diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000000000000000000000000000000000000..485dee64bcfb48793379b200a1afd14e85a8aaf4 --- /dev/null +++ b/.dockerignore @@ -0,0 +1 @@ +.idea diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 0000000000000000000000000000000000000000..585eb87aa06cbf03b2fe940f25ad5b94853ec39d --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1 @@ +* @lllyasviel diff --git a/.github/ISSUE_TEMPLATE/bug_report.yml b/.github/ISSUE_TEMPLATE/bug_report.yml new file mode 100644 index 0000000000000000000000000000000000000000..483e0de14bfcf3432cd46508a8612295ac4820c0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.yml @@ -0,0 +1,106 @@ +name: Bug Report +description: You think something is broken in Fooocus +title: "[Bug]: " +labels: ["bug", "triage"] + +body: + - type: markdown + attributes: + value: | + > The title of the bug report should be short and descriptive. + > Use relevant keywords for searchability. + > Do not leave it blank, but also do not put an entire error log in it. + - type: checkboxes + attributes: + label: Checklist + description: | + Please perform basic debugging to see if your configuration is the cause of the issue. + Basic debug procedure + 　2. Update Fooocus - sometimes things just need to be updated + 　3. Backup and remove your config.txt - check if the issue is caused by bad configuration + 　5. Try a fresh installation of Fooocus in a different directory - see if a clean installation solves the issue + Before making a issue report please, check that the issue hasn't been reported recently. + options: + - label: The issue exists on a clean installation of Fooocus + - label: The issue exists in the current version of Fooocus + - label: The issue has not been reported before recently + - label: The issue has been reported before but has not been fixed yet + - type: markdown + attributes: + value: | + > Please fill this form with as much information as possible. Don't forget to add information about "What browsers" and provide screenshots if possible + - type: textarea + id: what-did + attributes: + label: What happened? + description: Tell us what happened in a very clear and simple way + placeholder: | + image generation is not working as intended. + validations: + required: true + - type: textarea + id: steps + attributes: + label: Steps to reproduce the problem + description: Please provide us with precise step by step instructions on how to reproduce the bug + placeholder: | + 1. Go to ... + 2. Press ... + 3. ... + validations: + required: true + - type: textarea + id: what-should + attributes: + label: What should have happened? + description: Tell us what you think the normal behavior should be + placeholder: | + Fooocus should ... + validations: + required: true + - type: dropdown + id: browsers + attributes: + label: What browsers do you use to access Fooocus? + multiple: true + options: + - Mozilla Firefox + - Google Chrome + - Brave + - Apple Safari + - Microsoft Edge + - Android + - iOS + - Other + - type: dropdown + id: hosting + attributes: + label: Where are you running Fooocus? + multiple: false + options: + - Locally + - Locally with virtualization (e.g. Docker) + - Cloud (Google Colab) + - Cloud (other) + - type: input + id: operating-system + attributes: + label: What operating system are you using? + placeholder: | + Windows 10 + - type: textarea + id: logs + attributes: + label: Console logs + description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occured. If it's very long, provide a link to pastebin or similar service. + render: Shell + validations: + required: true + - type: textarea + id: misc + attributes: + label: Additional information + description: | + Please provide us with any relevant additional info or context. + Examples: + 　I have updated my GPU driver recently. \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..7bbf022a352ba012025029837cf7026105e1494b --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: false +contact_links: + - name: Ask a question + url: https://github.com/lllyasviel/Fooocus/discussions/new?category=q-a + about: Ask the community for help \ No newline at end of file diff --git a/.github/ISSUE_TEMPLATE/feature_request.yml b/.github/ISSUE_TEMPLATE/feature_request.yml new file mode 100644 index 0000000000000000000000000000000000000000..90e594e4b94d691472d1d70072f73b144984c9c4 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.yml @@ -0,0 +1,40 @@ +name: Feature request +description: Suggest an idea for this project +title: "[Feature Request]: " +labels: ["enhancement", "triage"] + +body: + - type: checkboxes + attributes: + label: Is there an existing issue for this? + description: Please search to see if an issue already exists for the feature you want, and that it's not implemented in a recent build/commit. + options: + - label: I have searched the existing issues and checked the recent builds/commits + required: true + - type: markdown + attributes: + value: | + *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible* + - type: textarea + id: feature + attributes: + label: What would your feature do? + description: Tell us about your feature in a very clear and simple way, and what problem it would solve + validations: + required: true + - type: textarea + id: workflow + attributes: + label: Proposed workflow + description: Please provide us with step by step information on how you'd like the feature to be accessed and used + value: | + 1. Go to .... + 2. Press .... + 3. ... + validations: + required: true + - type: textarea + id: misc + attributes: + label: Additional information + description: Add any other context or screenshots about the feature request here. \ No newline at end of file diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..859149866a1881bab5563c79297d87adf5255c2c --- /dev/null +++ b/.gitignore @@ -0,0 +1,54 @@ +__pycache__ +*.ckpt +*.safetensors +*.pth +*.pt +*.bin +*.patch +*.backup +*.corrupted +*.partial +*.onnx +sorted_styles.json +/input +/cache +/language/default.json +/test_imgs +config.txt +config_modification_tutorial.txt +user_path_config.txt +user_path_config-deprecated.txt +/modules/*.png +/repositories +/fooocus_env +/venv +/tmp +/ui-config.json +/outputs +/config.json +/log +/webui.settings.bat +/embeddings +/styles.csv +/params.txt +/styles.csv.bak +/webui-user.bat +/webui-user.sh +/interrogate +/user.css +/.idea +/notification.ogg +/notification.mp3 +/SwinIR +/textual_inversion +.vscode +/extensions +/test/stdout.txt +/test/stderr.txt +/cache.json* +/config_states/ +/node_modules +/package-lock.json +/.coverage* +/auth.json +.DS_Store diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..2aea28106615004e65b4270e40e08ba857823361 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,29 @@ +FROM nvidia/cuda:12.3.1-base-ubuntu22.04 +ENV DEBIAN_FRONTEND noninteractive +ENV CMDARGS --listen + +RUN apt-get update -y && \ + apt-get install -y curl libgl1 libglib2.0-0 python3-pip python-is-python3 git && \ + apt-get clean && \ + rm -rf /var/lib/apt/lists/* + +COPY requirements_docker.txt requirements_versions.txt /tmp/ +RUN pip install --no-cache-dir -r /tmp/requirements_docker.txt -r /tmp/requirements_versions.txt && \ + rm -f /tmp/requirements_docker.txt /tmp/requirements_versions.txt +RUN pip install --no-cache-dir xformers==0.0.22 --no-dependencies +RUN curl -fsL -o /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 https://cdn-media.huggingface.co/frpc-gradio-0.2/frpc_linux_amd64 && \ + chmod +x /usr/local/lib/python3.10/dist-packages/gradio/frpc_linux_amd64_v0.2 + +RUN adduser --disabled-password --gecos '' user && \ + mkdir -p /content/app /content/data + +COPY entrypoint.sh /content/ +RUN chown -R user:user /content + +WORKDIR /content +USER user + +RUN git clone https://github.com/lllyasviel/Fooocus /content/app +RUN mv /content/app/models /content/app/models.org + +CMD [ "sh", "-c", "/content/entrypoint.sh ${CMDARGS}" ] diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..f288702d2fa16d3cdf0035b15a9fcbc552cd88e7 --- /dev/null +++ b/LICENSE @@ -0,0 +1,674 @@ + GNU GENERAL PUBLIC LICENSE + Version 3, 29 June 2007 + + Copyright (C) 2007 Free Software Foundation, Inc. + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The GNU General Public License is a free, copyleft license for +software and other kinds of works. + + The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/args_manager.py b/args_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..c7c1b7ab113c81bdfa3f37ba8a4775ca882bab18 --- /dev/null +++ b/args_manager.py @@ -0,0 +1,55 @@ +import ldm_patched.modules.args_parser as args_parser +import os + +from tempfile import gettempdir + +args_parser.parser.add_argument("--share", action='store_true', help="Set whether to share on Gradio.") +args_parser.parser.add_argument("--preset", type=str, default=None, help="Apply specified UI preset.") + +args_parser.parser.add_argument("--language", type=str, default='default', + help="Translate UI using json files in [language] folder. " + "For example, [--language example] will use [language/example.json] for translation.") + +# For example, https://github.com/lllyasviel/Fooocus/issues/849 +args_parser.parser.add_argument("--disable-offload-from-vram", action="store_true", + help="Force loading models to vram when the unload can be avoided. " + "Some Mac users may need this.") + +args_parser.parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) +args_parser.parser.add_argument("--disable-image-log", action='store_true', + help="Prevent writing images and logs to hard drive.") + +args_parser.parser.add_argument("--disable-analytics", action='store_true', + help="Disables analytics for Gradio.") + +args_parser.parser.add_argument("--disable-metadata", action='store_true', + help="Disables saving metadata to images.") + +args_parser.parser.add_argument("--disable-preset-download", action='store_true', + help="Disables downloading models for presets", default=False) + +args_parser.parser.add_argument("--always-download-new-model", action='store_true', + help="Always download newer models ", default=False) + +args_parser.parser.set_defaults( + disable_cuda_malloc=True, + in_browser=True, + port=None +) + +args_parser.args = args_parser.parser.parse_args() + +# (Disable by default because of issues like https://github.com/lllyasviel/Fooocus/issues/724) +args_parser.args.always_offload_from_vram = not args_parser.args.disable_offload_from_vram + +if args_parser.args.disable_analytics: + import os + os.environ["GRADIO_ANALYTICS_ENABLED"] = "False" + +if args_parser.args.disable_in_browser: + args_parser.args.in_browser = False + +if args_parser.args.temp_path is None: + args_parser.args.temp_path = os.path.join(gettempdir(), 'Fooocus') + +args = args_parser.args diff --git a/auth-example.json b/auth-example.json new file mode 100644 index 0000000000000000000000000000000000000000..d8dc70bc5efdb25a38d3979978456c35ead7428e --- /dev/null +++ b/auth-example.json @@ -0,0 +1,6 @@ +[ + { + "user": "x", + "pass": "x" + } +] diff --git a/build_launcher.py b/build_launcher.py new file mode 100644 index 0000000000000000000000000000000000000000..4443888b20c595d5233405d614f104bd991fe93c --- /dev/null +++ b/build_launcher.py @@ -0,0 +1,26 @@ +import os + +win32_root = os.path.dirname(os.path.dirname(__file__)) +python_embeded_path = os.path.join(win32_root, 'python_embeded') + +is_win32_standalone_build = os.path.exists(python_embeded_path) and os.path.isdir(python_embeded_path) + +win32_cmd = ''' +.\python_embeded\python.exe -s Fooocus\entry_with_update.py {cmds} %* +pause +''' + + +def build_launcher(): + if not is_win32_standalone_build: + return + + presets = [None, 'anime', 'realistic'] + + for preset in presets: + win32_cmd_preset = win32_cmd.replace('{cmds}', '' if preset is None else f'--preset {preset}') + bat_path = os.path.join(win32_root, 'run.bat' if preset is None else f'run_{preset}.bat') + if not os.path.exists(bat_path): + with open(bat_path, "w", encoding="utf-8") as f: + f.write(win32_cmd_preset) + return diff --git a/css/style.css b/css/style.css new file mode 100644 index 0000000000000000000000000000000000000000..010c8e7f6def1663633610755d84049978b97072 --- /dev/null +++ b/css/style.css @@ -0,0 +1,220 @@ +/* based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/style.css */ + +#context-menu{ + z-index:9999; + position:absolute; + display:block; + padding:0px 0; + border:2px solid #a55000; + border-radius:8px; + box-shadow:1px 1px 2px #CE6400; + width: 200px; +} + +.context-menu-items{ + list-style: none; + margin: 0; + padding: 0; +} + +.context-menu-items a{ + display:block; + padding:5px; + cursor:pointer; +} + +.context-menu-items a:hover{ + background: #a55000; +} + +.canvas-tooltip-info { + position: absolute; + top: 28px; + left: 2px; + cursor: help; + background-color: rgba(0, 0, 0, 0.3); + width: 20px; + height: 20px; + border-radius: 50%; + display: flex; + align-items: center; + justify-content: center; + flex-direction: column; + + z-index: 100; +} + +.canvas-tooltip-info::after { + content: ''; + display: block; + width: 2px; + height: 7px; + background-color: white; + margin-top: 2px; +} + +.canvas-tooltip-info::before { + content: ''; + display: block; + width: 2px; + height: 2px; + background-color: white; +} + +.canvas-tooltip-content { + display: none; + background-color: #f9f9f9; + color: #333; + border: 1px solid #ddd; + padding: 15px; + position: absolute; + top: 40px; + left: 10px; + width: 250px; + font-size: 16px; + opacity: 0; + border-radius: 8px; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + + z-index: 100; +} + +.canvas-tooltip:hover .canvas-tooltip-content { + display: block; + animation: fadeIn 0.5s; + opacity: 1; +} + +@keyframes fadeIn { + from {opacity: 0;} + to {opacity: 1;} +} + +.styler { + overflow:inherit !important; +} + +.gradio-container{ + overflow: visible; +} + +/* fullpage image viewer */ + +#lightboxModal{ + display: none; + position: fixed; + z-index: 1001; + left: 0; + top: 0; + width: 100%; + height: 100%; + overflow: auto; + background-color: rgba(20, 20, 20, 0.95); + user-select: none; + -webkit-user-select: none; + flex-direction: column; +} + +.modalControls { + display: flex; + position: absolute; + right: 0px; + left: 0px; + gap: 1em; + padding: 1em; + background-color:rgba(0,0,0,0); + z-index: 1; + transition: 0.2s ease background-color; +} +.modalControls:hover { + background-color:rgba(0,0,0,0.9); +} +.modalClose { + margin-left: auto; +} +.modalControls span{ + color: white; + text-shadow: 0px 0px 0.25em black; + font-size: 35px; + font-weight: bold; + cursor: pointer; + width: 1em; +} + +.modalControls span:hover, .modalControls span:focus{ + color: #999; + text-decoration: none; +} + +#lightboxModal > img { + display: block; + margin: auto; + width: auto; +} + +#lightboxModal > img.modalImageFullscreen{ + object-fit: contain; + height: 100%; + width: 100%; + min-height: 0; +} + +.modalPrev, +.modalNext { + cursor: pointer; + position: absolute; + top: 50%; + width: auto; + padding: 16px; + margin-top: -50px; + color: white; + font-weight: bold; + font-size: 20px; + transition: 0.6s ease; + border-radius: 0 3px 3px 0; + user-select: none; + -webkit-user-select: none; +} + +.modalNext { + right: 0; + border-radius: 3px 0 0 3px; +} + +.modalPrev:hover, +.modalNext:hover { + background-color: rgba(0, 0, 0, 0.8); +} + +#imageARPreview { + position: absolute; + top: 0px; + left: 0px; + border: 2px solid red; + background: rgba(255, 0, 0, 0.3); + z-index: 900; + pointer-events: none; + display: none; +} + +#stylePreviewOverlay { + opacity: 0; + pointer-events: none; + width: 128px; + height: 128px; + position: fixed; + top: 0px; + left: 0px; + border: solid 1px lightgrey; + transform: translate(-140px, 20px); + background-size: cover; + background-position: center; + background-color: rgba(0, 0, 0, 0.3); + border-radius: 5px; + z-index: 100; + transition: transform 0.1s ease, opacity 0.3s ease; +} + +#stylePreviewOverlay.lower-half { + transform: translate(-140px, -140px); +} diff --git a/docker-compose.yml b/docker-compose.yml new file mode 100644 index 0000000000000000000000000000000000000000..dee7b3e7cafcdd08a8d8cddbd93fb29b527dcf5e --- /dev/null +++ b/docker-compose.yml @@ -0,0 +1,38 @@ +version: '3.9' + +volumes: + fooocus-data: + +services: + app: + build: . + image: fooocus + ports: + - "7865:7865" + environment: + - CMDARGS=--listen # Arguments for launch.py. + - DATADIR=/content/data # Directory which stores models, outputs dir + - config_path=/content/data/config.txt + - config_example_path=/content/data/config_modification_tutorial.txt + - path_checkpoints=/content/data/models/checkpoints/ + - path_loras=/content/data/models/loras/ + - path_embeddings=/content/data/models/embeddings/ + - path_vae_approx=/content/data/models/vae_approx/ + - path_upscale_models=/content/data/models/upscale_models/ + - path_inpaint=/content/data/models/inpaint/ + - path_controlnet=/content/data/models/controlnet/ + - path_clip_vision=/content/data/models/clip_vision/ + - path_fooocus_expansion=/content/data/models/prompt_expansion/fooocus_expansion/ + - path_outputs=/content/app/outputs/ # Warning: If it is not located under '/content/app', you can't see history log! + volumes: + - fooocus-data:/content/data + #- ./models:/import/models # Once you import files, you don't need to mount again. + #- ./outputs:/import/outputs # Once you import files, you don't need to mount again. + tty: true + deploy: + resources: + reservations: + devices: + - driver: nvidia + device_ids: ['0'] + capabilities: [compute, utility] diff --git a/docker.md b/docker.md new file mode 100644 index 0000000000000000000000000000000000000000..36cfa632ada9c9fc949eb437263224be54a6c024 --- /dev/null +++ b/docker.md @@ -0,0 +1,66 @@ +# Fooocus on Docker + +The docker image is based on NVIDIA CUDA 12.3 and PyTorch 2.0, see [Dockerfile](Dockerfile) and [requirements_docker.txt](requirements_docker.txt) for details. + +## Quick start + +**This is just an easy way for testing. Please find more information in the [notes](#notes).** + +1. Clone this repository +2. Build the image with `docker compose build` +3. Run the docker container with `docker compose up`. Building the image takes some time. + +When you see the message `Use the app with http://0.0.0.0:7865/` in the console, you can access the URL in your browser. + +Your models and outputs are stored in the `fooocus-data` volume, which, depending on OS, is stored in `/var/lib/docker/volumes`. + +## Details + +### Update the container manually + +When you are using `docker compose up` continuously, the container is not updated to the latest version of Fooocus automatically. +Run `git pull` before executing `docker compose build --no-cache` to build an image with the latest Fooocus version. +You can then start it with `docker compose up` + +### Import models, outputs +If you want to import files from models or the outputs folder, you can uncomment the following settings in the [docker-compose.yml](docker-compose.yml): +``` +#- ./models:/import/models # Once you import files, you don't need to mount again. +#- ./outputs:/import/outputs # Once you import files, you don't need to mount again. +``` +After running `docker compose up`, your files will be copied into `/content/data/models` and `/content/data/outputs` +Since `/content/data` is a persistent volume folder, your files will be persisted even when you re-run `docker compose up --build` without above volume settings. + + +### Paths inside the container + +|Path|Details| +|-|-| +|/content/app|The application stored folder| +|/content/app/models.org|Original 'models' folder.
Files are copied to the '/content/app/models' which is symlinked to '/content/data/models' every time the container boots. (Existing files will not be overwritten.) | +|/content/data|Persistent volume mount point| +|/content/data/models|The folder is symlinked to '/content/app/models'| +|/content/data/outputs|The folder is symlinked to '/content/app/outputs'| + +### Environments + +You can change `config.txt` parameters by using environment variables. +**The priority of using the environments is higher than the values defined in `config.txt`, and they will be saved to the `config_modification_tutorial.txt`** + +Docker specified environments are there. They are used by 'entrypoint.sh' +|Environment|Details| +|-|-| +|DATADIR|'/content/data' location.| +|CMDARGS|Arguments for [entry_with_update.py](entry_with_update.py) which is called by [entrypoint.sh](entrypoint.sh)| +|config_path|'config.txt' location| +|config_example_path|'config_modification_tutorial.txt' location| + +You can also use the same json key names and values explained in the 'config_modification_tutorial.txt' as the environments. +See examples in the [docker-compose.yml](docker-compose.yml) + +## Notes + +- Please keep 'path_outputs' under '/content/app'. Otherwise, you may get an error when you open the history log. +- Docker on Mac/Windows still has issues in the form of slow volume access when you use "bind mount" volumes. Please refer to [this article](https://docs.docker.com/storage/volumes/#use-a-volume-with-docker-compose) for not using "bind mount". +- The MPS backend (Metal Performance Shaders, Apple Silicon M1/M2/etc.) is not yet supported in Docker, see https://github.com/pytorch/pytorch/issues/81224 +- You can also use `docker compose up -d` to start the container detached and connect to the logs with `docker compose logs -f`. This way you can also close the terminal and keep the container running. \ No newline at end of file diff --git a/entry_with_update.py b/entry_with_update.py new file mode 100644 index 0000000000000000000000000000000000000000..4b66ac2d2822e3d50efb629d81aede4c0c62b1d5 --- /dev/null +++ b/entry_with_update.py @@ -0,0 +1,46 @@ +import os +import sys + + +root = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(root) +os.chdir(root) + + +try: + import pygit2 + pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0) + + repo = pygit2.Repository(os.path.abspath(os.path.dirname(__file__))) + + branch_name = repo.head.shorthand + + remote_name = 'origin' + remote = repo.remotes[remote_name] + + remote.fetch() + + local_branch_ref = f'refs/heads/{branch_name}' + local_branch = repo.lookup_reference(local_branch_ref) + + remote_reference = f'refs/remotes/{remote_name}/{branch_name}' + remote_commit = repo.revparse_single(remote_reference) + + merge_result, _ = repo.merge_analysis(remote_commit.id) + + if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE: + print("Already up-to-date") + elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD: + local_branch.set_target(remote_commit.id) + repo.head.set_target(remote_commit.id) + repo.checkout_tree(repo.get(remote_commit.id)) + repo.reset(local_branch.target, pygit2.GIT_RESET_HARD) + print("Fast-forward merge") + elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL: + print("Update failed - Did you modify any file?") +except Exception as e: + print('Update failed.') + print(str(e)) + +print('Update succeeded.') +from launch import * diff --git a/entrypoint.sh b/entrypoint.sh new file mode 100644 index 0000000000000000000000000000000000000000..d0dba09c266d42819c3fe789daa3781c00089796 --- /dev/null +++ b/entrypoint.sh @@ -0,0 +1,33 @@ +#!/bin/bash + +ORIGINALDIR=/content/app +# Use predefined DATADIR if it is defined +[[ x"${DATADIR}" == "x" ]] && DATADIR=/content/data + +# Make persistent dir from original dir +function mklink () { + mkdir -p $DATADIR/$1 + ln -s $DATADIR/$1 $ORIGINALDIR +} + +# Copy old files from import dir +function import () { + (test -d /import/$1 && cd /import/$1 && cp -Rpn . $DATADIR/$1/) +} + +cd $ORIGINALDIR + +# models +mklink models +# Copy original files +(cd $ORIGINALDIR/models.org && cp -Rpn . $ORIGINALDIR/models/) +# Import old files +import models + +# outputs +mklink outputs +# Import old files +import outputs + +# Start application +python launch.py $* diff --git a/environment.yaml b/environment.yaml new file mode 100644 index 0000000000000000000000000000000000000000..55826b707c7c9813c7b7d5fd806340e8348e3683 --- /dev/null +++ b/environment.yaml @@ -0,0 +1,7 @@ +name: fooocus +channels: + - defaults +dependencies: + - python=3.10 + - pip=23.0 + - packaging diff --git a/experiments_expansion.py b/experiments_expansion.py new file mode 100644 index 0000000000000000000000000000000000000000..5a2a946a88326328b57ebe01e16bbeea7d37b300 --- /dev/null +++ b/experiments_expansion.py @@ -0,0 +1,8 @@ +from modules.expansion import FooocusExpansion + +expansion = FooocusExpansion() + +text = 'a handsome man' + +for i in range(64): + print(expansion(text, seed=i)) diff --git a/experiments_face.py b/experiments_face.py new file mode 100644 index 0000000000000000000000000000000000000000..3b4909fa87bd79786cae9116457bd3d6539281f0 --- /dev/null +++ b/experiments_face.py @@ -0,0 +1,7 @@ +import cv2 +import extras.face_crop as cropper + + +img = cv2.imread('lena.png') +result = cropper.crop_image(img) +cv2.imwrite('lena_result.png', result) diff --git a/experiments_interrogate.py b/experiments_interrogate.py new file mode 100644 index 0000000000000000000000000000000000000000..16639d6281166e3dd01bbeb3a32af48e2d8299af --- /dev/null +++ b/experiments_interrogate.py @@ -0,0 +1,8 @@ +import cv2 +from extras.interrogate import default_interrogator as default_interrogator_photo +from extras.wd14tagger import default_interrogator as default_interrogator_anime + +img = cv2.imread('./test_imgs/red_box.jpg')[:, :, ::-1].copy() +print(default_interrogator_photo(img)) +img = cv2.imread('./test_imgs/miku.jpg')[:, :, ::-1].copy() +print(default_interrogator_anime(img)) diff --git a/extras/BLIP/configs/bert_config.json b/extras/BLIP/configs/bert_config.json new file mode 100644 index 0000000000000000000000000000000000000000..3ef38aabc7f966b53079e9d559dc59e459cc0051 --- /dev/null +++ b/extras/BLIP/configs/bert_config.json @@ -0,0 +1,21 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30522, + "encoder_width": 768, + "add_cross_attention": true +} diff --git a/extras/BLIP/configs/caption_coco.yaml b/extras/BLIP/configs/caption_coco.yaml new file mode 100644 index 0000000000000000000000000000000000000000..42eab7030c0310ba2f265baf36fa1400aa6e5846 --- /dev/null +++ b/extras/BLIP/configs/caption_coco.yaml @@ -0,0 +1,33 @@ +image_root: '/export/share/datasets/vision/coco/images/' +ann_root: 'annotation' +coco_gt_root: 'annotation/coco_gt' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' + +# size of vit model; base or large +vit: 'base' +vit_grad_ckpt: False +vit_ckpt_layer: 0 +batch_size: 32 +init_lr: 1e-5 + +# vit: 'large' +# vit_grad_ckpt: True +# vit_ckpt_layer: 5 +# batch_size: 16 +# init_lr: 2e-6 + +image_size: 384 + +# generation configs +max_length: 20 +min_length: 5 +num_beams: 3 +prompt: 'a picture of ' + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 5 + diff --git a/extras/BLIP/configs/med_config.json b/extras/BLIP/configs/med_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0ffad0a6f3c2f9f11b8faa84529d9860bb70327a --- /dev/null +++ b/extras/BLIP/configs/med_config.json @@ -0,0 +1,21 @@ +{ + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 0, + "type_vocab_size": 2, + "vocab_size": 30524, + "encoder_width": 768, + "add_cross_attention": true +} diff --git a/extras/BLIP/configs/nlvr.yaml b/extras/BLIP/configs/nlvr.yaml new file mode 100644 index 0000000000000000000000000000000000000000..2d1122aadb1a776bd347068233096b0c984f648b --- /dev/null +++ b/extras/BLIP/configs/nlvr.yaml @@ -0,0 +1,21 @@ +image_root: '/export/share/datasets/vision/NLVR2/' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_nlvr.pth' + +#size of vit model; base or large +vit: 'base' +batch_size_train: 16 +batch_size_test: 64 +vit_grad_ckpt: False +vit_ckpt_layer: 0 +max_epoch: 15 + +image_size: 384 + +# optimizer +weight_decay: 0.05 +init_lr: 3e-5 +min_lr: 0 + diff --git a/extras/BLIP/configs/nocaps.yaml b/extras/BLIP/configs/nocaps.yaml new file mode 100644 index 0000000000000000000000000000000000000000..9028135859b94aef5324c85c80e376c609d8a089 --- /dev/null +++ b/extras/BLIP/configs/nocaps.yaml @@ -0,0 +1,15 @@ +image_root: '/export/share/datasets/vision/nocaps/' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_caption_capfilt_large.pth' + +vit: 'base' +batch_size: 32 + +image_size: 384 + +max_length: 20 +min_length: 5 +num_beams: 3 +prompt: 'a picture of ' \ No newline at end of file diff --git a/extras/BLIP/configs/pretrain.yaml b/extras/BLIP/configs/pretrain.yaml new file mode 100644 index 0000000000000000000000000000000000000000..02355ee0228932803c661616485bf315e862b826 --- /dev/null +++ b/extras/BLIP/configs/pretrain.yaml @@ -0,0 +1,27 @@ +train_file: ['/export/share/junnan-li/VL_pretrain/annotation/coco_karpathy_train.json', + '/export/share/junnan-li/VL_pretrain/annotation/vg_caption.json', + ] +laion_path: '' + +# size of vit model; base or large +vit: 'base' +vit_grad_ckpt: False +vit_ckpt_layer: 0 + +image_size: 224 +batch_size: 75 + +queue_size: 57600 +alpha: 0.4 + +# optimizer +weight_decay: 0.05 +init_lr: 3e-4 +min_lr: 1e-6 +warmup_lr: 1e-6 +lr_decay_rate: 0.9 +max_epoch: 20 +warmup_steps: 3000 + + + diff --git a/extras/BLIP/configs/retrieval_coco.yaml b/extras/BLIP/configs/retrieval_coco.yaml new file mode 100644 index 0000000000000000000000000000000000000000..a8569e9b67112fe3605ac25e4fdc0231f7975378 --- /dev/null +++ b/extras/BLIP/configs/retrieval_coco.yaml @@ -0,0 +1,34 @@ +image_root: '/export/share/datasets/vision/coco/images/' +ann_root: 'annotation' +dataset: 'coco' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' + +# size of vit model; base or large + +vit: 'base' +batch_size_train: 32 +batch_size_test: 64 +vit_grad_ckpt: True +vit_ckpt_layer: 4 +init_lr: 1e-5 + +# vit: 'large' +# batch_size_train: 16 +# batch_size_test: 32 +# vit_grad_ckpt: True +# vit_ckpt_layer: 12 +# init_lr: 5e-6 + +image_size: 384 +queue_size: 57600 +alpha: 0.4 +k_test: 256 +negative_all_rank: True + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 6 + diff --git a/extras/BLIP/configs/retrieval_flickr.yaml b/extras/BLIP/configs/retrieval_flickr.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d75ea4eed87c9a001523c5e5914998c5e737594d --- /dev/null +++ b/extras/BLIP/configs/retrieval_flickr.yaml @@ -0,0 +1,34 @@ +image_root: '/export/share/datasets/vision/flickr30k/' +ann_root: 'annotation' +dataset: 'flickr' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_flickr.pth' + +# size of vit model; base or large + +vit: 'base' +batch_size_train: 32 +batch_size_test: 64 +vit_grad_ckpt: True +vit_ckpt_layer: 4 +init_lr: 1e-5 + +# vit: 'large' +# batch_size_train: 16 +# batch_size_test: 32 +# vit_grad_ckpt: True +# vit_ckpt_layer: 10 +# init_lr: 5e-6 + +image_size: 384 +queue_size: 57600 +alpha: 0.4 +k_test: 128 +negative_all_rank: False + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 6 + diff --git a/extras/BLIP/configs/retrieval_msrvtt.yaml b/extras/BLIP/configs/retrieval_msrvtt.yaml new file mode 100644 index 0000000000000000000000000000000000000000..395f62542bb22d706b8e19e2455d2c7298984d0b --- /dev/null +++ b/extras/BLIP/configs/retrieval_msrvtt.yaml @@ -0,0 +1,12 @@ +video_root: '/export/share/dongxuli/data/msrvtt_retrieval/videos' +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth' + +# size of vit model; base or large +vit: 'base' +batch_size: 64 +k_test: 128 +image_size: 384 +num_frm_test: 8 \ No newline at end of file diff --git a/extras/BLIP/configs/vqa.yaml b/extras/BLIP/configs/vqa.yaml new file mode 100644 index 0000000000000000000000000000000000000000..74327e6d0a34672023b44569558fe8beeb052548 --- /dev/null +++ b/extras/BLIP/configs/vqa.yaml @@ -0,0 +1,25 @@ +vqa_root: '/export/share/datasets/vision/VQA/Images/mscoco/' #followed by train2014/ +vg_root: '/export/share/datasets/vision/visual-genome/' #followed by image/ +train_files: ['vqa_train','vqa_val','vg_qa'] +ann_root: 'annotation' + +# set pretrained as a file path or an url +pretrained: 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth' + +# size of vit model; base or large +vit: 'base' +batch_size_train: 16 +batch_size_test: 32 +vit_grad_ckpt: False +vit_ckpt_layer: 0 +init_lr: 2e-5 + +image_size: 480 + +k_test: 128 +inference: 'rank' + +# optimizer +weight_decay: 0.05 +min_lr: 0 +max_epoch: 10 \ No newline at end of file diff --git a/extras/BLIP/models/bert_tokenizer/config.json b/extras/BLIP/models/bert_tokenizer/config.json new file mode 100644 index 0000000000000000000000000000000000000000..45a2321a7ecfdaaf60a6c1fd7f5463994cc8907d --- /dev/null +++ b/extras/BLIP/models/bert_tokenizer/config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "BertForMaskedLM" + ], + "attention_probs_dropout_prob": 0.1, + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + 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+##fi +##fl +##！ +##（ +##） +##， +##－ +##． +##／ +##： +##？ +##～ diff --git a/extras/BLIP/models/blip.py b/extras/BLIP/models/blip.py new file mode 100644 index 0000000000000000000000000000000000000000..a2566331dd659c14e708d8cc2fe0649f479d221a --- /dev/null +++ b/extras/BLIP/models/blip.py @@ -0,0 +1,239 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li +''' +import warnings +warnings.filterwarnings("ignore") + +from extras.BLIP.models.vit import VisionTransformer, interpolate_pos_embed +from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +import os +from urllib.parse import urlparse +from timm.models.hub import download_cached_file + +class BLIP_Base(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 224, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + + def forward(self, image, caption, mode): + + assert mode in ['image', 'text', 'multimodal'], "mode parameter must be image, text, or multimodal" + text = self.tokenizer(caption, return_tensors="pt").to(image.device) + + if mode=='image': + # return image features + image_embeds = self.visual_encoder(image) + return image_embeds + + elif mode=='text': + # return text features + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + return text_output.last_hidden_state + + elif mode=='multimodal': + # return multimodel features + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text.input_ids[:,0] = self.tokenizer.enc_token_id + output = self.text_encoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + return output.last_hidden_state + + + +class BLIP_Decoder(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + prompt = 'a picture of ', + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_decoder = BertLMHeadModel(config=med_config) + + self.prompt = prompt + self.prompt_length = len(self.tokenizer(self.prompt).input_ids)-1 + + + def forward(self, image, caption): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text = self.tokenizer(caption, padding='longest', truncation=True, max_length=40, return_tensors="pt").to(image.device) + + text.input_ids[:,0] = self.tokenizer.bos_token_id + + decoder_targets = text.input_ids.masked_fill(text.input_ids == self.tokenizer.pad_token_id, -100) + decoder_targets[:,:self.prompt_length] = -100 + + decoder_output = self.text_decoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + labels = decoder_targets, + return_dict = True, + ) + loss_lm = decoder_output.loss + + return loss_lm + + def generate(self, image, sample=False, num_beams=3, max_length=30, min_length=10, top_p=0.9, repetition_penalty=1.0): + image_embeds = self.visual_encoder(image) + + if not sample: + image_embeds = image_embeds.repeat_interleave(num_beams,dim=0) + + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + model_kwargs = {"encoder_hidden_states": image_embeds, "encoder_attention_mask":image_atts} + + prompt = [self.prompt] * image.size(0) + input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(image.device) + input_ids[:,0] = self.tokenizer.bos_token_id + input_ids = input_ids[:, :-1] + + if sample: + #nucleus sampling + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + do_sample=True, + top_p=top_p, + num_return_sequences=1, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=1.1, + **model_kwargs) + else: + #beam search + outputs = self.text_decoder.generate(input_ids=input_ids, + max_length=max_length, + min_length=min_length, + num_beams=num_beams, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + repetition_penalty=repetition_penalty, + **model_kwargs) + + captions = [] + for output in outputs: + caption = self.tokenizer.decode(output, skip_special_tokens=True) + captions.append(caption[len(self.prompt):]) + return captions + + +def blip_decoder(pretrained='',**kwargs): + model = BLIP_Decoder(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + +def blip_feature_extractor(pretrained='',**kwargs): + model = BLIP_Base(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + +def init_tokenizer(): + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "bert_tokenizer") + tokenizer = BertTokenizer.from_pretrained(tokenizer_path) + tokenizer.add_special_tokens({'bos_token':'[DEC]'}) + tokenizer.add_special_tokens({'additional_special_tokens':['[ENC]']}) + tokenizer.enc_token_id = tokenizer.additional_special_tokens_ids[0] + return tokenizer + + +def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0): + + assert vit in ['base', 'large'], "vit parameter must be base or large" + if vit=='base': + vision_width = 768 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=12, + num_heads=12, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0 or drop_path_rate + ) + elif vit=='large': + vision_width = 1024 + visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_width, depth=24, + num_heads=16, use_grad_checkpointing=use_grad_checkpointing, ckpt_layer=ckpt_layer, + drop_path_rate=0.1 or drop_path_rate + ) + return visual_encoder, vision_width + +def is_url(url_or_filename): + parsed = urlparse(url_or_filename) + return parsed.scheme in ("http", "https") + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + if 'visual_encoder_m.pos_embed' in model.state_dict().keys(): + state_dict['visual_encoder_m.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder_m.pos_embed'], + model.visual_encoder_m) + for key in model.state_dict().keys(): + if key in state_dict.keys(): + if state_dict[key].shape!=model.state_dict()[key].shape: + del state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + diff --git a/extras/BLIP/models/blip_itm.py b/extras/BLIP/models/blip_itm.py new file mode 100644 index 0000000000000000000000000000000000000000..6f4da82180a463ccad6b0a4d61d6eed9b0d24bd6 --- /dev/null +++ b/extras/BLIP/models/blip_itm.py @@ -0,0 +1,76 @@ +from extras.BLIP.models.med import BertConfig, BertModel +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +class BLIP_ITM(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + + self.itm_head = nn.Linear(text_width, 2) + + + def forward(self, image, caption, match_head='itm'): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, + return_tensors="pt").to(image.device) + + + if match_head=='itm': + output = self.text_encoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + itm_output = self.itm_head(output.last_hidden_state[:,0,:]) + return itm_output + + elif match_head=='itc': + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) + text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) + + sim = image_feat @ text_feat.t() + return sim + + +def blip_itm(pretrained='',**kwargs): + model = BLIP_ITM(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + assert(len(msg.missing_keys)==0) + return model + \ No newline at end of file diff --git a/extras/BLIP/models/blip_nlvr.py b/extras/BLIP/models/blip_nlvr.py new file mode 100644 index 0000000000000000000000000000000000000000..0eb9eaa696753d0c9e610507b765c2468dfe7adb --- /dev/null +++ b/extras/BLIP/models/blip_nlvr.py @@ -0,0 +1,105 @@ +from extras.BLIP.models.med import BertConfig +from extras.BLIP.models.nlvr_encoder import BertModel +from extras.BLIP.models.vit import interpolate_pos_embed +from extras.BLIP.models.blip import create_vit, init_tokenizer, is_url + +from timm.models.hub import download_cached_file + +import torch +from torch import nn +import torch.nn.functional as F +from transformers import BertTokenizer +import numpy as np +import os + + +class BLIP_NLVR(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 480, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + self.cls_head = nn.Sequential( + nn.Linear(self.text_encoder.config.hidden_size, self.text_encoder.config.hidden_size), + nn.ReLU(), + nn.Linear(self.text_encoder.config.hidden_size, 2) + ) + + def forward(self, image, text, targets, train=True): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + image0_embeds, image1_embeds = torch.split(image_embeds,targets.size(0)) + + text = self.tokenizer(text, padding='longest', return_tensors="pt").to(image.device) + text.input_ids[:,0] = self.tokenizer.enc_token_id + + output = self.text_encoder(text.input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = [image0_embeds,image1_embeds], + encoder_attention_mask = [image_atts[:image0_embeds.size(0)], + image_atts[image0_embeds.size(0):]], + return_dict = True, + ) + hidden_state = output.last_hidden_state[:,0,:] + prediction = self.cls_head(hidden_state) + + if train: + loss = F.cross_entropy(prediction, targets) + return loss + else: + return prediction + +def blip_nlvr(pretrained='',**kwargs): + model = BLIP_NLVR(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + print("missing keys:") + print(msg.missing_keys) + return model + + +def load_checkpoint(model,url_or_filename): + if is_url(url_or_filename): + cached_file = download_cached_file(url_or_filename, check_hash=False, progress=True) + checkpoint = torch.load(cached_file, map_location='cpu') + elif os.path.isfile(url_or_filename): + checkpoint = torch.load(url_or_filename, map_location='cpu') + else: + raise RuntimeError('checkpoint url or path is invalid') + state_dict = checkpoint['model'] + + state_dict['visual_encoder.pos_embed'] = interpolate_pos_embed(state_dict['visual_encoder.pos_embed'],model.visual_encoder) + + for key in list(state_dict.keys()): + if 'crossattention.self.' in key: + new_key0 = key.replace('self','self0') + new_key1 = key.replace('self','self1') + state_dict[new_key0] = state_dict[key] + state_dict[new_key1] = state_dict[key] + elif 'crossattention.output.dense.' in key: + new_key0 = key.replace('dense','dense0') + new_key1 = key.replace('dense','dense1') + state_dict[new_key0] = state_dict[key] + state_dict[new_key1] = state_dict[key] + + msg = model.load_state_dict(state_dict,strict=False) + print('load checkpoint from %s'%url_or_filename) + return model,msg + \ No newline at end of file diff --git a/extras/BLIP/models/blip_pretrain.py b/extras/BLIP/models/blip_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..9b8a3a47539c35b2e15def23acb392963f5b33ac --- /dev/null +++ b/extras/BLIP/models/blip_pretrain.py @@ -0,0 +1,339 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li +''' +from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel +from transformers import BertTokenizer +import transformers +transformers.logging.set_verbosity_error() + +import torch +from torch import nn +import torch.nn.functional as F + +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +class BLIP_Pretrain(nn.Module): + def __init__(self, + med_config = 'configs/bert_config.json', + image_size = 224, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + queue_size = 57600, + momentum = 0.995, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer, 0) + + if vit=='base': + checkpoint = torch.hub.load_state_dict_from_url( + url="https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth", + map_location="cpu", check_hash=True) + state_dict = checkpoint["model"] + msg = self.visual_encoder.load_state_dict(state_dict,strict=False) + elif vit=='large': + from timm.models.helpers import load_custom_pretrained + from timm.models.vision_transformer import default_cfgs + load_custom_pretrained(self.visual_encoder,default_cfgs['vit_large_patch16_224_in21k']) + + self.tokenizer = init_tokenizer() + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.text_encoder = BertModel.from_pretrained('bert-base-uncased',config=encoder_config, add_pooling_layer=False) + self.text_encoder.resize_token_embeddings(len(self.tokenizer)) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + + self.itm_head = nn.Linear(text_width, 2) + + # create momentum encoders + self.visual_encoder_m, vision_width = create_vit(vit,image_size) + self.vision_proj_m = nn.Linear(vision_width, embed_dim) + self.text_encoder_m = BertModel(config=encoder_config, add_pooling_layer=False) + self.text_proj_m = nn.Linear(text_width, embed_dim) + + self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], + [self.vision_proj,self.vision_proj_m], + [self.text_encoder,self.text_encoder_m], + [self.text_proj,self.text_proj_m], + ] + self.copy_params() + + # create the queue + self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("queue_ptr", torch.zeros(1, dtype=torch.long)) + + self.image_queue = nn.functional.normalize(self.image_queue, dim=0) + self.text_queue = nn.functional.normalize(self.text_queue, dim=0) + + self.queue_size = queue_size + self.momentum = momentum + self.temp = nn.Parameter(0.07*torch.ones([])) + + # create the decoder + decoder_config = BertConfig.from_json_file(med_config) + decoder_config.encoder_width = vision_width + self.text_decoder = BertLMHeadModel.from_pretrained('bert-base-uncased',config=decoder_config) + self.text_decoder.resize_token_embeddings(len(self.tokenizer)) + tie_encoder_decoder_weights(self.text_encoder,self.text_decoder.bert,'','/attention') + + + def forward(self, image, caption, alpha): + with torch.no_grad(): + self.temp.clamp_(0.001,0.5) + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) + + text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=30, + return_tensors="pt").to(image.device) + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) + + # get momentum features + with torch.no_grad(): + self._momentum_update() + image_embeds_m = self.visual_encoder_m(image) + image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) + image_feat_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) + + text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) + text_feat_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) + + sim_i2t_m = image_feat_m @ text_feat_all / self.temp + sim_t2i_m = text_feat_m @ image_feat_all / self.temp + + sim_targets = torch.zeros(sim_i2t_m.size()).to(image.device) + sim_targets.fill_diagonal_(1) + + sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets + sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets + + sim_i2t = image_feat @ text_feat_all / self.temp + sim_t2i = text_feat @ image_feat_all / self.temp + + loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() + loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() + + loss_ita = (loss_i2t+loss_t2i)/2 + + self._dequeue_and_enqueue(image_feat_m, text_feat_m) + + ###============== Image-text Matching ===================### + encoder_input_ids = text.input_ids.clone() + encoder_input_ids[:,0] = self.tokenizer.enc_token_id + + # forward the positve image-text pair + bs = image.size(0) + output_pos = self.text_encoder(encoder_input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + with torch.no_grad(): + weights_t2i = F.softmax(sim_t2i[:,:bs],dim=1)+1e-4 + weights_t2i.fill_diagonal_(0) + weights_i2t = F.softmax(sim_i2t[:,:bs],dim=1)+1e-4 + weights_i2t.fill_diagonal_(0) + + # select a negative image for each text + image_embeds_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_t2i[b], 1).item() + image_embeds_neg.append(image_embeds[neg_idx]) + image_embeds_neg = torch.stack(image_embeds_neg,dim=0) + + # select a negative text for each image + text_ids_neg = [] + text_atts_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_i2t[b], 1).item() + text_ids_neg.append(encoder_input_ids[neg_idx]) + text_atts_neg.append(text.attention_mask[neg_idx]) + + text_ids_neg = torch.stack(text_ids_neg,dim=0) + text_atts_neg = torch.stack(text_atts_neg,dim=0) + + text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) + text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) + + image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) + image_atts_all = torch.cat([image_atts,image_atts],dim=0) + + output_neg = self.text_encoder(text_ids_all, + attention_mask = text_atts_all, + encoder_hidden_states = image_embeds_all, + encoder_attention_mask = image_atts_all, + return_dict = True, + ) + + vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) + vl_output = self.itm_head(vl_embeddings) + + itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], + dim=0).to(image.device) + loss_itm = F.cross_entropy(vl_output, itm_labels) + + ##================= LM ========================## + decoder_input_ids = text.input_ids.clone() + decoder_input_ids[:,0] = self.tokenizer.bos_token_id + decoder_targets = decoder_input_ids.masked_fill(decoder_input_ids == self.tokenizer.pad_token_id, -100) + + decoder_output = self.text_decoder(decoder_input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + labels = decoder_targets, + return_dict = True, + ) + + loss_lm = decoder_output.loss + return loss_ita, loss_itm, loss_lm + + + + @torch.no_grad() + def copy_params(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data.copy_(param.data) # initialize + param_m.requires_grad = False # not update by gradient + + + @torch.no_grad() + def _momentum_update(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) + + + @torch.no_grad() + def _dequeue_and_enqueue(self, image_feat, text_feat): + # gather keys before updating queue + image_feats = concat_all_gather(image_feat) + text_feats = concat_all_gather(text_feat) + + batch_size = image_feats.shape[0] + + ptr = int(self.queue_ptr) + assert self.queue_size % batch_size == 0 # for simplicity + + # replace the keys at ptr (dequeue and enqueue) + self.image_queue[:, ptr:ptr + batch_size] = image_feats.T + self.text_queue[:, ptr:ptr + batch_size] = text_feats.T + ptr = (ptr + batch_size) % self.queue_size # move pointer + + self.queue_ptr[0] = ptr + + +def blip_pretrain(**kwargs): + model = BLIP_Pretrain(**kwargs) + return model + + +@torch.no_grad() +def concat_all_gather(tensor): + """ + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + tensors_gather = [torch.ones_like(tensor) + for _ in range(torch.distributed.get_world_size())] + torch.distributed.all_gather(tensors_gather, tensor, async_op=False) + + output = torch.cat(tensors_gather, dim=0) + return output + + +from typing import List +def tie_encoder_decoder_weights(encoder: nn.Module, decoder: nn.Module, base_model_prefix: str, skip_key:str): + uninitialized_encoder_weights: List[str] = [] + if decoder.__class__ != encoder.__class__: + print( + f"{decoder.__class__} and {encoder.__class__} are not equal. In this case make sure that all encoder weights are correctly initialized." + ) + + def tie_encoder_to_decoder_recursively( + decoder_pointer: nn.Module, + encoder_pointer: nn.Module, + module_name: str, + uninitialized_encoder_weights: List[str], + skip_key: str, + depth=0, + ): + assert isinstance(decoder_pointer, nn.Module) and isinstance( + encoder_pointer, nn.Module + ), f"{decoder_pointer} and {encoder_pointer} have to be of type torch.nn.Module" + if hasattr(decoder_pointer, "weight") and skip_key not in module_name: + assert hasattr(encoder_pointer, "weight") + encoder_pointer.weight = decoder_pointer.weight + if hasattr(decoder_pointer, "bias"): + assert hasattr(encoder_pointer, "bias") + encoder_pointer.bias = decoder_pointer.bias + print(module_name+' is tied') + return + + encoder_modules = encoder_pointer._modules + decoder_modules = decoder_pointer._modules + if len(decoder_modules) > 0: + assert ( + len(encoder_modules) > 0 + ), f"Encoder module {encoder_pointer} does not match decoder module {decoder_pointer}" + + all_encoder_weights = set([module_name + "/" + sub_name for sub_name in encoder_modules.keys()]) + encoder_layer_pos = 0 + for name, module in decoder_modules.items(): + if name.isdigit(): + encoder_name = str(int(name) + encoder_layer_pos) + decoder_name = name + if not isinstance(decoder_modules[decoder_name], type(encoder_modules[encoder_name])) and len( + encoder_modules + ) != len(decoder_modules): + # this can happen if the name corresponds to the position in a list module list of layers + # in this case the decoder has added a cross-attention that the encoder does not have + # thus skip this step and subtract one layer pos from encoder + encoder_layer_pos -= 1 + continue + elif name not in encoder_modules: + continue + elif depth > 500: + raise ValueError( + "Max depth of recursive function `tie_encoder_to_decoder` reached. It seems that there is a circular dependency between two or more `nn.Modules` of your model." + ) + else: + decoder_name = encoder_name = name + tie_encoder_to_decoder_recursively( + decoder_modules[decoder_name], + encoder_modules[encoder_name], + module_name + "/" + name, + uninitialized_encoder_weights, + skip_key, + depth=depth + 1, + ) + all_encoder_weights.remove(module_name + "/" + encoder_name) + + uninitialized_encoder_weights += list(all_encoder_weights) + + # tie weights recursively + tie_encoder_to_decoder_recursively(decoder, encoder, base_model_prefix, uninitialized_encoder_weights, skip_key) diff --git a/extras/BLIP/models/blip_retrieval.py b/extras/BLIP/models/blip_retrieval.py new file mode 100644 index 0000000000000000000000000000000000000000..09493586d60db9807f2dbfd5f7ff0117bcc89dad --- /dev/null +++ b/extras/BLIP/models/blip_retrieval.py @@ -0,0 +1,319 @@ +from extras.BLIP.models.med import BertConfig, BertModel +from transformers import BertTokenizer + +import torch +from torch import nn +import torch.nn.functional as F + +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +class BLIP_Retrieval(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 384, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + embed_dim = 256, + queue_size = 57600, + momentum = 0.995, + negative_all_rank = False, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit,image_size, vit_grad_ckpt, vit_ckpt_layer) + self.tokenizer = init_tokenizer() + med_config = BertConfig.from_json_file(med_config) + med_config.encoder_width = vision_width + self.text_encoder = BertModel(config=med_config, add_pooling_layer=False) + + text_width = self.text_encoder.config.hidden_size + + self.vision_proj = nn.Linear(vision_width, embed_dim) + self.text_proj = nn.Linear(text_width, embed_dim) + + self.itm_head = nn.Linear(text_width, 2) + + # create momentum encoders + self.visual_encoder_m, vision_width = create_vit(vit,image_size) + self.vision_proj_m = nn.Linear(vision_width, embed_dim) + self.text_encoder_m = BertModel(config=med_config, add_pooling_layer=False) + self.text_proj_m = nn.Linear(text_width, embed_dim) + + self.model_pairs = [[self.visual_encoder,self.visual_encoder_m], + [self.vision_proj,self.vision_proj_m], + [self.text_encoder,self.text_encoder_m], + [self.text_proj,self.text_proj_m], + ] + self.copy_params() + + # create the queue + self.register_buffer("image_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("text_queue", torch.randn(embed_dim, queue_size)) + self.register_buffer("idx_queue", torch.full((1,queue_size),-100)) + self.register_buffer("ptr_queue", torch.zeros(1, dtype=torch.long)) + + self.image_queue = nn.functional.normalize(self.image_queue, dim=0) + self.text_queue = nn.functional.normalize(self.text_queue, dim=0) + + self.queue_size = queue_size + self.momentum = momentum + self.temp = nn.Parameter(0.07*torch.ones([])) + + self.negative_all_rank = negative_all_rank + + + def forward(self, image, caption, alpha, idx): + with torch.no_grad(): + self.temp.clamp_(0.001,0.5) + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + image_feat = F.normalize(self.vision_proj(image_embeds[:,0,:]),dim=-1) + + text = self.tokenizer(caption, padding='max_length', truncation=True, max_length=35, + return_tensors="pt").to(image.device) + + text_output = self.text_encoder(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat = F.normalize(self.text_proj(text_output.last_hidden_state[:,0,:]),dim=-1) + + ###============== Image-text Contrastive Learning ===================### + idx = idx.view(-1,1) + idx_all = torch.cat([idx.t(), self.idx_queue.clone().detach()],dim=1) + pos_idx = torch.eq(idx, idx_all).float() + sim_targets = pos_idx / pos_idx.sum(1,keepdim=True) + + # get momentum features + with torch.no_grad(): + self._momentum_update() + image_embeds_m = self.visual_encoder_m(image) + image_feat_m = F.normalize(self.vision_proj_m(image_embeds_m[:,0,:]),dim=-1) + image_feat_m_all = torch.cat([image_feat_m.t(),self.image_queue.clone().detach()],dim=1) + + text_output_m = self.text_encoder_m(text.input_ids, attention_mask = text.attention_mask, + return_dict = True, mode = 'text') + text_feat_m = F.normalize(self.text_proj_m(text_output_m.last_hidden_state[:,0,:]),dim=-1) + text_feat_m_all = torch.cat([text_feat_m.t(),self.text_queue.clone().detach()],dim=1) + + sim_i2t_m = image_feat_m @ text_feat_m_all / self.temp + sim_t2i_m = text_feat_m @ image_feat_m_all / self.temp + + sim_i2t_targets = alpha * F.softmax(sim_i2t_m, dim=1) + (1 - alpha) * sim_targets + sim_t2i_targets = alpha * F.softmax(sim_t2i_m, dim=1) + (1 - alpha) * sim_targets + + sim_i2t = image_feat @ text_feat_m_all / self.temp + sim_t2i = text_feat @ image_feat_m_all / self.temp + + loss_i2t = -torch.sum(F.log_softmax(sim_i2t, dim=1)*sim_i2t_targets,dim=1).mean() + loss_t2i = -torch.sum(F.log_softmax(sim_t2i, dim=1)*sim_t2i_targets,dim=1).mean() + + loss_ita = (loss_i2t+loss_t2i)/2 + + idxs = concat_all_gather(idx) + self._dequeue_and_enqueue(image_feat_m, text_feat_m, idxs) + + ###============== Image-text Matching ===================### + encoder_input_ids = text.input_ids.clone() + encoder_input_ids[:,0] = self.tokenizer.enc_token_id + + # forward the positve image-text pair + bs = image.size(0) + output_pos = self.text_encoder(encoder_input_ids, + attention_mask = text.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True, + ) + + + if self.negative_all_rank: + # compute sample similarity + with torch.no_grad(): + mask = torch.eq(idx, idxs.t()) + + image_feat_world = concat_all_gather(image_feat) + text_feat_world = concat_all_gather(text_feat) + + sim_i2t = image_feat @ text_feat_world.t() / self.temp + sim_t2i = text_feat @ image_feat_world.t() / self.temp + + weights_i2t = F.softmax(sim_i2t,dim=1) + weights_i2t.masked_fill_(mask, 0) + + weights_t2i = F.softmax(sim_t2i,dim=1) + weights_t2i.masked_fill_(mask, 0) + + image_embeds_world = all_gather_with_grad(image_embeds) + + # select a negative image (from all ranks) for each text + image_embeds_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_t2i[b], 1).item() + image_embeds_neg.append(image_embeds_world[neg_idx]) + image_embeds_neg = torch.stack(image_embeds_neg,dim=0) + + # select a negative text (from all ranks) for each image + input_ids_world = concat_all_gather(encoder_input_ids) + att_mask_world = concat_all_gather(text.attention_mask) + + text_ids_neg = [] + text_atts_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_i2t[b], 1).item() + text_ids_neg.append(input_ids_world[neg_idx]) + text_atts_neg.append(att_mask_world[neg_idx]) + + else: + with torch.no_grad(): + mask = torch.eq(idx, idx.t()) + + sim_i2t = image_feat @ text_feat.t() / self.temp + sim_t2i = text_feat @ image_feat.t() / self.temp + + weights_i2t = F.softmax(sim_i2t,dim=1) + weights_i2t.masked_fill_(mask, 0) + + weights_t2i = F.softmax(sim_t2i,dim=1) + weights_t2i.masked_fill_(mask, 0) + + # select a negative image (from same rank) for each text + image_embeds_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_t2i[b], 1).item() + image_embeds_neg.append(image_embeds[neg_idx]) + image_embeds_neg = torch.stack(image_embeds_neg,dim=0) + + # select a negative text (from same rank) for each image + text_ids_neg = [] + text_atts_neg = [] + for b in range(bs): + neg_idx = torch.multinomial(weights_i2t[b], 1).item() + text_ids_neg.append(encoder_input_ids[neg_idx]) + text_atts_neg.append(text.attention_mask[neg_idx]) + + text_ids_neg = torch.stack(text_ids_neg,dim=0) + text_atts_neg = torch.stack(text_atts_neg,dim=0) + + text_ids_all = torch.cat([encoder_input_ids, text_ids_neg],dim=0) + text_atts_all = torch.cat([text.attention_mask, text_atts_neg],dim=0) + + image_embeds_all = torch.cat([image_embeds_neg,image_embeds],dim=0) + image_atts_all = torch.cat([image_atts,image_atts],dim=0) + + output_neg = self.text_encoder(text_ids_all, + attention_mask = text_atts_all, + encoder_hidden_states = image_embeds_all, + encoder_attention_mask = image_atts_all, + return_dict = True, + ) + + + vl_embeddings = torch.cat([output_pos.last_hidden_state[:,0,:], output_neg.last_hidden_state[:,0,:]],dim=0) + vl_output = self.itm_head(vl_embeddings) + + itm_labels = torch.cat([torch.ones(bs,dtype=torch.long),torch.zeros(2*bs,dtype=torch.long)], + dim=0).to(image.device) + loss_itm = F.cross_entropy(vl_output, itm_labels) + + return loss_ita, loss_itm + + + @torch.no_grad() + def copy_params(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data.copy_(param.data) # initialize + param_m.requires_grad = False # not update by gradient + + + @torch.no_grad() + def _momentum_update(self): + for model_pair in self.model_pairs: + for param, param_m in zip(model_pair[0].parameters(), model_pair[1].parameters()): + param_m.data = param_m.data * self.momentum + param.data * (1. - self.momentum) + + + @torch.no_grad() + def _dequeue_and_enqueue(self, image_feat, text_feat, idxs): + # gather keys before updating queue + image_feats = concat_all_gather(image_feat) + text_feats = concat_all_gather(text_feat) + + + batch_size = image_feats.shape[0] + + ptr = int(self.ptr_queue) + assert self.queue_size % batch_size == 0 # for simplicity + + # replace the keys at ptr (dequeue and enqueue) + self.image_queue[:, ptr:ptr + batch_size] = image_feats.T + self.text_queue[:, ptr:ptr + batch_size] = text_feats.T + self.idx_queue[:, ptr:ptr + batch_size] = idxs.T + ptr = (ptr + batch_size) % self.queue_size # move pointer + + self.ptr_queue[0] = ptr + + +def blip_retrieval(pretrained='',**kwargs): + model = BLIP_Retrieval(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) + print("missing keys:") + print(msg.missing_keys) + return model + + +@torch.no_grad() +def concat_all_gather(tensor): + """ + Performs all_gather operation on the provided tensors. + *** Warning ***: torch.distributed.all_gather has no gradient. + """ + tensors_gather = [torch.ones_like(tensor) + for _ in range(torch.distributed.get_world_size())] + torch.distributed.all_gather(tensors_gather, tensor, async_op=False) + + output = torch.cat(tensors_gather, dim=0) + return output + + +class GatherLayer(torch.autograd.Function): + """ + Gather tensors from all workers with support for backward propagation: + This implementation does not cut the gradients as torch.distributed.all_gather does. + """ + + @staticmethod + def forward(ctx, x): + output = [torch.zeros_like(x) for _ in range(torch.distributed.get_world_size())] + torch.distributed.all_gather(output, x) + return tuple(output) + + @staticmethod + def backward(ctx, *grads): + all_gradients = torch.stack(grads) + torch.distributed.all_reduce(all_gradients) + return all_gradients[torch.distributed.get_rank()] + + +def all_gather_with_grad(tensors): + """ + Performs all_gather operation on the provided tensors. + Graph remains connected for backward grad computation. + """ + # Queue the gathered tensors + world_size = torch.distributed.get_world_size() + # There is no need for reduction in the single-proc case + if world_size == 1: + return tensors + + tensor_all = GatherLayer.apply(tensors) + + return torch.cat(tensor_all, dim=0) diff --git a/extras/BLIP/models/blip_vqa.py b/extras/BLIP/models/blip_vqa.py new file mode 100644 index 0000000000000000000000000000000000000000..99928a8b50a4521e2f8ea074ee8dafd197e6fcc4 --- /dev/null +++ b/extras/BLIP/models/blip_vqa.py @@ -0,0 +1,186 @@ +from extras.BLIP.models.med import BertConfig, BertModel, BertLMHeadModel +from extras.BLIP.models.blip import create_vit, init_tokenizer, load_checkpoint + +import torch +from torch import nn +import torch.nn.functional as F +from transformers import BertTokenizer +import numpy as np + +class BLIP_VQA(nn.Module): + def __init__(self, + med_config = 'configs/med_config.json', + image_size = 480, + vit = 'base', + vit_grad_ckpt = False, + vit_ckpt_layer = 0, + ): + """ + Args: + med_config (str): path for the mixture of encoder-decoder model's configuration file + image_size (int): input image size + vit (str): model size of vision transformer + """ + super().__init__() + + self.visual_encoder, vision_width = create_vit(vit, image_size, vit_grad_ckpt, vit_ckpt_layer, drop_path_rate=0.1) + self.tokenizer = init_tokenizer() + + encoder_config = BertConfig.from_json_file(med_config) + encoder_config.encoder_width = vision_width + self.text_encoder = BertModel(config=encoder_config, add_pooling_layer=False) + + decoder_config = BertConfig.from_json_file(med_config) + self.text_decoder = BertLMHeadModel(config=decoder_config) + + + def forward(self, image, question, answer=None, n=None, weights=None, train=True, inference='rank', k_test=128): + + image_embeds = self.visual_encoder(image) + image_atts = torch.ones(image_embeds.size()[:-1],dtype=torch.long).to(image.device) + + question = self.tokenizer(question, padding='longest', truncation=True, max_length=35, + return_tensors="pt").to(image.device) + question.input_ids[:,0] = self.tokenizer.enc_token_id + + if train: + ''' + n: number of answers for each question + weights: weight for each answer + ''' + answer = self.tokenizer(answer, padding='longest', return_tensors="pt").to(image.device) + answer.input_ids[:,0] = self.tokenizer.bos_token_id + answer_targets = answer.input_ids.masked_fill(answer.input_ids == self.tokenizer.pad_token_id, -100) + + question_output = self.text_encoder(question.input_ids, + attention_mask = question.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True) + + question_states = [] + question_atts = [] + for b, n in enumerate(n): + question_states += [question_output.last_hidden_state[b]]*n + question_atts += [question.attention_mask[b]]*n + question_states = torch.stack(question_states,0) + question_atts = torch.stack(question_atts,0) + + answer_output = self.text_decoder(answer.input_ids, + attention_mask = answer.attention_mask, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + labels = answer_targets, + return_dict = True, + reduction = 'none', + ) + + loss = weights * answer_output.loss + loss = loss.sum()/image.size(0) + + return loss + + + else: + question_output = self.text_encoder(question.input_ids, + attention_mask = question.attention_mask, + encoder_hidden_states = image_embeds, + encoder_attention_mask = image_atts, + return_dict = True) + + if inference=='generate': + num_beams = 3 + question_states = question_output.last_hidden_state.repeat_interleave(num_beams,dim=0) + question_atts = torch.ones(question_states.size()[:-1],dtype=torch.long).to(question_states.device) + model_kwargs = {"encoder_hidden_states": question_states, "encoder_attention_mask":question_atts} + + bos_ids = torch.full((image.size(0),1),fill_value=self.tokenizer.bos_token_id,device=image.device) + + outputs = self.text_decoder.generate(input_ids=bos_ids, + max_length=10, + min_length=1, + num_beams=num_beams, + eos_token_id=self.tokenizer.sep_token_id, + pad_token_id=self.tokenizer.pad_token_id, + **model_kwargs) + + answers = [] + for output in outputs: + answer = self.tokenizer.decode(output, skip_special_tokens=True) + answers.append(answer) + return answers + + elif inference=='rank': + max_ids = self.rank_answer(question_output.last_hidden_state, question.attention_mask, + answer.input_ids, answer.attention_mask, k_test) + return max_ids + + + + def rank_answer(self, question_states, question_atts, answer_ids, answer_atts, k): + + num_ques = question_states.size(0) + start_ids = answer_ids[0,0].repeat(num_ques,1) # bos token + + start_output = self.text_decoder(start_ids, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + return_dict = True, + reduction = 'none') + logits = start_output.logits[:,0,:] # first token's logit + + # topk_probs: top-k probability + # topk_ids: [num_question, k] + answer_first_token = answer_ids[:,1] + prob_first_token = F.softmax(logits,dim=1).index_select(dim=1, index=answer_first_token) + topk_probs, topk_ids = prob_first_token.topk(k,dim=1) + + # answer input: [num_question*k, answer_len] + input_ids = [] + input_atts = [] + for b, topk_id in enumerate(topk_ids): + input_ids.append(answer_ids.index_select(dim=0, index=topk_id)) + input_atts.append(answer_atts.index_select(dim=0, index=topk_id)) + input_ids = torch.cat(input_ids,dim=0) + input_atts = torch.cat(input_atts,dim=0) + + targets_ids = input_ids.masked_fill(input_ids == self.tokenizer.pad_token_id, -100) + + # repeat encoder's output for top-k answers + question_states = tile(question_states, 0, k) + question_atts = tile(question_atts, 0, k) + + output = self.text_decoder(input_ids, + attention_mask = input_atts, + encoder_hidden_states = question_states, + encoder_attention_mask = question_atts, + labels = targets_ids, + return_dict = True, + reduction = 'none') + + log_probs_sum = -output.loss + log_probs_sum = log_probs_sum.view(num_ques,k) + + max_topk_ids = log_probs_sum.argmax(dim=1) + max_ids = topk_ids[max_topk_ids>=0,max_topk_ids] + + return max_ids + + +def blip_vqa(pretrained='',**kwargs): + model = BLIP_VQA(**kwargs) + if pretrained: + model,msg = load_checkpoint(model,pretrained) +# assert(len(msg.missing_keys)==0) + return model + + +def tile(x, dim, n_tile): + init_dim = x.size(dim) + repeat_idx = [1] * x.dim() + repeat_idx[dim] = n_tile + x = x.repeat(*(repeat_idx)) + order_index = torch.LongTensor(np.concatenate([init_dim * np.arange(n_tile) + i for i in range(init_dim)])) + return torch.index_select(x, dim, order_index.to(x.device)) + + \ No newline at end of file diff --git a/extras/BLIP/models/med.py b/extras/BLIP/models/med.py new file mode 100644 index 0000000000000000000000000000000000000000..7b00a35450b736180a805d4f4664b4fb95aeba01 --- /dev/null +++ b/extras/BLIP/models/med.py @@ -0,0 +1,955 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on huggingface code base + * https://github.com/huggingface/transformers/blob/v4.15.0/src/transformers/models/bert +''' + +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +from torch import Tensor, device, dtype, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +import torch.nn.functional as F + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False): + super().__init__() + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + + + +class BertLMHeadModel(BertPreTrainedModel): + + _keys_to_ignore_on_load_unexpected = [r"pooler"] + _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] + + def __init__(self, config): + super().__init__(config) + + self.bert = BertModel(config, add_pooling_layer=False) + self.cls = BertOnlyMLMHead(config) + + self.init_weights() + + def get_output_embeddings(self): + return self.cls.predictions.decoder + + def set_output_embeddings(self, new_embeddings): + self.cls.predictions.decoder = new_embeddings + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + labels=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + return_logits=False, + is_decoder=True, + reduction='mean', + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in + ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are + ignored (masked), the loss is only computed for the tokens with labels n ``[0, ..., config.vocab_size]`` + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + Returns: + Example:: + >>> from transformers import BertTokenizer, BertLMHeadModel, BertConfig + >>> import torch + >>> tokenizer = BertTokenizer.from_pretrained('bert-base-cased') + >>> config = BertConfig.from_pretrained("bert-base-cased") + >>> model = BertLMHeadModel.from_pretrained('bert-base-cased', config=config) + >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") + >>> outputs = model(**inputs) + >>> prediction_logits = outputs.logits + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + if labels is not None: + use_cache = False + + outputs = self.bert( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + is_decoder=is_decoder, + mode=mode, + ) + + sequence_output = outputs[0] + prediction_scores = self.cls(sequence_output) + + if return_logits: + return prediction_scores[:, :-1, :].contiguous() + + lm_loss = None + if labels is not None: + # we are doing next-token prediction; shift prediction scores and input ids by one + shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous() + labels = labels[:, 1:].contiguous() + loss_fct = CrossEntropyLoss(reduction=reduction, label_smoothing=0.1) + lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) + if reduction=='none': + lm_loss = lm_loss.view(prediction_scores.size(0),-1).sum(1) + + if not return_dict: + output = (prediction_scores,) + outputs[2:] + return ((lm_loss,) + output) if lm_loss is not None else output + + return CausalLMOutputWithCrossAttentions( + loss=lm_loss, + logits=prediction_scores, + past_key_values=outputs.past_key_values, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + cross_attentions=outputs.cross_attentions, + ) + + def prepare_inputs_for_generation(self, input_ids, past=None, attention_mask=None, **model_kwargs): + input_shape = input_ids.shape + # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly + if attention_mask is None: + attention_mask = input_ids.new_ones(input_shape) + + # cut decoder_input_ids if past is used + if past is not None: + input_ids = input_ids[:, -1:] + + return { + "input_ids": input_ids, + "attention_mask": attention_mask, + "past_key_values": past, + "encoder_hidden_states": model_kwargs.get("encoder_hidden_states", None), + "encoder_attention_mask": model_kwargs.get("encoder_attention_mask", None), + "is_decoder": True, + } + + def _reorder_cache(self, past, beam_idx): + reordered_past = () + for layer_past in past: + reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),) + return reordered_past diff --git a/extras/BLIP/models/nlvr_encoder.py b/extras/BLIP/models/nlvr_encoder.py new file mode 100644 index 0000000000000000000000000000000000000000..1946bb4a300f75afa4848f6622839445903c34a9 --- /dev/null +++ b/extras/BLIP/models/nlvr_encoder.py @@ -0,0 +1,843 @@ +import math +import os +import warnings +from dataclasses import dataclass +from typing import Optional, Tuple + +import torch +from torch import Tensor, device, dtype, nn +import torch.utils.checkpoint +from torch import nn +from torch.nn import CrossEntropyLoss +import torch.nn.functional as F + +from transformers.activations import ACT2FN +from transformers.file_utils import ( + ModelOutput, +) +from transformers.modeling_outputs import ( + BaseModelOutputWithPastAndCrossAttentions, + BaseModelOutputWithPoolingAndCrossAttentions, + CausalLMOutputWithCrossAttentions, + MaskedLMOutput, + MultipleChoiceModelOutput, + NextSentencePredictorOutput, + QuestionAnsweringModelOutput, + SequenceClassifierOutput, + TokenClassifierOutput, +) +from transformers.modeling_utils import ( + PreTrainedModel, + apply_chunking_to_forward, + find_pruneable_heads_and_indices, + prune_linear_layer, +) +from transformers.utils import logging +from transformers.models.bert.configuration_bert import BertConfig + + +logger = logging.get_logger(__name__) + + +class BertEmbeddings(nn.Module): + """Construct the embeddings from word and position embeddings.""" + + def __init__(self, config): + super().__init__() + self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id) + self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size) + + # self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load + # any TensorFlow checkpoint file + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + # position_ids (1, len position emb) is contiguous in memory and exported when serialized + self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1))) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + + self.config = config + + def forward( + self, input_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0 + ): + if input_ids is not None: + input_shape = input_ids.size() + else: + input_shape = inputs_embeds.size()[:-1] + + seq_length = input_shape[1] + + if position_ids is None: + position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] + + if inputs_embeds is None: + inputs_embeds = self.word_embeddings(input_ids) + + embeddings = inputs_embeds + + if self.position_embedding_type == "absolute": + position_embeddings = self.position_embeddings(position_ids) + embeddings += position_embeddings + embeddings = self.LayerNorm(embeddings) + embeddings = self.dropout(embeddings) + return embeddings + + +class BertSelfAttention(nn.Module): + def __init__(self, config, is_cross_attention): + super().__init__() + self.config = config + if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): + raise ValueError( + "The hidden size (%d) is not a multiple of the number of attention " + "heads (%d)" % (config.hidden_size, config.num_attention_heads) + ) + + self.num_attention_heads = config.num_attention_heads + self.attention_head_size = int(config.hidden_size / config.num_attention_heads) + self.all_head_size = self.num_attention_heads * self.attention_head_size + + self.query = nn.Linear(config.hidden_size, self.all_head_size) + if is_cross_attention: + self.key = nn.Linear(config.encoder_width, self.all_head_size) + self.value = nn.Linear(config.encoder_width, self.all_head_size) + else: + self.key = nn.Linear(config.hidden_size, self.all_head_size) + self.value = nn.Linear(config.hidden_size, self.all_head_size) + + self.dropout = nn.Dropout(config.attention_probs_dropout_prob) + self.position_embedding_type = getattr(config, "position_embedding_type", "absolute") + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + self.max_position_embeddings = config.max_position_embeddings + self.distance_embedding = nn.Embedding(2 * config.max_position_embeddings - 1, self.attention_head_size) + self.save_attention = False + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def transpose_for_scores(self, x): + new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size) + x = x.view(*new_x_shape) + return x.permute(0, 2, 1, 3) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + mixed_query_layer = self.query(hidden_states) + + # If this is instantiated as a cross-attention module, the keys + # and values come from an encoder; the attention mask needs to be + # such that the encoder's padding tokens are not attended to. + is_cross_attention = encoder_hidden_states is not None + + if is_cross_attention: + key_layer = self.transpose_for_scores(self.key(encoder_hidden_states)) + value_layer = self.transpose_for_scores(self.value(encoder_hidden_states)) + attention_mask = encoder_attention_mask + elif past_key_value is not None: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + key_layer = torch.cat([past_key_value[0], key_layer], dim=2) + value_layer = torch.cat([past_key_value[1], value_layer], dim=2) + else: + key_layer = self.transpose_for_scores(self.key(hidden_states)) + value_layer = self.transpose_for_scores(self.value(hidden_states)) + + query_layer = self.transpose_for_scores(mixed_query_layer) + + past_key_value = (key_layer, value_layer) + + # Take the dot product between "query" and "key" to get the raw attention scores. + attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2)) + + if self.position_embedding_type == "relative_key" or self.position_embedding_type == "relative_key_query": + seq_length = hidden_states.size()[1] + position_ids_l = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(-1, 1) + position_ids_r = torch.arange(seq_length, dtype=torch.long, device=hidden_states.device).view(1, -1) + distance = position_ids_l - position_ids_r + positional_embedding = self.distance_embedding(distance + self.max_position_embeddings - 1) + positional_embedding = positional_embedding.to(dtype=query_layer.dtype) # fp16 compatibility + + if self.position_embedding_type == "relative_key": + relative_position_scores = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores + elif self.position_embedding_type == "relative_key_query": + relative_position_scores_query = torch.einsum("bhld,lrd->bhlr", query_layer, positional_embedding) + relative_position_scores_key = torch.einsum("bhrd,lrd->bhlr", key_layer, positional_embedding) + attention_scores = attention_scores + relative_position_scores_query + relative_position_scores_key + + attention_scores = attention_scores / math.sqrt(self.attention_head_size) + if attention_mask is not None: + # Apply the attention mask is (precomputed for all layers in BertModel forward() function) + attention_scores = attention_scores + attention_mask + + # Normalize the attention scores to probabilities. + attention_probs = nn.Softmax(dim=-1)(attention_scores) + + if is_cross_attention and self.save_attention: + self.save_attention_map(attention_probs) + attention_probs.register_hook(self.save_attn_gradients) + + # This is actually dropping out entire tokens to attend to, which might + # seem a bit unusual, but is taken from the original Transformer paper. + attention_probs_dropped = self.dropout(attention_probs) + + # Mask heads if we want to + if head_mask is not None: + attention_probs_dropped = attention_probs_dropped * head_mask + + context_layer = torch.matmul(attention_probs_dropped, value_layer) + + context_layer = context_layer.permute(0, 2, 1, 3).contiguous() + new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,) + context_layer = context_layer.view(*new_context_layer_shape) + + outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) + + outputs = outputs + (past_key_value,) + return outputs + + +class BertSelfOutput(nn.Module): + def __init__(self, config, twin=False, merge=False): + super().__init__() + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + if twin: + self.dense0 = nn.Linear(config.hidden_size, config.hidden_size) + self.dense1 = nn.Linear(config.hidden_size, config.hidden_size) + else: + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if merge: + self.act = ACT2FN[config.hidden_act] + self.merge_layer = nn.Linear(config.hidden_size * 2, config.hidden_size) + self.merge = True + else: + self.merge = False + + def forward(self, hidden_states, input_tensor): + if type(hidden_states) == list: + hidden_states0 = self.dense0(hidden_states[0]) + hidden_states1 = self.dense1(hidden_states[1]) + if self.merge: + #hidden_states = self.merge_layer(self.act(torch.cat([hidden_states0,hidden_states1],dim=-1))) + hidden_states = self.merge_layer(torch.cat([hidden_states0,hidden_states1],dim=-1)) + else: + hidden_states = (hidden_states0+hidden_states1)/2 + else: + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertAttention(nn.Module): + def __init__(self, config, is_cross_attention=False, layer_num=-1): + super().__init__() + if is_cross_attention: + self.self0 = BertSelfAttention(config, is_cross_attention) + self.self1 = BertSelfAttention(config, is_cross_attention) + else: + self.self = BertSelfAttention(config, is_cross_attention) + self.output = BertSelfOutput(config, twin=is_cross_attention, merge=(is_cross_attention and layer_num>=6)) + self.pruned_heads = set() + + def prune_heads(self, heads): + if len(heads) == 0: + return + heads, index = find_pruneable_heads_and_indices( + heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads + ) + + # Prune linear layers + self.self.query = prune_linear_layer(self.self.query, index) + self.self.key = prune_linear_layer(self.self.key, index) + self.self.value = prune_linear_layer(self.self.value, index) + self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) + + # Update hyper params and store pruned heads + self.self.num_attention_heads = self.self.num_attention_heads - len(heads) + self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads + self.pruned_heads = self.pruned_heads.union(heads) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + ): + if type(encoder_hidden_states)==list: + self_outputs0 = self.self0( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states[0], + encoder_attention_mask[0], + past_key_value, + output_attentions, + ) + self_outputs1 = self.self1( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states[1], + encoder_attention_mask[1], + past_key_value, + output_attentions, + ) + attention_output = self.output([self_outputs0[0],self_outputs1[0]], hidden_states) + + outputs = (attention_output,) + self_outputs0[1:] # add attentions if we output them + else: + self_outputs = self.self( + hidden_states, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + ) + attention_output = self.output(self_outputs[0], hidden_states) + outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them + return outputs + + +class BertIntermediate(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.intermediate_size) + if isinstance(config.hidden_act, str): + self.intermediate_act_fn = ACT2FN[config.hidden_act] + else: + self.intermediate_act_fn = config.hidden_act + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.intermediate_act_fn(hidden_states) + return hidden_states + + +class BertOutput(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.intermediate_size, config.hidden_size) + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + self.dropout = nn.Dropout(config.hidden_dropout_prob) + + def forward(self, hidden_states, input_tensor): + hidden_states = self.dense(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.LayerNorm(hidden_states + input_tensor) + return hidden_states + + +class BertLayer(nn.Module): + def __init__(self, config, layer_num): + super().__init__() + self.config = config + self.chunk_size_feed_forward = config.chunk_size_feed_forward + self.seq_len_dim = 1 + self.attention = BertAttention(config) + self.layer_num = layer_num + if self.config.add_cross_attention: + self.crossattention = BertAttention(config, is_cross_attention=self.config.add_cross_attention, layer_num=layer_num) + self.intermediate = BertIntermediate(config) + self.output = BertOutput(config) + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_value=None, + output_attentions=False, + mode=None, + ): + # decoder uni-directional self-attention cached key/values tuple is at positions 1,2 + self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None + self_attention_outputs = self.attention( + hidden_states, + attention_mask, + head_mask, + output_attentions=output_attentions, + past_key_value=self_attn_past_key_value, + ) + attention_output = self_attention_outputs[0] + + outputs = self_attention_outputs[1:-1] + present_key_value = self_attention_outputs[-1] + + if mode=='multimodal': + assert encoder_hidden_states is not None, "encoder_hidden_states must be given for cross-attention layers" + cross_attention_outputs = self.crossattention( + attention_output, + attention_mask, + head_mask, + encoder_hidden_states, + encoder_attention_mask, + output_attentions=output_attentions, + ) + attention_output = cross_attention_outputs[0] + outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights + layer_output = apply_chunking_to_forward( + self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output + ) + outputs = (layer_output,) + outputs + + outputs = outputs + (present_key_value,) + + return outputs + + def feed_forward_chunk(self, attention_output): + intermediate_output = self.intermediate(attention_output) + layer_output = self.output(intermediate_output, attention_output) + return layer_output + + +class BertEncoder(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.layer = nn.ModuleList([BertLayer(config,i) for i in range(config.num_hidden_layers)]) + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + attention_mask=None, + head_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=False, + output_hidden_states=False, + return_dict=True, + mode='multimodal', + ): + all_hidden_states = () if output_hidden_states else None + all_self_attentions = () if output_attentions else None + all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None + + next_decoder_cache = () if use_cache else None + + for i in range(self.config.num_hidden_layers): + layer_module = self.layer[i] + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + layer_head_mask = head_mask[i] if head_mask is not None else None + past_key_value = past_key_values[i] if past_key_values is not None else None + + if self.gradient_checkpointing and self.training: + + if use_cache: + logger.warn( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs, past_key_value, output_attentions) + + return custom_forward + + layer_outputs = torch.utils.checkpoint.checkpoint( + create_custom_forward(layer_module), + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + mode=mode, + ) + else: + layer_outputs = layer_module( + hidden_states, + attention_mask, + layer_head_mask, + encoder_hidden_states, + encoder_attention_mask, + past_key_value, + output_attentions, + mode=mode, + ) + + hidden_states = layer_outputs[0] + if use_cache: + next_decoder_cache += (layer_outputs[-1],) + if output_attentions: + all_self_attentions = all_self_attentions + (layer_outputs[1],) + + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + next_decoder_cache, + all_hidden_states, + all_self_attentions, + all_cross_attentions, + ] + if v is not None + ) + return BaseModelOutputWithPastAndCrossAttentions( + last_hidden_state=hidden_states, + past_key_values=next_decoder_cache, + hidden_states=all_hidden_states, + attentions=all_self_attentions, + cross_attentions=all_cross_attentions, + ) + + +class BertPooler(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.activation = nn.Tanh() + + def forward(self, hidden_states): + # We "pool" the model by simply taking the hidden state corresponding + # to the first token. + first_token_tensor = hidden_states[:, 0] + pooled_output = self.dense(first_token_tensor) + pooled_output = self.activation(pooled_output) + return pooled_output + + +class BertPredictionHeadTransform(nn.Module): + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + if isinstance(config.hidden_act, str): + self.transform_act_fn = ACT2FN[config.hidden_act] + else: + self.transform_act_fn = config.hidden_act + self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) + + def forward(self, hidden_states): + hidden_states = self.dense(hidden_states) + hidden_states = self.transform_act_fn(hidden_states) + hidden_states = self.LayerNorm(hidden_states) + return hidden_states + + +class BertLMPredictionHead(nn.Module): + def __init__(self, config): + super().__init__() + self.transform = BertPredictionHeadTransform(config) + + # The output weights are the same as the input embeddings, but there is + # an output-only bias for each token. + self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + + self.bias = nn.Parameter(torch.zeros(config.vocab_size)) + + # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings` + self.decoder.bias = self.bias + + def forward(self, hidden_states): + hidden_states = self.transform(hidden_states) + hidden_states = self.decoder(hidden_states) + return hidden_states + + +class BertOnlyMLMHead(nn.Module): + def __init__(self, config): + super().__init__() + self.predictions = BertLMPredictionHead(config) + + def forward(self, sequence_output): + prediction_scores = self.predictions(sequence_output) + return prediction_scores + + +class BertPreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = BertConfig + base_model_prefix = "bert" + _keys_to_ignore_on_load_missing = [r"position_ids"] + + def _init_weights(self, module): + """ Initialize the weights """ + if isinstance(module, (nn.Linear, nn.Embedding)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + + +class BertModel(BertPreTrainedModel): + """ + The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of + cross-attention is added between the self-attention layers, following the architecture described in `Attention is + all you need `__ by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, + Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. + argument and :obj:`add_cross_attention` set to :obj:`True`; an :obj:`encoder_hidden_states` is then expected as an + input to the forward pass. + """ + + def __init__(self, config, add_pooling_layer=True): + super().__init__(config) + self.config = config + + self.embeddings = BertEmbeddings(config) + + self.encoder = BertEncoder(config) + + self.pooler = BertPooler(config) if add_pooling_layer else None + + self.init_weights() + + + def get_input_embeddings(self): + return self.embeddings.word_embeddings + + def set_input_embeddings(self, value): + self.embeddings.word_embeddings = value + + def _prune_heads(self, heads_to_prune): + """ + Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base + class PreTrainedModel + """ + for layer, heads in heads_to_prune.items(): + self.encoder.layer[layer].attention.prune_heads(heads) + + + def get_extended_attention_mask(self, attention_mask: Tensor, input_shape: Tuple[int], device: device, is_decoder: bool) -> Tensor: + """ + Makes broadcastable attention and causal masks so that future and masked tokens are ignored. + + Arguments: + attention_mask (:obj:`torch.Tensor`): + Mask with ones indicating tokens to attend to, zeros for tokens to ignore. + input_shape (:obj:`Tuple[int]`): + The shape of the input to the model. + device: (:obj:`torch.device`): + The device of the input to the model. + + Returns: + :obj:`torch.Tensor` The extended attention mask, with a the same dtype as :obj:`attention_mask.dtype`. + """ + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + if attention_mask.dim() == 3: + extended_attention_mask = attention_mask[:, None, :, :] + elif attention_mask.dim() == 2: + # Provided a padding mask of dimensions [batch_size, seq_length] + # - if the model is a decoder, apply a causal mask in addition to the padding mask + # - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, seq_length, seq_length] + if is_decoder: + batch_size, seq_length = input_shape + + seq_ids = torch.arange(seq_length, device=device) + causal_mask = seq_ids[None, None, :].repeat(batch_size, seq_length, 1) <= seq_ids[None, :, None] + # in case past_key_values are used we need to add a prefix ones mask to the causal mask + # causal and attention masks must have same type with pytorch version < 1.3 + causal_mask = causal_mask.to(attention_mask.dtype) + + if causal_mask.shape[1] < attention_mask.shape[1]: + prefix_seq_len = attention_mask.shape[1] - causal_mask.shape[1] + causal_mask = torch.cat( + [ + torch.ones((batch_size, seq_length, prefix_seq_len), device=device, dtype=causal_mask.dtype), + causal_mask, + ], + axis=-1, + ) + + extended_attention_mask = causal_mask[:, None, :, :] * attention_mask[:, None, None, :] + else: + extended_attention_mask = attention_mask[:, None, None, :] + else: + raise ValueError( + "Wrong shape for input_ids (shape {}) or attention_mask (shape {})".format( + input_shape, attention_mask.shape + ) + ) + + # Since attention_mask is 1.0 for positions we want to attend and 0.0 for + # masked positions, this operation will create a tensor which is 0.0 for + # positions we want to attend and -10000.0 for masked positions. + # Since we are adding it to the raw scores before the softmax, this is + # effectively the same as removing these entirely. + extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility + extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 + return extended_attention_mask + + def forward( + self, + input_ids=None, + attention_mask=None, + position_ids=None, + head_mask=None, + inputs_embeds=None, + encoder_embeds=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + past_key_values=None, + use_cache=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + is_decoder=False, + mode='multimodal', + ): + r""" + encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if + the model is configured as a decoder. + encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): + Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in + the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``: + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): + Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. + If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids` + (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)` + instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`. + use_cache (:obj:`bool`, `optional`): + If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up + decoding (see :obj:`past_key_values`). + """ + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if is_decoder: + use_cache = use_cache if use_cache is not None else self.config.use_cache + else: + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + input_shape = input_ids.size() + batch_size, seq_length = input_shape + device = input_ids.device + elif inputs_embeds is not None: + input_shape = inputs_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = inputs_embeds.device + elif encoder_embeds is not None: + input_shape = encoder_embeds.size()[:-1] + batch_size, seq_length = input_shape + device = encoder_embeds.device + else: + raise ValueError("You have to specify either input_ids or inputs_embeds or encoder_embeds") + + # past_key_values_length + past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 + + if attention_mask is None: + attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) + + # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] + # ourselves in which case we just need to make it broadcastable to all heads. + extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape, + device, is_decoder) + + # If a 2D or 3D attention mask is provided for the cross-attention + # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] + if encoder_hidden_states is not None: + if type(encoder_hidden_states) == list: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states[0].size() + else: + encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() + encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) + + if type(encoder_attention_mask) == list: + encoder_extended_attention_mask = [self.invert_attention_mask(mask) for mask in encoder_attention_mask] + elif encoder_attention_mask is None: + encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) + else: + encoder_extended_attention_mask = None + + # Prepare head mask if needed + # 1.0 in head_mask indicate we keep the head + # attention_probs has shape bsz x n_heads x N x N + # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] + # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] + head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) + + if encoder_embeds is None: + embedding_output = self.embeddings( + input_ids=input_ids, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + past_key_values_length=past_key_values_length, + ) + else: + embedding_output = encoder_embeds + + encoder_outputs = self.encoder( + embedding_output, + attention_mask=extended_attention_mask, + head_mask=head_mask, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_extended_attention_mask, + past_key_values=past_key_values, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + mode=mode, + ) + sequence_output = encoder_outputs[0] + pooled_output = self.pooler(sequence_output) if self.pooler is not None else None + + if not return_dict: + return (sequence_output, pooled_output) + encoder_outputs[1:] + + return BaseModelOutputWithPoolingAndCrossAttentions( + last_hidden_state=sequence_output, + pooler_output=pooled_output, + past_key_values=encoder_outputs.past_key_values, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + cross_attentions=encoder_outputs.cross_attentions, + ) + diff --git a/extras/BLIP/models/vit.py b/extras/BLIP/models/vit.py new file mode 100644 index 0000000000000000000000000000000000000000..91c0adad7c8728ae01666bfbdd83d5fd040f5be9 --- /dev/null +++ b/extras/BLIP/models/vit.py @@ -0,0 +1,308 @@ +''' + * Copyright (c) 2022, salesforce.com, inc. + * All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause + * By Junnan Li + * Based on timm code base + * https://github.com/rwightman/pytorch-image-models/tree/master/timm +''' + +import torch +import torch.nn as nn +import torch.nn.functional as F +from functools import partial + +from timm.models.vision_transformer import _cfg, PatchEmbed +from timm.models.registry import register_model +from timm.models.layers import trunc_normal_, DropPath +from timm.models.helpers import named_apply, adapt_input_conv + + +def checkpoint_wrapper(x): + return x + + +class Mlp(nn.Module): + """ MLP as used in Vision Transformer, MLP-Mixer and related networks + """ + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +class Attention(nn.Module): + def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights + self.scale = qk_scale or head_dim ** -0.5 + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.attn_gradients = None + self.attention_map = None + + def save_attn_gradients(self, attn_gradients): + self.attn_gradients = attn_gradients + + def get_attn_gradients(self): + return self.attn_gradients + + def save_attention_map(self, attention_map): + self.attention_map = attention_map + + def get_attention_map(self): + return self.attention_map + + def forward(self, x, register_hook=False): + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + if register_hook: + self.save_attention_map(attn) + attn.register_hook(self.save_attn_gradients) + + x = (attn @ v).transpose(1, 2).reshape(B, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., + drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_grad_checkpointing=False): + super().__init__() + self.norm1 = norm_layer(dim) + self.attn = Attention( + dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if use_grad_checkpointing: + self.attn = checkpoint_wrapper(self.attn) + self.mlp = checkpoint_wrapper(self.mlp) + + def forward(self, x, register_hook=False): + x = x + self.drop_path(self.attn(self.norm1(x), register_hook=register_hook)) + x = x + self.drop_path(self.mlp(self.norm2(x))) + return x + + +class VisionTransformer(nn.Module): + """ Vision Transformer + A PyTorch impl of : `An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale` - + https://arxiv.org/abs/2010.11929 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, + num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, + drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=None, + use_grad_checkpointing=False, ckpt_layer=0): + """ + Args: + img_size (int, tuple): input image size + patch_size (int, tuple): patch size + in_chans (int): number of input channels + num_classes (int): number of classes for classification head + embed_dim (int): embedding dimension + depth (int): depth of transformer + num_heads (int): number of attention heads + mlp_ratio (int): ratio of mlp hidden dim to embedding dim + qkv_bias (bool): enable bias for qkv if True + qk_scale (float): override default qk scale of head_dim ** -0.5 if set + representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set + drop_rate (float): dropout rate + attn_drop_rate (float): attention dropout rate + drop_path_rate (float): stochastic depth rate + norm_layer: (nn.Module): normalization layer + """ + super().__init__() + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + norm_layer = norm_layer or partial(nn.LayerNorm, eps=1e-6) + + self.patch_embed = PatchEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim) + + num_patches = self.patch_embed.num_patches + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, + drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, + use_grad_checkpointing=(use_grad_checkpointing and i>=depth-ckpt_layer) + ) + for i in range(depth)]) + self.norm = norm_layer(embed_dim) + + trunc_normal_(self.pos_embed, std=.02) + trunc_normal_(self.cls_token, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def forward(self, x, register_blk=-1): + B = x.shape[0] + x = self.patch_embed(x) + + cls_tokens = self.cls_token.expand(B, -1, -1) # stole cls_tokens impl from Phil Wang, thanks + x = torch.cat((cls_tokens, x), dim=1) + + x = x + self.pos_embed[:,:x.size(1),:] + x = self.pos_drop(x) + + for i,blk in enumerate(self.blocks): + x = blk(x, register_blk==i) + x = self.norm(x) + + return x + + @torch.jit.ignore() + def load_pretrained(self, checkpoint_path, prefix=''): + _load_weights(self, checkpoint_path, prefix) + + +@torch.no_grad() +def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): + """ Load weights from .npz checkpoints for official Google Brain Flax implementation + """ + import numpy as np + + def _n2p(w, t=True): + if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1: + w = w.flatten() + if t: + if w.ndim == 4: + w = w.transpose([3, 2, 0, 1]) + elif w.ndim == 3: + w = w.transpose([2, 0, 1]) + elif w.ndim == 2: + w = w.transpose([1, 0]) + return torch.from_numpy(w) + + w = np.load(checkpoint_path) + if not prefix and 'opt/target/embedding/kernel' in w: + prefix = 'opt/target/' + + if hasattr(model.patch_embed, 'backbone'): + # hybrid + backbone = model.patch_embed.backbone + stem_only = not hasattr(backbone, 'stem') + stem = backbone if stem_only else backbone.stem + stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel']))) + stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale'])) + stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias'])) + if not stem_only: + for i, stage in enumerate(backbone.stages): + for j, block in enumerate(stage.blocks): + bp = f'{prefix}block{i + 1}/unit{j + 1}/' + for r in range(3): + getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel'])) + getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale'])) + getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias'])) + if block.downsample is not None: + block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel'])) + block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale'])) + block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias'])) + embed_conv_w = _n2p(w[f'{prefix}embedding/kernel']) + else: + embed_conv_w = adapt_input_conv( + model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) + model.patch_embed.proj.weight.copy_(embed_conv_w) + model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) + model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) + pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) + if pos_embed_w.shape != model.pos_embed.shape: + pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights + pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size) + model.pos_embed.copy_(pos_embed_w) + model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) + model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias'])) +# if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]: +# model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel'])) +# model.head.bias.copy_(_n2p(w[f'{prefix}head/bias'])) +# if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: +# model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) +# model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) + for i, block in enumerate(model.blocks.children()): + block_prefix = f'{prefix}Transformer/encoderblock_{i}/' + mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' + block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) + block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) + block.attn.qkv.weight.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')])) + block.attn.qkv.bias.copy_(torch.cat([ + _n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')])) + block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) + block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) + for r in range(2): + getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) + getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) + block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) + block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) + + +def interpolate_pos_embed(pos_embed_checkpoint, visual_encoder): + # interpolate position embedding + embedding_size = pos_embed_checkpoint.shape[-1] + num_patches = visual_encoder.patch_embed.num_patches + num_extra_tokens = visual_encoder.pos_embed.shape[-2] - num_patches + # height (== width) for the checkpoint position embedding + orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5) + # height (== width) for the new position embedding + new_size = int(num_patches ** 0.5) + + if orig_size!=new_size: + # class_token and dist_token are kept unchanged + extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens] + # only the position tokens are interpolated + pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:] + pos_tokens = pos_tokens.reshape(-1, orig_size, orig_size, embedding_size).permute(0, 3, 1, 2) + pos_tokens = torch.nn.functional.interpolate( + pos_tokens, size=(new_size, new_size), mode='bicubic', align_corners=False) + pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2) + new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1) + print('reshape position embedding from %d to %d'%(orig_size ** 2,new_size ** 2)) + + return new_pos_embed + else: + return pos_embed_checkpoint \ No newline at end of file diff --git a/extras/expansion.py b/extras/expansion.py new file mode 100644 index 0000000000000000000000000000000000000000..34c1ee8d4e716cae5662055d703426e63c71b8e5 --- /dev/null +++ b/extras/expansion.py @@ -0,0 +1,129 @@ +# Fooocus GPT2 Expansion +# Algorithm created by Lvmin Zhang at 2023, Stanford +# If used inside Fooocus, any use is permitted. +# If used outside Fooocus, only non-commercial use is permitted (CC-By NC 4.0). +# This applies to the word list, vocab, model, and algorithm. + + +import os +import torch +import math +import ldm_patched.modules.model_management as model_management + +from transformers.generation.logits_process import LogitsProcessorList +from transformers import AutoTokenizer, AutoModelForCausalLM, set_seed +from modules.config import path_fooocus_expansion +from ldm_patched.modules.model_patcher import ModelPatcher + + +# limitation of np.random.seed(), called from transformers.set_seed() +SEED_LIMIT_NUMPY = 2**32 +neg_inf = - 8192.0 + + +def safe_str(x): + x = str(x) + for _ in range(16): + x = x.replace(' ', ' ') + return x.strip(",. \r\n") + + +def remove_pattern(x, pattern): + for p in pattern: + x = x.replace(p, '') + return x + + +class FooocusExpansion: + def __init__(self): + self.tokenizer = AutoTokenizer.from_pretrained(path_fooocus_expansion) + + positive_words = open(os.path.join(path_fooocus_expansion, 'positive.txt'), + encoding='utf-8').read().splitlines() + positive_words = ['Ġ' + x.lower() for x in positive_words if x != ''] + + self.logits_bias = torch.zeros((1, len(self.tokenizer.vocab)), dtype=torch.float32) + neg_inf + + debug_list = [] + for k, v in self.tokenizer.vocab.items(): + if k in positive_words: + self.logits_bias[0, v] = 0 + debug_list.append(k[1:]) + + print(f'Fooocus V2 Expansion: Vocab with {len(debug_list)} words.') + + # debug_list = '\n'.join(sorted(debug_list)) + # print(debug_list) + + # t11 = self.tokenizer(',', return_tensors="np") + # t198 = self.tokenizer('\n', return_tensors="np") + # eos = self.tokenizer.eos_token_id + + self.model = AutoModelForCausalLM.from_pretrained(path_fooocus_expansion) + self.model.eval() + + load_device = model_management.text_encoder_device() + offload_device = model_management.text_encoder_offload_device() + + # MPS hack + if model_management.is_device_mps(load_device): + load_device = torch.device('cpu') + offload_device = torch.device('cpu') + + use_fp16 = model_management.should_use_fp16(device=load_device) + + if use_fp16: + self.model.half() + + self.patcher = ModelPatcher(self.model, load_device=load_device, offload_device=offload_device) + print(f'Fooocus Expansion engine loaded for {load_device}, use_fp16 = {use_fp16}.') + + @torch.no_grad() + @torch.inference_mode() + def logits_processor(self, input_ids, scores): + assert scores.ndim == 2 and scores.shape[0] == 1 + self.logits_bias = self.logits_bias.to(scores) + + bias = self.logits_bias.clone() + bias[0, input_ids[0].to(bias.device).long()] = neg_inf + bias[0, 11] = 0 + + return scores + bias + + @torch.no_grad() + @torch.inference_mode() + def __call__(self, prompt, seed): + if prompt == '': + return '' + + if self.patcher.current_device != self.patcher.load_device: + print('Fooocus Expansion loaded by itself.') + model_management.load_model_gpu(self.patcher) + + seed = int(seed) % SEED_LIMIT_NUMPY + set_seed(seed) + prompt = safe_str(prompt) + ',' + + tokenized_kwargs = self.tokenizer(prompt, return_tensors="pt") + tokenized_kwargs.data['input_ids'] = tokenized_kwargs.data['input_ids'].to(self.patcher.load_device) + tokenized_kwargs.data['attention_mask'] = tokenized_kwargs.data['attention_mask'].to(self.patcher.load_device) + + current_token_length = int(tokenized_kwargs.data['input_ids'].shape[1]) + max_token_length = 75 * int(math.ceil(float(current_token_length) / 75.0)) + max_new_tokens = max_token_length - current_token_length + + if max_new_tokens == 0: + return prompt[:-1] + + # https://huggingface.co/blog/introducing-csearch + # https://huggingface.co/docs/transformers/generation_strategies + features = self.model.generate(**tokenized_kwargs, + top_k=100, + max_new_tokens=max_new_tokens, + do_sample=True, + logits_processor=LogitsProcessorList([self.logits_processor])) + + response = self.tokenizer.batch_decode(features, skip_special_tokens=True) + result = safe_str(response[0]) + + return result diff --git a/extras/face_crop.py b/extras/face_crop.py new file mode 100644 index 0000000000000000000000000000000000000000..d4da7e81d59334ecbd4d710d66a454cab796b349 --- /dev/null +++ b/extras/face_crop.py @@ -0,0 +1,50 @@ +import cv2 +import numpy as np +import modules.config + + +faceRestoreHelper = None + + +def align_warp_face(self, landmark, border_mode='constant'): + affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] + self.affine_matrices.append(affine_matrix) + if border_mode == 'constant': + border_mode = cv2.BORDER_CONSTANT + elif border_mode == 'reflect101': + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == 'reflect': + border_mode = cv2.BORDER_REFLECT + input_img = self.input_img + cropped_face = cv2.warpAffine(input_img, affine_matrix, self.face_size, + borderMode=border_mode, borderValue=(135, 133, 132)) + return cropped_face + + +def crop_image(img_rgb): + global faceRestoreHelper + + if faceRestoreHelper is None: + from extras.facexlib.utils.face_restoration_helper import FaceRestoreHelper + faceRestoreHelper = FaceRestoreHelper( + upscale_factor=1, + model_rootpath=modules.config.path_controlnet, + device='cpu' # use cpu is safer since we are out of memory management + ) + + faceRestoreHelper.clean_all() + faceRestoreHelper.read_image(np.ascontiguousarray(img_rgb[:, :, ::-1].copy())) + faceRestoreHelper.get_face_landmarks_5() + + landmarks = faceRestoreHelper.all_landmarks_5 + # landmarks are already sorted with confidence. + + if len(landmarks) == 0: + print('No face detected') + return img_rgb + else: + print(f'Detected {len(landmarks)} faces') + + result = align_warp_face(faceRestoreHelper, landmarks[0]) + + return np.ascontiguousarray(result[:, :, ::-1].copy()) diff --git a/extras/facexlib/detection/__init__.py b/extras/facexlib/detection/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..4e52fd74e56c74d6c8766c8ca4170863bcd215e0 --- /dev/null +++ b/extras/facexlib/detection/__init__.py @@ -0,0 +1,31 @@ +import torch +from copy import deepcopy + +from extras.facexlib.utils import load_file_from_url +from .retinaface import RetinaFace + + +def init_detection_model(model_name, half=False, device='cuda', model_rootpath=None): + if model_name == 'retinaface_resnet50': + model = RetinaFace(network_name='resnet50', half=half, device=device) + model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth' + elif model_name == 'retinaface_mobile0.25': + model = RetinaFace(network_name='mobile0.25', half=half, device=device) + model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth' + else: + raise NotImplementedError(f'{model_name} is not implemented.') + + model_path = load_file_from_url( + url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) + + # TODO: clean pretrained model + load_net = torch.load(model_path, map_location=lambda storage, loc: storage) + # remove unnecessary 'module.' + for k, v in deepcopy(load_net).items(): + if k.startswith('module.'): + load_net[k[7:]] = v + load_net.pop(k) + model.load_state_dict(load_net, strict=True) + model.eval() + model = model.to(device) + return model diff --git a/extras/facexlib/detection/align_trans.py b/extras/facexlib/detection/align_trans.py new file mode 100644 index 0000000000000000000000000000000000000000..07f1eb365462c2ec5bbac6d1854c786b6fd6be90 --- /dev/null +++ b/extras/facexlib/detection/align_trans.py @@ -0,0 +1,219 @@ +import cv2 +import numpy as np + +from .matlab_cp2tform import get_similarity_transform_for_cv2 + +# reference facial points, a list of coordinates (x,y) +REFERENCE_FACIAL_POINTS = [[30.29459953, 51.69630051], [65.53179932, 51.50139999], [48.02519989, 71.73660278], + [33.54930115, 92.3655014], [62.72990036, 92.20410156]] + +DEFAULT_CROP_SIZE = (96, 112) + + +class FaceWarpException(Exception): + + def __str__(self): + return 'In File {}:{}'.format(__file__, super.__str__(self)) + + +def get_reference_facial_points(output_size=None, inner_padding_factor=0.0, outer_padding=(0, 0), default_square=False): + """ + Function: + ---------- + get reference 5 key points according to crop settings: + 0. Set default crop_size: + if default_square: + crop_size = (112, 112) + else: + crop_size = (96, 112) + 1. Pad the crop_size by inner_padding_factor in each side; + 2. Resize crop_size into (output_size - outer_padding*2), + pad into output_size with outer_padding; + 3. Output reference_5point; + Parameters: + ---------- + @output_size: (w, h) or None + size of aligned face image + @inner_padding_factor: (w_factor, h_factor) + padding factor for inner (w, h) + @outer_padding: (w_pad, h_pad) + each row is a pair of coordinates (x, y) + @default_square: True or False + if True: + default crop_size = (112, 112) + else: + default crop_size = (96, 112); + !!! make sure, if output_size is not None: + (output_size - outer_padding) + = some_scale * (default crop_size * (1.0 + + inner_padding_factor)) + Returns: + ---------- + @reference_5point: 5x2 np.array + each row is a pair of transformed coordinates (x, y) + """ + + tmp_5pts = np.array(REFERENCE_FACIAL_POINTS) + tmp_crop_size = np.array(DEFAULT_CROP_SIZE) + + # 0) make the inner region a square + if default_square: + size_diff = max(tmp_crop_size) - tmp_crop_size + tmp_5pts += size_diff / 2 + tmp_crop_size += size_diff + + if (output_size and output_size[0] == tmp_crop_size[0] and output_size[1] == tmp_crop_size[1]): + + return tmp_5pts + + if (inner_padding_factor == 0 and outer_padding == (0, 0)): + if output_size is None: + return tmp_5pts + else: + raise FaceWarpException('No paddings to do, output_size must be None or {}'.format(tmp_crop_size)) + + # check output size + if not (0 <= inner_padding_factor <= 1.0): + raise FaceWarpException('Not (0 <= inner_padding_factor <= 1.0)') + + if ((inner_padding_factor > 0 or outer_padding[0] > 0 or outer_padding[1] > 0) and output_size is None): + output_size = tmp_crop_size * \ + (1 + inner_padding_factor * 2).astype(np.int32) + output_size += np.array(outer_padding) + if not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1]): + raise FaceWarpException('Not (outer_padding[0] < output_size[0] and outer_padding[1] < output_size[1])') + + # 1) pad the inner region according inner_padding_factor + if inner_padding_factor > 0: + size_diff = tmp_crop_size * inner_padding_factor * 2 + tmp_5pts += size_diff / 2 + tmp_crop_size += np.round(size_diff).astype(np.int32) + + # 2) resize the padded inner region + size_bf_outer_pad = np.array(output_size) - np.array(outer_padding) * 2 + + if size_bf_outer_pad[0] * tmp_crop_size[1] != size_bf_outer_pad[1] * tmp_crop_size[0]: + raise FaceWarpException('Must have (output_size - outer_padding)' + '= some_scale * (crop_size * (1.0 + inner_padding_factor)') + + scale_factor = size_bf_outer_pad[0].astype(np.float32) / tmp_crop_size[0] + tmp_5pts = tmp_5pts * scale_factor + # size_diff = tmp_crop_size * (scale_factor - min(scale_factor)) + # tmp_5pts = tmp_5pts + size_diff / 2 + tmp_crop_size = size_bf_outer_pad + + # 3) add outer_padding to make output_size + reference_5point = tmp_5pts + np.array(outer_padding) + tmp_crop_size = output_size + + return reference_5point + + +def get_affine_transform_matrix(src_pts, dst_pts): + """ + Function: + ---------- + get affine transform matrix 'tfm' from src_pts to dst_pts + Parameters: + ---------- + @src_pts: Kx2 np.array + source points matrix, each row is a pair of coordinates (x, y) + @dst_pts: Kx2 np.array + destination points matrix, each row is a pair of coordinates (x, y) + Returns: + ---------- + @tfm: 2x3 np.array + transform matrix from src_pts to dst_pts + """ + + tfm = np.float32([[1, 0, 0], [0, 1, 0]]) + n_pts = src_pts.shape[0] + ones = np.ones((n_pts, 1), src_pts.dtype) + src_pts_ = np.hstack([src_pts, ones]) + dst_pts_ = np.hstack([dst_pts, ones]) + + A, res, rank, s = np.linalg.lstsq(src_pts_, dst_pts_) + + if rank == 3: + tfm = np.float32([[A[0, 0], A[1, 0], A[2, 0]], [A[0, 1], A[1, 1], A[2, 1]]]) + elif rank == 2: + tfm = np.float32([[A[0, 0], A[1, 0], 0], [A[0, 1], A[1, 1], 0]]) + + return tfm + + +def warp_and_crop_face(src_img, facial_pts, reference_pts=None, crop_size=(96, 112), align_type='smilarity'): + """ + Function: + ---------- + apply affine transform 'trans' to uv + Parameters: + ---------- + @src_img: 3x3 np.array + input image + @facial_pts: could be + 1)a list of K coordinates (x,y) + or + 2) Kx2 or 2xK np.array + each row or col is a pair of coordinates (x, y) + @reference_pts: could be + 1) a list of K coordinates (x,y) + or + 2) Kx2 or 2xK np.array + each row or col is a pair of coordinates (x, y) + or + 3) None + if None, use default reference facial points + @crop_size: (w, h) + output face image size + @align_type: transform type, could be one of + 1) 'similarity': use similarity transform + 2) 'cv2_affine': use the first 3 points to do affine transform, + by calling cv2.getAffineTransform() + 3) 'affine': use all points to do affine transform + Returns: + ---------- + @face_img: output face image with size (w, h) = @crop_size + """ + + if reference_pts is None: + if crop_size[0] == 96 and crop_size[1] == 112: + reference_pts = REFERENCE_FACIAL_POINTS + else: + default_square = False + inner_padding_factor = 0 + outer_padding = (0, 0) + output_size = crop_size + + reference_pts = get_reference_facial_points(output_size, inner_padding_factor, outer_padding, + default_square) + + ref_pts = np.float32(reference_pts) + ref_pts_shp = ref_pts.shape + if max(ref_pts_shp) < 3 or min(ref_pts_shp) != 2: + raise FaceWarpException('reference_pts.shape must be (K,2) or (2,K) and K>2') + + if ref_pts_shp[0] == 2: + ref_pts = ref_pts.T + + src_pts = np.float32(facial_pts) + src_pts_shp = src_pts.shape + if max(src_pts_shp) < 3 or min(src_pts_shp) != 2: + raise FaceWarpException('facial_pts.shape must be (K,2) or (2,K) and K>2') + + if src_pts_shp[0] == 2: + src_pts = src_pts.T + + if src_pts.shape != ref_pts.shape: + raise FaceWarpException('facial_pts and reference_pts must have the same shape') + + if align_type == 'cv2_affine': + tfm = cv2.getAffineTransform(src_pts[0:3], ref_pts[0:3]) + elif align_type == 'affine': + tfm = get_affine_transform_matrix(src_pts, ref_pts) + else: + tfm = get_similarity_transform_for_cv2(src_pts, ref_pts) + + face_img = cv2.warpAffine(src_img, tfm, (crop_size[0], crop_size[1])) + + return face_img diff --git a/extras/facexlib/detection/matlab_cp2tform.py b/extras/facexlib/detection/matlab_cp2tform.py new file mode 100644 index 0000000000000000000000000000000000000000..b2a8b54a91709c71437e15c68d3be9a9b0a20a34 --- /dev/null +++ b/extras/facexlib/detection/matlab_cp2tform.py @@ -0,0 +1,317 @@ +import numpy as np +from numpy.linalg import inv, lstsq +from numpy.linalg import matrix_rank as rank +from numpy.linalg import norm + + +class MatlabCp2tormException(Exception): + + def __str__(self): + return 'In File {}:{}'.format(__file__, super.__str__(self)) + + +def tformfwd(trans, uv): + """ + Function: + ---------- + apply affine transform 'trans' to uv + + Parameters: + ---------- + @trans: 3x3 np.array + transform matrix + @uv: Kx2 np.array + each row is a pair of coordinates (x, y) + + Returns: + ---------- + @xy: Kx2 np.array + each row is a pair of transformed coordinates (x, y) + """ + uv = np.hstack((uv, np.ones((uv.shape[0], 1)))) + xy = np.dot(uv, trans) + xy = xy[:, 0:-1] + return xy + + +def tforminv(trans, uv): + """ + Function: + ---------- + apply the inverse of affine transform 'trans' to uv + + Parameters: + ---------- + @trans: 3x3 np.array + transform matrix + @uv: Kx2 np.array + each row is a pair of coordinates (x, y) + + Returns: + ---------- + @xy: Kx2 np.array + each row is a pair of inverse-transformed coordinates (x, y) + """ + Tinv = inv(trans) + xy = tformfwd(Tinv, uv) + return xy + + +def findNonreflectiveSimilarity(uv, xy, options=None): + options = {'K': 2} + + K = options['K'] + M = xy.shape[0] + x = xy[:, 0].reshape((-1, 1)) # use reshape to keep a column vector + y = xy[:, 1].reshape((-1, 1)) # use reshape to keep a column vector + + tmp1 = np.hstack((x, y, np.ones((M, 1)), np.zeros((M, 1)))) + tmp2 = np.hstack((y, -x, np.zeros((M, 1)), np.ones((M, 1)))) + X = np.vstack((tmp1, tmp2)) + + u = uv[:, 0].reshape((-1, 1)) # use reshape to keep a column vector + v = uv[:, 1].reshape((-1, 1)) # use reshape to keep a column vector + U = np.vstack((u, v)) + + # We know that X * r = U + if rank(X) >= 2 * K: + r, _, _, _ = lstsq(X, U, rcond=-1) + r = np.squeeze(r) + else: + raise Exception('cp2tform:twoUniquePointsReq') + sc = r[0] + ss = r[1] + tx = r[2] + ty = r[3] + + Tinv = np.array([[sc, -ss, 0], [ss, sc, 0], [tx, ty, 1]]) + T = inv(Tinv) + T[:, 2] = np.array([0, 0, 1]) + + return T, Tinv + + +def findSimilarity(uv, xy, options=None): + options = {'K': 2} + + # uv = np.array(uv) + # xy = np.array(xy) + + # Solve for trans1 + trans1, trans1_inv = findNonreflectiveSimilarity(uv, xy, options) + + # Solve for trans2 + + # manually reflect the xy data across the Y-axis + xyR = xy + xyR[:, 0] = -1 * xyR[:, 0] + + trans2r, trans2r_inv = findNonreflectiveSimilarity(uv, xyR, options) + + # manually reflect the tform to undo the reflection done on xyR + TreflectY = np.array([[-1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + trans2 = np.dot(trans2r, TreflectY) + + # Figure out if trans1 or trans2 is better + xy1 = tformfwd(trans1, uv) + norm1 = norm(xy1 - xy) + + xy2 = tformfwd(trans2, uv) + norm2 = norm(xy2 - xy) + + if norm1 <= norm2: + return trans1, trans1_inv + else: + trans2_inv = inv(trans2) + return trans2, trans2_inv + + +def get_similarity_transform(src_pts, dst_pts, reflective=True): + """ + Function: + ---------- + Find Similarity Transform Matrix 'trans': + u = src_pts[:, 0] + v = src_pts[:, 1] + x = dst_pts[:, 0] + y = dst_pts[:, 1] + [x, y, 1] = [u, v, 1] * trans + + Parameters: + ---------- + @src_pts: Kx2 np.array + source points, each row is a pair of coordinates (x, y) + @dst_pts: Kx2 np.array + destination points, each row is a pair of transformed + coordinates (x, y) + @reflective: True or False + if True: + use reflective similarity transform + else: + use non-reflective similarity transform + + Returns: + ---------- + @trans: 3x3 np.array + transform matrix from uv to xy + trans_inv: 3x3 np.array + inverse of trans, transform matrix from xy to uv + """ + + if reflective: + trans, trans_inv = findSimilarity(src_pts, dst_pts) + else: + trans, trans_inv = findNonreflectiveSimilarity(src_pts, dst_pts) + + return trans, trans_inv + + +def cvt_tform_mat_for_cv2(trans): + """ + Function: + ---------- + Convert Transform Matrix 'trans' into 'cv2_trans' which could be + directly used by cv2.warpAffine(): + u = src_pts[:, 0] + v = src_pts[:, 1] + x = dst_pts[:, 0] + y = dst_pts[:, 1] + [x, y].T = cv_trans * [u, v, 1].T + + Parameters: + ---------- + @trans: 3x3 np.array + transform matrix from uv to xy + + Returns: + ---------- + @cv2_trans: 2x3 np.array + transform matrix from src_pts to dst_pts, could be directly used + for cv2.warpAffine() + """ + cv2_trans = trans[:, 0:2].T + + return cv2_trans + + +def get_similarity_transform_for_cv2(src_pts, dst_pts, reflective=True): + """ + Function: + ---------- + Find Similarity Transform Matrix 'cv2_trans' which could be + directly used by cv2.warpAffine(): + u = src_pts[:, 0] + v = src_pts[:, 1] + x = dst_pts[:, 0] + y = dst_pts[:, 1] + [x, y].T = cv_trans * [u, v, 1].T + + Parameters: + ---------- + @src_pts: Kx2 np.array + source points, each row is a pair of coordinates (x, y) + @dst_pts: Kx2 np.array + destination points, each row is a pair of transformed + coordinates (x, y) + reflective: True or False + if True: + use reflective similarity transform + else: + use non-reflective similarity transform + + Returns: + ---------- + @cv2_trans: 2x3 np.array + transform matrix from src_pts to dst_pts, could be directly used + for cv2.warpAffine() + """ + trans, trans_inv = get_similarity_transform(src_pts, dst_pts, reflective) + cv2_trans = cvt_tform_mat_for_cv2(trans) + + return cv2_trans + + +if __name__ == '__main__': + """ + u = [0, 6, -2] + v = [0, 3, 5] + x = [-1, 0, 4] + y = [-1, -10, 4] + + # In Matlab, run: + # + # uv = [u'; v']; + # xy = [x'; y']; + # tform_sim=cp2tform(uv,xy,'similarity'); + # + # trans = tform_sim.tdata.T + # ans = + # -0.0764 -1.6190 0 + # 1.6190 -0.0764 0 + # -3.2156 0.0290 1.0000 + # trans_inv = tform_sim.tdata.Tinv + # ans = + # + # -0.0291 0.6163 0 + # -0.6163 -0.0291 0 + # -0.0756 1.9826 1.0000 + # xy_m=tformfwd(tform_sim, u,v) + # + # xy_m = + # + # -3.2156 0.0290 + # 1.1833 -9.9143 + # 5.0323 2.8853 + # uv_m=tforminv(tform_sim, x,y) + # + # uv_m = + # + # 0.5698 1.3953 + # 6.0872 2.2733 + # -2.6570 4.3314 + """ + u = [0, 6, -2] + v = [0, 3, 5] + x = [-1, 0, 4] + y = [-1, -10, 4] + + uv = np.array((u, v)).T + xy = np.array((x, y)).T + + print('\n--->uv:') + print(uv) + print('\n--->xy:') + print(xy) + + trans, trans_inv = get_similarity_transform(uv, xy) + + print('\n--->trans matrix:') + print(trans) + + print('\n--->trans_inv matrix:') + print(trans_inv) + + print('\n---> apply transform to uv') + print('\nxy_m = uv_augmented * trans') + uv_aug = np.hstack((uv, np.ones((uv.shape[0], 1)))) + xy_m = np.dot(uv_aug, trans) + print(xy_m) + + print('\nxy_m = tformfwd(trans, uv)') + xy_m = tformfwd(trans, uv) + print(xy_m) + + print('\n---> apply inverse transform to xy') + print('\nuv_m = xy_augmented * trans_inv') + xy_aug = np.hstack((xy, np.ones((xy.shape[0], 1)))) + uv_m = np.dot(xy_aug, trans_inv) + print(uv_m) + + print('\nuv_m = tformfwd(trans_inv, xy)') + uv_m = tformfwd(trans_inv, xy) + print(uv_m) + + uv_m = tforminv(trans, xy) + print('\nuv_m = tforminv(trans, xy)') + print(uv_m) diff --git a/extras/facexlib/detection/retinaface.py b/extras/facexlib/detection/retinaface.py new file mode 100644 index 0000000000000000000000000000000000000000..5e0b4f0a5962741915e38efabe07e4cf5a99bc3b --- /dev/null +++ b/extras/facexlib/detection/retinaface.py @@ -0,0 +1,366 @@ +import cv2 +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from PIL import Image +from torchvision.models._utils import IntermediateLayerGetter as IntermediateLayerGetter + +from extras.facexlib.detection.align_trans import get_reference_facial_points, warp_and_crop_face +from extras.facexlib.detection.retinaface_net import FPN, SSH, MobileNetV1, make_bbox_head, make_class_head, make_landmark_head +from extras.facexlib.detection.retinaface_utils import (PriorBox, batched_decode, batched_decode_landm, decode, decode_landm, + py_cpu_nms) + + +def generate_config(network_name): + + cfg_mnet = { + 'name': 'mobilenet0.25', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 32, + 'ngpu': 1, + 'epoch': 250, + 'decay1': 190, + 'decay2': 220, + 'image_size': 640, + 'return_layers': { + 'stage1': 1, + 'stage2': 2, + 'stage3': 3 + }, + 'in_channel': 32, + 'out_channel': 64 + } + + cfg_re50 = { + 'name': 'Resnet50', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 24, + 'ngpu': 4, + 'epoch': 100, + 'decay1': 70, + 'decay2': 90, + 'image_size': 840, + 'return_layers': { + 'layer2': 1, + 'layer3': 2, + 'layer4': 3 + }, + 'in_channel': 256, + 'out_channel': 256 + } + + if network_name == 'mobile0.25': + return cfg_mnet + elif network_name == 'resnet50': + return cfg_re50 + else: + raise NotImplementedError(f'network_name={network_name}') + + +class RetinaFace(nn.Module): + + def __init__(self, network_name='resnet50', half=False, phase='test', device=None): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if device is None else device + + super(RetinaFace, self).__init__() + self.half_inference = half + cfg = generate_config(network_name) + self.backbone = cfg['name'] + + self.model_name = f'retinaface_{network_name}' + self.cfg = cfg + self.phase = phase + self.target_size, self.max_size = 1600, 2150 + self.resize, self.scale, self.scale1 = 1., None, None + self.mean_tensor = torch.tensor([[[[104.]], [[117.]], [[123.]]]], device=self.device) + self.reference = get_reference_facial_points(default_square=True) + # Build network. + backbone = None + if cfg['name'] == 'mobilenet0.25': + backbone = MobileNetV1() + self.body = IntermediateLayerGetter(backbone, cfg['return_layers']) + elif cfg['name'] == 'Resnet50': + import torchvision.models as models + backbone = models.resnet50(weights=None) + self.body = IntermediateLayerGetter(backbone, cfg['return_layers']) + + in_channels_stage2 = cfg['in_channel'] + in_channels_list = [ + in_channels_stage2 * 2, + in_channels_stage2 * 4, + in_channels_stage2 * 8, + ] + + out_channels = cfg['out_channel'] + self.fpn = FPN(in_channels_list, out_channels) + self.ssh1 = SSH(out_channels, out_channels) + self.ssh2 = SSH(out_channels, out_channels) + self.ssh3 = SSH(out_channels, out_channels) + + self.ClassHead = make_class_head(fpn_num=3, inchannels=cfg['out_channel']) + self.BboxHead = make_bbox_head(fpn_num=3, inchannels=cfg['out_channel']) + self.LandmarkHead = make_landmark_head(fpn_num=3, inchannels=cfg['out_channel']) + + self.to(self.device) + self.eval() + if self.half_inference: + self.half() + + def forward(self, inputs): + out = self.body(inputs) + + if self.backbone == 'mobilenet0.25' or self.backbone == 'Resnet50': + out = list(out.values()) + # FPN + fpn = self.fpn(out) + + # SSH + feature1 = self.ssh1(fpn[0]) + feature2 = self.ssh2(fpn[1]) + feature3 = self.ssh3(fpn[2]) + features = [feature1, feature2, feature3] + + bbox_regressions = torch.cat([self.BboxHead[i](feature) for i, feature in enumerate(features)], dim=1) + classifications = torch.cat([self.ClassHead[i](feature) for i, feature in enumerate(features)], dim=1) + tmp = [self.LandmarkHead[i](feature) for i, feature in enumerate(features)] + ldm_regressions = (torch.cat(tmp, dim=1)) + + if self.phase == 'train': + output = (bbox_regressions, classifications, ldm_regressions) + else: + output = (bbox_regressions, F.softmax(classifications, dim=-1), ldm_regressions) + return output + + def __detect_faces(self, inputs): + # get scale + height, width = inputs.shape[2:] + self.scale = torch.tensor([width, height, width, height], dtype=torch.float32, device=self.device) + tmp = [width, height, width, height, width, height, width, height, width, height] + self.scale1 = torch.tensor(tmp, dtype=torch.float32, device=self.device) + + # forawrd + inputs = inputs.to(self.device) + if self.half_inference: + inputs = inputs.half() + loc, conf, landmarks = self(inputs) + + # get priorbox + priorbox = PriorBox(self.cfg, image_size=inputs.shape[2:]) + priors = priorbox.forward().to(self.device) + + return loc, conf, landmarks, priors + + # single image detection + def transform(self, image, use_origin_size): + # convert to opencv format + if isinstance(image, Image.Image): + image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR) + image = image.astype(np.float32) + + # testing scale + im_size_min = np.min(image.shape[0:2]) + im_size_max = np.max(image.shape[0:2]) + resize = float(self.target_size) / float(im_size_min) + + # prevent bigger axis from being more than max_size + if np.round(resize * im_size_max) > self.max_size: + resize = float(self.max_size) / float(im_size_max) + resize = 1 if use_origin_size else resize + + # resize + if resize != 1: + image = cv2.resize(image, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) + + # convert to torch.tensor format + # image -= (104, 117, 123) + image = image.transpose(2, 0, 1) + image = torch.from_numpy(image).unsqueeze(0) + + return image, resize + + def detect_faces( + self, + image, + conf_threshold=0.8, + nms_threshold=0.4, + use_origin_size=True, + ): + image, self.resize = self.transform(image, use_origin_size) + image = image.to(self.device) + if self.half_inference: + image = image.half() + image = image - self.mean_tensor + + loc, conf, landmarks, priors = self.__detect_faces(image) + + boxes = decode(loc.data.squeeze(0), priors.data, self.cfg['variance']) + boxes = boxes * self.scale / self.resize + boxes = boxes.cpu().numpy() + + scores = conf.squeeze(0).data.cpu().numpy()[:, 1] + + landmarks = decode_landm(landmarks.squeeze(0), priors, self.cfg['variance']) + landmarks = landmarks * self.scale1 / self.resize + landmarks = landmarks.cpu().numpy() + + # ignore low scores + inds = np.where(scores > conf_threshold)[0] + boxes, landmarks, scores = boxes[inds], landmarks[inds], scores[inds] + + # sort + order = scores.argsort()[::-1] + boxes, landmarks, scores = boxes[order], landmarks[order], scores[order] + + # do NMS + bounding_boxes = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False) + keep = py_cpu_nms(bounding_boxes, nms_threshold) + bounding_boxes, landmarks = bounding_boxes[keep, :], landmarks[keep] + # self.t['forward_pass'].toc() + # print(self.t['forward_pass'].average_time) + # import sys + # sys.stdout.flush() + return np.concatenate((bounding_boxes, landmarks), axis=1) + + def __align_multi(self, image, boxes, landmarks, limit=None): + + if len(boxes) < 1: + return [], [] + + if limit: + boxes = boxes[:limit] + landmarks = landmarks[:limit] + + faces = [] + for landmark in landmarks: + facial5points = [[landmark[2 * j], landmark[2 * j + 1]] for j in range(5)] + + warped_face = warp_and_crop_face(np.array(image), facial5points, self.reference, crop_size=(112, 112)) + faces.append(warped_face) + + return np.concatenate((boxes, landmarks), axis=1), faces + + def align_multi(self, img, conf_threshold=0.8, limit=None): + + rlt = self.detect_faces(img, conf_threshold=conf_threshold) + boxes, landmarks = rlt[:, 0:5], rlt[:, 5:] + + return self.__align_multi(img, boxes, landmarks, limit) + + # batched detection + def batched_transform(self, frames, use_origin_size): + """ + Arguments: + frames: a list of PIL.Image, or torch.Tensor(shape=[n, h, w, c], + type=np.float32, BGR format). + use_origin_size: whether to use origin size. + """ + from_PIL = True if isinstance(frames[0], Image.Image) else False + + # convert to opencv format + if from_PIL: + frames = [cv2.cvtColor(np.asarray(frame), cv2.COLOR_RGB2BGR) for frame in frames] + frames = np.asarray(frames, dtype=np.float32) + + # testing scale + im_size_min = np.min(frames[0].shape[0:2]) + im_size_max = np.max(frames[0].shape[0:2]) + resize = float(self.target_size) / float(im_size_min) + + # prevent bigger axis from being more than max_size + if np.round(resize * im_size_max) > self.max_size: + resize = float(self.max_size) / float(im_size_max) + resize = 1 if use_origin_size else resize + + # resize + if resize != 1: + if not from_PIL: + frames = F.interpolate(frames, scale_factor=resize) + else: + frames = [ + cv2.resize(frame, None, None, fx=resize, fy=resize, interpolation=cv2.INTER_LINEAR) + for frame in frames + ] + + # convert to torch.tensor format + if not from_PIL: + frames = frames.transpose(1, 2).transpose(1, 3).contiguous() + else: + frames = frames.transpose((0, 3, 1, 2)) + frames = torch.from_numpy(frames) + + return frames, resize + + def batched_detect_faces(self, frames, conf_threshold=0.8, nms_threshold=0.4, use_origin_size=True): + """ + Arguments: + frames: a list of PIL.Image, or np.array(shape=[n, h, w, c], + type=np.uint8, BGR format). + conf_threshold: confidence threshold. + nms_threshold: nms threshold. + use_origin_size: whether to use origin size. + Returns: + final_bounding_boxes: list of np.array ([n_boxes, 5], + type=np.float32). + final_landmarks: list of np.array ([n_boxes, 10], type=np.float32). + """ + # self.t['forward_pass'].tic() + frames, self.resize = self.batched_transform(frames, use_origin_size) + frames = frames.to(self.device) + frames = frames - self.mean_tensor + + b_loc, b_conf, b_landmarks, priors = self.__detect_faces(frames) + + final_bounding_boxes, final_landmarks = [], [] + + # decode + priors = priors.unsqueeze(0) + b_loc = batched_decode(b_loc, priors, self.cfg['variance']) * self.scale / self.resize + b_landmarks = batched_decode_landm(b_landmarks, priors, self.cfg['variance']) * self.scale1 / self.resize + b_conf = b_conf[:, :, 1] + + # index for selection + b_indice = b_conf > conf_threshold + + # concat + b_loc_and_conf = torch.cat((b_loc, b_conf.unsqueeze(-1)), dim=2).float() + + for pred, landm, inds in zip(b_loc_and_conf, b_landmarks, b_indice): + + # ignore low scores + pred, landm = pred[inds, :], landm[inds, :] + if pred.shape[0] == 0: + final_bounding_boxes.append(np.array([], dtype=np.float32)) + final_landmarks.append(np.array([], dtype=np.float32)) + continue + + # sort + # order = score.argsort(descending=True) + # box, landm, score = box[order], landm[order], score[order] + + # to CPU + bounding_boxes, landm = pred.cpu().numpy(), landm.cpu().numpy() + + # NMS + keep = py_cpu_nms(bounding_boxes, nms_threshold) + bounding_boxes, landmarks = bounding_boxes[keep, :], landm[keep] + + # append + final_bounding_boxes.append(bounding_boxes) + final_landmarks.append(landmarks) + # self.t['forward_pass'].toc(average=True) + # self.batch_time += self.t['forward_pass'].diff + # self.total_frame += len(frames) + # print(self.batch_time / self.total_frame) + + return final_bounding_boxes, final_landmarks diff --git a/extras/facexlib/detection/retinaface_net.py b/extras/facexlib/detection/retinaface_net.py new file mode 100644 index 0000000000000000000000000000000000000000..ab6aa82d3e9055a838f1f9076b12f05fdfc154d0 --- /dev/null +++ b/extras/facexlib/detection/retinaface_net.py @@ -0,0 +1,196 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def conv_bn(inp, oup, stride=1, leaky=0): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), + nn.LeakyReLU(negative_slope=leaky, inplace=True)) + + +def conv_bn_no_relu(inp, oup, stride): + return nn.Sequential( + nn.Conv2d(inp, oup, 3, stride, 1, bias=False), + nn.BatchNorm2d(oup), + ) + + +def conv_bn1X1(inp, oup, stride, leaky=0): + return nn.Sequential( + nn.Conv2d(inp, oup, 1, stride, padding=0, bias=False), nn.BatchNorm2d(oup), + nn.LeakyReLU(negative_slope=leaky, inplace=True)) + + +def conv_dw(inp, oup, stride, leaky=0.1): + return nn.Sequential( + nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), + nn.BatchNorm2d(inp), + nn.LeakyReLU(negative_slope=leaky, inplace=True), + nn.Conv2d(inp, oup, 1, 1, 0, bias=False), + nn.BatchNorm2d(oup), + nn.LeakyReLU(negative_slope=leaky, inplace=True), + ) + + +class SSH(nn.Module): + + def __init__(self, in_channel, out_channel): + super(SSH, self).__init__() + assert out_channel % 4 == 0 + leaky = 0 + if (out_channel <= 64): + leaky = 0.1 + self.conv3X3 = conv_bn_no_relu(in_channel, out_channel // 2, stride=1) + + self.conv5X5_1 = conv_bn(in_channel, out_channel // 4, stride=1, leaky=leaky) + self.conv5X5_2 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) + + self.conv7X7_2 = conv_bn(out_channel // 4, out_channel // 4, stride=1, leaky=leaky) + self.conv7x7_3 = conv_bn_no_relu(out_channel // 4, out_channel // 4, stride=1) + + def forward(self, input): + conv3X3 = self.conv3X3(input) + + conv5X5_1 = self.conv5X5_1(input) + conv5X5 = self.conv5X5_2(conv5X5_1) + + conv7X7_2 = self.conv7X7_2(conv5X5_1) + conv7X7 = self.conv7x7_3(conv7X7_2) + + out = torch.cat([conv3X3, conv5X5, conv7X7], dim=1) + out = F.relu(out) + return out + + +class FPN(nn.Module): + + def __init__(self, in_channels_list, out_channels): + super(FPN, self).__init__() + leaky = 0 + if (out_channels <= 64): + leaky = 0.1 + self.output1 = conv_bn1X1(in_channels_list[0], out_channels, stride=1, leaky=leaky) + self.output2 = conv_bn1X1(in_channels_list[1], out_channels, stride=1, leaky=leaky) + self.output3 = conv_bn1X1(in_channels_list[2], out_channels, stride=1, leaky=leaky) + + self.merge1 = conv_bn(out_channels, out_channels, leaky=leaky) + self.merge2 = conv_bn(out_channels, out_channels, leaky=leaky) + + def forward(self, input): + # names = list(input.keys()) + # input = list(input.values()) + + output1 = self.output1(input[0]) + output2 = self.output2(input[1]) + output3 = self.output3(input[2]) + + up3 = F.interpolate(output3, size=[output2.size(2), output2.size(3)], mode='nearest') + output2 = output2 + up3 + output2 = self.merge2(output2) + + up2 = F.interpolate(output2, size=[output1.size(2), output1.size(3)], mode='nearest') + output1 = output1 + up2 + output1 = self.merge1(output1) + + out = [output1, output2, output3] + return out + + +class MobileNetV1(nn.Module): + + def __init__(self): + super(MobileNetV1, self).__init__() + self.stage1 = nn.Sequential( + conv_bn(3, 8, 2, leaky=0.1), # 3 + conv_dw(8, 16, 1), # 7 + conv_dw(16, 32, 2), # 11 + conv_dw(32, 32, 1), # 19 + conv_dw(32, 64, 2), # 27 + conv_dw(64, 64, 1), # 43 + ) + self.stage2 = nn.Sequential( + conv_dw(64, 128, 2), # 43 + 16 = 59 + conv_dw(128, 128, 1), # 59 + 32 = 91 + conv_dw(128, 128, 1), # 91 + 32 = 123 + conv_dw(128, 128, 1), # 123 + 32 = 155 + conv_dw(128, 128, 1), # 155 + 32 = 187 + conv_dw(128, 128, 1), # 187 + 32 = 219 + ) + self.stage3 = nn.Sequential( + conv_dw(128, 256, 2), # 219 +3 2 = 241 + conv_dw(256, 256, 1), # 241 + 64 = 301 + ) + self.avg = nn.AdaptiveAvgPool2d((1, 1)) + self.fc = nn.Linear(256, 1000) + + def forward(self, x): + x = self.stage1(x) + x = self.stage2(x) + x = self.stage3(x) + x = self.avg(x) + # x = self.model(x) + x = x.view(-1, 256) + x = self.fc(x) + return x + + +class ClassHead(nn.Module): + + def __init__(self, inchannels=512, num_anchors=3): + super(ClassHead, self).__init__() + self.num_anchors = num_anchors + self.conv1x1 = nn.Conv2d(inchannels, self.num_anchors * 2, kernel_size=(1, 1), stride=1, padding=0) + + def forward(self, x): + out = self.conv1x1(x) + out = out.permute(0, 2, 3, 1).contiguous() + + return out.view(out.shape[0], -1, 2) + + +class BboxHead(nn.Module): + + def __init__(self, inchannels=512, num_anchors=3): + super(BboxHead, self).__init__() + self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 4, kernel_size=(1, 1), stride=1, padding=0) + + def forward(self, x): + out = self.conv1x1(x) + out = out.permute(0, 2, 3, 1).contiguous() + + return out.view(out.shape[0], -1, 4) + + +class LandmarkHead(nn.Module): + + def __init__(self, inchannels=512, num_anchors=3): + super(LandmarkHead, self).__init__() + self.conv1x1 = nn.Conv2d(inchannels, num_anchors * 10, kernel_size=(1, 1), stride=1, padding=0) + + def forward(self, x): + out = self.conv1x1(x) + out = out.permute(0, 2, 3, 1).contiguous() + + return out.view(out.shape[0], -1, 10) + + +def make_class_head(fpn_num=3, inchannels=64, anchor_num=2): + classhead = nn.ModuleList() + for i in range(fpn_num): + classhead.append(ClassHead(inchannels, anchor_num)) + return classhead + + +def make_bbox_head(fpn_num=3, inchannels=64, anchor_num=2): + bboxhead = nn.ModuleList() + for i in range(fpn_num): + bboxhead.append(BboxHead(inchannels, anchor_num)) + return bboxhead + + +def make_landmark_head(fpn_num=3, inchannels=64, anchor_num=2): + landmarkhead = nn.ModuleList() + for i in range(fpn_num): + landmarkhead.append(LandmarkHead(inchannels, anchor_num)) + return landmarkhead diff --git a/extras/facexlib/detection/retinaface_utils.py b/extras/facexlib/detection/retinaface_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8c357757741c6d9bd7ce4d8ce740fefd51850fbf --- /dev/null +++ b/extras/facexlib/detection/retinaface_utils.py @@ -0,0 +1,421 @@ +import numpy as np +import torch +import torchvision +from itertools import product as product +from math import ceil + + +class PriorBox(object): + + def __init__(self, cfg, image_size=None, phase='train'): + super(PriorBox, self).__init__() + self.min_sizes = cfg['min_sizes'] + self.steps = cfg['steps'] + self.clip = cfg['clip'] + self.image_size = image_size + self.feature_maps = [[ceil(self.image_size[0] / step), ceil(self.image_size[1] / step)] for step in self.steps] + self.name = 's' + + def forward(self): + anchors = [] + for k, f in enumerate(self.feature_maps): + min_sizes = self.min_sizes[k] + for i, j in product(range(f[0]), range(f[1])): + for min_size in min_sizes: + s_kx = min_size / self.image_size[1] + s_ky = min_size / self.image_size[0] + dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]] + dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]] + for cy, cx in product(dense_cy, dense_cx): + anchors += [cx, cy, s_kx, s_ky] + + # back to torch land + output = torch.Tensor(anchors).view(-1, 4) + if self.clip: + output.clamp_(max=1, min=0) + return output + + +def py_cpu_nms(dets, thresh): + """Pure Python NMS baseline.""" + keep = torchvision.ops.nms( + boxes=torch.Tensor(dets[:, :4]), + scores=torch.Tensor(dets[:, 4]), + iou_threshold=thresh, + ) + + return list(keep) + + +def point_form(boxes): + """ Convert prior_boxes to (xmin, ymin, xmax, ymax) + representation for comparison to point form ground truth data. + Args: + boxes: (tensor) center-size default boxes from priorbox layers. + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat( + ( + boxes[:, :2] - boxes[:, 2:] / 2, # xmin, ymin + boxes[:, :2] + boxes[:, 2:] / 2), + 1) # xmax, ymax + + +def center_size(boxes): + """ Convert prior_boxes to (cx, cy, w, h) + representation for comparison to center-size form ground truth data. + Args: + boxes: (tensor) point_form boxes + Return: + boxes: (tensor) Converted xmin, ymin, xmax, ymax form of boxes. + """ + return torch.cat( + (boxes[:, 2:] + boxes[:, :2]) / 2, # cx, cy + boxes[:, 2:] - boxes[:, :2], + 1) # w, h + + +def intersect(box_a, box_b): + """ We resize both tensors to [A,B,2] without new malloc: + [A,2] -> [A,1,2] -> [A,B,2] + [B,2] -> [1,B,2] -> [A,B,2] + Then we compute the area of intersect between box_a and box_b. + Args: + box_a: (tensor) bounding boxes, Shape: [A,4]. + box_b: (tensor) bounding boxes, Shape: [B,4]. + Return: + (tensor) intersection area, Shape: [A,B]. + """ + A = box_a.size(0) + B = box_b.size(0) + max_xy = torch.min(box_a[:, 2:].unsqueeze(1).expand(A, B, 2), box_b[:, 2:].unsqueeze(0).expand(A, B, 2)) + min_xy = torch.max(box_a[:, :2].unsqueeze(1).expand(A, B, 2), box_b[:, :2].unsqueeze(0).expand(A, B, 2)) + inter = torch.clamp((max_xy - min_xy), min=0) + return inter[:, :, 0] * inter[:, :, 1] + + +def jaccard(box_a, box_b): + """Compute the jaccard overlap of two sets of boxes. The jaccard overlap + is simply the intersection over union of two boxes. Here we operate on + ground truth boxes and default boxes. + E.g.: + A ∩ B / A ∪ B = A ∩ B / (area(A) + area(B) - A ∩ B) + Args: + box_a: (tensor) Ground truth bounding boxes, Shape: [num_objects,4] + box_b: (tensor) Prior boxes from priorbox layers, Shape: [num_priors,4] + Return: + jaccard overlap: (tensor) Shape: [box_a.size(0), box_b.size(0)] + """ + inter = intersect(box_a, box_b) + area_a = ((box_a[:, 2] - box_a[:, 0]) * (box_a[:, 3] - box_a[:, 1])).unsqueeze(1).expand_as(inter) # [A,B] + area_b = ((box_b[:, 2] - box_b[:, 0]) * (box_b[:, 3] - box_b[:, 1])).unsqueeze(0).expand_as(inter) # [A,B] + union = area_a + area_b - inter + return inter / union # [A,B] + + +def matrix_iou(a, b): + """ + return iou of a and b, numpy version for data augenmentation + """ + lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) + rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) + + area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) + area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) + area_b = np.prod(b[:, 2:] - b[:, :2], axis=1) + return area_i / (area_a[:, np.newaxis] + area_b - area_i) + + +def matrix_iof(a, b): + """ + return iof of a and b, numpy version for data augenmentation + """ + lt = np.maximum(a[:, np.newaxis, :2], b[:, :2]) + rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:]) + + area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2) + area_a = np.prod(a[:, 2:] - a[:, :2], axis=1) + return area_i / np.maximum(area_a[:, np.newaxis], 1) + + +def match(threshold, truths, priors, variances, labels, landms, loc_t, conf_t, landm_t, idx): + """Match each prior box with the ground truth box of the highest jaccard + overlap, encode the bounding boxes, then return the matched indices + corresponding to both confidence and location preds. + Args: + threshold: (float) The overlap threshold used when matching boxes. + truths: (tensor) Ground truth boxes, Shape: [num_obj, 4]. + priors: (tensor) Prior boxes from priorbox layers, Shape: [n_priors,4]. + variances: (tensor) Variances corresponding to each prior coord, + Shape: [num_priors, 4]. + labels: (tensor) All the class labels for the image, Shape: [num_obj]. + landms: (tensor) Ground truth landms, Shape [num_obj, 10]. + loc_t: (tensor) Tensor to be filled w/ encoded location targets. + conf_t: (tensor) Tensor to be filled w/ matched indices for conf preds. + landm_t: (tensor) Tensor to be filled w/ encoded landm targets. + idx: (int) current batch index + Return: + The matched indices corresponding to 1)location 2)confidence + 3)landm preds. + """ + # jaccard index + overlaps = jaccard(truths, point_form(priors)) + # (Bipartite Matching) + # [1,num_objects] best prior for each ground truth + best_prior_overlap, best_prior_idx = overlaps.max(1, keepdim=True) + + # ignore hard gt + valid_gt_idx = best_prior_overlap[:, 0] >= 0.2 + best_prior_idx_filter = best_prior_idx[valid_gt_idx, :] + if best_prior_idx_filter.shape[0] <= 0: + loc_t[idx] = 0 + conf_t[idx] = 0 + return + + # [1,num_priors] best ground truth for each prior + best_truth_overlap, best_truth_idx = overlaps.max(0, keepdim=True) + best_truth_idx.squeeze_(0) + best_truth_overlap.squeeze_(0) + best_prior_idx.squeeze_(1) + best_prior_idx_filter.squeeze_(1) + best_prior_overlap.squeeze_(1) + best_truth_overlap.index_fill_(0, best_prior_idx_filter, 2) # ensure best prior + # TODO refactor: index best_prior_idx with long tensor + # ensure every gt matches with its prior of max overlap + for j in range(best_prior_idx.size(0)): # 判别此anchor是预测哪一个boxes + best_truth_idx[best_prior_idx[j]] = j + matches = truths[best_truth_idx] # Shape: [num_priors,4] 此处为每一个anchor对应的bbox取出来 + conf = labels[best_truth_idx] # Shape: [num_priors] 此处为每一个anchor对应的label取出来 + conf[best_truth_overlap < threshold] = 0 # label as background overlap<0.35的全部作为负样本 + loc = encode(matches, priors, variances) + + matches_landm = landms[best_truth_idx] + landm = encode_landm(matches_landm, priors, variances) + loc_t[idx] = loc # [num_priors,4] encoded offsets to learn + conf_t[idx] = conf # [num_priors] top class label for each prior + landm_t[idx] = landm + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + + +def encode_landm(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 10]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded landm (tensor), Shape: [num_priors, 10] + """ + + # dist b/t match center and prior's center + matched = torch.reshape(matched, (matched.size(0), 5, 2)) + priors_cx = priors[:, 0].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors_cy = priors[:, 1].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors_w = priors[:, 2].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors_h = priors[:, 3].unsqueeze(1).expand(matched.size(0), 5).unsqueeze(2) + priors = torch.cat([priors_cx, priors_cy, priors_w, priors_h], dim=2) + g_cxcy = matched[:, :, :2] - priors[:, :, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, :, 2:]) + # g_cxcy /= priors[:, :, 2:] + g_cxcy = g_cxcy.reshape(g_cxcy.size(0), -1) + # return target for smooth_l1_loss + return g_cxcy + + +# Adapted from https://github.com/Hakuyume/chainer-ssd +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat((priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + + +def decode_landm(pre, priors, variances): + """Decode landm from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + pre (tensor): landm predictions for loc layers, + Shape: [num_priors,10] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded landm predictions + """ + tmp = ( + priors[:, :2] + pre[:, :2] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 2:4] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 4:6] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 6:8] * variances[0] * priors[:, 2:], + priors[:, :2] + pre[:, 8:10] * variances[0] * priors[:, 2:], + ) + landms = torch.cat(tmp, dim=1) + return landms + + +def batched_decode(b_loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + b_loc (tensor): location predictions for loc layers, + Shape: [num_batches,num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [1,num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + boxes = ( + priors[:, :, :2] + b_loc[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, 2:] * torch.exp(b_loc[:, :, 2:] * variances[1]), + ) + boxes = torch.cat(boxes, dim=2) + + boxes[:, :, :2] -= boxes[:, :, 2:] / 2 + boxes[:, :, 2:] += boxes[:, :, :2] + return boxes + + +def batched_decode_landm(pre, priors, variances): + """Decode landm from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + pre (tensor): landm predictions for loc layers, + Shape: [num_batches,num_priors,10] + priors (tensor): Prior boxes in center-offset form. + Shape: [1,num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded landm predictions + """ + landms = ( + priors[:, :, :2] + pre[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, :2] + pre[:, :, 2:4] * variances[0] * priors[:, :, 2:], + priors[:, :, :2] + pre[:, :, 4:6] * variances[0] * priors[:, :, 2:], + priors[:, :, :2] + pre[:, :, 6:8] * variances[0] * priors[:, :, 2:], + priors[:, :, :2] + pre[:, :, 8:10] * variances[0] * priors[:, :, 2:], + ) + landms = torch.cat(landms, dim=2) + return landms + + +def log_sum_exp(x): + """Utility function for computing log_sum_exp while determining + This will be used to determine unaveraged confidence loss across + all examples in a batch. + Args: + x (Variable(tensor)): conf_preds from conf layers + """ + x_max = x.data.max() + return torch.log(torch.sum(torch.exp(x - x_max), 1, keepdim=True)) + x_max + + +# Original author: Francisco Massa: +# https://github.com/fmassa/object-detection.torch +# Ported to PyTorch by Max deGroot (02/01/2017) +def nms(boxes, scores, overlap=0.5, top_k=200): + """Apply non-maximum suppression at test time to avoid detecting too many + overlapping bounding boxes for a given object. + Args: + boxes: (tensor) The location preds for the img, Shape: [num_priors,4]. + scores: (tensor) The class predscores for the img, Shape:[num_priors]. + overlap: (float) The overlap thresh for suppressing unnecessary boxes. + top_k: (int) The Maximum number of box preds to consider. + Return: + The indices of the kept boxes with respect to num_priors. + """ + + keep = torch.Tensor(scores.size(0)).fill_(0).long() + if boxes.numel() == 0: + return keep + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + area = torch.mul(x2 - x1, y2 - y1) + v, idx = scores.sort(0) # sort in ascending order + # I = I[v >= 0.01] + idx = idx[-top_k:] # indices of the top-k largest vals + xx1 = boxes.new() + yy1 = boxes.new() + xx2 = boxes.new() + yy2 = boxes.new() + w = boxes.new() + h = boxes.new() + + # keep = torch.Tensor() + count = 0 + while idx.numel() > 0: + i = idx[-1] # index of current largest val + # keep.append(i) + keep[count] = i + count += 1 + if idx.size(0) == 1: + break + idx = idx[:-1] # remove kept element from view + # load bboxes of next highest vals + torch.index_select(x1, 0, idx, out=xx1) + torch.index_select(y1, 0, idx, out=yy1) + torch.index_select(x2, 0, idx, out=xx2) + torch.index_select(y2, 0, idx, out=yy2) + # store element-wise max with next highest score + xx1 = torch.clamp(xx1, min=x1[i]) + yy1 = torch.clamp(yy1, min=y1[i]) + xx2 = torch.clamp(xx2, max=x2[i]) + yy2 = torch.clamp(yy2, max=y2[i]) + w.resize_as_(xx2) + h.resize_as_(yy2) + w = xx2 - xx1 + h = yy2 - yy1 + # check sizes of xx1 and xx2.. after each iteration + w = torch.clamp(w, min=0.0) + h = torch.clamp(h, min=0.0) + inter = w * h + # IoU = i / (area(a) + area(b) - i) + rem_areas = torch.index_select(area, 0, idx) # load remaining areas) + union = (rem_areas - inter) + area[i] + IoU = inter / union # store result in iou + # keep only elements with an IoU <= overlap + idx = idx[IoU.le(overlap)] + return keep, count diff --git a/extras/facexlib/parsing/__init__.py b/extras/facexlib/parsing/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8b4758bdda6e6f1528b943a37e32d901a32fa569 --- /dev/null +++ b/extras/facexlib/parsing/__init__.py @@ -0,0 +1,24 @@ +import torch + +from extras.facexlib.utils import load_file_from_url +from .bisenet import BiSeNet +from .parsenet import ParseNet + + +def init_parsing_model(model_name='bisenet', half=False, device='cuda', model_rootpath=None): + if model_name == 'bisenet': + model = BiSeNet(num_class=19) + model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.0/parsing_bisenet.pth' + elif model_name == 'parsenet': + model = ParseNet(in_size=512, out_size=512, parsing_ch=19) + model_url = 'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth' + else: + raise NotImplementedError(f'{model_name} is not implemented.') + + model_path = load_file_from_url( + url=model_url, model_dir='facexlib/weights', progress=True, file_name=None, save_dir=model_rootpath) + load_net = torch.load(model_path, map_location=lambda storage, loc: storage) + model.load_state_dict(load_net, strict=True) + model.eval() + model = model.to(device) + return model diff --git a/extras/facexlib/parsing/bisenet.py b/extras/facexlib/parsing/bisenet.py new file mode 100644 index 0000000000000000000000000000000000000000..3898cab76ae5876459cd4899c54cafa14234971d --- /dev/null +++ b/extras/facexlib/parsing/bisenet.py @@ -0,0 +1,140 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .resnet import ResNet18 + + +class ConvBNReLU(nn.Module): + + def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d(in_chan, out_chan, kernel_size=ks, stride=stride, padding=padding, bias=False) + self.bn = nn.BatchNorm2d(out_chan) + + def forward(self, x): + x = self.conv(x) + x = F.relu(self.bn(x)) + return x + + +class BiSeNetOutput(nn.Module): + + def __init__(self, in_chan, mid_chan, num_class): + super(BiSeNetOutput, self).__init__() + self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) + self.conv_out = nn.Conv2d(mid_chan, num_class, kernel_size=1, bias=False) + + def forward(self, x): + feat = self.conv(x) + out = self.conv_out(feat) + return out, feat + + +class AttentionRefinementModule(nn.Module): + + def __init__(self, in_chan, out_chan): + super(AttentionRefinementModule, self).__init__() + self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) + self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False) + self.bn_atten = nn.BatchNorm2d(out_chan) + self.sigmoid_atten = nn.Sigmoid() + + def forward(self, x): + feat = self.conv(x) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv_atten(atten) + atten = self.bn_atten(atten) + atten = self.sigmoid_atten(atten) + out = torch.mul(feat, atten) + return out + + +class ContextPath(nn.Module): + + def __init__(self): + super(ContextPath, self).__init__() + self.resnet = ResNet18() + self.arm16 = AttentionRefinementModule(256, 128) + self.arm32 = AttentionRefinementModule(512, 128) + self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) + + def forward(self, x): + feat8, feat16, feat32 = self.resnet(x) + h8, w8 = feat8.size()[2:] + h16, w16 = feat16.size()[2:] + h32, w32 = feat32.size()[2:] + + avg = F.avg_pool2d(feat32, feat32.size()[2:]) + avg = self.conv_avg(avg) + avg_up = F.interpolate(avg, (h32, w32), mode='nearest') + + feat32_arm = self.arm32(feat32) + feat32_sum = feat32_arm + avg_up + feat32_up = F.interpolate(feat32_sum, (h16, w16), mode='nearest') + feat32_up = self.conv_head32(feat32_up) + + feat16_arm = self.arm16(feat16) + feat16_sum = feat16_arm + feat32_up + feat16_up = F.interpolate(feat16_sum, (h8, w8), mode='nearest') + feat16_up = self.conv_head16(feat16_up) + + return feat8, feat16_up, feat32_up # x8, x8, x16 + + +class FeatureFusionModule(nn.Module): + + def __init__(self, in_chan, out_chan): + super(FeatureFusionModule, self).__init__() + self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) + self.conv1 = nn.Conv2d(out_chan, out_chan // 4, kernel_size=1, stride=1, padding=0, bias=False) + self.conv2 = nn.Conv2d(out_chan // 4, out_chan, kernel_size=1, stride=1, padding=0, bias=False) + self.relu = nn.ReLU(inplace=True) + self.sigmoid = nn.Sigmoid() + + def forward(self, fsp, fcp): + fcat = torch.cat([fsp, fcp], dim=1) + feat = self.convblk(fcat) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv1(atten) + atten = self.relu(atten) + atten = self.conv2(atten) + atten = self.sigmoid(atten) + feat_atten = torch.mul(feat, atten) + feat_out = feat_atten + feat + return feat_out + + +class BiSeNet(nn.Module): + + def __init__(self, num_class): + super(BiSeNet, self).__init__() + self.cp = ContextPath() + self.ffm = FeatureFusionModule(256, 256) + self.conv_out = BiSeNetOutput(256, 256, num_class) + self.conv_out16 = BiSeNetOutput(128, 64, num_class) + self.conv_out32 = BiSeNetOutput(128, 64, num_class) + + def forward(self, x, return_feat=False): + h, w = x.size()[2:] + feat_res8, feat_cp8, feat_cp16 = self.cp(x) # return res3b1 feature + feat_sp = feat_res8 # replace spatial path feature with res3b1 feature + feat_fuse = self.ffm(feat_sp, feat_cp8) + + out, feat = self.conv_out(feat_fuse) + out16, feat16 = self.conv_out16(feat_cp8) + out32, feat32 = self.conv_out32(feat_cp16) + + out = F.interpolate(out, (h, w), mode='bilinear', align_corners=True) + out16 = F.interpolate(out16, (h, w), mode='bilinear', align_corners=True) + out32 = F.interpolate(out32, (h, w), mode='bilinear', align_corners=True) + + if return_feat: + feat = F.interpolate(feat, (h, w), mode='bilinear', align_corners=True) + feat16 = F.interpolate(feat16, (h, w), mode='bilinear', align_corners=True) + feat32 = F.interpolate(feat32, (h, w), mode='bilinear', align_corners=True) + return out, out16, out32, feat, feat16, feat32 + else: + return out, out16, out32 diff --git a/extras/facexlib/parsing/parsenet.py b/extras/facexlib/parsing/parsenet.py new file mode 100644 index 0000000000000000000000000000000000000000..e178ebe43a1ef666aaea0bc0faf629485c22a24f --- /dev/null +++ b/extras/facexlib/parsing/parsenet.py @@ -0,0 +1,194 @@ +"""Modified from https://github.com/chaofengc/PSFRGAN +""" +import numpy as np +import torch.nn as nn +from torch.nn import functional as F + + +class NormLayer(nn.Module): + """Normalization Layers. + + Args: + channels: input channels, for batch norm and instance norm. + input_size: input shape without batch size, for layer norm. + """ + + def __init__(self, channels, normalize_shape=None, norm_type='bn'): + super(NormLayer, self).__init__() + norm_type = norm_type.lower() + self.norm_type = norm_type + if norm_type == 'bn': + self.norm = nn.BatchNorm2d(channels, affine=True) + elif norm_type == 'in': + self.norm = nn.InstanceNorm2d(channels, affine=False) + elif norm_type == 'gn': + self.norm = nn.GroupNorm(32, channels, affine=True) + elif norm_type == 'pixel': + self.norm = lambda x: F.normalize(x, p=2, dim=1) + elif norm_type == 'layer': + self.norm = nn.LayerNorm(normalize_shape) + elif norm_type == 'none': + self.norm = lambda x: x * 1.0 + else: + assert 1 == 0, f'Norm type {norm_type} not support.' + + def forward(self, x, ref=None): + if self.norm_type == 'spade': + return self.norm(x, ref) + else: + return self.norm(x) + + +class ReluLayer(nn.Module): + """Relu Layer. + + Args: + relu type: type of relu layer, candidates are + - ReLU + - LeakyReLU: default relu slope 0.2 + - PRelu + - SELU + - none: direct pass + """ + + def __init__(self, channels, relu_type='relu'): + super(ReluLayer, self).__init__() + relu_type = relu_type.lower() + if relu_type == 'relu': + self.func = nn.ReLU(True) + elif relu_type == 'leakyrelu': + self.func = nn.LeakyReLU(0.2, inplace=True) + elif relu_type == 'prelu': + self.func = nn.PReLU(channels) + elif relu_type == 'selu': + self.func = nn.SELU(True) + elif relu_type == 'none': + self.func = lambda x: x * 1.0 + else: + assert 1 == 0, f'Relu type {relu_type} not support.' + + def forward(self, x): + return self.func(x) + + +class ConvLayer(nn.Module): + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + scale='none', + norm_type='none', + relu_type='none', + use_pad=True, + bias=True): + super(ConvLayer, self).__init__() + self.use_pad = use_pad + self.norm_type = norm_type + if norm_type in ['bn']: + bias = False + + stride = 2 if scale == 'down' else 1 + + self.scale_func = lambda x: x + if scale == 'up': + self.scale_func = lambda x: nn.functional.interpolate(x, scale_factor=2, mode='nearest') + + self.reflection_pad = nn.ReflectionPad2d(int(np.ceil((kernel_size - 1.) / 2))) + self.conv2d = nn.Conv2d(in_channels, out_channels, kernel_size, stride, bias=bias) + + self.relu = ReluLayer(out_channels, relu_type) + self.norm = NormLayer(out_channels, norm_type=norm_type) + + def forward(self, x): + out = self.scale_func(x) + if self.use_pad: + out = self.reflection_pad(out) + out = self.conv2d(out) + out = self.norm(out) + out = self.relu(out) + return out + + +class ResidualBlock(nn.Module): + """ + Residual block recommended in: http://torch.ch/blog/2016/02/04/resnets.html + """ + + def __init__(self, c_in, c_out, relu_type='prelu', norm_type='bn', scale='none'): + super(ResidualBlock, self).__init__() + + if scale == 'none' and c_in == c_out: + self.shortcut_func = lambda x: x + else: + self.shortcut_func = ConvLayer(c_in, c_out, 3, scale) + + scale_config_dict = {'down': ['none', 'down'], 'up': ['up', 'none'], 'none': ['none', 'none']} + scale_conf = scale_config_dict[scale] + + self.conv1 = ConvLayer(c_in, c_out, 3, scale_conf[0], norm_type=norm_type, relu_type=relu_type) + self.conv2 = ConvLayer(c_out, c_out, 3, scale_conf[1], norm_type=norm_type, relu_type='none') + + def forward(self, x): + identity = self.shortcut_func(x) + + res = self.conv1(x) + res = self.conv2(res) + return identity + res + + +class ParseNet(nn.Module): + + def __init__(self, + in_size=128, + out_size=128, + min_feat_size=32, + base_ch=64, + parsing_ch=19, + res_depth=10, + relu_type='LeakyReLU', + norm_type='bn', + ch_range=[32, 256]): + super().__init__() + self.res_depth = res_depth + act_args = {'norm_type': norm_type, 'relu_type': relu_type} + min_ch, max_ch = ch_range + + ch_clip = lambda x: max(min_ch, min(x, max_ch)) # noqa: E731 + min_feat_size = min(in_size, min_feat_size) + + down_steps = int(np.log2(in_size // min_feat_size)) + up_steps = int(np.log2(out_size // min_feat_size)) + + # =============== define encoder-body-decoder ==================== + self.encoder = [] + self.encoder.append(ConvLayer(3, base_ch, 3, 1)) + head_ch = base_ch + for i in range(down_steps): + cin, cout = ch_clip(head_ch), ch_clip(head_ch * 2) + self.encoder.append(ResidualBlock(cin, cout, scale='down', **act_args)) + head_ch = head_ch * 2 + + self.body = [] + for i in range(res_depth): + self.body.append(ResidualBlock(ch_clip(head_ch), ch_clip(head_ch), **act_args)) + + self.decoder = [] + for i in range(up_steps): + cin, cout = ch_clip(head_ch), ch_clip(head_ch // 2) + self.decoder.append(ResidualBlock(cin, cout, scale='up', **act_args)) + head_ch = head_ch // 2 + + self.encoder = nn.Sequential(*self.encoder) + self.body = nn.Sequential(*self.body) + self.decoder = nn.Sequential(*self.decoder) + self.out_img_conv = ConvLayer(ch_clip(head_ch), 3) + self.out_mask_conv = ConvLayer(ch_clip(head_ch), parsing_ch) + + def forward(self, x): + feat = self.encoder(x) + x = feat + self.body(feat) + x = self.decoder(x) + out_img = self.out_img_conv(x) + out_mask = self.out_mask_conv(x) + return out_mask, out_img diff --git a/extras/facexlib/parsing/resnet.py b/extras/facexlib/parsing/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..fec8e82cf64469fb51be21ad5130217052addbda --- /dev/null +++ b/extras/facexlib/parsing/resnet.py @@ -0,0 +1,69 @@ +import torch.nn as nn +import torch.nn.functional as F + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False) + + +class BasicBlock(nn.Module): + + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = self.conv1(x) + residual = F.relu(self.bn1(residual)) + residual = self.conv2(residual) + residual = self.bn2(residual) + + shortcut = x + if self.downsample is not None: + shortcut = self.downsample(x) + + out = shortcut + residual + out = self.relu(out) + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum - 1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class ResNet18(nn.Module): + + def __init__(self): + super(ResNet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + + def forward(self, x): + x = self.conv1(x) + x = F.relu(self.bn1(x)) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 diff --git a/extras/facexlib/utils/__init__.py b/extras/facexlib/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..706e077a4f6d32ad8e8dd839f19f955a97844e01 --- /dev/null +++ b/extras/facexlib/utils/__init__.py @@ -0,0 +1,7 @@ +from .face_utils import align_crop_face_landmarks, compute_increased_bbox, get_valid_bboxes, paste_face_back +from .misc import img2tensor, load_file_from_url, scandir + +__all__ = [ + 'align_crop_face_landmarks', 'compute_increased_bbox', 'get_valid_bboxes', 'load_file_from_url', 'paste_face_back', + 'img2tensor', 'scandir' +] diff --git a/extras/facexlib/utils/face_restoration_helper.py b/extras/facexlib/utils/face_restoration_helper.py new file mode 100644 index 0000000000000000000000000000000000000000..2a39361aae2d62cdb752f8f7ba3e0e524400ad97 --- /dev/null +++ b/extras/facexlib/utils/face_restoration_helper.py @@ -0,0 +1,374 @@ +import cv2 +import numpy as np +import os +import torch +from torchvision.transforms.functional import normalize + +from extras.facexlib.detection import init_detection_model +from extras.facexlib.parsing import init_parsing_model +from extras.facexlib.utils.misc import img2tensor, imwrite + + +def get_largest_face(det_faces, h, w): + + def get_location(val, length): + if val < 0: + return 0 + elif val > length: + return length + else: + return val + + face_areas = [] + for det_face in det_faces: + left = get_location(det_face[0], w) + right = get_location(det_face[2], w) + top = get_location(det_face[1], h) + bottom = get_location(det_face[3], h) + face_area = (right - left) * (bottom - top) + face_areas.append(face_area) + largest_idx = face_areas.index(max(face_areas)) + return det_faces[largest_idx], largest_idx + + +def get_center_face(det_faces, h=0, w=0, center=None): + if center is not None: + center = np.array(center) + else: + center = np.array([w / 2, h / 2]) + center_dist = [] + for det_face in det_faces: + face_center = np.array([(det_face[0] + det_face[2]) / 2, (det_face[1] + det_face[3]) / 2]) + dist = np.linalg.norm(face_center - center) + center_dist.append(dist) + center_idx = center_dist.index(min(center_dist)) + return det_faces[center_idx], center_idx + + +class FaceRestoreHelper(object): + """Helper for the face restoration pipeline (base class).""" + + def __init__(self, + upscale_factor, + face_size=512, + crop_ratio=(1, 1), + det_model='retinaface_resnet50', + save_ext='png', + template_3points=False, + pad_blur=False, + use_parse=False, + device=None, + model_rootpath=None): + self.template_3points = template_3points # improve robustness + self.upscale_factor = upscale_factor + # the cropped face ratio based on the square face + self.crop_ratio = crop_ratio # (h, w) + assert (self.crop_ratio[0] >= 1 and self.crop_ratio[1] >= 1), 'crop ration only supports >=1' + self.face_size = (int(face_size * self.crop_ratio[1]), int(face_size * self.crop_ratio[0])) + + if self.template_3points: + self.face_template = np.array([[192, 240], [319, 240], [257, 371]]) + else: + # standard 5 landmarks for FFHQ faces with 512 x 512 + self.face_template = np.array([[192.98138, 239.94708], [318.90277, 240.1936], [256.63416, 314.01935], + [201.26117, 371.41043], [313.08905, 371.15118]]) + self.face_template = self.face_template * (face_size / 512.0) + if self.crop_ratio[0] > 1: + self.face_template[:, 1] += face_size * (self.crop_ratio[0] - 1) / 2 + if self.crop_ratio[1] > 1: + self.face_template[:, 0] += face_size * (self.crop_ratio[1] - 1) / 2 + self.save_ext = save_ext + self.pad_blur = pad_blur + if self.pad_blur is True: + self.template_3points = False + + self.all_landmarks_5 = [] + self.det_faces = [] + self.affine_matrices = [] + self.inverse_affine_matrices = [] + self.cropped_faces = [] + self.restored_faces = [] + self.pad_input_imgs = [] + + if device is None: + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + else: + self.device = device + + # init face detection model + self.face_det = init_detection_model(det_model, half=False, device=self.device, model_rootpath=model_rootpath) + + # init face parsing model + self.use_parse = use_parse + self.face_parse = init_parsing_model(model_name='parsenet', device=self.device, model_rootpath=model_rootpath) + + def set_upscale_factor(self, upscale_factor): + self.upscale_factor = upscale_factor + + def read_image(self, img): + """img can be image path or cv2 loaded image.""" + # self.input_img is Numpy array, (h, w, c), BGR, uint8, [0, 255] + if isinstance(img, str): + img = cv2.imread(img) + + if np.max(img) > 256: # 16-bit image + img = img / 65535 * 255 + if len(img.shape) == 2: # gray image + img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) + elif img.shape[2] == 4: # RGBA image with alpha channel + img = img[:, :, 0:3] + + self.input_img = img + + def get_face_landmarks_5(self, + only_keep_largest=False, + only_center_face=False, + resize=None, + blur_ratio=0.01, + eye_dist_threshold=None): + if resize is None: + scale = 1 + input_img = self.input_img + else: + h, w = self.input_img.shape[0:2] + scale = min(h, w) / resize + h, w = int(h / scale), int(w / scale) + input_img = cv2.resize(self.input_img, (w, h), interpolation=cv2.INTER_LANCZOS4) + + with torch.no_grad(): + bboxes = self.face_det.detect_faces(input_img, 0.97) * scale + for bbox in bboxes: + # remove faces with too small eye distance: side faces or too small faces + eye_dist = np.linalg.norm([bbox[5] - bbox[7], bbox[6] - bbox[8]]) + if eye_dist_threshold is not None and (eye_dist < eye_dist_threshold): + continue + + if self.template_3points: + landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 11, 2)]) + else: + landmark = np.array([[bbox[i], bbox[i + 1]] for i in range(5, 15, 2)]) + self.all_landmarks_5.append(landmark) + self.det_faces.append(bbox[0:5]) + if len(self.det_faces) == 0: + return 0 + if only_keep_largest: + h, w, _ = self.input_img.shape + self.det_faces, largest_idx = get_largest_face(self.det_faces, h, w) + self.all_landmarks_5 = [self.all_landmarks_5[largest_idx]] + elif only_center_face: + h, w, _ = self.input_img.shape + self.det_faces, center_idx = get_center_face(self.det_faces, h, w) + self.all_landmarks_5 = [self.all_landmarks_5[center_idx]] + + # pad blurry images + if self.pad_blur: + self.pad_input_imgs = [] + for landmarks in self.all_landmarks_5: + # get landmarks + eye_left = landmarks[0, :] + eye_right = landmarks[1, :] + eye_avg = (eye_left + eye_right) * 0.5 + mouth_avg = (landmarks[3, :] + landmarks[4, :]) * 0.5 + eye_to_eye = eye_right - eye_left + eye_to_mouth = mouth_avg - eye_avg + + # Get the oriented crop rectangle + # x: half width of the oriented crop rectangle + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise + # norm with the hypotenuse: get the direction + x /= np.hypot(*x) # get the hypotenuse of a right triangle + rect_scale = 1.5 + x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) + # y: half height of the oriented crop rectangle + y = np.flipud(x) * [-1, 1] + + # c: center + c = eye_avg + eye_to_mouth * 0.1 + # quad: (left_top, left_bottom, right_bottom, right_top) + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + # qsize: side length of the square + qsize = np.hypot(*x) * 2 + border = max(int(np.rint(qsize * 0.1)), 3) + + # get pad + # pad: (width_left, height_top, width_right, height_bottom) + pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + pad = [ + max(-pad[0] + border, 1), + max(-pad[1] + border, 1), + max(pad[2] - self.input_img.shape[0] + border, 1), + max(pad[3] - self.input_img.shape[1] + border, 1) + ] + + if max(pad) > 1: + # pad image + pad_img = np.pad(self.input_img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + # modify landmark coords + landmarks[:, 0] += pad[0] + landmarks[:, 1] += pad[1] + # blur pad images + h, w, _ = pad_img.shape + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], + np.float32(w - 1 - x) / pad[2]), + 1.0 - np.minimum(np.float32(y) / pad[1], + np.float32(h - 1 - y) / pad[3])) + blur = int(qsize * blur_ratio) + if blur % 2 == 0: + blur += 1 + blur_img = cv2.boxFilter(pad_img, 0, ksize=(blur, blur)) + # blur_img = cv2.GaussianBlur(pad_img, (blur, blur), 0) + + pad_img = pad_img.astype('float32') + pad_img += (blur_img - pad_img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + pad_img += (np.median(pad_img, axis=(0, 1)) - pad_img) * np.clip(mask, 0.0, 1.0) + pad_img = np.clip(pad_img, 0, 255) # float32, [0, 255] + self.pad_input_imgs.append(pad_img) + else: + self.pad_input_imgs.append(np.copy(self.input_img)) + + return len(self.all_landmarks_5) + + def align_warp_face(self, save_cropped_path=None, border_mode='constant'): + """Align and warp faces with face template. + """ + if self.pad_blur: + assert len(self.pad_input_imgs) == len( + self.all_landmarks_5), f'Mismatched samples: {len(self.pad_input_imgs)} and {len(self.all_landmarks_5)}' + for idx, landmark in enumerate(self.all_landmarks_5): + # use 5 landmarks to get affine matrix + # use cv2.LMEDS method for the equivalence to skimage transform + # ref: https://blog.csdn.net/yichxi/article/details/115827338 + affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template, method=cv2.LMEDS)[0] + self.affine_matrices.append(affine_matrix) + # warp and crop faces + if border_mode == 'constant': + border_mode = cv2.BORDER_CONSTANT + elif border_mode == 'reflect101': + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == 'reflect': + border_mode = cv2.BORDER_REFLECT + if self.pad_blur: + input_img = self.pad_input_imgs[idx] + else: + input_img = self.input_img + cropped_face = cv2.warpAffine( + input_img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132)) # gray + self.cropped_faces.append(cropped_face) + # save the cropped face + if save_cropped_path is not None: + path = os.path.splitext(save_cropped_path)[0] + save_path = f'{path}_{idx:02d}.{self.save_ext}' + imwrite(cropped_face, save_path) + + def get_inverse_affine(self, save_inverse_affine_path=None): + """Get inverse affine matrix.""" + for idx, affine_matrix in enumerate(self.affine_matrices): + inverse_affine = cv2.invertAffineTransform(affine_matrix) + inverse_affine *= self.upscale_factor + self.inverse_affine_matrices.append(inverse_affine) + # save inverse affine matrices + if save_inverse_affine_path is not None: + path, _ = os.path.splitext(save_inverse_affine_path) + save_path = f'{path}_{idx:02d}.pth' + torch.save(inverse_affine, save_path) + + def add_restored_face(self, face): + self.restored_faces.append(face) + + def paste_faces_to_input_image(self, save_path=None, upsample_img=None): + h, w, _ = self.input_img.shape + h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) + + if upsample_img is None: + # simply resize the background + upsample_img = cv2.resize(self.input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + else: + upsample_img = cv2.resize(upsample_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + + assert len(self.restored_faces) == len( + self.inverse_affine_matrices), ('length of restored_faces and affine_matrices are different.') + for restored_face, inverse_affine in zip(self.restored_faces, self.inverse_affine_matrices): + # Add an offset to inverse affine matrix, for more precise back alignment + if self.upscale_factor > 1: + extra_offset = 0.5 * self.upscale_factor + else: + extra_offset = 0 + inverse_affine[:, 2] += extra_offset + inv_restored = cv2.warpAffine(restored_face, inverse_affine, (w_up, h_up)) + + if self.use_parse: + # inference + face_input = cv2.resize(restored_face, (512, 512), interpolation=cv2.INTER_LINEAR) + face_input = img2tensor(face_input.astype('float32') / 255., bgr2rgb=True, float32=True) + normalize(face_input, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True) + face_input = torch.unsqueeze(face_input, 0).to(self.device) + with torch.no_grad(): + out = self.face_parse(face_input)[0] + out = out.argmax(dim=1).squeeze().cpu().numpy() + + mask = np.zeros(out.shape) + MASK_COLORMAP = [0, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 255, 0, 255, 0, 0, 0] + for idx, color in enumerate(MASK_COLORMAP): + mask[out == idx] = color + # blur the mask + mask = cv2.GaussianBlur(mask, (101, 101), 11) + mask = cv2.GaussianBlur(mask, (101, 101), 11) + # remove the black borders + thres = 10 + mask[:thres, :] = 0 + mask[-thres:, :] = 0 + mask[:, :thres] = 0 + mask[:, -thres:] = 0 + mask = mask / 255. + + mask = cv2.resize(mask, restored_face.shape[:2]) + mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up), flags=3) + inv_soft_mask = mask[:, :, None] + pasted_face = inv_restored + + else: # use square parse maps + mask = np.ones(self.face_size, dtype=np.float32) + inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) + # remove the black borders + inv_mask_erosion = cv2.erode( + inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8)) + pasted_face = inv_mask_erosion[:, :, None] * inv_restored + total_face_area = np.sum(inv_mask_erosion) # // 3 + # compute the fusion edge based on the area of face + w_edge = int(total_face_area**0.5) // 20 + erosion_radius = w_edge * 2 + inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) + blur_size = w_edge * 2 + inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) + if len(upsample_img.shape) == 2: # upsample_img is gray image + upsample_img = upsample_img[:, :, None] + inv_soft_mask = inv_soft_mask[:, :, None] + + if len(upsample_img.shape) == 3 and upsample_img.shape[2] == 4: # alpha channel + alpha = upsample_img[:, :, 3:] + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img[:, :, 0:3] + upsample_img = np.concatenate((upsample_img, alpha), axis=2) + else: + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img + + if np.max(upsample_img) > 256: # 16-bit image + upsample_img = upsample_img.astype(np.uint16) + else: + upsample_img = upsample_img.astype(np.uint8) + if save_path is not None: + path = os.path.splitext(save_path)[0] + save_path = f'{path}.{self.save_ext}' + imwrite(upsample_img, save_path) + return upsample_img + + def clean_all(self): + self.all_landmarks_5 = [] + self.restored_faces = [] + self.affine_matrices = [] + self.cropped_faces = [] + self.inverse_affine_matrices = [] + self.det_faces = [] + self.pad_input_imgs = [] diff --git a/extras/facexlib/utils/face_utils.py b/extras/facexlib/utils/face_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0bbe43c81110b82daaef09e6277d9c11f62e4bb4 --- /dev/null +++ b/extras/facexlib/utils/face_utils.py @@ -0,0 +1,250 @@ +import cv2 +import numpy as np +import torch + + +def compute_increased_bbox(bbox, increase_area, preserve_aspect=True): + left, top, right, bot = bbox + width = right - left + height = bot - top + + if preserve_aspect: + width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width)) + height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height)) + else: + width_increase = height_increase = increase_area + left = int(left - width_increase * width) + top = int(top - height_increase * height) + right = int(right + width_increase * width) + bot = int(bot + height_increase * height) + return (left, top, right, bot) + + +def get_valid_bboxes(bboxes, h, w): + left = max(bboxes[0], 0) + top = max(bboxes[1], 0) + right = min(bboxes[2], w) + bottom = min(bboxes[3], h) + return (left, top, right, bottom) + + +def align_crop_face_landmarks(img, + landmarks, + output_size, + transform_size=None, + enable_padding=True, + return_inverse_affine=False, + shrink_ratio=(1, 1)): + """Align and crop face with landmarks. + + The output_size and transform_size are based on width. The height is + adjusted based on shrink_ratio_h/shring_ration_w. + + Modified from: + https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py + + Args: + img (Numpy array): Input image. + landmarks (Numpy array): 5 or 68 or 98 landmarks. + output_size (int): Output face size. + transform_size (ing): Transform size. Usually the four time of + output_size. + enable_padding (float): Default: True. + shrink_ratio (float | tuple[float] | list[float]): Shring the whole + face for height and width (crop larger area). Default: (1, 1). + + Returns: + (Numpy array): Cropped face. + """ + lm_type = 'retinaface_5' # Options: dlib_5, retinaface_5 + + if isinstance(shrink_ratio, (float, int)): + shrink_ratio = (shrink_ratio, shrink_ratio) + if transform_size is None: + transform_size = output_size * 4 + + # Parse landmarks + lm = np.array(landmarks) + if lm.shape[0] == 5 and lm_type == 'retinaface_5': + eye_left = lm[0] + eye_right = lm[1] + mouth_avg = (lm[3] + lm[4]) * 0.5 + elif lm.shape[0] == 5 and lm_type == 'dlib_5': + lm_eye_left = lm[2:4] + lm_eye_right = lm[0:2] + eye_left = np.mean(lm_eye_left, axis=0) + eye_right = np.mean(lm_eye_right, axis=0) + mouth_avg = lm[4] + elif lm.shape[0] == 68: + lm_eye_left = lm[36:42] + lm_eye_right = lm[42:48] + eye_left = np.mean(lm_eye_left, axis=0) + eye_right = np.mean(lm_eye_right, axis=0) + mouth_avg = (lm[48] + lm[54]) * 0.5 + elif lm.shape[0] == 98: + lm_eye_left = lm[60:68] + lm_eye_right = lm[68:76] + eye_left = np.mean(lm_eye_left, axis=0) + eye_right = np.mean(lm_eye_right, axis=0) + mouth_avg = (lm[76] + lm[82]) * 0.5 + + eye_avg = (eye_left + eye_right) * 0.5 + eye_to_eye = eye_right - eye_left + eye_to_mouth = mouth_avg - eye_avg + + # Get the oriented crop rectangle + # x: half width of the oriented crop rectangle + x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1] + # - np.flipud(eye_to_mouth) * [-1, 1]: rotate 90 clockwise + # norm with the hypotenuse: get the direction + x /= np.hypot(*x) # get the hypotenuse of a right triangle + rect_scale = 1 # TODO: you can edit it to get larger rect + x *= max(np.hypot(*eye_to_eye) * 2.0 * rect_scale, np.hypot(*eye_to_mouth) * 1.8 * rect_scale) + # y: half height of the oriented crop rectangle + y = np.flipud(x) * [-1, 1] + + x *= shrink_ratio[1] # width + y *= shrink_ratio[0] # height + + # c: center + c = eye_avg + eye_to_mouth * 0.1 + # quad: (left_top, left_bottom, right_bottom, right_top) + quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y]) + # qsize: side length of the square + qsize = np.hypot(*x) * 2 + + quad_ori = np.copy(quad) + # Shrink, for large face + # TODO: do we really need shrink + shrink = int(np.floor(qsize / output_size * 0.5)) + if shrink > 1: + h, w = img.shape[0:2] + rsize = (int(np.rint(float(w) / shrink)), int(np.rint(float(h) / shrink))) + img = cv2.resize(img, rsize, interpolation=cv2.INTER_AREA) + quad /= shrink + qsize /= shrink + + # Crop + h, w = img.shape[0:2] + border = max(int(np.rint(qsize * 0.1)), 3) + crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, w), min(crop[3] + border, h)) + if crop[2] - crop[0] < w or crop[3] - crop[1] < h: + img = img[crop[1]:crop[3], crop[0]:crop[2], :] + quad -= crop[0:2] + + # Pad + # pad: (width_left, height_top, width_right, height_bottom) + h, w = img.shape[0:2] + pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))), + int(np.ceil(max(quad[:, 1])))) + pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - w + border, 0), max(pad[3] - h + border, 0)) + if enable_padding and max(pad) > border - 4: + pad = np.maximum(pad, int(np.rint(qsize * 0.3))) + img = np.pad(img, ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect') + h, w = img.shape[0:2] + y, x, _ = np.ogrid[:h, :w, :1] + mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], + np.float32(w - 1 - x) / pad[2]), + 1.0 - np.minimum(np.float32(y) / pad[1], + np.float32(h - 1 - y) / pad[3])) + blur = int(qsize * 0.02) + if blur % 2 == 0: + blur += 1 + blur_img = cv2.boxFilter(img, 0, ksize=(blur, blur)) + + img = img.astype('float32') + img += (blur_img - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0) + img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0) + img = np.clip(img, 0, 255) # float32, [0, 255] + quad += pad[:2] + + # Transform use cv2 + h_ratio = shrink_ratio[0] / shrink_ratio[1] + dst_h, dst_w = int(transform_size * h_ratio), transform_size + template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) + # use cv2.LMEDS method for the equivalence to skimage transform + # ref: https://blog.csdn.net/yichxi/article/details/115827338 + affine_matrix = cv2.estimateAffinePartial2D(quad, template, method=cv2.LMEDS)[0] + cropped_face = cv2.warpAffine( + img, affine_matrix, (dst_w, dst_h), borderMode=cv2.BORDER_CONSTANT, borderValue=(135, 133, 132)) # gray + + if output_size < transform_size: + cropped_face = cv2.resize( + cropped_face, (output_size, int(output_size * h_ratio)), interpolation=cv2.INTER_LINEAR) + + if return_inverse_affine: + dst_h, dst_w = int(output_size * h_ratio), output_size + template = np.array([[0, 0], [0, dst_h], [dst_w, dst_h], [dst_w, 0]]) + # use cv2.LMEDS method for the equivalence to skimage transform + # ref: https://blog.csdn.net/yichxi/article/details/115827338 + affine_matrix = cv2.estimateAffinePartial2D( + quad_ori, np.array([[0, 0], [0, output_size], [dst_w, dst_h], [dst_w, 0]]), method=cv2.LMEDS)[0] + inverse_affine = cv2.invertAffineTransform(affine_matrix) + else: + inverse_affine = None + return cropped_face, inverse_affine + + +def paste_face_back(img, face, inverse_affine): + h, w = img.shape[0:2] + face_h, face_w = face.shape[0:2] + inv_restored = cv2.warpAffine(face, inverse_affine, (w, h)) + mask = np.ones((face_h, face_w, 3), dtype=np.float32) + inv_mask = cv2.warpAffine(mask, inverse_affine, (w, h)) + # remove the black borders + inv_mask_erosion = cv2.erode(inv_mask, np.ones((2, 2), np.uint8)) + inv_restored_remove_border = inv_mask_erosion * inv_restored + total_face_area = np.sum(inv_mask_erosion) // 3 + # compute the fusion edge based on the area of face + w_edge = int(total_face_area**0.5) // 20 + erosion_radius = w_edge * 2 + inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) + blur_size = w_edge * 2 + inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) + img = inv_soft_mask * inv_restored_remove_border + (1 - inv_soft_mask) * img + # float32, [0, 255] + return img + + +if __name__ == '__main__': + import os + + from extras.facexlib.detection import init_detection_model + from extras.facexlib.utils.face_restoration_helper import get_largest_face + from extras.facexlib.visualization import visualize_detection + + img_path = '/home/wxt/datasets/ffhq/ffhq_wild/00009.png' + img_name = os.splitext(os.path.basename(img_path))[0] + + # initialize model + det_net = init_detection_model('retinaface_resnet50', half=False) + img_ori = cv2.imread(img_path) + h, w = img_ori.shape[0:2] + # if larger than 800, scale it + scale = max(h / 800, w / 800) + if scale > 1: + img = cv2.resize(img_ori, (int(w / scale), int(h / scale)), interpolation=cv2.INTER_LINEAR) + + with torch.no_grad(): + bboxes = det_net.detect_faces(img, 0.97) + if scale > 1: + bboxes *= scale # the score is incorrect + bboxes = get_largest_face(bboxes, h, w)[0] + visualize_detection(img_ori, [bboxes], f'tmp/{img_name}_det.png') + + landmarks = np.array([[bboxes[i], bboxes[i + 1]] for i in range(5, 15, 2)]) + + cropped_face, inverse_affine = align_crop_face_landmarks( + img_ori, + landmarks, + output_size=512, + transform_size=None, + enable_padding=True, + return_inverse_affine=True, + shrink_ratio=(1, 1)) + + cv2.imwrite(f'tmp/{img_name}_cropeed_face.png', cropped_face) + img = paste_face_back(img_ori, cropped_face, inverse_affine) + cv2.imwrite(f'tmp/{img_name}_back.png', img) diff --git a/extras/facexlib/utils/misc.py b/extras/facexlib/utils/misc.py new file mode 100644 index 0000000000000000000000000000000000000000..b1a597ce7ecc476ef74ee3eb83d5fdfdfdbf0679 --- /dev/null +++ b/extras/facexlib/utils/misc.py @@ -0,0 +1,118 @@ +import cv2 +import os +import os.path as osp +import torch +from torch.hub import download_url_to_file, get_dir +from urllib.parse import urlparse + +ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + + +def imwrite(img, file_path, params=None, auto_mkdir=True): + """Write image to file. + + Args: + img (ndarray): Image array to be written. + file_path (str): Image file path. + params (None or list): Same as opencv's :func:`imwrite` interface. + auto_mkdir (bool): If the parent folder of `file_path` does not exist, + whether to create it automatically. + + Returns: + bool: Successful or not. + """ + if auto_mkdir: + dir_name = os.path.abspath(os.path.dirname(file_path)) + os.makedirs(dir_name, exist_ok=True) + return cv2.imwrite(file_path, img, params) + + +def img2tensor(imgs, bgr2rgb=True, float32=True): + """Numpy array to tensor. + + Args: + imgs (list[ndarray] | ndarray): Input images. + bgr2rgb (bool): Whether to change bgr to rgb. + float32 (bool): Whether to change to float32. + + Returns: + list[tensor] | tensor: Tensor images. If returned results only have + one element, just return tensor. + """ + + def _totensor(img, bgr2rgb, float32): + if img.shape[2] == 3 and bgr2rgb: + if img.dtype == 'float64': + img = img.astype('float32') + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + img = torch.from_numpy(img.transpose(2, 0, 1)) + if float32: + img = img.float() + return img + + if isinstance(imgs, list): + return [_totensor(img, bgr2rgb, float32) for img in imgs] + else: + return _totensor(imgs, bgr2rgb, float32) + + +def load_file_from_url(url, model_dir=None, progress=True, file_name=None, save_dir=None): + """Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py + """ + if model_dir is None: + hub_dir = get_dir() + model_dir = os.path.join(hub_dir, 'checkpoints') + + if save_dir is None: + save_dir = os.path.join(ROOT_DIR, model_dir) + os.makedirs(save_dir, exist_ok=True) + + parts = urlparse(url) + filename = os.path.basename(parts.path) + if file_name is not None: + filename = file_name + cached_file = os.path.abspath(os.path.join(save_dir, filename)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) + return cached_file + + +def scandir(dir_path, suffix=None, recursive=False, full_path=False): + """Scan a directory to find the interested files. + Args: + dir_path (str): Path of the directory. + suffix (str | tuple(str), optional): File suffix that we are + interested in. Default: None. + recursive (bool, optional): If set to True, recursively scan the + directory. Default: False. + full_path (bool, optional): If set to True, include the dir_path. + Default: False. + Returns: + A generator for all the interested files with relative paths. + """ + + if (suffix is not None) and not isinstance(suffix, (str, tuple)): + raise TypeError('"suffix" must be a string or tuple of strings') + + root = dir_path + + def _scandir(dir_path, suffix, recursive): + for entry in os.scandir(dir_path): + if not entry.name.startswith('.') and entry.is_file(): + if full_path: + return_path = entry.path + else: + return_path = osp.relpath(entry.path, root) + + if suffix is None: + yield return_path + elif return_path.endswith(suffix): + yield return_path + else: + if recursive: + yield from _scandir(entry.path, suffix=suffix, recursive=recursive) + else: + continue + + return _scandir(dir_path, suffix=suffix, recursive=recursive) diff --git a/extras/interrogate.py b/extras/interrogate.py new file mode 100644 index 0000000000000000000000000000000000000000..410d685f649421f594453ae01ee71c1f88ac2222 --- /dev/null +++ b/extras/interrogate.py @@ -0,0 +1,63 @@ +import os +import torch +import ldm_patched.modules.model_management as model_management + +from torchvision import transforms +from torchvision.transforms.functional import InterpolationMode +from modules.model_loader import load_file_from_url +from modules.config import path_clip_vision +from ldm_patched.modules.model_patcher import ModelPatcher +from extras.BLIP.models.blip import blip_decoder + + +blip_image_eval_size = 384 +blip_repo_root = os.path.join(os.path.dirname(__file__), 'BLIP') + + +class Interrogator: + def __init__(self): + self.blip_model = None + self.load_device = torch.device('cpu') + self.offload_device = torch.device('cpu') + self.dtype = torch.float32 + + @torch.no_grad() + @torch.inference_mode() + def interrogate(self, img_rgb): + if self.blip_model is None: + filename = load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/model_base_caption_capfilt_large.pth', + model_dir=path_clip_vision, + file_name='model_base_caption_capfilt_large.pth', + ) + + model = blip_decoder(pretrained=filename, image_size=blip_image_eval_size, vit='base', + med_config=os.path.join(blip_repo_root, "configs", "med_config.json")) + model.eval() + + self.load_device = model_management.text_encoder_device() + self.offload_device = model_management.text_encoder_offload_device() + self.dtype = torch.float32 + + model.to(self.offload_device) + + if model_management.should_use_fp16(device=self.load_device): + model.half() + self.dtype = torch.float16 + + self.blip_model = ModelPatcher(model, load_device=self.load_device, offload_device=self.offload_device) + + model_management.load_model_gpu(self.blip_model) + + gpu_image = transforms.Compose([ + transforms.ToTensor(), + transforms.Resize((blip_image_eval_size, blip_image_eval_size), interpolation=InterpolationMode.BICUBIC), + transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) + ])(img_rgb).unsqueeze(0).to(device=self.load_device, dtype=self.dtype) + + caption = self.blip_model.model.generate(gpu_image, sample=True, num_beams=1, max_length=75)[0] + + return caption + + +default_interrogator = Interrogator().interrogate diff --git a/extras/ip_adapter.py b/extras/ip_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..22527d244954db606b5eff72e30037938b0cab2d --- /dev/null +++ b/extras/ip_adapter.py @@ -0,0 +1,284 @@ +import torch +import ldm_patched.modules.clip_vision +import safetensors.torch as sf +import ldm_patched.modules.model_management as model_management +import ldm_patched.ldm.modules.attention as attention + +from extras.resampler import Resampler +from ldm_patched.modules.model_patcher import ModelPatcher +from modules.core import numpy_to_pytorch +from modules.ops import use_patched_ops +from ldm_patched.modules.ops import manual_cast + + +SD_V12_CHANNELS = [320] * 4 + [640] * 4 + [1280] * 4 + [1280] * 6 + [640] * 6 + [320] * 6 + [1280] * 2 +SD_XL_CHANNELS = [640] * 8 + [1280] * 40 + [1280] * 60 + [640] * 12 + [1280] * 20 + + +def sdp(q, k, v, extra_options): + return attention.optimized_attention(q, k, v, heads=extra_options["n_heads"], mask=None) + + +class ImageProjModel(torch.nn.Module): + def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4): + super().__init__() + + self.cross_attention_dim = cross_attention_dim + self.clip_extra_context_tokens = clip_extra_context_tokens + self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim) + self.norm = torch.nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds): + embeds = image_embeds + clip_extra_context_tokens = self.proj(embeds).reshape(-1, self.clip_extra_context_tokens, + self.cross_attention_dim) + clip_extra_context_tokens = self.norm(clip_extra_context_tokens) + return clip_extra_context_tokens + + +class To_KV(torch.nn.Module): + def __init__(self, cross_attention_dim): + super().__init__() + + channels = SD_XL_CHANNELS if cross_attention_dim == 2048 else SD_V12_CHANNELS + self.to_kvs = torch.nn.ModuleList( + [torch.nn.Linear(cross_attention_dim, channel, bias=False) for channel in channels]) + + def load_state_dict_ordered(self, sd): + state_dict = [] + for i in range(4096): + for k in ['k', 'v']: + key = f'{i}.to_{k}_ip.weight' + if key in sd: + state_dict.append(sd[key]) + for i, v in enumerate(state_dict): + self.to_kvs[i].weight = torch.nn.Parameter(v, requires_grad=False) + + +class IPAdapterModel(torch.nn.Module): + def __init__(self, state_dict, plus, cross_attention_dim=768, clip_embeddings_dim=1024, clip_extra_context_tokens=4, + sdxl_plus=False): + super().__init__() + self.plus = plus + if self.plus: + self.image_proj_model = Resampler( + dim=1280 if sdxl_plus else cross_attention_dim, + depth=4, + dim_head=64, + heads=20 if sdxl_plus else 12, + num_queries=clip_extra_context_tokens, + embedding_dim=clip_embeddings_dim, + output_dim=cross_attention_dim, + ff_mult=4 + ) + else: + self.image_proj_model = ImageProjModel( + cross_attention_dim=cross_attention_dim, + clip_embeddings_dim=clip_embeddings_dim, + clip_extra_context_tokens=clip_extra_context_tokens + ) + + self.image_proj_model.load_state_dict(state_dict["image_proj"]) + self.ip_layers = To_KV(cross_attention_dim) + self.ip_layers.load_state_dict_ordered(state_dict["ip_adapter"]) + + +clip_vision: ldm_patched.modules.clip_vision.ClipVisionModel = None +ip_negative: torch.Tensor = None +ip_adapters: dict = {} + + +def load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path): + global clip_vision, ip_negative, ip_adapters + + if clip_vision is None and isinstance(clip_vision_path, str): + clip_vision = ldm_patched.modules.clip_vision.load(clip_vision_path) + + if ip_negative is None and isinstance(ip_negative_path, str): + ip_negative = sf.load_file(ip_negative_path)['data'] + + if not isinstance(ip_adapter_path, str) or ip_adapter_path in ip_adapters: + return + + load_device = model_management.get_torch_device() + offload_device = torch.device('cpu') + + use_fp16 = model_management.should_use_fp16(device=load_device) + ip_state_dict = torch.load(ip_adapter_path, map_location="cpu") + plus = "latents" in ip_state_dict["image_proj"] + cross_attention_dim = ip_state_dict["ip_adapter"]["1.to_k_ip.weight"].shape[1] + sdxl = cross_attention_dim == 2048 + sdxl_plus = sdxl and plus + + if plus: + clip_extra_context_tokens = ip_state_dict["image_proj"]["latents"].shape[1] + clip_embeddings_dim = ip_state_dict["image_proj"]["latents"].shape[2] + else: + clip_extra_context_tokens = ip_state_dict["image_proj"]["proj.weight"].shape[0] // cross_attention_dim + clip_embeddings_dim = None + + with use_patched_ops(manual_cast): + ip_adapter = IPAdapterModel( + ip_state_dict, + plus=plus, + cross_attention_dim=cross_attention_dim, + clip_embeddings_dim=clip_embeddings_dim, + clip_extra_context_tokens=clip_extra_context_tokens, + sdxl_plus=sdxl_plus + ) + + ip_adapter.sdxl = sdxl + ip_adapter.load_device = load_device + ip_adapter.offload_device = offload_device + ip_adapter.dtype = torch.float16 if use_fp16 else torch.float32 + ip_adapter.to(offload_device, dtype=ip_adapter.dtype) + + image_proj_model = ModelPatcher(model=ip_adapter.image_proj_model, load_device=load_device, + offload_device=offload_device) + ip_layers = ModelPatcher(model=ip_adapter.ip_layers, load_device=load_device, + offload_device=offload_device) + + ip_adapters[ip_adapter_path] = dict( + ip_adapter=ip_adapter, + image_proj_model=image_proj_model, + ip_layers=ip_layers, + ip_unconds=None + ) + + return + + +@torch.no_grad() +@torch.inference_mode() +def clip_preprocess(image): + mean = torch.tensor([0.48145466, 0.4578275, 0.40821073], device=image.device, dtype=image.dtype).view([1, 3, 1, 1]) + std = torch.tensor([0.26862954, 0.26130258, 0.27577711], device=image.device, dtype=image.dtype).view([1, 3, 1, 1]) + image = image.movedim(-1, 1) + + # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 + B, C, H, W = image.shape + assert H == 224 and W == 224 + + return (image - mean) / std + + +@torch.no_grad() +@torch.inference_mode() +def preprocess(img, ip_adapter_path): + global ip_adapters + entry = ip_adapters[ip_adapter_path] + + ldm_patched.modules.model_management.load_model_gpu(clip_vision.patcher) + pixel_values = clip_preprocess(numpy_to_pytorch(img).to(clip_vision.load_device)) + outputs = clip_vision.model(pixel_values=pixel_values, output_hidden_states=True) + + ip_adapter = entry['ip_adapter'] + ip_layers = entry['ip_layers'] + image_proj_model = entry['image_proj_model'] + ip_unconds = entry['ip_unconds'] + + if ip_adapter.plus: + cond = outputs.hidden_states[-2] + else: + cond = outputs.image_embeds + + cond = cond.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) + + ldm_patched.modules.model_management.load_model_gpu(image_proj_model) + cond = image_proj_model.model(cond).to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) + + ldm_patched.modules.model_management.load_model_gpu(ip_layers) + + if ip_unconds is None: + uncond = ip_negative.to(device=ip_adapter.load_device, dtype=ip_adapter.dtype) + ip_unconds = [m(uncond).cpu() for m in ip_layers.model.to_kvs] + entry['ip_unconds'] = ip_unconds + + ip_conds = [m(cond).cpu() for m in ip_layers.model.to_kvs] + + return ip_conds, ip_unconds + + +@torch.no_grad() +@torch.inference_mode() +def patch_model(model, tasks): + new_model = model.clone() + + def make_attn_patcher(ip_index): + def patcher(n, context_attn2, value_attn2, extra_options): + org_dtype = n.dtype + current_step = float(model.model.diffusion_model.current_step.detach().cpu().numpy()[0]) + cond_or_uncond = extra_options['cond_or_uncond'] + + q = n + k = [context_attn2] + v = [value_attn2] + b, _, _ = q.shape + + for (cs, ucs), cn_stop, cn_weight in tasks: + if current_step < cn_stop: + ip_k_c = cs[ip_index * 2].to(q) + ip_v_c = cs[ip_index * 2 + 1].to(q) + ip_k_uc = ucs[ip_index * 2].to(q) + ip_v_uc = ucs[ip_index * 2 + 1].to(q) + + ip_k = torch.cat([(ip_k_c, ip_k_uc)[i] for i in cond_or_uncond], dim=0) + ip_v = torch.cat([(ip_v_c, ip_v_uc)[i] for i in cond_or_uncond], dim=0) + + # Midjourney's attention formulation of image prompt (non-official reimplementation) + # Written by Lvmin Zhang at Stanford University, 2023 Dec + # For non-commercial use only - if you use this in commercial project then + # probably it has some intellectual property issues. + # Contact lvminzhang@acm.org if you are not sure. + + # Below is the sensitive part with potential intellectual property issues. + + ip_v_mean = torch.mean(ip_v, dim=1, keepdim=True) + ip_v_offset = ip_v - ip_v_mean + + B, F, C = ip_k.shape + channel_penalty = float(C) / 1280.0 + weight = cn_weight * channel_penalty + + ip_k = ip_k * weight + ip_v = ip_v_offset + ip_v_mean * weight + + k.append(ip_k) + v.append(ip_v) + + k = torch.cat(k, dim=1) + v = torch.cat(v, dim=1) + out = sdp(q, k, v, extra_options) + + + return out.to(dtype=org_dtype) + return patcher + + def set_model_patch_replace(model, number, key): + to = model.model_options["transformer_options"] + if "patches_replace" not in to: + to["patches_replace"] = {} + if "attn2" not in to["patches_replace"]: + to["patches_replace"]["attn2"] = {} + if key not in to["patches_replace"]["attn2"]: + to["patches_replace"]["attn2"][key] = make_attn_patcher(number) + + number = 0 + + for id in [4, 5, 7, 8]: + block_indices = range(2) if id in [4, 5] else range(10) + for index in block_indices: + set_model_patch_replace(new_model, number, ("input", id, index)) + number += 1 + + for id in range(6): + block_indices = range(2) if id in [3, 4, 5] else range(10) + for index in block_indices: + set_model_patch_replace(new_model, number, ("output", id, index)) + number += 1 + + for index in range(10): + set_model_patch_replace(new_model, number, ("middle", 0, index)) + number += 1 + + return new_model diff --git a/extras/preprocessors.py b/extras/preprocessors.py new file mode 100644 index 0000000000000000000000000000000000000000..0aa83109aac2b1228317ea12ec22d5f8bb9de8d0 --- /dev/null +++ b/extras/preprocessors.py @@ -0,0 +1,81 @@ +import cv2 +import numpy as np + + +def centered_canny(x: np.ndarray, canny_low_threshold, canny_high_threshold): + assert isinstance(x, np.ndarray) + assert x.ndim == 2 and x.dtype == np.uint8 + + y = cv2.Canny(x, int(canny_low_threshold), int(canny_high_threshold)) + y = y.astype(np.float32) / 255.0 + return y + + +def centered_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): + assert isinstance(x, np.ndarray) + assert x.ndim == 3 and x.shape[2] == 3 + + result = [centered_canny(x[..., i], canny_low_threshold, canny_high_threshold) for i in range(3)] + result = np.stack(result, axis=2) + return result + + +def pyramid_canny_color(x: np.ndarray, canny_low_threshold, canny_high_threshold): + assert isinstance(x, np.ndarray) + assert x.ndim == 3 and x.shape[2] == 3 + + H, W, C = x.shape + acc_edge = None + + for k in [0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]: + Hs, Ws = int(H * k), int(W * k) + small = cv2.resize(x, (Ws, Hs), interpolation=cv2.INTER_AREA) + edge = centered_canny_color(small, canny_low_threshold, canny_high_threshold) + if acc_edge is None: + acc_edge = edge + else: + acc_edge = cv2.resize(acc_edge, (edge.shape[1], edge.shape[0]), interpolation=cv2.INTER_LINEAR) + acc_edge = acc_edge * 0.75 + edge * 0.25 + + return acc_edge + + +def norm255(x, low=4, high=96): + assert isinstance(x, np.ndarray) + assert x.ndim == 2 and x.dtype == np.float32 + + v_min = np.percentile(x, low) + v_max = np.percentile(x, high) + + x -= v_min + x /= v_max - v_min + + return x * 255.0 + + +def canny_pyramid(x, canny_low_threshold, canny_high_threshold): + # For some reasons, SAI's Control-lora Canny seems to be trained on canny maps with non-standard resolutions. + # Then we use pyramid to use all resolutions to avoid missing any structure in specific resolutions. + + color_canny = pyramid_canny_color(x, canny_low_threshold, canny_high_threshold) + result = np.sum(color_canny, axis=2) + + return norm255(result, low=1, high=99).clip(0, 255).astype(np.uint8) + + +def cpds(x): + # cv2.decolor is not "decolor", it is Cewu Lu's method + # See http://www.cse.cuhk.edu.hk/leojia/projects/color2gray/index.html + # See https://docs.opencv.org/3.0-beta/modules/photo/doc/decolor.html + + raw = cv2.GaussianBlur(x, (0, 0), 0.8) + density, boost = cv2.decolor(raw) + + raw = raw.astype(np.float32) + density = density.astype(np.float32) + boost = boost.astype(np.float32) + + offset = np.sum((raw - boost) ** 2.0, axis=2) ** 0.5 + result = density + offset + + return norm255(result, low=4, high=96).clip(0, 255).astype(np.uint8) diff --git a/extras/resampler.py b/extras/resampler.py new file mode 100644 index 0000000000000000000000000000000000000000..539f309d4621cc432ec2937c48ef24b852652b8f --- /dev/null +++ b/extras/resampler.py @@ -0,0 +1,120 @@ +# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py +import math + +import torch +import torch.nn as nn + + +# FFN +def FeedForward(dim, mult=4): + inner_dim = int(dim * mult) + return nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, inner_dim, bias=False), + nn.GELU(), + nn.Linear(inner_dim, dim, bias=False), + ) + + +def reshape_tensor(x, heads): + bs, length, width = x.shape + #(bs, length, width) --> (bs, length, n_heads, dim_per_head) + x = x.view(bs, length, heads, -1) + # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head) + x = x.transpose(1, 2) + # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head) + x = x.reshape(bs, heads, length, -1) + return x + + +class PerceiverAttention(nn.Module): + def __init__(self, *, dim, dim_head=64, heads=8): + super().__init__() + self.scale = dim_head**-0.5 + self.dim_head = dim_head + self.heads = heads + inner_dim = dim_head * heads + + self.norm1 = nn.LayerNorm(dim) + self.norm2 = nn.LayerNorm(dim) + + self.to_q = nn.Linear(dim, inner_dim, bias=False) + self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + + def forward(self, x, latents): + """ + Args: + x (torch.Tensor): image features + shape (b, n1, D) + latent (torch.Tensor): latent features + shape (b, n2, D) + """ + x = self.norm1(x) + latents = self.norm2(latents) + + b, l, _ = latents.shape + + q = self.to_q(latents) + kv_input = torch.cat((x, latents), dim=-2) + k, v = self.to_kv(kv_input).chunk(2, dim=-1) + + q = reshape_tensor(q, self.heads) + k = reshape_tensor(k, self.heads) + v = reshape_tensor(v, self.heads) + + # attention + scale = 1 / math.sqrt(math.sqrt(self.dim_head)) + weight = (q * scale) @ (k * scale).transpose(-2, -1) # More stable with f16 than dividing afterwards + weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype) + out = weight @ v + + out = out.permute(0, 2, 1, 3).reshape(b, l, -1) + + return self.to_out(out) + + +class Resampler(nn.Module): + def __init__( + self, + dim=1024, + depth=8, + dim_head=64, + heads=16, + num_queries=8, + embedding_dim=768, + output_dim=1024, + ff_mult=4, + ): + super().__init__() + + self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5) + + self.proj_in = nn.Linear(embedding_dim, dim) + + self.proj_out = nn.Linear(dim, output_dim) + self.norm_out = nn.LayerNorm(output_dim) + + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append( + nn.ModuleList( + [ + PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads), + FeedForward(dim=dim, mult=ff_mult), + ] + ) + ) + + def forward(self, x): + latents = self.latents.repeat(x.size(0), 1, 1).to(x) + + x = self.proj_in(x) + + for attn, ff in self.layers: + latents = attn(x, latents) + latents + latents = ff(latents) + latents + + latents = self.proj_out(latents) + return self.norm_out(latents) diff --git a/extras/vae_interpose.py b/extras/vae_interpose.py new file mode 100644 index 0000000000000000000000000000000000000000..72fb09a415b3d283c1cdf10902ebe337c22c10a5 --- /dev/null +++ b/extras/vae_interpose.py @@ -0,0 +1,93 @@ +# https://github.com/city96/SD-Latent-Interposer/blob/main/interposer.py + +import os +import torch +import safetensors.torch as sf +import torch.nn as nn +import ldm_patched.modules.model_management + +from ldm_patched.modules.model_patcher import ModelPatcher +from modules.config import path_vae_approx + + +class Block(nn.Module): + def __init__(self, size): + super().__init__() + self.join = nn.ReLU() + self.long = nn.Sequential( + nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1), + nn.LeakyReLU(0.1), + nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1), + nn.LeakyReLU(0.1), + nn.Conv2d(size, size, kernel_size=3, stride=1, padding=1), + ) + + def forward(self, x): + y = self.long(x) + z = self.join(y + x) + return z + + +class Interposer(nn.Module): + def __init__(self): + super().__init__() + self.chan = 4 + self.hid = 128 + + self.head_join = nn.ReLU() + self.head_short = nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1) + self.head_long = nn.Sequential( + nn.Conv2d(self.chan, self.hid, kernel_size=3, stride=1, padding=1), + nn.LeakyReLU(0.1), + nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1), + nn.LeakyReLU(0.1), + nn.Conv2d(self.hid, self.hid, kernel_size=3, stride=1, padding=1), + ) + self.core = nn.Sequential( + Block(self.hid), + Block(self.hid), + Block(self.hid), + ) + self.tail = nn.Sequential( + nn.ReLU(), + nn.Conv2d(self.hid, self.chan, kernel_size=3, stride=1, padding=1) + ) + + def forward(self, x): + y = self.head_join( + self.head_long(x) + + self.head_short(x) + ) + z = self.core(y) + return self.tail(z) + + +vae_approx_model = None +vae_approx_filename = os.path.join(path_vae_approx, 'xl-to-v1_interposer-v3.1.safetensors') + + +def parse(x): + global vae_approx_model + + x_origin = x.clone() + + if vae_approx_model is None: + model = Interposer() + model.eval() + sd = sf.load_file(vae_approx_filename) + model.load_state_dict(sd) + fp16 = ldm_patched.modules.model_management.should_use_fp16() + if fp16: + model = model.half() + vae_approx_model = ModelPatcher( + model=model, + load_device=ldm_patched.modules.model_management.get_torch_device(), + offload_device=torch.device('cpu') + ) + vae_approx_model.dtype = torch.float16 if fp16 else torch.float32 + + ldm_patched.modules.model_management.load_model_gpu(vae_approx_model) + + x = x_origin.to(device=vae_approx_model.load_device, dtype=vae_approx_model.dtype) + x = vae_approx_model.model(x).to(x_origin) + return x diff --git a/extras/wd14tagger.py b/extras/wd14tagger.py new file mode 100644 index 0000000000000000000000000000000000000000..368c13dfa7cecefa655097602a5b9e249ceedaef --- /dev/null +++ b/extras/wd14tagger.py @@ -0,0 +1,98 @@ +# https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags +# https://github.com/pythongosssss/ComfyUI-WD14-Tagger/blob/main/wd14tagger.py + +# { +# "wd-v1-4-moat-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2", +# "wd-v1-4-convnextv2-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2", +# "wd-v1-4-convnext-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2", +# "wd-v1-4-convnext-tagger": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger", +# "wd-v1-4-vit-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2" +# } + + +import numpy as np +import csv +import onnxruntime as ort + +from PIL import Image +from onnxruntime import InferenceSession +from modules.config import path_clip_vision +from modules.model_loader import load_file_from_url + + +global_model = None +global_csv = None + + +def default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, exclude_tags=""): + global global_model, global_csv + + model_name = "wd-v1-4-moat-tagger-v2" + + model_onnx_filename = load_file_from_url( + url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx', + model_dir=path_clip_vision, + file_name=f'{model_name}.onnx', + ) + + model_csv_filename = load_file_from_url( + url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv', + model_dir=path_clip_vision, + file_name=f'{model_name}.csv', + ) + + if global_model is not None: + model = global_model + else: + model = InferenceSession(model_onnx_filename, providers=ort.get_available_providers()) + global_model = model + + input = model.get_inputs()[0] + height = input.shape[1] + + image = Image.fromarray(image_rgb) # RGB + ratio = float(height)/max(image.size) + new_size = tuple([int(x*ratio) for x in image.size]) + image = image.resize(new_size, Image.LANCZOS) + square = Image.new("RGB", (height, height), (255, 255, 255)) + square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2)) + + image = np.array(square).astype(np.float32) + image = image[:, :, ::-1] # RGB -> BGR + image = np.expand_dims(image, 0) + + if global_csv is not None: + csv_lines = global_csv + else: + csv_lines = [] + with open(model_csv_filename) as f: + reader = csv.reader(f) + next(reader) + for row in reader: + csv_lines.append(row) + global_csv = csv_lines + + tags = [] + general_index = None + character_index = None + for line_num, row in enumerate(csv_lines): + if general_index is None and row[2] == "0": + general_index = line_num + elif character_index is None and row[2] == "4": + character_index = line_num + tags.append(row[1]) + + label_name = model.get_outputs()[0].name + probs = model.run([label_name], {input.name: image})[0] + + result = list(zip(tags, probs[0])) + + general = [item for item in result[general_index:character_index] if item[1] > threshold] + character = [item for item in result[character_index:] if item[1] > character_threshold] + + all = character + general + remove = [s.strip() for s in exclude_tags.lower().split(",")] + all = [tag for tag in all if tag[0] not in remove] + + res = ", ".join((item[0].replace("(", "\\(").replace(")", "\\)") for item in all)).replace('_', ' ') + return res diff --git a/fooocus_colab.ipynb b/fooocus_colab.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..205dac55d4ed3aac4a1154d627465ac0e5474069 --- /dev/null +++ b/fooocus_colab.ipynb @@ -0,0 +1,35 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VjYy0F2gZIPR" + }, + "outputs": [], + "source": [ + "!pip install pygit2==1.12.2\n", + "%cd /content\n", + "!git clone https://github.com/lllyasviel/Fooocus.git\n", + "%cd /content/Fooocus\n", + "!python entry_with_update.py --share\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/fooocus_version.py b/fooocus_version.py new file mode 100644 index 0000000000000000000000000000000000000000..6c3c2c903805ba9f7ea4aa4d0b15aa1621ec3577 --- /dev/null +++ b/fooocus_version.py @@ -0,0 +1 @@ +version = '2.2.1' diff --git a/javascript/contextMenus.js b/javascript/contextMenus.js new file mode 100644 index 0000000000000000000000000000000000000000..7494674d336d03b05d1618746962c39f8cf5d440 --- /dev/null +++ b/javascript/contextMenus.js @@ -0,0 +1,166 @@ +// based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/javascript/contextMenus.js + +var contextMenuInit = function() { + let eventListenerApplied = false; + let menuSpecs = new Map(); + + const uid = function() { + return Date.now().toString(36) + Math.random().toString(36).substring(2); + }; + + function showContextMenu(event, element, menuEntries) { + let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft; + let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop; + + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + + let baseStyle = window.getComputedStyle(gradioApp().querySelector('button.selected')); + + const contextMenu = document.createElement('nav'); + contextMenu.id = "context-menu"; + contextMenu.style.background = baseStyle.background; + contextMenu.style.color = baseStyle.color; + contextMenu.style.fontFamily = baseStyle.fontFamily; + contextMenu.style.top = posy + 'px'; + contextMenu.style.left = posx + 'px'; + + const contextMenuList = document.createElement('ul'); + contextMenuList.className = 'context-menu-items'; + contextMenu.append(contextMenuList); + + menuEntries.forEach(function(entry) { + let contextMenuEntry = document.createElement('a'); + contextMenuEntry.innerHTML = entry['name']; + contextMenuEntry.addEventListener("click", function() { + entry['func'](); + }); + contextMenuList.append(contextMenuEntry); + + }); + + gradioApp().appendChild(contextMenu); + + let menuWidth = contextMenu.offsetWidth + 4; + let menuHeight = contextMenu.offsetHeight + 4; + + let windowWidth = window.innerWidth; + let windowHeight = window.innerHeight; + + if ((windowWidth - posx) < menuWidth) { + contextMenu.style.left = windowWidth - menuWidth + "px"; + } + + if ((windowHeight - posy) < menuHeight) { + contextMenu.style.top = windowHeight - menuHeight + "px"; + } + + } + + function appendContextMenuOption(targetElementSelector, entryName, entryFunction) { + + var currentItems = menuSpecs.get(targetElementSelector); + + if (!currentItems) { + currentItems = []; + menuSpecs.set(targetElementSelector, currentItems); + } + let newItem = { + id: targetElementSelector + '_' + uid(), + name: entryName, + func: entryFunction, + isNew: true + }; + + currentItems.push(newItem); + return newItem['id']; + } + + function removeContextMenuOption(uid) { + menuSpecs.forEach(function(v) { + let index = -1; + v.forEach(function(e, ei) { + if (e['id'] == uid) { + index = ei; + } + }); + if (index >= 0) { + v.splice(index, 1); + } + }); + } + + function addContextMenuEventListener() { + if (eventListenerApplied) { + return; + } + gradioApp().addEventListener("click", function(e) { + if (!e.isTrusted) { + return; + } + + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + }); + gradioApp().addEventListener("contextmenu", function(e) { + let oldMenu = gradioApp().querySelector('#context-menu'); + if (oldMenu) { + oldMenu.remove(); + } + menuSpecs.forEach(function(v, k) { + if (e.composedPath()[0].matches(k)) { + showContextMenu(e, e.composedPath()[0], v); + e.preventDefault(); + } + }); + }); + eventListenerApplied = true; + + } + + return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]; +}; + +var initResponse = contextMenuInit(); +var appendContextMenuOption = initResponse[0]; +var removeContextMenuOption = initResponse[1]; +var addContextMenuEventListener = initResponse[2]; + +let cancelGenerateForever = function() { + clearInterval(window.generateOnRepeatInterval); +}; + +(function() { + //Start example Context Menu Items + let generateOnRepeat = function(genbuttonid, interruptbuttonid) { + let genbutton = gradioApp().querySelector(genbuttonid); + let interruptbutton = gradioApp().querySelector(interruptbuttonid); + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + clearInterval(window.generateOnRepeatInterval); + window.generateOnRepeatInterval = setInterval(function() { + if (!interruptbutton.offsetParent) { + genbutton.click(); + } + }, + 500); + }; + + let generateOnRepeatForButtons = function() { + generateOnRepeat('#generate_button', '#stop_button'); + }; + appendContextMenuOption('#generate_button', 'Generate forever', generateOnRepeatForButtons); + +})(); +//End example Context Menu Items + +document.onreadystatechange = function () { + if (document.readyState == "complete") { + addContextMenuEventListener(); + } +}; diff --git a/javascript/edit-attention.js b/javascript/edit-attention.js new file mode 100644 index 0000000000000000000000000000000000000000..3f38f14b52831679eed1491291e4602112f1880c --- /dev/null +++ b/javascript/edit-attention.js @@ -0,0 +1,128 @@ +function updateInput(target) { + let e = new Event("input", {bubbles: true}); + Object.defineProperty(e, "target", {value: target}); + target.dispatchEvent(e); +} + +function keyupEditAttention(event) { + let target = event.originalTarget || event.composedPath()[0]; + if (!target.matches("*:is([id*='_prompt'], .prompt) textarea")) return; + if (!(event.metaKey || event.ctrlKey)) return; + + let isPlus = event.key == "ArrowUp"; + let isMinus = event.key == "ArrowDown"; + if (!isPlus && !isMinus) return; + + let selectionStart = target.selectionStart; + let selectionEnd = target.selectionEnd; + let text = target.value; + + function selectCurrentParenthesisBlock(OPEN, CLOSE) { + if (selectionStart !== selectionEnd) return false; + + // Find opening parenthesis around current cursor + const before = text.substring(0, selectionStart); + let beforeParen = before.lastIndexOf(OPEN); + if (beforeParen == -1) return false; + let beforeParenClose = before.lastIndexOf(CLOSE); + while (beforeParenClose !== -1 && beforeParenClose > beforeParen) { + beforeParen = before.lastIndexOf(OPEN, beforeParen - 1); + beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1); + } + + // Find closing parenthesis around current cursor + const after = text.substring(selectionStart); + let afterParen = after.indexOf(CLOSE); + if (afterParen == -1) return false; + let afterParenOpen = after.indexOf(OPEN); + while (afterParenOpen !== -1 && afterParen > afterParenOpen) { + afterParen = after.indexOf(CLOSE, afterParen + 1); + afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1); + } + if (beforeParen === -1 || afterParen === -1) return false; + + // Set the selection to the text between the parenthesis + const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen); + const lastColon = parenContent.lastIndexOf(":"); + selectionStart = beforeParen + 1; + selectionEnd = selectionStart + lastColon; + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + function selectCurrentWord() { + if (selectionStart !== selectionEnd) return false; + const delimiters = ".,\\/!?%^*;:{}=`~() \r\n\t"; + + // seek backward until to find beggining + while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) { + selectionStart--; + } + + // seek forward to find end + while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) { + selectionEnd++; + } + + target.setSelectionRange(selectionStart, selectionEnd); + return true; + } + + // If the user hasn't selected anything, let's select their current parenthesis block or word + if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) { + selectCurrentWord(); + } + + event.preventDefault(); + + var closeCharacter = ')'; + var delta = 0.1; + + if (selectionStart > 0 && text[selectionStart - 1] == '<') { + closeCharacter = '>'; + delta = 0.05; + } else if (selectionStart == 0 || text[selectionStart - 1] != "(") { + + // do not include spaces at the end + while (selectionEnd > selectionStart && text[selectionEnd - 1] == ' ') { + selectionEnd -= 1; + } + if (selectionStart == selectionEnd) { + return; + } + + text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd); + + selectionStart += 1; + selectionEnd += 1; + } + + var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1; + var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end)); + if (isNaN(weight)) return; + + weight += isPlus ? delta : -delta; + weight = parseFloat(weight.toPrecision(12)); + if (String(weight).length == 1) weight += ".0"; + + if (closeCharacter == ')' && weight == 1) { + var endParenPos = text.substring(selectionEnd).indexOf(')'); + text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + endParenPos + 1); + selectionStart--; + selectionEnd--; + } else { + text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + end); + } + + target.focus(); + target.value = text; + target.selectionStart = selectionStart; + target.selectionEnd = selectionEnd; + + updateInput(target); + +} + +addEventListener('keydown', (event) => { + keyupEditAttention(event); +}); diff --git a/javascript/imageviewer.js b/javascript/imageviewer.js new file mode 100644 index 0000000000000000000000000000000000000000..29f0f312bc02eae80f8e4a0245a7949d43dfad8d --- /dev/null +++ b/javascript/imageviewer.js @@ -0,0 +1,260 @@ +// From A1111 + +function closeModal() { + gradioApp().getElementById("lightboxModal").style.display = "none"; +} + +function showModal(event) { + const source = event.target || event.srcElement; + const modalImage = gradioApp().getElementById("modalImage"); + const lb = gradioApp().getElementById("lightboxModal"); + modalImage.src = source.src; + if (modalImage.style.display === 'none') { + lb.style.setProperty('background-image', 'url(' + source.src + ')'); + } + lb.style.display = "flex"; + lb.focus(); + + event.stopPropagation(); +} + +function negmod(n, m) { + return ((n % m) + m) % m; +} + +function updateOnBackgroundChange() { + const modalImage = gradioApp().getElementById("modalImage"); + if (modalImage && modalImage.offsetParent) { + let currentButton = selected_gallery_button(); + + if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { + modalImage.src = currentButton.children[0].src; + if (modalImage.style.display === 'none') { + const modal = gradioApp().getElementById("lightboxModal"); + modal.style.setProperty('background-image', `url(${modalImage.src})`); + } + } + } +} + +function all_gallery_buttons() { + var allGalleryButtons = gradioApp().querySelectorAll('.image_gallery .thumbnails > .thumbnail-item.thumbnail-small'); + var visibleGalleryButtons = []; + allGalleryButtons.forEach(function(elem) { + if (elem.parentElement.offsetParent) { + visibleGalleryButtons.push(elem); + } + }); + return visibleGalleryButtons; +} + +function selected_gallery_button() { + return all_gallery_buttons().find(elem => elem.classList.contains('selected')) ?? null; +} + +function selected_gallery_index() { + return all_gallery_buttons().findIndex(elem => elem.classList.contains('selected')); +} + +function modalImageSwitch(offset) { + var galleryButtons = all_gallery_buttons(); + + if (galleryButtons.length > 1) { + var currentButton = selected_gallery_button(); + + var result = -1; + galleryButtons.forEach(function(v, i) { + if (v == currentButton) { + result = i; + } + }); + + if (result != -1) { + var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]; + nextButton.click(); + const modalImage = gradioApp().getElementById("modalImage"); + const modal = gradioApp().getElementById("lightboxModal"); + modalImage.src = nextButton.children[0].src; + if (modalImage.style.display === 'none') { + modal.style.setProperty('background-image', `url(${modalImage.src})`); + } + setTimeout(function() { + modal.focus(); + }, 10); + } + } +} + +function saveImage() { + +} + +function modalSaveImage(event) { + event.stopPropagation(); +} + +function modalNextImage(event) { + modalImageSwitch(1); + event.stopPropagation(); +} + +function modalPrevImage(event) { + modalImageSwitch(-1); + event.stopPropagation(); +} + +function modalKeyHandler(event) { + switch (event.key) { + case "s": + saveImage(); + break; + case "ArrowLeft": + modalPrevImage(event); + break; + case "ArrowRight": + modalNextImage(event); + break; + case "Escape": + closeModal(); + break; + } +} + +function setupImageForLightbox(e) { + if (e.dataset.modded) { + return; + } + + e.dataset.modded = true; + e.style.cursor = 'pointer'; + e.style.userSelect = 'none'; + + var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1; + + // For Firefox, listening on click first switched to next image then shows the lightbox. + // If you know how to fix this without switching to mousedown event, please. + // For other browsers the event is click to make it possiblr to drag picture. + var event = isFirefox ? 'mousedown' : 'click'; + + e.addEventListener(event, function(evt) { + if (evt.button == 1) { + open(evt.target.src); + evt.preventDefault(); + return; + } + if (evt.button != 0) return; + + modalZoomSet(gradioApp().getElementById('modalImage'), true); + evt.preventDefault(); + showModal(evt); + }, true); + +} + +function modalZoomSet(modalImage, enable) { + if (modalImage) modalImage.classList.toggle('modalImageFullscreen', !!enable); +} + +function modalZoomToggle(event) { + var modalImage = gradioApp().getElementById("modalImage"); + modalZoomSet(modalImage, !modalImage.classList.contains('modalImageFullscreen')); + event.stopPropagation(); +} + +function modalTileImageToggle(event) { + const modalImage = gradioApp().getElementById("modalImage"); + const modal = gradioApp().getElementById("lightboxModal"); + const isTiling = modalImage.style.display === 'none'; + if (isTiling) { + modalImage.style.display = 'block'; + modal.style.setProperty('background-image', 'none'); + } else { + modalImage.style.display = 'none'; + modal.style.setProperty('background-image', `url(${modalImage.src})`); + } + + event.stopPropagation(); +} + +onAfterUiUpdate(function() { + var fullImg_preview = gradioApp().querySelectorAll('.image_gallery > div > img'); + if (fullImg_preview != null) { + fullImg_preview.forEach(setupImageForLightbox); + } + updateOnBackgroundChange(); +}); + +document.addEventListener("DOMContentLoaded", function() { + //const modalFragment = document.createDocumentFragment(); + const modal = document.createElement('div'); + modal.onclick = closeModal; + modal.id = "lightboxModal"; + modal.tabIndex = 0; + modal.addEventListener('keydown', modalKeyHandler, true); + + const modalControls = document.createElement('div'); + modalControls.className = 'modalControls gradio-container'; + modal.append(modalControls); + + const modalZoom = document.createElement('span'); + modalZoom.className = 'modalZoom cursor'; + modalZoom.innerHTML = '⤡'; + modalZoom.addEventListener('click', modalZoomToggle, true); + modalZoom.title = "Toggle zoomed view"; + modalControls.appendChild(modalZoom); + + // const modalTileImage = document.createElement('span'); + // modalTileImage.className = 'modalTileImage cursor'; + // modalTileImage.innerHTML = '⊞'; + // modalTileImage.addEventListener('click', modalTileImageToggle, true); + // modalTileImage.title = "Preview tiling"; + // modalControls.appendChild(modalTileImage); + // + // const modalSave = document.createElement("span"); + // modalSave.className = "modalSave cursor"; + // modalSave.id = "modal_save"; + // modalSave.innerHTML = "🖫"; + // modalSave.addEventListener("click", modalSaveImage, true); + // modalSave.title = "Save Image(s)"; + // modalControls.appendChild(modalSave); + + const modalClose = document.createElement('span'); + modalClose.className = 'modalClose cursor'; + modalClose.innerHTML = '×'; + modalClose.onclick = closeModal; + modalClose.title = "Close image viewer"; + modalControls.appendChild(modalClose); + + const modalImage = document.createElement('img'); + modalImage.id = 'modalImage'; + modalImage.onclick = closeModal; + modalImage.tabIndex = 0; + modalImage.addEventListener('keydown', modalKeyHandler, true); + modal.appendChild(modalImage); + + const modalPrev = document.createElement('a'); + modalPrev.className = 'modalPrev'; + modalPrev.innerHTML = '❮'; + modalPrev.tabIndex = 0; + modalPrev.addEventListener('click', modalPrevImage, true); + modalPrev.addEventListener('keydown', modalKeyHandler, true); + modal.appendChild(modalPrev); + + const modalNext = document.createElement('a'); + modalNext.className = 'modalNext'; + modalNext.innerHTML = '❯'; + modalNext.tabIndex = 0; + modalNext.addEventListener('click', modalNextImage, true); + modalNext.addEventListener('keydown', modalKeyHandler, true); + + modal.appendChild(modalNext); + + try { + gradioApp().appendChild(modal); + } catch (e) { + gradioApp().body.appendChild(modal); + } + + document.body.appendChild(modal); + +}); diff --git a/javascript/localization.js b/javascript/localization.js new file mode 100644 index 0000000000000000000000000000000000000000..0a8394ca2e7e82d5c8c5708655fffb811b43f430 --- /dev/null +++ b/javascript/localization.js @@ -0,0 +1,144 @@ +var re_num = /^[.\d]+$/; + +var original_lines = {}; +var translated_lines = {}; + +function hasLocalization() { + return window.localization && Object.keys(window.localization).length > 0; +} + +function textNodesUnder(el) { + var n, a = [], walk = document.createTreeWalker(el, NodeFilter.SHOW_TEXT, null, false); + while ((n = walk.nextNode())) a.push(n); + return a; +} + +function canBeTranslated(node, text) { + if (!text) return false; + if (!node.parentElement) return false; + var parentType = node.parentElement.nodeName; + if (parentType == 'SCRIPT' || parentType == 'STYLE' || parentType == 'TEXTAREA') return false; + if (re_num.test(text)) return false; + return true; +} + +function getTranslation(text) { + if (!text) return undefined; + + if (translated_lines[text] === undefined) { + original_lines[text] = 1; + } + + var tl = localization[text]; + if (tl !== undefined) { + translated_lines[tl] = 1; + } + + return tl; +} + +function processTextNode(node) { + var text = node.textContent.trim(); + + if (!canBeTranslated(node, text)) return; + + var tl = getTranslation(text); + if (tl !== undefined) { + node.textContent = tl; + if (text && node.parentElement) { + node.parentElement.setAttribute("data-original-text", text); + } + } +} + +function processNode(node) { + if (node.nodeType == 3) { + processTextNode(node); + return; + } + + if (node.title) { + let tl = getTranslation(node.title); + if (tl !== undefined) { + node.title = tl; + } + } + + if (node.placeholder) { + let tl = getTranslation(node.placeholder); + if (tl !== undefined) { + node.placeholder = tl; + } + } + + textNodesUnder(node).forEach(function(node) { + processTextNode(node); + }); +} + +function refresh_style_localization() { + processNode(document.querySelector('.style_selections')); +} + +function localizeWholePage() { + processNode(gradioApp()); + + function elem(comp) { + var elem_id = comp.props.elem_id ? comp.props.elem_id : "component-" + comp.id; + return gradioApp().getElementById(elem_id); + } + + for (var comp of window.gradio_config.components) { + if (comp.props.webui_tooltip) { + let e = elem(comp); + + let tl = e ? getTranslation(e.title) : undefined; + if (tl !== undefined) { + e.title = tl; + } + } + if (comp.props.placeholder) { + let e = elem(comp); + let textbox = e ? e.querySelector('[placeholder]') : null; + + let tl = textbox ? getTranslation(textbox.placeholder) : undefined; + if (tl !== undefined) { + textbox.placeholder = tl; + } + } + } +} + +document.addEventListener("DOMContentLoaded", function() { + if (!hasLocalization()) { + return; + } + + onUiUpdate(function(m) { + m.forEach(function(mutation) { + mutation.addedNodes.forEach(function(node) { + processNode(node); + }); + }); + }); + + localizeWholePage(); + + if (localization.rtl) { // if the language is from right to left, + (new MutationObserver((mutations, observer) => { // wait for the style to load + mutations.forEach(mutation => { + mutation.addedNodes.forEach(node => { + if (node.tagName === 'STYLE') { + observer.disconnect(); + + for (const x of node.sheet.rules) { // find all rtl media rules + if (Array.from(x.media || []).includes('rtl')) { + x.media.appendMedium('all'); // enable them + } + } + } + }); + }); + })).observe(gradioApp(), {childList: true}); + } +}); diff --git a/javascript/script.js b/javascript/script.js new file mode 100644 index 0000000000000000000000000000000000000000..8f4cac58f52686a6d850e4d3744470435d3a3929 --- /dev/null +++ b/javascript/script.js @@ -0,0 +1,215 @@ +// based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/script.js +function gradioApp() { + const elems = document.getElementsByTagName('gradio-app'); + const elem = elems.length == 0 ? document : elems[0]; + + if (elem !== document) { + elem.getElementById = function(id) { + return document.getElementById(id); + }; + } + return elem.shadowRoot ? elem.shadowRoot : elem; +} + +/** + * Get the currently selected top-level UI tab button (e.g. the button that says "Extras"). + */ +function get_uiCurrentTab() { + return gradioApp().querySelector('#tabs > .tab-nav > button.selected'); +} + +/** + * Get the first currently visible top-level UI tab content (e.g. the div hosting the "txt2img" UI). + */ +function get_uiCurrentTabContent() { + return gradioApp().querySelector('#tabs > .tabitem[id^=tab_]:not([style*="display: none"])'); +} + +var uiUpdateCallbacks = []; +var uiAfterUpdateCallbacks = []; +var uiLoadedCallbacks = []; +var uiTabChangeCallbacks = []; +var optionsChangedCallbacks = []; +var uiAfterUpdateTimeout = null; +var uiCurrentTab = null; + +/** + * Register callback to be called at each UI update. + * The callback receives an array of MutationRecords as an argument. + */ +function onUiUpdate(callback) { + uiUpdateCallbacks.push(callback); +} + +/** + * Register callback to be called soon after UI updates. + * The callback receives no arguments. + * + * This is preferred over `onUiUpdate` if you don't need + * access to the MutationRecords, as your function will + * not be called quite as often. + */ +function onAfterUiUpdate(callback) { + uiAfterUpdateCallbacks.push(callback); +} + +/** + * Register callback to be called when the UI is loaded. + * The callback receives no arguments. + */ +function onUiLoaded(callback) { + uiLoadedCallbacks.push(callback); +} + +/** + * Register callback to be called when the UI tab is changed. + * The callback receives no arguments. + */ +function onUiTabChange(callback) { + uiTabChangeCallbacks.push(callback); +} + +/** + * Register callback to be called when the options are changed. + * The callback receives no arguments. + * @param callback + */ +function onOptionsChanged(callback) { + optionsChangedCallbacks.push(callback); +} + +function executeCallbacks(queue, arg) { + for (const callback of queue) { + try { + callback(arg); + } catch (e) { + console.error("error running callback", callback, ":", e); + } + } +} + +/** + * Schedule the execution of the callbacks registered with onAfterUiUpdate. + * The callbacks are executed after a short while, unless another call to this function + * is made before that time. IOW, the callbacks are executed only once, even + * when there are multiple mutations observed. + */ +function scheduleAfterUiUpdateCallbacks() { + clearTimeout(uiAfterUpdateTimeout); + uiAfterUpdateTimeout = setTimeout(function() { + executeCallbacks(uiAfterUpdateCallbacks); + }, 200); +} + +var executedOnLoaded = false; + +document.addEventListener("DOMContentLoaded", function() { + var mutationObserver = new MutationObserver(function(m) { + if (!executedOnLoaded && gradioApp().querySelector('#generate_button')) { + executedOnLoaded = true; + executeCallbacks(uiLoadedCallbacks); + } + + executeCallbacks(uiUpdateCallbacks, m); + scheduleAfterUiUpdateCallbacks(); + const newTab = get_uiCurrentTab(); + if (newTab && (newTab !== uiCurrentTab)) { + uiCurrentTab = newTab; + executeCallbacks(uiTabChangeCallbacks); + } + }); + mutationObserver.observe(gradioApp(), {childList: true, subtree: true}); + initStylePreviewOverlay(); +}); + +/** + * Add a ctrl+enter as a shortcut to start a generation + */ +document.addEventListener('keydown', function(e) { + const isModifierKey = (e.metaKey || e.ctrlKey || e.altKey); + const isEnterKey = (e.key == "Enter" || e.keyCode == 13); + + if(isModifierKey && isEnterKey) { + const generateButton = gradioApp().querySelector('button:not(.hidden)[id=generate_button]'); + if (generateButton) { + generateButton.click(); + e.preventDefault(); + return; + } + + const stopButton = gradioApp().querySelector('button:not(.hidden)[id=stop_button]') + if(stopButton) { + stopButton.click(); + e.preventDefault(); + return; + } + } +}); + +function initStylePreviewOverlay() { + let overlayVisible = false; + const samplesPath = document.querySelector("meta[name='samples-path']").getAttribute("content") + const overlay = document.createElement('div'); + overlay.id = 'stylePreviewOverlay'; + document.body.appendChild(overlay); + document.addEventListener('mouseover', function(e) { + const label = e.target.closest('.style_selections label'); + if (!label) return; + label.removeEventListener("mouseout", onMouseLeave); + label.addEventListener("mouseout", onMouseLeave); + overlayVisible = true; + overlay.style.opacity = "1"; + const originalText = label.querySelector("span").getAttribute("data-original-text"); + const name = originalText || label.querySelector("span").textContent; + overlay.style.backgroundImage = `url("${samplesPath.replace( + "fooocus_v2", + name.toLowerCase().replaceAll(" ", "_") + ).replaceAll("\\", "\\\\")}")`; + function onMouseLeave() { + overlayVisible = false; + overlay.style.opacity = "0"; + overlay.style.backgroundImage = ""; + label.removeEventListener("mouseout", onMouseLeave); + } + }); + document.addEventListener('mousemove', function(e) { + if(!overlayVisible) return; + overlay.style.left = `${e.clientX}px`; + overlay.style.top = `${e.clientY}px`; + overlay.className = e.clientY > window.innerHeight / 2 ? "lower-half" : "upper-half"; + }); +} + +/** + * checks that a UI element is not in another hidden element or tab content + */ +function uiElementIsVisible(el) { + if (el === document) { + return true; + } + + const computedStyle = getComputedStyle(el); + const isVisible = computedStyle.display !== 'none'; + + if (!isVisible) return false; + return uiElementIsVisible(el.parentNode); +} + +function uiElementInSight(el) { + const clRect = el.getBoundingClientRect(); + const windowHeight = window.innerHeight; + const isOnScreen = clRect.bottom > 0 && clRect.top < windowHeight; + + return isOnScreen; +} + +function playNotification() { + gradioApp().querySelector('#audio_notification audio')?.play(); +} + +function set_theme(theme) { + var gradioURL = window.location.href; + if (!gradioURL.includes('?__theme=')) { + window.location.replace(gradioURL + '?__theme=' + theme); + } +} diff --git a/javascript/viewer.js b/javascript/viewer.js new file mode 100644 index 0000000000000000000000000000000000000000..3df32ccf4e78fe07f0d71f6877015f51e6154f2f --- /dev/null +++ b/javascript/viewer.js @@ -0,0 +1,88 @@ +window.main_viewer_height = 512; + +function refresh_grid() { + let gridContainer = document.querySelector('#final_gallery .grid-container'); + let final_gallery = document.getElementById('final_gallery'); + + if (gridContainer) if (final_gallery) { + let rect = final_gallery.getBoundingClientRect(); + let cols = Math.ceil((rect.width - 16.0) / rect.height); + if (cols < 2) cols = 2; + gridContainer.style.setProperty('--grid-cols', cols); + } +} + +function refresh_grid_delayed() { + refresh_grid(); + setTimeout(refresh_grid, 100); + setTimeout(refresh_grid, 500); + setTimeout(refresh_grid, 1000); +} + +function resized() { + let windowHeight = window.innerHeight - 260; + let elements = document.getElementsByClassName('main_view'); + + if (windowHeight > 745) windowHeight = 745; + + for (let i = 0; i < elements.length; i++) { + elements[i].style.height = windowHeight + 'px'; + } + + window.main_viewer_height = windowHeight; + + refresh_grid(); +} + +function viewer_to_top(delay = 100) { + setTimeout(() => window.scrollTo({top: 0, behavior: 'smooth'}), delay); +} + +function viewer_to_bottom(delay = 100) { + let element = document.getElementById('positive_prompt'); + let yPos = window.main_viewer_height; + + if (element) { + yPos = element.getBoundingClientRect().top + window.scrollY; + } + + setTimeout(() => window.scrollTo({top: yPos - 8, behavior: 'smooth'}), delay); +} + +window.addEventListener('resize', (e) => { + resized(); +}); + +onUiLoaded(async () => { + resized(); +}); + +function on_style_selection_blur() { + let target = document.querySelector("#gradio_receiver_style_selections textarea"); + target.value = "on_style_selection_blur " + Math.random(); + let e = new Event("input", {bubbles: true}) + Object.defineProperty(e, "target", {value: target}) + target.dispatchEvent(e); +} + +onUiLoaded(async () => { + let spans = document.querySelectorAll('.aspect_ratios span'); + + spans.forEach(function (span) { + span.innerHTML = span.innerHTML.replace(/</g, '<').replace(/>/g, '>'); + }); + + document.querySelector('.style_selections').addEventListener('focusout', function (event) { + setTimeout(() => { + if (!this.contains(document.activeElement)) { + on_style_selection_blur(); + } + }, 200); + }); + + let inputs = document.querySelectorAll('.lora_weight input[type="range"]'); + + inputs.forEach(function (input) { + input.style.marginTop = '12px'; + }); +}); diff --git a/javascript/zoom.js b/javascript/zoom.js new file mode 100644 index 0000000000000000000000000000000000000000..450a034729cbdb598ff2bbf84a7b76572f13c573 --- /dev/null +++ b/javascript/zoom.js @@ -0,0 +1,645 @@ +onUiLoaded(async() => { + // Helper functions + + // Detect whether the element has a horizontal scroll bar + function hasHorizontalScrollbar(element) { + return element.scrollWidth > element.clientWidth; + } + + // Function for defining the "Ctrl", "Shift" and "Alt" keys + function isModifierKey(event, key) { + switch (key) { + case "Ctrl": + return event.ctrlKey; + case "Shift": + return event.shiftKey; + case "Alt": + return event.altKey; + default: + return false; + } + } + + // Create hotkey configuration with the provided options + function createHotkeyConfig(defaultHotkeysConfig) { + const result = {}; // Resulting hotkey configuration + for (const key in defaultHotkeysConfig) { + result[key] = defaultHotkeysConfig[key]; + } + return result; + } + + // Default config + const defaultHotkeysConfig = { + canvas_hotkey_zoom: "Shift", + canvas_hotkey_adjust: "Ctrl", + canvas_zoom_undo_extra_key: "Ctrl", + canvas_zoom_hotkey_undo: "KeyZ", + canvas_hotkey_reset: "KeyR", + canvas_hotkey_fullscreen: "KeyS", + canvas_hotkey_move: "KeyF", + canvas_show_tooltip: true, + canvas_auto_expand: true, + canvas_blur_prompt: true, + }; + + // Loading the configuration from opts + const hotkeysConfig = createHotkeyConfig( + defaultHotkeysConfig + ); + + let isMoving = false; + let activeElement; + + const elemData = {}; + + function applyZoomAndPan(elemId) { + const targetElement = gradioApp().querySelector(elemId); + + if (!targetElement) { + console.log("Element not found"); + return; + } + + targetElement.style.transformOrigin = "0 0"; + + elemData[elemId] = { + zoom: 1, + panX: 0, + panY: 0 + }; + + let fullScreenMode = false; + + // Create tooltip + function createTooltip() { + const toolTipElemnt = + targetElement.querySelector(".image-container"); + const tooltip = document.createElement("div"); + tooltip.className = "canvas-tooltip"; + + // Creating an item of information + const info = document.createElement("i"); + info.className = "canvas-tooltip-info"; + info.textContent = ""; + + // Create a container for the contents of the tooltip + const tooltipContent = document.createElement("div"); + tooltipContent.className = "canvas-tooltip-content"; + + // Define an array with hotkey information and their actions + const hotkeysInfo = [ + { + configKey: "canvas_hotkey_zoom", + action: "Zoom canvas", + keySuffix: " + wheel" + }, + { + configKey: "canvas_hotkey_adjust", + action: "Adjust brush size", + keySuffix: " + wheel" + }, + {configKey: "canvas_zoom_hotkey_undo", action: "Undo last action", keyPrefix: `${hotkeysConfig.canvas_zoom_undo_extra_key} + ` }, + {configKey: "canvas_hotkey_reset", action: "Reset zoom"}, + { + configKey: "canvas_hotkey_fullscreen", + action: "Fullscreen mode" + }, + {configKey: "canvas_hotkey_move", action: "Move canvas"} + ]; + + // Create hotkeys array based on the config values + const hotkeys = hotkeysInfo.map((info) => { + const configValue = hotkeysConfig[info.configKey]; + + let key = configValue.slice(-1); + + if (info.keySuffix) { + key = `${configValue}${info.keySuffix}`; + } + + if (info.keyPrefix && info.keyPrefix !== "None + ") { + key = `${info.keyPrefix}${configValue[3]}`; + } + + return { + key, + action: info.action, + }; + }); + + hotkeys + .forEach(hotkey => { + const p = document.createElement("p"); + p.innerHTML = `${hotkey.key} - ${hotkey.action}`; + tooltipContent.appendChild(p); + }); + + tooltip.append(info, tooltipContent); + + // Add a hint element to the target element + toolTipElemnt.appendChild(tooltip); + } + + //Show tool tip if setting enable + if (hotkeysConfig.canvas_show_tooltip) { + createTooltip(); + } + + // Reset the zoom level and pan position of the target element to their initial values + function resetZoom() { + elemData[elemId] = { + zoomLevel: 1, + panX: 0, + panY: 0 + }; + + targetElement.style.overflow = "hidden"; + + targetElement.isZoomed = false; + + targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`; + + const canvas = gradioApp().querySelector( + `${elemId} canvas[key="interface"]` + ); + + toggleOverlap("off"); + fullScreenMode = false; + + const closeBtn = targetElement.querySelector("button[aria-label='Remove Image']"); + if (closeBtn) { + closeBtn.addEventListener("click", resetZoom); + } + + if (canvas) { + const parentElement = targetElement.closest('[id^="component-"]'); + if ( + canvas && + parseFloat(canvas.style.width) > parentElement.offsetWidth && + parseFloat(targetElement.style.width) > parentElement.offsetWidth + ) { + fitToElement(); + return; + } + + } + + targetElement.style.width = ""; + } + + // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements + function toggleOverlap(forced = "") { + const zIndex1 = "0"; + const zIndex2 = "998"; + + targetElement.style.zIndex = + targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1; + + if (forced === "off") { + targetElement.style.zIndex = zIndex1; + } else if (forced === "on") { + targetElement.style.zIndex = zIndex2; + } + } + + // Adjust the brush size based on the deltaY value from a mouse wheel event + function adjustBrushSize( + elemId, + deltaY, + withoutValue = false, + percentage = 5 + ) { + const input = + gradioApp().querySelector( + `${elemId} input[aria-label='Brush radius']` + ) || + gradioApp().querySelector( + `${elemId} button[aria-label="Use brush"]` + ); + + if (input) { + input.click(); + if (!withoutValue) { + const maxValue = + parseFloat(input.getAttribute("max")) || 100; + const changeAmount = maxValue * (percentage / 100); + const newValue = + parseFloat(input.value) + + (deltaY > 0 ? -changeAmount : changeAmount); + input.value = Math.min(Math.max(newValue, 0), maxValue); + input.dispatchEvent(new Event("change")); + } + } + } + + // Reset zoom when uploading a new image + const fileInput = gradioApp().querySelector( + `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv` + ); + fileInput.addEventListener("click", resetZoom); + + // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables + function updateZoom(newZoomLevel, mouseX, mouseY) { + newZoomLevel = Math.max(0.1, Math.min(newZoomLevel, 15)); + + elemData[elemId].panX += + mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel; + elemData[elemId].panY += + mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel; + + targetElement.style.transformOrigin = "0 0"; + targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`; + targetElement.style.overflow = "visible"; + + toggleOverlap("on"); + + return newZoomLevel; + } + + // Change the zoom level based on user interaction + function changeZoomLevel(operation, e) { + if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) { + e.preventDefault(); + + let zoomPosX, zoomPosY; + let delta = 0.2; + + if (elemData[elemId].zoomLevel > 7) { + delta = 0.9; + } else if (elemData[elemId].zoomLevel > 2) { + delta = 0.6; + } + + zoomPosX = e.clientX; + zoomPosY = e.clientY; + + fullScreenMode = false; + elemData[elemId].zoomLevel = updateZoom( + elemData[elemId].zoomLevel + + (operation === "+" ? delta : -delta), + zoomPosX - targetElement.getBoundingClientRect().left, + zoomPosY - targetElement.getBoundingClientRect().top + ); + + targetElement.isZoomed = true; + } + } + + /** + * This function fits the target element to the screen by calculating + * the required scale and offsets. It also updates the global variables + * zoomLevel, panX, and panY to reflect the new state. + */ + + function fitToElement() { + //Reset Zoom + targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; + + let parentElement; + + parentElement = targetElement.closest('[id^="component-"]'); + + // Get element and screen dimensions + const elementWidth = targetElement.offsetWidth; + const elementHeight = targetElement.offsetHeight; + + const screenWidth = parentElement.clientWidth - 24; + const screenHeight = parentElement.clientHeight; + + // Calculate scale and offsets + const scaleX = screenWidth / elementWidth; + const scaleY = screenHeight / elementHeight; + const scale = Math.min(scaleX, scaleY); + + const offsetX =0; + const offsetY =0; + + // Apply scale and offsets to the element + targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; + + // Update global variables + elemData[elemId].zoomLevel = scale; + elemData[elemId].panX = offsetX; + elemData[elemId].panY = offsetY; + + fullScreenMode = false; + toggleOverlap("off"); + } + + // Undo last action + function undoLastAction(e) { + let isCtrlPressed = isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key) + const isAuxButton = e.button >= 3; + + if (isAuxButton) { + isCtrlPressed = true + } else { + if (!isModifierKey(e, hotkeysConfig.canvas_zoom_undo_extra_key)) return; + } + + // Move undoBtn query outside the if statement to avoid unnecessary queries + const undoBtn = document.querySelector(`${activeElement} button[aria-label="Undo"]`); + + if ((isCtrlPressed) && undoBtn ) { + e.preventDefault(); + undoBtn.click(); + } + } + + /** + * This function fits the target element to the screen by calculating + * the required scale and offsets. It also updates the global variables + * zoomLevel, panX, and panY to reflect the new state. + */ + + // Fullscreen mode + function fitToScreen() { + const canvas = gradioApp().querySelector( + `${elemId} canvas[key="interface"]` + ); + + if (!canvas) return; + + targetElement.style.width = (canvas.offsetWidth + 2) + "px"; + targetElement.style.overflow = "visible"; + + if (fullScreenMode) { + resetZoom(); + fullScreenMode = false; + return; + } + + //Reset Zoom + targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`; + + // Get scrollbar width to right-align the image + const scrollbarWidth = + window.innerWidth - document.documentElement.clientWidth; + + // Get element and screen dimensions + const elementWidth = targetElement.offsetWidth; + const elementHeight = targetElement.offsetHeight; + const screenWidth = window.innerWidth - scrollbarWidth; + const screenHeight = window.innerHeight; + + // Get element's coordinates relative to the page + const elementRect = targetElement.getBoundingClientRect(); + const elementY = elementRect.y; + const elementX = elementRect.x; + + // Calculate scale and offsets + const scaleX = screenWidth / elementWidth; + const scaleY = screenHeight / elementHeight; + const scale = Math.min(scaleX, scaleY); + + // Get the current transformOrigin + const computedStyle = window.getComputedStyle(targetElement); + const transformOrigin = computedStyle.transformOrigin; + const [originX, originY] = transformOrigin.split(" "); + const originXValue = parseFloat(originX); + const originYValue = parseFloat(originY); + + // Calculate offsets with respect to the transformOrigin + const offsetX = + (screenWidth - elementWidth * scale) / 2 - + elementX - + originXValue * (1 - scale); + const offsetY = + (screenHeight - elementHeight * scale) / 2 - + elementY - + originYValue * (1 - scale); + + // Apply scale and offsets to the element + targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`; + + // Update global variables + elemData[elemId].zoomLevel = scale; + elemData[elemId].panX = offsetX; + elemData[elemId].panY = offsetY; + + fullScreenMode = true; + toggleOverlap("on"); + } + + // Handle keydown events + function handleKeyDown(event) { + // Disable key locks to make pasting from the buffer work correctly + if ((event.ctrlKey && event.code === 'KeyV') || (event.ctrlKey && event.code === 'KeyC') || event.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (event.target.nodeName === 'TEXTAREA' || event.target.nodeName === 'INPUT') { + return; + } + } + + const hotkeyActions = { + [hotkeysConfig.canvas_hotkey_reset]: resetZoom, + [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap, + [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen, + [hotkeysConfig.canvas_zoom_hotkey_undo]: undoLastAction, + }; + + const action = hotkeyActions[event.code]; + if (action) { + event.preventDefault(); + action(event); + } + + if ( + isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) || + isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust) + ) { + event.preventDefault(); + } + } + + // Get Mouse position + function getMousePosition(e) { + mouseX = e.offsetX; + mouseY = e.offsetY; + } + + // Simulation of the function to put a long image into the screen. + // We detect if an image has a scroll bar or not, make a fullscreen to reveal the image, then reduce it to fit into the element. + // We hide the image and show it to the user when it is ready. + + targetElement.isExpanded = false; + function autoExpand() { + const canvas = document.querySelector(`${elemId} canvas[key="interface"]`); + if (canvas) { + if (hasHorizontalScrollbar(targetElement) && targetElement.isExpanded === false) { + targetElement.style.visibility = "hidden"; + setTimeout(() => { + fitToScreen(); + resetZoom(); + targetElement.style.visibility = "visible"; + targetElement.isExpanded = true; + }, 10); + } + } + } + + targetElement.addEventListener("mousemove", getMousePosition); + targetElement.addEventListener("auxclick", undoLastAction); + + //observers + // Creating an observer with a callback function to handle DOM changes + const observer = new MutationObserver((mutationsList, observer) => { + for (let mutation of mutationsList) { + // If the style attribute of the canvas has changed, by observation it happens only when the picture changes + if (mutation.type === 'attributes' && mutation.attributeName === 'style' && + mutation.target.tagName.toLowerCase() === 'canvas') { + targetElement.isExpanded = false; + setTimeout(resetZoom, 10); + } + } + }); + + // Apply auto expand if enabled + if (hotkeysConfig.canvas_auto_expand) { + targetElement.addEventListener("mousemove", autoExpand); + // Set up an observer to track attribute changes + observer.observe(targetElement, { attributes: true, childList: true, subtree: true }); + } + + // Handle events only inside the targetElement + let isKeyDownHandlerAttached = false; + + function handleMouseMove() { + if (!isKeyDownHandlerAttached) { + document.addEventListener("keydown", handleKeyDown); + isKeyDownHandlerAttached = true; + + activeElement = elemId; + } + } + + function handleMouseLeave() { + if (isKeyDownHandlerAttached) { + document.removeEventListener("keydown", handleKeyDown); + isKeyDownHandlerAttached = false; + + activeElement = null; + } + } + + // Add mouse event handlers + targetElement.addEventListener("mousemove", handleMouseMove); + targetElement.addEventListener("mouseleave", handleMouseLeave); + + targetElement.addEventListener("wheel", e => { + // change zoom level + const operation = e.deltaY > 0 ? "-" : "+"; + changeZoomLevel(operation, e); + + // Handle brush size adjustment with ctrl key pressed + if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) { + e.preventDefault(); + + // Increase or decrease brush size based on scroll direction + adjustBrushSize(elemId, e.deltaY); + } + }); + + // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element. + function handleMoveKeyDown(e) { + + // Disable key locks to make pasting from the buffer work correctly + if ((e.ctrlKey && e.code === 'KeyV') || (e.ctrlKey && e.code === 'KeyC') || e.code === "F5") { + return; + } + + // before activating shortcut, ensure user is not actively typing in an input field + if (!hotkeysConfig.canvas_blur_prompt) { + if (e.target.nodeName === 'TEXTAREA' || e.target.nodeName === 'INPUT') { + return; + } + } + + + if (e.code === hotkeysConfig.canvas_hotkey_move) { + if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) { + e.preventDefault(); + document.activeElement.blur(); + isMoving = true; + } + } + } + + function handleMoveKeyUp(e) { + if (e.code === hotkeysConfig.canvas_hotkey_move) { + isMoving = false; + } + } + + document.addEventListener("keydown", handleMoveKeyDown); + document.addEventListener("keyup", handleMoveKeyUp); + + // Detect zoom level and update the pan speed. + function updatePanPosition(movementX, movementY) { + let panSpeed = 2; + + if (elemData[elemId].zoomLevel > 8) { + panSpeed = 3.5; + } + + elemData[elemId].panX += movementX * panSpeed; + elemData[elemId].panY += movementY * panSpeed; + + // Delayed redraw of an element + requestAnimationFrame(() => { + targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`; + toggleOverlap("on"); + }); + } + + function handleMoveByKey(e) { + if (isMoving && elemId === activeElement) { + updatePanPosition(e.movementX, e.movementY); + targetElement.style.pointerEvents = "none"; + targetElement.style.overflow = "visible"; + } else { + targetElement.style.pointerEvents = "auto"; + } + } + + // Prevents sticking to the mouse + window.onblur = function() { + isMoving = false; + }; + + // Checks for extension + function checkForOutBox() { + const parentElement = targetElement.closest('[id^="component-"]'); + if (parentElement.offsetWidth < targetElement.offsetWidth && !targetElement.isExpanded) { + resetZoom(); + targetElement.isExpanded = true; + } + + if (parentElement.offsetWidth < targetElement.offsetWidth && elemData[elemId].zoomLevel == 1) { + resetZoom(); + } + + if (parentElement.offsetWidth < targetElement.offsetWidth && targetElement.offsetWidth * elemData[elemId].zoomLevel > parentElement.offsetWidth && elemData[elemId].zoomLevel < 1 && !targetElement.isZoomed) { + resetZoom(); + } + } + + targetElement.addEventListener("mousemove", checkForOutBox); + + window.addEventListener('resize', (e) => { + resetZoom(); + + targetElement.isExpanded = false; + targetElement.isZoomed = false; + }); + + gradioApp().addEventListener("mousemove", handleMoveByKey); + } + + applyZoomAndPan("#inpaint_canvas"); +}); diff --git a/language/en.json b/language/en.json new file mode 100644 index 0000000000000000000000000000000000000000..f61255c963d3f2be4c2f23df45a52b56ef18bf88 --- /dev/null +++ b/language/en.json @@ -0,0 +1,385 @@ +{ + "Preview": "Preview", + "Gallery": "Gallery", + "Generate": "Generate", + "Skip": "Skip", + "Stop": "Stop", + "Input Image": "Input Image", + "Advanced": "Advanced", + "Upscale or Variation": "Upscale or Variation", + "Image Prompt": "Image Prompt", + "Inpaint or Outpaint (beta)": "Inpaint or Outpaint (beta)", + "Drag above image to here": "Drag above image to here", + "Upscale or Variation:": "Upscale or Variation:", + "Disabled": "Disabled", + "Vary (Subtle)": "Vary (Subtle)", + "Vary (Strong)": "Vary (Strong)", + "Upscale (1.5x)": "Upscale (1.5x)", + "Upscale (2x)": "Upscale (2x)", + "Upscale (Fast 2x)": "Upscale (Fast 2x)", + "\ud83d\udcd4 Document": "\uD83D\uDCD4 Document", + "Image": "Image", + "Stop At": "Stop At", + "Weight": "Weight", + "Type": "Type", + "PyraCanny": "PyraCanny", + "CPDS": "CPDS", + "* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1).": "* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1).", + "The scaler multiplied to positive ADM (use 1.0 to disable).": "The scaler multiplied to positive ADM (use 1.0 to disable).", + "The scaler multiplied to negative ADM (use 1.0 to disable).": "The scaler multiplied to negative ADM (use 1.0 to disable).", + "When to end the guidance from positive/negative ADM.": "When to end the guidance from positive/negative ADM.", + "Similar to the Control Mode in A1111 (use 0.0 to disable).": "Similar to the Control Mode in A1111 (use 0.0 to disable).", + "Outpaint Expansion (": "Outpaint Expansion (", + "Outpaint": "Outpaint", + "Left": "Left", + "Right": "Right", + "Top": "Top", + "Bottom": "Bottom", + "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)": "* \"Inpaint or Outpaint\" is powered by the sampler \"DPMPP Fooocus Seamless 2M SDE Karras Inpaint Sampler\" (beta)", + "Setting": "Setting", + "Style": "Style", + "Performance": "Performance", + "Speed": "Speed", + "Quality": "Quality", + "Aspect Ratios": "Aspect Ratios", + "width \u00d7 height": "width \u00d7 height", + "Image Number": "Image Number", + "Negative Prompt": "Negative Prompt", + "Describing what you do not want to see.": "Describing what you do not want to see.", + "Random": "Random", + "Seed": "Seed", + "Disable seed increment": "Disable seed increment", + "Disable automatic seed increment when image number is > 1.": "Disable automatic seed increment when image number is > 1.", + "\ud83d\udcda History Log": "\uD83D\uDCDA History Log", + "Image Style": "Image Style", + "Fooocus V2": "Fooocus V2", + "Default (Slightly Cinematic)": "Default (Slightly Cinematic)", + "Fooocus Masterpiece": "Fooocus Masterpiece", + "Fooocus Photograph": "Fooocus Photograph", + "Fooocus Negative": "Fooocus Negative", + "SAI 3D Model": "SAI 3D Model", + "SAI Analog Film": "SAI Analog Film", + "SAI Anime": "SAI Anime", + "SAI Cinematic": "SAI Cinematic", + "SAI Comic Book": "SAI Comic Book", + "SAI Craft Clay": "SAI Craft Clay", + "SAI Digital Art": "SAI Digital Art", + "SAI Enhance": "SAI Enhance", + "SAI Fantasy Art": "SAI Fantasy Art", + "SAI Isometric": "SAI Isometric", + "SAI Line Art": "SAI Line Art", + "SAI Lowpoly": "SAI Lowpoly", + "SAI Neonpunk": "SAI Neonpunk", + "SAI Origami": "SAI Origami", + "SAI Photographic": "SAI Photographic", + "SAI Pixel Art": "SAI Pixel Art", + "SAI Texture": "SAI Texture", + "MRE Cinematic Dynamic": "MRE Cinematic Dynamic", + "MRE Spontaneous Picture": "MRE Spontaneous Picture", + "MRE Artistic Vision": "MRE Artistic Vision", + "MRE Dark Dream": "MRE Dark Dream", + "MRE Gloomy Art": "MRE Gloomy Art", + "MRE Bad Dream": "MRE Bad Dream", + "MRE Underground": "MRE Underground", + "MRE Surreal Painting": "MRE Surreal Painting", + "MRE Dynamic Illustration": "MRE Dynamic Illustration", + "MRE Undead Art": "MRE Undead Art", + "MRE Elemental Art": "MRE Elemental Art", + "MRE Space Art": "MRE Space Art", + "MRE Ancient Illustration": "MRE Ancient Illustration", + "MRE Brave Art": "MRE Brave Art", + "MRE Heroic Fantasy": "MRE Heroic Fantasy", + "MRE Dark Cyberpunk": "MRE Dark Cyberpunk", + "MRE Lyrical Geometry": "MRE Lyrical Geometry", + "MRE Sumi E Symbolic": "MRE Sumi E Symbolic", + "MRE Sumi E Detailed": "MRE Sumi E Detailed", + "MRE Manga": "MRE Manga", + "MRE Anime": "MRE Anime", + "MRE Comic": "MRE Comic", + "Ads Advertising": "Ads Advertising", + "Ads Automotive": "Ads Automotive", + "Ads Corporate": "Ads Corporate", + "Ads Fashion Editorial": "Ads Fashion Editorial", + "Ads Food Photography": "Ads Food Photography", + "Ads Gourmet Food Photography": "Ads Gourmet Food Photography", + "Ads Luxury": "Ads Luxury", + "Ads Real Estate": "Ads Real Estate", + "Ads Retail": "Ads Retail", + "Artstyle Abstract": "Artstyle Abstract", + "Artstyle Abstract Expressionism": "Artstyle Abstract Expressionism", + "Artstyle Art Deco": "Artstyle Art Deco", + "Artstyle Art Nouveau": "Artstyle Art Nouveau", + "Artstyle Constructivist": "Artstyle Constructivist", + "Artstyle Cubist": "Artstyle Cubist", + "Artstyle Expressionist": "Artstyle Expressionist", + "Artstyle Graffiti": "Artstyle Graffiti", + "Artstyle Hyperrealism": "Artstyle Hyperrealism", + "Artstyle Impressionist": "Artstyle Impressionist", + "Artstyle Pointillism": "Artstyle Pointillism", + "Artstyle Pop Art": "Artstyle Pop Art", + "Artstyle Psychedelic": "Artstyle Psychedelic", + "Artstyle Renaissance": "Artstyle Renaissance", + "Artstyle Steampunk": "Artstyle Steampunk", + "Artstyle Surrealist": "Artstyle Surrealist", + "Artstyle Typography": "Artstyle Typography", + "Artstyle Watercolor": "Artstyle Watercolor", + "Futuristic Biomechanical": "Futuristic Biomechanical", + "Futuristic Biomechanical Cyberpunk": "Futuristic Biomechanical Cyberpunk", + "Futuristic Cybernetic": "Futuristic Cybernetic", + "Futuristic Cybernetic Robot": "Futuristic Cybernetic Robot", + "Futuristic Cyberpunk Cityscape": "Futuristic Cyberpunk Cityscape", + "Futuristic Futuristic": "Futuristic Futuristic", + "Futuristic Retro Cyberpunk": "Futuristic Retro Cyberpunk", + "Futuristic Retro Futurism": "Futuristic Retro Futurism", + "Futuristic Sci Fi": "Futuristic Sci Fi", + "Futuristic Vaporwave": "Futuristic Vaporwave", + "Game Bubble Bobble": "Game Bubble Bobble", + "Game Cyberpunk Game": "Game Cyberpunk Game", + "Game Fighting Game": "Game Fighting Game", + "Game Gta": "Game Gta", + "Game Mario": "Game Mario", + "Game Minecraft": "Game Minecraft", + "Game Pokemon": "Game Pokemon", + "Game Retro Arcade": "Game Retro Arcade", + "Game Retro Game": "Game Retro Game", + "Game Rpg Fantasy Game": "Game Rpg Fantasy Game", + "Game Strategy Game": "Game Strategy Game", + "Game Streetfighter": "Game Streetfighter", + "Game Zelda": "Game Zelda", + "Misc Architectural": "Misc Architectural", + "Misc Disco": "Misc Disco", + "Misc Dreamscape": "Misc Dreamscape", + "Misc Dystopian": "Misc Dystopian", + "Misc Fairy Tale": "Misc Fairy Tale", + "Misc Gothic": "Misc Gothic", + "Misc Grunge": "Misc Grunge", + "Misc Horror": "Misc Horror", + "Misc Kawaii": "Misc Kawaii", + "Misc Lovecraftian": "Misc Lovecraftian", + "Misc Macabre": "Misc Macabre", + "Misc Manga": "Misc Manga", + "Misc Metropolis": "Misc Metropolis", + "Misc Minimalist": "Misc Minimalist", + "Misc Monochrome": "Misc Monochrome", + "Misc Nautical": "Misc Nautical", + "Misc Space": "Misc Space", + "Misc Stained Glass": "Misc Stained Glass", + "Misc Techwear Fashion": "Misc Techwear Fashion", + "Misc Tribal": "Misc Tribal", + "Misc Zentangle": "Misc Zentangle", + "Papercraft Collage": "Papercraft Collage", + "Papercraft Flat Papercut": "Papercraft Flat Papercut", + "Papercraft Kirigami": "Papercraft Kirigami", + "Papercraft Paper Mache": "Papercraft Paper Mache", + "Papercraft Paper Quilling": "Papercraft Paper Quilling", + "Papercraft Papercut Collage": "Papercraft Papercut Collage", + "Papercraft Papercut Shadow Box": "Papercraft Papercut Shadow Box", + "Papercraft Stacked Papercut": "Papercraft Stacked Papercut", + "Papercraft Thick Layered Papercut": "Papercraft Thick Layered Papercut", + "Photo Alien": "Photo Alien", + "Photo Film Noir": "Photo Film Noir", + "Photo Glamour": "Photo Glamour", + "Photo Hdr": "Photo Hdr", + "Photo Iphone Photographic": "Photo Iphone Photographic", + "Photo Long Exposure": "Photo Long Exposure", + "Photo Neon Noir": "Photo Neon Noir", + "Photo Silhouette": "Photo Silhouette", + "Photo Tilt Shift": "Photo Tilt Shift", + "Cinematic Diva": "Cinematic Diva", + "Abstract Expressionism": "Abstract Expressionism", + "Academia": "Academia", + "Action Figure": "Action Figure", + "Adorable 3D Character": "Adorable 3D Character", + "Adorable Kawaii": "Adorable Kawaii", + "Art Deco": "Art Deco", + "Art Nouveau": "Art Nouveau", + "Astral Aura": "Astral Aura", + "Avant Garde": "Avant Garde", + "Baroque": "Baroque", + "Bauhaus Style Poster": "Bauhaus Style Poster", + "Blueprint Schematic Drawing": "Blueprint Schematic Drawing", + "Caricature": "Caricature", + "Cel Shaded Art": "Cel Shaded Art", + "Character Design Sheet": "Character Design Sheet", + "Classicism Art": "Classicism Art", + "Color Field Painting": "Color Field Painting", + "Colored Pencil Art": "Colored Pencil Art", + "Conceptual Art": "Conceptual Art", + "Constructivism": "Constructivism", + "Cubism": "Cubism", + "Dadaism": "Dadaism", + "Dark Fantasy": "Dark Fantasy", + "Dark Moody Atmosphere": "Dark Moody Atmosphere", + "Dmt Art Style": "Dmt Art Style", + "Doodle Art": "Doodle Art", + "Double Exposure": "Double Exposure", + "Dripping Paint Splatter Art": "Dripping Paint Splatter Art", + "Expressionism": "Expressionism", + "Faded Polaroid Photo": "Faded Polaroid Photo", + "Fauvism": "Fauvism", + "Flat 2d Art": "Flat 2d Art", + "Fortnite Art Style": "Fortnite Art Style", + "Futurism": "Futurism", + "Glitchcore": "Glitchcore", + "Glo Fi": "Glo Fi", + "Googie Art Style": "Googie Art Style", + "Graffiti Art": "Graffiti Art", + "Harlem Renaissance Art": "Harlem Renaissance Art", + "High Fashion": "High Fashion", + "Idyllic": "Idyllic", + "Impressionism": "Impressionism", + "Infographic Drawing": "Infographic Drawing", + "Ink Dripping Drawing": "Ink Dripping Drawing", + "Japanese Ink Drawing": "Japanese Ink Drawing", + "Knolling Photography": "Knolling Photography", + "Light Cheery Atmosphere": "Light Cheery Atmosphere", + "Logo Design": "Logo Design", + "Luxurious Elegance": "Luxurious Elegance", + "Macro Photography": "Macro Photography", + "Mandola Art": "Mandola Art", + "Marker Drawing": "Marker Drawing", + "Medievalism": "Medievalism", + "Minimalism": "Minimalism", + "Neo Baroque": "Neo Baroque", + "Neo Byzantine": "Neo Byzantine", + "Neo Futurism": "Neo Futurism", + "Neo Impressionism": "Neo Impressionism", + "Neo Rococo": "Neo Rococo", + "Neoclassicism": "Neoclassicism", + "Op Art": "Op Art", + "Ornate And Intricate": "Ornate And Intricate", + "Pencil Sketch Drawing": "Pencil Sketch Drawing", + "Pop Art 2": "Pop Art 2", + "Rococo": "Rococo", + "Silhouette Art": "Silhouette Art", + "Simple Vector Art": "Simple Vector Art", + "Sketchup": "Sketchup", + "Steampunk 2": "Steampunk 2", + "Surrealism": "Surrealism", + "Suprematism": "Suprematism", + "Terragen": "Terragen", + "Tranquil Relaxing Atmosphere": "Tranquil Relaxing Atmosphere", + "Sticker Designs": "Sticker Designs", + "Vibrant Rim Light": "Vibrant Rim Light", + "Volumetric Lighting": "Volumetric Lighting", + "Watercolor 2": "Watercolor 2", + "Whimsical And Playful": "Whimsical And Playful", + "Model": "Model", + "Base Model (SDXL only)": "Base Model (SDXL only)", + "sd_xl_base_1.0_0.9vae.safetensors": "sd_xl_base_1.0_0.9vae.safetensors", + "bluePencilXL_v009.safetensors": "bluePencilXL_v009.safetensors", + "bluePencilXL_v050.safetensors": "bluePencilXL_v050.safetensors", + "DreamShaper_8_pruned.safetensors": "DreamShaper_8_pruned.safetensors", + "realisticStockPhoto_v10.safetensors": "realisticStockPhoto_v10.safetensors", + "realisticVisionV51_v51VAE.safetensors": "realisticVisionV51_v51VAE.safetensors", + "sd_xl_refiner_1.0_0.9vae.safetensors": "sd_xl_refiner_1.0_0.9vae.safetensors", + "Refiner (SDXL or SD 1.5)": "Refiner (SDXL or SD 1.5)", + "None": "None", + "LoRAs": "LoRAs", + "SDXL LoRA 1": "SDXL LoRA 1", + "sd_xl_offset_example-lora_1.0.safetensors": "sd_xl_offset_example-lora_1.0.safetensors", + "3d_render_style_xl.safetensors": "3d_render_style_xl.safetensors", + "Bloodstained-XL-V1.safetensors": "Bloodstained-XL-V1.safetensors", + "SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors": "SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors", + "SDXL LoRA 2": "SDXL LoRA 2", + "SDXL LoRA 3": "SDXL LoRA 3", + "SDXL LoRA 4": "SDXL LoRA 4", + "SDXL LoRA 5": "SDXL LoRA 5", + "Refresh": "Refresh", + "\ud83d\udd04 Refresh All Files": "\ud83d\udd04 Refresh All Files", + "Sampling Sharpness": "Sampling Sharpness", + "Higher value means image and texture are sharper.": "Higher value means image and texture are sharper.", + "Guidance Scale": "Guidance Scale", + "Higher value means style is cleaner, vivider, and more artistic.": "Higher value means style is cleaner, vivider, and more artistic.", + "Developer Debug Mode": "Developer Debug Mode", + "Developer Debug Tools": "Developer Debug Tools", + "Positive ADM Guidance Scaler": "Positive ADM Guidance Scaler", + "The scaler multiplied to positive ADM (use 1.0 to disable). ": "The scaler multiplied to positive ADM (use 1.0 to disable). ", + "Negative ADM Guidance Scaler": "Negative ADM Guidance Scaler", + "The scaler multiplied to negative ADM (use 1.0 to disable). ": "The scaler multiplied to negative ADM (use 1.0 to disable). ", + "ADM Guidance End At Step": "ADM Guidance End At Step", + "When to end the guidance from positive/negative ADM. ": "When to end the guidance from positive/negative ADM. ", + "Refiner swap method": "Refiner swap method", + "joint": "joint", + "separate": "separate", + "vae": "vae", + "CFG Mimicking from TSNR": "CFG Mimicking from TSNR", + "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).": "Enabling Fooocus's implementation of CFG mimicking for TSNR (effective when real CFG > mimicked CFG).", + "Sampler": "Sampler", + "dpmpp_2m_sde_gpu": "dpmpp_2m_sde_gpu", + "Only effective in non-inpaint mode.": "Only effective in non-inpaint mode.", + "euler": "euler", + "euler_ancestral": "euler_ancestral", + "heun": "heun", + "dpm_2": "dpm_2", + "dpm_2_ancestral": "dpm_2_ancestral", + "lms": "lms", + "dpm_fast": "dpm_fast", + "dpm_adaptive": "dpm_adaptive", + "dpmpp_2s_ancestral": "dpmpp_2s_ancestral", + "dpmpp_sde": "dpmpp_sde", + "dpmpp_sde_gpu": "dpmpp_sde_gpu", + "dpmpp_2m": "dpmpp_2m", + "dpmpp_2m_sde": "dpmpp_2m_sde", + "dpmpp_3m_sde": "dpmpp_3m_sde", + "dpmpp_3m_sde_gpu": "dpmpp_3m_sde_gpu", + "ddpm": "ddpm", + "ddim": "ddim", + "uni_pc": "uni_pc", + "uni_pc_bh2": "uni_pc_bh2", + "Scheduler": "Scheduler", + "karras": "karras", + "Scheduler of Sampler.": "Scheduler of Sampler.", + "normal": "normal", + "exponential": "exponential", + "sgm_uniform": "sgm_uniform", + "simple": "simple", + "ddim_uniform": "ddim_uniform", + "Forced Overwrite of Sampling Step": "Forced Overwrite of Sampling Step", + "Set as -1 to disable. For developer debugging.": "Set as -1 to disable. For developer debugging.", + "Forced Overwrite of Refiner Switch Step": "Forced Overwrite of Refiner Switch Step", + "Forced Overwrite of Generating Width": "Forced Overwrite of Generating Width", + "Set as -1 to disable. For developer debugging. Results will be worse for non-standard numbers that SDXL is not trained on.": "Set as -1 to disable. For developer debugging. Results will be worse for non-standard numbers that SDXL is not trained on.", + "Forced Overwrite of Generating Height": "Forced Overwrite of Generating Height", + "Forced Overwrite of Denoising Strength of \"Vary\"": "Forced Overwrite of Denoising Strength of \"Vary\"", + "Set as negative number to disable. For developer debugging.": "Set as negative number to disable. For developer debugging.", + "Forced Overwrite of Denoising Strength of \"Upscale\"": "Forced Overwrite of Denoising Strength of \"Upscale\"", + "Disable Preview": "Disable Preview", + "Disable preview during generation.": "Disable preview during generation.", + "Disable Intermediate Results": "Disable Intermediate Results", + "Disable intermediate results during generation, only show final gallery.": "Disable intermediate results during generation, only show final gallery.", + "Inpaint Engine": "Inpaint Engine", + "v1": "v1", + "Version of Fooocus inpaint model": "Version of Fooocus inpaint model", + "v2.5": "v2.5", + "Control Debug": "Control Debug", + "Debug Preprocessors": "Debug Preprocessors", + "Mixing Image Prompt and Vary/Upscale": "Mixing Image Prompt and Vary/Upscale", + "Mixing Image Prompt and Inpaint": "Mixing Image Prompt and Inpaint", + "Softness of ControlNet": "Softness of ControlNet", + "Similar to the Control Mode in A1111 (use 0.0 to disable). ": "Similar to the Control Mode in A1111 (use 0.0 to disable). ", + "Canny": "Canny", + "Canny Low Threshold": "Canny Low Threshold", + "Canny High Threshold": "Canny High Threshold", + "FreeU": "FreeU", + "Enabled": "Enabled", + "B1": "B1", + "B2": "B2", + "S1": "S1", + "S2": "S2", + "Extreme Speed": "Extreme Speed", + "\uD83D\uDD0E Type here to search styles ...": "\uD83D\uDD0E Type here to search styles ...", + "Type prompt here.": "Type prompt here.", + "Outpaint Expansion Direction:": "Outpaint Expansion Direction:", + "* Powered by Fooocus Inpaint Engine (beta)": "* Powered by Fooocus Inpaint Engine (beta)", + "Fooocus Enhance": "Fooocus Enhance", + "Fooocus Cinematic": "Fooocus Cinematic", + "Fooocus Sharp": "Fooocus Sharp", + "Drag any image generated by Fooocus here": "Drag any image generated by Fooocus here", + "Metadata": "Metadata", + "Apply Metadata": "Apply Metadata", + "Metadata Scheme": "Metadata Scheme", + "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.": "Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.", + "fooocus (json)": "fooocus (json)", + "a1111 (plain text)": "a1111 (plain text)" +} \ No newline at end of file diff --git a/language/example.json b/language/example.json new file mode 100644 index 0000000000000000000000000000000000000000..9b792449029bba64f2e190a03ac8459f6ccb84a9 --- /dev/null +++ b/language/example.json @@ -0,0 +1,6 @@ +{ + "Generate": "生成", + "Input Image": "入力画像", + "Advanced": "고급", + "SAI 3D Model": "SAI 3D Modèle" +} diff --git a/launch.py b/launch.py new file mode 100644 index 0000000000000000000000000000000000000000..4269f1fcbb120c65ffffd66101b718957907ca24 --- /dev/null +++ b/launch.py @@ -0,0 +1,127 @@ +import os +import sys +import ssl + +print('[System ARGV] ' + str(sys.argv)) + +root = os.path.dirname(os.path.abspath(__file__)) +sys.path.append(root) +os.chdir(root) + +os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1" +os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0" +if "GRADIO_SERVER_PORT" not in os.environ: + os.environ["GRADIO_SERVER_PORT"] = "7865" + +ssl._create_default_https_context = ssl._create_unverified_context + + +import platform +import fooocus_version + +from build_launcher import build_launcher +from modules.launch_util import is_installed, run, python, run_pip, requirements_met +from modules.model_loader import load_file_from_url + + +REINSTALL_ALL = False +TRY_INSTALL_XFORMERS = False + + +def prepare_environment(): + torch_index_url = os.environ.get('TORCH_INDEX_URL', "https://download.pytorch.org/whl/cu121") + torch_command = os.environ.get('TORCH_COMMAND', + f"pip install torch==2.1.0 torchvision==0.16.0 --extra-index-url {torch_index_url}") + requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt") + + print(f"Python {sys.version}") + print(f"Fooocus version: {fooocus_version.version}") + + if REINSTALL_ALL or not is_installed("torch") or not is_installed("torchvision"): + run(f'"{python}" -m {torch_command}', "Installing torch and torchvision", "Couldn't install torch", live=True) + + if TRY_INSTALL_XFORMERS: + if REINSTALL_ALL or not is_installed("xformers"): + xformers_package = os.environ.get('XFORMERS_PACKAGE', 'xformers==0.0.20') + if platform.system() == "Windows": + if platform.python_version().startswith("3.10"): + run_pip(f"install -U -I --no-deps {xformers_package}", "xformers", live=True) + else: + print("Installation of xformers is not supported in this version of Python.") + print( + "You can also check this and build manually: https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers#building-xformers-on-windows-by-duckness") + if not is_installed("xformers"): + exit(0) + elif platform.system() == "Linux": + run_pip(f"install -U -I --no-deps {xformers_package}", "xformers") + + if REINSTALL_ALL or not requirements_met(requirements_file): + run_pip(f"install -r \"{requirements_file}\"", "requirements") + + return + + +vae_approx_filenames = [ + ('xlvaeapp.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/xlvaeapp.pth'), + ('vaeapp_sd15.pth', 'https://huggingface.co/lllyasviel/misc/resolve/main/vaeapp_sd15.pt'), + ('xl-to-v1_interposer-v3.1.safetensors', + 'https://huggingface.co/lllyasviel/misc/resolve/main/xl-to-v1_interposer-v3.1.safetensors') +] + +def ini_args(): + from args_manager import args + return args + + +prepare_environment() +build_launcher() +args = ini_args() + + +if args.gpu_device_id is not None: + os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpu_device_id) + print("Set device to:", args.gpu_device_id) + + +from modules import config + +def download_models(): + for file_name, url in vae_approx_filenames: + load_file_from_url(url=url, model_dir=config.path_vae_approx, file_name=file_name) + + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_expansion.bin', + model_dir=config.path_fooocus_expansion, + file_name='pytorch_model.bin' + ) + + if args.disable_preset_download: + print('Skipped model download.') + return + + if not args.always_download_new_model: + if not os.path.exists(os.path.join(config.paths_checkpoints[0], config.default_base_model_name)): + for alternative_model_name in config.previous_default_models: + if os.path.exists(os.path.join(config.paths_checkpoints[0], alternative_model_name)): + print(f'You do not have [{config.default_base_model_name}] but you have [{alternative_model_name}].') + print(f'Fooocus will use [{alternative_model_name}] to avoid downloading new models, ' + f'but you are not using latest models.') + print('Use --always-download-new-model to avoid fallback and always get new models.') + config.checkpoint_downloads = {} + config.default_base_model_name = alternative_model_name + break + + for file_name, url in config.checkpoint_downloads.items(): + load_file_from_url(url=url, model_dir=config.paths_checkpoints[0], file_name=file_name) + for file_name, url in config.embeddings_downloads.items(): + load_file_from_url(url=url, model_dir=config.path_embeddings, file_name=file_name) + for file_name, url in config.lora_downloads.items(): + load_file_from_url(url=url, model_dir=config.paths_loras[0], file_name=file_name) + + return + + +download_models() + + +from webui import * diff --git a/ldm_patched/contrib/external.py b/ldm_patched/contrib/external.py new file mode 100644 index 0000000000000000000000000000000000000000..927cd3f38a1750168b740f7d32686e20abcb5304 --- /dev/null +++ b/ldm_patched/contrib/external.py @@ -0,0 +1,1954 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch + +import os +import sys +import json +import hashlib +import traceback +import math +import time +import random + +from PIL import Image, ImageOps, ImageSequence +from PIL.PngImagePlugin import PngInfo +import numpy as np +import safetensors.torch + +pass # sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched")) + + +import ldm_patched.modules.diffusers_load +import ldm_patched.modules.samplers +import ldm_patched.modules.sample +import ldm_patched.modules.sd +import ldm_patched.modules.utils +import ldm_patched.modules.controlnet + +import ldm_patched.modules.clip_vision + +import ldm_patched.modules.model_management +from ldm_patched.modules.args_parser import args + +import importlib + +import ldm_patched.utils.path_utils +import ldm_patched.utils.latent_visualization + +def before_node_execution(): + ldm_patched.modules.model_management.throw_exception_if_processing_interrupted() + +def interrupt_processing(value=True): + ldm_patched.modules.model_management.interrupt_current_processing(value) + +MAX_RESOLUTION=8192 + +class CLIPTextEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled}]], ) + +class ConditioningCombine: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "combine" + + CATEGORY = "conditioning" + + def combine(self, conditioning_1, conditioning_2): + return (conditioning_1 + conditioning_2, ) + +class ConditioningAverage : + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ), + "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "addWeighted" + + CATEGORY = "conditioning" + + def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength): + out = [] + + if len(conditioning_from) > 1: + print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + pooled_output_from = conditioning_from[0][1].get("pooled_output", None) + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from) + t0 = cond_from[:,:t1.shape[1]] + if t0.shape[1] < t1.shape[1]: + t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1) + + tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength)) + t_to = conditioning_to[i][1].copy() + if pooled_output_from is not None and pooled_output_to is not None: + t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength)) + elif pooled_output_from is not None: + t_to["pooled_output"] = pooled_output_from + + n = [tw, t_to] + out.append(n) + return (out, ) + +class ConditioningConcat: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "conditioning_to": ("CONDITIONING",), + "conditioning_from": ("CONDITIONING",), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "concat" + + CATEGORY = "conditioning" + + def concat(self, conditioning_to, conditioning_from): + out = [] + + if len(conditioning_from) > 1: + print("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.") + + cond_from = conditioning_from[0][0] + + for i in range(len(conditioning_to)): + t1 = conditioning_to[i][0] + tw = torch.cat((t1, cond_from),1) + n = [tw, conditioning_to[i][1].copy()] + out.append(n) + + return (out, ) + +class ConditioningSetArea: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['area'] = (height // 8, width // 8, y // 8, x // 8) + n[1]['strength'] = strength + n[1]['set_area_to_bounds'] = False + c.append(n) + return (c, ) + +class ConditioningSetAreaPercentage: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, width, height, x, y, strength): + c = [] + for t in conditioning: + n = [t[0], t[1].copy()] + n[1]['area'] = ("percentage", height, width, y, x) + n[1]['strength'] = strength + n[1]['set_area_to_bounds'] = False + c.append(n) + return (c, ) + +class ConditioningSetMask: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "mask": ("MASK", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "set_cond_area": (["default", "mask bounds"],), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning" + + def append(self, conditioning, mask, set_cond_area, strength): + c = [] + set_area_to_bounds = False + if set_cond_area != "default": + set_area_to_bounds = True + if len(mask.shape) < 3: + mask = mask.unsqueeze(0) + for t in conditioning: + n = [t[0], t[1].copy()] + _, h, w = mask.shape + n[1]['mask'] = mask + n[1]['set_area_to_bounds'] = set_area_to_bounds + n[1]['mask_strength'] = strength + c.append(n) + return (c, ) + +class ConditioningZeroOut: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", )}} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "zero_out" + + CATEGORY = "advanced/conditioning" + + def zero_out(self, conditioning): + c = [] + for t in conditioning: + d = t[1].copy() + if "pooled_output" in d: + d["pooled_output"] = torch.zeros_like(d["pooled_output"]) + n = [torch.zeros_like(t[0]), d] + c.append(n) + return (c, ) + +class ConditioningSetTimestepRange: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "set_range" + + CATEGORY = "advanced/conditioning" + + def set_range(self, conditioning, start, end): + c = [] + for t in conditioning: + d = t[1].copy() + d['start_percent'] = start + d['end_percent'] = end + n = [t[0], d] + c.append(n) + return (c, ) + +class VAEDecode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + CATEGORY = "latent" + + def decode(self, vae, samples): + return (vae.decode(samples["samples"]), ) + +class VAEDecodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "decode" + + CATEGORY = "_for_testing" + + def decode(self, vae, samples, tile_size): + return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), ) + +class VAEEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", )}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent" + + @staticmethod + def vae_encode_crop_pixels(pixels): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] + return pixels + + def encode(self, vae, pixels): + pixels = self.vae_encode_crop_pixels(pixels) + t = vae.encode(pixels[:,:,:,:3]) + return ({"samples":t}, ) + +class VAEEncodeTiled: + @classmethod + def INPUT_TYPES(s): + return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), + "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}) + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "_for_testing" + + def encode(self, vae, pixels, tile_size): + pixels = VAEEncode.vae_encode_crop_pixels(pixels) + t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, ) + return ({"samples":t}, ) + +class VAEEncodeForInpaint: + @classmethod + def INPUT_TYPES(s): + return {"required": { "pixels": ("IMAGE", ), "vae": ("VAE", ), "mask": ("MASK", ), "grow_mask_by": ("INT", {"default": 6, "min": 0, "max": 64, "step": 1}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "encode" + + CATEGORY = "latent/inpaint" + + def encode(self, vae, pixels, mask, grow_mask_by=6): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + pixels = pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + #grow mask by a few pixels to keep things seamless in latent space + if grow_mask_by == 0: + mask_erosion = mask + else: + kernel_tensor = torch.ones((1, 1, grow_mask_by, grow_mask_by)) + padding = math.ceil((grow_mask_by - 1) / 2) + + mask_erosion = torch.clamp(torch.nn.functional.conv2d(mask.round(), kernel_tensor, padding=padding), 0, 1) + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + t = vae.encode(pixels) + + return ({"samples":t, "noise_mask": (mask_erosion[:,:,:x,:y].round())}, ) + + +class InpaintModelConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "vae": ("VAE", ), + "pixels": ("IMAGE", ), + "mask": ("MASK", ), + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + FUNCTION = "encode" + + CATEGORY = "conditioning/inpaint" + + def encode(self, positive, negative, pixels, vae, mask): + x = (pixels.shape[1] // 8) * 8 + y = (pixels.shape[2] // 8) * 8 + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear") + + orig_pixels = pixels + pixels = orig_pixels.clone() + if pixels.shape[1] != x or pixels.shape[2] != y: + x_offset = (pixels.shape[1] % 8) // 2 + y_offset = (pixels.shape[2] % 8) // 2 + pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:] + mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset] + + m = (1.0 - mask.round()).squeeze(1) + for i in range(3): + pixels[:,:,:,i] -= 0.5 + pixels[:,:,:,i] *= m + pixels[:,:,:,i] += 0.5 + concat_latent = vae.encode(pixels) + orig_latent = vae.encode(orig_pixels) + + out_latent = {} + + out_latent["samples"] = orig_latent + out_latent["noise_mask"] = mask + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + d["concat_latent_image"] = concat_latent + d["concat_mask"] = mask + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1], out_latent) + + +class SaveLatent: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT", ), + "filename_prefix": ("STRING", {"default": "latents/ldm_patched"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + RETURN_TYPES = () + FUNCTION = "save" + + OUTPUT_NODE = True + + CATEGORY = "_for_testing" + + def save(self, samples, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir) + + # support save metadata for latent sharing + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = None + if not args.disable_server_info: + metadata = {"prompt": prompt_info} + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + file = f"{filename}_{counter:05}_.latent" + + results = list() + results.append({ + "filename": file, + "subfolder": subfolder, + "type": "output" + }) + + file = os.path.join(full_output_folder, file) + + output = {} + output["latent_tensor"] = samples["samples"] + output["latent_format_version_0"] = torch.tensor([]) + + ldm_patched.modules.utils.save_torch_file(output, file, metadata=metadata) + return { "ui": { "latents": results } } + + +class LoadLatent: + @classmethod + def INPUT_TYPES(s): + input_dir = ldm_patched.utils.path_utils.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f)) and f.endswith(".latent")] + return {"required": {"latent": [sorted(files), ]}, } + + CATEGORY = "_for_testing" + + RETURN_TYPES = ("LATENT", ) + FUNCTION = "load" + + def load(self, latent): + latent_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent) + latent = safetensors.torch.load_file(latent_path, device="cpu") + multiplier = 1.0 + if "latent_format_version_0" not in latent: + multiplier = 1.0 / 0.18215 + samples = {"samples": latent["latent_tensor"].float() * multiplier} + return (samples, ) + + @classmethod + def IS_CHANGED(s, latent): + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(latent) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, latent): + if not ldm_patched.utils.path_utils.exists_annotated_filepath(latent): + return "Invalid latent file: {}".format(latent) + return True + + +class CheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "config_name": (ldm_patched.utils.path_utils.get_filename_list("configs"), ), + "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), )}} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders" + + def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True): + config_path = ldm_patched.utils.path_utils.get_full_path("configs", config_name) + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + return ldm_patched.modules.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + +class CheckpointLoaderSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + return out[:3] + +class DiffusersLoader: + @classmethod + def INPUT_TYPES(cls): + paths = [] + for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"): + if os.path.exists(search_path): + for root, subdir, files in os.walk(search_path, followlinks=True): + if "model_index.json" in files: + paths.append(os.path.relpath(root, start=search_path)) + + return {"required": {"model_path": (paths,), }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "advanced/loaders/deprecated" + + def load_checkpoint(self, model_path, output_vae=True, output_clip=True): + for search_path in ldm_patched.utils.path_utils.get_folder_paths("diffusers"): + if os.path.exists(search_path): + path = os.path.join(search_path, model_path) + if os.path.exists(path): + model_path = path + break + + return ldm_patched.modules.diffusers_load.load_diffusers(model_path, output_vae=output_vae, output_clip=output_clip, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + + +class unCLIPCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP", "VAE", "CLIP_VISION") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + return out + +class CLIPSetLastLayer: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP", ), + "stop_at_clip_layer": ("INT", {"default": -1, "min": -24, "max": -1, "step": 1}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "set_last_layer" + + CATEGORY = "conditioning" + + def set_last_layer(self, clip, stop_at_clip_layer): + clip = clip.clone() + clip.clip_layer(stop_at_clip_layer) + return (clip,) + +class LoraLoader: + def __init__(self): + self.loaded_lora = None + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip": ("CLIP", ), + "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL", "CLIP") + FUNCTION = "load_lora" + + CATEGORY = "loaders" + + def load_lora(self, model, clip, lora_name, strength_model, strength_clip): + if strength_model == 0 and strength_clip == 0: + return (model, clip) + + lora_path = ldm_patched.utils.path_utils.get_full_path("loras", lora_name) + lora = None + if self.loaded_lora is not None: + if self.loaded_lora[0] == lora_path: + lora = self.loaded_lora[1] + else: + temp = self.loaded_lora + self.loaded_lora = None + del temp + + if lora is None: + lora = ldm_patched.modules.utils.load_torch_file(lora_path, safe_load=True) + self.loaded_lora = (lora_path, lora) + + model_lora, clip_lora = ldm_patched.modules.sd.load_lora_for_models(model, clip, lora, strength_model, strength_clip) + return (model_lora, clip_lora) + +class LoraLoaderModelOnly(LoraLoader): + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "lora_name": (ldm_patched.utils.path_utils.get_filename_list("loras"), ), + "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lora_model_only" + + def load_lora_model_only(self, model, lora_name, strength_model): + return (self.load_lora(model, None, lora_name, strength_model, 0)[0],) + +class VAELoader: + @staticmethod + def vae_list(): + vaes = ldm_patched.utils.path_utils.get_filename_list("vae") + approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx") + sdxl_taesd_enc = False + sdxl_taesd_dec = False + sd1_taesd_enc = False + sd1_taesd_dec = False + + for v in approx_vaes: + if v.startswith("taesd_decoder."): + sd1_taesd_dec = True + elif v.startswith("taesd_encoder."): + sd1_taesd_enc = True + elif v.startswith("taesdxl_decoder."): + sdxl_taesd_dec = True + elif v.startswith("taesdxl_encoder."): + sdxl_taesd_enc = True + if sd1_taesd_dec and sd1_taesd_enc: + vaes.append("taesd") + if sdxl_taesd_dec and sdxl_taesd_enc: + vaes.append("taesdxl") + return vaes + + @staticmethod + def load_taesd(name): + sd = {} + approx_vaes = ldm_patched.utils.path_utils.get_filename_list("vae_approx") + + encoder = next(filter(lambda a: a.startswith("{}_encoder.".format(name)), approx_vaes)) + decoder = next(filter(lambda a: a.startswith("{}_decoder.".format(name)), approx_vaes)) + + enc = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", encoder)) + for k in enc: + sd["taesd_encoder.{}".format(k)] = enc[k] + + dec = ldm_patched.modules.utils.load_torch_file(ldm_patched.utils.path_utils.get_full_path("vae_approx", decoder)) + for k in dec: + sd["taesd_decoder.{}".format(k)] = dec[k] + + if name == "taesd": + sd["vae_scale"] = torch.tensor(0.18215) + elif name == "taesdxl": + sd["vae_scale"] = torch.tensor(0.13025) + return sd + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae_name": (s.vae_list(), )}} + RETURN_TYPES = ("VAE",) + FUNCTION = "load_vae" + + CATEGORY = "loaders" + + #TODO: scale factor? + def load_vae(self, vae_name): + if vae_name in ["taesd", "taesdxl"]: + sd = self.load_taesd(vae_name) + else: + vae_path = ldm_patched.utils.path_utils.get_full_path("vae", vae_name) + sd = ldm_patched.modules.utils.load_torch_file(vae_path) + vae = ldm_patched.modules.sd.VAE(sd=sd) + return (vae,) + +class ControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, control_net_name): + controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name) + controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path) + return (controlnet,) + +class DiffControlNetLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "control_net_name": (ldm_patched.utils.path_utils.get_filename_list("controlnet"), )}} + + RETURN_TYPES = ("CONTROL_NET",) + FUNCTION = "load_controlnet" + + CATEGORY = "loaders" + + def load_controlnet(self, model, control_net_name): + controlnet_path = ldm_patched.utils.path_utils.get_full_path("controlnet", control_net_name) + controlnet = ldm_patched.modules.controlnet.load_controlnet(controlnet_path, model) + return (controlnet,) + + +class ControlNetApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_controlnet" + + CATEGORY = "conditioning" + + def apply_controlnet(self, conditioning, control_net, image, strength): + if strength == 0: + return (conditioning, ) + + c = [] + control_hint = image.movedim(-1,1) + for t in conditioning: + n = [t[0], t[1].copy()] + c_net = control_net.copy().set_cond_hint(control_hint, strength) + if 'control' in t[1]: + c_net.set_previous_controlnet(t[1]['control']) + n[1]['control'] = c_net + n[1]['control_apply_to_uncond'] = True + c.append(n) + return (c, ) + + +class ControlNetApplyAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": {"positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "control_net": ("CONTROL_NET", ), + "image": ("IMAGE", ), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}) + }} + + RETURN_TYPES = ("CONDITIONING","CONDITIONING") + RETURN_NAMES = ("positive", "negative") + FUNCTION = "apply_controlnet" + + CATEGORY = "conditioning" + + def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent): + if strength == 0: + return (positive, negative) + + control_hint = image.movedim(-1,1) + cnets = {} + + out = [] + for conditioning in [positive, negative]: + c = [] + for t in conditioning: + d = t[1].copy() + + prev_cnet = d.get('control', None) + if prev_cnet in cnets: + c_net = cnets[prev_cnet] + else: + c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent)) + c_net.set_previous_controlnet(prev_cnet) + cnets[prev_cnet] = c_net + + d['control'] = c_net + d['control_apply_to_uncond'] = False + n = [t[0], d] + c.append(n) + out.append(c) + return (out[0], out[1]) + + +class UNETLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "unet_name": (ldm_patched.utils.path_utils.get_filename_list("unet"), ), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_unet" + + CATEGORY = "advanced/loaders" + + def load_unet(self, unet_name): + unet_path = ldm_patched.utils.path_utils.get_full_path("unet", unet_name) + model = ldm_patched.modules.sd.load_unet(unet_path) + return (model,) + +class CLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name): + clip_path = ldm_patched.utils.path_utils.get_full_path("clip", clip_name) + clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + return (clip,) + +class DualCLIPLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name1": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), "clip_name2": (ldm_patched.utils.path_utils.get_filename_list("clip"), ), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "load_clip" + + CATEGORY = "advanced/loaders" + + def load_clip(self, clip_name1, clip_name2): + clip_path1 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name1) + clip_path2 = ldm_patched.utils.path_utils.get_full_path("clip", clip_name2) + clip = ldm_patched.modules.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + return (clip,) + +class CLIPVisionLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_name": (ldm_patched.utils.path_utils.get_filename_list("clip_vision"), ), + }} + RETURN_TYPES = ("CLIP_VISION",) + FUNCTION = "load_clip" + + CATEGORY = "loaders" + + def load_clip(self, clip_name): + clip_path = ldm_patched.utils.path_utils.get_full_path("clip_vision", clip_name) + clip_vision = ldm_patched.modules.clip_vision.load(clip_path) + return (clip_vision,) + +class CLIPVisionEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "image": ("IMAGE",) + }} + RETURN_TYPES = ("CLIP_VISION_OUTPUT",) + FUNCTION = "encode" + + CATEGORY = "conditioning" + + def encode(self, clip_vision, image): + output = clip_vision.encode_image(image) + return (output,) + +class StyleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "style_model_name": (ldm_patched.utils.path_utils.get_filename_list("style_models"), )}} + + RETURN_TYPES = ("STYLE_MODEL",) + FUNCTION = "load_style_model" + + CATEGORY = "loaders" + + def load_style_model(self, style_model_name): + style_model_path = ldm_patched.utils.path_utils.get_full_path("style_models", style_model_name) + style_model = ldm_patched.modules.sd.load_style_model(style_model_path) + return (style_model,) + + +class StyleModelApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "style_model": ("STYLE_MODEL", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_stylemodel" + + CATEGORY = "conditioning/style_model" + + def apply_stylemodel(self, clip_vision_output, style_model, conditioning): + cond = style_model.get_cond(clip_vision_output).flatten(start_dim=0, end_dim=1).unsqueeze(dim=0) + c = [] + for t in conditioning: + n = [torch.cat((t[0], cond), dim=1), t[1].copy()] + c.append(n) + return (c, ) + +class unCLIPConditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning": ("CONDITIONING", ), + "clip_vision_output": ("CLIP_VISION_OUTPUT", ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_adm" + + CATEGORY = "conditioning" + + def apply_adm(self, conditioning, clip_vision_output, strength, noise_augmentation): + if strength == 0: + return (conditioning, ) + + c = [] + for t in conditioning: + o = t[1].copy() + x = {"clip_vision_output": clip_vision_output, "strength": strength, "noise_augmentation": noise_augmentation} + if "unclip_conditioning" in o: + o["unclip_conditioning"] = o["unclip_conditioning"][:] + [x] + else: + o["unclip_conditioning"] = [x] + n = [t[0], o] + c.append(n) + return (c, ) + +class GLIGENLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "gligen_name": (ldm_patched.utils.path_utils.get_filename_list("gligen"), )}} + + RETURN_TYPES = ("GLIGEN",) + FUNCTION = "load_gligen" + + CATEGORY = "loaders" + + def load_gligen(self, gligen_name): + gligen_path = ldm_patched.utils.path_utils.get_full_path("gligen", gligen_name) + gligen = ldm_patched.modules.sd.load_gligen(gligen_path) + return (gligen,) + +class GLIGENTextBoxApply: + @classmethod + def INPUT_TYPES(s): + return {"required": {"conditioning_to": ("CONDITIONING", ), + "clip": ("CLIP", ), + "gligen_textbox_model": ("GLIGEN", ), + "text": ("STRING", {"multiline": True}), + "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "append" + + CATEGORY = "conditioning/gligen" + + def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y): + c = [] + cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True) + for t in conditioning_to: + n = [t[0], t[1].copy()] + position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)] + prev = [] + if "gligen" in n[1]: + prev = n[1]['gligen'][2] + + n[1]['gligen'] = ("position", gligen_textbox_model, prev + position_params) + c.append(n) + return (c, ) + +class EmptyLatentImage: + def __init__(self): + self.device = ldm_patched.modules.model_management.intermediate_device() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "generate" + + CATEGORY = "latent" + + def generate(self, width, height, batch_size=1): + latent = torch.zeros([batch_size, 4, height // 8, width // 8], device=self.device) + return ({"samples":latent}, ) + + +class LatentFromBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "batch_index": ("INT", {"default": 0, "min": 0, "max": 63}), + "length": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "frombatch" + + CATEGORY = "latent/batch" + + def frombatch(self, samples, batch_index, length): + s = samples.copy() + s_in = samples["samples"] + batch_index = min(s_in.shape[0] - 1, batch_index) + length = min(s_in.shape[0] - batch_index, length) + s["samples"] = s_in[batch_index:batch_index + length].clone() + if "noise_mask" in samples: + masks = samples["noise_mask"] + if masks.shape[0] == 1: + s["noise_mask"] = masks.clone() + else: + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = masks[batch_index:batch_index + length].clone() + if "batch_index" not in s: + s["batch_index"] = [x for x in range(batch_index, batch_index+length)] + else: + s["batch_index"] = samples["batch_index"][batch_index:batch_index + length] + return (s,) + +class RepeatLatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "amount": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "repeat" + + CATEGORY = "latent/batch" + + def repeat(self, samples, amount): + s = samples.copy() + s_in = samples["samples"] + + s["samples"] = s_in.repeat((amount, 1,1,1)) + if "noise_mask" in samples and samples["noise_mask"].shape[0] > 1: + masks = samples["noise_mask"] + if masks.shape[0] < s_in.shape[0]: + masks = masks.repeat(math.ceil(s_in.shape[0] / masks.shape[0]), 1, 1, 1)[:s_in.shape[0]] + s["noise_mask"] = samples["noise_mask"].repeat((amount, 1,1,1)) + if "batch_index" in s: + offset = max(s["batch_index"]) - min(s["batch_index"]) + 1 + s["batch_index"] = s["batch_index"] + [x + (i * offset) for i in range(1, amount) for x in s["batch_index"]] + return (s,) + +class LatentUpscale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = samples + else: + s = samples.copy() + + if width == 0: + height = max(64, height) + width = max(64, round(samples["samples"].shape[3] * height / samples["samples"].shape[2])) + elif height == 0: + width = max(64, width) + height = max(64, round(samples["samples"].shape[2] * width / samples["samples"].shape[3])) + else: + width = max(64, width) + height = max(64, height) + + s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width // 8, height // 8, upscale_method, crop) + return (s,) + +class LatentUpscaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "bislerp"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.5, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("LATENT",) + FUNCTION = "upscale" + + CATEGORY = "latent" + + def upscale(self, samples, upscale_method, scale_by): + s = samples.copy() + width = round(samples["samples"].shape[3] * scale_by) + height = round(samples["samples"].shape[2] * scale_by) + s["samples"] = ldm_patched.modules.utils.common_upscale(samples["samples"], width, height, upscale_method, "disabled") + return (s,) + +class LatentRotate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "rotation": (["none", "90 degrees", "180 degrees", "270 degrees"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "rotate" + + CATEGORY = "latent/transform" + + def rotate(self, samples, rotation): + s = samples.copy() + rotate_by = 0 + if rotation.startswith("90"): + rotate_by = 1 + elif rotation.startswith("180"): + rotate_by = 2 + elif rotation.startswith("270"): + rotate_by = 3 + + s["samples"] = torch.rot90(samples["samples"], k=rotate_by, dims=[3, 2]) + return (s,) + +class LatentFlip: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "flip_method": (["x-axis: vertically", "y-axis: horizontally"],), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "flip" + + CATEGORY = "latent/transform" + + def flip(self, samples, flip_method): + s = samples.copy() + if flip_method.startswith("x"): + s["samples"] = torch.flip(samples["samples"], dims=[2]) + elif flip_method.startswith("y"): + s["samples"] = torch.flip(samples["samples"], dims=[3]) + + return (s,) + +class LatentComposite: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples_to": ("LATENT",), + "samples_from": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0): + x = x // 8 + y = y // 8 + feather = feather // 8 + samples_out = samples_to.copy() + s = samples_to["samples"].clone() + samples_to = samples_to["samples"] + samples_from = samples_from["samples"] + if feather == 0: + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + else: + samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] + mask = torch.ones_like(samples_from) + for t in range(feather): + if y != 0: + mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) + + if y + samples_from.shape[2] < samples_to.shape[2]: + mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) + if x != 0: + mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) + if x + samples_from.shape[3] < samples_to.shape[3]: + mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) + rev_mask = torch.ones_like(mask) - mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask + samples_out["samples"] = s + return (samples_out,) + +class LatentBlend: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "samples1": ("LATENT",), + "samples2": ("LATENT",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0, + "max": 1, + "step": 0.01 + }), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "blend" + + CATEGORY = "_for_testing" + + def blend(self, samples1, samples2, blend_factor:float, blend_mode: str="normal"): + + samples_out = samples1.copy() + samples1 = samples1["samples"] + samples2 = samples2["samples"] + + if samples1.shape != samples2.shape: + samples2.permute(0, 3, 1, 2) + samples2 = ldm_patched.modules.utils.common_upscale(samples2, samples1.shape[3], samples1.shape[2], 'bicubic', crop='center') + samples2.permute(0, 2, 3, 1) + + samples_blended = self.blend_mode(samples1, samples2, blend_mode) + samples_blended = samples1 * blend_factor + samples_blended * (1 - blend_factor) + samples_out["samples"] = samples_blended + return (samples_out,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + +class LatentCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "width": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 512, "min": 64, "max": MAX_RESOLUTION, "step": 8}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "crop" + + CATEGORY = "latent/transform" + + def crop(self, samples, width, height, x, y): + s = samples.copy() + samples = samples['samples'] + x = x // 8 + y = y // 8 + + #enfonce minimum size of 64 + if x > (samples.shape[3] - 8): + x = samples.shape[3] - 8 + if y > (samples.shape[2] - 8): + y = samples.shape[2] - 8 + + new_height = height // 8 + new_width = width // 8 + to_x = new_width + x + to_y = new_height + y + s['samples'] = samples[:,:,y:to_y, x:to_x] + return (s,) + +class SetLatentNoiseMask: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "mask": ("MASK",), + }} + RETURN_TYPES = ("LATENT",) + FUNCTION = "set_mask" + + CATEGORY = "latent/inpaint" + + def set_mask(self, samples, mask): + s = samples.copy() + s["noise_mask"] = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])) + return (s,) + +def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): + latent_image = latent["samples"] + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + callback = ldm_patched.utils.latent_visualization.prepare_callback(model, steps) + disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED + samples = ldm_patched.modules.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + out = latent.copy() + out["samples"] = samples + return (out, ) + +class KSampler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ), + "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0): + return common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise) + +class KSamplerAdvanced: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": (["enable", "disable"], ), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "sampler_name": (ldm_patched.modules.samplers.KSampler.SAMPLERS, ), + "scheduler": (ldm_patched.modules.samplers.KSampler.SCHEDULERS, ), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "latent_image": ("LATENT", ), + "start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000}), + "end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000}), + "return_with_leftover_noise": (["disable", "enable"], ), + } + } + + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" + + CATEGORY = "sampling" + + def sample(self, model, add_noise, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, start_at_step, end_at_step, return_with_leftover_noise, denoise=1.0): + force_full_denoise = True + if return_with_leftover_noise == "enable": + force_full_denoise = False + disable_noise = False + if add_noise == "disable": + disable_noise = True + return common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise) + +class SaveImage: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + self.type = "output" + self.prefix_append = "" + self.compress_level = 4 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ldm_patched"})}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image" + + def save_images(self, images, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + metadata = None + if not args.disable_server_info: + metadata = PngInfo() + if prompt is not None: + metadata.add_text("prompt", json.dumps(prompt)) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add_text(x, json.dumps(extra_pnginfo[x])) + + file = f"{filename}_{counter:05}_.png" + img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + return { "ui": { "images": results } } + +class PreviewImage(SaveImage): + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_temp_directory() + self.type = "temp" + self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5)) + self.compress_level = 1 + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + +class LoadImage: + @classmethod + def INPUT_TYPES(s): + input_dir = ldm_patched.utils.path_utils.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True})}, + } + + CATEGORY = "image" + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "load_image" + def load_image(self, image): + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) + img = Image.open(image_path) + output_images = [] + output_masks = [] + for i in ImageSequence.Iterator(img): + i = ImageOps.exif_transpose(i) + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + image = i.convert("RGB") + image = np.array(image).astype(np.float32) / 255.0 + image = torch.from_numpy(image)[None,] + if 'A' in i.getbands(): + mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0 + mask = 1. - torch.from_numpy(mask) + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + output_images.append(image) + output_masks.append(mask.unsqueeze(0)) + + if len(output_images) > 1: + output_image = torch.cat(output_images, dim=0) + output_mask = torch.cat(output_masks, dim=0) + else: + output_image = output_images[0] + output_mask = output_masks[0] + + return (output_image, output_mask) + + @classmethod + def IS_CHANGED(s, image): + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not ldm_patched.utils.path_utils.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class LoadImageMask: + _color_channels = ["alpha", "red", "green", "blue"] + @classmethod + def INPUT_TYPES(s): + input_dir = ldm_patched.utils.path_utils.get_input_directory() + files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))] + return {"required": + {"image": (sorted(files), {"image_upload": True}), + "channel": (s._color_channels, ), } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "load_image" + def load_image(self, image, channel): + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) + i = Image.open(image_path) + i = ImageOps.exif_transpose(i) + if i.getbands() != ("R", "G", "B", "A"): + if i.mode == 'I': + i = i.point(lambda i: i * (1 / 255)) + i = i.convert("RGBA") + mask = None + c = channel[0].upper() + if c in i.getbands(): + mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0 + mask = torch.from_numpy(mask) + if c == 'A': + mask = 1. - mask + else: + mask = torch.zeros((64,64), dtype=torch.float32, device="cpu") + return (mask.unsqueeze(0),) + + @classmethod + def IS_CHANGED(s, image, channel): + image_path = ldm_patched.utils.path_utils.get_annotated_filepath(image) + m = hashlib.sha256() + with open(image_path, 'rb') as f: + m.update(f.read()) + return m.digest().hex() + + @classmethod + def VALIDATE_INPUTS(s, image): + if not ldm_patched.utils.path_utils.exists_annotated_filepath(image): + return "Invalid image file: {}".format(image) + + return True + +class ImageScale: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "crop": (s.crop_methods,)}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, width, height, crop): + if width == 0 and height == 0: + s = image + else: + samples = image.movedim(-1,1) + + if width == 0: + width = max(1, round(samples.shape[3] * height / samples.shape[2])) + elif height == 0: + height = max(1, round(samples.shape[2] * width / samples.shape[3])) + + s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, crop) + s = s.movedim(1,-1) + return (s,) + +class ImageScaleBy: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "scale_by": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 8.0, "step": 0.01}),}} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, scale_by): + samples = image.movedim(-1,1) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +class ImageInvert: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "invert" + + CATEGORY = "image" + + def invert(self, image): + s = 1.0 - image + return (s,) + +class ImageBatch: + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image1": ("IMAGE",), "image2": ("IMAGE",)}} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "batch" + + CATEGORY = "image" + + def batch(self, image1, image2): + if image1.shape[1:] != image2.shape[1:]: + image2 = ldm_patched.modules.utils.common_upscale(image2.movedim(-1,1), image1.shape[2], image1.shape[1], "bilinear", "center").movedim(1,-1) + s = torch.cat((image1, image2), dim=0) + return (s,) + +class EmptyImage: + def __init__(self, device="cpu"): + self.device = device + + @classmethod + def INPUT_TYPES(s): + return {"required": { "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "generate" + + CATEGORY = "image" + + def generate(self, width, height, batch_size=1, color=0): + r = torch.full([batch_size, height, width, 1], ((color >> 16) & 0xFF) / 0xFF) + g = torch.full([batch_size, height, width, 1], ((color >> 8) & 0xFF) / 0xFF) + b = torch.full([batch_size, height, width, 1], ((color) & 0xFF) / 0xFF) + return (torch.cat((r, g, b), dim=-1), ) + +class ImagePadForOutpaint: + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "feathering": ("INT", {"default": 40, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "expand_image" + + CATEGORY = "image" + + def expand_image(self, image, left, top, right, bottom, feathering): + d1, d2, d3, d4 = image.size() + + new_image = torch.ones( + (d1, d2 + top + bottom, d3 + left + right, d4), + dtype=torch.float32, + ) * 0.5 + + new_image[:, top:top + d2, left:left + d3, :] = image + + mask = torch.ones( + (d2 + top + bottom, d3 + left + right), + dtype=torch.float32, + ) + + t = torch.zeros( + (d2, d3), + dtype=torch.float32 + ) + + if feathering > 0 and feathering * 2 < d2 and feathering * 2 < d3: + + for i in range(d2): + for j in range(d3): + dt = i if top != 0 else d2 + db = d2 - i if bottom != 0 else d2 + + dl = j if left != 0 else d3 + dr = d3 - j if right != 0 else d3 + + d = min(dt, db, dl, dr) + + if d >= feathering: + continue + + v = (feathering - d) / feathering + + t[i, j] = v * v + + mask[top:top + d2, left:left + d3] = t + + return (new_image, mask) + + +NODE_CLASS_MAPPINGS = { + "KSampler": KSampler, + "CheckpointLoaderSimple": CheckpointLoaderSimple, + "CLIPTextEncode": CLIPTextEncode, + "CLIPSetLastLayer": CLIPSetLastLayer, + "VAEDecode": VAEDecode, + "VAEEncode": VAEEncode, + "VAEEncodeForInpaint": VAEEncodeForInpaint, + "VAELoader": VAELoader, + "EmptyLatentImage": EmptyLatentImage, + "LatentUpscale": LatentUpscale, + "LatentUpscaleBy": LatentUpscaleBy, + "LatentFromBatch": LatentFromBatch, + "RepeatLatentBatch": RepeatLatentBatch, + "SaveImage": SaveImage, + "PreviewImage": PreviewImage, + "LoadImage": LoadImage, + "LoadImageMask": LoadImageMask, + "ImageScale": ImageScale, + "ImageScaleBy": ImageScaleBy, + "ImageInvert": ImageInvert, + "ImageBatch": ImageBatch, + "ImagePadForOutpaint": ImagePadForOutpaint, + "EmptyImage": EmptyImage, + "ConditioningAverage": ConditioningAverage , + "ConditioningCombine": ConditioningCombine, + "ConditioningConcat": ConditioningConcat, + "ConditioningSetArea": ConditioningSetArea, + "ConditioningSetAreaPercentage": ConditioningSetAreaPercentage, + "ConditioningSetMask": ConditioningSetMask, + "KSamplerAdvanced": KSamplerAdvanced, + "SetLatentNoiseMask": SetLatentNoiseMask, + "LatentComposite": LatentComposite, + "LatentBlend": LatentBlend, + "LatentRotate": LatentRotate, + "LatentFlip": LatentFlip, + "LatentCrop": LatentCrop, + "LoraLoader": LoraLoader, + "CLIPLoader": CLIPLoader, + "UNETLoader": UNETLoader, + "DualCLIPLoader": DualCLIPLoader, + "CLIPVisionEncode": CLIPVisionEncode, + "StyleModelApply": StyleModelApply, + "unCLIPConditioning": unCLIPConditioning, + "ControlNetApply": ControlNetApply, + "ControlNetApplyAdvanced": ControlNetApplyAdvanced, + "ControlNetLoader": ControlNetLoader, + "DiffControlNetLoader": DiffControlNetLoader, + "StyleModelLoader": StyleModelLoader, + "CLIPVisionLoader": CLIPVisionLoader, + "VAEDecodeTiled": VAEDecodeTiled, + "VAEEncodeTiled": VAEEncodeTiled, + "unCLIPCheckpointLoader": unCLIPCheckpointLoader, + "GLIGENLoader": GLIGENLoader, + "GLIGENTextBoxApply": GLIGENTextBoxApply, + "InpaintModelConditioning": InpaintModelConditioning, + + "CheckpointLoader": CheckpointLoader, + "DiffusersLoader": DiffusersLoader, + + "LoadLatent": LoadLatent, + "SaveLatent": SaveLatent, + + "ConditioningZeroOut": ConditioningZeroOut, + "ConditioningSetTimestepRange": ConditioningSetTimestepRange, + "LoraLoaderModelOnly": LoraLoaderModelOnly, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "KSampler": "KSampler", + "KSamplerAdvanced": "KSampler (Advanced)", + # Loaders + "CheckpointLoader": "Load Checkpoint With Config (DEPRECATED)", + "CheckpointLoaderSimple": "Load Checkpoint", + "VAELoader": "Load VAE", + "LoraLoader": "Load LoRA", + "CLIPLoader": "Load CLIP", + "ControlNetLoader": "Load ControlNet Model", + "DiffControlNetLoader": "Load ControlNet Model (diff)", + "StyleModelLoader": "Load Style Model", + "CLIPVisionLoader": "Load CLIP Vision", + "UpscaleModelLoader": "Load Upscale Model", + # Conditioning + "CLIPVisionEncode": "CLIP Vision Encode", + "StyleModelApply": "Apply Style Model", + "CLIPTextEncode": "CLIP Text Encode (Prompt)", + "CLIPSetLastLayer": "CLIP Set Last Layer", + "ConditioningCombine": "Conditioning (Combine)", + "ConditioningAverage ": "Conditioning (Average)", + "ConditioningConcat": "Conditioning (Concat)", + "ConditioningSetArea": "Conditioning (Set Area)", + "ConditioningSetAreaPercentage": "Conditioning (Set Area with Percentage)", + "ConditioningSetMask": "Conditioning (Set Mask)", + "ControlNetApply": "Apply ControlNet", + "ControlNetApplyAdvanced": "Apply ControlNet (Advanced)", + # Latent + "VAEEncodeForInpaint": "VAE Encode (for Inpainting)", + "SetLatentNoiseMask": "Set Latent Noise Mask", + "VAEDecode": "VAE Decode", + "VAEEncode": "VAE Encode", + "LatentRotate": "Rotate Latent", + "LatentFlip": "Flip Latent", + "LatentCrop": "Crop Latent", + "EmptyLatentImage": "Empty Latent Image", + "LatentUpscale": "Upscale Latent", + "LatentUpscaleBy": "Upscale Latent By", + "LatentComposite": "Latent Composite", + "LatentBlend": "Latent Blend", + "LatentFromBatch" : "Latent From Batch", + "RepeatLatentBatch": "Repeat Latent Batch", + # Image + "SaveImage": "Save Image", + "PreviewImage": "Preview Image", + "LoadImage": "Load Image", + "LoadImageMask": "Load Image (as Mask)", + "ImageScale": "Upscale Image", + "ImageScaleBy": "Upscale Image By", + "ImageUpscaleWithModel": "Upscale Image (using Model)", + "ImageInvert": "Invert Image", + "ImagePadForOutpaint": "Pad Image for Outpainting", + "ImageBatch": "Batch Images", + # _for_testing + "VAEDecodeTiled": "VAE Decode (Tiled)", + "VAEEncodeTiled": "VAE Encode (Tiled)", +} + +EXTENSION_WEB_DIRS = {} + +def load_custom_node(module_path, ignore=set()): + module_name = os.path.basename(module_path) + if os.path.isfile(module_path): + sp = os.path.splitext(module_path) + module_name = sp[0] + try: + if os.path.isfile(module_path): + module_spec = importlib.util.spec_from_file_location(module_name, module_path) + module_dir = os.path.split(module_path)[0] + else: + module_spec = importlib.util.spec_from_file_location(module_name, os.path.join(module_path, "__init__.py")) + module_dir = module_path + + module = importlib.util.module_from_spec(module_spec) + sys.modules[module_name] = module + module_spec.loader.exec_module(module) + + if hasattr(module, "WEB_DIRECTORY") and getattr(module, "WEB_DIRECTORY") is not None: + web_dir = os.path.abspath(os.path.join(module_dir, getattr(module, "WEB_DIRECTORY"))) + if os.path.isdir(web_dir): + EXTENSION_WEB_DIRS[module_name] = web_dir + + if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None: + for name in module.NODE_CLASS_MAPPINGS: + if name not in ignore: + NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name] + if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None: + NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS) + return True + else: + print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.") + return False + except Exception as e: + print(traceback.format_exc()) + print(f"Cannot import {module_path} module for custom nodes:", e) + return False + +def load_custom_nodes(): + base_node_names = set(NODE_CLASS_MAPPINGS.keys()) + node_paths = ldm_patched.utils.path_utils.get_folder_paths("custom_nodes") + node_import_times = [] + for custom_node_path in node_paths: + possible_modules = os.listdir(os.path.realpath(custom_node_path)) + if "__pycache__" in possible_modules: + possible_modules.remove("__pycache__") + + for possible_module in possible_modules: + module_path = os.path.join(custom_node_path, possible_module) + if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue + if module_path.endswith(".disabled"): continue + time_before = time.perf_counter() + success = load_custom_node(module_path, base_node_names) + node_import_times.append((time.perf_counter() - time_before, module_path, success)) + + if len(node_import_times) > 0: + print("\nImport times for custom nodes:") + for n in sorted(node_import_times): + if n[2]: + import_message = "" + else: + import_message = " (IMPORT FAILED)" + print("{:6.1f} seconds{}:".format(n[0], import_message), n[1]) + print() + +def init_custom_nodes(): + extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "ldm_patched_extras") + extras_files = [ + "nodes_latent.py", + "nodes_hypernetwork.py", + "nodes_upscale_model.py", + "nodes_post_processing.py", + "nodes_mask.py", + "nodes_compositing.py", + "nodes_rebatch.py", + "nodes_model_merging.py", + "nodes_tomesd.py", + "nodes_clip_sdxl.py", + "nodes_canny.py", + "nodes_freelunch.py", + "nodes_custom_sampler.py", + "nodes_hypertile.py", + "nodes_model_advanced.py", + "nodes_model_downscale.py", + "nodes_images.py", + "nodes_video_model.py", + "nodes_sag.py", + "nodes_perpneg.py", + "nodes_stable3d.py", + "nodes_sdupscale.py", + "nodes_photomaker.py", + ] + + for node_file in extras_files: + load_custom_node(os.path.join(extras_dir, node_file)) + + load_custom_nodes() diff --git a/ldm_patched/contrib/external_canny.py b/ldm_patched/contrib/external_canny.py new file mode 100644 index 0000000000000000000000000000000000000000..7347ba1edcdf530720aa4c0689afe866391065e8 --- /dev/null +++ b/ldm_patched/contrib/external_canny.py @@ -0,0 +1,301 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +#From https://github.com/kornia/kornia +import math + +import torch +import torch.nn.functional as F +import ldm_patched.modules.model_management + +def get_canny_nms_kernel(device=None, dtype=None): + """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.""" + return torch.tensor( + [ + [[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]], + [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + [[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]], + ], + device=device, + dtype=dtype, + ) + + +def get_hysteresis_kernel(device=None, dtype=None): + """Utility function that returns the 3x3 kernels for the Canny hysteresis.""" + return torch.tensor( + [ + [[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]], + [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]], + [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]], + [[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + [[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]], + ], + device=device, + dtype=dtype, + ) + +def gaussian_blur_2d(img, kernel_size, sigma): + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + + x_kernel = pdf / pdf.sum() + x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) + + kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) + kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) + + padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] + + img = torch.nn.functional.pad(img, padding, mode="reflect") + img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3]) + + return img + +def get_sobel_kernel2d(device=None, dtype=None): + kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype) + kernel_y = kernel_x.transpose(0, 1) + return torch.stack([kernel_x, kernel_y]) + +def spatial_gradient(input, normalized: bool = True): + r"""Compute the first order image derivative in both x and y using a Sobel operator. + .. image:: _static/img/spatial_gradient.png + Args: + input: input image tensor with shape :math:`(B, C, H, W)`. + mode: derivatives modality, can be: `sobel` or `diff`. + order: the order of the derivatives. + normalized: whether the output is normalized. + Return: + the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`. + .. note:: + See a working example `here `__. + Examples: + >>> input = torch.rand(1, 3, 4, 4) + >>> output = spatial_gradient(input) # 1x3x2x4x4 + >>> output.shape + torch.Size([1, 3, 2, 4, 4]) + """ + # KORNIA_CHECK_IS_TENSOR(input) + # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) + + # allocate kernel + kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype) + if normalized: + kernel = normalize_kernel2d(kernel) + + # prepare kernel + b, c, h, w = input.shape + tmp_kernel = kernel[:, None, ...] + + # Pad with "replicate for spatial dims, but with zeros for channel + spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2] + out_channels: int = 2 + padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate') + out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1) + return out.reshape(b, c, out_channels, h, w) + +def rgb_to_grayscale(image, rgb_weights = None): + r"""Convert a RGB image to grayscale version of image. + + .. image:: _static/img/rgb_to_grayscale.png + + The image data is assumed to be in the range of (0, 1). + + Args: + image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`. + rgb_weights: Weights that will be applied on each channel (RGB). + The sum of the weights should add up to one. + Returns: + grayscale version of the image with shape :math:`(*,1,H,W)`. + + .. note:: + See a working example `here `__. + + Example: + >>> input = torch.rand(2, 3, 4, 5) + >>> gray = rgb_to_grayscale(input) # 2x1x4x5 + """ + + if len(image.shape) < 3 or image.shape[-3] != 3: + raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}") + + if rgb_weights is None: + # 8 bit images + if image.dtype == torch.uint8: + rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8) + # floating point images + elif image.dtype in (torch.float16, torch.float32, torch.float64): + rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype) + else: + raise TypeError(f"Unknown data type: {image.dtype}") + else: + # is tensor that we make sure is in the same device/dtype + rgb_weights = rgb_weights.to(image) + + # unpack the color image channels with RGB order + r: Tensor = image[..., 0:1, :, :] + g: Tensor = image[..., 1:2, :, :] + b: Tensor = image[..., 2:3, :, :] + + w_r, w_g, w_b = rgb_weights.unbind() + return w_r * r + w_g * g + w_b * b + +def canny( + input, + low_threshold = 0.1, + high_threshold = 0.2, + kernel_size = 5, + sigma = 1, + hysteresis = True, + eps = 1e-6, +): + r"""Find edges of the input image and filters them using the Canny algorithm. + .. image:: _static/img/canny.png + Args: + input: input image tensor with shape :math:`(B,C,H,W)`. + low_threshold: lower threshold for the hysteresis procedure. + high_threshold: upper threshold for the hysteresis procedure. + kernel_size: the size of the kernel for the gaussian blur. + sigma: the standard deviation of the kernel for the gaussian blur. + hysteresis: if True, applies the hysteresis edge tracking. + Otherwise, the edges are divided between weak (0.5) and strong (1) edges. + eps: regularization number to avoid NaN during backprop. + Returns: + - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`. + - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`. + .. note:: + See a working example `here `__. + Example: + >>> input = torch.rand(5, 3, 4, 4) + >>> magnitude, edges = canny(input) # 5x3x4x4 + >>> magnitude.shape + torch.Size([5, 1, 4, 4]) + >>> edges.shape + torch.Size([5, 1, 4, 4]) + """ + # KORNIA_CHECK_IS_TENSOR(input) + # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W']) + # KORNIA_CHECK( + # low_threshold <= high_threshold, + # "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: " + # f"{low_threshold}>{high_threshold}", + # ) + # KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}') + # KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}') + + device = input.device + dtype = input.dtype + + # To Grayscale + if input.shape[1] == 3: + input = rgb_to_grayscale(input) + + # Gaussian filter + blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma) + + # Compute the gradients + gradients: Tensor = spatial_gradient(blurred, normalized=False) + + # Unpack the edges + gx: Tensor = gradients[:, :, 0] + gy: Tensor = gradients[:, :, 1] + + # Compute gradient magnitude and angle + magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps) + angle: Tensor = torch.atan2(gy, gx) + + # Radians to Degrees + angle = 180.0 * angle / math.pi + + # Round angle to the nearest 45 degree + angle = torch.round(angle / 45) * 45 + + # Non-maximal suppression + nms_kernels: Tensor = get_canny_nms_kernel(device, dtype) + nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2) + + # Get the indices for both directions + positive_idx: Tensor = (angle / 45) % 8 + positive_idx = positive_idx.long() + + negative_idx: Tensor = ((angle / 45) + 4) % 8 + negative_idx = negative_idx.long() + + # Apply the non-maximum suppression to the different directions + channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx) + channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx) + + channel_select_filtered: Tensor = torch.stack( + [channel_select_filtered_positive, channel_select_filtered_negative], 1 + ) + + is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0 + + magnitude = magnitude * is_max + + # Threshold + edges: Tensor = F.threshold(magnitude, low_threshold, 0.0) + + low: Tensor = magnitude > low_threshold + high: Tensor = magnitude > high_threshold + + edges = low * 0.5 + high * 0.5 + edges = edges.to(dtype) + + # Hysteresis + if hysteresis: + edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype) + hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype) + + while ((edges_old - edges).abs() != 0).any(): + weak: Tensor = (edges == 0.5).float() + strong: Tensor = (edges == 1).float() + + hysteresis_magnitude: Tensor = F.conv2d( + edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2 + ) + hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype) + hysteresis_magnitude = hysteresis_magnitude * weak + strong + + edges_old = edges.clone() + edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5 + + edges = hysteresis_magnitude + + return magnitude, edges + + +class Canny: + @classmethod + def INPUT_TYPES(s): + return {"required": {"image": ("IMAGE",), + "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}), + "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01}) + }} + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "detect_edge" + + CATEGORY = "image/preprocessors" + + def detect_edge(self, image, low_threshold, high_threshold): + output = canny(image.to(ldm_patched.modules.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) + img_out = output[1].to(ldm_patched.modules.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) + return (img_out,) + +NODE_CLASS_MAPPINGS = { + "Canny": Canny, +} diff --git a/ldm_patched/contrib/external_clip_sdxl.py b/ldm_patched/contrib/external_clip_sdxl.py new file mode 100644 index 0000000000000000000000000000000000000000..230321a87a1344409459ed9803fda09083cb98f1 --- /dev/null +++ b/ldm_patched/contrib/external_clip_sdxl.py @@ -0,0 +1,58 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +from ldm_patched.contrib.external import MAX_RESOLUTION + +class CLIPTextEncodeSDXLRefiner: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}), + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text": ("STRING", {"multiline": True}), "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, ascore, width, height, text): + tokens = clip.tokenize(text) + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled, "aesthetic_score": ascore, "width": width,"height": height}]], ) + +class CLIPTextEncodeSDXL: + @classmethod + def INPUT_TYPES(s): + return {"required": { + "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}), + "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}), + "text_g": ("STRING", {"multiline": True, "default": "CLIP_G"}), "clip": ("CLIP", ), + "text_l": ("STRING", {"multiline": True, "default": "CLIP_L"}), "clip": ("CLIP", ), + }} + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "encode" + + CATEGORY = "advanced/conditioning" + + def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l): + tokens = clip.tokenize(text_g) + tokens["l"] = clip.tokenize(text_l)["l"] + if len(tokens["l"]) != len(tokens["g"]): + empty = clip.tokenize("") + while len(tokens["l"]) < len(tokens["g"]): + tokens["l"] += empty["l"] + while len(tokens["l"]) > len(tokens["g"]): + tokens["g"] += empty["g"] + cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True) + return ([[cond, {"pooled_output": pooled, "width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}]], ) + +NODE_CLASS_MAPPINGS = { + "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner, + "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL, +} diff --git a/ldm_patched/contrib/external_compositing.py b/ldm_patched/contrib/external_compositing.py new file mode 100644 index 0000000000000000000000000000000000000000..0cf91d9a75bb72d89925d0eb14014fbcb41e8adf --- /dev/null +++ b/ldm_patched/contrib/external_compositing.py @@ -0,0 +1,204 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import numpy as np +import torch +import ldm_patched.modules.utils +from enum import Enum + +def resize_mask(mask, shape): + return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1) + +class PorterDuffMode(Enum): + ADD = 0 + CLEAR = 1 + DARKEN = 2 + DST = 3 + DST_ATOP = 4 + DST_IN = 5 + DST_OUT = 6 + DST_OVER = 7 + LIGHTEN = 8 + MULTIPLY = 9 + OVERLAY = 10 + SCREEN = 11 + SRC = 12 + SRC_ATOP = 13 + SRC_IN = 14 + SRC_OUT = 15 + SRC_OVER = 16 + XOR = 17 + + +def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode): + if mode == PorterDuffMode.ADD: + out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1) + out_image = torch.clamp(src_image + dst_image, 0, 1) + elif mode == PorterDuffMode.CLEAR: + out_alpha = torch.zeros_like(dst_alpha) + out_image = torch.zeros_like(dst_image) + elif mode == PorterDuffMode.DARKEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image) + elif mode == PorterDuffMode.DST: + out_alpha = dst_alpha + out_image = dst_image + elif mode == PorterDuffMode.DST_ATOP: + out_alpha = src_alpha + out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.DST_IN: + out_alpha = src_alpha * dst_alpha + out_image = dst_image * src_alpha + elif mode == PorterDuffMode.DST_OUT: + out_alpha = (1 - src_alpha) * dst_alpha + out_image = (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.DST_OVER: + out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha + out_image = dst_image + (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.LIGHTEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image) + elif mode == PorterDuffMode.MULTIPLY: + out_alpha = src_alpha * dst_alpha + out_image = src_image * dst_image + elif mode == PorterDuffMode.OVERLAY: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image, + src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image)) + elif mode == PorterDuffMode.SCREEN: + out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha + out_image = src_image + dst_image - src_image * dst_image + elif mode == PorterDuffMode.SRC: + out_alpha = src_alpha + out_image = src_image + elif mode == PorterDuffMode.SRC_ATOP: + out_alpha = dst_alpha + out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.SRC_IN: + out_alpha = src_alpha * dst_alpha + out_image = src_image * dst_alpha + elif mode == PorterDuffMode.SRC_OUT: + out_alpha = (1 - dst_alpha) * src_alpha + out_image = (1 - dst_alpha) * src_image + elif mode == PorterDuffMode.SRC_OVER: + out_alpha = src_alpha + (1 - src_alpha) * dst_alpha + out_image = src_image + (1 - src_alpha) * dst_image + elif mode == PorterDuffMode.XOR: + out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha + out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + else: + out_alpha = None + out_image = None + return out_image, out_alpha + + +class PorterDuffImageComposite: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "source": ("IMAGE",), + "source_alpha": ("MASK",), + "destination": ("IMAGE",), + "destination_alpha": ("MASK",), + "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}), + }, + } + + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "composite" + CATEGORY = "mask/compositing" + + def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode): + batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha)) + out_images = [] + out_alphas = [] + + for i in range(batch_size): + src_image = source[i] + dst_image = destination[i] + + assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels + + src_alpha = source_alpha[i].unsqueeze(2) + dst_alpha = destination_alpha[i].unsqueeze(2) + + if dst_alpha.shape[:2] != dst_image.shape[:2]: + upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) + if src_image.shape != dst_image.shape: + upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center') + src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0) + if src_alpha.shape != dst_alpha.shape: + upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2) + upscale_output = ldm_patched.modules.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center') + src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0) + + out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode]) + + out_images.append(out_image) + out_alphas.append(out_alpha.squeeze(2)) + + result = (torch.stack(out_images), torch.stack(out_alphas)) + return result + + +class SplitImageWithAlpha: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + } + } + + CATEGORY = "mask/compositing" + RETURN_TYPES = ("IMAGE", "MASK") + FUNCTION = "split_image_with_alpha" + + def split_image_with_alpha(self, image: torch.Tensor): + out_images = [i[:,:,:3] for i in image] + out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image] + result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas)) + return result + + +class JoinImageWithAlpha: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "alpha": ("MASK",), + } + } + + CATEGORY = "mask/compositing" + RETURN_TYPES = ("IMAGE",) + FUNCTION = "join_image_with_alpha" + + def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor): + batch_size = min(len(image), len(alpha)) + out_images = [] + + alpha = 1.0 - resize_mask(alpha, image.shape[1:]) + for i in range(batch_size): + out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2)) + + result = (torch.stack(out_images),) + return result + + +NODE_CLASS_MAPPINGS = { + "PorterDuffImageComposite": PorterDuffImageComposite, + "SplitImageWithAlpha": SplitImageWithAlpha, + "JoinImageWithAlpha": JoinImageWithAlpha, +} + + +NODE_DISPLAY_NAME_MAPPINGS = { + "PorterDuffImageComposite": "Porter-Duff Image Composite", + "SplitImageWithAlpha": "Split Image with Alpha", + "JoinImageWithAlpha": "Join Image with Alpha", +} diff --git a/ldm_patched/contrib/external_custom_sampler.py b/ldm_patched/contrib/external_custom_sampler.py new file mode 100644 index 0000000000000000000000000000000000000000..8f92e841f4d1a3741c970f05392be5758be79d33 --- /dev/null +++ b/ldm_patched/contrib/external_custom_sampler.py @@ -0,0 +1,297 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.modules.samplers +import ldm_patched.modules.sample +from ldm_patched.k_diffusion import sampling as k_diffusion_sampling +import ldm_patched.utils.latent_visualization +import torch +import ldm_patched.modules.utils + + +class BasicScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "scheduler": (ldm_patched.modules.samplers.SCHEDULER_NAMES, ), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, scheduler, steps, denoise): + total_steps = steps + if denoise < 1.0: + total_steps = int(steps/denoise) + + ldm_patched.modules.model_management.load_models_gpu([model]) + sigmas = ldm_patched.modules.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu() + sigmas = sigmas[-(steps + 1):] + return (sigmas, ) + + +class KarrasScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, rho): + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) + return (sigmas, ) + +class ExponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min): + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max) + return (sigmas, ) + +class PolyexponentialScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, sigma_max, sigma_min, rho): + sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho) + return (sigmas, ) + +class SDTurboScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "steps": ("INT", {"default": 1, "min": 1, "max": 10}), + "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, model, steps, denoise): + start_step = 10 - int(10 * denoise) + timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps] + ldm_patched.modules.model_management.load_models_gpu([model]) + sigmas = model.model.model_sampling.sigma(timesteps) + sigmas = torch.cat([sigmas, sigmas.new_zeros([1])]) + return (sigmas, ) + +class VPScheduler: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), #TODO: fix default values + "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), + "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/schedulers" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, steps, beta_d, beta_min, eps_s): + sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s) + return (sigmas, ) + +class SplitSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + "step": ("INT", {"default": 0, "min": 0, "max": 10000}), + } + } + RETURN_TYPES = ("SIGMAS","SIGMAS") + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas, step): + sigmas1 = sigmas[:step + 1] + sigmas2 = sigmas[step:] + return (sigmas1, sigmas2) + +class FlipSigmas: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sigmas": ("SIGMAS", ), + } + } + RETURN_TYPES = ("SIGMAS",) + CATEGORY = "sampling/custom_sampling/sigmas" + + FUNCTION = "get_sigmas" + + def get_sigmas(self, sigmas): + sigmas = sigmas.flip(0) + if sigmas[0] == 0: + sigmas[0] = 0.0001 + return (sigmas,) + +class KSamplerSelect: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"sampler_name": (ldm_patched.modules.samplers.SAMPLER_NAMES, ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, sampler_name): + sampler = ldm_patched.modules.samplers.sampler_object(sampler_name) + return (sampler, ) + +class SamplerDPMPP_2M_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"solver_type": (['midpoint', 'heun'], ), + "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, solver_type, eta, s_noise, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_2m_sde" + else: + sampler_name = "dpmpp_2m_sde_gpu" + sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type}) + return (sampler, ) + + +class SamplerDPMPP_SDE: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}), + "noise_device": (['gpu', 'cpu'], ), + } + } + RETURN_TYPES = ("SAMPLER",) + CATEGORY = "sampling/custom_sampling/samplers" + + FUNCTION = "get_sampler" + + def get_sampler(self, eta, s_noise, r, noise_device): + if noise_device == 'cpu': + sampler_name = "dpmpp_sde" + else: + sampler_name = "dpmpp_sde_gpu" + sampler = ldm_patched.modules.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r}) + return (sampler, ) + +class SamplerCustom: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"model": ("MODEL",), + "add_noise": ("BOOLEAN", {"default": True}), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "positive": ("CONDITIONING", ), + "negative": ("CONDITIONING", ), + "sampler": ("SAMPLER", ), + "sigmas": ("SIGMAS", ), + "latent_image": ("LATENT", ), + } + } + + RETURN_TYPES = ("LATENT","LATENT") + RETURN_NAMES = ("output", "denoised_output") + + FUNCTION = "sample" + + CATEGORY = "sampling/custom_sampling" + + def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image): + latent = latent_image + latent_image = latent["samples"] + if not add_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = ldm_patched.modules.sample.prepare_noise(latent_image, noise_seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + x0_output = {} + callback = ldm_patched.utils.latent_visualization.prepare_callback(model, sigmas.shape[-1] - 1, x0_output) + + disable_pbar = not ldm_patched.modules.utils.PROGRESS_BAR_ENABLED + samples = ldm_patched.modules.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed) + + out = latent.copy() + out["samples"] = samples + if "x0" in x0_output: + out_denoised = latent.copy() + out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu()) + else: + out_denoised = out + return (out, out_denoised) + +NODE_CLASS_MAPPINGS = { + "SamplerCustom": SamplerCustom, + "BasicScheduler": BasicScheduler, + "KarrasScheduler": KarrasScheduler, + "ExponentialScheduler": ExponentialScheduler, + "PolyexponentialScheduler": PolyexponentialScheduler, + "VPScheduler": VPScheduler, + "SDTurboScheduler": SDTurboScheduler, + "KSamplerSelect": KSamplerSelect, + "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE, + "SamplerDPMPP_SDE": SamplerDPMPP_SDE, + "SplitSigmas": SplitSigmas, + "FlipSigmas": FlipSigmas, +} diff --git a/ldm_patched/contrib/external_freelunch.py b/ldm_patched/contrib/external_freelunch.py new file mode 100644 index 0000000000000000000000000000000000000000..59ec5babd632d7804b9c34442729910f0cb338d2 --- /dev/null +++ b/ldm_patched/contrib/external_freelunch.py @@ -0,0 +1,115 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +#code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License) + +import torch + + +def Fourier_filter(x, threshold, scale): + # FFT + x_freq = torch.fft.fftn(x.float(), dim=(-2, -1)) + x_freq = torch.fft.fftshift(x_freq, dim=(-2, -1)) + + B, C, H, W = x_freq.shape + mask = torch.ones((B, C, H, W), device=x.device) + + crow, ccol = H // 2, W //2 + mask[..., crow - threshold:crow + threshold, ccol - threshold:ccol + threshold] = scale + x_freq = x_freq * mask + + # IFFT + x_freq = torch.fft.ifftshift(x_freq, dim=(-2, -1)) + x_filtered = torch.fft.ifftn(x_freq, dim=(-2, -1)).real + + return x_filtered.to(x.dtype) + + +class FreeU: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "b1": ("FLOAT", {"default": 1.1, "min": 0.0, "max": 10.0, "step": 0.01}), + "b2": ("FLOAT", {"default": 1.2, "min": 0.0, "max": 10.0, "step": 0.01}), + "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), + "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches" + + def patch(self, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(h.shape[1], None) + if scale is not None: + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0] + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except: + print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +class FreeU_V2: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "b1": ("FLOAT", {"default": 1.3, "min": 0.0, "max": 10.0, "step": 0.01}), + "b2": ("FLOAT", {"default": 1.4, "min": 0.0, "max": 10.0, "step": 0.01}), + "s1": ("FLOAT", {"default": 0.9, "min": 0.0, "max": 10.0, "step": 0.01}), + "s2": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches" + + def patch(self, model, b1, b2, s1, s2): + model_channels = model.model.model_config.unet_config["model_channels"] + scale_dict = {model_channels * 4: (b1, s1), model_channels * 2: (b2, s2)} + on_cpu_devices = {} + + def output_block_patch(h, hsp, transformer_options): + scale = scale_dict.get(h.shape[1], None) + if scale is not None: + hidden_mean = h.mean(1).unsqueeze(1) + B = hidden_mean.shape[0] + hidden_max, _ = torch.max(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_min, _ = torch.min(hidden_mean.view(B, -1), dim=-1, keepdim=True) + hidden_mean = (hidden_mean - hidden_min.unsqueeze(2).unsqueeze(3)) / (hidden_max - hidden_min).unsqueeze(2).unsqueeze(3) + + h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * ((scale[0] - 1 ) * hidden_mean + 1) + + if hsp.device not in on_cpu_devices: + try: + hsp = Fourier_filter(hsp, threshold=1, scale=scale[1]) + except: + print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.") + on_cpu_devices[hsp.device] = True + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + else: + hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device) + + return h, hsp + + m = model.clone() + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "FreeU": FreeU, + "FreeU_V2": FreeU_V2, +} diff --git a/ldm_patched/contrib/external_hypernetwork.py b/ldm_patched/contrib/external_hypernetwork.py new file mode 100644 index 0000000000000000000000000000000000000000..17aaacb009d5d2e8de8af547645ef6a082b9da46 --- /dev/null +++ b/ldm_patched/contrib/external_hypernetwork.py @@ -0,0 +1,121 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.modules.utils +import ldm_patched.utils.path_utils +import torch + +def load_hypernetwork_patch(path, strength): + sd = ldm_patched.modules.utils.load_torch_file(path, safe_load=True) + activation_func = sd.get('activation_func', 'linear') + is_layer_norm = sd.get('is_layer_norm', False) + use_dropout = sd.get('use_dropout', False) + activate_output = sd.get('activate_output', False) + last_layer_dropout = sd.get('last_layer_dropout', False) + + valid_activation = { + "linear": torch.nn.Identity, + "relu": torch.nn.ReLU, + "leakyrelu": torch.nn.LeakyReLU, + "elu": torch.nn.ELU, + "swish": torch.nn.Hardswish, + "tanh": torch.nn.Tanh, + "sigmoid": torch.nn.Sigmoid, + "softsign": torch.nn.Softsign, + "mish": torch.nn.Mish, + } + + if activation_func not in valid_activation: + print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout) + return None + + out = {} + + for d in sd: + try: + dim = int(d) + except: + continue + + output = [] + for index in [0, 1]: + attn_weights = sd[dim][index] + keys = attn_weights.keys() + + linears = filter(lambda a: a.endswith(".weight"), keys) + linears = list(map(lambda a: a[:-len(".weight")], linears)) + layers = [] + + i = 0 + while i < len(linears): + lin_name = linears[i] + last_layer = (i == (len(linears) - 1)) + penultimate_layer = (i == (len(linears) - 2)) + + lin_weight = attn_weights['{}.weight'.format(lin_name)] + lin_bias = attn_weights['{}.bias'.format(lin_name)] + layer = torch.nn.Linear(lin_weight.shape[1], lin_weight.shape[0]) + layer.load_state_dict({"weight": lin_weight, "bias": lin_bias}) + layers.append(layer) + if activation_func != "linear": + if (not last_layer) or (activate_output): + layers.append(valid_activation[activation_func]()) + if is_layer_norm: + i += 1 + ln_name = linears[i] + ln_weight = attn_weights['{}.weight'.format(ln_name)] + ln_bias = attn_weights['{}.bias'.format(ln_name)] + ln = torch.nn.LayerNorm(ln_weight.shape[0]) + ln.load_state_dict({"weight": ln_weight, "bias": ln_bias}) + layers.append(ln) + if use_dropout: + if (not last_layer) and (not penultimate_layer or last_layer_dropout): + layers.append(torch.nn.Dropout(p=0.3)) + i += 1 + + output.append(torch.nn.Sequential(*layers)) + out[dim] = torch.nn.ModuleList(output) + + class hypernetwork_patch: + def __init__(self, hypernet, strength): + self.hypernet = hypernet + self.strength = strength + def __call__(self, q, k, v, extra_options): + dim = k.shape[-1] + if dim in self.hypernet: + hn = self.hypernet[dim] + k = k + hn[0](k) * self.strength + v = v + hn[1](v) * self.strength + + return q, k, v + + def to(self, device): + for d in self.hypernet.keys(): + self.hypernet[d] = self.hypernet[d].to(device) + return self + + return hypernetwork_patch(out, strength) + +class HypernetworkLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "hypernetwork_name": (ldm_patched.utils.path_utils.get_filename_list("hypernetworks"), ), + "strength": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_hypernetwork" + + CATEGORY = "loaders" + + def load_hypernetwork(self, model, hypernetwork_name, strength): + hypernetwork_path = ldm_patched.utils.path_utils.get_full_path("hypernetworks", hypernetwork_name) + model_hypernetwork = model.clone() + patch = load_hypernetwork_patch(hypernetwork_path, strength) + if patch is not None: + model_hypernetwork.set_model_attn1_patch(patch) + model_hypernetwork.set_model_attn2_patch(patch) + return (model_hypernetwork,) + +NODE_CLASS_MAPPINGS = { + "HypernetworkLoader": HypernetworkLoader +} diff --git a/ldm_patched/contrib/external_hypertile.py b/ldm_patched/contrib/external_hypertile.py new file mode 100644 index 0000000000000000000000000000000000000000..5cf7d9d6d1609b6a02a89a8a74aca9fdccaeda58 --- /dev/null +++ b/ldm_patched/contrib/external_hypertile.py @@ -0,0 +1,85 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +#Taken from: https://github.com/tfernd/HyperTile/ + +import math +from einops import rearrange +# Use torch rng for consistency across generations +from torch import randint + +def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int: + min_value = min(min_value, value) + + # All big divisors of value (inclusive) + divisors = [i for i in range(min_value, value + 1) if value % i == 0] + + ns = [value // i for i in divisors[:max_options]] # has at least 1 element + + if len(ns) - 1 > 0: + idx = randint(low=0, high=len(ns) - 1, size=(1,)).item() + else: + idx = 0 + + return ns[idx] + +class HyperTile: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}), + "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}), + "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}), + "scale_depth": ("BOOLEAN", {"default": False}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "model_patches" + + def patch(self, model, tile_size, swap_size, max_depth, scale_depth): + model_channels = model.model.model_config.unet_config["model_channels"] + + latent_tile_size = max(32, tile_size) // 8 + self.temp = None + + def hypertile_in(q, k, v, extra_options): + model_chans = q.shape[-2] + orig_shape = extra_options['original_shape'] + apply_to = [] + for i in range(max_depth + 1): + apply_to.append((orig_shape[-2] / (2 ** i)) * (orig_shape[-1] / (2 ** i))) + + if model_chans in apply_to: + shape = extra_options["original_shape"] + aspect_ratio = shape[-1] / shape[-2] + + hw = q.size(1) + h, w = round(math.sqrt(hw * aspect_ratio)), round(math.sqrt(hw / aspect_ratio)) + + factor = (2 ** apply_to.index(model_chans)) if scale_depth else 1 + nh = random_divisor(h, latent_tile_size * factor, swap_size) + nw = random_divisor(w, latent_tile_size * factor, swap_size) + + if nh * nw > 1: + q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw) + self.temp = (nh, nw, h, w) + return q, k, v + + return q, k, v + def hypertile_out(out, extra_options): + if self.temp is not None: + nh, nw, h, w = self.temp + self.temp = None + out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw) + out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw) + return out + + + m = model.clone() + m.set_model_attn1_patch(hypertile_in) + m.set_model_attn1_output_patch(hypertile_out) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "HyperTile": HyperTile, +} diff --git a/ldm_patched/contrib/external_images.py b/ldm_patched/contrib/external_images.py new file mode 100644 index 0000000000000000000000000000000000000000..17e9c4978101644f66e7db18fbb4e5d00d29bcd2 --- /dev/null +++ b/ldm_patched/contrib/external_images.py @@ -0,0 +1,177 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.contrib.external +import ldm_patched.utils.path_utils +from ldm_patched.modules.args_parser import args + +from PIL import Image +from PIL.PngImagePlugin import PngInfo + +import numpy as np +import json +import os + +MAX_RESOLUTION = ldm_patched.contrib.external.MAX_RESOLUTION + +class ImageCrop: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "crop" + + CATEGORY = "image/transform" + + def crop(self, image, width, height, x, y): + x = min(x, image.shape[2] - 1) + y = min(y, image.shape[1] - 1) + to_x = width + x + to_y = height + y + img = image[:,y:to_y, x:to_x, :] + return (img,) + +class RepeatImageBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), + "amount": ("INT", {"default": 1, "min": 1, "max": 64}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "repeat" + + CATEGORY = "image/batch" + + def repeat(self, image, amount): + s = image.repeat((amount, 1,1,1)) + return (s,) + +class SaveAnimatedWEBP: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + self.type = "output" + self.prefix_append = "" + + methods = {"default": 4, "fastest": 0, "slowest": 6} + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ldm_patched"}), + "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "lossless": ("BOOLEAN", {"default": True}), + "quality": ("INT", {"default": 80, "min": 0, "max": 100}), + "method": (list(s.methods.keys()),), + # "num_frames": ("INT", {"default": 0, "min": 0, "max": 8192}), + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image/animation" + + def save_images(self, images, fps, filename_prefix, lossless, quality, method, num_frames=0, prompt=None, extra_pnginfo=None): + method = self.methods.get(method) + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + pil_images = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + metadata = pil_images[0].getexif() + if not args.disable_server_info: + if prompt is not None: + metadata[0x0110] = "prompt:{}".format(json.dumps(prompt)) + if extra_pnginfo is not None: + inital_exif = 0x010f + for x in extra_pnginfo: + metadata[inital_exif] = "{}:{}".format(x, json.dumps(extra_pnginfo[x])) + inital_exif -= 1 + + if num_frames == 0: + num_frames = len(pil_images) + + c = len(pil_images) + for i in range(0, c, num_frames): + file = f"{filename}_{counter:05}_.webp" + pil_images[i].save(os.path.join(full_output_folder, file), save_all=True, duration=int(1000.0/fps), append_images=pil_images[i + 1:i + num_frames], exif=metadata, lossless=lossless, quality=quality, method=method) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + counter += 1 + + animated = num_frames != 1 + return { "ui": { "images": results, "animated": (animated,) } } + +class SaveAnimatedPNG: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + self.type = "output" + self.prefix_append = "" + + @classmethod + def INPUT_TYPES(s): + return {"required": + {"images": ("IMAGE", ), + "filename_prefix": ("STRING", {"default": "ldm_patched"}), + "fps": ("FLOAT", {"default": 6.0, "min": 0.01, "max": 1000.0, "step": 0.01}), + "compress_level": ("INT", {"default": 4, "min": 0, "max": 9}) + }, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"}, + } + + RETURN_TYPES = () + FUNCTION = "save_images" + + OUTPUT_NODE = True + + CATEGORY = "image/animation" + + def save_images(self, images, fps, compress_level, filename_prefix="ldm_patched", prompt=None, extra_pnginfo=None): + filename_prefix += self.prefix_append + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]) + results = list() + pil_images = [] + for image in images: + i = 255. * image.cpu().numpy() + img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8)) + pil_images.append(img) + + metadata = None + if not args.disable_server_info: + metadata = PngInfo() + if prompt is not None: + metadata.add(b"ldm_patched", "prompt".encode("latin-1", "strict") + b"\0" + json.dumps(prompt).encode("latin-1", "strict"), after_idat=True) + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata.add(b"ldm_patched", x.encode("latin-1", "strict") + b"\0" + json.dumps(extra_pnginfo[x]).encode("latin-1", "strict"), after_idat=True) + + file = f"{filename}_{counter:05}_.png" + pil_images[0].save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=compress_level, save_all=True, duration=int(1000.0/fps), append_images=pil_images[1:]) + results.append({ + "filename": file, + "subfolder": subfolder, + "type": self.type + }) + + return { "ui": { "images": results, "animated": (True,)} } + +NODE_CLASS_MAPPINGS = { + "ImageCrop": ImageCrop, + "RepeatImageBatch": RepeatImageBatch, + "SaveAnimatedWEBP": SaveAnimatedWEBP, + "SaveAnimatedPNG": SaveAnimatedPNG, +} diff --git a/ldm_patched/contrib/external_latent.py b/ldm_patched/contrib/external_latent.py new file mode 100644 index 0000000000000000000000000000000000000000..6d753d0f7cfb019a1c84fe487e6a0890d903abbd --- /dev/null +++ b/ldm_patched/contrib/external_latent.py @@ -0,0 +1,157 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.modules.utils +import torch + +def reshape_latent_to(target_shape, latent): + if latent.shape[1:] != target_shape[1:]: + latent = ldm_patched.modules.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center") + return ldm_patched.modules.utils.repeat_to_batch_size(latent, target_shape[0]) + + +class LatentAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 + s2 + return (samples_out,) + +class LatentSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + samples_out["samples"] = s1 - s2 + return (samples_out,) + +class LatentMultiply: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples, multiplier): + samples_out = samples.copy() + + s1 = samples["samples"] + samples_out["samples"] = s1 * multiplier + return (samples_out,) + +class LatentInterpolate: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), + "samples2": ("LATENT",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples1, samples2, ratio): + samples_out = samples1.copy() + + s1 = samples1["samples"] + s2 = samples2["samples"] + + s2 = reshape_latent_to(s1.shape, s2) + + m1 = torch.linalg.vector_norm(s1, dim=(1)) + m2 = torch.linalg.vector_norm(s2, dim=(1)) + + s1 = torch.nan_to_num(s1 / m1) + s2 = torch.nan_to_num(s2 / m2) + + t = (s1 * ratio + s2 * (1.0 - ratio)) + mt = torch.linalg.vector_norm(t, dim=(1)) + st = torch.nan_to_num(t / mt) + + samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio)) + return (samples_out,) + +class LatentBatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "batch" + + CATEGORY = "latent/batch" + + def batch(self, samples1, samples2): + samples_out = samples1.copy() + s1 = samples1["samples"] + s2 = samples2["samples"] + + if s1.shape[1:] != s2.shape[1:]: + s2 = ldm_patched.modules.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center") + s = torch.cat((s1, s2), dim=0) + samples_out["samples"] = s + samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])]) + return (samples_out,) + +class LatentBatchSeedBehavior: + @classmethod + def INPUT_TYPES(s): + return {"required": { "samples": ("LATENT",), + "seed_behavior": (["random", "fixed"],),}} + + RETURN_TYPES = ("LATENT",) + FUNCTION = "op" + + CATEGORY = "latent/advanced" + + def op(self, samples, seed_behavior): + samples_out = samples.copy() + latent = samples["samples"] + if seed_behavior == "random": + if 'batch_index' in samples_out: + samples_out.pop('batch_index') + elif seed_behavior == "fixed": + batch_number = samples_out.get("batch_index", [0])[0] + samples_out["batch_index"] = [batch_number] * latent.shape[0] + + return (samples_out,) + +NODE_CLASS_MAPPINGS = { + "LatentAdd": LatentAdd, + "LatentSubtract": LatentSubtract, + "LatentMultiply": LatentMultiply, + "LatentInterpolate": LatentInterpolate, + "LatentBatch": LatentBatch, + "LatentBatchSeedBehavior": LatentBatchSeedBehavior, +} diff --git a/ldm_patched/contrib/external_mask.py b/ldm_patched/contrib/external_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..a86a7fe69b67f1df14672bd480060142d4385ed6 --- /dev/null +++ b/ldm_patched/contrib/external_mask.py @@ -0,0 +1,365 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import numpy as np +import scipy.ndimage +import torch +import ldm_patched.modules.utils + +from ldm_patched.contrib.external import MAX_RESOLUTION + +def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False): + source = source.to(destination.device) + if resize_source: + source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear") + + source = ldm_patched.modules.utils.repeat_to_batch_size(source, destination.shape[0]) + + x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier)) + y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier)) + + left, top = (x // multiplier, y // multiplier) + right, bottom = (left + source.shape[3], top + source.shape[2],) + + if mask is None: + mask = torch.ones_like(source) + else: + mask = mask.to(destination.device, copy=True) + mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear") + mask = ldm_patched.modules.utils.repeat_to_batch_size(mask, source.shape[0]) + + # calculate the bounds of the source that will be overlapping the destination + # this prevents the source trying to overwrite latent pixels that are out of bounds + # of the destination + visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),) + + mask = mask[:, :, :visible_height, :visible_width] + inverse_mask = torch.ones_like(mask) - mask + + source_portion = mask * source[:, :, :visible_height, :visible_width] + destination_portion = inverse_mask * destination[:, :, top:bottom, left:right] + + destination[:, :, top:bottom, left:right] = source_portion + destination_portion + return destination + +class LatentCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("LATENT",), + "source": ("LATENT",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("LATENT",) + FUNCTION = "composite" + + CATEGORY = "latent" + + def composite(self, destination, source, x, y, resize_source, mask = None): + output = destination.copy() + destination = destination["samples"].clone() + source = source["samples"] + output["samples"] = composite(destination, source, x, y, mask, 8, resize_source) + return (output,) + +class ImageCompositeMasked: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "destination": ("IMAGE",), + "source": ("IMAGE",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "resize_source": ("BOOLEAN", {"default": False}), + }, + "optional": { + "mask": ("MASK",), + } + } + RETURN_TYPES = ("IMAGE",) + FUNCTION = "composite" + + CATEGORY = "image" + + def composite(self, destination, source, x, y, resize_source, mask = None): + destination = destination.clone().movedim(-1, 1) + output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1) + return (output,) + +class MaskToImage: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "mask_to_image" + + def mask_to_image(self, mask): + result = mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) + return (result,) + +class ImageToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "channel": (["red", "green", "blue", "alpha"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, channel): + channels = ["red", "green", "blue", "alpha"] + mask = image[:, :, :, channels.index(channel)] + return (mask,) + +class ImageColorToMask: + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "color": ("INT", {"default": 0, "min": 0, "max": 0xFFFFFF, "step": 1, "display": "color"}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + FUNCTION = "image_to_mask" + + def image_to_mask(self, image, color): + temp = (torch.clamp(image, 0, 1.0) * 255.0).round().to(torch.int) + temp = torch.bitwise_left_shift(temp[:,:,:,0], 16) + torch.bitwise_left_shift(temp[:,:,:,1], 8) + temp[:,:,:,2] + mask = torch.where(temp == color, 255, 0).float() + return (mask,) + +class SolidMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "solid" + + def solid(self, value, width, height): + out = torch.full((1, height, width), value, dtype=torch.float32, device="cpu") + return (out,) + +class InvertMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "invert" + + def invert(self, mask): + out = 1.0 - mask + return (out,) + +class CropMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "crop" + + def crop(self, mask, x, y, width, height): + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = mask[:, y:y + height, x:x + width] + return (out,) + +class MaskComposite: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "destination": ("MASK",), + "source": ("MASK",), + "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "operation": (["multiply", "add", "subtract", "and", "or", "xor"],), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "combine" + + def combine(self, destination, source, x, y, operation): + output = destination.reshape((-1, destination.shape[-2], destination.shape[-1])).clone() + source = source.reshape((-1, source.shape[-2], source.shape[-1])) + + left, top = (x, y,) + right, bottom = (min(left + source.shape[-1], destination.shape[-1]), min(top + source.shape[-2], destination.shape[-2])) + visible_width, visible_height = (right - left, bottom - top,) + + source_portion = source[:, :visible_height, :visible_width] + destination_portion = destination[:, top:bottom, left:right] + + if operation == "multiply": + output[:, top:bottom, left:right] = destination_portion * source_portion + elif operation == "add": + output[:, top:bottom, left:right] = destination_portion + source_portion + elif operation == "subtract": + output[:, top:bottom, left:right] = destination_portion - source_portion + elif operation == "and": + output[:, top:bottom, left:right] = torch.bitwise_and(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "or": + output[:, top:bottom, left:right] = torch.bitwise_or(destination_portion.round().bool(), source_portion.round().bool()).float() + elif operation == "xor": + output[:, top:bottom, left:right] = torch.bitwise_xor(destination_portion.round().bool(), source_portion.round().bool()).float() + + output = torch.clamp(output, 0.0, 1.0) + + return (output,) + +class FeatherMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}), + } + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "feather" + + def feather(self, mask, left, top, right, bottom): + output = mask.reshape((-1, mask.shape[-2], mask.shape[-1])).clone() + + left = min(left, output.shape[-1]) + right = min(right, output.shape[-1]) + top = min(top, output.shape[-2]) + bottom = min(bottom, output.shape[-2]) + + for x in range(left): + feather_rate = (x + 1.0) / left + output[:, :, x] *= feather_rate + + for x in range(right): + feather_rate = (x + 1) / right + output[:, :, -x] *= feather_rate + + for y in range(top): + feather_rate = (y + 1) / top + output[:, y, :] *= feather_rate + + for y in range(bottom): + feather_rate = (y + 1) / bottom + output[:, -y, :] *= feather_rate + + return (output,) + +class GrowMask: + @classmethod + def INPUT_TYPES(cls): + return { + "required": { + "mask": ("MASK",), + "expand": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1}), + "tapered_corners": ("BOOLEAN", {"default": True}), + }, + } + + CATEGORY = "mask" + + RETURN_TYPES = ("MASK",) + + FUNCTION = "expand_mask" + + def expand_mask(self, mask, expand, tapered_corners): + c = 0 if tapered_corners else 1 + kernel = np.array([[c, 1, c], + [1, 1, 1], + [c, 1, c]]) + mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1])) + out = [] + for m in mask: + output = m.numpy() + for _ in range(abs(expand)): + if expand < 0: + output = scipy.ndimage.grey_erosion(output, footprint=kernel) + else: + output = scipy.ndimage.grey_dilation(output, footprint=kernel) + output = torch.from_numpy(output) + out.append(output) + return (torch.stack(out, dim=0),) + + + +NODE_CLASS_MAPPINGS = { + "LatentCompositeMasked": LatentCompositeMasked, + "ImageCompositeMasked": ImageCompositeMasked, + "MaskToImage": MaskToImage, + "ImageToMask": ImageToMask, + "ImageColorToMask": ImageColorToMask, + "SolidMask": SolidMask, + "InvertMask": InvertMask, + "CropMask": CropMask, + "MaskComposite": MaskComposite, + "FeatherMask": FeatherMask, + "GrowMask": GrowMask, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageToMask": "Convert Image to Mask", + "MaskToImage": "Convert Mask to Image", +} diff --git a/ldm_patched/contrib/external_model_advanced.py b/ldm_patched/contrib/external_model_advanced.py new file mode 100644 index 0000000000000000000000000000000000000000..03a2f0454c74885371e7d9cc843c53a6f8bbb830 --- /dev/null +++ b/ldm_patched/contrib/external_model_advanced.py @@ -0,0 +1,177 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.utils.path_utils +import ldm_patched.modules.sd +import ldm_patched.modules.model_sampling +import torch + +class LCM(ldm_patched.modules.model_sampling.EPS): + def calculate_denoised(self, sigma, model_output, model_input): + timestep = self.timestep(sigma).view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + x0 = model_input - model_output * sigma + + sigma_data = 0.5 + scaled_timestep = timestep * 10.0 #timestep_scaling + + c_skip = sigma_data**2 / (scaled_timestep**2 + sigma_data**2) + c_out = scaled_timestep / (scaled_timestep**2 + sigma_data**2) ** 0.5 + + return c_out * x0 + c_skip * model_input + +class ModelSamplingDiscreteDistilled(ldm_patched.modules.model_sampling.ModelSamplingDiscrete): + original_timesteps = 50 + + def __init__(self, model_config=None): + super().__init__(model_config) + + self.skip_steps = self.num_timesteps // self.original_timesteps + + sigmas_valid = torch.zeros((self.original_timesteps), dtype=torch.float32) + for x in range(self.original_timesteps): + sigmas_valid[self.original_timesteps - 1 - x] = self.sigmas[self.num_timesteps - 1 - x * self.skip_steps] + + self.set_sigmas(sigmas_valid) + + def timestep(self, sigma): + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return (dists.abs().argmin(dim=0).view(sigma.shape) * self.skip_steps + (self.skip_steps - 1)).to(sigma.device) + + def sigma(self, timestep): + t = torch.clamp(((timestep.float().to(self.log_sigmas.device) - (self.skip_steps - 1)) / self.skip_steps).float(), min=0, max=(len(self.sigmas) - 1)) + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp().to(timestep.device) + + +def rescale_zero_terminal_snr_sigmas(sigmas): + alphas_cumprod = 1 / ((sigmas * sigmas) + 1) + alphas_bar_sqrt = alphas_cumprod.sqrt() + + # Store old values. + alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone() + alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone() + + # Shift so the last timestep is zero. + alphas_bar_sqrt -= (alphas_bar_sqrt_T) + + # Scale so the first timestep is back to the old value. + alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T) + + # Convert alphas_bar_sqrt to betas + alphas_bar = alphas_bar_sqrt**2 # Revert sqrt + alphas_bar[-1] = 4.8973451890853435e-08 + return ((1 - alphas_bar) / alphas_bar) ** 0.5 + +class ModelSamplingDiscrete: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["eps", "v_prediction", "lcm"],), + "zsnr": ("BOOLEAN", {"default": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, zsnr): + m = model.clone() + + sampling_base = ldm_patched.modules.model_sampling.ModelSamplingDiscrete + if sampling == "eps": + sampling_type = ldm_patched.modules.model_sampling.EPS + elif sampling == "v_prediction": + sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION + elif sampling == "lcm": + sampling_type = LCM + sampling_base = ModelSamplingDiscreteDistilled + + class ModelSamplingAdvanced(sampling_base, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + if zsnr: + model_sampling.set_sigmas(rescale_zero_terminal_snr_sigmas(model_sampling.sigmas)) + + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class ModelSamplingContinuousEDM: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "sampling": (["v_prediction", "eps"],), + "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}), + }} + + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, sampling, sigma_max, sigma_min): + m = model.clone() + + if sampling == "eps": + sampling_type = ldm_patched.modules.model_sampling.EPS + elif sampling == "v_prediction": + sampling_type = ldm_patched.modules.model_sampling.V_PREDICTION + + class ModelSamplingAdvanced(ldm_patched.modules.model_sampling.ModelSamplingContinuousEDM, sampling_type): + pass + + model_sampling = ModelSamplingAdvanced(model.model.model_config) + model_sampling.set_sigma_range(sigma_min, sigma_max) + m.add_object_patch("model_sampling", model_sampling) + return (m, ) + +class RescaleCFG: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "multiplier": ("FLOAT", {"default": 0.7, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "advanced/model" + + def patch(self, model, multiplier): + def rescale_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + sigma = args["sigma"] + sigma = sigma.view(sigma.shape[:1] + (1,) * (cond.ndim - 1)) + x_orig = args["input"] + + #rescale cfg has to be done on v-pred model output + x = x_orig / (sigma * sigma + 1.0) + cond = ((x - (x_orig - cond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) + uncond = ((x - (x_orig - uncond)) * (sigma ** 2 + 1.0) ** 0.5) / (sigma) + + #rescalecfg + x_cfg = uncond + cond_scale * (cond - uncond) + ro_pos = torch.std(cond, dim=(1,2,3), keepdim=True) + ro_cfg = torch.std(x_cfg, dim=(1,2,3), keepdim=True) + + x_rescaled = x_cfg * (ro_pos / ro_cfg) + x_final = multiplier * x_rescaled + (1.0 - multiplier) * x_cfg + + return x_orig - (x - x_final * sigma / (sigma * sigma + 1.0) ** 0.5) + + m = model.clone() + m.set_model_sampler_cfg_function(rescale_cfg) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "ModelSamplingDiscrete": ModelSamplingDiscrete, + "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM, + "RescaleCFG": RescaleCFG, +} diff --git a/ldm_patched/contrib/external_model_downscale.py b/ldm_patched/contrib/external_model_downscale.py new file mode 100644 index 0000000000000000000000000000000000000000..4f1da54de25425dea2c5eeabe10c2342cc1e3739 --- /dev/null +++ b/ldm_patched/contrib/external_model_downscale.py @@ -0,0 +1,55 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +import ldm_patched.modules.utils + +class PatchModelAddDownscale: + upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"] + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}), + "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}), + "downscale_after_skip": ("BOOLEAN", {"default": True}), + "downscale_method": (s.upscale_methods,), + "upscale_method": (s.upscale_methods,), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method): + sigma_start = model.model.model_sampling.percent_to_sigma(start_percent) + sigma_end = model.model.model_sampling.percent_to_sigma(end_percent) + + def input_block_patch(h, transformer_options): + if transformer_options["block"][1] == block_number: + sigma = transformer_options["sigmas"][0].item() + if sigma <= sigma_start and sigma >= sigma_end: + h = ldm_patched.modules.utils.common_upscale(h, round(h.shape[-1] * (1.0 / downscale_factor)), round(h.shape[-2] * (1.0 / downscale_factor)), downscale_method, "disabled") + return h + + def output_block_patch(h, hsp, transformer_options): + if h.shape[2] != hsp.shape[2]: + h = ldm_patched.modules.utils.common_upscale(h, hsp.shape[-1], hsp.shape[-2], upscale_method, "disabled") + return h, hsp + + m = model.clone() + if downscale_after_skip: + m.set_model_input_block_patch_after_skip(input_block_patch) + else: + m.set_model_input_block_patch(input_block_patch) + m.set_model_output_block_patch(output_block_patch) + return (m, ) + +NODE_CLASS_MAPPINGS = { + "PatchModelAddDownscale": PatchModelAddDownscale, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + # Sampling + "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)", +} diff --git a/ldm_patched/contrib/external_model_merging.py b/ldm_patched/contrib/external_model_merging.py new file mode 100644 index 0000000000000000000000000000000000000000..ae8145d4f698f5286d6f69962a25d06d78203614 --- /dev/null +++ b/ldm_patched/contrib/external_model_merging.py @@ -0,0 +1,286 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.modules.sd +import ldm_patched.modules.utils +import ldm_patched.modules.model_base +import ldm_patched.modules.model_management + +import ldm_patched.utils.path_utils +import json +import os + +from ldm_patched.modules.args_parser import args + +class ModelMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, ratio): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +class ModelSubtract: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, multiplier): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, - multiplier, multiplier) + return (m, ) + +class ModelAdd: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + for k in kp: + m.add_patches({k: kp[k]}, 1.0, 1.0) + return (m, ) + + +class CLIPMergeSimple: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip1": ("CLIP",), + "clip2": ("CLIP",), + "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("CLIP",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, clip1, clip2, ratio): + m = clip1.clone() + kp = clip2.get_key_patches() + for k in kp: + if k.endswith(".position_ids") or k.endswith(".logit_scale"): + continue + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +class ModelMergeBlocks: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model1": ("MODEL",), + "model2": ("MODEL",), + "input": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "middle": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), + "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}) + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "merge" + + CATEGORY = "advanced/model_merging" + + def merge(self, model1, model2, **kwargs): + m = model1.clone() + kp = model2.get_key_patches("diffusion_model.") + default_ratio = next(iter(kwargs.values())) + + for k in kp: + ratio = default_ratio + k_unet = k[len("diffusion_model."):] + + last_arg_size = 0 + for arg in kwargs: + if k_unet.startswith(arg) and last_arg_size < len(arg): + ratio = kwargs[arg] + last_arg_size = len(arg) + + m.add_patches({k: kp[k]}, 1.0 - ratio, ratio) + return (m, ) + +def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefix=None, output_dir=None, prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, output_dir) + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + + enable_modelspec = True + if isinstance(model.model, ldm_patched.modules.model_base.SDXL): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base" + elif isinstance(model.model, ldm_patched.modules.model_base.SDXLRefiner): + metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner" + else: + enable_modelspec = False + + if enable_modelspec: + metadata["modelspec.sai_model_spec"] = "1.0.0" + metadata["modelspec.implementation"] = "sgm" + metadata["modelspec.title"] = "{} {}".format(filename, counter) + + #TODO: + # "stable-diffusion-v1", "stable-diffusion-v1-inpainting", "stable-diffusion-v2-512", + # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h", + # "v2-inpainting" + + if model.model.model_type == ldm_patched.modules.model_base.ModelType.EPS: + metadata["modelspec.predict_key"] = "epsilon" + elif model.model.model_type == ldm_patched.modules.model_base.ModelType.V_PREDICTION: + metadata["modelspec.predict_key"] = "v" + + if not args.disable_server_info: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + ldm_patched.modules.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata) + +class CheckpointSave: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip": ("CLIP",), + "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, model, clip, vae, filename_prefix, prompt=None, extra_pnginfo=None): + save_checkpoint(model, clip=clip, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + +class CLIPSave: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip": ("CLIP",), + "filename_prefix": ("STRING", {"default": "clip/ldm_patched"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, clip, filename_prefix, prompt=None, extra_pnginfo=None): + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + if not args.disable_server_info: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + ldm_patched.modules.model_management.load_models_gpu([clip.load_model()]) + clip_sd = clip.get_sd() + + for prefix in ["clip_l.", "clip_g.", ""]: + k = list(filter(lambda a: a.startswith(prefix), clip_sd.keys())) + current_clip_sd = {} + for x in k: + current_clip_sd[x] = clip_sd.pop(x) + if len(current_clip_sd) == 0: + continue + + p = prefix[:-1] + replace_prefix = {} + filename_prefix_ = filename_prefix + if len(p) > 0: + filename_prefix_ = "{}_{}".format(filename_prefix_, p) + replace_prefix[prefix] = "" + replace_prefix["transformer."] = "" + + full_output_folder, filename, counter, subfolder, filename_prefix_ = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix_, self.output_dir) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + current_clip_sd = ldm_patched.modules.utils.state_dict_prefix_replace(current_clip_sd, replace_prefix) + + ldm_patched.modules.utils.save_torch_file(current_clip_sd, output_checkpoint, metadata=metadata) + return {} + +class VAESave: + def __init__(self): + self.output_dir = ldm_patched.utils.path_utils.get_output_directory() + + @classmethod + def INPUT_TYPES(s): + return {"required": { "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "vae/ldm_patched_vae"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + RETURN_TYPES = () + FUNCTION = "save" + OUTPUT_NODE = True + + CATEGORY = "advanced/model_merging" + + def save(self, vae, filename_prefix, prompt=None, extra_pnginfo=None): + full_output_folder, filename, counter, subfolder, filename_prefix = ldm_patched.utils.path_utils.get_save_image_path(filename_prefix, self.output_dir) + prompt_info = "" + if prompt is not None: + prompt_info = json.dumps(prompt) + + metadata = {} + if not args.disable_server_info: + metadata["prompt"] = prompt_info + if extra_pnginfo is not None: + for x in extra_pnginfo: + metadata[x] = json.dumps(extra_pnginfo[x]) + + output_checkpoint = f"{filename}_{counter:05}_.safetensors" + output_checkpoint = os.path.join(full_output_folder, output_checkpoint) + + ldm_patched.modules.utils.save_torch_file(vae.get_sd(), output_checkpoint, metadata=metadata) + return {} + +NODE_CLASS_MAPPINGS = { + "ModelMergeSimple": ModelMergeSimple, + "ModelMergeBlocks": ModelMergeBlocks, + "ModelMergeSubtract": ModelSubtract, + "ModelMergeAdd": ModelAdd, + "CheckpointSave": CheckpointSave, + "CLIPMergeSimple": CLIPMergeSimple, + "CLIPSave": CLIPSave, + "VAESave": VAESave, +} diff --git a/ldm_patched/contrib/external_perpneg.py b/ldm_patched/contrib/external_perpneg.py new file mode 100644 index 0000000000000000000000000000000000000000..ec91681fea1261767e9c2ae68e4c8a4bb6489be6 --- /dev/null +++ b/ldm_patched/contrib/external_perpneg.py @@ -0,0 +1,57 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +import ldm_patched.modules.model_management +import ldm_patched.modules.sample +import ldm_patched.modules.samplers +import ldm_patched.modules.utils + + +class PerpNeg: + @classmethod + def INPUT_TYPES(s): + return {"required": {"model": ("MODEL", ), + "empty_conditioning": ("CONDITIONING", ), + "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, empty_conditioning, neg_scale): + m = model.clone() + nocond = ldm_patched.modules.sample.convert_cond(empty_conditioning) + + def cfg_function(args): + model = args["model"] + noise_pred_pos = args["cond_denoised"] + noise_pred_neg = args["uncond_denoised"] + cond_scale = args["cond_scale"] + x = args["input"] + sigma = args["sigma"] + model_options = args["model_options"] + nocond_processed = ldm_patched.modules.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative") + + (noise_pred_nocond, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options) + + pos = noise_pred_pos - noise_pred_nocond + neg = noise_pred_neg - noise_pred_nocond + perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg + perp_neg = perp * neg_scale + cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg) + cfg_result = x - cfg_result + return cfg_result + + m.set_model_sampler_cfg_function(cfg_function) + + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "PerpNeg": PerpNeg, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "PerpNeg": "Perp-Neg", +} diff --git a/ldm_patched/contrib/external_photomaker.py b/ldm_patched/contrib/external_photomaker.py new file mode 100644 index 0000000000000000000000000000000000000000..cc7f67100670d6407531d56f232517a9216e9dcf --- /dev/null +++ b/ldm_patched/contrib/external_photomaker.py @@ -0,0 +1,189 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +import torch.nn as nn +import ldm_patched.utils.path_utils +import ldm_patched.modules.clip_model +import ldm_patched.modules.clip_vision +import ldm_patched.modules.ops + +# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0 +VISION_CONFIG_DICT = { + "hidden_size": 1024, + "image_size": 224, + "intermediate_size": 4096, + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "hidden_act": "quick_gelu", +} + +class MLP(nn.Module): + def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=ldm_patched.modules.ops): + super().__init__() + if use_residual: + assert in_dim == out_dim + self.layernorm = operations.LayerNorm(in_dim) + self.fc1 = operations.Linear(in_dim, hidden_dim) + self.fc2 = operations.Linear(hidden_dim, out_dim) + self.use_residual = use_residual + self.act_fn = nn.GELU() + + def forward(self, x): + residual = x + x = self.layernorm(x) + x = self.fc1(x) + x = self.act_fn(x) + x = self.fc2(x) + if self.use_residual: + x = x + residual + return x + + +class FuseModule(nn.Module): + def __init__(self, embed_dim, operations): + super().__init__() + self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations) + self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations) + self.layer_norm = operations.LayerNorm(embed_dim) + + def fuse_fn(self, prompt_embeds, id_embeds): + stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1) + stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds + stacked_id_embeds = self.mlp2(stacked_id_embeds) + stacked_id_embeds = self.layer_norm(stacked_id_embeds) + return stacked_id_embeds + + def forward( + self, + prompt_embeds, + id_embeds, + class_tokens_mask, + ) -> torch.Tensor: + # id_embeds shape: [b, max_num_inputs, 1, 2048] + id_embeds = id_embeds.to(prompt_embeds.dtype) + num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case + batch_size, max_num_inputs = id_embeds.shape[:2] + # seq_length: 77 + seq_length = prompt_embeds.shape[1] + # flat_id_embeds shape: [b*max_num_inputs, 1, 2048] + flat_id_embeds = id_embeds.view( + -1, id_embeds.shape[-2], id_embeds.shape[-1] + ) + # valid_id_mask [b*max_num_inputs] + valid_id_mask = ( + torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :] + < num_inputs[:, None] + ) + valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()] + + prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1]) + class_tokens_mask = class_tokens_mask.view(-1) + valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1]) + # slice out the image token embeddings + image_token_embeds = prompt_embeds[class_tokens_mask] + stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds) + assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}" + prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype)) + updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1) + return updated_prompt_embeds + +class PhotoMakerIDEncoder(ldm_patched.modules.clip_model.CLIPVisionModelProjection): + def __init__(self): + self.load_device = ldm_patched.modules.model_management.text_encoder_device() + offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() + dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device) + + super().__init__(VISION_CONFIG_DICT, dtype, offload_device, ldm_patched.modules.ops.manual_cast) + self.visual_projection_2 = ldm_patched.modules.ops.manual_cast.Linear(1024, 1280, bias=False) + self.fuse_module = FuseModule(2048, ldm_patched.modules.ops.manual_cast) + + def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask): + b, num_inputs, c, h, w = id_pixel_values.shape + id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w) + + shared_id_embeds = self.vision_model(id_pixel_values)[2] + id_embeds = self.visual_projection(shared_id_embeds) + id_embeds_2 = self.visual_projection_2(shared_id_embeds) + + id_embeds = id_embeds.view(b, num_inputs, 1, -1) + id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1) + + id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1) + updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask) + + return updated_prompt_embeds + + +class PhotoMakerLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "photomaker_model_name": (ldm_patched.utils.path_utils.get_filename_list("photomaker"), )}} + + RETURN_TYPES = ("PHOTOMAKER",) + FUNCTION = "load_photomaker_model" + + CATEGORY = "_for_testing/photomaker" + + def load_photomaker_model(self, photomaker_model_name): + photomaker_model_path = ldm_patched.utils.path_utils.get_full_path("photomaker", photomaker_model_name) + photomaker_model = PhotoMakerIDEncoder() + data = ldm_patched.modules.utils.load_torch_file(photomaker_model_path, safe_load=True) + if "id_encoder" in data: + data = data["id_encoder"] + photomaker_model.load_state_dict(data) + return (photomaker_model,) + + +class PhotoMakerEncode: + @classmethod + def INPUT_TYPES(s): + return {"required": { "photomaker": ("PHOTOMAKER",), + "image": ("IMAGE",), + "clip": ("CLIP", ), + "text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}), + }} + + RETURN_TYPES = ("CONDITIONING",) + FUNCTION = "apply_photomaker" + + CATEGORY = "_for_testing/photomaker" + + def apply_photomaker(self, photomaker, image, clip, text): + special_token = "photomaker" + pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float() + try: + index = text.split(" ").index(special_token) + 1 + except ValueError: + index = -1 + tokens = clip.tokenize(text, return_word_ids=True) + out_tokens = {} + for k in tokens: + out_tokens[k] = [] + for t in tokens[k]: + f = list(filter(lambda x: x[2] != index, t)) + while len(f) < len(t): + f.append(t[-1]) + out_tokens[k].append(f) + + cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True) + + if index > 0: + token_index = index - 1 + num_id_images = 1 + class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)] + out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device), + class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0)) + else: + out = cond + + return ([[out, {"pooled_output": pooled}]], ) + + +NODE_CLASS_MAPPINGS = { + "PhotoMakerLoader": PhotoMakerLoader, + "PhotoMakerEncode": PhotoMakerEncode, +} + diff --git a/ldm_patched/contrib/external_post_processing.py b/ldm_patched/contrib/external_post_processing.py new file mode 100644 index 0000000000000000000000000000000000000000..93cb12122c4f05ac0c0a4ed890f6a0af27916a02 --- /dev/null +++ b/ldm_patched/contrib/external_post_processing.py @@ -0,0 +1,278 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import numpy as np +import torch +import torch.nn.functional as F +from PIL import Image +import math + +import ldm_patched.modules.utils + + +class Blend: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image1": ("IMAGE",), + "image2": ("IMAGE",), + "blend_factor": ("FLOAT", { + "default": 0.5, + "min": 0.0, + "max": 1.0, + "step": 0.01 + }), + "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blend_images" + + CATEGORY = "image/postprocessing" + + def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str): + image2 = image2.to(image1.device) + if image1.shape != image2.shape: + image2 = image2.permute(0, 3, 1, 2) + image2 = ldm_patched.modules.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') + image2 = image2.permute(0, 2, 3, 1) + + blended_image = self.blend_mode(image1, image2, blend_mode) + blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor + blended_image = torch.clamp(blended_image, 0, 1) + return (blended_image,) + + def blend_mode(self, img1, img2, mode): + if mode == "normal": + return img2 + elif mode == "multiply": + return img1 * img2 + elif mode == "screen": + return 1 - (1 - img1) * (1 - img2) + elif mode == "overlay": + return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2)) + elif mode == "soft_light": + return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1)) + elif mode == "difference": + return img1 - img2 + else: + raise ValueError(f"Unsupported blend mode: {mode}") + + def g(self, x): + return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x)) + +def gaussian_kernel(kernel_size: int, sigma: float, device=None): + x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij") + d = torch.sqrt(x * x + y * y) + g = torch.exp(-(d * d) / (2.0 * sigma * sigma)) + return g / g.sum() + +class Blur: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "blur_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "blur" + + CATEGORY = "image/postprocessing" + + def blur(self, image: torch.Tensor, blur_radius: int, sigma: float): + if blur_radius == 0: + return (image,) + + batch_size, height, width, channels = image.shape + + kernel_size = blur_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1) + + image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect') + blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius] + blurred = blurred.permute(0, 2, 3, 1) + + return (blurred,) + +class Quantize: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "colors": ("INT", { + "default": 256, + "min": 1, + "max": 256, + "step": 1 + }), + "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "quantize" + + CATEGORY = "image/postprocessing" + + def bayer(im, pal_im, order): + def normalized_bayer_matrix(n): + if n == 0: + return np.zeros((1,1), "float32") + else: + q = 4 ** n + m = q * normalized_bayer_matrix(n - 1) + return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q + + num_colors = len(pal_im.getpalette()) // 3 + spread = 2 * 256 / num_colors + bayer_n = int(math.log2(order)) + bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5) + + result = torch.from_numpy(np.array(im).astype(np.float32)) + tw = math.ceil(result.shape[0] / bayer_matrix.shape[0]) + th = math.ceil(result.shape[1] / bayer_matrix.shape[1]) + tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1) + result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255) + result = result.to(dtype=torch.uint8) + + im = Image.fromarray(result.cpu().numpy()) + im = im.quantize(palette=pal_im, dither=Image.Dither.NONE) + return im + + def quantize(self, image: torch.Tensor, colors: int, dither: str): + batch_size, height, width, _ = image.shape + result = torch.zeros_like(image) + + for b in range(batch_size): + im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB') + + pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836 + + if dither == "none": + quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE) + elif dither == "floyd-steinberg": + quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG) + elif dither.startswith("bayer"): + order = int(dither.split('-')[-1]) + quantized_image = Quantize.bayer(im, pal_im, order) + + quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255 + result[b] = quantized_array + + return (result,) + +class Sharpen: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "image": ("IMAGE",), + "sharpen_radius": ("INT", { + "default": 1, + "min": 1, + "max": 31, + "step": 1 + }), + "sigma": ("FLOAT", { + "default": 1.0, + "min": 0.1, + "max": 10.0, + "step": 0.1 + }), + "alpha": ("FLOAT", { + "default": 1.0, + "min": 0.0, + "max": 5.0, + "step": 0.1 + }), + }, + } + + RETURN_TYPES = ("IMAGE",) + FUNCTION = "sharpen" + + CATEGORY = "image/postprocessing" + + def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float): + if sharpen_radius == 0: + return (image,) + + batch_size, height, width, channels = image.shape + + kernel_size = sharpen_radius * 2 + 1 + kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10) + center = kernel_size // 2 + kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0 + kernel = kernel.repeat(channels, 1, 1).unsqueeze(1) + + tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C) + tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect') + sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius] + sharpened = sharpened.permute(0, 2, 3, 1) + + result = torch.clamp(sharpened, 0, 1) + + return (result,) + +class ImageScaleToTotalPixels: + upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"] + crop_methods = ["disabled", "center"] + + @classmethod + def INPUT_TYPES(s): + return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,), + "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, image, upscale_method, megapixels): + samples = image.movedim(-1,1) + total = int(megapixels * 1024 * 1024) + + scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2])) + width = round(samples.shape[3] * scale_by) + height = round(samples.shape[2] * scale_by) + + s = ldm_patched.modules.utils.common_upscale(samples, width, height, upscale_method, "disabled") + s = s.movedim(1,-1) + return (s,) + +NODE_CLASS_MAPPINGS = { + "ImageBlend": Blend, + "ImageBlur": Blur, + "ImageQuantize": Quantize, + "ImageSharpen": Sharpen, + "ImageScaleToTotalPixels": ImageScaleToTotalPixels, +} diff --git a/ldm_patched/contrib/external_rebatch.py b/ldm_patched/contrib/external_rebatch.py new file mode 100644 index 0000000000000000000000000000000000000000..c24cc8c32c61b349fe416be398a805d8b2fc0bfc --- /dev/null +++ b/ldm_patched/contrib/external_rebatch.py @@ -0,0 +1,140 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch + +class LatentRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "latents": ("LATENT",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("LATENT",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "latent/batch" + + @staticmethod + def get_batch(latents, list_ind, offset): + '''prepare a batch out of the list of latents''' + samples = latents[list_ind]['samples'] + shape = samples.shape + mask = latents[list_ind]['noise_mask'] if 'noise_mask' in latents[list_ind] else torch.ones((shape[0], 1, shape[2]*8, shape[3]*8), device='cpu') + if mask.shape[-1] != shape[-1] * 8 or mask.shape[-2] != shape[-2]: + torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[-2]*8, shape[-1]*8), mode="bilinear") + if mask.shape[0] < samples.shape[0]: + mask = mask.repeat((shape[0] - 1) // mask.shape[0] + 1, 1, 1, 1)[:shape[0]] + if 'batch_index' in latents[list_ind]: + batch_inds = latents[list_ind]['batch_index'] + else: + batch_inds = [x+offset for x in range(shape[0])] + return samples, mask, batch_inds + + @staticmethod + def get_slices(indexable, num, batch_size): + '''divides an indexable object into num slices of length batch_size, and a remainder''' + slices = [] + for i in range(num): + slices.append(indexable[i*batch_size:(i+1)*batch_size]) + if num * batch_size < len(indexable): + return slices, indexable[num * batch_size:] + else: + return slices, None + + @staticmethod + def slice_batch(batch, num, batch_size): + result = [LatentRebatch.get_slices(x, num, batch_size) for x in batch] + return list(zip(*result)) + + @staticmethod + def cat_batch(batch1, batch2): + if batch1[0] is None: + return batch2 + result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)] + return result + + def rebatch(self, latents, batch_size): + batch_size = batch_size[0] + + output_list = [] + current_batch = (None, None, None) + processed = 0 + + for i in range(len(latents)): + # fetch new entry of list + #samples, masks, indices = self.get_batch(latents, i) + next_batch = self.get_batch(latents, i, processed) + processed += len(next_batch[2]) + # set to current if current is None + if current_batch[0] is None: + current_batch = next_batch + # add previous to list if dimensions do not match + elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + current_batch = next_batch + # cat if everything checks out + else: + current_batch = self.cat_batch(current_batch, next_batch) + + # add to list if dimensions gone above target batch size + if current_batch[0].shape[0] > batch_size: + num = current_batch[0].shape[0] // batch_size + sliced, remainder = self.slice_batch(current_batch, num, batch_size) + + for i in range(num): + output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]}) + + current_batch = remainder + + #add remainder + if current_batch[0] is not None: + sliced, _ = self.slice_batch(current_batch, 1, batch_size) + output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]}) + + #get rid of empty masks + for s in output_list: + if s['noise_mask'].mean() == 1.0: + del s['noise_mask'] + + return (output_list,) + +class ImageRebatch: + @classmethod + def INPUT_TYPES(s): + return {"required": { "images": ("IMAGE",), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + }} + RETURN_TYPES = ("IMAGE",) + INPUT_IS_LIST = True + OUTPUT_IS_LIST = (True, ) + + FUNCTION = "rebatch" + + CATEGORY = "image/batch" + + def rebatch(self, images, batch_size): + batch_size = batch_size[0] + + output_list = [] + all_images = [] + for img in images: + for i in range(img.shape[0]): + all_images.append(img[i:i+1]) + + for i in range(0, len(all_images), batch_size): + output_list.append(torch.cat(all_images[i:i+batch_size], dim=0)) + + return (output_list,) + +NODE_CLASS_MAPPINGS = { + "RebatchLatents": LatentRebatch, + "RebatchImages": ImageRebatch, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "RebatchLatents": "Rebatch Latents", + "RebatchImages": "Rebatch Images", +} diff --git a/ldm_patched/contrib/external_sag.py b/ldm_patched/contrib/external_sag.py new file mode 100644 index 0000000000000000000000000000000000000000..804d56113b281e573a27859fdb4bae82e125882d --- /dev/null +++ b/ldm_patched/contrib/external_sag.py @@ -0,0 +1,172 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +from torch import einsum +import torch.nn.functional as F +import math + +from einops import rearrange, repeat +import os +from ldm_patched.ldm.modules.attention import optimized_attention, _ATTN_PRECISION +import ldm_patched.modules.samplers + +# from ldm_patched.modules/ldm/modules/attention.py +# but modified to return attention scores as well as output +def attention_basic_with_sim(q, k, v, heads, mask=None): + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 + + h = heads + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + # force cast to fp32 to avoid overflowing + if _ATTN_PRECISION =="fp32": + sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * scale + + del q, k + + if mask is not None: + mask = rearrange(mask, 'b ... -> b (...)') + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return (out, sim) + +def create_blur_map(x0, attn, sigma=3.0, threshold=1.0): + # reshape and GAP the attention map + _, hw1, hw2 = attn.shape + b, _, lh, lw = x0.shape + attn = attn.reshape(b, -1, hw1, hw2) + # Global Average Pool + mask = attn.mean(1, keepdim=False).sum(1, keepdim=False) > threshold + ratio = 2**(math.ceil(math.sqrt(lh * lw / hw1)) - 1).bit_length() + mid_shape = [math.ceil(lh / ratio), math.ceil(lw / ratio)] + + # Reshape + mask = ( + mask.reshape(b, *mid_shape) + .unsqueeze(1) + .type(attn.dtype) + ) + # Upsample + mask = F.interpolate(mask, (lh, lw)) + + blurred = gaussian_blur_2d(x0, kernel_size=9, sigma=sigma) + blurred = blurred * mask + x0 * (1 - mask) + return blurred + +def gaussian_blur_2d(img, kernel_size, sigma): + ksize_half = (kernel_size - 1) * 0.5 + + x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size) + + pdf = torch.exp(-0.5 * (x / sigma).pow(2)) + + x_kernel = pdf / pdf.sum() + x_kernel = x_kernel.to(device=img.device, dtype=img.dtype) + + kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :]) + kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1]) + + padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2] + + img = F.pad(img, padding, mode="reflect") + img = F.conv2d(img, kernel2d, groups=img.shape[-3]) + return img + +class SelfAttentionGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.1}), + "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, scale, blur_sigma): + m = model.clone() + + attn_scores = None + + # TODO: make this work properly with chunked batches + # currently, we can only save the attn from one UNet call + def attn_and_record(q, k, v, extra_options): + nonlocal attn_scores + # if uncond, save the attention scores + heads = extra_options["n_heads"] + cond_or_uncond = extra_options["cond_or_uncond"] + b = q.shape[0] // len(cond_or_uncond) + if 1 in cond_or_uncond: + uncond_index = cond_or_uncond.index(1) + # do the entire attention operation, but save the attention scores to attn_scores + (out, sim) = attention_basic_with_sim(q, k, v, heads=heads) + # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn] + n_slices = heads * b + attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)] + return out + else: + return optimized_attention(q, k, v, heads=heads) + + def post_cfg_function(args): + nonlocal attn_scores + uncond_attn = attn_scores + + sag_scale = scale + sag_sigma = blur_sigma + sag_threshold = 1.0 + model = args["model"] + uncond_pred = args["uncond_denoised"] + uncond = args["uncond"] + cfg_result = args["denoised"] + sigma = args["sigma"] + model_options = args["model_options"] + x = args["input"] + if min(cfg_result.shape[2:]) <= 4: #skip when too small to add padding + return cfg_result + + # create the adversarially blurred image + degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold) + degraded_noised = degraded + x - uncond_pred + # call into the UNet + (sag, _) = ldm_patched.modules.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options) + return cfg_result + (degraded - sag) * sag_scale + + m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True) + + # from diffusers: + # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch + m.set_model_attn1_replace(attn_and_record, "middle", 0, 0) + + return (m, ) + +NODE_CLASS_MAPPINGS = { + "SelfAttentionGuidance": SelfAttentionGuidance, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "SelfAttentionGuidance": "Self-Attention Guidance", +} diff --git a/ldm_patched/contrib/external_sdupscale.py b/ldm_patched/contrib/external_sdupscale.py new file mode 100644 index 0000000000000000000000000000000000000000..68153c478bf3b216235ce6a0241c2e1d84eb46af --- /dev/null +++ b/ldm_patched/contrib/external_sdupscale.py @@ -0,0 +1,49 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +import ldm_patched.contrib.external +import ldm_patched.modules.utils + +class SD_4XUpscale_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "images": ("IMAGE",), + "positive": ("CONDITIONING",), + "negative": ("CONDITIONING",), + "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/upscale_diffusion" + + def encode(self, images, positive, negative, scale_ratio, noise_augmentation): + width = max(1, round(images.shape[-2] * scale_ratio)) + height = max(1, round(images.shape[-3] * scale_ratio)) + + pixels = ldm_patched.modules.utils.common_upscale((images.movedim(-1,1) * 2.0) - 1.0, width // 4, height // 4, "bilinear", "center") + + out_cp = [] + out_cn = [] + + for t in positive: + n = [t[0], t[1].copy()] + n[1]['concat_image'] = pixels + n[1]['noise_augmentation'] = noise_augmentation + out_cp.append(n) + + for t in negative: + n = [t[0], t[1].copy()] + n[1]['concat_image'] = pixels + n[1]['noise_augmentation'] = noise_augmentation + out_cn.append(n) + + latent = torch.zeros([images.shape[0], 4, height // 4, width // 4]) + return (out_cp, out_cn, {"samples":latent}) + +NODE_CLASS_MAPPINGS = { + "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning, +} diff --git a/ldm_patched/contrib/external_stable3d.py b/ldm_patched/contrib/external_stable3d.py new file mode 100644 index 0000000000000000000000000000000000000000..bae2623fada0efa2d70f93af9bf475e8a619ebb4 --- /dev/null +++ b/ldm_patched/contrib/external_stable3d.py @@ -0,0 +1,104 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import torch +import ldm_patched.contrib.external +import ldm_patched.modules.utils + +def camera_embeddings(elevation, azimuth): + elevation = torch.as_tensor([elevation]) + azimuth = torch.as_tensor([azimuth]) + embeddings = torch.stack( + [ + torch.deg2rad( + (90 - elevation) - (90) + ), # Zero123 polar is 90-elevation + torch.sin(torch.deg2rad(azimuth)), + torch.cos(torch.deg2rad(azimuth)), + torch.deg2rad( + 90 - torch.full_like(elevation, 0) + ), + ], dim=-1).unsqueeze(1) + + return embeddings + + +class StableZero123_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + cam_embeds = camera_embeddings(elevation, azimuth) + cond = torch.cat([pooled, cam_embeds.to(pooled.device).repeat((pooled.shape[0], 1, 1))], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +class StableZero123_Conditioning_Batched: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 256, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}), + "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}), + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/3d_models" + + def encode(self, clip_vision, init_image, vae, width, height, batch_size, elevation, azimuth, elevation_batch_increment, azimuth_batch_increment): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + t = vae.encode(encode_pixels) + + cam_embeds = [] + for i in range(batch_size): + cam_embeds.append(camera_embeddings(elevation, azimuth)) + elevation += elevation_batch_increment + azimuth += azimuth_batch_increment + + cam_embeds = torch.cat(cam_embeds, dim=0) + cond = torch.cat([ldm_patched.modules.utils.repeat_to_batch_size(pooled, batch_size), cam_embeds], dim=-1) + + positive = [[cond, {"concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([batch_size, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size}) + + +NODE_CLASS_MAPPINGS = { + "StableZero123_Conditioning": StableZero123_Conditioning, + "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched, +} diff --git a/ldm_patched/contrib/external_tomesd.py b/ldm_patched/contrib/external_tomesd.py new file mode 100644 index 0000000000000000000000000000000000000000..b01d6910f09c6ba1c7cfe3fe255acb9c4ea3545b --- /dev/null +++ b/ldm_patched/contrib/external_tomesd.py @@ -0,0 +1,179 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +#Taken from: https://github.com/dbolya/tomesd + +import torch +from typing import Tuple, Callable +import math + +def do_nothing(x: torch.Tensor, mode:str=None): + return x + + +def mps_gather_workaround(input, dim, index): + if input.shape[-1] == 1: + return torch.gather( + input.unsqueeze(-1), + dim - 1 if dim < 0 else dim, + index.unsqueeze(-1) + ).squeeze(-1) + else: + return torch.gather(input, dim, index) + + +def bipartite_soft_matching_random2d(metric: torch.Tensor, + w: int, h: int, sx: int, sy: int, r: int, + no_rand: bool = False) -> Tuple[Callable, Callable]: + """ + Partitions the tokens into src and dst and merges r tokens from src to dst. + Dst tokens are partitioned by choosing one randomy in each (sx, sy) region. + Args: + - metric [B, N, C]: metric to use for similarity + - w: image width in tokens + - h: image height in tokens + - sx: stride in the x dimension for dst, must divide w + - sy: stride in the y dimension for dst, must divide h + - r: number of tokens to remove (by merging) + - no_rand: if true, disable randomness (use top left corner only) + """ + B, N, _ = metric.shape + + if r <= 0 or w == 1 or h == 1: + return do_nothing, do_nothing + + gather = mps_gather_workaround if metric.device.type == "mps" else torch.gather + + with torch.no_grad(): + + hsy, wsx = h // sy, w // sx + + # For each sy by sx kernel, randomly assign one token to be dst and the rest src + if no_rand: + rand_idx = torch.zeros(hsy, wsx, 1, device=metric.device, dtype=torch.int64) + else: + rand_idx = torch.randint(sy*sx, size=(hsy, wsx, 1), device=metric.device) + + # The image might not divide sx and sy, so we need to work on a view of the top left if the idx buffer instead + idx_buffer_view = torch.zeros(hsy, wsx, sy*sx, device=metric.device, dtype=torch.int64) + idx_buffer_view.scatter_(dim=2, index=rand_idx, src=-torch.ones_like(rand_idx, dtype=rand_idx.dtype)) + idx_buffer_view = idx_buffer_view.view(hsy, wsx, sy, sx).transpose(1, 2).reshape(hsy * sy, wsx * sx) + + # Image is not divisible by sx or sy so we need to move it into a new buffer + if (hsy * sy) < h or (wsx * sx) < w: + idx_buffer = torch.zeros(h, w, device=metric.device, dtype=torch.int64) + idx_buffer[:(hsy * sy), :(wsx * sx)] = idx_buffer_view + else: + idx_buffer = idx_buffer_view + + # We set dst tokens to be -1 and src to be 0, so an argsort gives us dst|src indices + rand_idx = idx_buffer.reshape(1, -1, 1).argsort(dim=1) + + # We're finished with these + del idx_buffer, idx_buffer_view + + # rand_idx is currently dst|src, so split them + num_dst = hsy * wsx + a_idx = rand_idx[:, num_dst:, :] # src + b_idx = rand_idx[:, :num_dst, :] # dst + + def split(x): + C = x.shape[-1] + src = gather(x, dim=1, index=a_idx.expand(B, N - num_dst, C)) + dst = gather(x, dim=1, index=b_idx.expand(B, num_dst, C)) + return src, dst + + # Cosine similarity between A and B + metric = metric / metric.norm(dim=-1, keepdim=True) + a, b = split(metric) + scores = a @ b.transpose(-1, -2) + + # Can't reduce more than the # tokens in src + r = min(a.shape[1], r) + + # Find the most similar greedily + node_max, node_idx = scores.max(dim=-1) + edge_idx = node_max.argsort(dim=-1, descending=True)[..., None] + + unm_idx = edge_idx[..., r:, :] # Unmerged Tokens + src_idx = edge_idx[..., :r, :] # Merged Tokens + dst_idx = gather(node_idx[..., None], dim=-2, index=src_idx) + + def merge(x: torch.Tensor, mode="mean") -> torch.Tensor: + src, dst = split(x) + n, t1, c = src.shape + + unm = gather(src, dim=-2, index=unm_idx.expand(n, t1 - r, c)) + src = gather(src, dim=-2, index=src_idx.expand(n, r, c)) + dst = dst.scatter_reduce(-2, dst_idx.expand(n, r, c), src, reduce=mode) + + return torch.cat([unm, dst], dim=1) + + def unmerge(x: torch.Tensor) -> torch.Tensor: + unm_len = unm_idx.shape[1] + unm, dst = x[..., :unm_len, :], x[..., unm_len:, :] + _, _, c = unm.shape + + src = gather(dst, dim=-2, index=dst_idx.expand(B, r, c)) + + # Combine back to the original shape + out = torch.zeros(B, N, c, device=x.device, dtype=x.dtype) + out.scatter_(dim=-2, index=b_idx.expand(B, num_dst, c), src=dst) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=unm_idx).expand(B, unm_len, c), src=unm) + out.scatter_(dim=-2, index=gather(a_idx.expand(B, a_idx.shape[1], 1), dim=1, index=src_idx).expand(B, r, c), src=src) + + return out + + return merge, unmerge + + +def get_functions(x, ratio, original_shape): + b, c, original_h, original_w = original_shape + original_tokens = original_h * original_w + downsample = int(math.ceil(math.sqrt(original_tokens // x.shape[1]))) + stride_x = 2 + stride_y = 2 + max_downsample = 1 + + if downsample <= max_downsample: + w = int(math.ceil(original_w / downsample)) + h = int(math.ceil(original_h / downsample)) + r = int(x.shape[1] * ratio) + no_rand = False + m, u = bipartite_soft_matching_random2d(x, w, h, stride_x, stride_y, r, no_rand) + return m, u + + nothing = lambda y: y + return nothing, nothing + + + +class TomePatchModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "ratio": ("FLOAT", {"default": 0.3, "min": 0.0, "max": 1.0, "step": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "_for_testing" + + def patch(self, model, ratio): + self.u = None + def tomesd_m(q, k, v, extra_options): + #NOTE: In the reference code get_functions takes x (input of the transformer block) as the argument instead of q + #however from my basic testing it seems that using q instead gives better results + m, self.u = get_functions(q, ratio, extra_options["original_shape"]) + return m(q), k, v + def tomesd_u(n, extra_options): + return self.u(n) + + m = model.clone() + m.set_model_attn1_patch(tomesd_m) + m.set_model_attn1_output_patch(tomesd_u) + return (m, ) + + +NODE_CLASS_MAPPINGS = { + "TomePatchModel": TomePatchModel, +} diff --git a/ldm_patched/contrib/external_upscale_model.py b/ldm_patched/contrib/external_upscale_model.py new file mode 100644 index 0000000000000000000000000000000000000000..31d102f0e884af374d19eadbf6cda5eb928926d9 --- /dev/null +++ b/ldm_patched/contrib/external_upscale_model.py @@ -0,0 +1,68 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import os +from ldm_patched.pfn import model_loading +from ldm_patched.modules import model_management +import torch +import ldm_patched.modules.utils +import ldm_patched.utils.path_utils + +class UpscaleModelLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model_name": (ldm_patched.utils.path_utils.get_filename_list("upscale_models"), ), + }} + RETURN_TYPES = ("UPSCALE_MODEL",) + FUNCTION = "load_model" + + CATEGORY = "loaders" + + def load_model(self, model_name): + model_path = ldm_patched.utils.path_utils.get_full_path("upscale_models", model_name) + sd = ldm_patched.modules.utils.load_torch_file(model_path, safe_load=True) + if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd: + sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"module.":""}) + out = model_loading.load_state_dict(sd).eval() + return (out, ) + + +class ImageUpscaleWithModel: + @classmethod + def INPUT_TYPES(s): + return {"required": { "upscale_model": ("UPSCALE_MODEL",), + "image": ("IMAGE",), + }} + RETURN_TYPES = ("IMAGE",) + FUNCTION = "upscale" + + CATEGORY = "image/upscaling" + + def upscale(self, upscale_model, image): + device = model_management.get_torch_device() + upscale_model.to(device) + in_img = image.movedim(-1,-3).to(device) + free_memory = model_management.get_free_memory(device) + + tile = 512 + overlap = 32 + + oom = True + while oom: + try: + steps = in_img.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(in_img.shape[3], in_img.shape[2], tile_x=tile, tile_y=tile, overlap=overlap) + pbar = ldm_patched.modules.utils.ProgressBar(steps) + s = ldm_patched.modules.utils.tiled_scale(in_img, lambda a: upscale_model(a), tile_x=tile, tile_y=tile, overlap=overlap, upscale_amount=upscale_model.scale, pbar=pbar) + oom = False + except model_management.OOM_EXCEPTION as e: + tile //= 2 + if tile < 128: + raise e + + upscale_model.cpu() + s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0) + return (s,) + +NODE_CLASS_MAPPINGS = { + "UpscaleModelLoader": UpscaleModelLoader, + "ImageUpscaleWithModel": ImageUpscaleWithModel +} diff --git a/ldm_patched/contrib/external_video_model.py b/ldm_patched/contrib/external_video_model.py new file mode 100644 index 0000000000000000000000000000000000000000..503df0e181b445df5bd7879d9ffe4b251f3a43fe --- /dev/null +++ b/ldm_patched/contrib/external_video_model.py @@ -0,0 +1,108 @@ +# https://github.com/comfyanonymous/ComfyUI/blob/master/nodes.py + +import ldm_patched.contrib.external +import torch +import ldm_patched.modules.utils +import ldm_patched.modules.sd +import ldm_patched.utils.path_utils +import ldm_patched.contrib.external_model_merging + + +class ImageOnlyCheckpointLoader: + @classmethod + def INPUT_TYPES(s): + return {"required": { "ckpt_name": (ldm_patched.utils.path_utils.get_filename_list("checkpoints"), ), + }} + RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE") + FUNCTION = "load_checkpoint" + + CATEGORY = "loaders/video_models" + + def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True): + ckpt_path = ldm_patched.utils.path_utils.get_full_path("checkpoints", ckpt_name) + out = ldm_patched.modules.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=ldm_patched.utils.path_utils.get_folder_paths("embeddings")) + return (out[0], out[3], out[2]) + + +class SVD_img2vid_Conditioning: + @classmethod + def INPUT_TYPES(s): + return {"required": { "clip_vision": ("CLIP_VISION",), + "init_image": ("IMAGE",), + "vae": ("VAE",), + "width": ("INT", {"default": 1024, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "height": ("INT", {"default": 576, "min": 16, "max": ldm_patched.contrib.external.MAX_RESOLUTION, "step": 8}), + "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}), + "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}), + "fps": ("INT", {"default": 6, "min": 1, "max": 1024}), + "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01}) + }} + RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT") + RETURN_NAMES = ("positive", "negative", "latent") + + FUNCTION = "encode" + + CATEGORY = "conditioning/video_models" + + def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level): + output = clip_vision.encode_image(init_image) + pooled = output.image_embeds.unsqueeze(0) + pixels = ldm_patched.modules.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1) + encode_pixels = pixels[:,:,:,:3] + if augmentation_level > 0: + encode_pixels += torch.randn_like(pixels) * augmentation_level + t = vae.encode(encode_pixels) + positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]] + negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]] + latent = torch.zeros([video_frames, 4, height // 8, width // 8]) + return (positive, negative, {"samples":latent}) + +class VideoLinearCFGGuidance: + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), + }} + RETURN_TYPES = ("MODEL",) + FUNCTION = "patch" + + CATEGORY = "sampling/video_models" + + def patch(self, model, min_cfg): + def linear_cfg(args): + cond = args["cond"] + uncond = args["uncond"] + cond_scale = args["cond_scale"] + + scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1)) + return uncond + scale * (cond - uncond) + + m = model.clone() + m.set_model_sampler_cfg_function(linear_cfg) + return (m, ) + +class ImageOnlyCheckpointSave(ldm_patched.contrib.external_model_merging.CheckpointSave): + CATEGORY = "_for_testing" + + @classmethod + def INPUT_TYPES(s): + return {"required": { "model": ("MODEL",), + "clip_vision": ("CLIP_VISION",), + "vae": ("VAE",), + "filename_prefix": ("STRING", {"default": "checkpoints/ldm_patched"}),}, + "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},} + + def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None): + ldm_patched.contrib.external_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo) + return {} + +NODE_CLASS_MAPPINGS = { + "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader, + "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning, + "VideoLinearCFGGuidance": VideoLinearCFGGuidance, + "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave, +} + +NODE_DISPLAY_NAME_MAPPINGS = { + "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)", +} diff --git a/ldm_patched/controlnet/cldm.py b/ldm_patched/controlnet/cldm.py new file mode 100644 index 0000000000000000000000000000000000000000..82265ef955f6384895f0b530870195da7866fdbc --- /dev/null +++ b/ldm_patched/controlnet/cldm.py @@ -0,0 +1,312 @@ +#taken from: https://github.com/lllyasviel/ControlNet +#and modified + +import torch +import torch as th +import torch.nn as nn + +from ldm_patched.ldm.modules.diffusionmodules.util import ( + zero_module, + timestep_embedding, +) + +from ldm_patched.ldm.modules.attention import SpatialTransformer +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample +from ldm_patched.ldm.util import exists +import ldm_patched.modules.ops + +class ControlledUnetModel(UNetModel): + #implemented in the ldm unet + pass + +class ControlNet(nn.Module): + def __init__( + self, + image_size, + in_channels, + model_channels, + hint_channels, + num_res_blocks, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + dtype=torch.float32, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + transformer_depth_output=None, + device=None, + operations=ldm_patched.modules.ops.disable_weight_init, + **kwargs, + ): + super().__init__() + assert use_spatial_transformer == True, "use_spatial_transformer has to be true" + if use_spatial_transformer: + assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...' + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.dims = dims + self.image_size = image_size + self.in_channels = in_channels + self.model_channels = model_channels + + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks)))) + + transformer_depth = transformer_depth[:] + + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.predict_codebook_ids = n_embed is not None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim) + elif self.num_classes == "continuous": + print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels, operations=operations, dtype=self.dtype, device=device)]) + + self.input_hint_block = TimestepEmbedSequential( + operations.conv_nd(dims, hint_channels, 16, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 16, 16, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 16, 32, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 32, 32, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 32, 96, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 96, 96, 3, padding=1, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 96, 256, 3, padding=1, stride=2, dtype=self.dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, 256, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append( + SpatialTransformer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + self.zero_convs.append(self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + mid_block = [ + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + if transformer_depth_middle >= 0: + mid_block += [SpatialTransformer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + ), + ResBlock( + ch, + time_embed_dim, + dropout, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + self.middle_block = TimestepEmbedSequential(*mid_block) + self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device) + self._feature_size += ch + + def make_zero_conv(self, channels, operations=None, dtype=None, device=None): + return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device)) + + def forward(self, x, hint, timesteps, context, y=None, **kwargs): + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + guided_hint = self.input_hint_block(hint, emb, context) + + outs = [] + + hs = [] + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for module, zero_conv in zip(self.input_blocks, self.zero_convs): + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + outs.append(zero_conv(h, emb, context)) + + h = self.middle_block(h, emb, context) + outs.append(self.middle_block_out(h, emb, context)) + + return outs + diff --git a/ldm_patched/k_diffusion/sampling.py b/ldm_patched/k_diffusion/sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..761c2e0ef7cb66e2b2f918f7477bd5ca1801ea88 --- /dev/null +++ b/ldm_patched/k_diffusion/sampling.py @@ -0,0 +1,810 @@ +import math + +from scipy import integrate +import torch +from torch import nn +import torchsde +from tqdm.auto import trange, tqdm + +from . import utils + + +def append_zero(x): + return torch.cat([x, x.new_zeros([1])]) + + +def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'): + """Constructs the noise schedule of Karras et al. (2022).""" + ramp = torch.linspace(0, 1, n, device=device) + min_inv_rho = sigma_min ** (1 / rho) + max_inv_rho = sigma_max ** (1 / rho) + sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho + return append_zero(sigmas).to(device) + + +def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'): + """Constructs an exponential noise schedule.""" + sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp() + return append_zero(sigmas) + + +def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'): + """Constructs an polynomial in log sigma noise schedule.""" + ramp = torch.linspace(1, 0, n, device=device) ** rho + sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min)) + return append_zero(sigmas) + + +def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'): + """Constructs a continuous VP noise schedule.""" + t = torch.linspace(1, eps_s, n, device=device) + sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1) + return append_zero(sigmas) + + +def to_d(x, sigma, denoised): + """Converts a denoiser output to a Karras ODE derivative.""" + return (x - denoised) / utils.append_dims(sigma, x.ndim) + + +def get_ancestral_step(sigma_from, sigma_to, eta=1.): + """Calculates the noise level (sigma_down) to step down to and the amount + of noise to add (sigma_up) when doing an ancestral sampling step.""" + if not eta: + return sigma_to, 0. + sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5) + sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5 + return sigma_down, sigma_up + + +def default_noise_sampler(x): + return lambda sigma, sigma_next: torch.randn_like(x) + + +class BatchedBrownianTree: + """A wrapper around torchsde.BrownianTree that enables batches of entropy.""" + + def __init__(self, x, t0, t1, seed=None, **kwargs): + self.cpu_tree = True + if "cpu" in kwargs: + self.cpu_tree = kwargs.pop("cpu") + t0, t1, self.sign = self.sort(t0, t1) + w0 = kwargs.get('w0', torch.zeros_like(x)) + if seed is None: + seed = torch.randint(0, 2 ** 63 - 1, []).item() + self.batched = True + try: + assert len(seed) == x.shape[0] + w0 = w0[0] + except TypeError: + seed = [seed] + self.batched = False + if self.cpu_tree: + self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed] + else: + self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed] + + @staticmethod + def sort(a, b): + return (a, b, 1) if a < b else (b, a, -1) + + def __call__(self, t0, t1): + t0, t1, sign = self.sort(t0, t1) + if self.cpu_tree: + w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign) + else: + w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign) + + return w if self.batched else w[0] + + +class BrownianTreeNoiseSampler: + """A noise sampler backed by a torchsde.BrownianTree. + + Args: + x (Tensor): The tensor whose shape, device and dtype to use to generate + random samples. + sigma_min (float): The low end of the valid interval. + sigma_max (float): The high end of the valid interval. + seed (int or List[int]): The random seed. If a list of seeds is + supplied instead of a single integer, then the noise sampler will + use one BrownianTree per batch item, each with its own seed. + transform (callable): A function that maps sigma to the sampler's + internal timestep. + """ + + def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): + self.transform = transform + t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max)) + self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) + + def __call__(self, sigma, sigma_next): + t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next)) + return self.tree(t0, t1) / (t1 - t0).abs().sqrt() + + +@torch.no_grad() +def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Euler steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + # Euler method + x = x + d * dt + return x + + +@torch.no_grad() +def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with Euler method steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """Implements Algorithm 2 (Heun steps) from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == 0: + # Euler method + x = x + d * dt + else: + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + d_prime = (d + d_2) / 2 + x = x + d_prime * dt + return x + + +@torch.no_grad() +def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + eps = torch.randn_like(x) * s_noise + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + dt = sigmas[i + 1] - sigma_hat + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp() + dt_1 = sigma_mid - sigma_hat + dt_2 = sigmas[i + 1] - sigma_hat + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + return x + + +@torch.no_grad() +def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + d = to_d(x, sigmas[i], denoised) + if sigma_down == 0: + # Euler method + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver-2 + sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp() + dt_1 = sigma_mid - sigmas[i] + dt_2 = sigma_down - sigmas[i] + x_2 = x + d * dt_1 + denoised_2 = model(x_2, sigma_mid * s_in, **extra_args) + d_2 = to_d(x_2, sigma_mid, denoised_2) + x = x + d_2 * dt_2 + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +def linear_multistep_coeff(order, t, i, j): + if order - 1 > i: + raise ValueError(f'Order {order} too high for step {i}') + def fn(tau): + prod = 1. + for k in range(order): + if j == k: + continue + prod *= (tau - t[i - k]) / (t[i - j] - t[i - k]) + return prod + return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0] + + +@torch.no_grad() +def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4): + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigmas_cpu = sigmas.detach().cpu().numpy() + ds = [] + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + d = to_d(x, sigmas[i], denoised) + ds.append(d) + if len(ds) > order: + ds.pop(0) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + cur_order = min(i + 1, order) + coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)] + x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds))) + return x + + +class PIDStepSizeController: + """A PID controller for ODE adaptive step size control.""" + def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8): + self.h = h + self.b1 = (pcoeff + icoeff + dcoeff) / order + self.b2 = -(pcoeff + 2 * dcoeff) / order + self.b3 = dcoeff / order + self.accept_safety = accept_safety + self.eps = eps + self.errs = [] + + def limiter(self, x): + return 1 + math.atan(x - 1) + + def propose_step(self, error): + inv_error = 1 / (float(error) + self.eps) + if not self.errs: + self.errs = [inv_error, inv_error, inv_error] + self.errs[0] = inv_error + factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3 + factor = self.limiter(factor) + accept = factor >= self.accept_safety + if accept: + self.errs[2] = self.errs[1] + self.errs[1] = self.errs[0] + self.h *= factor + return accept + + +class DPMSolver(nn.Module): + """DPM-Solver. See https://arxiv.org/abs/2206.00927.""" + + def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None): + super().__init__() + self.model = model + self.extra_args = {} if extra_args is None else extra_args + self.eps_callback = eps_callback + self.info_callback = info_callback + + def t(self, sigma): + return -sigma.log() + + def sigma(self, t): + return t.neg().exp() + + def eps(self, eps_cache, key, x, t, *args, **kwargs): + if key in eps_cache: + return eps_cache[key], eps_cache + sigma = self.sigma(t) * x.new_ones([x.shape[0]]) + eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t) + if self.eps_callback is not None: + self.eps_callback() + return eps, {key: eps, **eps_cache} + + def dpm_solver_1_step(self, x, t, t_next, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + x_1 = x - self.sigma(t_next) * h.expm1() * eps + return x_1, eps_cache + + def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps) + return x_2, eps_cache + + def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None): + eps_cache = {} if eps_cache is None else eps_cache + h = t_next - t + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + s1 = t + r1 * h + s2 = t + r2 * h + u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps + eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1) + u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps) + eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2) + x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps) + return x_3, eps_cache + + def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if not t_end > t_start and eta: + raise ValueError('eta must be 0 for reverse sampling') + + m = math.floor(nfe / 3) + 1 + ts = torch.linspace(t_start, t_end, m + 1, device=x.device) + + if nfe % 3 == 0: + orders = [3] * (m - 2) + [2, 1] + else: + orders = [3] * (m - 1) + [nfe % 3] + + for i in range(len(orders)): + eps_cache = {} + t, t_next = ts[i], ts[i + 1] + if eta: + sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta) + t_next_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5 + else: + t_next_, su = t_next, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, t) + denoised = x - self.sigma(t) * eps + if self.info_callback is not None: + self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised}) + + if orders[i] == 1: + x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache) + elif orders[i] == 2: + x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache) + else: + x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache) + + x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next)) + + return x + + def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None): + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + if order not in {2, 3}: + raise ValueError('order should be 2 or 3') + forward = t_end > t_start + if not forward and eta: + raise ValueError('eta must be 0 for reverse sampling') + h_init = abs(h_init) * (1 if forward else -1) + atol = torch.tensor(atol) + rtol = torch.tensor(rtol) + s = t_start + x_prev = x + accept = True + pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety) + info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0} + + while s < t_end - 1e-5 if forward else s > t_end + 1e-5: + eps_cache = {} + t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h) + if eta: + sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta) + t_ = torch.minimum(t_end, self.t(sd)) + su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5 + else: + t_, su = t, 0. + + eps, eps_cache = self.eps(eps_cache, 'eps', x, s) + denoised = x - self.sigma(s) * eps + + if order == 2: + x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache) + else: + x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache) + x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache) + delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs())) + error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5 + accept = pid.propose_step(error) + if accept: + x_prev = x_low + x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t)) + s = t + info['n_accept'] += 1 + else: + info['n_reject'] += 1 + info['nfe'] += order + info['steps'] += 1 + + if self.info_callback is not None: + self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info}) + + return x, info + + +@torch.no_grad() +def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None): + """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(total=n, disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler) + + +@torch.no_grad() +def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False): + """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927.""" + if sigma_min <= 0 or sigma_max <= 0: + raise ValueError('sigma_min and sigma_max must not be 0') + with tqdm(disable=disable) as pbar: + dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update) + if callback is not None: + dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info}) + x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler) + if return_info: + return x, info + return x + + +@torch.no_grad() +def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """Ancestral sampling with DPM-Solver++(2S) second-order steps.""" + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigma_down == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigma_down - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++(2S) + t, t_next = t_fn(sigmas[i]), t_fn(sigma_down) + r = 1 / 2 + h = t_next - t + s = t + r * h + x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2 + # Noise addition + if sigmas[i + 1] > 0: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up + return x + + +@torch.no_grad() +def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + """DPM-Solver++ (stochastic).""" + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + seed = extra_args.get("seed", None) + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Euler method + d = to_d(x, sigmas[i], denoised) + dt = sigmas[i + 1] - sigmas[i] + x = x + d * dt + else: + # DPM-Solver++ + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + s = t + h * r + fac = 1 / (2 * r) + + # Step 1 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(s), eta) + s_ = t_fn(sd) + x_2 = (sigma_fn(s_) / sigma_fn(t)) * x - (t - s_).expm1() * denoised + x_2 = x_2 + noise_sampler(sigma_fn(t), sigma_fn(s)) * s_noise * su + denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args) + + # Step 2 + sd, su = get_ancestral_step(sigma_fn(t), sigma_fn(t_next), eta) + t_next_ = t_fn(sd) + denoised_d = (1 - fac) * denoised + fac * denoised_2 + x = (sigma_fn(t_next_) / sigma_fn(t)) * x - (t - t_next_).expm1() * denoised_d + x = x + noise_sampler(sigma_fn(t), sigma_fn(t_next)) * s_noise * su + return x + + +@torch.no_grad() +def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None): + """DPM-Solver++(2M).""" + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + sigma_fn = lambda t: t.neg().exp() + t_fn = lambda sigma: sigma.log().neg() + old_denoised = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1]) + h = t_next - t + if old_denoised is None or sigmas[i + 1] == 0: + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised + else: + h_last = t - t_fn(sigmas[i - 1]) + r = h_last / h + denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised + x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d + old_denoised = denoised + return x + +@torch.no_grad() +def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + """DPM-Solver++(2M) SDE.""" + + if solver_type not in {'heun', 'midpoint'}: + raise ValueError('solver_type must be \'heun\' or \'midpoint\'') + + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + old_denoised = None + h_last = None + h = None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + # DPM-Solver++(2M) SDE + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + eta_h = eta * h + + x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised + + if old_denoised is not None: + r = h_last / h + if solver_type == 'heun': + x = x + ((-h - eta_h).expm1().neg() / (-h - eta_h) + 1) * (1 / r) * (denoised - old_denoised) + elif solver_type == 'midpoint': + x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised) + + if eta: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * eta_h).expm1().neg().sqrt() * s_noise + + old_denoised = denoised + h_last = h + return x + +@torch.no_grad() +def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + """DPM-Solver++(3M) SDE.""" + + seed = extra_args.get("seed", None) + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + + denoised_1, denoised_2 = None, None + h, h_1, h_2 = None, None, None + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + if sigmas[i + 1] == 0: + # Denoising step + x = denoised + else: + t, s = -sigmas[i].log(), -sigmas[i + 1].log() + h = s - t + h_eta = h * (eta + 1) + + x = torch.exp(-h_eta) * x + (-h_eta).expm1().neg() * denoised + + if h_2 is not None: + r0 = h_1 / h + r1 = h_2 / h + d1_0 = (denoised - denoised_1) / r0 + d1_1 = (denoised_1 - denoised_2) / r1 + d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1) + d2 = (d1_0 - d1_1) / (r0 + r1) + phi_2 = h_eta.neg().expm1() / h_eta + 1 + phi_3 = phi_2 / h_eta - 0.5 + x = x + phi_2 * d1 - phi_3 * d2 + elif h_1 is not None: + r = h_1 / h + d = (denoised - denoised_1) / r + phi_2 = h_eta.neg().expm1() / h_eta + 1 + x = x + phi_2 * d + + if eta: + x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise + + denoised_1, denoised_2 = denoised, denoised_1 + h_1, h_2 = h, h_1 + return x + +@torch.no_grad() +def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None): + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler) + +@torch.no_grad() +def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'): + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type) + +@torch.no_grad() +def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2): + sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max() + noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler + return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r) + + +def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler): + alpha_cumprod = 1 / ((sigma * sigma) + 1) + alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1) + alpha = (alpha_cumprod / alpha_cumprod_prev) + + mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt()) + if sigma_prev > 0: + mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev) + return mu + +def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None): + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler) + if sigmas[i + 1] != 0: + x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0) + return x + + +@torch.no_grad() +def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step) + +@torch.no_grad() +def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None): + extra_args = {} if extra_args is None else extra_args + noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler + s_in = x.new_ones([x.shape[0]]) + for i in trange(len(sigmas) - 1, disable=disable): + denoised = model(x, sigmas[i] * s_in, **extra_args) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised}) + + x = denoised + if sigmas[i + 1] > 0: + x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1]) + return x + + + +@torch.no_grad() +def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.): + # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/ + extra_args = {} if extra_args is None else extra_args + s_in = x.new_ones([x.shape[0]]) + s_end = sigmas[-1] + for i in trange(len(sigmas) - 1, disable=disable): + gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0. + eps = torch.randn_like(x) * s_noise + sigma_hat = sigmas[i] * (gamma + 1) + if gamma > 0: + x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5 + denoised = model(x, sigma_hat * s_in, **extra_args) + d = to_d(x, sigma_hat, denoised) + if callback is not None: + callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised}) + dt = sigmas[i + 1] - sigma_hat + if sigmas[i + 1] == s_end: + # Euler method + x = x + d * dt + elif sigmas[i + 2] == s_end: + + # Heun's method + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + + w = 2 * sigmas[0] + w2 = sigmas[i+1]/w + w1 = 1 - w2 + + d_prime = d * w1 + d_2 * w2 + + + x = x + d_prime * dt + + else: + # Heun++ + x_2 = x + d * dt + denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args) + d_2 = to_d(x_2, sigmas[i + 1], denoised_2) + dt_2 = sigmas[i + 2] - sigmas[i + 1] + + x_3 = x_2 + d_2 * dt_2 + denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args) + d_3 = to_d(x_3, sigmas[i + 2], denoised_3) + + w = 3 * sigmas[0] + w2 = sigmas[i + 1] / w + w3 = sigmas[i + 2] / w + w1 = 1 - w2 - w3 + + d_prime = w1 * d + w2 * d_2 + w3 * d_3 + x = x + d_prime * dt + return x diff --git a/ldm_patched/k_diffusion/utils.py b/ldm_patched/k_diffusion/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a644df2f3cf82b32ac6e9bf2cb7bfc70c95e05f9 --- /dev/null +++ b/ldm_patched/k_diffusion/utils.py @@ -0,0 +1,313 @@ +from contextlib import contextmanager +import hashlib +import math +from pathlib import Path +import shutil +import urllib +import warnings + +from PIL import Image +import torch +from torch import nn, optim +from torch.utils import data + + +def hf_datasets_augs_helper(examples, transform, image_key, mode='RGB'): + """Apply passed in transforms for HuggingFace Datasets.""" + images = [transform(image.convert(mode)) for image in examples[image_key]] + return {image_key: images} + + +def append_dims(x, target_dims): + """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" + dims_to_append = target_dims - x.ndim + if dims_to_append < 0: + raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less') + expanded = x[(...,) + (None,) * dims_to_append] + # MPS will get inf values if it tries to index into the new axes, but detaching fixes this. + # https://github.com/pytorch/pytorch/issues/84364 + return expanded.detach().clone() if expanded.device.type == 'mps' else expanded + + +def n_params(module): + """Returns the number of trainable parameters in a module.""" + return sum(p.numel() for p in module.parameters()) + + +def download_file(path, url, digest=None): + """Downloads a file if it does not exist, optionally checking its SHA-256 hash.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + if not path.exists(): + with urllib.request.urlopen(url) as response, open(path, 'wb') as f: + shutil.copyfileobj(response, f) + if digest is not None: + file_digest = hashlib.sha256(open(path, 'rb').read()).hexdigest() + if digest != file_digest: + raise OSError(f'hash of {path} (url: {url}) failed to validate') + return path + + +@contextmanager +def train_mode(model, mode=True): + """A context manager that places a model into training mode and restores + the previous mode on exit.""" + modes = [module.training for module in model.modules()] + try: + yield model.train(mode) + finally: + for i, module in enumerate(model.modules()): + module.training = modes[i] + + +def eval_mode(model): + """A context manager that places a model into evaluation mode and restores + the previous mode on exit.""" + return train_mode(model, False) + + +@torch.no_grad() +def ema_update(model, averaged_model, decay): + """Incorporates updated model parameters into an exponential moving averaged + version of a model. It should be called after each optimizer step.""" + model_params = dict(model.named_parameters()) + averaged_params = dict(averaged_model.named_parameters()) + assert model_params.keys() == averaged_params.keys() + + for name, param in model_params.items(): + averaged_params[name].mul_(decay).add_(param, alpha=1 - decay) + + model_buffers = dict(model.named_buffers()) + averaged_buffers = dict(averaged_model.named_buffers()) + assert model_buffers.keys() == averaged_buffers.keys() + + for name, buf in model_buffers.items(): + averaged_buffers[name].copy_(buf) + + +class EMAWarmup: + """Implements an EMA warmup using an inverse decay schedule. + If inv_gamma=1 and power=1, implements a simple average. inv_gamma=1, power=2/3 are + good values for models you plan to train for a million or more steps (reaches decay + factor 0.999 at 31.6K steps, 0.9999 at 1M steps), inv_gamma=1, power=3/4 for models + you plan to train for less (reaches decay factor 0.999 at 10K steps, 0.9999 at + 215.4k steps). + Args: + inv_gamma (float): Inverse multiplicative factor of EMA warmup. Default: 1. + power (float): Exponential factor of EMA warmup. Default: 1. + min_value (float): The minimum EMA decay rate. Default: 0. + max_value (float): The maximum EMA decay rate. Default: 1. + start_at (int): The epoch to start averaging at. Default: 0. + last_epoch (int): The index of last epoch. Default: 0. + """ + + def __init__(self, inv_gamma=1., power=1., min_value=0., max_value=1., start_at=0, + last_epoch=0): + self.inv_gamma = inv_gamma + self.power = power + self.min_value = min_value + self.max_value = max_value + self.start_at = start_at + self.last_epoch = last_epoch + + def state_dict(self): + """Returns the state of the class as a :class:`dict`.""" + return dict(self.__dict__.items()) + + def load_state_dict(self, state_dict): + """Loads the class's state. + Args: + state_dict (dict): scaler state. Should be an object returned + from a call to :meth:`state_dict`. + """ + self.__dict__.update(state_dict) + + def get_value(self): + """Gets the current EMA decay rate.""" + epoch = max(0, self.last_epoch - self.start_at) + value = 1 - (1 + epoch / self.inv_gamma) ** -self.power + return 0. if epoch < 0 else min(self.max_value, max(self.min_value, value)) + + def step(self): + """Updates the step count.""" + self.last_epoch += 1 + + +class InverseLR(optim.lr_scheduler._LRScheduler): + """Implements an inverse decay learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. + inv_gamma is the number of steps/epochs required for the learning rate to decay to + (1 / 2)**power of its original value. + Args: + optimizer (Optimizer): Wrapped optimizer. + inv_gamma (float): Inverse multiplicative factor of learning rate decay. Default: 1. + power (float): Exponential factor of learning rate decay. Default: 1. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, inv_gamma=1., power=1., warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.inv_gamma = inv_gamma + self.power = power + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (1 + self.last_epoch / self.inv_gamma) ** -self.power + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +class ExponentialLR(optim.lr_scheduler._LRScheduler): + """Implements an exponential learning rate schedule with an optional exponential + warmup. When last_epoch=-1, sets initial lr as lr. Decays the learning rate + continuously by decay (default 0.5) every num_steps steps. + Args: + optimizer (Optimizer): Wrapped optimizer. + num_steps (float): The number of steps to decay the learning rate by decay in. + decay (float): The factor by which to decay the learning rate every num_steps + steps. Default: 0.5. + warmup (float): Exponential warmup factor (0 <= warmup < 1, 0 to disable) + Default: 0. + min_lr (float): The minimum learning rate. Default: 0. + last_epoch (int): The index of last epoch. Default: -1. + verbose (bool): If ``True``, prints a message to stdout for + each update. Default: ``False``. + """ + + def __init__(self, optimizer, num_steps, decay=0.5, warmup=0., min_lr=0., + last_epoch=-1, verbose=False): + self.num_steps = num_steps + self.decay = decay + if not 0. <= warmup < 1: + raise ValueError('Invalid value for warmup') + self.warmup = warmup + self.min_lr = min_lr + super().__init__(optimizer, last_epoch, verbose) + + def get_lr(self): + if not self._get_lr_called_within_step: + warnings.warn("To get the last learning rate computed by the scheduler, " + "please use `get_last_lr()`.") + + return self._get_closed_form_lr() + + def _get_closed_form_lr(self): + warmup = 1 - self.warmup ** (self.last_epoch + 1) + lr_mult = (self.decay ** (1 / self.num_steps)) ** self.last_epoch + return [warmup * max(self.min_lr, base_lr * lr_mult) + for base_lr in self.base_lrs] + + +def rand_log_normal(shape, loc=0., scale=1., device='cpu', dtype=torch.float32): + """Draws samples from an lognormal distribution.""" + return (torch.randn(shape, device=device, dtype=dtype) * scale + loc).exp() + + +def rand_log_logistic(shape, loc=0., scale=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from an optionally truncated log-logistic distribution.""" + min_value = torch.as_tensor(min_value, device=device, dtype=torch.float64) + max_value = torch.as_tensor(max_value, device=device, dtype=torch.float64) + min_cdf = min_value.log().sub(loc).div(scale).sigmoid() + max_cdf = max_value.log().sub(loc).div(scale).sigmoid() + u = torch.rand(shape, device=device, dtype=torch.float64) * (max_cdf - min_cdf) + min_cdf + return u.logit().mul(scale).add(loc).exp().to(dtype) + + +def rand_log_uniform(shape, min_value, max_value, device='cpu', dtype=torch.float32): + """Draws samples from an log-uniform distribution.""" + min_value = math.log(min_value) + max_value = math.log(max_value) + return (torch.rand(shape, device=device, dtype=dtype) * (max_value - min_value) + min_value).exp() + + +def rand_v_diffusion(shape, sigma_data=1., min_value=0., max_value=float('inf'), device='cpu', dtype=torch.float32): + """Draws samples from a truncated v-diffusion training timestep distribution.""" + min_cdf = math.atan(min_value / sigma_data) * 2 / math.pi + max_cdf = math.atan(max_value / sigma_data) * 2 / math.pi + u = torch.rand(shape, device=device, dtype=dtype) * (max_cdf - min_cdf) + min_cdf + return torch.tan(u * math.pi / 2) * sigma_data + + +def rand_split_log_normal(shape, loc, scale_1, scale_2, device='cpu', dtype=torch.float32): + """Draws samples from a split lognormal distribution.""" + n = torch.randn(shape, device=device, dtype=dtype).abs() + u = torch.rand(shape, device=device, dtype=dtype) + n_left = n * -scale_1 + loc + n_right = n * scale_2 + loc + ratio = scale_1 / (scale_1 + scale_2) + return torch.where(u < ratio, n_left, n_right).exp() + + +class FolderOfImages(data.Dataset): + """Recursively finds all images in a directory. It does not support + classes/targets.""" + + IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif', '.tiff', '.webp'} + + def __init__(self, root, transform=None): + super().__init__() + self.root = Path(root) + self.transform = nn.Identity() if transform is None else transform + self.paths = sorted(path for path in self.root.rglob('*') if path.suffix.lower() in self.IMG_EXTENSIONS) + + def __repr__(self): + return f'FolderOfImages(root="{self.root}", len: {len(self)})' + + def __len__(self): + return len(self.paths) + + def __getitem__(self, key): + path = self.paths[key] + with open(path, 'rb') as f: + image = Image.open(f).convert('RGB') + image = self.transform(image) + return image, + + +class CSVLogger: + def __init__(self, filename, columns): + self.filename = Path(filename) + self.columns = columns + if self.filename.exists(): + self.file = open(self.filename, 'a') + else: + self.file = open(self.filename, 'w') + self.write(*self.columns) + + def write(self, *args): + print(*args, sep=',', file=self.file, flush=True) + + +@contextmanager +def tf32_mode(cudnn=None, matmul=None): + """A context manager that sets whether TF32 is allowed on cuDNN or matmul.""" + cudnn_old = torch.backends.cudnn.allow_tf32 + matmul_old = torch.backends.cuda.matmul.allow_tf32 + try: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul + yield + finally: + if cudnn is not None: + torch.backends.cudnn.allow_tf32 = cudnn_old + if matmul is not None: + torch.backends.cuda.matmul.allow_tf32 = matmul_old diff --git a/ldm_patched/ldm/models/autoencoder.py b/ldm_patched/ldm/models/autoencoder.py new file mode 100644 index 0000000000000000000000000000000000000000..c809a0c31de0b4bf3bbb07395257e9014b4e3435 --- /dev/null +++ b/ldm_patched/ldm/models/autoencoder.py @@ -0,0 +1,228 @@ +import torch +# import pytorch_lightning as pl +import torch.nn.functional as F +from contextlib import contextmanager +from typing import Any, Dict, List, Optional, Tuple, Union + +from ldm_patched.ldm.modules.distributions.distributions import DiagonalGaussianDistribution + +from ldm_patched.ldm.util import instantiate_from_config +from ldm_patched.ldm.modules.ema import LitEma +import ldm_patched.modules.ops + +class DiagonalGaussianRegularizer(torch.nn.Module): + def __init__(self, sample: bool = True): + super().__init__() + self.sample = sample + + def get_trainable_parameters(self) -> Any: + yield from () + + def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]: + log = dict() + posterior = DiagonalGaussianDistribution(z) + if self.sample: + z = posterior.sample() + else: + z = posterior.mode() + kl_loss = posterior.kl() + kl_loss = torch.sum(kl_loss) / kl_loss.shape[0] + log["kl_loss"] = kl_loss + return z, log + + +class AbstractAutoencoder(torch.nn.Module): + """ + This is the base class for all autoencoders, including image autoencoders, image autoencoders with discriminators, + unCLIP models, etc. Hence, it is fairly general, and specific features + (e.g. discriminator training, encoding, decoding) must be implemented in subclasses. + """ + + def __init__( + self, + ema_decay: Union[None, float] = None, + monitor: Union[None, str] = None, + input_key: str = "jpg", + **kwargs, + ): + super().__init__() + + self.input_key = input_key + self.use_ema = ema_decay is not None + if monitor is not None: + self.monitor = monitor + + if self.use_ema: + self.model_ema = LitEma(self, decay=ema_decay) + logpy.info(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.") + + def get_input(self, batch) -> Any: + raise NotImplementedError() + + def on_train_batch_end(self, *args, **kwargs): + # for EMA computation + if self.use_ema: + self.model_ema(self) + + @contextmanager + def ema_scope(self, context=None): + if self.use_ema: + self.model_ema.store(self.parameters()) + self.model_ema.copy_to(self) + if context is not None: + logpy.info(f"{context}: Switched to EMA weights") + try: + yield None + finally: + if self.use_ema: + self.model_ema.restore(self.parameters()) + if context is not None: + logpy.info(f"{context}: Restored training weights") + + def encode(self, *args, **kwargs) -> torch.Tensor: + raise NotImplementedError("encode()-method of abstract base class called") + + def decode(self, *args, **kwargs) -> torch.Tensor: + raise NotImplementedError("decode()-method of abstract base class called") + + def instantiate_optimizer_from_config(self, params, lr, cfg): + logpy.info(f"loading >>> {cfg['target']} <<< optimizer from config") + return get_obj_from_str(cfg["target"])( + params, lr=lr, **cfg.get("params", dict()) + ) + + def configure_optimizers(self) -> Any: + raise NotImplementedError() + + +class AutoencodingEngine(AbstractAutoencoder): + """ + Base class for all image autoencoders that we train, like VQGAN or AutoencoderKL + (we also restore them explicitly as special cases for legacy reasons). + Regularizations such as KL or VQ are moved to the regularizer class. + """ + + def __init__( + self, + *args, + encoder_config: Dict, + decoder_config: Dict, + regularizer_config: Dict, + **kwargs, + ): + super().__init__(*args, **kwargs) + + self.encoder: torch.nn.Module = instantiate_from_config(encoder_config) + self.decoder: torch.nn.Module = instantiate_from_config(decoder_config) + self.regularization: AbstractRegularizer = instantiate_from_config( + regularizer_config + ) + + def get_last_layer(self): + return self.decoder.get_last_layer() + + def encode( + self, + x: torch.Tensor, + return_reg_log: bool = False, + unregularized: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: + z = self.encoder(x) + if unregularized: + return z, dict() + z, reg_log = self.regularization(z) + if return_reg_log: + return z, reg_log + return z + + def decode(self, z: torch.Tensor, **kwargs) -> torch.Tensor: + x = self.decoder(z, **kwargs) + return x + + def forward( + self, x: torch.Tensor, **additional_decode_kwargs + ) -> Tuple[torch.Tensor, torch.Tensor, dict]: + z, reg_log = self.encode(x, return_reg_log=True) + dec = self.decode(z, **additional_decode_kwargs) + return z, dec, reg_log + + +class AutoencodingEngineLegacy(AutoencodingEngine): + def __init__(self, embed_dim: int, **kwargs): + self.max_batch_size = kwargs.pop("max_batch_size", None) + ddconfig = kwargs.pop("ddconfig") + super().__init__( + encoder_config={ + "target": "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", + "params": ddconfig, + }, + decoder_config={ + "target": "ldm_patched.ldm.modules.diffusionmodules.model.Decoder", + "params": ddconfig, + }, + **kwargs, + ) + self.quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d( + (1 + ddconfig["double_z"]) * ddconfig["z_channels"], + (1 + ddconfig["double_z"]) * embed_dim, + 1, + ) + self.post_quant_conv = ldm_patched.modules.ops.disable_weight_init.Conv2d(embed_dim, ddconfig["z_channels"], 1) + self.embed_dim = embed_dim + + def get_autoencoder_params(self) -> list: + params = super().get_autoencoder_params() + return params + + def encode( + self, x: torch.Tensor, return_reg_log: bool = False + ) -> Union[torch.Tensor, Tuple[torch.Tensor, dict]]: + if self.max_batch_size is None: + z = self.encoder(x) + z = self.quant_conv(z) + else: + N = x.shape[0] + bs = self.max_batch_size + n_batches = int(math.ceil(N / bs)) + z = list() + for i_batch in range(n_batches): + z_batch = self.encoder(x[i_batch * bs : (i_batch + 1) * bs]) + z_batch = self.quant_conv(z_batch) + z.append(z_batch) + z = torch.cat(z, 0) + + z, reg_log = self.regularization(z) + if return_reg_log: + return z, reg_log + return z + + def decode(self, z: torch.Tensor, **decoder_kwargs) -> torch.Tensor: + if self.max_batch_size is None: + dec = self.post_quant_conv(z) + dec = self.decoder(dec, **decoder_kwargs) + else: + N = z.shape[0] + bs = self.max_batch_size + n_batches = int(math.ceil(N / bs)) + dec = list() + for i_batch in range(n_batches): + dec_batch = self.post_quant_conv(z[i_batch * bs : (i_batch + 1) * bs]) + dec_batch = self.decoder(dec_batch, **decoder_kwargs) + dec.append(dec_batch) + dec = torch.cat(dec, 0) + + return dec + + +class AutoencoderKL(AutoencodingEngineLegacy): + def __init__(self, **kwargs): + if "lossconfig" in kwargs: + kwargs["loss_config"] = kwargs.pop("lossconfig") + super().__init__( + regularizer_config={ + "target": ( + "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer" + ) + }, + **kwargs, + ) diff --git a/ldm_patched/ldm/modules/attention.py b/ldm_patched/ldm/modules/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..e10a868d26a4fb04292df4b6582711c660b17c40 --- /dev/null +++ b/ldm_patched/ldm/modules/attention.py @@ -0,0 +1,781 @@ +import math +import torch +import torch.nn.functional as F +from torch import nn, einsum +from einops import rearrange, repeat +from typing import Optional, Any + +from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding +from .sub_quadratic_attention import efficient_dot_product_attention + +from ldm_patched.modules import model_management + +if model_management.xformers_enabled(): + import xformers + import xformers.ops + +from ldm_patched.modules.args_parser import args +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init + +# CrossAttn precision handling +if args.disable_attention_upcast: + print("disabling upcasting of attention") + _ATTN_PRECISION = "fp16" +else: + _ATTN_PRECISION = "fp32" + + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d + + +def max_neg_value(t): + return -torch.finfo(t.dtype).max + + +def init_(tensor): + dim = tensor.shape[-1] + std = 1 / math.sqrt(dim) + tensor.uniform_(-std, std) + return tensor + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops): + super().__init__() + self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * F.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + operations.Linear(dim, inner_dim, dtype=dtype, device=device), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + operations.Linear(inner_dim, dim_out, dtype=dtype, device=device) + ) + + def forward(self, x): + return self.net(x) + +def Normalize(in_channels, dtype=None, device=None): + return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + +def attention_basic(q, k, v, heads, mask=None): + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 + + h = heads + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + # force cast to fp32 to avoid overflowing + if _ATTN_PRECISION =="fp32": + sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale + else: + sim = einsum('b i d, b j d -> b i j', q, k) * scale + + del q, k + + if exists(mask): + if mask.dtype == torch.bool: + mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention + max_neg_value = -torch.finfo(sim.dtype).max + mask = repeat(mask, 'b j -> (b h) () j', h=h) + sim.masked_fill_(~mask, max_neg_value) + else: + sim += mask + + # attention, what we cannot get enough of + sim = sim.softmax(dim=-1) + + out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v) + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return out + + +def attention_sub_quad(query, key, value, heads, mask=None): + b, _, dim_head = query.shape + dim_head //= heads + + scale = dim_head ** -0.5 + query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head) + + key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1) + + dtype = query.dtype + upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32 + if upcast_attention: + bytes_per_token = torch.finfo(torch.float32).bits//8 + else: + bytes_per_token = torch.finfo(query.dtype).bits//8 + batch_x_heads, q_tokens, _ = query.shape + _, _, k_tokens = key.shape + qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens + + mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True) + + kv_chunk_size_min = None + kv_chunk_size = None + query_chunk_size = None + + for x in [4096, 2048, 1024, 512, 256]: + count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0) + if count >= k_tokens: + kv_chunk_size = k_tokens + query_chunk_size = x + break + + if query_chunk_size is None: + query_chunk_size = 512 + + hidden_states = efficient_dot_product_attention( + query, + key, + value, + query_chunk_size=query_chunk_size, + kv_chunk_size=kv_chunk_size, + kv_chunk_size_min=kv_chunk_size_min, + use_checkpoint=False, + upcast_attention=upcast_attention, + mask=mask, + ) + + hidden_states = hidden_states.to(dtype) + + hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2) + return hidden_states + +def attention_split(q, k, v, heads, mask=None): + b, _, dim_head = q.shape + dim_head //= heads + scale = dim_head ** -0.5 + + h = heads + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype) + + mem_free_total = model_management.get_free_memory(q.device) + + if _ATTN_PRECISION =="fp32": + element_size = 4 + else: + element_size = q.element_size() + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size + modifier = 3 + mem_required = tensor_size * modifier + steps = 1 + + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + # print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB " + # f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}") + + if steps > 64: + max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64 + raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). ' + f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free') + + # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size) + first_op_done = False + cleared_cache = False + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + if _ATTN_PRECISION =="fp32": + with torch.autocast(enabled=False, device_type = 'cuda'): + s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale + else: + s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale + + if mask is not None: + if len(mask.shape) == 2: + s1 += mask[i:end] + else: + s1 += mask[:, i:end] + + s2 = s1.softmax(dim=-1).to(v.dtype) + del s1 + first_op_done = True + + r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v) + del s2 + break + except model_management.OOM_EXCEPTION as e: + if first_op_done == False: + model_management.soft_empty_cache(True) + if cleared_cache == False: + cleared_cache = True + print("out of memory error, emptying cache and trying again") + continue + steps *= 2 + if steps > 64: + raise e + print("out of memory error, increasing steps and trying again", steps) + else: + raise e + + del q, k, v + + r1 = ( + r1.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return r1 + +BROKEN_XFORMERS = False +try: + x_vers = xformers.__version__ + #I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error) + BROKEN_XFORMERS = x_vers.startswith("0.0.21") or x_vers.startswith("0.0.22") or x_vers.startswith("0.0.23") +except: + pass + +def attention_xformers(q, k, v, heads, mask=None): + b, _, dim_head = q.shape + dim_head //= heads + if BROKEN_XFORMERS: + if b * heads > 65535: + return attention_pytorch(q, k, v, heads, mask) + + q, k, v = map( + lambda t: t.unsqueeze(3) + .reshape(b, -1, heads, dim_head) + .permute(0, 2, 1, 3) + .reshape(b * heads, -1, dim_head) + .contiguous(), + (q, k, v), + ) + + if mask is not None: + pad = 8 - q.shape[1] % 8 + mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device) + mask_out[:, :, :mask.shape[-1]] = mask + mask = mask_out[:, :, :mask.shape[-1]] + + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask) + + out = ( + out.unsqueeze(0) + .reshape(b, heads, -1, dim_head) + .permute(0, 2, 1, 3) + .reshape(b, -1, heads * dim_head) + ) + return out + +def attention_pytorch(q, k, v, heads, mask=None): + b, _, dim_head = q.shape + dim_head //= heads + q, k, v = map( + lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2), + (q, k, v), + ) + + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False) + out = ( + out.transpose(1, 2).reshape(b, -1, heads * dim_head) + ) + return out + + +optimized_attention = attention_basic + +if model_management.xformers_enabled(): + print("Using xformers cross attention") + optimized_attention = attention_xformers +elif model_management.pytorch_attention_enabled(): + print("Using pytorch cross attention") + optimized_attention = attention_pytorch +else: + if args.attention_split: + print("Using split optimization for cross attention") + optimized_attention = attention_split + else: + print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --attention-split") + optimized_attention = attention_sub_quad + +optimized_attention_masked = optimized_attention + +def optimized_attention_for_device(device, mask=False, small_input=False): + if small_input: + if model_management.pytorch_attention_enabled(): + return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases + else: + return attention_basic + + if device == torch.device("cpu"): + return attention_sub_quad + + if mask: + return optimized_attention_masked + + return optimized_attention + + +class CrossAttention(nn.Module): + def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops): + super().__init__() + inner_dim = dim_head * heads + context_dim = default(context_dim, query_dim) + + self.heads = heads + self.dim_head = dim_head + + self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device) + + self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout)) + + def forward(self, x, context=None, value=None, mask=None): + q = self.to_q(x) + context = default(context, x) + k = self.to_k(context) + if value is not None: + v = self.to_v(value) + del value + else: + v = self.to_v(context) + + if mask is None: + out = optimized_attention(q, k, v, self.heads) + else: + out = optimized_attention_masked(q, k, v, self.heads, mask) + return self.to_out(out) + + +class BasicTransformerBlock(nn.Module): + def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None, + disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops): + super().__init__() + + self.ff_in = ff_in or inner_dim is not None + if inner_dim is None: + inner_dim = dim + + self.is_res = inner_dim == dim + + if self.ff_in: + self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device) + self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + + self.disable_self_attn = disable_self_attn + self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout, + context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn + self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations) + + if disable_temporal_crossattention: + if switch_temporal_ca_to_sa: + raise ValueError + else: + self.attn2 = None + else: + context_dim_attn2 = None + if not switch_temporal_ca_to_sa: + context_dim_attn2 = context_dim + + self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2, + heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none + self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + + self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device) + self.checkpoint = checkpoint + self.n_heads = n_heads + self.d_head = d_head + self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa + + def forward(self, x, context=None, transformer_options={}): + return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint) + + def _forward(self, x, context=None, transformer_options={}): + extra_options = {} + block = transformer_options.get("block", None) + block_index = transformer_options.get("block_index", 0) + transformer_patches = {} + transformer_patches_replace = {} + + for k in transformer_options: + if k == "patches": + transformer_patches = transformer_options[k] + elif k == "patches_replace": + transformer_patches_replace = transformer_options[k] + else: + extra_options[k] = transformer_options[k] + + extra_options["n_heads"] = self.n_heads + extra_options["dim_head"] = self.d_head + + if self.ff_in: + x_skip = x + x = self.ff_in(self.norm_in(x)) + if self.is_res: + x += x_skip + + n = self.norm1(x) + if self.disable_self_attn: + context_attn1 = context + else: + context_attn1 = None + value_attn1 = None + + if "attn1_patch" in transformer_patches: + patch = transformer_patches["attn1_patch"] + if context_attn1 is None: + context_attn1 = n + value_attn1 = context_attn1 + for p in patch: + n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options) + + if block is not None: + transformer_block = (block[0], block[1], block_index) + else: + transformer_block = None + attn1_replace_patch = transformer_patches_replace.get("attn1", {}) + block_attn1 = transformer_block + if block_attn1 not in attn1_replace_patch: + block_attn1 = block + + if block_attn1 in attn1_replace_patch: + if context_attn1 is None: + context_attn1 = n + value_attn1 = n + n = self.attn1.to_q(n) + context_attn1 = self.attn1.to_k(context_attn1) + value_attn1 = self.attn1.to_v(value_attn1) + n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options) + n = self.attn1.to_out(n) + else: + n = self.attn1(n, context=context_attn1, value=value_attn1) + + if "attn1_output_patch" in transformer_patches: + patch = transformer_patches["attn1_output_patch"] + for p in patch: + n = p(n, extra_options) + + x += n + if "middle_patch" in transformer_patches: + patch = transformer_patches["middle_patch"] + for p in patch: + x = p(x, extra_options) + + if self.attn2 is not None: + n = self.norm2(x) + if self.switch_temporal_ca_to_sa: + context_attn2 = n + else: + context_attn2 = context + value_attn2 = None + if "attn2_patch" in transformer_patches: + patch = transformer_patches["attn2_patch"] + value_attn2 = context_attn2 + for p in patch: + n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options) + + attn2_replace_patch = transformer_patches_replace.get("attn2", {}) + block_attn2 = transformer_block + if block_attn2 not in attn2_replace_patch: + block_attn2 = block + + if block_attn2 in attn2_replace_patch: + if value_attn2 is None: + value_attn2 = context_attn2 + n = self.attn2.to_q(n) + context_attn2 = self.attn2.to_k(context_attn2) + value_attn2 = self.attn2.to_v(value_attn2) + n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options) + n = self.attn2.to_out(n) + else: + n = self.attn2(n, context=context_attn2, value=value_attn2) + + if "attn2_output_patch" in transformer_patches: + patch = transformer_patches["attn2_output_patch"] + for p in patch: + n = p(n, extra_options) + + x += n + if self.is_res: + x_skip = x + x = self.ff(self.norm3(x)) + if self.is_res: + x += x_skip + + return x + + +class SpatialTransformer(nn.Module): + """ + Transformer block for image-like data. + First, project the input (aka embedding) + and reshape to b, t, d. + Then apply standard transformer action. + Finally, reshape to image + NEW: use_linear for more efficiency instead of the 1x1 convs + """ + def __init__(self, in_channels, n_heads, d_head, + depth=1, dropout=0., context_dim=None, + disable_self_attn=False, use_linear=False, + use_checkpoint=True, dtype=None, device=None, operations=ops): + super().__init__() + if exists(context_dim) and not isinstance(context_dim, list): + context_dim = [context_dim] * depth + self.in_channels = in_channels + inner_dim = n_heads * d_head + self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device) + if not use_linear: + self.proj_in = operations.Conv2d(in_channels, + inner_dim, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + + self.transformer_blocks = nn.ModuleList( + [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d], + disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations) + for d in range(depth)] + ) + if not use_linear: + self.proj_out = operations.Conv2d(inner_dim,in_channels, + kernel_size=1, + stride=1, + padding=0, dtype=dtype, device=device) + else: + self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device) + self.use_linear = use_linear + + def forward(self, x, context=None, transformer_options={}): + # note: if no context is given, cross-attention defaults to self-attention + if not isinstance(context, list): + context = [context] * len(self.transformer_blocks) + b, c, h, w = x.shape + x_in = x + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, 'b c h w -> b (h w) c').contiguous() + if self.use_linear: + x = self.proj_in(x) + for i, block in enumerate(self.transformer_blocks): + transformer_options["block_index"] = i + x = block(x, context=context[i], transformer_options=transformer_options) + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous() + if not self.use_linear: + x = self.proj_out(x) + return x + x_in + + +class SpatialVideoTransformer(SpatialTransformer): + def __init__( + self, + in_channels, + n_heads, + d_head, + depth=1, + dropout=0.0, + use_linear=False, + context_dim=None, + use_spatial_context=False, + timesteps=None, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + time_context_dim=None, + ff_in=False, + checkpoint=False, + time_depth=1, + disable_self_attn=False, + disable_temporal_crossattention=False, + max_time_embed_period: int = 10000, + dtype=None, device=None, operations=ops + ): + super().__init__( + in_channels, + n_heads, + d_head, + depth=depth, + dropout=dropout, + use_checkpoint=checkpoint, + context_dim=context_dim, + use_linear=use_linear, + disable_self_attn=disable_self_attn, + dtype=dtype, device=device, operations=operations + ) + self.time_depth = time_depth + self.depth = depth + self.max_time_embed_period = max_time_embed_period + + time_mix_d_head = d_head + n_time_mix_heads = n_heads + + time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads) + + inner_dim = n_heads * d_head + if use_spatial_context: + time_context_dim = context_dim + + self.time_stack = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + n_time_mix_heads, + time_mix_d_head, + dropout=dropout, + context_dim=time_context_dim, + # timesteps=timesteps, + checkpoint=checkpoint, + ff_in=ff_in, + inner_dim=time_mix_inner_dim, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + dtype=dtype, device=device, operations=operations + ) + for _ in range(self.depth) + ] + ) + + assert len(self.time_stack) == len(self.transformer_blocks) + + self.use_spatial_context = use_spatial_context + self.in_channels = in_channels + + time_embed_dim = self.in_channels * 4 + self.time_pos_embed = nn.Sequential( + operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device), + ) + + self.time_mixer = AlphaBlender( + alpha=merge_factor, merge_strategy=merge_strategy + ) + + def forward( + self, + x: torch.Tensor, + context: Optional[torch.Tensor] = None, + time_context: Optional[torch.Tensor] = None, + timesteps: Optional[int] = None, + image_only_indicator: Optional[torch.Tensor] = None, + transformer_options={} + ) -> torch.Tensor: + _, _, h, w = x.shape + x_in = x + spatial_context = None + if exists(context): + spatial_context = context + + if self.use_spatial_context: + assert ( + context.ndim == 3 + ), f"n dims of spatial context should be 3 but are {context.ndim}" + + if time_context is None: + time_context = context + time_context_first_timestep = time_context[::timesteps] + time_context = repeat( + time_context_first_timestep, "b ... -> (b n) ...", n=h * w + ) + elif time_context is not None and not self.use_spatial_context: + time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w) + if time_context.ndim == 2: + time_context = rearrange(time_context, "b c -> b 1 c") + + x = self.norm(x) + if not self.use_linear: + x = self.proj_in(x) + x = rearrange(x, "b c h w -> b (h w) c") + if self.use_linear: + x = self.proj_in(x) + + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype) + emb = self.time_pos_embed(t_emb) + emb = emb[:, None, :] + + for it_, (block, mix_block) in enumerate( + zip(self.transformer_blocks, self.time_stack) + ): + transformer_options["block_index"] = it_ + x = block( + x, + context=spatial_context, + transformer_options=transformer_options, + ) + + x_mix = x + x_mix = x_mix + emb + + B, S, C = x_mix.shape + x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps) + x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options + x_mix = rearrange( + x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps + ) + + x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator) + + if self.use_linear: + x = self.proj_out(x) + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + if not self.use_linear: + x = self.proj_out(x) + out = x + x_in + return out + + diff --git a/ldm_patched/ldm/modules/diffusionmodules/__init__.py b/ldm_patched/ldm/modules/diffusionmodules/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm_patched/ldm/modules/diffusionmodules/model.py b/ldm_patched/ldm/modules/diffusionmodules/model.py new file mode 100644 index 0000000000000000000000000000000000000000..1901145c59863b6503eb8bd28eb8c22b77219e17 --- /dev/null +++ b/ldm_patched/ldm/modules/diffusionmodules/model.py @@ -0,0 +1,650 @@ +# pytorch_diffusion + derived encoder decoder +import math +import torch +import torch.nn as nn +import numpy as np +from einops import rearrange +from typing import Optional, Any + +from ldm_patched.modules import model_management +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init + +if model_management.xformers_enabled_vae(): + import xformers + import xformers.ops + +def get_timestep_embedding(timesteps, embedding_dim): + """ + This matches the implementation in Denoising Diffusion Probabilistic Models: + From Fairseq. + Build sinusoidal embeddings. + This matches the implementation in tensor2tensor, but differs slightly + from the description in Section 3.5 of "Attention Is All You Need". + """ + assert len(timesteps.shape) == 1 + + half_dim = embedding_dim // 2 + emb = math.log(10000) / (half_dim - 1) + emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb) + emb = emb.to(device=timesteps.device) + emb = timesteps.float()[:, None] * emb[None, :] + emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) + if embedding_dim % 2 == 1: # zero pad + emb = torch.nn.functional.pad(emb, (0,1,0,0)) + return emb + + +def nonlinearity(x): + # swish + return x*torch.sigmoid(x) + + +def Normalize(in_channels, num_groups=32): + return ops.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = ops.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + try: + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + except: #operation not implemented for bf16 + b, c, h, w = x.shape + out = torch.empty((b, c, h*2, w*2), dtype=x.dtype, layout=x.layout, device=x.device) + split = 8 + l = out.shape[1] // split + for i in range(0, out.shape[1], l): + out[:,i:i+l] = torch.nn.functional.interpolate(x[:,i:i+l].to(torch.float32), scale_factor=2.0, mode="nearest").to(x.dtype) + del x + x = out + + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = ops.Conv2d(in_channels, + in_channels, + kernel_size=3, + stride=2, + padding=0) + + def forward(self, x): + if self.with_conv: + pad = (0,1,0,1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False, + dropout, temb_channels=512): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.swish = torch.nn.SiLU(inplace=True) + self.norm1 = Normalize(in_channels) + self.conv1 = ops.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if temb_channels > 0: + self.temb_proj = ops.Linear(temb_channels, + out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout, inplace=True) + self.conv2 = ops.Conv2d(out_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = ops.Conv2d(in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1) + else: + self.nin_shortcut = ops.Conv2d(in_channels, + out_channels, + kernel_size=1, + stride=1, + padding=0) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = self.swish(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(self.swish(temb))[:,:,None,None] + + h = self.norm2(h) + h = self.swish(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x+h + +def slice_attention(q, k, v): + r1 = torch.zeros_like(k, device=q.device) + scale = (int(q.shape[-1])**(-0.5)) + + mem_free_total = model_management.get_free_memory(q.device) + + gb = 1024 ** 3 + tensor_size = q.shape[0] * q.shape[1] * k.shape[2] * q.element_size() + modifier = 3 if q.element_size() == 2 else 2.5 + mem_required = tensor_size * modifier + steps = 1 + + if mem_required > mem_free_total: + steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2))) + + while True: + try: + slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1] + for i in range(0, q.shape[1], slice_size): + end = i + slice_size + s1 = torch.bmm(q[:, i:end], k) * scale + + s2 = torch.nn.functional.softmax(s1, dim=2).permute(0,2,1) + del s1 + + r1[:, :, i:end] = torch.bmm(v, s2) + del s2 + break + except model_management.OOM_EXCEPTION as e: + model_management.soft_empty_cache(True) + steps *= 2 + if steps > 128: + raise e + print("out of memory error, increasing steps and trying again", steps) + + return r1 + +def normal_attention(q, k, v): + # compute attention + b,c,h,w = q.shape + + q = q.reshape(b,c,h*w) + q = q.permute(0,2,1) # b,hw,c + k = k.reshape(b,c,h*w) # b,c,hw + v = v.reshape(b,c,h*w) + + r1 = slice_attention(q, k, v) + h_ = r1.reshape(b,c,h,w) + del r1 + return h_ + +def xformers_attention(q, k, v): + # compute attention + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, C, -1).transpose(1, 2).contiguous(), + (q, k, v), + ) + + try: + out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None) + out = out.transpose(1, 2).reshape(B, C, H, W) + except NotImplementedError as e: + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) + return out + +def pytorch_attention(q, k, v): + # compute attention + B, C, H, W = q.shape + q, k, v = map( + lambda t: t.view(B, 1, C, -1).transpose(2, 3).contiguous(), + (q, k, v), + ) + + try: + out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False) + out = out.transpose(2, 3).reshape(B, C, H, W) + except model_management.OOM_EXCEPTION as e: + print("scaled_dot_product_attention OOMed: switched to slice attention") + out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W) + return out + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = Normalize(in_channels) + self.q = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.k = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.v = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + self.proj_out = ops.Conv2d(in_channels, + in_channels, + kernel_size=1, + stride=1, + padding=0) + + if model_management.xformers_enabled_vae(): + print("Using xformers attention in VAE") + self.optimized_attention = xformers_attention + elif model_management.pytorch_attention_enabled(): + print("Using pytorch attention in VAE") + self.optimized_attention = pytorch_attention + else: + print("Using split attention in VAE") + self.optimized_attention = normal_attention + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + h_ = self.optimized_attention(q, k, v) + + h_ = self.proj_out(h_) + + return x+h_ + + +def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None): + return AttnBlock(in_channels) + + +class Model(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = self.ch*4 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + self.use_timestep = use_timestep + if self.use_timestep: + # timestep embedding + self.temb = nn.Module() + self.temb.dense = nn.ModuleList([ + ops.Linear(self.ch, + self.temb_ch), + ops.Linear(self.temb_ch, + self.temb_ch), + ]) + + # downsampling + self.conv_in = ops.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + skip_in = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + if i_block == self.num_res_blocks: + skip_in = ch*in_ch_mult[i_level] + block.append(ResnetBlock(in_channels=block_in+skip_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = ops.Conv2d(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x, t=None, context=None): + #assert x.shape[2] == x.shape[3] == self.resolution + if context is not None: + # assume aligned context, cat along channel axis + x = torch.cat((x, context), dim=1) + if self.use_timestep: + # timestep embedding + assert t is not None + temb = get_timestep_embedding(t, self.ch) + temb = self.temb.dense[0](temb) + temb = nonlinearity(temb) + temb = self.temb.dense[1](temb) + else: + temb = None + + # downsampling + hs = [self.conv_in(x)] + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](hs[-1], temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + hs.append(h) + if i_level != self.num_resolutions-1: + hs.append(self.down[i_level].downsample(hs[-1])) + + # middle + h = hs[-1] + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block]( + torch.cat([h, hs.pop()], dim=1), temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + def get_last_layer(self): + return self.conv_out.weight + + +class Encoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla", + **ignore_kwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + + # downsampling + self.conv_in = ops.Conv2d(in_channels, + self.ch, + kernel_size=3, + stride=1, + padding=1) + + curr_res = resolution + in_ch_mult = (1,)+tuple(ch_mult) + self.in_ch_mult = in_ch_mult + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch*in_ch_mult[i_level] + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append(ResnetBlock(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(make_attn(block_in, attn_type=attn_type)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions-1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = make_attn(block_in, attn_type=attn_type) + self.mid.block_2 = ResnetBlock(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = ops.Conv2d(block_in, + 2*z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, x): + # timestep embedding + temb = None + # downsampling + h = self.conv_in(x) + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + if i_level != self.num_resolutions-1: + h = self.down[i_level].downsample(h) + + # middle + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class Decoder(nn.Module): + def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks, + attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels, + resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False, + conv_out_op=ops.Conv2d, + resnet_op=ResnetBlock, + attn_op=AttnBlock, + **ignorekwargs): + super().__init__() + if use_linear_attn: attn_type = "linear" + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.tanh_out = tanh_out + + # compute in_ch_mult, block_in and curr_res at lowest res + in_ch_mult = (1,)+tuple(ch_mult) + block_in = ch*ch_mult[self.num_resolutions-1] + curr_res = resolution // 2**(self.num_resolutions-1) + self.z_shape = (1,z_channels,curr_res,curr_res) + print("Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape))) + + # z to block_in + self.conv_in = ops.Conv2d(z_channels, + block_in, + kernel_size=3, + stride=1, + padding=1) + + # middle + self.mid = nn.Module() + self.mid.block_1 = resnet_op(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + self.mid.attn_1 = attn_op(block_in) + self.mid.block_2 = resnet_op(in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch*ch_mult[i_level] + for i_block in range(self.num_res_blocks+1): + block.append(resnet_op(in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout)) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(attn_op(block_in)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = conv_out_op(block_in, + out_ch, + kernel_size=3, + stride=1, + padding=1) + + def forward(self, z, **kwargs): + #assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + h = self.mid.block_1(h, temb, **kwargs) + h = self.mid.attn_1(h, **kwargs) + h = self.mid.block_2(h, temb, **kwargs) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks+1): + h = self.up[i_level].block[i_block](h, temb, **kwargs) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h, **kwargs) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h, **kwargs) + if self.tanh_out: + h = torch.tanh(h) + return h diff --git a/ldm_patched/ldm/modules/diffusionmodules/openaimodel.py b/ldm_patched/ldm/modules/diffusionmodules/openaimodel.py new file mode 100644 index 0000000000000000000000000000000000000000..4b695f76ac7ef2d28e6548b0385991d353a0fa2c --- /dev/null +++ b/ldm_patched/ldm/modules/diffusionmodules/openaimodel.py @@ -0,0 +1,886 @@ +from abc import abstractmethod + +import torch as th +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from .util import ( + checkpoint, + avg_pool_nd, + zero_module, + timestep_embedding, + AlphaBlender, +) +from ..attention import SpatialTransformer, SpatialVideoTransformer, default +from ldm_patched.ldm.util import exists +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + +#This is needed because accelerate makes a copy of transformer_options which breaks "transformer_index" +def forward_timestep_embed(ts, x, emb, context=None, transformer_options={}, output_shape=None, time_context=None, num_video_frames=None, image_only_indicator=None): + for layer in ts: + if isinstance(layer, VideoResBlock): + x = layer(x, emb, num_video_frames, image_only_indicator) + elif isinstance(layer, TimestepBlock): + x = layer(x, emb) + elif isinstance(layer, SpatialVideoTransformer): + x = layer(x, context, time_context, num_video_frames, image_only_indicator, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 + elif isinstance(layer, SpatialTransformer): + x = layer(x, context, transformer_options) + if "transformer_index" in transformer_options: + transformer_options["transformer_index"] += 1 + elif isinstance(layer, Upsample): + x = layer(x, output_shape=output_shape) + else: + x = layer(x) + return x + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, *args, **kwargs): + return forward_timestep_embed(self, *args, **kwargs) + +class Upsample(nn.Module): + """ + An upsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + upsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + if use_conv: + self.conv = operations.conv_nd(dims, self.channels, self.out_channels, 3, padding=padding, dtype=dtype, device=device) + + def forward(self, x, output_shape=None): + assert x.shape[1] == self.channels + if self.dims == 3: + shape = [x.shape[2], x.shape[3] * 2, x.shape[4] * 2] + if output_shape is not None: + shape[1] = output_shape[3] + shape[2] = output_shape[4] + else: + shape = [x.shape[2] * 2, x.shape[3] * 2] + if output_shape is not None: + shape[0] = output_shape[2] + shape[1] = output_shape[3] + + x = F.interpolate(x, size=shape, mode="nearest") + if self.use_conv: + x = self.conv(x) + return x + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1, dtype=None, device=None, operations=ops): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = operations.conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding, dtype=dtype, device=device + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + return self.op(x) + + +class ResBlock(TimestepBlock): + """ + A residual block that can optionally change the number of channels. + :param channels: the number of input channels. + :param emb_channels: the number of timestep embedding channels. + :param dropout: the rate of dropout. + :param out_channels: if specified, the number of out channels. + :param use_conv: if True and out_channels is specified, use a spatial + convolution instead of a smaller 1x1 convolution to change the + channels in the skip connection. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param use_checkpoint: if True, use gradient checkpointing on this module. + :param up: if True, use this block for upsampling. + :param down: if True, use this block for downsampling. + """ + + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + use_conv=False, + use_scale_shift_norm=False, + dims=2, + use_checkpoint=False, + up=False, + down=False, + kernel_size=3, + exchange_temb_dims=False, + skip_t_emb=False, + dtype=None, + device=None, + operations=ops + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_checkpoint = use_checkpoint + self.use_scale_shift_norm = use_scale_shift_norm + self.exchange_temb_dims = exchange_temb_dims + + if isinstance(kernel_size, list): + padding = [k // 2 for k in kernel_size] + else: + padding = kernel_size // 2 + + self.in_layers = nn.Sequential( + operations.GroupNorm(32, channels, dtype=dtype, device=device), + nn.SiLU(), + operations.conv_nd(dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device), + ) + + self.updown = up or down + + if up: + self.h_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Upsample(channels, False, dims, dtype=dtype, device=device) + elif down: + self.h_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + self.x_upd = Downsample(channels, False, dims, dtype=dtype, device=device) + else: + self.h_upd = self.x_upd = nn.Identity() + + self.skip_t_emb = skip_t_emb + if self.skip_t_emb: + self.emb_layers = None + self.exchange_temb_dims = False + else: + self.emb_layers = nn.Sequential( + nn.SiLU(), + operations.Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, dtype=dtype, device=device + ), + ) + self.out_layers = nn.Sequential( + operations.GroupNorm(32, self.out_channels, dtype=dtype, device=device), + nn.SiLU(), + nn.Dropout(p=dropout), + operations.conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device) + , + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + elif use_conv: + self.skip_connection = operations.conv_nd( + dims, channels, self.out_channels, kernel_size, padding=padding, dtype=dtype, device=device + ) + else: + self.skip_connection = operations.conv_nd(dims, channels, self.out_channels, 1, dtype=dtype, device=device) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, (x, emb), self.parameters(), self.use_checkpoint + ) + + + def _forward(self, x, emb): + if self.updown: + in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] + h = in_rest(x) + h = self.h_upd(h) + x = self.x_upd(x) + h = in_conv(h) + else: + h = self.in_layers(x) + + emb_out = None + if not self.skip_t_emb: + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + h = out_norm(h) + if emb_out is not None: + scale, shift = th.chunk(emb_out, 2, dim=1) + h *= (1 + scale) + h += shift + h = out_rest(h) + else: + if emb_out is not None: + if self.exchange_temb_dims: + emb_out = rearrange(emb_out, "b t c ... -> b c t ...") + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class VideoResBlock(ResBlock): + def __init__( + self, + channels: int, + emb_channels: int, + dropout: float, + video_kernel_size=3, + merge_strategy: str = "fixed", + merge_factor: float = 0.5, + out_channels=None, + use_conv: bool = False, + use_scale_shift_norm: bool = False, + dims: int = 2, + use_checkpoint: bool = False, + up: bool = False, + down: bool = False, + dtype=None, + device=None, + operations=ops + ): + super().__init__( + channels, + emb_channels, + dropout, + out_channels=out_channels, + use_conv=use_conv, + use_scale_shift_norm=use_scale_shift_norm, + dims=dims, + use_checkpoint=use_checkpoint, + up=up, + down=down, + dtype=dtype, + device=device, + operations=operations + ) + + self.time_stack = ResBlock( + default(out_channels, channels), + emb_channels, + dropout=dropout, + dims=3, + out_channels=default(out_channels, channels), + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=use_checkpoint, + exchange_temb_dims=True, + dtype=dtype, + device=device, + operations=operations + ) + self.time_mixer = AlphaBlender( + alpha=merge_factor, + merge_strategy=merge_strategy, + rearrange_pattern="b t -> b 1 t 1 1", + ) + + def forward( + self, + x: th.Tensor, + emb: th.Tensor, + num_video_frames: int, + image_only_indicator = None, + ) -> th.Tensor: + x = super().forward(x, emb) + + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + x = rearrange(x, "(b t) c h w -> b c t h w", t=num_video_frames) + + x = self.time_stack( + x, rearrange(emb, "(b t) ... -> b t ...", t=num_video_frames) + ) + x = self.time_mixer( + x_spatial=x_mix, x_temporal=x, image_only_indicator=image_only_indicator + ) + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class Timestep(nn.Module): + def __init__(self, dim): + super().__init__() + self.dim = dim + + def forward(self, t): + return timestep_embedding(t, self.dim) + +def apply_control(h, control, name): + if control is not None and name in control and len(control[name]) > 0: + ctrl = control[name].pop() + if ctrl is not None: + try: + h += ctrl + except: + print("warning control could not be applied", h.shape, ctrl.shape) + return h + +class UNetModel(nn.Module): + """ + The full UNet model with attention and timestep embedding. + :param in_channels: channels in the input Tensor. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param dims: determines if the signal is 1D, 2D, or 3D. + :param num_classes: if specified (as an int), then this model will be + class-conditional with `num_classes` classes. + :param use_checkpoint: use gradient checkpointing to reduce memory usage. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + image_size, + in_channels, + model_channels, + out_channels, + num_res_blocks, + dropout=0, + channel_mult=(1, 2, 4, 8), + conv_resample=True, + dims=2, + num_classes=None, + use_checkpoint=False, + dtype=th.float32, + num_heads=-1, + num_head_channels=-1, + num_heads_upsample=-1, + use_scale_shift_norm=False, + resblock_updown=False, + use_new_attention_order=False, + use_spatial_transformer=False, # custom transformer support + transformer_depth=1, # custom transformer support + context_dim=None, # custom transformer support + n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model + legacy=True, + disable_self_attentions=None, + num_attention_blocks=None, + disable_middle_self_attn=False, + use_linear_in_transformer=False, + adm_in_channels=None, + transformer_depth_middle=None, + transformer_depth_output=None, + use_temporal_resblock=False, + use_temporal_attention=False, + time_context_dim=None, + extra_ff_mix_layer=False, + use_spatial_context=False, + merge_strategy=None, + merge_factor=0.0, + video_kernel_size=None, + disable_temporal_crossattention=False, + max_ddpm_temb_period=10000, + device=None, + operations=ops, + ): + super().__init__() + + if context_dim is not None: + assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...' + # from omegaconf.listconfig import ListConfig + # if type(context_dim) == ListConfig: + # context_dim = list(context_dim) + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if num_heads == -1: + assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set' + + if num_head_channels == -1: + assert num_heads != -1, 'Either num_heads or num_head_channels has to be set' + + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + + if isinstance(num_res_blocks, int): + self.num_res_blocks = len(channel_mult) * [num_res_blocks] + else: + if len(num_res_blocks) != len(channel_mult): + raise ValueError("provide num_res_blocks either as an int (globally constant) or " + "as a list/tuple (per-level) with the same length as channel_mult") + self.num_res_blocks = num_res_blocks + + if disable_self_attentions is not None: + # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not + assert len(disable_self_attentions) == len(channel_mult) + if num_attention_blocks is not None: + assert len(num_attention_blocks) == len(self.num_res_blocks) + + transformer_depth = transformer_depth[:] + transformer_depth_output = transformer_depth_output[:] + + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_classes = num_classes + self.use_checkpoint = use_checkpoint + self.dtype = dtype + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.use_temporal_resblocks = use_temporal_resblock + self.predict_codebook_ids = n_embed is not None + + self.default_num_video_frames = None + self.default_image_only_indicator = None + + time_embed_dim = model_channels * 4 + self.time_embed = nn.Sequential( + operations.Linear(model_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + + if self.num_classes is not None: + if isinstance(self.num_classes, int): + self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device) + elif self.num_classes == "continuous": + print("setting up linear c_adm embedding layer") + self.label_emb = nn.Linear(1, time_embed_dim) + elif self.num_classes == "sequential": + assert adm_in_channels is not None + self.label_emb = nn.Sequential( + nn.Sequential( + operations.Linear(adm_in_channels, time_embed_dim, dtype=self.dtype, device=device), + nn.SiLU(), + operations.Linear(time_embed_dim, time_embed_dim, dtype=self.dtype, device=device), + ) + ) + else: + raise ValueError() + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + operations.conv_nd(dims, in_channels, model_channels, 3, padding=1, dtype=self.dtype, device=device) + ) + ] + ) + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + + def get_attention_layer( + ch, + num_heads, + dim_head, + depth=1, + context_dim=None, + use_checkpoint=False, + disable_self_attn=False, + ): + if use_temporal_attention: + return SpatialVideoTransformer( + ch, + num_heads, + dim_head, + depth=depth, + context_dim=context_dim, + time_context_dim=time_context_dim, + dropout=dropout, + ff_in=extra_ff_mix_layer, + use_spatial_context=use_spatial_context, + merge_strategy=merge_strategy, + merge_factor=merge_factor, + checkpoint=use_checkpoint, + use_linear=use_linear_in_transformer, + disable_self_attn=disable_self_attn, + disable_temporal_crossattention=disable_temporal_crossattention, + max_time_embed_period=max_ddpm_temb_period, + dtype=self.dtype, device=device, operations=operations + ) + else: + return SpatialTransformer( + ch, num_heads, dim_head, depth=depth, context_dim=context_dim, + disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer, + use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations + ) + + def get_resblock( + merge_factor, + merge_strategy, + video_kernel_size, + ch, + time_embed_dim, + dropout, + out_channels, + dims, + use_checkpoint, + use_scale_shift_norm, + down=False, + up=False, + dtype=None, + device=None, + operations=ops + ): + if self.use_temporal_resblocks: + return VideoResBlock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + else: + return ResBlock( + channels=ch, + emb_channels=time_embed_dim, + dropout=dropout, + out_channels=out_channels, + use_checkpoint=use_checkpoint, + dims=dims, + use_scale_shift_norm=use_scale_shift_norm, + down=down, + up=up, + dtype=dtype, + device=device, + operations=operations + ) + + for level, mult in enumerate(channel_mult): + for nr in range(self.num_res_blocks[level]): + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=mult * model_channels, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations, + ) + ] + ch = mult * model_channels + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or nr < num_attention_blocks[level]: + layers.append(get_attention_layer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + down=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Downsample( + ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + mid_block = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + if transformer_depth_middle >= 0: + mid_block += [get_attention_layer( # always uses a self-attn + ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim, + disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint + ), + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=None, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + )] + self.middle_block = TimestepEmbedSequential(*mid_block) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, mult in list(enumerate(channel_mult))[::-1]: + for i in range(self.num_res_blocks[level] + 1): + ich = input_block_chans.pop() + layers = [ + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch + ich, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=model_channels * mult, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + dtype=self.dtype, + device=device, + operations=operations + ) + ] + ch = model_channels * mult + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + if num_head_channels == -1: + dim_head = ch // num_heads + else: + num_heads = ch // num_head_channels + dim_head = num_head_channels + if legacy: + #num_heads = 1 + dim_head = ch // num_heads if use_spatial_transformer else num_head_channels + if exists(disable_self_attentions): + disabled_sa = disable_self_attentions[level] + else: + disabled_sa = False + + if not exists(num_attention_blocks) or i < num_attention_blocks[level]: + layers.append( + get_attention_layer( + ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim, + disable_self_attn=disabled_sa, use_checkpoint=use_checkpoint + ) + ) + if level and i == self.num_res_blocks[level]: + out_ch = ch + layers.append( + get_resblock( + merge_factor=merge_factor, + merge_strategy=merge_strategy, + video_kernel_size=video_kernel_size, + ch=ch, + time_embed_dim=time_embed_dim, + dropout=dropout, + out_channels=out_ch, + dims=dims, + use_checkpoint=use_checkpoint, + use_scale_shift_norm=use_scale_shift_norm, + up=True, + dtype=self.dtype, + device=device, + operations=operations + ) + if resblock_updown + else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, dtype=self.dtype, device=device, operations=operations) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), + nn.SiLU(), + zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)), + ) + if self.predict_codebook_ids: + self.id_predictor = nn.Sequential( + operations.GroupNorm(32, ch, dtype=self.dtype, device=device), + operations.conv_nd(dims, model_channels, n_embed, 1, dtype=self.dtype, device=device), + #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits + ) + + def forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): + """ + Apply the model to an input batch. + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param context: conditioning plugged in via crossattn + :param y: an [N] Tensor of labels, if class-conditional. + :return: an [N x C x ...] Tensor of outputs. + """ + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for id, module in enumerate(self.input_blocks): + transformer_options["block"] = ("input", id) + h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'input') + if "input_block_patch" in transformer_patches: + patch = transformer_patches["input_block_patch"] + for p in patch: + h = p(h, transformer_options) + + hs.append(h) + if "input_block_patch_after_skip" in transformer_patches: + patch = transformer_patches["input_block_patch_after_skip"] + for p in patch: + h = p(h, transformer_options) + + transformer_options["block"] = ("middle", 0) + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'middle') + + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + hsp = apply_control(hsp, control, 'output') + + if "output_block_patch" in transformer_patches: + patch = transformer_patches["output_block_patch"] + for p in patch: + h, hsp = p(h, hsp, transformer_options) + + h = th.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) diff --git a/ldm_patched/ldm/modules/diffusionmodules/upscaling.py b/ldm_patched/ldm/modules/diffusionmodules/upscaling.py new file mode 100644 index 0000000000000000000000000000000000000000..a38bff57794be08f5175386384fe85d68b38536f --- /dev/null +++ b/ldm_patched/ldm/modules/diffusionmodules/upscaling.py @@ -0,0 +1,85 @@ +import torch +import torch.nn as nn +import numpy as np +from functools import partial + +from .util import extract_into_tensor, make_beta_schedule +from ldm_patched.ldm.util import default + + +class AbstractLowScaleModel(nn.Module): + # for concatenating a downsampled image to the latent representation + def __init__(self, noise_schedule_config=None): + super(AbstractLowScaleModel, self).__init__() + if noise_schedule_config is not None: + self.register_schedule(**noise_schedule_config) + + def register_schedule(self, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, + cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep' + + to_torch = partial(torch.tensor, dtype=torch.float32) + + self.register_buffer('betas', to_torch(betas)) + self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod)) + self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev)) + + # calculations for diffusion q(x_t | x_{t-1}) and others + self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod))) + self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod))) + self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod))) + self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod))) + self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1))) + + def q_sample(self, x_start, t, noise=None, seed=None): + if noise is None: + if seed is None: + noise = torch.randn_like(x_start) + else: + noise = torch.randn(x_start.size(), dtype=x_start.dtype, layout=x_start.layout, generator=torch.manual_seed(seed)).to(x_start.device) + return (extract_into_tensor(self.sqrt_alphas_cumprod.to(x_start.device), t, x_start.shape) * x_start + + extract_into_tensor(self.sqrt_one_minus_alphas_cumprod.to(x_start.device), t, x_start.shape) * noise) + + def forward(self, x): + return x, None + + def decode(self, x): + return x + + +class SimpleImageConcat(AbstractLowScaleModel): + # no noise level conditioning + def __init__(self): + super(SimpleImageConcat, self).__init__(noise_schedule_config=None) + self.max_noise_level = 0 + + def forward(self, x): + # fix to constant noise level + return x, torch.zeros(x.shape[0], device=x.device).long() + + +class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel): + def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False): + super().__init__(noise_schedule_config=noise_schedule_config) + self.max_noise_level = max_noise_level + + def forward(self, x, noise_level=None, seed=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + z = self.q_sample(x, noise_level, seed=seed) + return z, noise_level + + + diff --git a/ldm_patched/ldm/modules/diffusionmodules/util.py b/ldm_patched/ldm/modules/diffusionmodules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..e261e06a3fd74fe38a0f986891d92771a6a72ab4 --- /dev/null +++ b/ldm_patched/ldm/modules/diffusionmodules/util.py @@ -0,0 +1,304 @@ +# adopted from +# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py +# and +# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py +# and +# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py +# +# thanks! + + +import os +import math +import torch +import torch.nn as nn +import numpy as np +from einops import repeat, rearrange + +from ldm_patched.ldm.util import instantiate_from_config + +class AlphaBlender(nn.Module): + strategies = ["learned", "fixed", "learned_with_images"] + + def __init__( + self, + alpha: float, + merge_strategy: str = "learned_with_images", + rearrange_pattern: str = "b t -> (b t) 1 1", + ): + super().__init__() + self.merge_strategy = merge_strategy + self.rearrange_pattern = rearrange_pattern + + assert ( + merge_strategy in self.strategies + ), f"merge_strategy needs to be in {self.strategies}" + + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif ( + self.merge_strategy == "learned" + or self.merge_strategy == "learned_with_images" + ): + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor: + # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t) + if self.merge_strategy == "fixed": + # make shape compatible + # alpha = repeat(self.mix_factor, '1 -> b () t () ()', t=t, b=bs) + alpha = self.mix_factor.to(image_only_indicator.device) + elif self.merge_strategy == "learned": + alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device)) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + elif self.merge_strategy == "learned_with_images": + assert image_only_indicator is not None, "need image_only_indicator ..." + alpha = torch.where( + image_only_indicator.bool(), + torch.ones(1, 1, device=image_only_indicator.device), + rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"), + ) + alpha = rearrange(alpha, self.rearrange_pattern) + # make shape compatible + # alpha = repeat(alpha, '1 -> s () ()', s = t * bs) + else: + raise NotImplementedError() + return alpha + + def forward( + self, + x_spatial, + x_temporal, + image_only_indicator=None, + ) -> torch.Tensor: + alpha = self.get_alpha(image_only_indicator) + x = ( + alpha.to(x_spatial.dtype) * x_spatial + + (1.0 - alpha).to(x_spatial.dtype) * x_temporal + ) + return x + + +def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if schedule == "linear": + betas = ( + torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2 + ) + + elif schedule == "cosine": + timesteps = ( + torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s + ) + alphas = timesteps / (1 + cosine_s) * np.pi / 2 + alphas = torch.cos(alphas).pow(2) + alphas = alphas / alphas[0] + betas = 1 - alphas[1:] / alphas[:-1] + betas = np.clip(betas, a_min=0, a_max=0.999) + + elif schedule == "squaredcos_cap_v2": # used for karlo prior + # return early + return betas_for_alpha_bar( + n_timestep, + lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2, + ) + + elif schedule == "sqrt_linear": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) + elif schedule == "sqrt": + betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5 + else: + raise ValueError(f"schedule '{schedule}' unknown.") + return betas.numpy() + + +def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True): + if ddim_discr_method == 'uniform': + c = num_ddpm_timesteps // num_ddim_timesteps + ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c))) + elif ddim_discr_method == 'quad': + ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int) + else: + raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"') + + # assert ddim_timesteps.shape[0] == num_ddim_timesteps + # add one to get the final alpha values right (the ones from first scale to data during sampling) + steps_out = ddim_timesteps + 1 + if verbose: + print(f'Selected timesteps for ddim sampler: {steps_out}') + return steps_out + + +def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True): + # select alphas for computing the variance schedule + alphas = alphacums[ddim_timesteps] + alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist()) + + # according the the formula provided in https://arxiv.org/abs/2010.02502 + sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev)) + if verbose: + print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}') + print(f'For the chosen value of eta, which is {eta}, ' + f'this results in the following sigma_t schedule for ddim sampler {sigmas}') + return sigmas, alphas, alphas_prev + + +def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999): + """ + Create a beta schedule that discretizes the given alpha_t_bar function, + which defines the cumulative product of (1-beta) over time from t = [0,1]. + :param num_diffusion_timesteps: the number of betas to produce. + :param alpha_bar: a lambda that takes an argument t from 0 to 1 and + produces the cumulative product of (1-beta) up to that + part of the diffusion process. + :param max_beta: the maximum beta to use; use values lower than 1 to + prevent singularities. + """ + betas = [] + for i in range(num_diffusion_timesteps): + t1 = i / num_diffusion_timesteps + t2 = (i + 1) / num_diffusion_timesteps + betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta)) + return np.array(betas) + + +def extract_into_tensor(a, t, x_shape): + b, *_ = t.shape + out = a.gather(-1, t) + return out.reshape(b, *((1,) * (len(x_shape) - 1))) + + +def checkpoint(func, inputs, params, flag): + """ + Evaluate a function without caching intermediate activations, allowing for + reduced memory at the expense of extra compute in the backward pass. + :param func: the function to evaluate. + :param inputs: the argument sequence to pass to `func`. + :param params: a sequence of parameters `func` depends on but does not + explicitly take as arguments. + :param flag: if False, disable gradient checkpointing. + """ + if flag: + args = tuple(inputs) + tuple(params) + return CheckpointFunction.apply(func, len(inputs), *args) + else: + return func(*inputs) + + +class CheckpointFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, run_function, length, *args): + ctx.run_function = run_function + ctx.input_tensors = list(args[:length]) + ctx.input_params = list(args[length:]) + ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(), + "dtype": torch.get_autocast_gpu_dtype(), + "cache_enabled": torch.is_autocast_cache_enabled()} + with torch.no_grad(): + output_tensors = ctx.run_function(*ctx.input_tensors) + return output_tensors + + @staticmethod + def backward(ctx, *output_grads): + ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors] + with torch.enable_grad(), \ + torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs): + # Fixes a bug where the first op in run_function modifies the + # Tensor storage in place, which is not allowed for detach()'d + # Tensors. + shallow_copies = [x.view_as(x) for x in ctx.input_tensors] + output_tensors = ctx.run_function(*shallow_copies) + input_grads = torch.autograd.grad( + output_tensors, + ctx.input_tensors + ctx.input_params, + output_grads, + allow_unused=True, + ) + del ctx.input_tensors + del ctx.input_params + del output_tensors + return (None, None) + input_grads + + +def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + """ + Create sinusoidal timestep embeddings. + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device) / half + ) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def zero_module(module): + """ + Zero out the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().zero_() + return module + + +def scale_module(module, scale): + """ + Scale the parameters of a module and return it. + """ + for p in module.parameters(): + p.detach().mul_(scale) + return module + + +def mean_flat(tensor): + """ + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class HybridConditioner(nn.Module): + + def __init__(self, c_concat_config, c_crossattn_config): + super().__init__() + self.concat_conditioner = instantiate_from_config(c_concat_config) + self.crossattn_conditioner = instantiate_from_config(c_crossattn_config) + + def forward(self, c_concat, c_crossattn): + c_concat = self.concat_conditioner(c_concat) + c_crossattn = self.crossattn_conditioner(c_crossattn) + return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]} + + +def noise_like(shape, device, repeat=False): + repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1))) + noise = lambda: torch.randn(shape, device=device) + return repeat_noise() if repeat else noise() diff --git a/ldm_patched/ldm/modules/distributions/__init__.py b/ldm_patched/ldm/modules/distributions/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm_patched/ldm/modules/distributions/distributions.py b/ldm_patched/ldm/modules/distributions/distributions.py new file mode 100644 index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9 --- /dev/null +++ b/ldm_patched/ldm/modules/distributions/distributions.py @@ -0,0 +1,92 @@ +import torch +import numpy as np + + +class AbstractDistribution: + def sample(self): + raise NotImplementedError() + + def mode(self): + raise NotImplementedError() + + +class DiracDistribution(AbstractDistribution): + def __init__(self, value): + self.value = value + + def sample(self): + return self.value + + def mode(self): + return self.value + + +class DiagonalGaussianDistribution(object): + def __init__(self, parameters, deterministic=False): + self.parameters = parameters + self.mean, self.logvar = torch.chunk(parameters, 2, dim=1) + self.logvar = torch.clamp(self.logvar, -30.0, 20.0) + self.deterministic = deterministic + self.std = torch.exp(0.5 * self.logvar) + self.var = torch.exp(self.logvar) + if self.deterministic: + self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device) + + def sample(self): + x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device) + return x + + def kl(self, other=None): + if self.deterministic: + return torch.Tensor([0.]) + else: + if other is None: + return 0.5 * torch.sum(torch.pow(self.mean, 2) + + self.var - 1.0 - self.logvar, + dim=[1, 2, 3]) + else: + return 0.5 * torch.sum( + torch.pow(self.mean - other.mean, 2) / other.var + + self.var / other.var - 1.0 - self.logvar + other.logvar, + dim=[1, 2, 3]) + + def nll(self, sample, dims=[1,2,3]): + if self.deterministic: + return torch.Tensor([0.]) + logtwopi = np.log(2.0 * np.pi) + return 0.5 * torch.sum( + logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, + dim=dims) + + def mode(self): + return self.mean + + +def normal_kl(mean1, logvar1, mean2, logvar2): + """ + source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 + Compute the KL divergence between two gaussians. + Shapes are automatically broadcasted, so batches can be compared to + scalars, among other use cases. + """ + tensor = None + for obj in (mean1, logvar1, mean2, logvar2): + if isinstance(obj, torch.Tensor): + tensor = obj + break + assert tensor is not None, "at least one argument must be a Tensor" + + # Force variances to be Tensors. Broadcasting helps convert scalars to + # Tensors, but it does not work for torch.exp(). + logvar1, logvar2 = [ + x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor) + for x in (logvar1, logvar2) + ] + + return 0.5 * ( + -1.0 + + logvar2 + - logvar1 + + torch.exp(logvar1 - logvar2) + + ((mean1 - mean2) ** 2) * torch.exp(-logvar2) + ) diff --git a/ldm_patched/ldm/modules/ema.py b/ldm_patched/ldm/modules/ema.py new file mode 100644 index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4 --- /dev/null +++ b/ldm_patched/ldm/modules/ema.py @@ -0,0 +1,80 @@ +import torch +from torch import nn + + +class LitEma(nn.Module): + def __init__(self, model, decay=0.9999, use_num_upates=True): + super().__init__() + if decay < 0.0 or decay > 1.0: + raise ValueError('Decay must be between 0 and 1') + + self.m_name2s_name = {} + self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32)) + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates + else torch.tensor(-1, dtype=torch.int)) + + for name, p in model.named_parameters(): + if p.requires_grad: + # remove as '.'-character is not allowed in buffers + s_name = name.replace('.', '') + self.m_name2s_name.update({name: s_name}) + self.register_buffer(s_name, p.clone().detach().data) + + self.collected_params = [] + + def reset_num_updates(self): + del self.num_updates + self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int)) + + def forward(self, model): + decay = self.decay + + if self.num_updates >= 0: + self.num_updates += 1 + decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates)) + + one_minus_decay = 1.0 - decay + + with torch.no_grad(): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + + for key in m_param: + if m_param[key].requires_grad: + sname = self.m_name2s_name[key] + shadow_params[sname] = shadow_params[sname].type_as(m_param[key]) + shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key])) + else: + assert not key in self.m_name2s_name + + def copy_to(self, model): + m_param = dict(model.named_parameters()) + shadow_params = dict(self.named_buffers()) + for key in m_param: + if m_param[key].requires_grad: + m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data) + else: + assert not key in self.m_name2s_name + + def store(self, parameters): + """ + Save the current parameters for restoring later. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + temporarily stored. + """ + self.collected_params = [param.clone() for param in parameters] + + def restore(self, parameters): + """ + Restore the parameters stored with the `store` method. + Useful to validate the model with EMA parameters without affecting the + original optimization process. Store the parameters before the + `copy_to` method. After validation (or model saving), use this to + restore the former parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored parameters. + """ + for c_param, param in zip(self.collected_params, parameters): + param.data.copy_(c_param.data) diff --git a/ldm_patched/ldm/modules/encoders/__init__.py b/ldm_patched/ldm/modules/encoders/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm_patched/ldm/modules/encoders/noise_aug_modules.py b/ldm_patched/ldm/modules/encoders/noise_aug_modules.py new file mode 100644 index 0000000000000000000000000000000000000000..a5d8660301636fde75808cba50afa539cf1162e0 --- /dev/null +++ b/ldm_patched/ldm/modules/encoders/noise_aug_modules.py @@ -0,0 +1,35 @@ +from ..diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +from ..diffusionmodules.openaimodel import Timestep +import torch + +class CLIPEmbeddingNoiseAugmentation(ImageConcatWithNoiseAugmentation): + def __init__(self, *args, clip_stats_path=None, timestep_dim=256, **kwargs): + super().__init__(*args, **kwargs) + if clip_stats_path is None: + clip_mean, clip_std = torch.zeros(timestep_dim), torch.ones(timestep_dim) + else: + clip_mean, clip_std = torch.load(clip_stats_path, map_location="cpu") + self.register_buffer("data_mean", clip_mean[None, :], persistent=False) + self.register_buffer("data_std", clip_std[None, :], persistent=False) + self.time_embed = Timestep(timestep_dim) + + def scale(self, x): + # re-normalize to centered mean and unit variance + x = (x - self.data_mean.to(x.device)) * 1. / self.data_std.to(x.device) + return x + + def unscale(self, x): + # back to original data stats + x = (x * self.data_std.to(x.device)) + self.data_mean.to(x.device) + return x + + def forward(self, x, noise_level=None, seed=None): + if noise_level is None: + noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long() + else: + assert isinstance(noise_level, torch.Tensor) + x = self.scale(x) + z = self.q_sample(x, noise_level, seed=seed) + z = self.unscale(z) + noise_level = self.time_embed(noise_level) + return z, noise_level diff --git a/ldm_patched/ldm/modules/sub_quadratic_attention.py b/ldm_patched/ldm/modules/sub_quadratic_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..9f4c23c7ecdac38ef4d7c200cb342a04f3fd0cfb --- /dev/null +++ b/ldm_patched/ldm/modules/sub_quadratic_attention.py @@ -0,0 +1,273 @@ +# original source: +# https://github.com/AminRezaei0x443/memory-efficient-attention/blob/1bc0d9e6ac5f82ea43a375135c4e1d3896ee1694/memory_efficient_attention/attention_torch.py +# license: +# MIT +# credit: +# Amin Rezaei (original author) +# Alex Birch (optimized algorithm for 3D tensors, at the expense of removing bias, masking and callbacks) +# implementation of: +# Self-attention Does Not Need O(n2) Memory": +# https://arxiv.org/abs/2112.05682v2 + +from functools import partial +import torch +from torch import Tensor +from torch.utils.checkpoint import checkpoint +import math + +try: + from typing import Optional, NamedTuple, List, Protocol +except ImportError: + from typing import Optional, NamedTuple, List + from typing_extensions import Protocol + +from torch import Tensor +from typing import List + +from ldm_patched.modules import model_management + +def dynamic_slice( + x: Tensor, + starts: List[int], + sizes: List[int], +) -> Tensor: + slicing = [slice(start, start + size) for start, size in zip(starts, sizes)] + return x[slicing] + +class AttnChunk(NamedTuple): + exp_values: Tensor + exp_weights_sum: Tensor + max_score: Tensor + +class SummarizeChunk(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> AttnChunk: ... + +class ComputeQueryChunkAttn(Protocol): + @staticmethod + def __call__( + query: Tensor, + key_t: Tensor, + value: Tensor, + ) -> Tensor: ... + +def _summarize_chunk( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, + mask, +) -> AttnChunk: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_weights = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + max_score, _ = torch.max(attn_weights, -1, keepdim=True) + max_score = max_score.detach() + attn_weights -= max_score + if mask is not None: + attn_weights += mask + torch.exp(attn_weights, out=attn_weights) + exp_weights = attn_weights.to(value.dtype) + exp_values = torch.bmm(exp_weights, value) + max_score = max_score.squeeze(-1) + return AttnChunk(exp_values, exp_weights.sum(dim=-1), max_score) + +def _query_chunk_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + summarize_chunk: SummarizeChunk, + kv_chunk_size: int, + mask, +) -> Tensor: + batch_x_heads, k_channels_per_head, k_tokens = key_t.shape + _, _, v_channels_per_head = value.shape + + def chunk_scanner(chunk_idx: int, mask) -> AttnChunk: + key_chunk = dynamic_slice( + key_t, + (0, 0, chunk_idx), + (batch_x_heads, k_channels_per_head, kv_chunk_size) + ) + value_chunk = dynamic_slice( + value, + (0, chunk_idx, 0), + (batch_x_heads, kv_chunk_size, v_channels_per_head) + ) + if mask is not None: + mask = mask[:,:,chunk_idx:chunk_idx + kv_chunk_size] + + return summarize_chunk(query, key_chunk, value_chunk, mask=mask) + + chunks: List[AttnChunk] = [ + chunk_scanner(chunk, mask) for chunk in torch.arange(0, k_tokens, kv_chunk_size) + ] + acc_chunk = AttnChunk(*map(torch.stack, zip(*chunks))) + chunk_values, chunk_weights, chunk_max = acc_chunk + + global_max, _ = torch.max(chunk_max, 0, keepdim=True) + max_diffs = torch.exp(chunk_max - global_max) + chunk_values *= torch.unsqueeze(max_diffs, -1) + chunk_weights *= max_diffs + + all_values = chunk_values.sum(dim=0) + all_weights = torch.unsqueeze(chunk_weights, -1).sum(dim=0) + return all_values / all_weights + +# TODO: refactor CrossAttention#get_attention_scores to share code with this +def _get_attention_scores_no_kv_chunking( + query: Tensor, + key_t: Tensor, + value: Tensor, + scale: float, + upcast_attention: bool, + mask, +) -> Tensor: + if upcast_attention: + with torch.autocast(enabled=False, device_type = 'cuda'): + query = query.float() + key_t = key_t.float() + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + else: + attn_scores = torch.baddbmm( + torch.empty(1, 1, 1, device=query.device, dtype=query.dtype), + query, + key_t, + alpha=scale, + beta=0, + ) + + if mask is not None: + attn_scores += mask + try: + attn_probs = attn_scores.softmax(dim=-1) + del attn_scores + except model_management.OOM_EXCEPTION: + print("ran out of memory while running softmax in _get_attention_scores_no_kv_chunking, trying slower in place softmax instead") + attn_scores -= attn_scores.max(dim=-1, keepdim=True).values + torch.exp(attn_scores, out=attn_scores) + summed = torch.sum(attn_scores, dim=-1, keepdim=True) + attn_scores /= summed + attn_probs = attn_scores + + hidden_states_slice = torch.bmm(attn_probs.to(value.dtype), value) + return hidden_states_slice + +class ScannedChunk(NamedTuple): + chunk_idx: int + attn_chunk: AttnChunk + +def efficient_dot_product_attention( + query: Tensor, + key_t: Tensor, + value: Tensor, + query_chunk_size=1024, + kv_chunk_size: Optional[int] = None, + kv_chunk_size_min: Optional[int] = None, + use_checkpoint=True, + upcast_attention=False, + mask = None, +): + """Computes efficient dot-product attention given query, transposed key, and value. + This is efficient version of attention presented in + https://arxiv.org/abs/2112.05682v2 which comes with O(sqrt(n)) memory requirements. + Args: + query: queries for calculating attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + key_t: keys for calculating attention with shape of + `[batch * num_heads, channels_per_head, tokens]`. + value: values to be used in attention with shape of + `[batch * num_heads, tokens, channels_per_head]`. + query_chunk_size: int: query chunks size + kv_chunk_size: Optional[int]: key/value chunks size. if None: defaults to sqrt(key_tokens) + kv_chunk_size_min: Optional[int]: key/value minimum chunk size. only considered when kv_chunk_size is None. changes `sqrt(key_tokens)` into `max(sqrt(key_tokens), kv_chunk_size_min)`, to ensure our chunk sizes don't get too small (smaller chunks = more chunks = less concurrent work done). + use_checkpoint: bool: whether to use checkpointing (recommended True for training, False for inference) + Returns: + Output of shape `[batch * num_heads, query_tokens, channels_per_head]`. + """ + batch_x_heads, q_tokens, q_channels_per_head = query.shape + _, _, k_tokens = key_t.shape + scale = q_channels_per_head ** -0.5 + + kv_chunk_size = min(kv_chunk_size or int(math.sqrt(k_tokens)), k_tokens) + if kv_chunk_size_min is not None: + kv_chunk_size = max(kv_chunk_size, kv_chunk_size_min) + + if mask is not None and len(mask.shape) == 2: + mask = mask.unsqueeze(0) + + def get_query_chunk(chunk_idx: int) -> Tensor: + return dynamic_slice( + query, + (0, chunk_idx, 0), + (batch_x_heads, min(query_chunk_size, q_tokens), q_channels_per_head) + ) + + def get_mask_chunk(chunk_idx: int) -> Tensor: + if mask is None: + return None + chunk = min(query_chunk_size, q_tokens) + return mask[:,chunk_idx:chunk_idx + chunk] + + summarize_chunk: SummarizeChunk = partial(_summarize_chunk, scale=scale, upcast_attention=upcast_attention) + summarize_chunk: SummarizeChunk = partial(checkpoint, summarize_chunk) if use_checkpoint else summarize_chunk + compute_query_chunk_attn: ComputeQueryChunkAttn = partial( + _get_attention_scores_no_kv_chunking, + scale=scale, + upcast_attention=upcast_attention + ) if k_tokens <= kv_chunk_size else ( + # fast-path for when there's just 1 key-value chunk per query chunk (this is just sliced attention btw) + partial( + _query_chunk_attention, + kv_chunk_size=kv_chunk_size, + summarize_chunk=summarize_chunk, + ) + ) + + if q_tokens <= query_chunk_size: + # fast-path for when there's just 1 query chunk + return compute_query_chunk_attn( + query=query, + key_t=key_t, + value=value, + mask=mask, + ) + + # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance, + # and pass slices to be mutated, instead of torch.cat()ing the returned slices + res = torch.cat([ + compute_query_chunk_attn( + query=get_query_chunk(i * query_chunk_size), + key_t=key_t, + value=value, + mask=get_mask_chunk(i * query_chunk_size) + ) for i in range(math.ceil(q_tokens / query_chunk_size)) + ], dim=1) + return res diff --git a/ldm_patched/ldm/modules/temporal_ae.py b/ldm_patched/ldm/modules/temporal_ae.py new file mode 100644 index 0000000000000000000000000000000000000000..ee851921187f44c63a2459ce8461347dc0f8d8d1 --- /dev/null +++ b/ldm_patched/ldm/modules/temporal_ae.py @@ -0,0 +1,245 @@ +import functools +from typing import Callable, Iterable, Union + +import torch +from einops import rearrange, repeat + +import ldm_patched.modules.ops +ops = ldm_patched.modules.ops.disable_weight_init + +from .diffusionmodules.model import ( + AttnBlock, + Decoder, + ResnetBlock, +) +from .diffusionmodules.openaimodel import ResBlock, timestep_embedding +from .attention import BasicTransformerBlock + +def partialclass(cls, *args, **kwargs): + class NewCls(cls): + __init__ = functools.partialmethod(cls.__init__, *args, **kwargs) + + return NewCls + + +class VideoResBlock(ResnetBlock): + def __init__( + self, + out_channels, + *args, + dropout=0.0, + video_kernel_size=3, + alpha=0.0, + merge_strategy="learned", + **kwargs, + ): + super().__init__(out_channels=out_channels, dropout=dropout, *args, **kwargs) + if video_kernel_size is None: + video_kernel_size = [3, 1, 1] + self.time_stack = ResBlock( + channels=out_channels, + emb_channels=0, + dropout=dropout, + dims=3, + use_scale_shift_norm=False, + use_conv=False, + up=False, + down=False, + kernel_size=video_kernel_size, + use_checkpoint=False, + skip_t_emb=True, + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def get_alpha(self, bs): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError() + + def forward(self, x, temb, skip_video=False, timesteps=None): + b, c, h, w = x.shape + if timesteps is None: + timesteps = b + + x = super().forward(x, temb) + + if not skip_video: + x_mix = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + + x = self.time_stack(x, temb) + + alpha = self.get_alpha(bs=b // timesteps).to(x.device) + x = alpha * x + (1.0 - alpha) * x_mix + + x = rearrange(x, "b c t h w -> (b t) c h w") + return x + + +class AE3DConv(ops.Conv2d): + def __init__(self, in_channels, out_channels, video_kernel_size=3, *args, **kwargs): + super().__init__(in_channels, out_channels, *args, **kwargs) + if isinstance(video_kernel_size, Iterable): + padding = [int(k // 2) for k in video_kernel_size] + else: + padding = int(video_kernel_size // 2) + + self.time_mix_conv = ops.Conv3d( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=video_kernel_size, + padding=padding, + ) + + def forward(self, input, timesteps=None, skip_video=False): + if timesteps is None: + timesteps = input.shape[0] + x = super().forward(input) + if skip_video: + return x + x = rearrange(x, "(b t) c h w -> b c t h w", t=timesteps) + x = self.time_mix_conv(x) + return rearrange(x, "b c t h w -> (b t) c h w") + + +class AttnVideoBlock(AttnBlock): + def __init__( + self, in_channels: int, alpha: float = 0, merge_strategy: str = "learned" + ): + super().__init__(in_channels) + # no context, single headed, as in base class + self.time_mix_block = BasicTransformerBlock( + dim=in_channels, + n_heads=1, + d_head=in_channels, + checkpoint=False, + ff_in=True, + ) + + time_embed_dim = self.in_channels * 4 + self.video_time_embed = torch.nn.Sequential( + ops.Linear(self.in_channels, time_embed_dim), + torch.nn.SiLU(), + ops.Linear(time_embed_dim, self.in_channels), + ) + + self.merge_strategy = merge_strategy + if self.merge_strategy == "fixed": + self.register_buffer("mix_factor", torch.Tensor([alpha])) + elif self.merge_strategy == "learned": + self.register_parameter( + "mix_factor", torch.nn.Parameter(torch.Tensor([alpha])) + ) + else: + raise ValueError(f"unknown merge strategy {self.merge_strategy}") + + def forward(self, x, timesteps=None, skip_time_block=False): + if skip_time_block: + return super().forward(x) + + if timesteps is None: + timesteps = x.shape[0] + + x_in = x + x = self.attention(x) + h, w = x.shape[2:] + x = rearrange(x, "b c h w -> b (h w) c") + + x_mix = x + num_frames = torch.arange(timesteps, device=x.device) + num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps) + num_frames = rearrange(num_frames, "b t -> (b t)") + t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False) + emb = self.video_time_embed(t_emb) # b, n_channels + emb = emb[:, None, :] + x_mix = x_mix + emb + + alpha = self.get_alpha().to(x.device) + x_mix = self.time_mix_block(x_mix, timesteps=timesteps) + x = alpha * x + (1.0 - alpha) * x_mix # alpha merge + + x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w) + x = self.proj_out(x) + + return x_in + x + + def get_alpha( + self, + ): + if self.merge_strategy == "fixed": + return self.mix_factor + elif self.merge_strategy == "learned": + return torch.sigmoid(self.mix_factor) + else: + raise NotImplementedError(f"unknown merge strategy {self.merge_strategy}") + + + +def make_time_attn( + in_channels, + attn_type="vanilla", + attn_kwargs=None, + alpha: float = 0, + merge_strategy: str = "learned", +): + return partialclass( + AttnVideoBlock, in_channels, alpha=alpha, merge_strategy=merge_strategy + ) + + +class Conv2DWrapper(torch.nn.Conv2d): + def forward(self, input: torch.Tensor, **kwargs) -> torch.Tensor: + return super().forward(input) + + +class VideoDecoder(Decoder): + available_time_modes = ["all", "conv-only", "attn-only"] + + def __init__( + self, + *args, + video_kernel_size: Union[int, list] = 3, + alpha: float = 0.0, + merge_strategy: str = "learned", + time_mode: str = "conv-only", + **kwargs, + ): + self.video_kernel_size = video_kernel_size + self.alpha = alpha + self.merge_strategy = merge_strategy + self.time_mode = time_mode + assert ( + self.time_mode in self.available_time_modes + ), f"time_mode parameter has to be in {self.available_time_modes}" + + if self.time_mode != "attn-only": + kwargs["conv_out_op"] = partialclass(AE3DConv, video_kernel_size=self.video_kernel_size) + if self.time_mode not in ["conv-only", "only-last-conv"]: + kwargs["attn_op"] = partialclass(make_time_attn, alpha=self.alpha, merge_strategy=self.merge_strategy) + if self.time_mode not in ["attn-only", "only-last-conv"]: + kwargs["resnet_op"] = partialclass(VideoResBlock, video_kernel_size=self.video_kernel_size, alpha=self.alpha, merge_strategy=self.merge_strategy) + + super().__init__(*args, **kwargs) + + def get_last_layer(self, skip_time_mix=False, **kwargs): + if self.time_mode == "attn-only": + raise NotImplementedError("TODO") + else: + return ( + self.conv_out.time_mix_conv.weight + if not skip_time_mix + else self.conv_out.weight + ) diff --git a/ldm_patched/ldm/util.py b/ldm_patched/ldm/util.py new file mode 100644 index 0000000000000000000000000000000000000000..8c09ca1c72f7ceb3f9d7f9546aae5561baf62b13 --- /dev/null +++ b/ldm_patched/ldm/util.py @@ -0,0 +1,197 @@ +import importlib + +import torch +from torch import optim +import numpy as np + +from inspect import isfunction +from PIL import Image, ImageDraw, ImageFont + + +def log_txt_as_img(wh, xc, size=10): + # wh a tuple of (width, height) + # xc a list of captions to plot + b = len(xc) + txts = list() + for bi in range(b): + txt = Image.new("RGB", wh, color="white") + draw = ImageDraw.Draw(txt) + font = ImageFont.truetype('data/DejaVuSans.ttf', size=size) + nc = int(40 * (wh[0] / 256)) + lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc)) + + try: + draw.text((0, 0), lines, fill="black", font=font) + except UnicodeEncodeError: + print("Cant encode string for logging. Skipping.") + + txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0 + txts.append(txt) + txts = np.stack(txts) + txts = torch.tensor(txts) + return txts + + +def ismap(x): + if not isinstance(x, torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] > 3) + + +def isimage(x): + if not isinstance(x,torch.Tensor): + return False + return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1) + + +def exists(x): + return x is not None + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +def mean_flat(tensor): + """ + https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86 + Take the mean over all non-batch dimensions. + """ + return tensor.mean(dim=list(range(1, len(tensor.shape)))) + + +def count_params(model, verbose=False): + total_params = sum(p.numel() for p in model.parameters()) + if verbose: + print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.") + return total_params + + +def instantiate_from_config(config): + if not "target" in config: + if config == '__is_first_stage__': + return None + elif config == "__is_unconditional__": + return None + raise KeyError("Expected key `target` to instantiate.") + return get_obj_from_str(config["target"])(**config.get("params", dict())) + + +def get_obj_from_str(string, reload=False): + module, cls = string.rsplit(".", 1) + if reload: + module_imp = importlib.import_module(module) + importlib.reload(module_imp) + return getattr(importlib.import_module(module, package=None), cls) + + +class AdamWwithEMAandWings(optim.Optimizer): + # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298 + def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using + weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code + ema_power=1., param_names=()): + """AdamW that saves EMA versions of the parameters.""" + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + if not 0.0 <= weight_decay: + raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) + if not 0.0 <= ema_decay <= 1.0: + raise ValueError("Invalid ema_decay value: {}".format(ema_decay)) + defaults = dict(lr=lr, betas=betas, eps=eps, + weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay, + ema_power=ema_power, param_names=param_names) + super().__init__(params, defaults) + + def __setstate__(self, state): + super().__setstate__(state) + for group in self.param_groups: + group.setdefault('amsgrad', False) + + @torch.no_grad() + def step(self, closure=None): + """Performs a single optimization step. + Args: + closure (callable, optional): A closure that reevaluates the model + and returns the loss. + """ + loss = None + if closure is not None: + with torch.enable_grad(): + loss = closure() + + for group in self.param_groups: + params_with_grad = [] + grads = [] + exp_avgs = [] + exp_avg_sqs = [] + ema_params_with_grad = [] + state_sums = [] + max_exp_avg_sqs = [] + state_steps = [] + amsgrad = group['amsgrad'] + beta1, beta2 = group['betas'] + ema_decay = group['ema_decay'] + ema_power = group['ema_power'] + + for p in group['params']: + if p.grad is None: + continue + params_with_grad.append(p) + if p.grad.is_sparse: + raise RuntimeError('AdamW does not support sparse gradients') + grads.append(p.grad) + + state = self.state[p] + + # State initialization + if len(state) == 0: + state['step'] = 0 + # Exponential moving average of gradient values + state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of squared gradient values + state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + if amsgrad: + # Maintains max of all exp. moving avg. of sq. grad. values + state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format) + # Exponential moving average of parameter values + state['param_exp_avg'] = p.detach().float().clone() + + exp_avgs.append(state['exp_avg']) + exp_avg_sqs.append(state['exp_avg_sq']) + ema_params_with_grad.append(state['param_exp_avg']) + + if amsgrad: + max_exp_avg_sqs.append(state['max_exp_avg_sq']) + + # update the steps for each param group update + state['step'] += 1 + # record the step after step update + state_steps.append(state['step']) + + optim._functional.adamw(params_with_grad, + grads, + exp_avgs, + exp_avg_sqs, + max_exp_avg_sqs, + state_steps, + amsgrad=amsgrad, + beta1=beta1, + beta2=beta2, + lr=group['lr'], + weight_decay=group['weight_decay'], + eps=group['eps'], + maximize=False) + + cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power) + for param, ema_param in zip(params_with_grad, ema_params_with_grad): + ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay) + + return loss \ No newline at end of file diff --git a/ldm_patched/licenses-3rd/chainer b/ldm_patched/licenses-3rd/chainer new file mode 100644 index 0000000000000000000000000000000000000000..db8ef9d966d9b2d89f1b060912806b45797d8c14 --- /dev/null +++ b/ldm_patched/licenses-3rd/chainer @@ -0,0 +1,20 @@ +Copyright (c) 2015 Preferred Infrastructure, Inc. +Copyright (c) 2015 Preferred Networks, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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No Surrender of Others' Freedom. + + If conditions are imposed on you (whether by court order, agreement or +otherwise) that contradict the conditions of this License, they do not +excuse you from the conditions of this License. If you cannot convey a +covered work so as to satisfy simultaneously your obligations under this +License and any other pertinent obligations, then as a consequence you may +not convey it at all. For example, if you agree to terms that obligate you +to collect a royalty for further conveying from those to whom you convey +the Program, the only way you could satisfy both those terms and this +License would be to refrain entirely from conveying the Program. + + 13. 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If the +Program specifies that a certain numbered version of the GNU General +Public License "or any later version" applies to it, you have the +option of following the terms and conditions either of that numbered +version or of any later version published by the Free Software +Foundation. If the Program does not specify a version number of the +GNU General Public License, you may choose any version ever published +by the Free Software Foundation. + + If the Program specifies that a proxy can decide which future +versions of the GNU General Public License can be used, that proxy's +public statement of acceptance of a version permanently authorizes you +to choose that version for the Program. + + Later license versions may give you additional or different +permissions. However, no additional obligations are imposed on any +author or copyright holder as a result of your choosing to follow a +later version. + + 15. Disclaimer of Warranty. + + THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY +APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT +HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY +OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, +THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM +IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF +ALL NECESSARY SERVICING, REPAIR OR CORRECTION. + + 16. Limitation of Liability. + + IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS +THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY +GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE +USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF +DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD +PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), +EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF +SUCH DAMAGES. + + 17. Interpretation of Sections 15 and 16. + + If the disclaimer of warranty and limitation of liability provided +above cannot be given local legal effect according to their terms, +reviewing courts shall apply local law that most closely approximates +an absolute waiver of all civil liability in connection with the +Program, unless a warranty or assumption of liability accompanies a +copy of the Program in return for a fee. + + END OF TERMS AND CONDITIONS + + How to Apply These Terms to Your New Programs + + If you develop a new program, and you want it to be of the greatest +possible use to the public, the best way to achieve this is to make it +free software which everyone can redistribute and change under these terms. + + To do so, attach the following notices to the program. It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + + Copyright (C) + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. \ No newline at end of file diff --git a/ldm_patched/licenses-3rd/diffusers b/ldm_patched/licenses-3rd/diffusers new file mode 100644 index 0000000000000000000000000000000000000000..f49a4e16e68b128803cc2dcea614603632b04eac --- /dev/null +++ b/ldm_patched/licenses-3rd/diffusers @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/ldm_patched/licenses-3rd/kdiffusion b/ldm_patched/licenses-3rd/kdiffusion new file mode 100644 index 0000000000000000000000000000000000000000..e20684e521b3f0ce86ed26c2fc95ed665f52e05f --- /dev/null +++ b/ldm_patched/licenses-3rd/kdiffusion @@ -0,0 +1,19 @@ +Copyright (c) 2022 Katherine Crowson + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in +all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN +THE SOFTWARE. \ No newline at end of file diff --git a/ldm_patched/licenses-3rd/ldm b/ldm_patched/licenses-3rd/ldm new file mode 100644 index 0000000000000000000000000000000000000000..1a1c5058de8a8817b786ad440e9984c99e7df0ca --- /dev/null +++ b/ldm_patched/licenses-3rd/ldm @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/ldm_patched/licenses-3rd/taesd b/ldm_patched/licenses-3rd/taesd new file mode 100644 index 0000000000000000000000000000000000000000..62e6312e5e8ca8669aa15c250f1d40c001cd7f20 --- /dev/null +++ b/ldm_patched/licenses-3rd/taesd @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023 Ollin Boer Bohan + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/ldm_patched/licenses-3rd/transformers b/ldm_patched/licenses-3rd/transformers new file mode 100644 index 0000000000000000000000000000000000000000..e44d8f5b79a0643c99977835611e1da9d08fc3cf --- /dev/null +++ b/ldm_patched/licenses-3rd/transformers @@ -0,0 +1,203 @@ +Copyright 2018- The Hugging Face team. All rights reserved. + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/ldm_patched/modules/args_parser.py b/ldm_patched/modules/args_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..0c6165a7b9a89cede4ba450154f4f902a998017b --- /dev/null +++ b/ldm_patched/modules/args_parser.py @@ -0,0 +1,125 @@ +import argparse +import enum +import ldm_patched.modules.options + +class EnumAction(argparse.Action): + """ + Argparse action for handling Enums + """ + def __init__(self, **kwargs): + # Pop off the type value + enum_type = kwargs.pop("type", None) + + # Ensure an Enum subclass is provided + if enum_type is None: + raise ValueError("type must be assigned an Enum when using EnumAction") + if not issubclass(enum_type, enum.Enum): + raise TypeError("type must be an Enum when using EnumAction") + + # Generate choices from the Enum + choices = tuple(e.value for e in enum_type) + kwargs.setdefault("choices", choices) + kwargs.setdefault("metavar", f"[{','.join(list(choices))}]") + + super(EnumAction, self).__init__(**kwargs) + + self._enum = enum_type + + def __call__(self, parser, namespace, values, option_string=None): + # Convert value back into an Enum + value = self._enum(values) + setattr(namespace, self.dest, value) + + +parser = argparse.ArgumentParser() + +parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0") +parser.add_argument("--port", type=int, default=8188) +parser.add_argument("--disable-header-check", type=str, default=None, metavar="ORIGIN", nargs="?", const="*") +parser.add_argument("--web-upload-size", type=float, default=100) + +parser.add_argument("--external-working-path", type=str, default=None, metavar="PATH", nargs='+', action='append') +parser.add_argument("--output-path", type=str, default=None) +parser.add_argument("--temp-path", type=str, default=None) +parser.add_argument("--cache-path", type=str, default=None) +parser.add_argument("--in-browser", action="store_true") +parser.add_argument("--disable-in-browser", action="store_true") +parser.add_argument("--gpu-device-id", type=int, default=None, metavar="DEVICE_ID") +cm_group = parser.add_mutually_exclusive_group() +cm_group.add_argument("--async-cuda-allocation", action="store_true") +cm_group.add_argument("--disable-async-cuda-allocation", action="store_true") + +parser.add_argument("--disable-attention-upcast", action="store_true") + +fp_group = parser.add_mutually_exclusive_group() +fp_group.add_argument("--all-in-fp32", action="store_true") +fp_group.add_argument("--all-in-fp16", action="store_true") + +fpunet_group = parser.add_mutually_exclusive_group() +fpunet_group.add_argument("--unet-in-bf16", action="store_true") +fpunet_group.add_argument("--unet-in-fp16", action="store_true") +fpunet_group.add_argument("--unet-in-fp8-e4m3fn", action="store_true") +fpunet_group.add_argument("--unet-in-fp8-e5m2", action="store_true") + +fpvae_group = parser.add_mutually_exclusive_group() +fpvae_group.add_argument("--vae-in-fp16", action="store_true") +fpvae_group.add_argument("--vae-in-fp32", action="store_true") +fpvae_group.add_argument("--vae-in-bf16", action="store_true") + +parser.add_argument("--vae-in-cpu", action="store_true") + +fpte_group = parser.add_mutually_exclusive_group() +fpte_group.add_argument("--clip-in-fp8-e4m3fn", action="store_true") +fpte_group.add_argument("--clip-in-fp8-e5m2", action="store_true") +fpte_group.add_argument("--clip-in-fp16", action="store_true") +fpte_group.add_argument("--clip-in-fp32", action="store_true") + + +parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1) + +parser.add_argument("--disable-ipex-hijack", action="store_true") + +class LatentPreviewMethod(enum.Enum): + NoPreviews = "none" + Auto = "auto" + Latent2RGB = "fast" + TAESD = "taesd" + +parser.add_argument("--preview-option", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, action=EnumAction) + +attn_group = parser.add_mutually_exclusive_group() +attn_group.add_argument("--attention-split", action="store_true") +attn_group.add_argument("--attention-quad", action="store_true") +attn_group.add_argument("--attention-pytorch", action="store_true") + +parser.add_argument("--disable-xformers", action="store_true") + +vram_group = parser.add_mutually_exclusive_group() +vram_group.add_argument("--always-gpu", action="store_true") +vram_group.add_argument("--always-high-vram", action="store_true") +vram_group.add_argument("--always-normal-vram", action="store_true") +vram_group.add_argument("--always-low-vram", action="store_true") +vram_group.add_argument("--always-no-vram", action="store_true") +vram_group.add_argument("--always-cpu", type=int, nargs="?", metavar="CPU_NUM_THREADS", const=-1) + +parser.add_argument("--always-offload-from-vram", action="store_true") +parser.add_argument("--pytorch-deterministic", action="store_true") + +parser.add_argument("--disable-server-log", action="store_true") +parser.add_argument("--debug-mode", action="store_true") +parser.add_argument("--is-windows-embedded-python", action="store_true") + +parser.add_argument("--disable-server-info", action="store_true") + +parser.add_argument("--multi-user", action="store_true") + +if ldm_patched.modules.options.args_parsing: + args = parser.parse_args([]) +else: + args = parser.parse_args([]) + +if args.is_windows_embedded_python: + args.in_browser = True + +if args.disable_in_browser: + args.in_browser = False diff --git a/ldm_patched/modules/checkpoint_pickle.py b/ldm_patched/modules/checkpoint_pickle.py new file mode 100644 index 0000000000000000000000000000000000000000..206551d3c1cf0d654c907534629a800196ba138b --- /dev/null +++ b/ldm_patched/modules/checkpoint_pickle.py @@ -0,0 +1,13 @@ +import pickle + +load = pickle.load + +class Empty: + pass + +class Unpickler(pickle.Unpickler): + def find_class(self, module, name): + #TODO: safe unpickle + if module.startswith("pytorch_lightning"): + return Empty + return super().find_class(module, name) diff --git a/ldm_patched/modules/clip_config_bigg.json b/ldm_patched/modules/clip_config_bigg.json new file mode 100644 index 0000000000000000000000000000000000000000..32d82ff39ba66ba0be15ec101993e1c46cc3f7ab --- /dev/null +++ b/ldm_patched/modules/clip_config_bigg.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_size": 1280, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 20, + "num_hidden_layers": 32, + "pad_token_id": 1, + "projection_dim": 1280, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/ldm_patched/modules/clip_model.py b/ldm_patched/modules/clip_model.py new file mode 100644 index 0000000000000000000000000000000000000000..aceca86d641b6fcf116b1b57da232cc7336c566d --- /dev/null +++ b/ldm_patched/modules/clip_model.py @@ -0,0 +1,188 @@ +import torch +from ldm_patched.ldm.modules.attention import optimized_attention_for_device + +class CLIPAttention(torch.nn.Module): + def __init__(self, embed_dim, heads, dtype, device, operations): + super().__init__() + + self.heads = heads + self.q_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.k_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + self.v_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + self.out_proj = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x, mask=None, optimized_attention=None): + q = self.q_proj(x) + k = self.k_proj(x) + v = self.v_proj(x) + + out = optimized_attention(q, k, v, self.heads, mask) + return self.out_proj(out) + +ACTIVATIONS = {"quick_gelu": lambda a: a * torch.sigmoid(1.702 * a), + "gelu": torch.nn.functional.gelu, +} + +class CLIPMLP(torch.nn.Module): + def __init__(self, embed_dim, intermediate_size, activation, dtype, device, operations): + super().__init__() + self.fc1 = operations.Linear(embed_dim, intermediate_size, bias=True, dtype=dtype, device=device) + self.activation = ACTIVATIONS[activation] + self.fc2 = operations.Linear(intermediate_size, embed_dim, bias=True, dtype=dtype, device=device) + + def forward(self, x): + x = self.fc1(x) + x = self.activation(x) + x = self.fc2(x) + return x + +class CLIPLayer(torch.nn.Module): + def __init__(self, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layer_norm1 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.self_attn = CLIPAttention(embed_dim, heads, dtype, device, operations) + self.layer_norm2 = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + self.mlp = CLIPMLP(embed_dim, intermediate_size, intermediate_activation, dtype, device, operations) + + def forward(self, x, mask=None, optimized_attention=None): + x += self.self_attn(self.layer_norm1(x), mask, optimized_attention) + x += self.mlp(self.layer_norm2(x)) + return x + + +class CLIPEncoder(torch.nn.Module): + def __init__(self, num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations): + super().__init__() + self.layers = torch.nn.ModuleList([CLIPLayer(embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) for i in range(num_layers)]) + + def forward(self, x, mask=None, intermediate_output=None): + optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True) + + if intermediate_output is not None: + if intermediate_output < 0: + intermediate_output = len(self.layers) + intermediate_output + + intermediate = None + for i, l in enumerate(self.layers): + x = l(x, mask, optimized_attention) + if i == intermediate_output: + intermediate = x.clone() + return x, intermediate + +class CLIPEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None): + super().__init__() + self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device) + self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens): + return self.token_embedding(input_tokens) + self.position_embedding.weight + + +class CLIPTextModel_(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + + super().__init__() + self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device) + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device) + + def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True): + x = self.embeddings(input_tokens) + mask = None + if attention_mask is not None: + mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1]) + mask = mask.masked_fill(mask.to(torch.bool), float("-inf")) + + causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1) + if mask is not None: + mask += causal_mask + else: + mask = causal_mask + + x, i = self.encoder(x, mask=mask, intermediate_output=intermediate_output) + x = self.final_layer_norm(x) + if i is not None and final_layer_norm_intermediate: + i = self.final_layer_norm(i) + + pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),] + return x, i, pooled_output + +class CLIPTextModel(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.num_layers = config_dict["num_hidden_layers"] + self.text_model = CLIPTextModel_(config_dict, dtype, device, operations) + self.dtype = dtype + + def get_input_embeddings(self): + return self.text_model.embeddings.token_embedding + + def set_input_embeddings(self, embeddings): + self.text_model.embeddings.token_embedding = embeddings + + def forward(self, *args, **kwargs): + return self.text_model(*args, **kwargs) + +class CLIPVisionEmbeddings(torch.nn.Module): + def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None): + super().__init__() + self.class_embedding = torch.nn.Parameter(torch.empty(embed_dim, dtype=dtype, device=device)) + + self.patch_embedding = operations.Conv2d( + in_channels=num_channels, + out_channels=embed_dim, + kernel_size=patch_size, + stride=patch_size, + bias=False, + dtype=dtype, + device=device + ) + + num_patches = (image_size // patch_size) ** 2 + num_positions = num_patches + 1 + self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device) + + def forward(self, pixel_values): + embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2) + return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device) + + +class CLIPVision(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + num_layers = config_dict["num_hidden_layers"] + embed_dim = config_dict["hidden_size"] + heads = config_dict["num_attention_heads"] + intermediate_size = config_dict["intermediate_size"] + intermediate_activation = config_dict["hidden_act"] + + self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations) + self.pre_layrnorm = operations.LayerNorm(embed_dim) + self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations) + self.post_layernorm = operations.LayerNorm(embed_dim) + + def forward(self, pixel_values, attention_mask=None, intermediate_output=None): + x = self.embeddings(pixel_values) + x = self.pre_layrnorm(x) + #TODO: attention_mask? + x, i = self.encoder(x, mask=None, intermediate_output=intermediate_output) + pooled_output = self.post_layernorm(x[:, 0, :]) + return x, i, pooled_output + +class CLIPVisionModelProjection(torch.nn.Module): + def __init__(self, config_dict, dtype, device, operations): + super().__init__() + self.vision_model = CLIPVision(config_dict, dtype, device, operations) + self.visual_projection = operations.Linear(config_dict["hidden_size"], config_dict["projection_dim"], bias=False) + + def forward(self, *args, **kwargs): + x = self.vision_model(*args, **kwargs) + out = self.visual_projection(x[2]) + return (x[0], x[1], out) diff --git a/ldm_patched/modules/clip_vision.py b/ldm_patched/modules/clip_vision.py new file mode 100644 index 0000000000000000000000000000000000000000..affdb8b24e35eb64aad2f96de36a59c6d0d67b5d --- /dev/null +++ b/ldm_patched/modules/clip_vision.py @@ -0,0 +1,116 @@ +from .utils import load_torch_file, transformers_convert, state_dict_prefix_replace +import os +import torch +import json + +import ldm_patched.modules.ops +import ldm_patched.modules.model_patcher +import ldm_patched.modules.model_management +import ldm_patched.modules.utils +import ldm_patched.modules.clip_model + +class Output: + def __getitem__(self, key): + return getattr(self, key) + def __setitem__(self, key, item): + setattr(self, key, item) + +def clip_preprocess(image, size=224): + mean = torch.tensor([ 0.48145466,0.4578275,0.40821073], device=image.device, dtype=image.dtype) + std = torch.tensor([0.26862954,0.26130258,0.27577711], device=image.device, dtype=image.dtype) + image = image.movedim(-1, 1) + if not (image.shape[2] == size and image.shape[3] == size): + scale = (size / min(image.shape[2], image.shape[3])) + image = torch.nn.functional.interpolate(image, size=(round(scale * image.shape[2]), round(scale * image.shape[3])), mode="bicubic", antialias=True) + h = (image.shape[2] - size)//2 + w = (image.shape[3] - size)//2 + image = image[:,:,h:h+size,w:w+size] + image = torch.clip((255. * image), 0, 255).round() / 255.0 + return (image - mean.view([3,1,1])) / std.view([3,1,1]) + +class ClipVisionModel(): + def __init__(self, json_config): + with open(json_config) as f: + config = json.load(f) + + self.load_device = ldm_patched.modules.model_management.text_encoder_device() + offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() + self.dtype = ldm_patched.modules.model_management.text_encoder_dtype(self.load_device) + self.model = ldm_patched.modules.clip_model.CLIPVisionModelProjection(config, self.dtype, offload_device, ldm_patched.modules.ops.manual_cast) + self.model.eval() + + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device) + + def load_sd(self, sd): + return self.model.load_state_dict(sd, strict=False) + + def get_sd(self): + return self.model.state_dict() + + def encode_image(self, image): + ldm_patched.modules.model_management.load_model_gpu(self.patcher) + pixel_values = clip_preprocess(image.to(self.load_device)).float() + out = self.model(pixel_values=pixel_values, intermediate_output=-2) + + outputs = Output() + outputs["last_hidden_state"] = out[0].to(ldm_patched.modules.model_management.intermediate_device()) + outputs["image_embeds"] = out[2].to(ldm_patched.modules.model_management.intermediate_device()) + outputs["penultimate_hidden_states"] = out[1].to(ldm_patched.modules.model_management.intermediate_device()) + return outputs + +def convert_to_transformers(sd, prefix): + sd_k = sd.keys() + if "{}transformer.resblocks.0.attn.in_proj_weight".format(prefix) in sd_k: + keys_to_replace = { + "{}class_embedding".format(prefix): "vision_model.embeddings.class_embedding", + "{}conv1.weight".format(prefix): "vision_model.embeddings.patch_embedding.weight", + "{}positional_embedding".format(prefix): "vision_model.embeddings.position_embedding.weight", + "{}ln_post.bias".format(prefix): "vision_model.post_layernorm.bias", + "{}ln_post.weight".format(prefix): "vision_model.post_layernorm.weight", + "{}ln_pre.bias".format(prefix): "vision_model.pre_layrnorm.bias", + "{}ln_pre.weight".format(prefix): "vision_model.pre_layrnorm.weight", + } + + for x in keys_to_replace: + if x in sd_k: + sd[keys_to_replace[x]] = sd.pop(x) + + if "{}proj".format(prefix) in sd_k: + sd['visual_projection.weight'] = sd.pop("{}proj".format(prefix)).transpose(0, 1) + + sd = transformers_convert(sd, prefix, "vision_model.", 48) + else: + replace_prefix = {prefix: ""} + sd = state_dict_prefix_replace(sd, replace_prefix) + return sd + +def load_clipvision_from_sd(sd, prefix="", convert_keys=False): + if convert_keys: + sd = convert_to_transformers(sd, prefix) + if "vision_model.encoder.layers.47.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_g.json") + elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json") + elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd: + json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json") + else: + return None + + clip = ClipVisionModel(json_config) + m, u = clip.load_sd(sd) + if len(m) > 0: + print("extra clip vision:", m) + u = set(u) + keys = list(sd.keys()) + for k in keys: + if k not in u: + t = sd.pop(k) + del t + return clip + +def load(ckpt_path): + sd = load_torch_file(ckpt_path) + if "visual.transformer.resblocks.0.attn.in_proj_weight" in sd: + return load_clipvision_from_sd(sd, prefix="visual.", convert_keys=True) + else: + return load_clipvision_from_sd(sd) diff --git a/ldm_patched/modules/clip_vision_config_g.json b/ldm_patched/modules/clip_vision_config_g.json new file mode 100644 index 0000000000000000000000000000000000000000..708e7e21ac3513a719d6a49e88e756f5ef7e2c8d --- /dev/null +++ b/ldm_patched/modules/clip_vision_config_g.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1664, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 8192, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 48, + "patch_size": 14, + "projection_dim": 1280, + "torch_dtype": "float32" +} diff --git a/ldm_patched/modules/clip_vision_config_h.json b/ldm_patched/modules/clip_vision_config_h.json new file mode 100644 index 0000000000000000000000000000000000000000..bb71be419a4be0ad5c8c157850de032a65593cb9 --- /dev/null +++ b/ldm_patched/modules/clip_vision_config_h.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "gelu", + "hidden_size": 1280, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 5120, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 32, + "patch_size": 14, + "projection_dim": 1024, + "torch_dtype": "float32" +} diff --git a/ldm_patched/modules/clip_vision_config_vitl.json b/ldm_patched/modules/clip_vision_config_vitl.json new file mode 100644 index 0000000000000000000000000000000000000000..c59b8ed5a4c1f41fbcc9e6811d2c7dfe44273de7 --- /dev/null +++ b/ldm_patched/modules/clip_vision_config_vitl.json @@ -0,0 +1,18 @@ +{ + "attention_dropout": 0.0, + "dropout": 0.0, + "hidden_act": "quick_gelu", + "hidden_size": 1024, + "image_size": 224, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "model_type": "clip_vision_model", + "num_attention_heads": 16, + "num_channels": 3, + "num_hidden_layers": 24, + "patch_size": 14, + "projection_dim": 768, + "torch_dtype": "float32" +} diff --git a/ldm_patched/modules/conds.py b/ldm_patched/modules/conds.py new file mode 100644 index 0000000000000000000000000000000000000000..0ee184bc82d3f9988c5f9a2acea08ec5dee3e600 --- /dev/null +++ b/ldm_patched/modules/conds.py @@ -0,0 +1,76 @@ +import torch +import math +import ldm_patched.modules.utils + + + +class CONDRegular: + def __init__(self, cond): + self.cond = cond + + def _copy_with(self, cond): + return self.__class__(cond) + + def process_cond(self, batch_size, device, **kwargs): + return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(self.cond, batch_size).to(device)) + + def can_concat(self, other): + if self.cond.shape != other.cond.shape: + return False + return True + + def concat(self, others): + conds = [self.cond] + for x in others: + conds.append(x.cond) + return torch.cat(conds) + +class CONDNoiseShape(CONDRegular): + def process_cond(self, batch_size, device, area, **kwargs): + data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] + return self._copy_with(ldm_patched.modules.utils.repeat_to_batch_size(data, batch_size).to(device)) + + +class CONDCrossAttn(CONDRegular): + def can_concat(self, other): + s1 = self.cond.shape + s2 = other.cond.shape + if s1 != s2: + if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen + return False + + mult_min = math.lcm(s1[1], s2[1]) + diff = mult_min // min(s1[1], s2[1]) + if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much + return False + return True + + def concat(self, others): + conds = [self.cond] + crossattn_max_len = self.cond.shape[1] + for x in others: + c = x.cond + crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1]) + conds.append(c) + + out = [] + for c in conds: + if c.shape[1] < crossattn_max_len: + c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result + out.append(c) + return torch.cat(out) + +class CONDConstant(CONDRegular): + def __init__(self, cond): + self.cond = cond + + def process_cond(self, batch_size, device, **kwargs): + return self._copy_with(self.cond) + + def can_concat(self, other): + if self.cond != other.cond: + return False + return True + + def concat(self, others): + return self.cond diff --git a/ldm_patched/modules/controlnet.py b/ldm_patched/modules/controlnet.py new file mode 100644 index 0000000000000000000000000000000000000000..7e11497fe4982f2995056f2825e7273777e052e8 --- /dev/null +++ b/ldm_patched/modules/controlnet.py @@ -0,0 +1,516 @@ +import torch +import math +import os +import ldm_patched.modules.utils +import ldm_patched.modules.model_management +import ldm_patched.modules.model_detection +import ldm_patched.modules.model_patcher +import ldm_patched.modules.ops + +import ldm_patched.controlnet.cldm +import ldm_patched.t2ia.adapter + + +def broadcast_image_to(tensor, target_batch_size, batched_number): + current_batch_size = tensor.shape[0] + #print(current_batch_size, target_batch_size) + if current_batch_size == 1: + return tensor + + per_batch = target_batch_size // batched_number + tensor = tensor[:per_batch] + + if per_batch > tensor.shape[0]: + tensor = torch.cat([tensor] * (per_batch // tensor.shape[0]) + [tensor[:(per_batch % tensor.shape[0])]], dim=0) + + current_batch_size = tensor.shape[0] + if current_batch_size == target_batch_size: + return tensor + else: + return torch.cat([tensor] * batched_number, dim=0) + +class ControlBase: + def __init__(self, device=None): + self.cond_hint_original = None + self.cond_hint = None + self.strength = 1.0 + self.timestep_percent_range = (0.0, 1.0) + self.global_average_pooling = False + self.timestep_range = None + + if device is None: + device = ldm_patched.modules.model_management.get_torch_device() + self.device = device + self.previous_controlnet = None + + def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)): + self.cond_hint_original = cond_hint + self.strength = strength + self.timestep_percent_range = timestep_percent_range + return self + + def pre_run(self, model, percent_to_timestep_function): + self.timestep_range = (percent_to_timestep_function(self.timestep_percent_range[0]), percent_to_timestep_function(self.timestep_percent_range[1])) + if self.previous_controlnet is not None: + self.previous_controlnet.pre_run(model, percent_to_timestep_function) + + def set_previous_controlnet(self, controlnet): + self.previous_controlnet = controlnet + return self + + def cleanup(self): + if self.previous_controlnet is not None: + self.previous_controlnet.cleanup() + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + self.timestep_range = None + + def get_models(self): + out = [] + if self.previous_controlnet is not None: + out += self.previous_controlnet.get_models() + return out + + def copy_to(self, c): + c.cond_hint_original = self.cond_hint_original + c.strength = self.strength + c.timestep_percent_range = self.timestep_percent_range + c.global_average_pooling = self.global_average_pooling + + def inference_memory_requirements(self, dtype): + if self.previous_controlnet is not None: + return self.previous_controlnet.inference_memory_requirements(dtype) + return 0 + + def control_merge(self, control_input, control_output, control_prev, output_dtype): + out = {'input':[], 'middle':[], 'output': []} + + if control_input is not None: + for i in range(len(control_input)): + key = 'input' + x = control_input[i] + if x is not None: + x *= self.strength + if x.dtype != output_dtype: + x = x.to(output_dtype) + out[key].insert(0, x) + + if control_output is not None: + for i in range(len(control_output)): + if i == (len(control_output) - 1): + key = 'middle' + index = 0 + else: + key = 'output' + index = i + x = control_output[i] + if x is not None: + if self.global_average_pooling: + x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3]) + + x *= self.strength + if x.dtype != output_dtype: + x = x.to(output_dtype) + + out[key].append(x) + if control_prev is not None: + for x in ['input', 'middle', 'output']: + o = out[x] + for i in range(len(control_prev[x])): + prev_val = control_prev[x][i] + if i >= len(o): + o.append(prev_val) + elif prev_val is not None: + if o[i] is None: + o[i] = prev_val + else: + if o[i].shape[0] < prev_val.shape[0]: + o[i] = prev_val + o[i] + else: + o[i] += prev_val + return out + +class ControlNet(ControlBase): + def __init__(self, control_model, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None): + super().__init__(device) + self.control_model = control_model + self.load_device = load_device + self.control_model_wrapped = ldm_patched.modules.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=ldm_patched.modules.model_management.unet_offload_device()) + self.global_average_pooling = global_average_pooling + self.model_sampling_current = None + self.manual_cast_dtype = manual_cast_dtype + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return None + + dtype = self.control_model.dtype + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + + output_dtype = x_noisy.dtype + if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.cond_hint = None + self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + + context = cond['c_crossattn'] + y = cond.get('y', None) + if y is not None: + y = y.to(dtype) + timestep = self.model_sampling_current.timestep(t) + x_noisy = self.model_sampling_current.calculate_input(t, x_noisy) + + control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y) + return self.control_merge(None, control, control_prev, output_dtype) + + def copy(self): + c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype) + self.copy_to(c) + return c + + def get_models(self): + out = super().get_models() + out.append(self.control_model_wrapped) + return out + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + self.model_sampling_current = model.model_sampling + + def cleanup(self): + self.model_sampling_current = None + super().cleanup() + +class ControlLoraOps: + class Linear(torch.nn.Module): + def __init__(self, in_features: int, out_features: int, bias: bool = True, + device=None, dtype=None) -> None: + factory_kwargs = {'device': device, 'dtype': dtype} + super().__init__() + self.in_features = in_features + self.out_features = out_features + self.weight = None + self.up = None + self.down = None + self.bias = None + + def forward(self, input): + weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input) + if self.up is not None: + return torch.nn.functional.linear(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias) + else: + return torch.nn.functional.linear(input, weight, bias) + + class Conv2d(torch.nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=True, + padding_mode='zeros', + device=None, + dtype=None + ): + super().__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.dilation = dilation + self.transposed = False + self.output_padding = 0 + self.groups = groups + self.padding_mode = padding_mode + + self.weight = None + self.bias = None + self.up = None + self.down = None + + + def forward(self, input): + weight, bias = ldm_patched.modules.ops.cast_bias_weight(self, input) + if self.up is not None: + return torch.nn.functional.conv2d(input, weight + (torch.mm(self.up.flatten(start_dim=1), self.down.flatten(start_dim=1))).reshape(self.weight.shape).type(input.dtype), bias, self.stride, self.padding, self.dilation, self.groups) + else: + return torch.nn.functional.conv2d(input, weight, bias, self.stride, self.padding, self.dilation, self.groups) + + +class ControlLora(ControlNet): + def __init__(self, control_weights, global_average_pooling=False, device=None): + ControlBase.__init__(self, device) + self.control_weights = control_weights + self.global_average_pooling = global_average_pooling + + def pre_run(self, model, percent_to_timestep_function): + super().pre_run(model, percent_to_timestep_function) + controlnet_config = model.model_config.unet_config.copy() + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = self.control_weights["input_hint_block.0.weight"].shape[1] + self.manual_cast_dtype = model.manual_cast_dtype + dtype = model.get_dtype() + if self.manual_cast_dtype is None: + class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.disable_weight_init): + pass + else: + class control_lora_ops(ControlLoraOps, ldm_patched.modules.ops.manual_cast): + pass + dtype = self.manual_cast_dtype + + controlnet_config["operations"] = control_lora_ops + controlnet_config["dtype"] = dtype + self.control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config) + self.control_model.to(ldm_patched.modules.model_management.get_torch_device()) + diffusion_model = model.diffusion_model + sd = diffusion_model.state_dict() + cm = self.control_model.state_dict() + + for k in sd: + weight = sd[k] + try: + ldm_patched.modules.utils.set_attr(self.control_model, k, weight) + except: + pass + + for k in self.control_weights: + if k not in {"lora_controlnet"}: + ldm_patched.modules.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(ldm_patched.modules.model_management.get_torch_device())) + + def copy(self): + c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling) + self.copy_to(c) + return c + + def cleanup(self): + del self.control_model + self.control_model = None + super().cleanup() + + def get_models(self): + out = ControlBase.get_models(self) + return out + + def inference_memory_requirements(self, dtype): + return ldm_patched.modules.utils.calculate_parameters(self.control_weights) * ldm_patched.modules.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype) + +def load_controlnet(ckpt_path, model=None): + controlnet_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) + if "lora_controlnet" in controlnet_data: + return ControlLora(controlnet_data) + + controlnet_config = None + if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format + unet_dtype = ldm_patched.modules.model_management.unet_dtype() + controlnet_config = ldm_patched.modules.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype) + diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(controlnet_config) + diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight" + diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias" + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + k_in = "controlnet_down_blocks.{}{}".format(count, s) + k_out = "zero_convs.{}.0{}".format(count, s) + if k_in not in controlnet_data: + loop = False + break + diffusers_keys[k_in] = k_out + count += 1 + + count = 0 + loop = True + while loop: + suffix = [".weight", ".bias"] + for s in suffix: + if count == 0: + k_in = "controlnet_cond_embedding.conv_in{}".format(s) + else: + k_in = "controlnet_cond_embedding.blocks.{}{}".format(count - 1, s) + k_out = "input_hint_block.{}{}".format(count * 2, s) + if k_in not in controlnet_data: + k_in = "controlnet_cond_embedding.conv_out{}".format(s) + loop = False + diffusers_keys[k_in] = k_out + count += 1 + + new_sd = {} + for k in diffusers_keys: + if k in controlnet_data: + new_sd[diffusers_keys[k]] = controlnet_data.pop(k) + + leftover_keys = controlnet_data.keys() + if len(leftover_keys) > 0: + print("leftover keys:", leftover_keys) + controlnet_data = new_sd + + pth_key = 'control_model.zero_convs.0.0.weight' + pth = False + key = 'zero_convs.0.0.weight' + if pth_key in controlnet_data: + pth = True + key = pth_key + prefix = "control_model." + elif key in controlnet_data: + prefix = "" + else: + net = load_t2i_adapter(controlnet_data) + if net is None: + print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path) + return net + + if controlnet_config is None: + unet_dtype = ldm_patched.modules.model_management.unet_dtype() + controlnet_config = ldm_patched.modules.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config + load_device = ldm_patched.modules.model_management.get_torch_device() + manual_cast_dtype = ldm_patched.modules.model_management.unet_manual_cast(unet_dtype, load_device) + if manual_cast_dtype is not None: + controlnet_config["operations"] = ldm_patched.modules.ops.manual_cast + controlnet_config.pop("out_channels") + controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1] + control_model = ldm_patched.controlnet.cldm.ControlNet(**controlnet_config) + + if pth: + if 'difference' in controlnet_data: + if model is not None: + ldm_patched.modules.model_management.load_models_gpu([model]) + model_sd = model.model_state_dict() + for x in controlnet_data: + c_m = "control_model." + if x.startswith(c_m): + sd_key = "diffusion_model.{}".format(x[len(c_m):]) + if sd_key in model_sd: + cd = controlnet_data[x] + cd += model_sd[sd_key].type(cd.dtype).to(cd.device) + else: + print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.") + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.control_model = control_model + missing, unexpected = w.load_state_dict(controlnet_data, strict=False) + else: + missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False) + print(missing, unexpected) + + global_average_pooling = False + filename = os.path.splitext(ckpt_path)[0] + if filename.endswith("_shuffle") or filename.endswith("_shuffle_fp16"): #TODO: smarter way of enabling global_average_pooling + global_average_pooling = True + + control = ControlNet(control_model, global_average_pooling=global_average_pooling, load_device=load_device, manual_cast_dtype=manual_cast_dtype) + return control + +class T2IAdapter(ControlBase): + def __init__(self, t2i_model, channels_in, device=None): + super().__init__(device) + self.t2i_model = t2i_model + self.channels_in = channels_in + self.control_input = None + + def scale_image_to(self, width, height): + unshuffle_amount = self.t2i_model.unshuffle_amount + width = math.ceil(width / unshuffle_amount) * unshuffle_amount + height = math.ceil(height / unshuffle_amount) * unshuffle_amount + return width, height + + def get_control(self, x_noisy, t, cond, batched_number): + control_prev = None + if self.previous_controlnet is not None: + control_prev = self.previous_controlnet.get_control(x_noisy, t, cond, batched_number) + + if self.timestep_range is not None: + if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]: + if control_prev is not None: + return control_prev + else: + return None + + if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]: + if self.cond_hint is not None: + del self.cond_hint + self.control_input = None + self.cond_hint = None + width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8) + self.cond_hint = ldm_patched.modules.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device) + if self.channels_in == 1 and self.cond_hint.shape[1] > 1: + self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True) + if x_noisy.shape[0] != self.cond_hint.shape[0]: + self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number) + if self.control_input is None: + self.t2i_model.to(x_noisy.dtype) + self.t2i_model.to(self.device) + self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype)) + self.t2i_model.cpu() + + control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input)) + mid = None + if self.t2i_model.xl == True: + mid = control_input[-1:] + control_input = control_input[:-1] + return self.control_merge(control_input, mid, control_prev, x_noisy.dtype) + + def copy(self): + c = T2IAdapter(self.t2i_model, self.channels_in) + self.copy_to(c) + return c + +def load_t2i_adapter(t2i_data): + if 'adapter' in t2i_data: + t2i_data = t2i_data['adapter'] + if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format + prefix_replace = {} + for i in range(4): + for j in range(2): + prefix_replace["adapter.body.{}.resnets.{}.".format(i, j)] = "body.{}.".format(i * 2 + j) + prefix_replace["adapter.body.{}.".format(i, j)] = "body.{}.".format(i * 2) + prefix_replace["adapter."] = "" + t2i_data = ldm_patched.modules.utils.state_dict_prefix_replace(t2i_data, prefix_replace) + keys = t2i_data.keys() + + if "body.0.in_conv.weight" in keys: + cin = t2i_data['body.0.in_conv.weight'].shape[1] + model_ad = ldm_patched.t2ia.adapter.Adapter_light(cin=cin, channels=[320, 640, 1280, 1280], nums_rb=4) + elif 'conv_in.weight' in keys: + cin = t2i_data['conv_in.weight'].shape[1] + channel = t2i_data['conv_in.weight'].shape[0] + ksize = t2i_data['body.0.block2.weight'].shape[2] + use_conv = False + down_opts = list(filter(lambda a: a.endswith("down_opt.op.weight"), keys)) + if len(down_opts) > 0: + use_conv = True + xl = False + if cin == 256 or cin == 768: + xl = True + model_ad = ldm_patched.t2ia.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl) + else: + return None + missing, unexpected = model_ad.load_state_dict(t2i_data) + if len(missing) > 0: + print("t2i missing", missing) + + if len(unexpected) > 0: + print("t2i unexpected", unexpected) + + return T2IAdapter(model_ad, model_ad.input_channels) diff --git a/ldm_patched/modules/diffusers_convert.py b/ldm_patched/modules/diffusers_convert.py new file mode 100644 index 0000000000000000000000000000000000000000..a9eb9302f14f4fa2710c9652e0b58c6453c0cf7b --- /dev/null +++ b/ldm_patched/modules/diffusers_convert.py @@ -0,0 +1,261 @@ +import re +import torch + +# conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py + +# =================# +# UNet Conversion # +# =================# + +unet_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), +] + +unet_conversion_map_resnet = [ + # (stable-diffusion, HF Diffusers) + ("in_layers.0", "norm1"), + ("in_layers.2", "conv1"), + ("out_layers.0", "norm2"), + ("out_layers.3", "conv2"), + ("emb_layers.1", "time_emb_proj"), + ("skip_connection", "conv_shortcut"), +] + +unet_conversion_map_layer = [] +# hardcoded number of downblocks and resnets/attentions... +# would need smarter logic for other networks. +for i in range(4): + # loop over downblocks/upblocks + + for j in range(2): + # loop over resnets/attentions for downblocks + hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." + sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0." + unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) + + if i < 3: + # no attention layers in down_blocks.3 + hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." + sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1." + unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) + + for j in range(3): + # loop over resnets/attentions for upblocks + hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." + sd_up_res_prefix = f"output_blocks.{3 * i + j}.0." + unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) + + if i > 0: + # no attention layers in up_blocks.0 + hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." + sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1." + unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) + + if i < 3: + # no downsample in down_blocks.3 + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." + sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op." + unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) + + # no upsample in up_blocks.3 + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}." + unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) + +hf_mid_atn_prefix = "mid_block.attentions.0." +sd_mid_atn_prefix = "middle_block.1." +unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) + +for j in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{j}." + sd_mid_res_prefix = f"middle_block.{2 * j}." + unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) + + +def convert_unet_state_dict(unet_state_dict): + # buyer beware: this is a *brittle* function, + # and correct output requires that all of these pieces interact in + # the exact order in which I have arranged them. + mapping = {k: k for k in unet_state_dict.keys()} + for sd_name, hf_name in unet_conversion_map: + mapping[hf_name] = sd_name + for k, v in mapping.items(): + if "resnets" in k: + for sd_part, hf_part in unet_conversion_map_resnet: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + for sd_part, hf_part in unet_conversion_map_layer: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()} + return new_state_dict + + +# ================# +# VAE Conversion # +# ================# + +vae_conversion_map = [ + # (stable-diffusion, HF Diffusers) + ("nin_shortcut", "conv_shortcut"), + ("norm_out", "conv_norm_out"), + ("mid.attn_1.", "mid_block.attentions.0."), +] + +for i in range(4): + # down_blocks have two resnets + for j in range(2): + hf_down_prefix = f"encoder.down_blocks.{i}.resnets.{j}." + sd_down_prefix = f"encoder.down.{i}.block.{j}." + vae_conversion_map.append((sd_down_prefix, hf_down_prefix)) + + if i < 3: + hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0." + sd_downsample_prefix = f"down.{i}.downsample." + vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix)) + + hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." + sd_upsample_prefix = f"up.{3 - i}.upsample." + vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix)) + + # up_blocks have three resnets + # also, up blocks in hf are numbered in reverse from sd + for j in range(3): + hf_up_prefix = f"decoder.up_blocks.{i}.resnets.{j}." + sd_up_prefix = f"decoder.up.{3 - i}.block.{j}." + vae_conversion_map.append((sd_up_prefix, hf_up_prefix)) + +# this part accounts for mid blocks in both the encoder and the decoder +for i in range(2): + hf_mid_res_prefix = f"mid_block.resnets.{i}." + sd_mid_res_prefix = f"mid.block_{i + 1}." + vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix)) + +vae_conversion_map_attn = [ + # (stable-diffusion, HF Diffusers) + ("norm.", "group_norm."), + ("q.", "query."), + ("k.", "key."), + ("v.", "value."), + ("q.", "to_q."), + ("k.", "to_k."), + ("v.", "to_v."), + ("proj_out.", "to_out.0."), + ("proj_out.", "proj_attn."), +] + + +def reshape_weight_for_sd(w): + # convert HF linear weights to SD conv2d weights + return w.reshape(*w.shape, 1, 1) + + +def convert_vae_state_dict(vae_state_dict): + mapping = {k: k for k in vae_state_dict.keys()} + for k, v in mapping.items(): + for sd_part, hf_part in vae_conversion_map: + v = v.replace(hf_part, sd_part) + mapping[k] = v + for k, v in mapping.items(): + if "attentions" in k: + for sd_part, hf_part in vae_conversion_map_attn: + v = v.replace(hf_part, sd_part) + mapping[k] = v + new_state_dict = {v: vae_state_dict[k] for k, v in mapping.items()} + weights_to_convert = ["q", "k", "v", "proj_out"] + for k, v in new_state_dict.items(): + for weight_name in weights_to_convert: + if f"mid.attn_1.{weight_name}.weight" in k: + print(f"Reshaping {k} for SD format") + new_state_dict[k] = reshape_weight_for_sd(v) + return new_state_dict + + +# =========================# +# Text Encoder Conversion # +# =========================# + + +textenc_conversion_lst = [ + # (stable-diffusion, HF Diffusers) + ("resblocks.", "text_model.encoder.layers."), + ("ln_1", "layer_norm1"), + ("ln_2", "layer_norm2"), + (".c_fc.", ".fc1."), + (".c_proj.", ".fc2."), + (".attn", ".self_attn"), + ("ln_final.", "transformer.text_model.final_layer_norm."), + ("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"), + ("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"), +] +protected = {re.escape(x[1]): x[0] for x in textenc_conversion_lst} +textenc_pattern = re.compile("|".join(protected.keys())) + +# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp +code2idx = {"q": 0, "k": 1, "v": 2} + + +def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""): + new_state_dict = {} + capture_qkv_weight = {} + capture_qkv_bias = {} + for k, v in text_enc_dict.items(): + if not k.startswith(prefix): + continue + if ( + k.endswith(".self_attn.q_proj.weight") + or k.endswith(".self_attn.k_proj.weight") + or k.endswith(".self_attn.v_proj.weight") + ): + k_pre = k[: -len(".q_proj.weight")] + k_code = k[-len("q_proj.weight")] + if k_pre not in capture_qkv_weight: + capture_qkv_weight[k_pre] = [None, None, None] + capture_qkv_weight[k_pre][code2idx[k_code]] = v + continue + + if ( + k.endswith(".self_attn.q_proj.bias") + or k.endswith(".self_attn.k_proj.bias") + or k.endswith(".self_attn.v_proj.bias") + ): + k_pre = k[: -len(".q_proj.bias")] + k_code = k[-len("q_proj.bias")] + if k_pre not in capture_qkv_bias: + capture_qkv_bias[k_pre] = [None, None, None] + capture_qkv_bias[k_pre][code2idx[k_code]] = v + continue + + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k) + new_state_dict[relabelled_key] = v + + for k_pre, tensors in capture_qkv_weight.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors) + + for k_pre, tensors in capture_qkv_bias.items(): + if None in tensors: + raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing") + relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre) + new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors) + + return new_state_dict + + +def convert_text_enc_state_dict(text_enc_dict): + return text_enc_dict + + diff --git a/ldm_patched/modules/diffusers_load.py b/ldm_patched/modules/diffusers_load.py new file mode 100644 index 0000000000000000000000000000000000000000..62edc72b8b8c6b0cf49b1e675d4df45510b6f2ed --- /dev/null +++ b/ldm_patched/modules/diffusers_load.py @@ -0,0 +1,36 @@ +import os + +import ldm_patched.modules.sd + +def first_file(path, filenames): + for f in filenames: + p = os.path.join(path, f) + if os.path.exists(p): + return p + return None + +def load_diffusers(model_path, output_vae=True, output_clip=True, embedding_directory=None): + diffusion_model_names = ["diffusion_pytorch_model.fp16.safetensors", "diffusion_pytorch_model.safetensors", "diffusion_pytorch_model.fp16.bin", "diffusion_pytorch_model.bin"] + unet_path = first_file(os.path.join(model_path, "unet"), diffusion_model_names) + vae_path = first_file(os.path.join(model_path, "vae"), diffusion_model_names) + + text_encoder_model_names = ["model.fp16.safetensors", "model.safetensors", "pytorch_model.fp16.bin", "pytorch_model.bin"] + text_encoder1_path = first_file(os.path.join(model_path, "text_encoder"), text_encoder_model_names) + text_encoder2_path = first_file(os.path.join(model_path, "text_encoder_2"), text_encoder_model_names) + + text_encoder_paths = [text_encoder1_path] + if text_encoder2_path is not None: + text_encoder_paths.append(text_encoder2_path) + + unet = ldm_patched.modules.sd.load_unet(unet_path) + + clip = None + if output_clip: + clip = ldm_patched.modules.sd.load_clip(text_encoder_paths, embedding_directory=embedding_directory) + + vae = None + if output_vae: + sd = ldm_patched.modules.utils.load_torch_file(vae_path) + vae = ldm_patched.modules.sd.VAE(sd=sd) + + return (unet, clip, vae) diff --git a/ldm_patched/modules/gligen.py b/ldm_patched/modules/gligen.py new file mode 100644 index 0000000000000000000000000000000000000000..11f1ee938de04236a4e8f5eb50448548d345e490 --- /dev/null +++ b/ldm_patched/modules/gligen.py @@ -0,0 +1,341 @@ +import torch +from torch import nn +from ldm_patched.ldm.modules.attention import CrossAttention +from inspect import isfunction + + +def exists(val): + return val is not None + + +def uniq(arr): + return{el: True for el in arr}.keys() + + +def default(val, d): + if exists(val): + return val + return d() if isfunction(d) else d + + +# feedforward +class GEGLU(nn.Module): + def __init__(self, dim_in, dim_out): + super().__init__() + self.proj = nn.Linear(dim_in, dim_out * 2) + + def forward(self, x): + x, gate = self.proj(x).chunk(2, dim=-1) + return x * torch.nn.functional.gelu(gate) + + +class FeedForward(nn.Module): + def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.): + super().__init__() + inner_dim = int(dim * mult) + dim_out = default(dim_out, dim) + project_in = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU() + ) if not glu else GEGLU(dim, inner_dim) + + self.net = nn.Sequential( + project_in, + nn.Dropout(dropout), + nn.Linear(inner_dim, dim_out) + ) + + def forward(self, x): + return self.net(x) + + +class GatedCrossAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=context_dim, + heads=n_heads, + dim_head=d_head) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = nn.LayerNorm(query_dim) + self.norm2 = nn.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + x = x + self.scale * \ + torch.tanh(self.alpha_attn) * self.attn(self.norm1(x), objs, objs) + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = nn.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, + context_dim=query_dim, + heads=n_heads, + dim_head=d_head) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = nn.LayerNorm(query_dim) + self.norm2 = nn.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + N_visual = x.shape[1] + objs = self.linear(objs) + + x = x + self.scale * torch.tanh(self.alpha_attn) * self.attn( + self.norm1(torch.cat([x, objs], dim=1)))[:, 0:N_visual, :] + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class GatedSelfAttentionDense2(nn.Module): + def __init__(self, query_dim, context_dim, n_heads, d_head): + super().__init__() + + # we need a linear projection since we need cat visual feature and obj + # feature + self.linear = nn.Linear(context_dim, query_dim) + + self.attn = CrossAttention( + query_dim=query_dim, context_dim=query_dim, dim_head=d_head) + self.ff = FeedForward(query_dim, glu=True) + + self.norm1 = nn.LayerNorm(query_dim) + self.norm2 = nn.LayerNorm(query_dim) + + self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.))) + self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.))) + + # this can be useful: we can externally change magnitude of tanh(alpha) + # for example, when it is set to 0, then the entire model is same as + # original one + self.scale = 1 + + def forward(self, x, objs): + + B, N_visual, _ = x.shape + B, N_ground, _ = objs.shape + + objs = self.linear(objs) + + # sanity check + size_v = math.sqrt(N_visual) + size_g = math.sqrt(N_ground) + assert int(size_v) == size_v, "Visual tokens must be square rootable" + assert int(size_g) == size_g, "Grounding tokens must be square rootable" + size_v = int(size_v) + size_g = int(size_g) + + # select grounding token and resize it to visual token size as residual + out = self.attn(self.norm1(torch.cat([x, objs], dim=1)))[ + :, N_visual:, :] + out = out.permute(0, 2, 1).reshape(B, -1, size_g, size_g) + out = torch.nn.functional.interpolate( + out, (size_v, size_v), mode='bicubic') + residual = out.reshape(B, -1, N_visual).permute(0, 2, 1) + + # add residual to visual feature + x = x + self.scale * torch.tanh(self.alpha_attn) * residual + x = x + self.scale * \ + torch.tanh(self.alpha_dense) * self.ff(self.norm2(x)) + + return x + + +class FourierEmbedder(): + def __init__(self, num_freqs=64, temperature=100): + + self.num_freqs = num_freqs + self.temperature = temperature + self.freq_bands = temperature ** (torch.arange(num_freqs) / num_freqs) + + @torch.no_grad() + def __call__(self, x, cat_dim=-1): + "x: arbitrary shape of tensor. dim: cat dim" + out = [] + for freq in self.freq_bands: + out.append(torch.sin(freq * x)) + out.append(torch.cos(freq * x)) + return torch.cat(out, cat_dim) + + +class PositionNet(nn.Module): + def __init__(self, in_dim, out_dim, fourier_freqs=8): + super().__init__() + self.in_dim = in_dim + self.out_dim = out_dim + + self.fourier_embedder = FourierEmbedder(num_freqs=fourier_freqs) + self.position_dim = fourier_freqs * 2 * 4 # 2 is sin&cos, 4 is xyxy + + self.linears = nn.Sequential( + nn.Linear(self.in_dim + self.position_dim, 512), + nn.SiLU(), + nn.Linear(512, 512), + nn.SiLU(), + nn.Linear(512, out_dim), + ) + + self.null_positive_feature = torch.nn.Parameter( + torch.zeros([self.in_dim])) + self.null_position_feature = torch.nn.Parameter( + torch.zeros([self.position_dim])) + + def forward(self, boxes, masks, positive_embeddings): + B, N, _ = boxes.shape + dtype = self.linears[0].weight.dtype + masks = masks.unsqueeze(-1).to(dtype) + positive_embeddings = positive_embeddings.to(dtype) + + # embedding position (it may includes padding as placeholder) + xyxy_embedding = self.fourier_embedder(boxes.to(dtype)) # B*N*4 --> B*N*C + + # learnable null embedding + positive_null = self.null_positive_feature.view(1, 1, -1) + xyxy_null = self.null_position_feature.view(1, 1, -1) + + # replace padding with learnable null embedding + positive_embeddings = positive_embeddings * \ + masks + (1 - masks) * positive_null + xyxy_embedding = xyxy_embedding * masks + (1 - masks) * xyxy_null + + objs = self.linears( + torch.cat([positive_embeddings, xyxy_embedding], dim=-1)) + assert objs.shape == torch.Size([B, N, self.out_dim]) + return objs + + +class Gligen(nn.Module): + def __init__(self, modules, position_net, key_dim): + super().__init__() + self.module_list = nn.ModuleList(modules) + self.position_net = position_net + self.key_dim = key_dim + self.max_objs = 30 + self.current_device = torch.device("cpu") + + def _set_position(self, boxes, masks, positive_embeddings): + objs = self.position_net(boxes, masks, positive_embeddings) + def func(x, extra_options): + key = extra_options["transformer_index"] + module = self.module_list[key] + return module(x, objs) + return func + + def set_position(self, latent_image_shape, position_params, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu") + boxes = [] + positive_embeddings = [] + for p in position_params: + x1 = (p[4]) / w + y1 = (p[3]) / h + x2 = (p[4] + p[2]) / w + y2 = (p[3] + p[1]) / h + masks[len(boxes)] = 1.0 + boxes += [torch.tensor((x1, y1, x2, y2)).unsqueeze(0)] + positive_embeddings += [p[0]] + append_boxes = [] + append_conds = [] + if len(boxes) < self.max_objs: + append_boxes = [torch.zeros( + [self.max_objs - len(boxes), 4], device="cpu")] + append_conds = [torch.zeros( + [self.max_objs - len(boxes), self.key_dim], device="cpu")] + + box_out = torch.cat( + boxes + append_boxes).unsqueeze(0).repeat(batch, 1, 1) + masks = masks.unsqueeze(0).repeat(batch, 1) + conds = torch.cat(positive_embeddings + + append_conds).unsqueeze(0).repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + def set_empty(self, latent_image_shape, device): + batch, c, h, w = latent_image_shape + masks = torch.zeros([self.max_objs], device="cpu").repeat(batch, 1) + box_out = torch.zeros([self.max_objs, 4], + device="cpu").repeat(batch, 1, 1) + conds = torch.zeros([self.max_objs, self.key_dim], + device="cpu").repeat(batch, 1, 1) + return self._set_position( + box_out.to(device), + masks.to(device), + conds.to(device)) + + +def load_gligen(sd): + sd_k = sd.keys() + output_list = [] + key_dim = 768 + for a in ["input_blocks", "middle_block", "output_blocks"]: + for b in range(20): + k_temp = filter(lambda k: "{}.{}.".format(a, b) + in k and ".fuser." in k, sd_k) + k_temp = map(lambda k: (k, k.split(".fuser.")[-1]), k_temp) + + n_sd = {} + for k in k_temp: + n_sd[k[1]] = sd[k[0]] + if len(n_sd) > 0: + query_dim = n_sd["linear.weight"].shape[0] + key_dim = n_sd["linear.weight"].shape[1] + + if key_dim == 768: # SD1.x + n_heads = 8 + d_head = query_dim // n_heads + else: + d_head = 64 + n_heads = query_dim // d_head + + gated = GatedSelfAttentionDense( + query_dim, key_dim, n_heads, d_head) + gated.load_state_dict(n_sd, strict=False) + output_list.append(gated) + + if "position_net.null_positive_feature" in sd_k: + in_dim = sd["position_net.null_positive_feature"].shape[0] + out_dim = sd["position_net.linears.4.weight"].shape[0] + + class WeightsLoader(torch.nn.Module): + pass + w = WeightsLoader() + w.position_net = PositionNet(in_dim, out_dim) + w.load_state_dict(sd, strict=False) + + gligen = Gligen(output_list, w.position_net, key_dim) + return gligen diff --git a/ldm_patched/modules/latent_formats.py b/ldm_patched/modules/latent_formats.py new file mode 100644 index 0000000000000000000000000000000000000000..2252a075ed530fce5b6b6f81dfa7eede4b5395af --- /dev/null +++ b/ldm_patched/modules/latent_formats.py @@ -0,0 +1,39 @@ + +class LatentFormat: + scale_factor = 1.0 + latent_rgb_factors = None + taesd_decoder_name = None + + def process_in(self, latent): + return latent * self.scale_factor + + def process_out(self, latent): + return latent / self.scale_factor + +class SD15(LatentFormat): + def __init__(self, scale_factor=0.18215): + self.scale_factor = scale_factor + self.latent_rgb_factors = [ + # R G B + [ 0.3512, 0.2297, 0.3227], + [ 0.3250, 0.4974, 0.2350], + [-0.2829, 0.1762, 0.2721], + [-0.2120, -0.2616, -0.7177] + ] + self.taesd_decoder_name = "taesd_decoder" + +class SDXL(LatentFormat): + def __init__(self): + self.scale_factor = 0.13025 + self.latent_rgb_factors = [ + # R G B + [ 0.3920, 0.4054, 0.4549], + [-0.2634, -0.0196, 0.0653], + [ 0.0568, 0.1687, -0.0755], + [-0.3112, -0.2359, -0.2076] + ] + self.taesd_decoder_name = "taesdxl_decoder" + +class SD_X4(LatentFormat): + def __init__(self): + self.scale_factor = 0.08333 diff --git a/ldm_patched/modules/lora.py b/ldm_patched/modules/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..cc5a29da82eb6ed48afbb1eeff89074edfb4d49d --- /dev/null +++ b/ldm_patched/modules/lora.py @@ -0,0 +1,224 @@ +import ldm_patched.modules.utils + +LORA_CLIP_MAP = { + "mlp.fc1": "mlp_fc1", + "mlp.fc2": "mlp_fc2", + "self_attn.k_proj": "self_attn_k_proj", + "self_attn.q_proj": "self_attn_q_proj", + "self_attn.v_proj": "self_attn_v_proj", + "self_attn.out_proj": "self_attn_out_proj", +} + + +def load_lora(lora, to_load): + patch_dict = {} + loaded_keys = set() + for x in to_load: + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name ="{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + + + ######## loha + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + + + ######## lokr + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) + + #glora + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) + + w_norm_name = "{}.w_norm".format(x) + b_norm_name = "{}.b_norm".format(x) + w_norm = lora.get(w_norm_name, None) + b_norm = lora.get(b_norm_name, None) + + if w_norm is not None: + loaded_keys.add(w_norm_name) + patch_dict[to_load[x]] = ("diff", (w_norm,)) + if b_norm is not None: + loaded_keys.add(b_norm_name) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) + + diff_name = "{}.diff".format(x) + diff_weight = lora.get(diff_name, None) + if diff_weight is not None: + patch_dict[to_load[x]] = ("diff", (diff_weight,)) + loaded_keys.add(diff_name) + + diff_bias_name = "{}.diff_b".format(x) + diff_bias = lora.get(diff_bias_name, None) + if diff_bias is not None: + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) + loaded_keys.add(diff_bias_name) + + for x in lora.keys(): + if x not in loaded_keys: + print("lora key not loaded", x) + return patch_dict + +def model_lora_keys_clip(model, key_map={}): + sdk = model.state_dict().keys() + + text_model_lora_key = "lora_te_text_model_encoder_layers_{}_{}" + clip_l_present = False + for b in range(32): #TODO: clean up + for c in LORA_CLIP_MAP: + k = "clip_h.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_l.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + lora_key = text_model_lora_key.format(b, LORA_CLIP_MAP[c]) + key_map[lora_key] = k + lora_key = "lora_te1_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + clip_l_present = True + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + k = "clip_g.transformer.text_model.encoder.layers.{}.{}.weight".format(b, c) + if k in sdk: + if clip_l_present: + lora_key = "lora_te2_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #SDXL base + key_map[lora_key] = k + lora_key = "text_encoder_2.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + else: + lora_key = "lora_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #TODO: test if this is correct for SDXL-Refiner + key_map[lora_key] = k + lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora + key_map[lora_key] = k + + return key_map + +def model_lora_keys_unet(model, key_map={}): + sdk = model.state_dict().keys() + + for k in sdk: + if k.startswith("diffusion_model.") and k.endswith(".weight"): + key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = k + + diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model.model_config.unet_config) + for k in diffusers_keys: + if k.endswith(".weight"): + unet_key = "diffusion_model.{}".format(diffusers_keys[k]) + key_lora = k[:-len(".weight")].replace(".", "_") + key_map["lora_unet_{}".format(key_lora)] = unet_key + + diffusers_lora_prefix = ["", "unet."] + for p in diffusers_lora_prefix: + diffusers_lora_key = "{}{}".format(p, k[:-len(".weight")].replace(".to_", ".processor.to_")) + if diffusers_lora_key.endswith(".to_out.0"): + diffusers_lora_key = diffusers_lora_key[:-2] + key_map[diffusers_lora_key] = unet_key + return key_map diff --git a/ldm_patched/modules/model_base.py b/ldm_patched/modules/model_base.py new file mode 100644 index 0000000000000000000000000000000000000000..9c69e98b88d5eec2ffe02067d80a571926a112f6 --- /dev/null +++ b/ldm_patched/modules/model_base.py @@ -0,0 +1,425 @@ +import torch +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep +from ldm_patched.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation +from ldm_patched.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation +import ldm_patched.modules.model_management +import ldm_patched.modules.conds +import ldm_patched.modules.ops +from enum import Enum +from . import utils + +class ModelType(Enum): + EPS = 1 + V_PREDICTION = 2 + V_PREDICTION_EDM = 3 + + +from ldm_patched.modules.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM + + +def model_sampling(model_config, model_type): + s = ModelSamplingDiscrete + + if model_type == ModelType.EPS: + c = EPS + elif model_type == ModelType.V_PREDICTION: + c = V_PREDICTION + elif model_type == ModelType.V_PREDICTION_EDM: + c = V_PREDICTION + s = ModelSamplingContinuousEDM + + class ModelSampling(s, c): + pass + + return ModelSampling(model_config) + + +class BaseModel(torch.nn.Module): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__() + + unet_config = model_config.unet_config + self.latent_format = model_config.latent_format + self.model_config = model_config + self.manual_cast_dtype = model_config.manual_cast_dtype + + if not unet_config.get("disable_unet_model_creation", False): + if self.manual_cast_dtype is not None: + operations = ldm_patched.modules.ops.manual_cast + else: + operations = ldm_patched.modules.ops.disable_weight_init + self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations) + self.model_type = model_type + self.model_sampling = model_sampling(model_config, model_type) + + self.adm_channels = unet_config.get("adm_in_channels", None) + if self.adm_channels is None: + self.adm_channels = 0 + self.inpaint_model = False + print("model_type", model_type.name) + print("UNet ADM Dimension", self.adm_channels) + + def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs): + sigma = t + xc = self.model_sampling.calculate_input(sigma, x) + if c_concat is not None: + xc = torch.cat([xc] + [c_concat], dim=1) + + context = c_crossattn + dtype = self.get_dtype() + + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + + xc = xc.to(dtype) + t = self.model_sampling.timestep(t).float() + context = context.to(dtype) + extra_conds = {} + for o in kwargs: + extra = kwargs[o] + if hasattr(extra, "dtype"): + if extra.dtype != torch.int and extra.dtype != torch.long: + extra = extra.to(dtype) + extra_conds[o] = extra + + model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float() + return self.model_sampling.calculate_denoised(sigma, model_output, x) + + def get_dtype(self): + return self.diffusion_model.dtype + + def is_adm(self): + return self.adm_channels > 0 + + def encode_adm(self, **kwargs): + return None + + def extra_conds(self, **kwargs): + out = {} + if self.inpaint_model: + concat_keys = ("mask", "masked_image") + cond_concat = [] + denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None)) + concat_latent_image = kwargs.get("concat_latent_image", None) + if concat_latent_image is None: + concat_latent_image = kwargs.get("latent_image", None) + else: + concat_latent_image = self.process_latent_in(concat_latent_image) + + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if concat_latent_image.shape[1:] != noise.shape[1:]: + concat_latent_image = utils.common_upscale(concat_latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0]) + + if len(denoise_mask.shape) == len(noise.shape): + denoise_mask = denoise_mask[:,:1] + + denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1])) + if denoise_mask.shape[-2:] != noise.shape[-2:]: + denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center") + denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0]) + + def blank_inpaint_image_like(latent_image): + blank_image = torch.ones_like(latent_image) + # these are the values for "zero" in pixel space translated to latent space + blank_image[:,0] *= 0.8223 + blank_image[:,1] *= -0.6876 + blank_image[:,2] *= 0.6364 + blank_image[:,3] *= 0.1380 + return blank_image + + for ck in concat_keys: + if denoise_mask is not None: + if ck == "mask": + cond_concat.append(denoise_mask.to(device)) + elif ck == "masked_image": + cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space + else: + if ck == "mask": + cond_concat.append(torch.ones_like(noise)[:,:1]) + elif ck == "masked_image": + cond_concat.append(blank_inpaint_image_like(noise)) + data = torch.cat(cond_concat, dim=1) + out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(data) + + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = ldm_patched.modules.conds.CONDRegular(adm) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) + + return out + + def load_model_weights(self, sd, unet_prefix=""): + to_load = {} + keys = list(sd.keys()) + for k in keys: + if k.startswith(unet_prefix): + to_load[k[len(unet_prefix):]] = sd.pop(k) + + to_load = self.model_config.process_unet_state_dict(to_load) + m, u = self.diffusion_model.load_state_dict(to_load, strict=False) + if len(m) > 0: + print("unet missing:", m) + + if len(u) > 0: + print("unet unexpected:", u) + del to_load + return self + + def process_latent_in(self, latent): + return self.latent_format.process_in(latent) + + def process_latent_out(self, latent): + return self.latent_format.process_out(latent) + + def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None): + extra_sds = [] + if clip_state_dict is not None: + extra_sds.append(self.model_config.process_clip_state_dict_for_saving(clip_state_dict)) + if vae_state_dict is not None: + extra_sds.append(self.model_config.process_vae_state_dict_for_saving(vae_state_dict)) + if clip_vision_state_dict is not None: + extra_sds.append(self.model_config.process_clip_vision_state_dict_for_saving(clip_vision_state_dict)) + + unet_state_dict = self.diffusion_model.state_dict() + unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict) + + if self.get_dtype() == torch.float16: + extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds) + + if self.model_type == ModelType.V_PREDICTION: + unet_state_dict["v_pred"] = torch.tensor([]) + + for sd in extra_sds: + unet_state_dict.update(sd) + + return unet_state_dict + + def set_inpaint(self): + self.inpaint_model = True + + def memory_required(self, input_shape): + if ldm_patched.modules.model_management.xformers_enabled() or ldm_patched.modules.model_management.pytorch_attention_flash_attention(): + dtype = self.get_dtype() + if self.manual_cast_dtype is not None: + dtype = self.manual_cast_dtype + #TODO: this needs to be tweaked + area = input_shape[0] * input_shape[2] * input_shape[3] + return (area * ldm_patched.modules.model_management.dtype_size(dtype) / 50) * (1024 * 1024) + else: + #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory. + area = input_shape[0] * input_shape[2] * input_shape[3] + return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024) + + +def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None): + adm_inputs = [] + weights = [] + noise_aug = [] + for unclip_cond in unclip_conditioning: + for adm_cond in unclip_cond["clip_vision_output"].image_embeds: + weight = unclip_cond["strength"] + noise_augment = unclip_cond["noise_augmentation"] + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_cond.to(device), noise_level=torch.tensor([noise_level], device=device), seed=seed) + adm_out = torch.cat((c_adm, noise_level_emb), 1) * weight + weights.append(weight) + noise_aug.append(noise_augment) + adm_inputs.append(adm_out) + + if len(noise_aug) > 1: + adm_out = torch.stack(adm_inputs).sum(0) + noise_augment = noise_augment_merge + noise_level = round((noise_augmentor.max_noise_level - 1) * noise_augment) + c_adm, noise_level_emb = noise_augmentor(adm_out[:, :noise_augmentor.time_embed.dim], noise_level=torch.tensor([noise_level], device=device)) + adm_out = torch.cat((c_adm, noise_level_emb), 1) + + return adm_out + +class SD21UNCLIP(BaseModel): + def __init__(self, model_config, noise_aug_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**noise_aug_config) + + def encode_adm(self, **kwargs): + unclip_conditioning = kwargs.get("unclip_conditioning", None) + device = kwargs["device"] + if unclip_conditioning is None: + return torch.zeros((1, self.adm_channels)) + else: + return unclip_adm(unclip_conditioning, device, self.noise_augmentor, kwargs.get("unclip_noise_augment_merge", 0.05), kwargs.get("seed", 0) - 10) + +def sdxl_pooled(args, noise_augmentor): + if "unclip_conditioning" in args: + return unclip_adm(args.get("unclip_conditioning", None), args["device"], noise_augmentor, seed=args.get("seed", 0) - 10)[:,:1280] + else: + return args["pooled_output"] + +class SDXLRefiner(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + + if kwargs.get("prompt_type", "") == "negative": + aesthetic_score = kwargs.get("aesthetic_score", 2.5) + else: + aesthetic_score = kwargs.get("aesthetic_score", 6) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([aesthetic_score]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + +class SDXL(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + self.noise_augmentor = CLIPEmbeddingNoiseAugmentation(**{"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1280}) + + def encode_adm(self, **kwargs): + clip_pooled = sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 768) + height = kwargs.get("height", 768) + crop_w = kwargs.get("crop_w", 0) + crop_h = kwargs.get("crop_h", 0) + target_width = kwargs.get("target_width", width) + target_height = kwargs.get("target_height", height) + + out = [] + out.append(self.embedder(torch.Tensor([height]))) + out.append(self.embedder(torch.Tensor([width]))) + out.append(self.embedder(torch.Tensor([crop_h]))) + out.append(self.embedder(torch.Tensor([crop_w]))) + out.append(self.embedder(torch.Tensor([target_height]))) + out.append(self.embedder(torch.Tensor([target_width]))) + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return torch.cat((clip_pooled.to(flat.device), flat), dim=1) + +class SVD_img2vid(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None): + super().__init__(model_config, model_type, device=device) + self.embedder = Timestep(256) + + def encode_adm(self, **kwargs): + fps_id = kwargs.get("fps", 6) - 1 + motion_bucket_id = kwargs.get("motion_bucket_id", 127) + augmentation = kwargs.get("augmentation_level", 0) + + out = [] + out.append(self.embedder(torch.Tensor([fps_id]))) + out.append(self.embedder(torch.Tensor([motion_bucket_id]))) + out.append(self.embedder(torch.Tensor([augmentation]))) + + flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0) + return flat + + def extra_conds(self, **kwargs): + out = {} + adm = self.encode_adm(**kwargs) + if adm is not None: + out['y'] = ldm_patched.modules.conds.CONDRegular(adm) + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + device = kwargs["device"] + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) + + if "time_conditioning" in kwargs: + out["time_context"] = ldm_patched.modules.conds.CONDCrossAttn(kwargs["time_conditioning"]) + + out['image_only_indicator'] = ldm_patched.modules.conds.CONDConstant(torch.zeros((1,), device=device)) + out['num_video_frames'] = ldm_patched.modules.conds.CONDConstant(noise.shape[0]) + return out + +class Stable_Zero123(BaseModel): + def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None): + super().__init__(model_config, model_type, device=device) + self.cc_projection = ldm_patched.modules.ops.manual_cast.Linear(cc_projection_weight.shape[1], cc_projection_weight.shape[0], dtype=self.get_dtype(), device=device) + self.cc_projection.weight.copy_(cc_projection_weight) + self.cc_projection.bias.copy_(cc_projection_bias) + + def extra_conds(self, **kwargs): + out = {} + + latent_image = kwargs.get("concat_latent_image", None) + noise = kwargs.get("noise", None) + + if latent_image is None: + latent_image = torch.zeros_like(noise) + + if latent_image.shape[1:] != noise.shape[1:]: + latent_image = utils.common_upscale(latent_image, noise.shape[-1], noise.shape[-2], "bilinear", "center") + + latent_image = utils.resize_to_batch_size(latent_image, noise.shape[0]) + + out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(latent_image) + + cross_attn = kwargs.get("cross_attn", None) + if cross_attn is not None: + if cross_attn.shape[-1] != 768: + cross_attn = self.cc_projection(cross_attn) + out['c_crossattn'] = ldm_patched.modules.conds.CONDCrossAttn(cross_attn) + return out + +class SD_X4Upscaler(BaseModel): + def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None): + super().__init__(model_config, model_type, device=device) + self.noise_augmentor = ImageConcatWithNoiseAugmentation(noise_schedule_config={"linear_start": 0.0001, "linear_end": 0.02}, max_noise_level=350) + + def extra_conds(self, **kwargs): + out = {} + + image = kwargs.get("concat_image", None) + noise = kwargs.get("noise", None) + noise_augment = kwargs.get("noise_augmentation", 0.0) + device = kwargs["device"] + seed = kwargs["seed"] - 10 + + noise_level = round((self.noise_augmentor.max_noise_level) * noise_augment) + + if image is None: + image = torch.zeros_like(noise)[:,:3] + + if image.shape[1:] != noise.shape[1:]: + image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center") + + noise_level = torch.tensor([noise_level], device=device) + if noise_augment > 0: + image, noise_level = self.noise_augmentor(image.to(device), noise_level=noise_level, seed=seed) + + image = utils.resize_to_batch_size(image, noise.shape[0]) + + out['c_concat'] = ldm_patched.modules.conds.CONDNoiseShape(image) + out['y'] = ldm_patched.modules.conds.CONDRegular(noise_level) + return out diff --git a/ldm_patched/modules/model_detection.py b/ldm_patched/modules/model_detection.py new file mode 100644 index 0000000000000000000000000000000000000000..126386ca80e236e8205a3e8918c24923bfe23e87 --- /dev/null +++ b/ldm_patched/modules/model_detection.py @@ -0,0 +1,320 @@ +import ldm_patched.modules.supported_models +import ldm_patched.modules.supported_models_base + +def count_blocks(state_dict_keys, prefix_string): + count = 0 + while True: + c = False + for k in state_dict_keys: + if k.startswith(prefix_string.format(count)): + c = True + break + if c == False: + break + count += 1 + return count + +def calculate_transformer_depth(prefix, state_dict_keys, state_dict): + context_dim = None + use_linear_in_transformer = False + + transformer_prefix = prefix + "1.transformer_blocks." + transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys))) + if len(transformer_keys) > 0: + last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}') + context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1] + use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2 + time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict + return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack + return None + +def detect_unet_config(state_dict, key_prefix, dtype): + state_dict_keys = list(state_dict.keys()) + + unet_config = { + "use_checkpoint": False, + "image_size": 32, + "use_spatial_transformer": True, + "legacy": False + } + + y_input = '{}label_emb.0.0.weight'.format(key_prefix) + if y_input in state_dict_keys: + unet_config["num_classes"] = "sequential" + unet_config["adm_in_channels"] = state_dict[y_input].shape[1] + else: + unet_config["adm_in_channels"] = None + + unet_config["dtype"] = dtype + model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0] + in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1] + + out_key = '{}out.2.weight'.format(key_prefix) + if out_key in state_dict: + out_channels = state_dict[out_key].shape[0] + else: + out_channels = 4 + + num_res_blocks = [] + channel_mult = [] + attention_resolutions = [] + transformer_depth = [] + transformer_depth_output = [] + context_dim = None + use_linear_in_transformer = False + + video_model = False + + current_res = 1 + count = 0 + + last_res_blocks = 0 + last_channel_mult = 0 + + input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.') + for count in range(input_block_count): + prefix = '{}input_blocks.{}.'.format(key_prefix, count) + prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1) + + block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys))) + if len(block_keys) == 0: + break + + block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys))) + + if "{}0.op.weight".format(prefix) in block_keys: #new layer + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + + current_res *= 2 + last_res_blocks = 0 + last_channel_mult = 0 + out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + else: + res_block_prefix = "{}0.in_layers.0.weight".format(prefix) + if res_block_prefix in block_keys: + last_res_blocks += 1 + last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels + + out = calculate_transformer_depth(prefix, state_dict_keys, state_dict) + if out is not None: + transformer_depth.append(out[0]) + if context_dim is None: + context_dim = out[1] + use_linear_in_transformer = out[2] + video_model = out[3] + else: + transformer_depth.append(0) + + res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output) + if res_block_prefix in block_keys_output: + out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict) + if out is not None: + transformer_depth_output.append(out[0]) + else: + transformer_depth_output.append(0) + + + num_res_blocks.append(last_res_blocks) + channel_mult.append(last_channel_mult) + if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys: + transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}') + else: + transformer_depth_middle = -1 + + unet_config["in_channels"] = in_channels + unet_config["out_channels"] = out_channels + unet_config["model_channels"] = model_channels + unet_config["num_res_blocks"] = num_res_blocks + unet_config["transformer_depth"] = transformer_depth + unet_config["transformer_depth_output"] = transformer_depth_output + unet_config["channel_mult"] = channel_mult + unet_config["transformer_depth_middle"] = transformer_depth_middle + unet_config['use_linear_in_transformer'] = use_linear_in_transformer + unet_config["context_dim"] = context_dim + + if video_model: + unet_config["extra_ff_mix_layer"] = True + unet_config["use_spatial_context"] = True + unet_config["merge_strategy"] = "learned_with_images" + unet_config["merge_factor"] = 0.0 + unet_config["video_kernel_size"] = [3, 1, 1] + unet_config["use_temporal_resblock"] = True + unet_config["use_temporal_attention"] = True + else: + unet_config["use_temporal_resblock"] = False + unet_config["use_temporal_attention"] = False + + return unet_config + +def model_config_from_unet_config(unet_config): + for model_config in ldm_patched.modules.supported_models.models: + if model_config.matches(unet_config): + return model_config(unet_config) + + print("no match", unet_config) + return None + +def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False): + unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype) + model_config = model_config_from_unet_config(unet_config) + if model_config is None and use_base_if_no_match: + return ldm_patched.modules.supported_models_base.BASE(unet_config) + else: + return model_config + +def convert_config(unet_config): + new_config = unet_config.copy() + num_res_blocks = new_config.get("num_res_blocks", None) + channel_mult = new_config.get("channel_mult", None) + + if isinstance(num_res_blocks, int): + num_res_blocks = len(channel_mult) * [num_res_blocks] + + if "attention_resolutions" in new_config: + attention_resolutions = new_config.pop("attention_resolutions") + transformer_depth = new_config.get("transformer_depth", None) + transformer_depth_middle = new_config.get("transformer_depth_middle", None) + + if isinstance(transformer_depth, int): + transformer_depth = len(channel_mult) * [transformer_depth] + if transformer_depth_middle is None: + transformer_depth_middle = transformer_depth[-1] + t_in = [] + t_out = [] + s = 1 + for i in range(len(num_res_blocks)): + res = num_res_blocks[i] + d = 0 + if s in attention_resolutions: + d = transformer_depth[i] + + t_in += [d] * res + t_out += [d] * (res + 1) + s *= 2 + transformer_depth = t_in + transformer_depth_output = t_out + new_config["transformer_depth"] = t_in + new_config["transformer_depth_output"] = t_out + new_config["transformer_depth_middle"] = transformer_depth_middle + + new_config["num_res_blocks"] = num_res_blocks + return new_config + + +def unet_config_from_diffusers_unet(state_dict, dtype): + match = {} + transformer_depth = [] + + attn_res = 1 + down_blocks = count_blocks(state_dict, "down_blocks.{}") + for i in range(down_blocks): + attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}') + for ab in range(attn_blocks): + transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}') + transformer_depth.append(transformer_count) + if transformer_count > 0: + match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1] + + attn_res *= 2 + if attn_blocks == 0: + transformer_depth.append(0) + transformer_depth.append(0) + + match["transformer_depth"] = transformer_depth + + match["model_channels"] = state_dict["conv_in.weight"].shape[0] + match["in_channels"] = state_dict["conv_in.weight"].shape[1] + match["adm_in_channels"] = None + if "class_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1] + elif "add_embedding.linear_1.weight" in state_dict: + match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1] + + SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4, + 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], + 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, + 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None, + 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], + 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8, + 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0, + 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10, + 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10], + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, + 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, + 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2], + 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, + 'use_temporal_attention': False, 'use_temporal_resblock': False} + + supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega] + + for unet_config in supported_models: + matches = True + for k in match: + if match[k] != unet_config[k]: + matches = False + break + if matches: + return convert_config(unet_config) + return None + +def model_config_from_diffusers_unet(state_dict, dtype): + unet_config = unet_config_from_diffusers_unet(state_dict, dtype) + if unet_config is not None: + return model_config_from_unet_config(unet_config) + return None diff --git a/ldm_patched/modules/model_management.py b/ldm_patched/modules/model_management.py new file mode 100644 index 0000000000000000000000000000000000000000..840d79a07b607818ddc8596a9128ca6b7b0c2f2b --- /dev/null +++ b/ldm_patched/modules/model_management.py @@ -0,0 +1,807 @@ +import psutil +from enum import Enum +from ldm_patched.modules.args_parser import args +import ldm_patched.modules.utils +import torch +import sys + +class VRAMState(Enum): + DISABLED = 0 #No vram present: no need to move models to vram + NO_VRAM = 1 #Very low vram: enable all the options to save vram + LOW_VRAM = 2 + NORMAL_VRAM = 3 + HIGH_VRAM = 4 + SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. + +class CPUState(Enum): + GPU = 0 + CPU = 1 + MPS = 2 + +# Determine VRAM State +vram_state = VRAMState.NORMAL_VRAM +set_vram_to = VRAMState.NORMAL_VRAM +cpu_state = CPUState.GPU + +total_vram = 0 + +lowvram_available = True +xpu_available = False + +if args.pytorch_deterministic: + print("Using deterministic algorithms for pytorch") + torch.use_deterministic_algorithms(True, warn_only=True) + +directml_enabled = False +if args.directml is not None: + import torch_directml + directml_enabled = True + device_index = args.directml + if device_index < 0: + directml_device = torch_directml.device() + else: + directml_device = torch_directml.device(device_index) + print("Using directml with device:", torch_directml.device_name(device_index)) + # torch_directml.disable_tiled_resources(True) + lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default. + +try: + import intel_extension_for_pytorch as ipex + if torch.xpu.is_available(): + xpu_available = True +except: + pass + +try: + if torch.backends.mps.is_available(): + cpu_state = CPUState.MPS + import torch.mps +except: + pass + +if args.always_cpu: + if args.always_cpu > 0: + torch.set_num_threads(args.always_cpu) + print(f"Running on {torch.get_num_threads()} CPU threads") + cpu_state = CPUState.CPU + +def is_intel_xpu(): + global cpu_state + global xpu_available + if cpu_state == CPUState.GPU: + if xpu_available: + return True + return False + +def get_torch_device(): + global directml_enabled + global cpu_state + if directml_enabled: + global directml_device + return directml_device + if cpu_state == CPUState.MPS: + return torch.device("mps") + if cpu_state == CPUState.CPU: + return torch.device("cpu") + else: + if is_intel_xpu(): + return torch.device("xpu") + else: + return torch.device(torch.cuda.current_device()) + +def get_total_memory(dev=None, torch_total_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_total = psutil.virtual_memory().total + mem_total_torch = mem_total + else: + if directml_enabled: + mem_total = 1024 * 1024 * 1024 #TODO + mem_total_torch = mem_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + mem_total = torch.xpu.get_device_properties(dev).total_memory + mem_total_torch = mem_reserved + else: + stats = torch.cuda.memory_stats(dev) + mem_reserved = stats['reserved_bytes.all.current'] + _, mem_total_cuda = torch.cuda.mem_get_info(dev) + mem_total_torch = mem_reserved + mem_total = mem_total_cuda + + if torch_total_too: + return (mem_total, mem_total_torch) + else: + return mem_total + +total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) +total_ram = psutil.virtual_memory().total / (1024 * 1024) +print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram)) +if not args.always_normal_vram and not args.always_cpu: + if lowvram_available and total_vram <= 4096: + print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --always-normal-vram") + set_vram_to = VRAMState.LOW_VRAM + +try: + OOM_EXCEPTION = torch.cuda.OutOfMemoryError +except: + OOM_EXCEPTION = Exception + +XFORMERS_VERSION = "" +XFORMERS_ENABLED_VAE = True +if args.disable_xformers: + XFORMERS_IS_AVAILABLE = False +else: + try: + import xformers + import xformers.ops + XFORMERS_IS_AVAILABLE = True + try: + XFORMERS_IS_AVAILABLE = xformers._has_cpp_library + except: + pass + try: + XFORMERS_VERSION = xformers.version.__version__ + print("xformers version:", XFORMERS_VERSION) + if XFORMERS_VERSION.startswith("0.0.18"): + print() + print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.") + print("Please downgrade or upgrade xformers to a different version.") + print() + XFORMERS_ENABLED_VAE = False + except: + pass + except: + XFORMERS_IS_AVAILABLE = False + +def is_nvidia(): + global cpu_state + if cpu_state == CPUState.GPU: + if torch.version.cuda: + return True + return False + +ENABLE_PYTORCH_ATTENTION = False +if args.attention_pytorch: + ENABLE_PYTORCH_ATTENTION = True + XFORMERS_IS_AVAILABLE = False + +VAE_DTYPE = torch.float32 + +try: + if is_nvidia(): + torch_version = torch.version.__version__ + if int(torch_version[0]) >= 2: + if ENABLE_PYTORCH_ATTENTION == False and args.attention_split == False and args.attention_quad == False: + ENABLE_PYTORCH_ATTENTION = True + if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8: + VAE_DTYPE = torch.bfloat16 + if is_intel_xpu(): + if args.attention_split == False and args.attention_quad == False: + ENABLE_PYTORCH_ATTENTION = True +except: + pass + +if is_intel_xpu(): + VAE_DTYPE = torch.bfloat16 + +if args.vae_in_cpu: + VAE_DTYPE = torch.float32 + +if args.vae_in_fp16: + VAE_DTYPE = torch.float16 +elif args.vae_in_bf16: + VAE_DTYPE = torch.bfloat16 +elif args.vae_in_fp32: + VAE_DTYPE = torch.float32 + + +if ENABLE_PYTORCH_ATTENTION: + torch.backends.cuda.enable_math_sdp(True) + torch.backends.cuda.enable_flash_sdp(True) + torch.backends.cuda.enable_mem_efficient_sdp(True) + +if args.always_low_vram: + set_vram_to = VRAMState.LOW_VRAM + lowvram_available = True +elif args.always_no_vram: + set_vram_to = VRAMState.NO_VRAM +elif args.always_high_vram or args.always_gpu: + vram_state = VRAMState.HIGH_VRAM + +FORCE_FP32 = False +FORCE_FP16 = False +if args.all_in_fp32: + print("Forcing FP32, if this improves things please report it.") + FORCE_FP32 = True + +if args.all_in_fp16: + print("Forcing FP16.") + FORCE_FP16 = True + +if lowvram_available: + if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM): + vram_state = set_vram_to + + +if cpu_state != CPUState.GPU: + vram_state = VRAMState.DISABLED + +if cpu_state == CPUState.MPS: + vram_state = VRAMState.SHARED + +print(f"Set vram state to: {vram_state.name}") + +ALWAYS_VRAM_OFFLOAD = args.always_offload_from_vram + +if ALWAYS_VRAM_OFFLOAD: + print("Always offload VRAM") + +def get_torch_device_name(device): + if hasattr(device, 'type'): + if device.type == "cuda": + try: + allocator_backend = torch.cuda.get_allocator_backend() + except: + allocator_backend = "" + return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend) + else: + return "{}".format(device.type) + elif is_intel_xpu(): + return "{} {}".format(device, torch.xpu.get_device_name(device)) + else: + return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device)) + +try: + print("Device:", get_torch_device_name(get_torch_device())) +except: + print("Could not pick default device.") + +print("VAE dtype:", VAE_DTYPE) + +current_loaded_models = [] + +def module_size(module): + module_mem = 0 + sd = module.state_dict() + for k in sd: + t = sd[k] + module_mem += t.nelement() * t.element_size() + return module_mem + +class LoadedModel: + def __init__(self, model): + self.model = model + self.model_accelerated = False + self.device = model.load_device + + def model_memory(self): + return self.model.model_size() + + def model_memory_required(self, device): + if device == self.model.current_device: + return 0 + else: + return self.model_memory() + + def model_load(self, lowvram_model_memory=0): + patch_model_to = None + if lowvram_model_memory == 0: + patch_model_to = self.device + + self.model.model_patches_to(self.device) + self.model.model_patches_to(self.model.model_dtype()) + + try: + self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU + except Exception as e: + self.model.unpatch_model(self.model.offload_device) + self.model_unload() + raise e + + if lowvram_model_memory > 0: + print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024)) + mem_counter = 0 + for m in self.real_model.modules(): + if hasattr(m, "ldm_patched_cast_weights"): + m.prev_ldm_patched_cast_weights = m.ldm_patched_cast_weights + m.ldm_patched_cast_weights = True + module_mem = module_size(m) + if mem_counter + module_mem < lowvram_model_memory: + m.to(self.device) + mem_counter += module_mem + elif hasattr(m, "weight"): #only modules with ldm_patched_cast_weights can be set to lowvram mode + m.to(self.device) + mem_counter += module_size(m) + print("lowvram: loaded module regularly", m) + + self.model_accelerated = True + + if is_intel_xpu() and not args.disable_ipex_hijack: + self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True) + + return self.real_model + + def model_unload(self): + if self.model_accelerated: + for m in self.real_model.modules(): + if hasattr(m, "prev_ldm_patched_cast_weights"): + m.ldm_patched_cast_weights = m.prev_ldm_patched_cast_weights + del m.prev_ldm_patched_cast_weights + + self.model_accelerated = False + + self.model.unpatch_model(self.model.offload_device) + self.model.model_patches_to(self.model.offload_device) + + def __eq__(self, other): + return self.model is other.model + +def minimum_inference_memory(): + return (1024 * 1024 * 1024) + +def unload_model_clones(model): + to_unload = [] + for i in range(len(current_loaded_models)): + if model.is_clone(current_loaded_models[i].model): + to_unload = [i] + to_unload + + for i in to_unload: + print("unload clone", i) + current_loaded_models.pop(i).model_unload() + +def free_memory(memory_required, device, keep_loaded=[]): + unloaded_model = False + for i in range(len(current_loaded_models) -1, -1, -1): + if not ALWAYS_VRAM_OFFLOAD: + if get_free_memory(device) > memory_required: + break + shift_model = current_loaded_models[i] + if shift_model.device == device: + if shift_model not in keep_loaded: + m = current_loaded_models.pop(i) + m.model_unload() + del m + unloaded_model = True + + if unloaded_model: + soft_empty_cache() + else: + if vram_state != VRAMState.HIGH_VRAM: + mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True) + if mem_free_torch > mem_free_total * 0.25: + soft_empty_cache() + +def load_models_gpu(models, memory_required=0): + global vram_state + + inference_memory = minimum_inference_memory() + extra_mem = max(inference_memory, memory_required) + + models_to_load = [] + models_already_loaded = [] + for x in models: + loaded_model = LoadedModel(x) + + if loaded_model in current_loaded_models: + index = current_loaded_models.index(loaded_model) + current_loaded_models.insert(0, current_loaded_models.pop(index)) + models_already_loaded.append(loaded_model) + else: + if hasattr(x, "model"): + print(f"Requested to load {x.model.__class__.__name__}") + models_to_load.append(loaded_model) + + if len(models_to_load) == 0: + devs = set(map(lambda a: a.device, models_already_loaded)) + for d in devs: + if d != torch.device("cpu"): + free_memory(extra_mem, d, models_already_loaded) + return + + print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") + + total_memory_required = {} + for loaded_model in models_to_load: + unload_model_clones(loaded_model.model) + total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device) + + for device in total_memory_required: + if device != torch.device("cpu"): + free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) + + for loaded_model in models_to_load: + model = loaded_model.model + torch_dev = model.load_device + if is_device_cpu(torch_dev): + vram_set_state = VRAMState.DISABLED + else: + vram_set_state = vram_state + lowvram_model_memory = 0 + if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM): + model_size = loaded_model.model_memory_required(torch_dev) + current_free_mem = get_free_memory(torch_dev) + lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 )) + if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary + vram_set_state = VRAMState.LOW_VRAM + else: + lowvram_model_memory = 0 + + if vram_set_state == VRAMState.NO_VRAM: + lowvram_model_memory = 64 * 1024 * 1024 + + cur_loaded_model = loaded_model.model_load(lowvram_model_memory) + current_loaded_models.insert(0, loaded_model) + return + + +def load_model_gpu(model): + return load_models_gpu([model]) + +def cleanup_models(): + to_delete = [] + for i in range(len(current_loaded_models)): + if sys.getrefcount(current_loaded_models[i].model) <= 2: + to_delete = [i] + to_delete + + for i in to_delete: + x = current_loaded_models.pop(i) + x.model_unload() + del x + +def dtype_size(dtype): + dtype_size = 4 + if dtype == torch.float16 or dtype == torch.bfloat16: + dtype_size = 2 + elif dtype == torch.float32: + dtype_size = 4 + else: + try: + dtype_size = dtype.itemsize + except: #Old pytorch doesn't have .itemsize + pass + return dtype_size + +def unet_offload_device(): + if vram_state == VRAMState.HIGH_VRAM: + return get_torch_device() + else: + return torch.device("cpu") + +def unet_inital_load_device(parameters, dtype): + torch_dev = get_torch_device() + if vram_state == VRAMState.HIGH_VRAM: + return torch_dev + + cpu_dev = torch.device("cpu") + if ALWAYS_VRAM_OFFLOAD: + return cpu_dev + + model_size = dtype_size(dtype) * parameters + + mem_dev = get_free_memory(torch_dev) + mem_cpu = get_free_memory(cpu_dev) + if mem_dev > mem_cpu and model_size < mem_dev: + return torch_dev + else: + return cpu_dev + +def unet_dtype(device=None, model_params=0): + if args.unet_in_bf16: + return torch.bfloat16 + if args.unet_in_fp16: + return torch.float16 + if args.unet_in_fp8_e4m3fn: + return torch.float8_e4m3fn + if args.unet_in_fp8_e5m2: + return torch.float8_e5m2 + if should_use_fp16(device=device, model_params=model_params): + return torch.float16 + return torch.float32 + +# None means no manual cast +def unet_manual_cast(weight_dtype, inference_device): + if weight_dtype == torch.float32: + return None + + fp16_supported = ldm_patched.modules.model_management.should_use_fp16(inference_device, prioritize_performance=False) + if fp16_supported and weight_dtype == torch.float16: + return None + + if fp16_supported: + return torch.float16 + else: + return torch.float32 + +def text_encoder_offload_device(): + if args.always_gpu: + return get_torch_device() + else: + return torch.device("cpu") + +def text_encoder_device(): + if args.always_gpu: + return get_torch_device() + elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM: + if is_intel_xpu(): + return torch.device("cpu") + if should_use_fp16(prioritize_performance=False): + return get_torch_device() + else: + return torch.device("cpu") + else: + return torch.device("cpu") + +def text_encoder_dtype(device=None): + if args.clip_in_fp8_e4m3fn: + return torch.float8_e4m3fn + elif args.clip_in_fp8_e5m2: + return torch.float8_e5m2 + elif args.clip_in_fp16: + return torch.float16 + elif args.clip_in_fp32: + return torch.float32 + + if is_device_cpu(device): + return torch.float16 + + if should_use_fp16(device, prioritize_performance=False): + return torch.float16 + else: + return torch.float32 + +def intermediate_device(): + if args.always_gpu: + return get_torch_device() + else: + return torch.device("cpu") + +def vae_device(): + if args.vae_in_cpu: + return torch.device("cpu") + return get_torch_device() + +def vae_offload_device(): + if args.always_gpu: + return get_torch_device() + else: + return torch.device("cpu") + +def vae_dtype(): + global VAE_DTYPE + return VAE_DTYPE + +def get_autocast_device(dev): + if hasattr(dev, 'type'): + return dev.type + return "cuda" + +def supports_dtype(device, dtype): #TODO + if dtype == torch.float32: + return True + if is_device_cpu(device): + return False + if dtype == torch.float16: + return True + if dtype == torch.bfloat16: + return True + return False + +def device_supports_non_blocking(device): + if is_device_mps(device): + return False #pytorch bug? mps doesn't support non blocking + return True + +def cast_to_device(tensor, device, dtype, copy=False): + device_supports_cast = False + if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: + device_supports_cast = True + elif tensor.dtype == torch.bfloat16: + if hasattr(device, 'type') and device.type.startswith("cuda"): + device_supports_cast = True + elif is_intel_xpu(): + device_supports_cast = True + + non_blocking = device_supports_non_blocking(device) + + if device_supports_cast: + if copy: + if tensor.device == device: + return tensor.to(dtype, copy=copy, non_blocking=non_blocking) + return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) + else: + return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) + else: + return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking) + +def xformers_enabled(): + global directml_enabled + global cpu_state + if cpu_state != CPUState.GPU: + return False + if is_intel_xpu(): + return False + if directml_enabled: + return False + return XFORMERS_IS_AVAILABLE + + +def xformers_enabled_vae(): + enabled = xformers_enabled() + if not enabled: + return False + + return XFORMERS_ENABLED_VAE + +def pytorch_attention_enabled(): + global ENABLE_PYTORCH_ATTENTION + return ENABLE_PYTORCH_ATTENTION + +def pytorch_attention_flash_attention(): + global ENABLE_PYTORCH_ATTENTION + if ENABLE_PYTORCH_ATTENTION: + #TODO: more reliable way of checking for flash attention? + if is_nvidia(): #pytorch flash attention only works on Nvidia + return True + return False + +def get_free_memory(dev=None, torch_free_too=False): + global directml_enabled + if dev is None: + dev = get_torch_device() + + if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'): + mem_free_total = psutil.virtual_memory().available + mem_free_torch = mem_free_total + else: + if directml_enabled: + mem_free_total = 1024 * 1024 * 1024 #TODO + mem_free_torch = mem_free_total + elif is_intel_xpu(): + stats = torch.xpu.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_allocated = stats['allocated_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_torch = mem_reserved - mem_active + mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated + else: + stats = torch.cuda.memory_stats(dev) + mem_active = stats['active_bytes.all.current'] + mem_reserved = stats['reserved_bytes.all.current'] + mem_free_cuda, _ = torch.cuda.mem_get_info(dev) + mem_free_torch = mem_reserved - mem_active + mem_free_total = mem_free_cuda + mem_free_torch + + if torch_free_too: + return (mem_free_total, mem_free_torch) + else: + return mem_free_total + +def cpu_mode(): + global cpu_state + return cpu_state == CPUState.CPU + +def mps_mode(): + global cpu_state + return cpu_state == CPUState.MPS + +def is_device_cpu(device): + if hasattr(device, 'type'): + if (device.type == 'cpu'): + return True + return False + +def is_device_mps(device): + if hasattr(device, 'type'): + if (device.type == 'mps'): + return True + return False + +def should_use_fp16(device=None, model_params=0, prioritize_performance=True): + global directml_enabled + + if device is not None: + if is_device_cpu(device): + return False + + if FORCE_FP16: + return True + + if device is not None: #TODO + if is_device_mps(device): + return False + + if FORCE_FP32: + return False + + if directml_enabled: + return False + + if cpu_mode() or mps_mode(): + return False #TODO ? + + if is_intel_xpu(): + return True + + if torch.cuda.is_bf16_supported(): + return True + + props = torch.cuda.get_device_properties("cuda") + if props.major < 6: + return False + + fp16_works = False + #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled + #when the model doesn't actually fit on the card + #TODO: actually test if GP106 and others have the same type of behavior + nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050"] + for x in nvidia_10_series: + if x in props.name.lower(): + fp16_works = True + + if fp16_works: + free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory()) + if (not prioritize_performance) or model_params * 4 > free_model_memory: + return True + + if props.major < 7: + return False + + #FP16 is just broken on these cards + nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"] + for x in nvidia_16_series: + if x in props.name: + return False + + return True + +def soft_empty_cache(force=False): + global cpu_state + if cpu_state == CPUState.MPS: + torch.mps.empty_cache() + elif is_intel_xpu(): + torch.xpu.empty_cache() + elif torch.cuda.is_available(): + if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda + torch.cuda.empty_cache() + torch.cuda.ipc_collect() + +def unload_all_models(): + free_memory(1e30, get_torch_device()) + + +def resolve_lowvram_weight(weight, model, key): #TODO: remove + return weight + +#TODO: might be cleaner to put this somewhere else +import threading + +class InterruptProcessingException(Exception): + pass + +interrupt_processing_mutex = threading.RLock() + +interrupt_processing = False +def interrupt_current_processing(value=True): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + interrupt_processing = value + +def processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + return interrupt_processing + +def throw_exception_if_processing_interrupted(): + global interrupt_processing + global interrupt_processing_mutex + with interrupt_processing_mutex: + if interrupt_processing: + interrupt_processing = False + raise InterruptProcessingException() diff --git a/ldm_patched/modules/model_patcher.py b/ldm_patched/modules/model_patcher.py new file mode 100644 index 0000000000000000000000000000000000000000..dd816e52e1ee05c2c279af0823e91b71f2567fb9 --- /dev/null +++ b/ldm_patched/modules/model_patcher.py @@ -0,0 +1,357 @@ +import torch +import copy +import inspect + +import ldm_patched.modules.utils +import ldm_patched.modules.model_management + +class ModelPatcher: + def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False): + self.size = size + self.model = model + self.patches = {} + self.backup = {} + self.object_patches = {} + self.object_patches_backup = {} + self.model_options = {"transformer_options":{}} + self.model_size() + self.load_device = load_device + self.offload_device = offload_device + if current_device is None: + self.current_device = self.offload_device + else: + self.current_device = current_device + + self.weight_inplace_update = weight_inplace_update + + def model_size(self): + if self.size > 0: + return self.size + model_sd = self.model.state_dict() + self.size = ldm_patched.modules.model_management.module_size(self.model) + self.model_keys = set(model_sd.keys()) + return self.size + + def clone(self): + n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update) + n.patches = {} + for k in self.patches: + n.patches[k] = self.patches[k][:] + + n.object_patches = self.object_patches.copy() + n.model_options = copy.deepcopy(self.model_options) + n.model_keys = self.model_keys + return n + + def is_clone(self, other): + if hasattr(other, 'model') and self.model is other.model: + return True + return False + + def memory_required(self, input_shape): + return self.model.memory_required(input_shape=input_shape) + + def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False): + if len(inspect.signature(sampler_cfg_function).parameters) == 3: + self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way + else: + self.model_options["sampler_cfg_function"] = sampler_cfg_function + if disable_cfg1_optimization: + self.model_options["disable_cfg1_optimization"] = True + + def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False): + self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function] + if disable_cfg1_optimization: + self.model_options["disable_cfg1_optimization"] = True + + def set_model_unet_function_wrapper(self, unet_wrapper_function): + self.model_options["model_function_wrapper"] = unet_wrapper_function + + def set_model_patch(self, patch, name): + to = self.model_options["transformer_options"] + if "patches" not in to: + to["patches"] = {} + to["patches"][name] = to["patches"].get(name, []) + [patch] + + def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None): + to = self.model_options["transformer_options"] + if "patches_replace" not in to: + to["patches_replace"] = {} + if name not in to["patches_replace"]: + to["patches_replace"][name] = {} + if transformer_index is not None: + block = (block_name, number, transformer_index) + else: + block = (block_name, number) + to["patches_replace"][name][block] = patch + + def set_model_attn1_patch(self, patch): + self.set_model_patch(patch, "attn1_patch") + + def set_model_attn2_patch(self, patch): + self.set_model_patch(patch, "attn2_patch") + + def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index) + + def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None): + self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index) + + def set_model_attn1_output_patch(self, patch): + self.set_model_patch(patch, "attn1_output_patch") + + def set_model_attn2_output_patch(self, patch): + self.set_model_patch(patch, "attn2_output_patch") + + def set_model_input_block_patch(self, patch): + self.set_model_patch(patch, "input_block_patch") + + def set_model_input_block_patch_after_skip(self, patch): + self.set_model_patch(patch, "input_block_patch_after_skip") + + def set_model_output_block_patch(self, patch): + self.set_model_patch(patch, "output_block_patch") + + def add_object_patch(self, name, obj): + self.object_patches[name] = obj + + def model_patches_to(self, device): + to = self.model_options["transformer_options"] + if "patches" in to: + patches = to["patches"] + for name in patches: + patch_list = patches[name] + for i in range(len(patch_list)): + if hasattr(patch_list[i], "to"): + patch_list[i] = patch_list[i].to(device) + if "patches_replace" in to: + patches = to["patches_replace"] + for name in patches: + patch_list = patches[name] + for k in patch_list: + if hasattr(patch_list[k], "to"): + patch_list[k] = patch_list[k].to(device) + if "model_function_wrapper" in self.model_options: + wrap_func = self.model_options["model_function_wrapper"] + if hasattr(wrap_func, "to"): + self.model_options["model_function_wrapper"] = wrap_func.to(device) + + def model_dtype(self): + if hasattr(self.model, "get_dtype"): + return self.model.get_dtype() + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + p = set() + for k in patches: + if k in self.model_keys: + p.add(k) + current_patches = self.patches.get(k, []) + current_patches.append((strength_patch, patches[k], strength_model)) + self.patches[k] = current_patches + + return list(p) + + def get_key_patches(self, filter_prefix=None): + ldm_patched.modules.model_management.unload_model_clones(self) + model_sd = self.model_state_dict() + p = {} + for k in model_sd: + if filter_prefix is not None: + if not k.startswith(filter_prefix): + continue + if k in self.patches: + p[k] = [model_sd[k]] + self.patches[k] + else: + p[k] = (model_sd[k],) + return p + + def model_state_dict(self, filter_prefix=None): + sd = self.model.state_dict() + keys = list(sd.keys()) + if filter_prefix is not None: + for k in keys: + if not k.startswith(filter_prefix): + sd.pop(k) + return sd + + def patch_model(self, device_to=None, patch_weights=True): + for k in self.object_patches: + old = getattr(self.model, k) + if k not in self.object_patches_backup: + self.object_patches_backup[k] = old + setattr(self.model, k, self.object_patches[k]) + + if patch_weights: + model_sd = self.model_state_dict() + for key in self.patches: + if key not in model_sd: + print("could not patch. key doesn't exist in model:", key) + continue + + weight = model_sd[key] + + inplace_update = self.weight_inplace_update + + if key not in self.backup: + self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update) + + if device_to is not None: + temp_weight = ldm_patched.modules.model_management.cast_to_device(weight, device_to, torch.float32, copy=True) + else: + temp_weight = weight.to(torch.float32, copy=True) + out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype) + if inplace_update: + ldm_patched.modules.utils.copy_to_param(self.model, key, out_weight) + else: + ldm_patched.modules.utils.set_attr(self.model, key, out_weight) + del temp_weight + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + + return self.model + + def calculate_weight(self, patches, weight, key): + for p in patches: + alpha = p[0] + v = p[1] + strength_model = p[2] + + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (self.calculate_weight(v[1:], v[0].clone(), key), ) + + if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": + w1 = v[0] + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lora": #lora/locon + mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) + if v[2] is not None: + alpha *= v[2] / mat2.shape[0] + if v[3] is not None: + #locon mid weights, hopefully the math is fine because I didn't properly test it + mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) + try: + weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "lokr": + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) + else: + w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) + else: + w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha *= v[2] / dim + + try: + weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "loha": + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha *= v[2] / w1b.shape[0] + w2a = v[3] + w2b = v[4] + if v[5] is not None: #cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) + else: + m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) + m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32)) + + try: + weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "glora": + if v[4] is not None: + alpha *= v[4] / v[0].shape[0] + + a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) + a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) + b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) + b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) + + weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + else: + print("patch type not recognized", patch_type, key) + + return weight + + def unpatch_model(self, device_to=None): + keys = list(self.backup.keys()) + + if self.weight_inplace_update: + for k in keys: + ldm_patched.modules.utils.copy_to_param(self.model, k, self.backup[k]) + else: + for k in keys: + ldm_patched.modules.utils.set_attr(self.model, k, self.backup[k]) + + self.backup = {} + + if device_to is not None: + self.model.to(device_to) + self.current_device = device_to + + keys = list(self.object_patches_backup.keys()) + for k in keys: + setattr(self.model, k, self.object_patches_backup[k]) + + self.object_patches_backup = {} diff --git a/ldm_patched/modules/model_sampling.py b/ldm_patched/modules/model_sampling.py new file mode 100644 index 0000000000000000000000000000000000000000..f39e275d3824e87eb1e1e069d16869d0abdad630 --- /dev/null +++ b/ldm_patched/modules/model_sampling.py @@ -0,0 +1,136 @@ +import torch +import numpy as np +from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule +import math + +class EPS: + def calculate_input(self, sigma, noise): + sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1)) + return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input - model_output * sigma + + +class V_PREDICTION(EPS): + def calculate_denoised(self, sigma, model_output, model_input): + sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1)) + return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5 + + +class ModelSamplingDiscrete(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + beta_schedule = sampling_settings.get("beta_schedule", "linear") + linear_start = sampling_settings.get("linear_start", 0.00085) + linear_end = sampling_settings.get("linear_end", 0.012) + + self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3) + self.sigma_data = 1.0 + + def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + if given_betas is not None: + betas = given_betas + else: + betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s) + alphas = 1. - betas + alphas_cumprod = torch.tensor(np.cumprod(alphas, axis=0), dtype=torch.float32) + # alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1]) + + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + + # self.register_buffer('betas', torch.tensor(betas, dtype=torch.float32)) + # self.register_buffer('alphas_cumprod', torch.tensor(alphas_cumprod, dtype=torch.float32)) + # self.register_buffer('alphas_cumprod_prev', torch.tensor(alphas_cumprod_prev, dtype=torch.float32)) + + sigmas = ((1 - alphas_cumprod) / alphas_cumprod) ** 0.5 + self.set_sigmas(sigmas) + + def set_sigmas(self, sigmas): + self.register_buffer('sigmas', sigmas) + self.register_buffer('log_sigmas', sigmas.log()) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + log_sigma = sigma.log() + dists = log_sigma.to(self.log_sigmas.device) - self.log_sigmas[:, None] + return dists.abs().argmin(dim=0).view(sigma.shape).to(sigma.device) + + def sigma(self, timestep): + t = torch.clamp(timestep.float().to(self.log_sigmas.device), min=0, max=(len(self.sigmas) - 1)) + low_idx = t.floor().long() + high_idx = t.ceil().long() + w = t.frac() + log_sigma = (1 - w) * self.log_sigmas[low_idx] + w * self.log_sigmas[high_idx] + return log_sigma.exp().to(timestep.device) + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + return self.sigma(torch.tensor(percent * 999.0)).item() + + +class ModelSamplingContinuousEDM(torch.nn.Module): + def __init__(self, model_config=None): + super().__init__() + self.sigma_data = 1.0 + + if model_config is not None: + sampling_settings = model_config.sampling_settings + else: + sampling_settings = {} + + sigma_min = sampling_settings.get("sigma_min", 0.002) + sigma_max = sampling_settings.get("sigma_max", 120.0) + self.set_sigma_range(sigma_min, sigma_max) + + def set_sigma_range(self, sigma_min, sigma_max): + sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp() + + self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers + self.register_buffer('log_sigmas', sigmas.log()) + + @property + def sigma_min(self): + return self.sigmas[0] + + @property + def sigma_max(self): + return self.sigmas[-1] + + def timestep(self, sigma): + return 0.25 * sigma.log() + + def sigma(self, timestep): + return (timestep / 0.25).exp() + + def percent_to_sigma(self, percent): + if percent <= 0.0: + return 999999999.9 + if percent >= 1.0: + return 0.0 + percent = 1.0 - percent + + log_sigma_min = math.log(self.sigma_min) + return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min) diff --git a/ldm_patched/modules/ops.py b/ldm_patched/modules/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..2d7fa3776bc9a490563bcebac7d7f27c3e90a39e --- /dev/null +++ b/ldm_patched/modules/ops.py @@ -0,0 +1,114 @@ +import torch +import ldm_patched.modules.model_management + +def cast_bias_weight(s, input): + bias = None + non_blocking = ldm_patched.modules.model_management.device_supports_non_blocking(input.device) + if s.bias is not None: + bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) + weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking) + return weight, bias + + +class disable_weight_init: + class Linear(torch.nn.Linear): + ldm_patched_cast_weights = False + def reset_parameters(self): + return None + + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.linear(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv2d(torch.nn.Conv2d): + ldm_patched_cast_weights = False + def reset_parameters(self): + return None + + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class Conv3d(torch.nn.Conv3d): + ldm_patched_cast_weights = False + def reset_parameters(self): + return None + + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return self._conv_forward(input, weight, bias) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + class GroupNorm(torch.nn.GroupNorm): + ldm_patched_cast_weights = False + def reset_parameters(self): + return None + + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + + class LayerNorm(torch.nn.LayerNorm): + ldm_patched_cast_weights = False + def reset_parameters(self): + return None + + def forward_ldm_patched_cast_weights(self, input): + weight, bias = cast_bias_weight(self, input) + return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps) + + def forward(self, *args, **kwargs): + if self.ldm_patched_cast_weights: + return self.forward_ldm_patched_cast_weights(*args, **kwargs) + else: + return super().forward(*args, **kwargs) + + @classmethod + def conv_nd(s, dims, *args, **kwargs): + if dims == 2: + return s.Conv2d(*args, **kwargs) + elif dims == 3: + return s.Conv3d(*args, **kwargs) + else: + raise ValueError(f"unsupported dimensions: {dims}") + + +class manual_cast(disable_weight_init): + class Linear(disable_weight_init.Linear): + ldm_patched_cast_weights = True + + class Conv2d(disable_weight_init.Conv2d): + ldm_patched_cast_weights = True + + class Conv3d(disable_weight_init.Conv3d): + ldm_patched_cast_weights = True + + class GroupNorm(disable_weight_init.GroupNorm): + ldm_patched_cast_weights = True + + class LayerNorm(disable_weight_init.LayerNorm): + ldm_patched_cast_weights = True diff --git a/ldm_patched/modules/options.py b/ldm_patched/modules/options.py new file mode 100644 index 0000000000000000000000000000000000000000..f7f8af41ebd8b9669ef0ef21827ea6195bcb4752 --- /dev/null +++ b/ldm_patched/modules/options.py @@ -0,0 +1,6 @@ + +args_parsing = False + +def enable_args_parsing(enable=True): + global args_parsing + args_parsing = enable diff --git a/ldm_patched/modules/sample.py b/ldm_patched/modules/sample.py new file mode 100644 index 0000000000000000000000000000000000000000..0f4839503758aa4bbfb76c26ef7a383627eeff4a --- /dev/null +++ b/ldm_patched/modules/sample.py @@ -0,0 +1,118 @@ +import torch +import ldm_patched.modules.model_management +import ldm_patched.modules.samplers +import ldm_patched.modules.conds +import ldm_patched.modules.utils +import math +import numpy as np + +def prepare_noise(latent_image, seed, noise_inds=None): + """ + creates random noise given a latent image and a seed. + optional arg skip can be used to skip and discard x number of noise generations for a given seed + """ + generator = torch.manual_seed(seed) + if noise_inds is None: + return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + + unique_inds, inverse = np.unique(noise_inds, return_inverse=True) + noises = [] + for i in range(unique_inds[-1]+1): + noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu") + if i in unique_inds: + noises.append(noise) + noises = [noises[i] for i in inverse] + noises = torch.cat(noises, axis=0) + return noises + +def prepare_mask(noise_mask, shape, device): + """ensures noise mask is of proper dimensions""" + noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear") + noise_mask = torch.cat([noise_mask] * shape[1], dim=1) + noise_mask = ldm_patched.modules.utils.repeat_to_batch_size(noise_mask, shape[0]) + noise_mask = noise_mask.to(device) + return noise_mask + +def get_models_from_cond(cond, model_type): + models = [] + for c in cond: + if model_type in c: + models += [c[model_type]] + return models + +def convert_cond(cond): + out = [] + for c in cond: + temp = c[1].copy() + model_conds = temp.get("model_conds", {}) + if c[0] is not None: + model_conds["c_crossattn"] = ldm_patched.modules.conds.CONDCrossAttn(c[0]) #TODO: remove + temp["cross_attn"] = c[0] + temp["model_conds"] = model_conds + out.append(temp) + return out + +def get_additional_models(positive, negative, dtype): + """loads additional models in positive and negative conditioning""" + control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control")) + + inference_memory = 0 + control_models = [] + for m in control_nets: + control_models += m.get_models() + inference_memory += m.inference_memory_requirements(dtype) + + gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen") + gligen = [x[1] for x in gligen] + models = control_models + gligen + return models, inference_memory + +def cleanup_additional_models(models): + """cleanup additional models that were loaded""" + for m in models: + if hasattr(m, 'cleanup'): + m.cleanup() + +def prepare_sampling(model, noise_shape, positive, negative, noise_mask): + device = model.load_device + positive = convert_cond(positive) + negative = convert_cond(negative) + + if noise_mask is not None: + noise_mask = prepare_mask(noise_mask, noise_shape, device) + + real_model = None + models, inference_memory = get_additional_models(positive, negative, model.model_dtype()) + ldm_patched.modules.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory) + real_model = model.model + + return real_model, positive, negative, noise_mask, models + + +def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) + + noise = noise.to(model.load_device) + latent_image = latent_image.to(model.load_device) + + sampler = ldm_patched.modules.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options) + + samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(ldm_patched.modules.model_management.intermediate_device()) + + cleanup_additional_models(models) + cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) + return samples + +def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None): + real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask) + noise = noise.to(model.load_device) + latent_image = latent_image.to(model.load_device) + sigmas = sigmas.to(model.load_device) + + samples = ldm_patched.modules.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + samples = samples.to(ldm_patched.modules.model_management.intermediate_device()) + cleanup_additional_models(models) + cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control"))) + return samples + diff --git a/ldm_patched/modules/samplers.py b/ldm_patched/modules/samplers.py new file mode 100644 index 0000000000000000000000000000000000000000..1f69d2b101403fb2fcaf3f2ab020bc10f4058c45 --- /dev/null +++ b/ldm_patched/modules/samplers.py @@ -0,0 +1,712 @@ +from ldm_patched.k_diffusion import sampling as k_diffusion_sampling +from ldm_patched.unipc import uni_pc +import torch +import collections +from ldm_patched.modules import model_management +import math + +def get_area_and_mult(conds, x_in, timestep_in): + area = (x_in.shape[2], x_in.shape[3], 0, 0) + strength = 1.0 + + if 'timestep_start' in conds: + timestep_start = conds['timestep_start'] + if timestep_in[0] > timestep_start: + return None + if 'timestep_end' in conds: + timestep_end = conds['timestep_end'] + if timestep_in[0] < timestep_end: + return None + if 'area' in conds: + area = conds['area'] + if 'strength' in conds: + strength = conds['strength'] + + input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] + if 'mask' in conds: + # Scale the mask to the size of the input + # The mask should have been resized as we began the sampling process + mask_strength = 1.0 + if "mask_strength" in conds: + mask_strength = conds["mask_strength"] + mask = conds['mask'] + assert(mask.shape[1] == x_in.shape[2]) + assert(mask.shape[2] == x_in.shape[3]) + mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength + mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1) + else: + mask = torch.ones_like(input_x) + mult = mask * strength + + if 'mask' not in conds: + rr = 8 + if area[2] != 0: + for t in range(rr): + mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1)) + if (area[0] + area[2]) < x_in.shape[2]: + for t in range(rr): + mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1)) + if area[3] != 0: + for t in range(rr): + mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1)) + if (area[1] + area[3]) < x_in.shape[3]: + for t in range(rr): + mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1)) + + conditioning = {} + model_conds = conds["model_conds"] + for c in model_conds: + conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area) + + control = conds.get('control', None) + + patches = None + if 'gligen' in conds: + gligen = conds['gligen'] + patches = {} + gligen_type = gligen[0] + gligen_model = gligen[1] + if gligen_type == "position": + gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device) + else: + gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device) + + patches['middle_patch'] = [gligen_patch] + + cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches']) + return cond_obj(input_x, mult, conditioning, area, control, patches) + +def cond_equal_size(c1, c2): + if c1 is c2: + return True + if c1.keys() != c2.keys(): + return False + for k in c1: + if not c1[k].can_concat(c2[k]): + return False + return True + +def can_concat_cond(c1, c2): + if c1.input_x.shape != c2.input_x.shape: + return False + + def objects_concatable(obj1, obj2): + if (obj1 is None) != (obj2 is None): + return False + if obj1 is not None: + if obj1 is not obj2: + return False + return True + + if not objects_concatable(c1.control, c2.control): + return False + + if not objects_concatable(c1.patches, c2.patches): + return False + + return cond_equal_size(c1.conditioning, c2.conditioning) + +def cond_cat(c_list): + c_crossattn = [] + c_concat = [] + c_adm = [] + crossattn_max_len = 0 + + temp = {} + for x in c_list: + for k in x: + cur = temp.get(k, []) + cur.append(x[k]) + temp[k] = cur + + out = {} + for k in temp: + conds = temp[k] + out[k] = conds[0].concat(conds[1:]) + + return out + +def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): + out_cond = torch.zeros_like(x_in) + out_count = torch.ones_like(x_in) * 1e-37 + + out_uncond = torch.zeros_like(x_in) + out_uncond_count = torch.ones_like(x_in) * 1e-37 + + COND = 0 + UNCOND = 1 + + to_run = [] + for x in cond: + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + + to_run += [(p, COND)] + if uncond is not None: + for x in uncond: + p = get_area_and_mult(x, x_in, timestep) + if p is None: + continue + + to_run += [(p, UNCOND)] + + while len(to_run) > 0: + first = to_run[0] + first_shape = first[0][0].shape + to_batch_temp = [] + for x in range(len(to_run)): + if can_concat_cond(to_run[x][0], first[0]): + to_batch_temp += [x] + + to_batch_temp.reverse() + to_batch = to_batch_temp[:1] + + free_memory = model_management.get_free_memory(x_in.device) + for i in range(1, len(to_batch_temp) + 1): + batch_amount = to_batch_temp[:len(to_batch_temp)//i] + input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:] + if model.memory_required(input_shape) < free_memory: + to_batch = batch_amount + break + + input_x = [] + mult = [] + c = [] + cond_or_uncond = [] + area = [] + control = None + patches = None + for x in to_batch: + o = to_run.pop(x) + p = o[0] + input_x.append(p.input_x) + mult.append(p.mult) + c.append(p.conditioning) + area.append(p.area) + cond_or_uncond.append(o[1]) + control = p.control + patches = p.patches + + batch_chunks = len(cond_or_uncond) + input_x = torch.cat(input_x) + c = cond_cat(c) + timestep_ = torch.cat([timestep] * batch_chunks) + + if control is not None: + c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond)) + + transformer_options = {} + if 'transformer_options' in model_options: + transformer_options = model_options['transformer_options'].copy() + + if patches is not None: + if "patches" in transformer_options: + cur_patches = transformer_options["patches"].copy() + for p in patches: + if p in cur_patches: + cur_patches[p] = cur_patches[p] + patches[p] + else: + cur_patches[p] = patches[p] + else: + transformer_options["patches"] = patches + + transformer_options["cond_or_uncond"] = cond_or_uncond[:] + transformer_options["sigmas"] = timestep + + c['transformer_options'] = transformer_options + + if 'model_function_wrapper' in model_options: + output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks) + else: + output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks) + del input_x + + for o in range(batch_chunks): + if cond_or_uncond[o] == COND: + out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] + out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] + else: + out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o] + out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o] + del mult + + out_cond /= out_count + del out_count + out_uncond /= out_uncond_count + del out_uncond_count + return out_cond, out_uncond + +#The main sampling function shared by all the samplers +#Returns denoised +def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None): + if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False: + uncond_ = None + else: + uncond_ = uncond + + cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options) + if "sampler_cfg_function" in model_options: + args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep, + "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options} + cfg_result = x - model_options["sampler_cfg_function"](args) + else: + cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale + + for fn in model_options.get("sampler_post_cfg_function", []): + args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred, + "sigma": timestep, "model_options": model_options, "input": x} + cfg_result = fn(args) + + return cfg_result + +class CFGNoisePredictor(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None): + out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed) + return out + def forward(self, *args, **kwargs): + return self.apply_model(*args, **kwargs) + +class KSamplerX0Inpaint(torch.nn.Module): + def __init__(self, model): + super().__init__() + self.inner_model = model + def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): + if denoise_mask is not None: + latent_mask = 1. - denoise_mask + x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask + out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed) + if denoise_mask is not None: + out = out * denoise_mask + self.latent_image * latent_mask + return out + +def simple_scheduler(model, steps): + s = model.model_sampling + sigs = [] + ss = len(s.sigmas) / steps + for x in range(steps): + sigs += [float(s.sigmas[-(1 + int(x * ss))])] + sigs += [0.0] + return torch.FloatTensor(sigs) + +def ddim_scheduler(model, steps): + s = model.model_sampling + sigs = [] + ss = len(s.sigmas) // steps + x = 1 + while x < len(s.sigmas): + sigs += [float(s.sigmas[x])] + x += ss + sigs = sigs[::-1] + sigs += [0.0] + return torch.FloatTensor(sigs) + +def normal_scheduler(model, steps, sgm=False, floor=False): + s = model.model_sampling + start = s.timestep(s.sigma_max) + end = s.timestep(s.sigma_min) + + if sgm: + timesteps = torch.linspace(start, end, steps + 1)[:-1] + else: + timesteps = torch.linspace(start, end, steps) + + sigs = [] + for x in range(len(timesteps)): + ts = timesteps[x] + sigs.append(s.sigma(ts)) + sigs += [0.0] + return torch.FloatTensor(sigs) + +def get_mask_aabb(masks): + if masks.numel() == 0: + return torch.zeros((0, 4), device=masks.device, dtype=torch.int) + + b = masks.shape[0] + + bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int) + is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool) + for i in range(b): + mask = masks[i] + if mask.numel() == 0: + continue + if torch.max(mask != 0) == False: + is_empty[i] = True + continue + y, x = torch.where(mask) + bounding_boxes[i, 0] = torch.min(x) + bounding_boxes[i, 1] = torch.min(y) + bounding_boxes[i, 2] = torch.max(x) + bounding_boxes[i, 3] = torch.max(y) + + return bounding_boxes, is_empty + +def resolve_areas_and_cond_masks(conditions, h, w, device): + # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes. + # While we're doing this, we can also resolve the mask device and scaling for performance reasons + for i in range(len(conditions)): + c = conditions[i] + if 'area' in c: + area = c['area'] + if area[0] == "percentage": + modified = c.copy() + area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w)) + modified['area'] = area + c = modified + conditions[i] = c + + if 'mask' in c: + mask = c['mask'] + mask = mask.to(device=device) + modified = c.copy() + if len(mask.shape) == 2: + mask = mask.unsqueeze(0) + if mask.shape[1] != h or mask.shape[2] != w: + mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1) + + if modified.get("set_area_to_bounds", False): + bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0) + boxes, is_empty = get_mask_aabb(bounds) + if is_empty[0]: + # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway) + modified['area'] = (8, 8, 0, 0) + else: + box = boxes[0] + H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0]) + H = max(8, H) + W = max(8, W) + area = (int(H), int(W), int(Y), int(X)) + modified['area'] = area + + modified['mask'] = mask + conditions[i] = modified + +def create_cond_with_same_area_if_none(conds, c): + if 'area' not in c: + return + + c_area = c['area'] + smallest = None + for x in conds: + if 'area' in x: + a = x['area'] + if c_area[2] >= a[2] and c_area[3] >= a[3]: + if a[0] + a[2] >= c_area[0] + c_area[2]: + if a[1] + a[3] >= c_area[1] + c_area[3]: + if smallest is None: + smallest = x + elif 'area' not in smallest: + smallest = x + else: + if smallest['area'][0] * smallest['area'][1] > a[0] * a[1]: + smallest = x + else: + if smallest is None: + smallest = x + if smallest is None: + return + if 'area' in smallest: + if smallest['area'] == c_area: + return + + out = c.copy() + out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied? + conds += [out] + +def calculate_start_end_timesteps(model, conds): + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + if 'start_percent' in x: + timestep_start = s.percent_to_sigma(x['start_percent']) + if 'end_percent' in x: + timestep_end = s.percent_to_sigma(x['end_percent']) + + if (timestep_start is not None) or (timestep_end is not None): + n = x.copy() + if (timestep_start is not None): + n['timestep_start'] = timestep_start + if (timestep_end is not None): + n['timestep_end'] = timestep_end + conds[t] = n + +def pre_run_control(model, conds): + s = model.model_sampling + for t in range(len(conds)): + x = conds[t] + + timestep_start = None + timestep_end = None + percent_to_timestep_function = lambda a: s.percent_to_sigma(a) + if 'control' in x: + x['control'].pre_run(model, percent_to_timestep_function) + +def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func): + cond_cnets = [] + cond_other = [] + uncond_cnets = [] + uncond_other = [] + for t in range(len(conds)): + x = conds[t] + if 'area' not in x: + if name in x and x[name] is not None: + cond_cnets.append(x[name]) + else: + cond_other.append((x, t)) + for t in range(len(uncond)): + x = uncond[t] + if 'area' not in x: + if name in x and x[name] is not None: + uncond_cnets.append(x[name]) + else: + uncond_other.append((x, t)) + + if len(uncond_cnets) > 0: + return + + for x in range(len(cond_cnets)): + temp = uncond_other[x % len(uncond_other)] + o = temp[0] + if name in o and o[name] is not None: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond += [n] + else: + n = o.copy() + n[name] = uncond_fill_func(cond_cnets, x) + uncond[temp[1]] = n + +def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs): + for t in range(len(conds)): + x = conds[t] + params = x.copy() + params["device"] = device + params["noise"] = noise + params["width"] = params.get("width", noise.shape[3] * 8) + params["height"] = params.get("height", noise.shape[2] * 8) + params["prompt_type"] = params.get("prompt_type", prompt_type) + for k in kwargs: + if k not in params: + params[k] = kwargs[k] + + out = model_function(**params) + x = x.copy() + model_conds = x['model_conds'].copy() + for k in out: + model_conds[k] = out[k] + x['model_conds'] = model_conds + conds[t] = x + return conds + +class Sampler: + def sample(self): + pass + + def max_denoise(self, model_wrap, sigmas): + max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max) + sigma = float(sigmas[0]) + return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma + +class UNIPC(Sampler): + def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar) + +class UNIPCBH2(Sampler): + def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar) + +KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", + "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", + "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"] + +class KSAMPLER(Sampler): + def __init__(self, sampler_function, extra_options={}, inpaint_options={}): + self.sampler_function = sampler_function + self.extra_options = extra_options + self.inpaint_options = inpaint_options + + def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False): + extra_args["denoise_mask"] = denoise_mask + model_k = KSamplerX0Inpaint(model_wrap) + model_k.latent_image = latent_image + if self.inpaint_options.get("random", False): #TODO: Should this be the default? + generator = torch.manual_seed(extra_args.get("seed", 41) + 1) + model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device) + else: + model_k.noise = noise + + if self.max_denoise(model_wrap, sigmas): + noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0) + else: + noise = noise * sigmas[0] + + k_callback = None + total_steps = len(sigmas) - 1 + if callback is not None: + k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps) + + if latent_image is not None: + noise += latent_image + + samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options) + return samples + + +def ksampler(sampler_name, extra_options={}, inpaint_options={}): + if sampler_name == "dpm_fast": + def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable): + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + total_steps = len(sigmas) - 1 + return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable) + sampler_function = dpm_fast_function + elif sampler_name == "dpm_adaptive": + def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable): + sigma_min = sigmas[-1] + if sigma_min == 0: + sigma_min = sigmas[-2] + return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable) + sampler_function = dpm_adaptive_function + else: + sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name)) + + return KSAMPLER(sampler_function, extra_options, inpaint_options) + +def wrap_model(model): + model_denoise = CFGNoisePredictor(model) + return model_denoise + +def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + positive = positive[:] + negative = negative[:] + + resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device) + resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device) + + model_wrap = wrap_model(model) + + calculate_start_end_timesteps(model, negative) + calculate_start_end_timesteps(model, positive) + + if latent_image is not None: + latent_image = model.process_latent_in(latent_image) + + if hasattr(model, 'extra_conds'): + positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) + negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed) + + #make sure each cond area has an opposite one with the same area + for c in positive: + create_cond_with_same_area_if_none(negative, c) + for c in negative: + create_cond_with_same_area_if_none(positive, c) + + pre_run_control(model, negative + positive) + + apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) + apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) + + extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} + + samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar) + return model.process_latent_out(samples.to(torch.float32)) + +SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"] +SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"] + +def calculate_sigmas_scheduler(model, scheduler_name, steps): + if scheduler_name == "karras": + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "exponential": + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "normal": + sigmas = normal_scheduler(model, steps) + elif scheduler_name == "simple": + sigmas = simple_scheduler(model, steps) + elif scheduler_name == "ddim_uniform": + sigmas = ddim_scheduler(model, steps) + elif scheduler_name == "sgm_uniform": + sigmas = normal_scheduler(model, steps, sgm=True) + else: + print("error invalid scheduler", scheduler_name) + return sigmas + +def sampler_object(name): + if name == "uni_pc": + sampler = UNIPC() + elif name == "uni_pc_bh2": + sampler = UNIPCBH2() + elif name == "ddim": + sampler = ksampler("euler", inpaint_options={"random": True}) + else: + sampler = ksampler(name) + return sampler + +class KSampler: + SCHEDULERS = SCHEDULER_NAMES + SAMPLERS = SAMPLER_NAMES + + def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}): + self.model = model + self.device = device + if scheduler not in self.SCHEDULERS: + scheduler = self.SCHEDULERS[0] + if sampler not in self.SAMPLERS: + sampler = self.SAMPLERS[0] + self.scheduler = scheduler + self.sampler = sampler + self.set_steps(steps, denoise) + self.denoise = denoise + self.model_options = model_options + + def calculate_sigmas(self, steps): + sigmas = None + + discard_penultimate_sigma = False + if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']: + steps += 1 + discard_penultimate_sigma = True + + sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + def set_steps(self, steps, denoise=None): + self.steps = steps + if denoise is None or denoise > 0.9999: + self.sigmas = self.calculate_sigmas(steps).to(self.device) + else: + new_steps = int(steps/denoise) + sigmas = self.calculate_sigmas(new_steps).to(self.device) + self.sigmas = sigmas[-(steps + 1):] + + def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None): + if sigmas is None: + sigmas = self.sigmas + + if last_step is not None and last_step < (len(sigmas) - 1): + sigmas = sigmas[:last_step + 1] + if force_full_denoise: + sigmas[-1] = 0 + + if start_step is not None: + if start_step < (len(sigmas) - 1): + sigmas = sigmas[start_step:] + else: + if latent_image is not None: + return latent_image + else: + return torch.zeros_like(noise) + + sampler = sampler_object(self.sampler) + + return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) diff --git a/ldm_patched/modules/sd.py b/ldm_patched/modules/sd.py new file mode 100644 index 0000000000000000000000000000000000000000..e197c39caf79bc03e136101feb46f4ad06756d3a --- /dev/null +++ b/ldm_patched/modules/sd.py @@ -0,0 +1,544 @@ +import torch + +from ldm_patched.modules import model_management +from ldm_patched.ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine +import yaml + +import ldm_patched.modules.utils + +from . import clip_vision +from . import gligen +from . import diffusers_convert +from . import model_base +from . import model_detection + +from . import sd1_clip +from . import sd2_clip +from . import sdxl_clip + +import ldm_patched.modules.model_patcher +import ldm_patched.modules.lora +import ldm_patched.t2ia.adapter +import ldm_patched.modules.supported_models_base +import ldm_patched.taesd.taesd + +def load_model_weights(model, sd): + m, u = model.load_state_dict(sd, strict=False) + m = set(m) + unexpected_keys = set(u) + + k = list(sd.keys()) + for x in k: + if x not in unexpected_keys: + w = sd.pop(x) + del w + if len(m) > 0: + print("extra", m) + return model + +def load_clip_weights(model, sd): + k = list(sd.keys()) + for x in k: + if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): + y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") + sd[y] = sd.pop(x) + + if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd: + ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] + if ids.dtype == torch.float32: + sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() + + sd = ldm_patched.modules.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24) + return load_model_weights(model, sd) + + +def load_lora_for_models(model, clip, lora, strength_model, strength_clip): + key_map = {} + if model is not None: + key_map = ldm_patched.modules.lora.model_lora_keys_unet(model.model, key_map) + if clip is not None: + key_map = ldm_patched.modules.lora.model_lora_keys_clip(clip.cond_stage_model, key_map) + + loaded = ldm_patched.modules.lora.load_lora(lora, key_map) + if model is not None: + new_modelpatcher = model.clone() + k = new_modelpatcher.add_patches(loaded, strength_model) + else: + k = () + new_modelpatcher = None + + if clip is not None: + new_clip = clip.clone() + k1 = new_clip.add_patches(loaded, strength_clip) + else: + k1 = () + new_clip = None + k = set(k) + k1 = set(k1) + for x in loaded: + if (x not in k) and (x not in k1): + print("NOT LOADED", x) + + return (new_modelpatcher, new_clip) + + +class CLIP: + def __init__(self, target=None, embedding_directory=None, no_init=False): + if no_init: + return + params = target.params.copy() + clip = target.clip + tokenizer = target.tokenizer + + load_device = model_management.text_encoder_device() + offload_device = model_management.text_encoder_offload_device() + params['device'] = offload_device + params['dtype'] = model_management.text_encoder_dtype(load_device) + + self.cond_stage_model = clip(**(params)) + + self.tokenizer = tokenizer(embedding_directory=embedding_directory) + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device) + self.layer_idx = None + + def clone(self): + n = CLIP(no_init=True) + n.patcher = self.patcher.clone() + n.cond_stage_model = self.cond_stage_model + n.tokenizer = self.tokenizer + n.layer_idx = self.layer_idx + return n + + def add_patches(self, patches, strength_patch=1.0, strength_model=1.0): + return self.patcher.add_patches(patches, strength_patch, strength_model) + + def clip_layer(self, layer_idx): + self.layer_idx = layer_idx + + def tokenize(self, text, return_word_ids=False): + return self.tokenizer.tokenize_with_weights(text, return_word_ids) + + def encode_from_tokens(self, tokens, return_pooled=False): + if self.layer_idx is not None: + self.cond_stage_model.clip_layer(self.layer_idx) + else: + self.cond_stage_model.reset_clip_layer() + + self.load_model() + cond, pooled = self.cond_stage_model.encode_token_weights(tokens) + if return_pooled: + return cond, pooled + return cond + + def encode(self, text): + tokens = self.tokenize(text) + return self.encode_from_tokens(tokens) + + def load_sd(self, sd): + return self.cond_stage_model.load_sd(sd) + + def get_sd(self): + return self.cond_stage_model.state_dict() + + def load_model(self): + model_management.load_model_gpu(self.patcher) + return self.patcher + + def get_key_patches(self): + return self.patcher.get_key_patches() + +class VAE: + def __init__(self, sd=None, device=None, config=None, dtype=None): + if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format + sd = diffusers_convert.convert_vae_state_dict(sd) + + self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower) + self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) + self.downscale_ratio = 8 + self.latent_channels = 4 + + if config is None: + if "decoder.mid.block_1.mix_factor" in sd: + encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + decoder_config = encoder_config.copy() + decoder_config["video_kernel_size"] = [3, 1, 1] + decoder_config["alpha"] = 0.0 + self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "ldm_patched.ldm.models.autoencoder.DiagonalGaussianRegularizer"}, + encoder_config={'target': "ldm_patched.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config}, + decoder_config={'target': "ldm_patched.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config}) + elif "taesd_decoder.1.weight" in sd: + self.first_stage_model = ldm_patched.taesd.taesd.TAESD() + else: + #default SD1.x/SD2.x VAE parameters + ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0} + + if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE + ddconfig['ch_mult'] = [1, 2, 4] + self.downscale_ratio = 4 + + self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4) + else: + self.first_stage_model = AutoencoderKL(**(config['params'])) + self.first_stage_model = self.first_stage_model.eval() + + m, u = self.first_stage_model.load_state_dict(sd, strict=False) + if len(m) > 0: + print("Missing VAE keys", m) + + if len(u) > 0: + print("Leftover VAE keys", u) + + if device is None: + device = model_management.vae_device() + self.device = device + offload_device = model_management.vae_offload_device() + if dtype is None: + dtype = model_management.vae_dtype() + self.vae_dtype = dtype + self.first_stage_model.to(self.vae_dtype) + self.output_device = model_management.intermediate_device() + + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device) + + def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16): + steps = samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap) + steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = ldm_patched.modules.utils.ProgressBar(steps) + + decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float() + output = torch.clamp(( + (ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) + + ldm_patched.modules.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar)) + / 3.0) / 2.0, min=0.0, max=1.0) + return output + + def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + steps = pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) + steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap) + steps += pixel_samples.shape[0] * ldm_patched.modules.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap) + pbar = ldm_patched.modules.utils.ProgressBar(steps) + + encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float() + samples = ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples += ldm_patched.modules.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar) + samples /= 3.0 + return samples + + def decode(self, samples_in): + try: + memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) + model_management.load_models_gpu([self.patcher], memory_required=memory_used) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + + pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device) + for x in range(0, samples_in.shape[0], batch_number): + samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device) + pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0) + except model_management.OOM_EXCEPTION as e: + print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.") + pixel_samples = self.decode_tiled_(samples_in) + + pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1) + return pixel_samples + + def decode_tiled(self, samples, tile_x=64, tile_y=64, overlap = 16): + model_management.load_model_gpu(self.patcher) + output = self.decode_tiled_(samples, tile_x, tile_y, overlap) + return output.movedim(1,-1) + + def encode(self, pixel_samples): + pixel_samples = pixel_samples.movedim(-1,1) + try: + memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype) + model_management.load_models_gpu([self.patcher], memory_required=memory_used) + free_memory = model_management.get_free_memory(self.device) + batch_number = int(free_memory / memory_used) + batch_number = max(1, batch_number) + samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device) + for x in range(0, pixel_samples.shape[0], batch_number): + pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device) + samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float() + + except model_management.OOM_EXCEPTION as e: + print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") + samples = self.encode_tiled_(pixel_samples) + + return samples + + def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): + model_management.load_model_gpu(self.patcher) + pixel_samples = pixel_samples.movedim(-1,1) + samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap) + return samples + + def get_sd(self): + return self.first_stage_model.state_dict() + +class StyleModel: + def __init__(self, model, device="cpu"): + self.model = model + + def get_cond(self, input): + return self.model(input.last_hidden_state) + + +def load_style_model(ckpt_path): + model_data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) + keys = model_data.keys() + if "style_embedding" in keys: + model = ldm_patched.t2ia.adapter.StyleAdapter(width=1024, context_dim=768, num_head=8, n_layes=3, num_token=8) + else: + raise Exception("invalid style model {}".format(ckpt_path)) + model.load_state_dict(model_data) + return StyleModel(model) + + +def load_clip(ckpt_paths, embedding_directory=None): + clip_data = [] + for p in ckpt_paths: + clip_data.append(ldm_patched.modules.utils.load_torch_file(p, safe_load=True)) + + class EmptyClass: + pass + + for i in range(len(clip_data)): + if "transformer.resblocks.0.ln_1.weight" in clip_data[i]: + clip_data[i] = ldm_patched.modules.utils.transformers_convert(clip_data[i], "", "text_model.", 32) + + clip_target = EmptyClass() + clip_target.params = {} + if len(clip_data) == 1: + if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]: + clip_target.clip = sdxl_clip.SDXLRefinerClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]: + clip_target.clip = sd2_clip.SD2ClipModel + clip_target.tokenizer = sd2_clip.SD2Tokenizer + else: + clip_target.clip = sd1_clip.SD1ClipModel + clip_target.tokenizer = sd1_clip.SD1Tokenizer + else: + clip_target.clip = sdxl_clip.SDXLClipModel + clip_target.tokenizer = sdxl_clip.SDXLTokenizer + + clip = CLIP(clip_target, embedding_directory=embedding_directory) + for c in clip_data: + m, u = clip.load_sd(c) + if len(m) > 0: + print("clip missing:", m) + + if len(u) > 0: + print("clip unexpected:", u) + return clip + +def load_gligen(ckpt_path): + data = ldm_patched.modules.utils.load_torch_file(ckpt_path, safe_load=True) + model = gligen.load_gligen(data) + if model_management.should_use_fp16(): + model = model.half() + return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device()) + +def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None): + #TODO: this function is a mess and should be removed eventually + if config is None: + with open(config_path, 'r') as stream: + config = yaml.safe_load(stream) + model_config_params = config['model']['params'] + clip_config = model_config_params['cond_stage_config'] + scale_factor = model_config_params['scale_factor'] + vae_config = model_config_params['first_stage_config'] + + fp16 = False + if "unet_config" in model_config_params: + if "params" in model_config_params["unet_config"]: + unet_config = model_config_params["unet_config"]["params"] + if "use_fp16" in unet_config: + fp16 = unet_config.pop("use_fp16") + if fp16: + unet_config["dtype"] = torch.float16 + + noise_aug_config = None + if "noise_aug_config" in model_config_params: + noise_aug_config = model_config_params["noise_aug_config"] + + model_type = model_base.ModelType.EPS + + if "parameterization" in model_config_params: + if model_config_params["parameterization"] == "v": + model_type = model_base.ModelType.V_PREDICTION + + clip = None + vae = None + + class WeightsLoader(torch.nn.Module): + pass + + if state_dict is None: + state_dict = ldm_patched.modules.utils.load_torch_file(ckpt_path) + + class EmptyClass: + pass + + model_config = ldm_patched.modules.supported_models_base.BASE({}) + + from . import latent_formats + model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor) + model_config.unet_config = model_detection.convert_config(unet_config) + + if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"): + model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type) + else: + model = model_base.BaseModel(model_config, model_type=model_type) + + if config['model']["target"].endswith("LatentInpaintDiffusion"): + model.set_inpaint() + + if fp16: + model = model.half() + + offload_device = model_management.unet_offload_device() + model = model.to(offload_device) + model.load_model_weights(state_dict, "model.diffusion_model.") + + if output_vae: + vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True) + vae = VAE(sd=vae_sd, config=vae_config) + + if output_clip: + w = WeightsLoader() + clip_target = EmptyClass() + clip_target.params = clip_config.get("params", {}) + if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"): + clip_target.clip = sd2_clip.SD2ClipModel + clip_target.tokenizer = sd2_clip.SD2Tokenizer + clip = CLIP(clip_target, embedding_directory=embedding_directory) + w.cond_stage_model = clip.cond_stage_model.clip_h + elif clip_config["target"].endswith("FrozenCLIPEmbedder"): + clip_target.clip = sd1_clip.SD1ClipModel + clip_target.tokenizer = sd1_clip.SD1Tokenizer + clip = CLIP(clip_target, embedding_directory=embedding_directory) + w.cond_stage_model = clip.cond_stage_model.clip_l + load_clip_weights(w, state_dict) + + return (ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae) + +def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True): + sd = ldm_patched.modules.utils.load_torch_file(ckpt_path) + sd_keys = sd.keys() + clip = None + clipvision = None + vae = None + model = None + model_patcher = None + clip_target = None + + parameters = ldm_patched.modules.utils.calculate_parameters(sd, "model.diffusion_model.") + unet_dtype = model_management.unet_dtype(model_params=parameters) + load_device = model_management.get_torch_device() + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) + + class WeightsLoader(torch.nn.Module): + pass + + model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype) + model_config.set_manual_cast(manual_cast_dtype) + + if model_config is None: + raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path)) + + if model_config.clip_vision_prefix is not None: + if output_clipvision: + clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True) + + if output_model: + inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype) + offload_device = model_management.unet_offload_device() + model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device) + model.load_model_weights(sd, "model.diffusion_model.") + + if output_vae: + vae_sd = ldm_patched.modules.utils.state_dict_prefix_replace(sd, {"first_stage_model.": ""}, filter_keys=True) + vae_sd = model_config.process_vae_state_dict(vae_sd) + vae = VAE(sd=vae_sd) + + if output_clip: + w = WeightsLoader() + clip_target = model_config.clip_target() + if clip_target is not None: + clip = CLIP(clip_target, embedding_directory=embedding_directory) + w.cond_stage_model = clip.cond_stage_model + sd = model_config.process_clip_state_dict(sd) + load_model_weights(w, sd) + + left_over = sd.keys() + if len(left_over) > 0: + print("left over keys:", left_over) + + if output_model: + model_patcher = ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device) + if inital_load_device != torch.device("cpu"): + print("loaded straight to GPU") + model_management.load_model_gpu(model_patcher) + + return (model_patcher, clip, vae, clipvision) + + +def load_unet_state_dict(sd): #load unet in diffusers format + parameters = ldm_patched.modules.utils.calculate_parameters(sd) + unet_dtype = model_management.unet_dtype(model_params=parameters) + load_device = model_management.get_torch_device() + manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device) + + if "input_blocks.0.0.weight" in sd: #ldm + model_config = model_detection.model_config_from_unet(sd, "", unet_dtype) + if model_config is None: + return None + new_sd = sd + + else: #diffusers + model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype) + if model_config is None: + return None + + diffusers_keys = ldm_patched.modules.utils.unet_to_diffusers(model_config.unet_config) + + new_sd = {} + for k in diffusers_keys: + if k in sd: + new_sd[diffusers_keys[k]] = sd.pop(k) + else: + print(diffusers_keys[k], k) + offload_device = model_management.unet_offload_device() + model_config.set_manual_cast(manual_cast_dtype) + model = model_config.get_model(new_sd, "") + model = model.to(offload_device) + model.load_model_weights(new_sd, "") + left_over = sd.keys() + if len(left_over) > 0: + print("left over keys in unet:", left_over) + return ldm_patched.modules.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device) + +def load_unet(unet_path): + sd = ldm_patched.modules.utils.load_torch_file(unet_path) + model = load_unet_state_dict(sd) + if model is None: + print("ERROR UNSUPPORTED UNET", unet_path) + raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path)) + return model + +def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None): + clip_sd = None + load_models = [model] + if clip is not None: + load_models.append(clip.load_model()) + clip_sd = clip.get_sd() + + model_management.load_models_gpu(load_models) + clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None + sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd) + ldm_patched.modules.utils.save_torch_file(sd, output_path, metadata=metadata) diff --git a/ldm_patched/modules/sd1_clip.py b/ldm_patched/modules/sd1_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..3727fb48242153a7f87b6e6359367b18f19041c5 --- /dev/null +++ b/ldm_patched/modules/sd1_clip.py @@ -0,0 +1,518 @@ +import os + +from transformers import CLIPTokenizer +import ldm_patched.modules.ops +import torch +import traceback +import zipfile +from . import model_management +import ldm_patched.modules.clip_model +import json + +def gen_empty_tokens(special_tokens, length): + start_token = special_tokens.get("start", None) + end_token = special_tokens.get("end", None) + pad_token = special_tokens.get("pad") + output = [] + if start_token is not None: + output.append(start_token) + if end_token is not None: + output.append(end_token) + output += [pad_token] * (length - len(output)) + return output + +class ClipTokenWeightEncoder: + def encode_token_weights(self, token_weight_pairs): + to_encode = list() + max_token_len = 0 + has_weights = False + for x in token_weight_pairs: + tokens = list(map(lambda a: a[0], x)) + max_token_len = max(len(tokens), max_token_len) + has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) + to_encode.append(tokens) + + sections = len(to_encode) + if has_weights or sections == 0: + to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len)) + + out, pooled = self.encode(to_encode) + if pooled is not None: + first_pooled = pooled[0:1].to(model_management.intermediate_device()) + else: + first_pooled = pooled + + output = [] + for k in range(0, sections): + z = out[k:k+1] + if has_weights: + z_empty = out[-1] + for i in range(len(z)): + for j in range(len(z[i])): + weight = token_weight_pairs[k][j][1] + if weight != 1.0: + z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] + output.append(z) + + if (len(output) == 0): + return out[-1:].to(model_management.intermediate_device()), first_pooled + return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled + +class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder): + """Uses the CLIP transformer encoder for text (from huggingface)""" + LAYERS = [ + "last", + "pooled", + "hidden" + ] + def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77, + freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=ldm_patched.modules.clip_model.CLIPTextModel, + special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True): # clip-vit-base-patch32 + super().__init__() + assert layer in self.LAYERS + + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json") + + with open(textmodel_json_config) as f: + config = json.load(f) + + self.transformer = model_class(config, dtype, device, ldm_patched.modules.ops.manual_cast) + self.num_layers = self.transformer.num_layers + + self.max_length = max_length + if freeze: + self.freeze() + self.layer = layer + self.layer_idx = None + self.special_tokens = special_tokens + self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.enable_attention_masks = False + + self.layer_norm_hidden_state = layer_norm_hidden_state + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) < self.num_layers + self.clip_layer(layer_idx) + self.layer_default = (self.layer, self.layer_idx) + + def freeze(self): + self.transformer = self.transformer.eval() + #self.train = disabled_train + for param in self.parameters(): + param.requires_grad = False + + def clip_layer(self, layer_idx): + if abs(layer_idx) > self.num_layers: + self.layer = "last" + else: + self.layer = "hidden" + self.layer_idx = layer_idx + + def reset_clip_layer(self): + self.layer = self.layer_default[0] + self.layer_idx = self.layer_default[1] + + def set_up_textual_embeddings(self, tokens, current_embeds): + out_tokens = [] + next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1 + embedding_weights = [] + + for x in tokens: + tokens_temp = [] + for y in x: + if isinstance(y, int): + if y == token_dict_size: #EOS token + y = -1 + tokens_temp += [y] + else: + if y.shape[0] == current_embeds.weight.shape[1]: + embedding_weights += [y] + tokens_temp += [next_new_token] + next_new_token += 1 + else: + print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1]) + while len(tokens_temp) < len(x): + tokens_temp += [self.special_tokens["pad"]] + out_tokens += [tokens_temp] + + n = token_dict_size + if len(embedding_weights) > 0: + new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype) + new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1] + for x in embedding_weights: + new_embedding.weight[n] = x + n += 1 + new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding + self.transformer.set_input_embeddings(new_embedding) + + processed_tokens = [] + for x in out_tokens: + processed_tokens += [list(map(lambda a: n if a == -1 else a, x))] #The EOS token should always be the largest one + + return processed_tokens + + def forward(self, tokens): + backup_embeds = self.transformer.get_input_embeddings() + device = backup_embeds.weight.device + tokens = self.set_up_textual_embeddings(tokens, backup_embeds) + tokens = torch.LongTensor(tokens).to(device) + + attention_mask = None + if self.enable_attention_masks: + attention_mask = torch.zeros_like(tokens) + max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 + for x in range(attention_mask.shape[0]): + for y in range(attention_mask.shape[1]): + attention_mask[x, y] = 1 + if tokens[x, y] == max_token: + break + + outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) + self.transformer.set_input_embeddings(backup_embeds) + + if self.layer == "last": + z = outputs[0] + else: + z = outputs[1] + + if outputs[2] is not None: + pooled_output = outputs[2].float() + else: + pooled_output = None + + if self.text_projection is not None and pooled_output is not None: + pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() + return z.float(), pooled_output + + def encode(self, tokens): + return self(tokens) + + def load_sd(self, sd): + if "text_projection" in sd: + self.text_projection[:] = sd.pop("text_projection") + if "text_projection.weight" in sd: + self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1) + return self.transformer.load_state_dict(sd, strict=False) + +def parse_parentheses(string): + result = [] + current_item = "" + nesting_level = 0 + for char in string: + if char == "(": + if nesting_level == 0: + if current_item: + result.append(current_item) + current_item = "(" + else: + current_item = "(" + else: + current_item += char + nesting_level += 1 + elif char == ")": + nesting_level -= 1 + if nesting_level == 0: + result.append(current_item + ")") + current_item = "" + else: + current_item += char + else: + current_item += char + if current_item: + result.append(current_item) + return result + +def token_weights(string, current_weight): + a = parse_parentheses(string) + out = [] + for x in a: + weight = current_weight + if len(x) >= 2 and x[-1] == ')' and x[0] == '(': + x = x[1:-1] + xx = x.rfind(":") + weight *= 1.1 + if xx > 0: + try: + weight = float(x[xx+1:]) + x = x[:xx] + except: + pass + out += token_weights(x, weight) + else: + out += [(x, current_weight)] + return out + +def escape_important(text): + text = text.replace("\\)", "\0\1") + text = text.replace("\\(", "\0\2") + return text + +def unescape_important(text): + text = text.replace("\0\1", ")") + text = text.replace("\0\2", "(") + return text + +def safe_load_embed_zip(embed_path): + with zipfile.ZipFile(embed_path) as myzip: + names = list(filter(lambda a: "data/" in a, myzip.namelist())) + names.reverse() + for n in names: + with myzip.open(n) as myfile: + data = myfile.read() + number = len(data) // 4 + length_embed = 1024 #sd2.x + if number < 768: + continue + if number % 768 == 0: + length_embed = 768 #sd1.x + num_embeds = number // length_embed + embed = torch.frombuffer(data, dtype=torch.float) + out = embed.reshape((num_embeds, length_embed)).clone() + del embed + return out + +def expand_directory_list(directories): + dirs = set() + for x in directories: + dirs.add(x) + for root, subdir, file in os.walk(x, followlinks=True): + dirs.add(root) + return list(dirs) + +def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None): + if isinstance(embedding_directory, str): + embedding_directory = [embedding_directory] + + embedding_directory = expand_directory_list(embedding_directory) + + valid_file = None + for embed_dir in embedding_directory: + embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name)) + embed_dir = os.path.abspath(embed_dir) + try: + if os.path.commonpath((embed_dir, embed_path)) != embed_dir: + continue + except: + continue + if not os.path.isfile(embed_path): + extensions = ['.safetensors', '.pt', '.bin'] + for x in extensions: + t = embed_path + x + if os.path.isfile(t): + valid_file = t + break + else: + valid_file = embed_path + if valid_file is not None: + break + + if valid_file is None: + return None + + embed_path = valid_file + + embed_out = None + + try: + if embed_path.lower().endswith(".safetensors"): + import safetensors.torch + embed = safetensors.torch.load_file(embed_path, device="cpu") + else: + if 'weights_only' in torch.load.__code__.co_varnames: + try: + embed = torch.load(embed_path, weights_only=True, map_location="cpu") + except: + embed_out = safe_load_embed_zip(embed_path) + else: + embed = torch.load(embed_path, map_location="cpu") + except Exception as e: + print(traceback.format_exc()) + print() + print("error loading embedding, skipping loading:", embedding_name) + return None + + if embed_out is None: + if 'string_to_param' in embed: + values = embed['string_to_param'].values() + embed_out = next(iter(values)) + elif isinstance(embed, list): + out_list = [] + for x in range(len(embed)): + for k in embed[x]: + t = embed[x][k] + if t.shape[-1] != embedding_size: + continue + out_list.append(t.reshape(-1, t.shape[-1])) + embed_out = torch.cat(out_list, dim=0) + elif embed_key is not None and embed_key in embed: + embed_out = embed[embed_key] + else: + values = embed.values() + embed_out = next(iter(values)) + return embed_out + +class SDTokenizer: + def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True): + if tokenizer_path is None: + tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer") + self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path) + self.max_length = max_length + + empty = self.tokenizer('')["input_ids"] + if has_start_token: + self.tokens_start = 1 + self.start_token = empty[0] + self.end_token = empty[1] + else: + self.tokens_start = 0 + self.start_token = None + self.end_token = empty[0] + self.pad_with_end = pad_with_end + self.pad_to_max_length = pad_to_max_length + + vocab = self.tokenizer.get_vocab() + self.inv_vocab = {v: k for k, v in vocab.items()} + self.embedding_directory = embedding_directory + self.max_word_length = 8 + self.embedding_identifier = "embedding:" + self.embedding_size = embedding_size + self.embedding_key = embedding_key + + def _try_get_embedding(self, embedding_name:str): + ''' + Takes a potential embedding name and tries to retrieve it. + Returns a Tuple consisting of the embedding and any leftover string, embedding can be None. + ''' + embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key) + if embed is None: + stripped = embedding_name.strip(',') + if len(stripped) < len(embedding_name): + embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key) + return (embed, embedding_name[len(stripped):]) + return (embed, "") + + + def tokenize_with_weights(self, text:str, return_word_ids=False): + ''' + Takes a prompt and converts it to a list of (token, weight, word id) elements. + Tokens can both be integer tokens and pre computed CLIP tensors. + Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens. + Returned list has the dimensions NxM where M is the input size of CLIP + ''' + if self.pad_with_end: + pad_token = self.end_token + else: + pad_token = 0 + + text = escape_important(text) + parsed_weights = token_weights(text, 1.0) + + #tokenize words + tokens = [] + for weighted_segment, weight in parsed_weights: + to_tokenize = unescape_important(weighted_segment).replace("\n", " ").split(' ') + to_tokenize = [x for x in to_tokenize if x != ""] + for word in to_tokenize: + #if we find an embedding, deal with the embedding + if word.startswith(self.embedding_identifier) and self.embedding_directory is not None: + embedding_name = word[len(self.embedding_identifier):].strip('\n') + embed, leftover = self._try_get_embedding(embedding_name) + if embed is None: + print(f"warning, embedding:{embedding_name} does not exist, ignoring") + else: + if len(embed.shape) == 1: + tokens.append([(embed, weight)]) + else: + tokens.append([(embed[x], weight) for x in range(embed.shape[0])]) + #if we accidentally have leftover text, continue parsing using leftover, else move on to next word + if leftover != "": + word = leftover + else: + continue + #parse word + tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:-1]]) + + #reshape token array to CLIP input size + batched_tokens = [] + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + for i, t_group in enumerate(tokens): + #determine if we're going to try and keep the tokens in a single batch + is_large = len(t_group) >= self.max_word_length + + while len(t_group) > 0: + if len(t_group) + len(batch) > self.max_length - 1: + remaining_length = self.max_length - len(batch) - 1 + #break word in two and add end token + if is_large: + batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]]) + batch.append((self.end_token, 1.0, 0)) + t_group = t_group[remaining_length:] + #add end token and pad + else: + batch.append((self.end_token, 1.0, 0)) + if self.pad_to_max_length: + batch.extend([(pad_token, 1.0, 0)] * (remaining_length)) + #start new batch + batch = [] + if self.start_token is not None: + batch.append((self.start_token, 1.0, 0)) + batched_tokens.append(batch) + else: + batch.extend([(t,w,i+1) for t,w in t_group]) + t_group = [] + + #fill last batch + batch.append((self.end_token, 1.0, 0)) + if self.pad_to_max_length: + batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch))) + + if not return_word_ids: + batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens] + + return batched_tokens + + + def untokenize(self, token_weight_pair): + return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair)) + + +class SD1Tokenizer: + def __init__(self, embedding_directory=None, clip_name="l", tokenizer=SDTokenizer): + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory)) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return getattr(self, self.clip).untokenize(token_weight_pair) + + +class SD1ClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs): + super().__init__() + self.clip_name = clip_name + self.clip = "clip_{}".format(self.clip_name) + setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs)) + + def clip_layer(self, layer_idx): + getattr(self, self.clip).clip_layer(layer_idx) + + def reset_clip_layer(self): + getattr(self, self.clip).reset_clip_layer() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs = token_weight_pairs[self.clip_name] + out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs) + return out, pooled + + def load_sd(self, sd): + return getattr(self, self.clip).load_sd(sd) diff --git a/ldm_patched/modules/sd1_clip_config.json b/ldm_patched/modules/sd1_clip_config.json new file mode 100644 index 0000000000000000000000000000000000000000..0158a1fd52727adf22359238285afafb150f66f2 --- /dev/null +++ b/ldm_patched/modules/sd1_clip_config.json @@ -0,0 +1,25 @@ +{ + "_name_or_path": "openai/clip-vit-large-patch14", + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "quick_gelu", + "hidden_size": 768, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "pad_token_id": 1, + "projection_dim": 768, + "torch_dtype": "float32", + "transformers_version": "4.24.0", + "vocab_size": 49408 +} diff --git a/ldm_patched/modules/sd1_tokenizer/merges.txt b/ldm_patched/modules/sd1_tokenizer/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b --- /dev/null +++ b/ldm_patched/modules/sd1_tokenizer/merges.txt @@ -0,0 +1,48895 @@ +#version: 0.2 +i n +t h +a n +r e +a r +e r +th e +in g +o u +o n +s t +o r +e n +o n +a l +a t +e r +i t +i n +t o +r o +i s +l e +i c +a t +an d +e d +o f +c h +o r +e s +i l +e l +s t +a c +o m +a m +l o +a n +a y +s h +r i +l i +t i +f or +n e +ð Ł +r a +h a +d e +o l +v e +s i +u r +a l +s e +' s +u n +d i +b e +l a +w h +o o +d ay +e n +m a +n o +l e +t o +ou r +i r +g h +w it +i t +y o +a s +s p +th is +t s +at i +yo u +wit h +a d 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255, + "Ń": 511 +} diff --git a/ldm_patched/modules/sd2_clip.py b/ldm_patched/modules/sd2_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..41f9e388dc40966e7d68a1cba7d7df11a05983c7 --- /dev/null +++ b/ldm_patched/modules/sd2_clip.py @@ -0,0 +1,24 @@ +from ldm_patched.modules import sd1_clip +import torch +import os + +class SD2ClipHModel(sd1_clip.SDClipModel): + def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): + if layer == "penultimate": + layer="hidden" + layer_idx=-2 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}) + +class SD2ClipHTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024) + +class SD2Tokenizer(sd1_clip.SD1Tokenizer): + def __init__(self, embedding_directory=None): + super().__init__(embedding_directory=embedding_directory, clip_name="h", tokenizer=SD2ClipHTokenizer) + +class SD2ClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None, **kwargs): + super().__init__(device=device, dtype=dtype, clip_name="h", clip_model=SD2ClipHModel, **kwargs) diff --git a/ldm_patched/modules/sd2_clip_config.json b/ldm_patched/modules/sd2_clip_config.json new file mode 100644 index 0000000000000000000000000000000000000000..85cec832be9a1d0957245a8d125af398829f247e --- /dev/null +++ b/ldm_patched/modules/sd2_clip_config.json @@ -0,0 +1,23 @@ +{ + "architectures": [ + "CLIPTextModel" + ], + "attention_dropout": 0.0, + "bos_token_id": 0, + "dropout": 0.0, + "eos_token_id": 2, + "hidden_act": "gelu", + "hidden_size": 1024, + "initializer_factor": 1.0, + "initializer_range": 0.02, + "intermediate_size": 4096, + "layer_norm_eps": 1e-05, + "max_position_embeddings": 77, + "model_type": "clip_text_model", + "num_attention_heads": 16, + "num_hidden_layers": 24, + "pad_token_id": 1, + "projection_dim": 1024, + "torch_dtype": "float32", + "vocab_size": 49408 +} diff --git a/ldm_patched/modules/sdxl_clip.py b/ldm_patched/modules/sdxl_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..9d3d83d82314ac116943601e07eb4bad7b2adb56 --- /dev/null +++ b/ldm_patched/modules/sdxl_clip.py @@ -0,0 +1,66 @@ +from ldm_patched.modules import sd1_clip +import torch +import os + +class SDXLClipG(sd1_clip.SDClipModel): + def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): + if layer == "penultimate": + layer="hidden" + layer_idx=-2 + + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") + super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, + special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False) + + def load_sd(self, sd): + return super().load_sd(sd) + +class SDXLClipGTokenizer(sd1_clip.SDTokenizer): + def __init__(self, tokenizer_path=None, embedding_directory=None): + super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') + + +class SDXLTokenizer: + def __init__(self, embedding_directory=None): + self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) + self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) + + def tokenize_with_weights(self, text:str, return_word_ids=False): + out = {} + out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) + out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) + return out + + def untokenize(self, token_weight_pair): + return self.clip_g.untokenize(token_weight_pair) + +class SDXLClipModel(torch.nn.Module): + def __init__(self, device="cpu", dtype=None): + super().__init__() + self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) + self.clip_g = SDXLClipG(device=device, dtype=dtype) + + def clip_layer(self, layer_idx): + self.clip_l.clip_layer(layer_idx) + self.clip_g.clip_layer(layer_idx) + + def reset_clip_layer(self): + self.clip_g.reset_clip_layer() + self.clip_l.reset_clip_layer() + + def encode_token_weights(self, token_weight_pairs): + token_weight_pairs_g = token_weight_pairs["g"] + token_weight_pairs_l = token_weight_pairs["l"] + g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) + l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) + return torch.cat([l_out, g_out], dim=-1), g_pooled + + def load_sd(self, sd): + if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: + return self.clip_g.load_sd(sd) + else: + return self.clip_l.load_sd(sd) + +class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): + def __init__(self, device="cpu", dtype=None): + super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG) diff --git a/ldm_patched/modules/supported_models.py b/ldm_patched/modules/supported_models.py new file mode 100644 index 0000000000000000000000000000000000000000..1d442d4dd9ca2c56b7bc92a0f142b7dabd600c5a --- /dev/null +++ b/ldm_patched/modules/supported_models.py @@ -0,0 +1,310 @@ +import torch +from . import model_base +from . import utils + +from . import sd1_clip +from . import sd2_clip +from . import sdxl_clip + +from . import supported_models_base +from . import latent_formats + +from . import diffusers_convert + +class SD15(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format = latent_formats.SD15 + + def process_clip_state_dict(self, state_dict): + k = list(state_dict.keys()) + for x in k: + if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."): + y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.") + state_dict[y] = state_dict.pop(x) + + if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict: + ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] + if ids.dtype == torch.float32: + state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round() + + replace_prefix = {} + replace_prefix["cond_stage_model."] = "cond_stage_model.clip_l." + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {"clip_l.": "cond_stage_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def clip_target(self): + return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel) + +class SD20(supported_models_base.BASE): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + latent_format = latent_formats.SD15 + + def model_type(self, state_dict, prefix=""): + if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction + k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix) + out = state_dict[k] + if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out. + return model_base.ModelType.V_PREDICTION + return model_base.ModelType.EPS + + def process_clip_state_dict(self, state_dict): + replace_prefix = {} + replace_prefix["conditioner.embedders.0.model."] = "cond_stage_model.model." #SD2 in sgm format + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + + state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + replace_prefix["clip_h"] = "cond_stage_model.model" + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict) + return state_dict + + def clip_target(self): + return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel) + +class SD21UnclipL(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 1536, + "use_temporal_attention": False, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768} + + +class SD21UnclipH(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 320, + "use_linear_in_transformer": True, + "adm_in_channels": 2048, + "use_temporal_attention": False, + } + + clip_vision_prefix = "embedder.model.visual." + noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024} + +class SDXLRefiner(supported_models_base.BASE): + unet_config = { + "model_channels": 384, + "use_linear_in_transformer": True, + "context_dim": 1280, + "adm_in_channels": 2560, + "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0], + "use_temporal_attention": False, + } + + latent_format = latent_formats.SDXL + + def get_model(self, state_dict, prefix="", device=None): + return model_base.SDXLRefiner(self, device=device) + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + + state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) + keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection" + keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" + + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: + state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") + replace_prefix["clip_g"] = "conditioner.embedders.0.model" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel) + +class SDXL(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 10, 10], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + + latent_format = latent_formats.SDXL + + def model_type(self, state_dict, prefix=""): + if "v_pred" in state_dict: + return model_base.ModelType.V_PREDICTION + else: + return model_base.ModelType.EPS + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + keys_to_replace = {} + replace_prefix = {} + + replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model" + state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32) + keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection" + keys_to_replace["conditioner.embedders.1.model.text_projection.weight"] = "cond_stage_model.clip_g.text_projection" + keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale" + + state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix) + state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace) + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {} + keys_to_replace = {} + state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g") + if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g: + state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids") + for k in state_dict: + if k.startswith("clip_l"): + state_dict_g[k] = state_dict[k] + + replace_prefix["clip_g"] = "conditioner.embedders.1.model" + replace_prefix["clip_l"] = "conditioner.embedders.0" + state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix) + return state_dict_g + + def clip_target(self): + return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel) + +class SSD1B(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 2, 2, 4, 4], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class Segmind_Vega(SDXL): + unet_config = { + "model_channels": 320, + "use_linear_in_transformer": True, + "transformer_depth": [0, 0, 1, 1, 2, 2], + "context_dim": 2048, + "adm_in_channels": 2816, + "use_temporal_attention": False, + } + +class SVD_img2vid(supported_models_base.BASE): + unet_config = { + "model_channels": 320, + "in_channels": 8, + "use_linear_in_transformer": True, + "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0], + "context_dim": 1024, + "adm_in_channels": 768, + "use_temporal_attention": True, + "use_temporal_resblock": True + } + + clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual." + + latent_format = latent_formats.SD15 + + sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002} + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SVD_img2vid(self, device=device) + return out + + def clip_target(self): + return None + +class Stable_Zero123(supported_models_base.BASE): + unet_config = { + "context_dim": 768, + "model_channels": 320, + "use_linear_in_transformer": False, + "adm_in_channels": None, + "use_temporal_attention": False, + "in_channels": 8, + } + + unet_extra_config = { + "num_heads": 8, + "num_head_channels": -1, + } + + clip_vision_prefix = "cond_stage_model.model.visual." + + latent_format = latent_formats.SD15 + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"]) + return out + + def clip_target(self): + return None + +class SD_X4Upscaler(SD20): + unet_config = { + "context_dim": 1024, + "model_channels": 256, + 'in_channels': 7, + "use_linear_in_transformer": True, + "adm_in_channels": None, + "use_temporal_attention": False, + } + + unet_extra_config = { + "disable_self_attentions": [True, True, True, False], + "num_classes": 1000, + "num_heads": 8, + "num_head_channels": -1, + } + + latent_format = latent_formats.SD_X4 + + sampling_settings = { + "linear_start": 0.0001, + "linear_end": 0.02, + } + + def get_model(self, state_dict, prefix="", device=None): + out = model_base.SD_X4Upscaler(self, device=device) + return out + +models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler] +models += [SVD_img2vid] diff --git a/ldm_patched/modules/supported_models_base.py b/ldm_patched/modules/supported_models_base.py new file mode 100644 index 0000000000000000000000000000000000000000..5baf4bca6c60ad5e449041e6ce72438811f81d20 --- /dev/null +++ b/ldm_patched/modules/supported_models_base.py @@ -0,0 +1,83 @@ +import torch +from . import model_base +from . import utils +from . import latent_formats + +class ClipTarget: + def __init__(self, tokenizer, clip): + self.clip = clip + self.tokenizer = tokenizer + self.params = {} + +class BASE: + unet_config = {} + unet_extra_config = { + "num_heads": -1, + "num_head_channels": 64, + } + + clip_prefix = [] + clip_vision_prefix = None + noise_aug_config = None + sampling_settings = {} + latent_format = latent_formats.LatentFormat + + manual_cast_dtype = None + + @classmethod + def matches(s, unet_config): + for k in s.unet_config: + if s.unet_config[k] != unet_config[k]: + return False + return True + + def model_type(self, state_dict, prefix=""): + return model_base.ModelType.EPS + + def inpaint_model(self): + return self.unet_config["in_channels"] > 4 + + def __init__(self, unet_config): + self.unet_config = unet_config + self.latent_format = self.latent_format() + for x in self.unet_extra_config: + self.unet_config[x] = self.unet_extra_config[x] + + def get_model(self, state_dict, prefix="", device=None): + if self.noise_aug_config is not None: + out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device) + else: + out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device) + if self.inpaint_model(): + out.set_inpaint() + return out + + def process_clip_state_dict(self, state_dict): + return state_dict + + def process_unet_state_dict(self, state_dict): + return state_dict + + def process_vae_state_dict(self, state_dict): + return state_dict + + def process_clip_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "cond_stage_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_clip_vision_state_dict_for_saving(self, state_dict): + replace_prefix = {} + if self.clip_vision_prefix is not None: + replace_prefix[""] = self.clip_vision_prefix + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_unet_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "model.diffusion_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def process_vae_state_dict_for_saving(self, state_dict): + replace_prefix = {"": "first_stage_model."} + return utils.state_dict_prefix_replace(state_dict, replace_prefix) + + def set_manual_cast(self, manual_cast_dtype): + self.manual_cast_dtype = manual_cast_dtype diff --git a/ldm_patched/modules/utils.py b/ldm_patched/modules/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..f8283a86e4682d56aae608a7ffa918c6980d8bac --- /dev/null +++ b/ldm_patched/modules/utils.py @@ -0,0 +1,461 @@ +import torch +import math +import struct +import ldm_patched.modules.checkpoint_pickle +import safetensors.torch +import numpy as np +from PIL import Image + +def load_torch_file(ckpt, safe_load=False, device=None): + if device is None: + device = torch.device("cpu") + if ckpt.lower().endswith(".safetensors"): + sd = safetensors.torch.load_file(ckpt, device=device.type) + else: + if safe_load: + if not 'weights_only' in torch.load.__code__.co_varnames: + print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.") + safe_load = False + if safe_load: + pl_sd = torch.load(ckpt, map_location=device, weights_only=True) + else: + pl_sd = torch.load(ckpt, map_location=device, pickle_module=ldm_patched.modules.checkpoint_pickle) + if "global_step" in pl_sd: + print(f"Global Step: {pl_sd['global_step']}") + if "state_dict" in pl_sd: + sd = pl_sd["state_dict"] + else: + sd = pl_sd + return sd + +def save_torch_file(sd, ckpt, metadata=None): + if metadata is not None: + safetensors.torch.save_file(sd, ckpt, metadata=metadata) + else: + safetensors.torch.save_file(sd, ckpt) + +def calculate_parameters(sd, prefix=""): + params = 0 + for k in sd.keys(): + if k.startswith(prefix): + params += sd[k].nelement() + return params + +def state_dict_key_replace(state_dict, keys_to_replace): + for x in keys_to_replace: + if x in state_dict: + state_dict[keys_to_replace[x]] = state_dict.pop(x) + return state_dict + +def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False): + if filter_keys: + out = {} + else: + out = state_dict + for rp in replace_prefix: + replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys()))) + for x in replace: + w = state_dict.pop(x[0]) + out[x[1]] = w + return out + + +def transformers_convert(sd, prefix_from, prefix_to, number): + keys_to_replace = { + "{}positional_embedding": "{}embeddings.position_embedding.weight", + "{}token_embedding.weight": "{}embeddings.token_embedding.weight", + "{}ln_final.weight": "{}final_layer_norm.weight", + "{}ln_final.bias": "{}final_layer_norm.bias", + } + + for k in keys_to_replace: + x = k.format(prefix_from) + if x in sd: + sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x) + + resblock_to_replace = { + "ln_1": "layer_norm1", + "ln_2": "layer_norm2", + "mlp.c_fc": "mlp.fc1", + "mlp.c_proj": "mlp.fc2", + "attn.out_proj": "self_attn.out_proj", + } + + for resblock in range(number): + for x in resblock_to_replace: + for y in ["weight", "bias"]: + k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y) + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y) + if k in sd: + sd[k_to] = sd.pop(k) + + for y in ["weight", "bias"]: + k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y) + if k_from in sd: + weights = sd.pop(k_from) + shape_from = weights.shape[0] // 3 + for x in range(3): + p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"] + k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y) + sd[k_to] = weights[shape_from*x:shape_from*(x + 1)] + return sd + +UNET_MAP_ATTENTIONS = { + "proj_in.weight", + "proj_in.bias", + "proj_out.weight", + "proj_out.bias", + "norm.weight", + "norm.bias", +} + +TRANSFORMER_BLOCKS = { + "norm1.weight", + "norm1.bias", + "norm2.weight", + "norm2.bias", + "norm3.weight", + "norm3.bias", + "attn1.to_q.weight", + "attn1.to_k.weight", + "attn1.to_v.weight", + "attn1.to_out.0.weight", + "attn1.to_out.0.bias", + "attn2.to_q.weight", + "attn2.to_k.weight", + "attn2.to_v.weight", + "attn2.to_out.0.weight", + "attn2.to_out.0.bias", + "ff.net.0.proj.weight", + "ff.net.0.proj.bias", + "ff.net.2.weight", + "ff.net.2.bias", +} + +UNET_MAP_RESNET = { + "in_layers.2.weight": "conv1.weight", + "in_layers.2.bias": "conv1.bias", + "emb_layers.1.weight": "time_emb_proj.weight", + "emb_layers.1.bias": "time_emb_proj.bias", + "out_layers.3.weight": "conv2.weight", + "out_layers.3.bias": "conv2.bias", + "skip_connection.weight": "conv_shortcut.weight", + "skip_connection.bias": "conv_shortcut.bias", + "in_layers.0.weight": "norm1.weight", + "in_layers.0.bias": "norm1.bias", + "out_layers.0.weight": "norm2.weight", + "out_layers.0.bias": "norm2.bias", +} + +UNET_MAP_BASIC = { + ("label_emb.0.0.weight", "class_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "class_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "class_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "class_embedding.linear_2.bias"), + ("label_emb.0.0.weight", "add_embedding.linear_1.weight"), + ("label_emb.0.0.bias", "add_embedding.linear_1.bias"), + ("label_emb.0.2.weight", "add_embedding.linear_2.weight"), + ("label_emb.0.2.bias", "add_embedding.linear_2.bias"), + ("input_blocks.0.0.weight", "conv_in.weight"), + ("input_blocks.0.0.bias", "conv_in.bias"), + ("out.0.weight", "conv_norm_out.weight"), + ("out.0.bias", "conv_norm_out.bias"), + ("out.2.weight", "conv_out.weight"), + ("out.2.bias", "conv_out.bias"), + ("time_embed.0.weight", "time_embedding.linear_1.weight"), + ("time_embed.0.bias", "time_embedding.linear_1.bias"), + ("time_embed.2.weight", "time_embedding.linear_2.weight"), + ("time_embed.2.bias", "time_embedding.linear_2.bias") +} + +def unet_to_diffusers(unet_config): + num_res_blocks = unet_config["num_res_blocks"] + channel_mult = unet_config["channel_mult"] + transformer_depth = unet_config["transformer_depth"][:] + transformer_depth_output = unet_config["transformer_depth_output"][:] + num_blocks = len(channel_mult) + + transformers_mid = unet_config.get("transformer_depth_middle", None) + + diffusers_unet_map = {} + for x in range(num_blocks): + n = 1 + (num_res_blocks[x] + 1) * x + for i in range(num_res_blocks[x]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b) + num_transformers = transformer_depth.pop(0) + if num_transformers > 0: + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + n += 1 + for k in ["weight", "bias"]: + diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k) + + i = 0 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b) + for t in range(transformers_mid): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b) + + for i, n in enumerate([0, 2]): + for b in UNET_MAP_RESNET: + diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b) + + num_res_blocks = list(reversed(num_res_blocks)) + for x in range(num_blocks): + n = (num_res_blocks[x] + 1) * x + l = num_res_blocks[x] + 1 + for i in range(l): + c = 0 + for b in UNET_MAP_RESNET: + diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b) + c += 1 + num_transformers = transformer_depth_output.pop() + if num_transformers > 0: + c += 1 + for b in UNET_MAP_ATTENTIONS: + diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b) + for t in range(num_transformers): + for b in TRANSFORMER_BLOCKS: + diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b) + if i == l - 1: + for k in ["weight", "bias"]: + diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k) + n += 1 + + for k in UNET_MAP_BASIC: + diffusers_unet_map[k[1]] = k[0] + + return diffusers_unet_map + +def repeat_to_batch_size(tensor, batch_size): + if tensor.shape[0] > batch_size: + return tensor[:batch_size] + elif tensor.shape[0] < batch_size: + return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size] + return tensor + +def resize_to_batch_size(tensor, batch_size): + in_batch_size = tensor.shape[0] + if in_batch_size == batch_size: + return tensor + + if batch_size <= 1: + return tensor[:batch_size] + + output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device) + if batch_size < in_batch_size: + scale = (in_batch_size - 1) / (batch_size - 1) + for i in range(batch_size): + output[i] = tensor[min(round(i * scale), in_batch_size - 1)] + else: + scale = in_batch_size / batch_size + for i in range(batch_size): + output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)] + + return output + +def convert_sd_to(state_dict, dtype): + keys = list(state_dict.keys()) + for k in keys: + state_dict[k] = state_dict[k].to(dtype) + return state_dict + +def safetensors_header(safetensors_path, max_size=100*1024*1024): + with open(safetensors_path, "rb") as f: + header = f.read(8) + length_of_header = struct.unpack(' max_size: + return None + return f.read(length_of_header) + +def set_attr(obj, attr, value): + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False)) + del prev + +def copy_to_param(obj, attr, value): + # inplace update tensor instead of replacing it + attrs = attr.split(".") + for name in attrs[:-1]: + obj = getattr(obj, name) + prev = getattr(obj, attrs[-1]) + prev.data.copy_(value) + +def get_attr(obj, attr): + attrs = attr.split(".") + for name in attrs: + obj = getattr(obj, name) + return obj + +def bislerp(samples, width, height): + def slerp(b1, b2, r): + '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC''' + + c = b1.shape[-1] + + #norms + b1_norms = torch.norm(b1, dim=-1, keepdim=True) + b2_norms = torch.norm(b2, dim=-1, keepdim=True) + + #normalize + b1_normalized = b1 / b1_norms + b2_normalized = b2 / b2_norms + + #zero when norms are zero + b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0 + b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0 + + #slerp + dot = (b1_normalized*b2_normalized).sum(1) + omega = torch.acos(dot) + so = torch.sin(omega) + + #technically not mathematically correct, but more pleasing? + res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized + res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c) + + #edge cases for same or polar opposites + res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] + res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1] + return res + + def generate_bilinear_data(length_old, length_new, device): + coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear") + ratios = coords_1 - coords_1.floor() + coords_1 = coords_1.to(torch.int64) + + coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1 + coords_2[:,:,:,-1] -= 1 + coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear") + coords_2 = coords_2.to(torch.int64) + return ratios, coords_1, coords_2 + + orig_dtype = samples.dtype + samples = samples.float() + n,c,h,w = samples.shape + h_new, w_new = (height, width) + + #linear w + ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device) + coords_1 = coords_1.expand((n, c, h, -1)) + coords_2 = coords_2.expand((n, c, h, -1)) + ratios = ratios.expand((n, 1, h, -1)) + + pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h, w_new, c).movedim(-1, 1) + + #linear h + ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device) + coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new)) + ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new)) + + pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c)) + pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c)) + ratios = ratios.movedim(1, -1).reshape((-1,1)) + + result = slerp(pass_1, pass_2, ratios) + result = result.reshape(n, h_new, w_new, c).movedim(-1, 1) + return result.to(orig_dtype) + +def lanczos(samples, width, height): + images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples] + images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images] + images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images] + result = torch.stack(images) + return result.to(samples.device, samples.dtype) + +def common_upscale(samples, width, height, upscale_method, crop): + if crop == "center": + old_width = samples.shape[3] + old_height = samples.shape[2] + old_aspect = old_width / old_height + new_aspect = width / height + x = 0 + y = 0 + if old_aspect > new_aspect: + x = round((old_width - old_width * (new_aspect / old_aspect)) / 2) + elif old_aspect < new_aspect: + y = round((old_height - old_height * (old_aspect / new_aspect)) / 2) + s = samples[:,:,y:old_height-y,x:old_width-x] + else: + s = samples + + if upscale_method == "bislerp": + return bislerp(s, width, height) + elif upscale_method == "lanczos": + return lanczos(s, width, height) + else: + return torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method) + +def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap): + return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) + +@torch.inference_mode() +def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): + output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device) + for b in range(samples.shape[0]): + s = samples[b:b+1] + out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) + out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) + for y in range(0, s.shape[2], tile_y - overlap): + for x in range(0, s.shape[3], tile_x - overlap): + s_in = s[:,:,y:y+tile_y,x:x+tile_x] + + ps = function(s_in).to(output_device) + mask = torch.ones_like(ps) + feather = round(overlap * upscale_amount) + for t in range(feather): + mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) + mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) + mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) + mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) + out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask + out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask + if pbar is not None: + pbar.update(1) + + output[b:b+1] = out/out_div + return output + +PROGRESS_BAR_ENABLED = True +def set_progress_bar_enabled(enabled): + global PROGRESS_BAR_ENABLED + PROGRESS_BAR_ENABLED = enabled + +PROGRESS_BAR_HOOK = None +def set_progress_bar_global_hook(function): + global PROGRESS_BAR_HOOK + PROGRESS_BAR_HOOK = function + +class ProgressBar: + def __init__(self, total): + global PROGRESS_BAR_HOOK + self.total = total + self.current = 0 + self.hook = PROGRESS_BAR_HOOK + + def update_absolute(self, value, total=None, preview=None): + if total is not None: + self.total = total + if value > self.total: + value = self.total + self.current = value + if self.hook is not None: + self.hook(self.current, self.total, preview) + + def update(self, value): + self.update_absolute(self.current + value) diff --git a/ldm_patched/pfn/__init__.py b/ldm_patched/pfn/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm_patched/pfn/architecture/DAT.py b/ldm_patched/pfn/architecture/DAT.py new file mode 100644 index 0000000000000000000000000000000000000000..0bcc26ef422b73cef41744e2203901a3d290c2f0 --- /dev/null +++ b/ldm_patched/pfn/architecture/DAT.py @@ -0,0 +1,1182 @@ +# pylint: skip-file +import math +import re + +import numpy as np +import torch +import torch.nn as nn +import torch.utils.checkpoint as checkpoint +from einops import rearrange +from einops.layers.torch import Rearrange +from torch import Tensor +from torch.nn import functional as F + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +def img2windows(img, H_sp, W_sp): + """ + Input: Image (B, C, H, W) + Output: Window Partition (B', N, C) + """ + B, C, H, W = img.shape + img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp) + img_perm = ( + img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C) + ) + return img_perm + + +def windows2img(img_splits_hw, H_sp, W_sp, H, W): + """ + Input: Window Partition (B', N, C) + Output: Image (B, H, W, C) + """ + B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp)) + + img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1) + img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return img + + +class SpatialGate(nn.Module): + """Spatial-Gate. + Args: + dim (int): Half of input channels. + """ + + def __init__(self, dim): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.conv = nn.Conv2d( + dim, dim, kernel_size=3, stride=1, padding=1, groups=dim + ) # DW Conv + + def forward(self, x, H, W): + # Split + x1, x2 = x.chunk(2, dim=-1) + B, N, C = x.shape + x2 = ( + self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W)) + .flatten(2) + .transpose(-1, -2) + .contiguous() + ) + + return x1 * x2 + + +class SGFN(nn.Module): + """Spatial-Gate Feed-Forward Network. + Args: + in_features (int): Number of input channels. + hidden_features (int | None): Number of hidden channels. Default: None + out_features (int | None): Number of output channels. Default: None + act_layer (nn.Module): Activation layer. Default: nn.GELU + drop (float): Dropout rate. Default: 0.0 + """ + + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.sg = SpatialGate(hidden_features // 2) + self.fc2 = nn.Linear(hidden_features // 2, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + + x = self.sg(x, H, W) + x = self.drop(x) + + x = self.fc2(x) + x = self.drop(x) + return x + + +class DynamicPosBias(nn.Module): + # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py + """Dynamic Relative Position Bias. + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. + residual (bool): If True, use residual strage to connect conv. + """ + + def __init__(self, dim, num_heads, residual): + super().__init__() + self.residual = residual + self.num_heads = num_heads + self.pos_dim = dim // 4 + self.pos_proj = nn.Linear(2, self.pos_dim) + self.pos1 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos2 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.pos_dim), + ) + self.pos3 = nn.Sequential( + nn.LayerNorm(self.pos_dim), + nn.ReLU(inplace=True), + nn.Linear(self.pos_dim, self.num_heads), + ) + + def forward(self, biases): + if self.residual: + pos = self.pos_proj(biases) # 2Gh-1 * 2Gw-1, heads + pos = pos + self.pos1(pos) + pos = pos + self.pos2(pos) + pos = self.pos3(pos) + else: + pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases)))) + return pos + + +class Spatial_Attention(nn.Module): + """Spatial Window Self-Attention. + It supports rectangle window (containing square window). + Args: + dim (int): Number of input channels. + idx (int): The indentix of window. (0/1) + split_size (tuple(int)): Height and Width of spatial window. + dim_out (int | None): The dimension of the attention output. Default: None + num_heads (int): Number of attention heads. Default: 6 + attn_drop (float): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float): Dropout ratio of output. Default: 0.0 + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set + position_bias (bool): The dynamic relative position bias. Default: True + """ + + def __init__( + self, + dim, + idx, + split_size=[8, 8], + dim_out=None, + num_heads=6, + attn_drop=0.0, + proj_drop=0.0, + qk_scale=None, + position_bias=True, + ): + super().__init__() + self.dim = dim + self.dim_out = dim_out or dim + self.split_size = split_size + self.num_heads = num_heads + self.idx = idx + self.position_bias = position_bias + + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + if idx == 0: + H_sp, W_sp = self.split_size[0], self.split_size[1] + elif idx == 1: + W_sp, H_sp = self.split_size[0], self.split_size[1] + else: + print("ERROR MODE", idx) + exit(0) + self.H_sp = H_sp + self.W_sp = W_sp + + if self.position_bias: + self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False) + # generate mother-set + position_bias_h = torch.arange(1 - self.H_sp, self.H_sp) + position_bias_w = torch.arange(1 - self.W_sp, self.W_sp) + biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w])) + biases = biases.flatten(1).transpose(0, 1).contiguous().float() + self.register_buffer("rpe_biases", biases) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.H_sp) + coords_w = torch.arange(self.W_sp) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) + coords_flatten = torch.flatten(coords, 1) + relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] + relative_coords = relative_coords.permute(1, 2, 0).contiguous() + relative_coords[:, :, 0] += self.H_sp - 1 + relative_coords[:, :, 1] += self.W_sp - 1 + relative_coords[:, :, 0] *= 2 * self.W_sp - 1 + relative_position_index = relative_coords.sum(-1) + self.register_buffer("relative_position_index", relative_position_index) + + self.attn_drop = nn.Dropout(attn_drop) + + def im2win(self, x, H, W): + B, N, C = x.shape + x = x.transpose(-2, -1).contiguous().view(B, C, H, W) + x = img2windows(x, self.H_sp, self.W_sp) + x = ( + x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads) + .permute(0, 2, 1, 3) + .contiguous() + ) + return x + + def forward(self, qkv, H, W, mask=None): + """ + Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size + Output: x (B, H, W, C) + """ + q, k, v = qkv[0], qkv[1], qkv[2] + + B, L, C = q.shape + assert L == H * W, "flatten img_tokens has wrong size" + + # partition the q,k,v, image to window + q = self.im2win(q, H, W) + k = self.im2win(k, H, W) + v = self.im2win(v, H, W) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) # B head N C @ B head C N --> B head N N + + # calculate drpe + if self.position_bias: + pos = self.pos(self.rpe_biases) + # select position bias + relative_position_bias = pos[self.relative_position_index.view(-1)].view( + self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1 + ) + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() + attn = attn + relative_position_bias.unsqueeze(0) + + N = attn.shape[3] + + # use mask for shift window + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze( + 0 + ) + attn = attn.view(-1, self.num_heads, N, N) + + attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype) + attn = self.attn_drop(attn) + + x = attn @ v + x = x.transpose(1, 2).reshape( + -1, self.H_sp * self.W_sp, C + ) # B head N N @ B head N C + + # merge the window, window to image + x = windows2img(x, self.H_sp, self.W_sp, H, W) # B H' W' C + + return x + + +class Adaptive_Spatial_Attention(nn.Module): + # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT + """Adaptive Spatial Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + split_size (tuple(int)): Height and Width of spatial window. + shift_size (tuple(int)): Shift size for spatial window. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + drop (float): Dropout rate. Default: 0.0 + attn_drop (float): Attention dropout rate. Default: 0.0 + rg_idx (int): The indentix of Residual Group (RG) + b_idx (int): The indentix of Block in each RG + """ + + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[8, 8], + shift_size=[1, 2], + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + rg_idx=0, + b_idx=0, + ): + super().__init__() + self.dim = dim + self.num_heads = num_heads + self.split_size = split_size + self.shift_size = shift_size + self.b_idx = b_idx + self.rg_idx = rg_idx + self.patches_resolution = reso + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + + assert ( + 0 <= self.shift_size[0] < self.split_size[0] + ), "shift_size must in 0-split_size0" + assert ( + 0 <= self.shift_size[1] < self.split_size[1] + ), "shift_size must in 0-split_size1" + + self.branch_num = 2 + + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(drop) + + self.attns = nn.ModuleList( + [ + Spatial_Attention( + dim // 2, + idx=i, + split_size=split_size, + num_heads=num_heads // 2, + dim_out=dim // 2, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + position_bias=True, + ) + for i in range(self.branch_num) + ] + ) + + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + attn_mask = self.calculate_mask( + self.patches_resolution, self.patches_resolution + ) + self.register_buffer("attn_mask_0", attn_mask[0]) + self.register_buffer("attn_mask_1", attn_mask[1]) + else: + attn_mask = None + self.register_buffer("attn_mask_0", None) + self.register_buffer("attn_mask_1", None) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def calculate_mask(self, H, W): + # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py + # calculate attention mask for shift window + img_mask_0 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=0 + img_mask_1 = torch.zeros((1, H, W, 1)) # 1 H W 1 idx=1 + h_slices_0 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + w_slices_0 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + + h_slices_1 = ( + slice(0, -self.split_size[1]), + slice(-self.split_size[1], -self.shift_size[1]), + slice(-self.shift_size[1], None), + ) + w_slices_1 = ( + slice(0, -self.split_size[0]), + slice(-self.split_size[0], -self.shift_size[0]), + slice(-self.shift_size[0], None), + ) + cnt = 0 + for h in h_slices_0: + for w in w_slices_0: + img_mask_0[:, h, w, :] = cnt + cnt += 1 + cnt = 0 + for h in h_slices_1: + for w in w_slices_1: + img_mask_1[:, h, w, :] = cnt + cnt += 1 + + # calculate mask for window-0 + img_mask_0 = img_mask_0.view( + 1, + H // self.split_size[0], + self.split_size[0], + W // self.split_size[1], + self.split_size[1], + 1, + ) + img_mask_0 = ( + img_mask_0.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[0], self.split_size[1], 1) + ) # nW, sw[0], sw[1], 1 + mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1]) + attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2) + attn_mask_0 = attn_mask_0.masked_fill( + attn_mask_0 != 0, float(-100.0) + ).masked_fill(attn_mask_0 == 0, float(0.0)) + + # calculate mask for window-1 + img_mask_1 = img_mask_1.view( + 1, + H // self.split_size[1], + self.split_size[1], + W // self.split_size[0], + self.split_size[0], + 1, + ) + img_mask_1 = ( + img_mask_1.permute(0, 1, 3, 2, 4, 5) + .contiguous() + .view(-1, self.split_size[1], self.split_size[0], 1) + ) # nW, sw[1], sw[0], 1 + mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0]) + attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2) + attn_mask_1 = attn_mask_1.masked_fill( + attn_mask_1 != 0, float(-100.0) + ).masked_fill(attn_mask_1 == 0, float(0.0)) + + return attn_mask_0, attn_mask_1 + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, L, C = x.shape + assert L == H * W, "flatten img_tokens has wrong size" + + qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3) # 3, B, HW, C + # V without partition + v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W) + + # image padding + max_split_size = max(self.split_size[0], self.split_size[1]) + pad_l = pad_t = 0 + pad_r = (max_split_size - W % max_split_size) % max_split_size + pad_b = (max_split_size - H % max_split_size) % max_split_size + + qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2) # 3B C H W + qkv = ( + F.pad(qkv, (pad_l, pad_r, pad_t, pad_b)) + .reshape(3, B, C, -1) + .transpose(-2, -1) + ) # l r t b + _H = pad_b + H + _W = pad_r + W + _L = _H * _W + + # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged + # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ... + if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or ( + self.rg_idx % 2 != 0 and self.b_idx % 4 == 0 + ): + qkv = qkv.view(3, B, _H, _W, C) + qkv_0 = torch.roll( + qkv[:, :, :, :, : C // 2], + shifts=(-self.shift_size[0], -self.shift_size[1]), + dims=(2, 3), + ) + qkv_0 = qkv_0.view(3, B, _L, C // 2) + qkv_1 = torch.roll( + qkv[:, :, :, :, C // 2 :], + shifts=(-self.shift_size[1], -self.shift_size[0]), + dims=(2, 3), + ) + qkv_1 = qkv_1.view(3, B, _L, C // 2) + + if self.patches_resolution != _H or self.patches_resolution != _W: + mask_tmp = self.calculate_mask(_H, _W) + x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device)) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device)) + else: + x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0) + x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1) + + x1 = torch.roll( + x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2) + ) + x2 = torch.roll( + x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2) + ) + x1 = x1[:, :H, :W, :].reshape(B, L, C // 2) + x2 = x2[:, :H, :W, :].reshape(B, L, C // 2) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + else: + x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape( + B, L, C // 2 + ) + # attention output + attened_x = torch.cat([x1, x2], dim=2) + + # convolution output + conv_x = self.dwconv(v) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + channel_map = ( + self.channel_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, 1, C) + ) + # S-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + spatial_map = self.spatial_interaction(attention_reshape) + + # C-I + attened_x = attened_x * torch.sigmoid(channel_map) + # S-I + conv_x = torch.sigmoid(spatial_map) * conv_x + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class Adaptive_Channel_Attention(nn.Module): + # The implementation builds on XCiT code https://github.com/facebookresearch/xcit + """Adaptive Channel Self-Attention + Args: + dim (int): Number of input channels. + num_heads (int): Number of attention heads. Default: 6 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. + attn_drop (float): Attention dropout rate. Default: 0.0 + drop_path (float): Stochastic depth rate. Default: 0.0 + """ + + def __init__( + self, + dim, + num_heads=8, + qkv_bias=False, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.num_heads = num_heads + self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1)) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + self.dwconv = nn.Sequential( + nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim), + nn.BatchNorm2d(dim), + nn.GELU(), + ) + self.channel_interaction = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(dim, dim // 8, kernel_size=1), + nn.BatchNorm2d(dim // 8), + nn.GELU(), + nn.Conv2d(dim // 8, dim, kernel_size=1), + ) + self.spatial_interaction = nn.Sequential( + nn.Conv2d(dim, dim // 16, kernel_size=1), + nn.BatchNorm2d(dim // 16), + nn.GELU(), + nn.Conv2d(dim // 16, 1, kernel_size=1), + ) + + def forward(self, x, H, W): + """ + Input: x: (B, H*W, C), H, W + Output: x: (B, H*W, C) + """ + B, N, C = x.shape + qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads) + qkv = qkv.permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] + + q = q.transpose(-2, -1) + k = k.transpose(-2, -1) + v = v.transpose(-2, -1) + + v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W) + + q = torch.nn.functional.normalize(q, dim=-1) + k = torch.nn.functional.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + # attention output + attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C) + + # convolution output + conv_x = self.dwconv(v_) + + # Adaptive Interaction Module (AIM) + # C-Map (before sigmoid) + attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W) + channel_map = self.channel_interaction(attention_reshape) + # S-Map (before sigmoid) + spatial_map = ( + self.spatial_interaction(conv_x) + .permute(0, 2, 3, 1) + .contiguous() + .view(B, N, 1) + ) + + # S-I + attened_x = attened_x * torch.sigmoid(spatial_map) + # C-I + conv_x = conv_x * torch.sigmoid(channel_map) + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C) + + x = attened_x + conv_x + + x = self.proj(x) + x = self.proj_drop(x) + + return x + + +class DATB(nn.Module): + def __init__( + self, + dim, + num_heads, + reso=64, + split_size=[2, 4], + shift_size=[1, 2], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + rg_idx=0, + b_idx=0, + ): + super().__init__() + + self.norm1 = norm_layer(dim) + + if b_idx % 2 == 0: + # DSTB + self.attn = Adaptive_Spatial_Attention( + dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=shift_size, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + rg_idx=rg_idx, + b_idx=b_idx, + ) + else: + # DCTB + self.attn = Adaptive_Channel_Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + + ffn_hidden_dim = int(dim * expansion_factor) + self.ffn = SGFN( + in_features=dim, + hidden_features=ffn_hidden_dim, + out_features=dim, + act_layer=act_layer, + ) + self.norm2 = norm_layer(dim) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + x = x + self.drop_path(self.attn(self.norm1(x), H, W)) + x = x + self.drop_path(self.ffn(self.norm2(x), H, W)) + + return x + + +class ResidualGroup(nn.Module): + """ResidualGroup + Args: + dim (int): Number of input channels. + reso (int): Input resolution. + num_heads (int): Number of attention heads. + split_size (tuple(int)): Height and Width of spatial window. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop (float): Dropout rate. Default: 0 + attn_drop(float): Attention dropout rate. Default: 0 + drop_paths (float | None): Stochastic depth rate. + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + depth (int): Number of dual aggregation Transformer blocks in residual group. + use_chk (bool): Whether to use checkpointing to save memory. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + dim, + reso, + num_heads, + split_size=[2, 4], + expansion_factor=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_paths=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + depth=2, + use_chk=False, + resi_connection="1conv", + rg_idx=0, + ): + super().__init__() + self.use_chk = use_chk + self.reso = reso + + self.blocks = nn.ModuleList( + [ + DATB( + dim=dim, + num_heads=num_heads, + reso=reso, + split_size=split_size, + shift_size=[split_size[0] // 2, split_size[1] // 2], + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_paths[i], + act_layer=act_layer, + norm_layer=norm_layer, + rg_idx=rg_idx, + b_idx=i, + ) + for i in range(depth) + ] + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + def forward(self, x, x_size): + """ + Input: x: (B, H*W, C), x_size: (H, W) + Output: x: (B, H*W, C) + """ + H, W = x_size + res = x + for blk in self.blocks: + if self.use_chk: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + x = self.conv(x) + x = rearrange(x, "b c h w -> b (h w) c") + x = res + x + + return x + + +class Upsample(nn.Sequential): + """Upsample module. + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + h, w = self.input_resolution + flops = h * w * self.num_feat * 3 * 9 + return flops + + +class DAT(nn.Module): + """Dual Aggregation Transformer + Args: + img_size (int): Input image size. Default: 64 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 180 + depths (tuple(int)): Depth of each residual group (number of DATB in each RG). + split_size (tuple(int)): Height and Width of spatial window. + num_heads (tuple(int)): Number of attention heads in different residual groups. + expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + act_layer (nn.Module): Activation layer. Default: nn.GELU + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm + use_chk (bool): Whether to use checkpointing to save memory. + upscale: Upscale factor. 2/3/4 for image SR + img_range: Image range. 1. or 255. + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__(self, state_dict): + super().__init__() + + # defaults + img_size = 64 + in_chans = 3 + embed_dim = 180 + split_size = [2, 4] + depth = [2, 2, 2, 2] + num_heads = [2, 2, 2, 2] + expansion_factor = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + act_layer = nn.GELU + norm_layer = nn.LayerNorm + use_chk = False + upscale = 2 + img_range = 1.0 + resi_connection = "1conv" + upsampler = "pixelshuffle" + + self.model_arch = "DAT" + self.sub_type = "SR" + self.state = state_dict + + state_keys = state_dict.keys() + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + state_dict.get("conv_before_upsample.0.weight", None).shape[1] + if state_dict.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = state_dict["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = state_dict["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = state_dict[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depth = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if "layers.0.blocks.1.attn.temperature" in state_keys: + num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depth + + embed_dim = state_dict["conv_first.weight"].shape[0] + expansion_factor = float( + state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + if "layers.0.blocks.2.attn.attn_mask_0" in state_keys: + attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[ + "layers.0.blocks.2.attn.attn_mask_0" + ].shape + + img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y)) + + if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys: + split_sizes = ( + state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1 + ) + split_size = [int(x) for x in split_sizes] + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depth = depth + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.expansion_factor = expansion_factor + self.resi_connection = resi_connection + self.split_size = split_size + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # ------------------------- 1, Shallow Feature Extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, Deep Feature Extraction ------------------------- # + self.num_layers = len(depth) + self.use_chk = use_chk + self.num_features = ( + self.embed_dim + ) = embed_dim # num_features for consistency with other models + heads = num_heads + + self.before_RG = nn.Sequential( + Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim) + ) + + curr_dim = embed_dim + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth)) + ] # stochastic depth decay rule + + self.layers = nn.ModuleList() + for i in range(self.num_layers): + layer = ResidualGroup( + dim=embed_dim, + num_heads=heads[i], + reso=img_size, + split_size=split_size, + expansion_factor=expansion_factor, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])], + act_layer=act_layer, + norm_layer=norm_layer, + depth=depth[i], + use_chk=use_chk, + resi_connection=resi_connection, + rg_idx=i, + ) + self.layers.append(layer) + + self.norm = norm_layer(curr_dim) + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + # ------------------------- 3, Reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, embed_dim, num_out_ch, (img_size, img_size) + ) + + self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance( + m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d) + ): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def forward_features(self, x): + _, _, H, W = x.shape + x_size = [H, W] + x = self.before_RG(x) + for layer in self.layers: + x = layer(x, x_size) + x = self.norm(x) + x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W) + + return x + + def forward(self, x): + """ + Input: x: (B, C, H, W) + """ + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for image SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + + x = x / self.img_range + self.mean + return x diff --git a/ldm_patched/pfn/architecture/HAT.py b/ldm_patched/pfn/architecture/HAT.py new file mode 100644 index 0000000000000000000000000000000000000000..7e12ad0fd47a223c4b2a902d296ebeff3403cffb --- /dev/null +++ b/ldm_patched/pfn/architecture/HAT.py @@ -0,0 +1,1277 @@ +# pylint: skip-file +# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py +import math +import re + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange + +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + From: https://github.com/huggingface/pytorch-image-models/blob/main/timm/layers/drop.py + """ + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) # type: ignore + + +class ChannelAttention(nn.Module): + """Channel attention used in RCAN. + Args: + num_feat (int): Channel number of intermediate features. + squeeze_factor (int): Channel squeeze factor. Default: 16. + """ + + def __init__(self, num_feat, squeeze_factor=16): + super(ChannelAttention, self).__init__() + self.attention = nn.Sequential( + nn.AdaptiveAvgPool2d(1), + nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0), + nn.ReLU(inplace=True), + nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0), + nn.Sigmoid(), + ) + + def forward(self, x): + y = self.attention(x) + return x * y + + +class CAB(nn.Module): + def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30): + super(CAB, self).__init__() + + self.cab = nn.Sequential( + nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1), + nn.GELU(), + nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1), + ChannelAttention(num_feat, squeeze_factor), + ) + + def forward(self, x): + return self.cab(x) + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (b, h, w, c) + window_size (int): window size + Returns: + windows: (num_windows*b, window_size, window_size, c) + """ + b, h, w, c = x.shape + x = x.view(b, h // window_size, window_size, w // window_size, window_size, c) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c) + ) + return windows + + +def window_reverse(windows, window_size, h, w): + """ + Args: + windows: (num_windows*b, window_size, window_size, c) + window_size (int): Window size + h (int): Height of image + w (int): Width of image + Returns: + x: (b, h, w, c) + """ + b = int(windows.shape[0] / (h * w / window_size / window_size)) + x = windows.view( + b, h // window_size, w // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, rpi, mask=None): + """ + Args: + x: input features with shape of (num_windows*b, n, c) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + b_, n, c = x.shape + qkv = ( + self.qkv(x) + .reshape(b_, n, 3, self.num_heads, c // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nw = mask.shape[0] + attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, n, n) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(b_, n, c) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class HAB(nn.Module): + r"""Hybrid Attention Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + compress_ratio=3, + squeeze_factor=30, + conv_scale=0.01, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.conv_scale = conv_scale + self.conv_block = CAB( + num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + def forward(self, x, x_size, rpi_sa, attn_mask): + h, w = x_size + b, _, c = x.shape + # assert seq_len == h * w, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(b, h, w, c) + + # Conv_X + conv_x = self.conv_block(x.permute(0, 3, 1, 2)) + conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + attn_mask = attn_mask + else: + shifted_x = x + attn_mask = None + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nw*b, window_size, window_size, c + x_windows = x_windows.view( + -1, self.window_size * self.window_size, c + ) # nw*b, window_size*window_size, c + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c) + shifted_x = window_reverse(attn_windows, self.window_size, h, w) # b h' w' c + + # reverse cyclic shift + if self.shift_size > 0: + attn_x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + attn_x = shifted_x + attn_x = attn_x.view(b, h * w, c) + + # FFN + x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: b, h*w, c + """ + h, w = self.input_resolution + b, seq_len, c = x.shape + assert seq_len == h * w, "input feature has wrong size" + assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even." + + x = x.view(b, h, w, c) + + x0 = x[:, 0::2, 0::2, :] # b h/2 w/2 c + x1 = x[:, 1::2, 0::2, :] # b h/2 w/2 c + x2 = x[:, 0::2, 1::2, :] # b h/2 w/2 c + x3 = x[:, 1::2, 1::2, :] # b h/2 w/2 c + x = torch.cat([x0, x1, x2, x3], -1) # b h/2 w/2 4*c + x = x.view(b, -1, 4 * c) # b h/2*w/2 4*c + + x = self.norm(x) + x = self.reduction(x) + + return x + + +class OCAB(nn.Module): + # overlapping cross-attention block + + def __init__( + self, + dim, + input_resolution, + window_size, + overlap_ratio, + num_heads, + qkv_bias=True, + qk_scale=None, + mlp_ratio=2, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.window_size = window_size + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + self.overlap_win_size = int(window_size * overlap_ratio) + window_size + + self.norm1 = norm_layer(dim) + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.unfold = nn.Unfold( + kernel_size=(self.overlap_win_size, self.overlap_win_size), + stride=window_size, + padding=(self.overlap_win_size - window_size) // 2, + ) + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros( + (window_size + self.overlap_win_size - 1) + * (window_size + self.overlap_win_size - 1), + num_heads, + ) + ) # 2*Wh-1 * 2*Ww-1, nH + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + self.proj = nn.Linear(dim, dim) + + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU + ) + + def forward(self, x, x_size, rpi): + h, w = x_size + b, _, c = x.shape + + shortcut = x + x = self.norm1(x) + x = x.view(b, h, w, c) + + qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2) # 3, b, c, h, w + q = qkv[0].permute(0, 2, 3, 1) # b, h, w, c + kv = torch.cat((qkv[1], qkv[2]), dim=1) # b, 2*c, h, w + + # partition windows + q_windows = window_partition( + q, self.window_size + ) # nw*b, window_size, window_size, c + q_windows = q_windows.view( + -1, self.window_size * self.window_size, c + ) # nw*b, window_size*window_size, c + + kv_windows = self.unfold(kv) # b, c*w*w, nw + kv_windows = rearrange( + kv_windows, + "b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch", + nc=2, + ch=c, + owh=self.overlap_win_size, + oww=self.overlap_win_size, + ).contiguous() # 2, nw*b, ow*ow, c + # Do the above rearrangement without the rearrange function + # kv_windows = kv_windows.view( + # 2, b, self.overlap_win_size, self.overlap_win_size, c, -1 + # ) + # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous() + # kv_windows = kv_windows.view( + # 2, -1, self.overlap_win_size * self.overlap_win_size, c + # ) + + k_windows, v_windows = kv_windows[0], kv_windows[1] # nw*b, ow*ow, c + + b_, nq, _ = q_windows.shape + _, n, _ = k_windows.shape + d = self.dim // self.num_heads + q = q_windows.reshape(b_, nq, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, nq, d + k = k_windows.reshape(b_, n, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, n, d + v = v_windows.reshape(b_, n, self.num_heads, d).permute( + 0, 2, 1, 3 + ) # nw*b, nH, n, d + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view( + self.window_size * self.window_size, + self.overlap_win_size * self.overlap_win_size, + -1, + ) # ws*ws, wse*wse, nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, ws*ws, wse*wse + attn = attn + relative_position_bias.unsqueeze(0) + + attn = self.softmax(attn) + attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim) + + # merge windows + attn_windows = attn_windows.view( + -1, self.window_size, self.window_size, self.dim + ) + x = window_reverse(attn_windows, self.window_size, h, w) # b h w c + x = x.view(b, h * w, self.dim) + + x = self.proj(x) + shortcut + + x = x + self.mlp(self.norm2(x)) + return x + + +class AttenBlocks(nn.Module): + """A series of attention blocks for one RHAG. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + compress_ratio, + squeeze_factor, + conv_scale, + overlap_ratio, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + HAB( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # OCAB + self.overlap_attn = OCAB( + dim=dim, + input_resolution=input_resolution, + window_size=window_size, + overlap_ratio=overlap_ratio, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + mlp_ratio=mlp_ratio, # type: ignore + norm_layer=norm_layer, + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size, params): + for blk in self.blocks: + x = blk(x, x_size, params["rpi_sa"], params["attn_mask"]) + + x = self.overlap_attn(x, x_size, params["rpi_oca"]) + + if self.downsample is not None: + x = self.downsample(x) + return x + + +class RHAG(nn.Module): + """Residual Hybrid Attention Group (RHAG). + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + compress_ratio, + squeeze_factor, + conv_scale, + overlap_ratio, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RHAG, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = AttenBlocks( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + overlap_ratio=overlap_ratio, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "identity": + self.conv = nn.Identity() + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size, params): + return ( + self.patch_embed( + self.conv( + self.patch_unembed(self.residual_group(x, x_size, params), x_size) + ) + ) + + x + ) + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # b Ph*Pw c + if self.norm is not None: + x = self.norm(x) + return x + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + x = ( + x.transpose(1, 2) + .contiguous() + .view(x.shape[0], self.embed_dim, x_size[0], x_size[1]) + ) # b Ph*Pw c + return x + + +class Upsample(nn.Sequential): + """Upsample module. + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class HAT(nn.Module): + r"""Hybrid Attention Transformer + A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`. + Some codes are based on SwinIR. + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(HAT, self).__init__() + + # Defaults + img_size = 64 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = (6, 6, 6, 6) + num_heads = (6, 6, 6, 6) + window_size = 7 + compress_ratio = 3 + squeeze_factor = 30 + conv_scale = 0.01 + overlap_ratio = 0.5 + mlp_ratio = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + + self.state = state_dict + self.model_arch = "HAT" + self.sub_type = "SR" + self.supports_fp16 = False + self.support_bf16 = True + self.min_size_restriction = 16 + + state_keys = list(state_dict.keys()) + + num_feat = state_dict["conv_last.weight"].shape[1] + in_chans = state_dict["conv_first.weight"].shape[1] + num_out_ch = state_dict["conv_last.weight"].shape[0] + embed_dim = state_dict["conv_first.weight"].shape[0] + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0])) + + # Not sure if this is needed or used at all anywhere in HAT's config + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + self.window_size = window_size + self.shift_size = window_size // 2 + self.overlap_ratio = overlap_ratio + + self.in_nc = in_chans + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + num_in_ch = in_chans + # num_out_ch = in_chans + # num_feat = 64 + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + + # relative position index + relative_position_index_SA = self.calculate_rpi_sa() + relative_position_index_OCA = self.calculate_rpi_oca() + self.register_buffer("relative_position_index_SA", relative_position_index_SA) + self.register_buffer("relative_position_index_OCA", relative_position_index_OCA) + + # ------------------------- 1, shallow feature extraction ------------------------- # + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + # ------------------------- 2, deep feature extraction ------------------------- # + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter( # type: ignore[arg-type] + torch.zeros(1, num_patches, embed_dim) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Hybrid Attention Groups (RHAG) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RHAG( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + compress_ratio=compress_ratio, + squeeze_factor=squeeze_factor, + conv_scale=conv_scale, + overlap_ratio=overlap_ratio, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[ + sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore + ], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "identity": + self.conv_after_body = nn.Identity() + + # ------------------------- 3, high quality image reconstruction ------------------------- # + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + self.load_state_dict(self.state, strict=False) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def calculate_rpi_sa(self): + # calculate relative position index for SA + coords_h = torch.arange(self.window_size) + coords_w = torch.arange(self.window_size) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size - 1 + relative_coords[:, :, 0] *= 2 * self.window_size - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + return relative_position_index + + def calculate_rpi_oca(self): + # calculate relative position index for OCA + window_size_ori = self.window_size + window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size) + + coords_h = torch.arange(window_size_ori) + coords_w = torch.arange(window_size_ori) + coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, ws, ws + coords_ori_flatten = torch.flatten(coords_ori, 1) # 2, ws*ws + + coords_h = torch.arange(window_size_ext) + coords_w = torch.arange(window_size_ext) + coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, wse, wse + coords_ext_flatten = torch.flatten(coords_ext, 1) # 2, wse*wse + + relative_coords = ( + coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None] + ) # 2, ws*ws, wse*wse + + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # ws*ws, wse*wse, 2 + relative_coords[:, :, 0] += ( + window_size_ori - window_size_ext + 1 + ) # shift to start from 0 + relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1 + + relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1 + relative_position_index = relative_coords.sum(-1) + return relative_position_index + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + h, w = x_size + img_mask = torch.zeros((1, h, w, 1)) # 1 h w 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nw, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + + # Calculate attention mask and relative position index in advance to speed up inference. + # The original code is very time-cosuming for large window size. + attn_mask = self.calculate_mask(x_size).to(x.device) + params = { + "attn_mask": attn_mask, + "rpi_sa": self.relative_position_index_SA, + "rpi_oca": self.relative_position_index_OCA, + } + + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size, params) + + x = self.norm(x) # b seq_len c + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + x = self.check_image_size(x) + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + + x = x / self.img_range + self.mean + + return x[:, :, : H * self.upscale, : W * self.upscale] diff --git a/ldm_patched/pfn/architecture/LICENSE-DAT b/ldm_patched/pfn/architecture/LICENSE-DAT new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-DAT @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ldm_patched/pfn/architecture/LICENSE-HAT b/ldm_patched/pfn/architecture/LICENSE-HAT new file mode 100644 index 0000000000000000000000000000000000000000..003e97e96cbed07d07b5ff15831711181607edb3 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-HAT @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Xiangyu Chen + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2022 Kai Zhang (cskaizhang@gmail.com, https://cszn.github.io/). All rights reserved. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ldm_patched/pfn/architecture/LICENSE-SPSR b/ldm_patched/pfn/architecture/LICENSE-SPSR new file mode 100644 index 0000000000000000000000000000000000000000..3245f3f9e4f476ee3a283f41dd0d9db65544c222 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-SPSR @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. 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Should any part of the License for any +reason be judged legally invalid or ineffective under applicable law, such +partial invalidity or ineffectiveness shall not invalidate the remainder +of the License, and in such case Affirmer hereby affirms that he or she +will not (i) exercise any of his or her remaining Copyright and Related +Rights in the Work or (ii) assert any associated claims and causes of +action with respect to the Work, in either case contrary to Affirmer's +express Statement of Purpose. + +4. Limitations and Disclaimers. + + a. No trademark or patent rights held by Affirmer are waived, abandoned, + surrendered, licensed or otherwise affected by this document. + b. Affirmer offers the Work as-is and makes no representations or + warranties of any kind concerning the Work, express, implied, + statutory or otherwise, including without limitation warranties of + title, merchantability, fitness for a particular purpose, non + infringement, or the absence of latent or other defects, accuracy, or + the present or absence of errors, whether or not discoverable, all to + the greatest extent permissible under applicable law. + c. Affirmer disclaims responsibility for clearing rights of other persons + that may apply to the Work or any use thereof, including without + limitation any person's Copyright and Related Rights in the Work. + Further, Affirmer disclaims responsibility for obtaining any necessary + consents, permissions or other rights required for any use of the + Work. + d. Affirmer understands and acknowledges that Creative Commons is not a + party to this document and has no duty or obligation with respect to + this CC0 or use of the Work. diff --git a/ldm_patched/pfn/architecture/LICENSE-Swin2SR b/ldm_patched/pfn/architecture/LICENSE-Swin2SR new file mode 100644 index 0000000000000000000000000000000000000000..e5e4ee061a3f3fbad64bc837425716af7fb108f5 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-Swin2SR @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] [SwinIR Authors] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ldm_patched/pfn/architecture/LICENSE-SwinIR b/ldm_patched/pfn/architecture/LICENSE-SwinIR new file mode 100644 index 0000000000000000000000000000000000000000..e5e4ee061a3f3fbad64bc837425716af7fb108f5 --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-SwinIR @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] [SwinIR Authors] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ldm_patched/pfn/architecture/LICENSE-lama b/ldm_patched/pfn/architecture/LICENSE-lama new file mode 100644 index 0000000000000000000000000000000000000000..ca822bb5f62a37a5a73f56a2d563b16dab46c03f --- /dev/null +++ b/ldm_patched/pfn/architecture/LICENSE-lama @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. For the purposes of this definition, + "control" means (i) the power, direct or indirect, to cause the + direction or management of such entity, whether by contract or + otherwise, or (ii) ownership of fifty percent (50%) or more of the + outstanding shares, or (iii) beneficial ownership of such entity. + + "You" (or "Your") shall mean an individual or Legal Entity + exercising permissions granted by this License. + + "Source" form shall mean the preferred form for making modifications, + including but not limited to software source code, documentation + source, and configuration files. + + "Object" form shall mean any form resulting from mechanical + transformation or translation of a Source form, including but + not limited to compiled object code, generated documentation, + and conversions to other media types. + + "Work" shall mean the work of authorship, whether in Source or + Object form, made available under the License, as indicated by a + copyright notice that is included in or attached to the work + (an example is provided in the Appendix below). + + "Derivative Works" shall mean any work, whether in Source or Object + form, that is based on (or derived from) the Work and for which the + editorial revisions, annotations, elaborations, or other modifications + represent, as a whole, an original work of authorship. For the purposes + of this License, Derivative Works shall not include works that remain + separable from, or merely link (or bind by name) to the interfaces of, + the Work and Derivative Works thereof. + + "Contribution" shall mean any work of authorship, including + the original version of the Work and any modifications or additions + to that Work or Derivative Works thereof, that is intentionally + submitted to Licensor for inclusion in the Work by the copyright owner + or by an individual or Legal Entity authorized to submit on behalf of + the copyright owner. For the purposes of this definition, "submitted" + means any form of electronic, verbal, or written communication sent + to the Licensor or its representatives, including but not limited to + communication on electronic mailing lists, source code control systems, + and issue tracking systems that are managed by, or on behalf of, the + Licensor for the purpose of discussing and improving the Work, but + excluding communication that is conspicuously marked or otherwise + designated in writing by the copyright owner as "Not a Contribution." + + "Contributor" shall mean Licensor and any individual or Legal Entity + on behalf of whom a Contribution has been received by Licensor and + subsequently incorporated within the Work. + + 2. Grant of Copyright License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + copyright license to reproduce, prepare Derivative Works of, + publicly display, publicly perform, sublicense, and distribute the + Work and such Derivative Works in Source or Object form. + + 3. Grant of Patent License. Subject to the terms and conditions of + this License, each Contributor hereby grants to You a perpetual, + worldwide, non-exclusive, no-charge, royalty-free, irrevocable + (except as stated in this section) patent license to make, have made, + use, offer to sell, sell, import, and otherwise transfer the Work, + where such license applies only to those patent claims licensable + by such Contributor that are necessarily infringed by their + Contribution(s) alone or by combination of their Contribution(s) + with the Work to which such Contribution(s) was submitted. If You + institute patent litigation against any entity (including a + cross-claim or counterclaim in a lawsuit) alleging that the Work + or a Contribution incorporated within the Work constitutes direct + or contributory patent infringement, then any patent licenses + granted to You under this License for that Work shall terminate + as of the date such litigation is filed. + + 4. Redistribution. You may reproduce and distribute copies of the + Work or Derivative Works thereof in any medium, with or without + modifications, and in Source or Object form, provided that You + meet the following conditions: + + (a) You must give any other recipients of the Work or + Derivative Works a copy of this License; and + + (b) You must cause any modified files to carry prominent notices + stating that You changed the files; and + + (c) You must retain, in the Source form of any Derivative Works + that You distribute, all copyright, patent, trademark, and + attribution notices from the Source form of the Work, + excluding those notices that do not pertain to any part of + the Derivative Works; and + + (d) If the Work includes a "NOTICE" text file as part of its + distribution, then any Derivative Works that You distribute must + include a readable copy of the attribution notices contained + within such NOTICE file, excluding those notices that do not + pertain to any part of the Derivative Works, in at least one + of the following places: within a NOTICE text file distributed + as part of the Derivative Works; within the Source form or + documentation, if provided along with the Derivative Works; or, + within a display generated by the Derivative Works, if and + wherever such third-party notices normally appear. The contents + of the NOTICE file are for informational purposes only and + do not modify the License. You may add Your own attribution + notices within Derivative Works that You distribute, alongside + or as an addendum to the NOTICE text from the Work, provided + that such additional attribution notices cannot be construed + as modifying the License. + + You may add Your own copyright statement to Your modifications and + may provide additional or different license terms and conditions + for use, reproduction, or distribution of Your modifications, or + for any such Derivative Works as a whole, provided Your use, + reproduction, and distribution of the Work otherwise complies with + the conditions stated in this License. + + 5. Submission of Contributions. Unless You explicitly state otherwise, + any Contribution intentionally submitted for inclusion in the Work + by You to the Licensor shall be under the terms and conditions of + this License, without any additional terms or conditions. + Notwithstanding the above, nothing herein shall supersede or modify + the terms of any separate license agreement you may have executed + with Licensor regarding such Contributions. + + 6. Trademarks. This License does not grant permission to use the trade + names, trademarks, service marks, or product names of the Licensor, + except as required for reasonable and customary use in describing the + origin of the Work and reproducing the content of the NOTICE file. + + 7. Disclaimer of Warranty. Unless required by applicable law or + agreed to in writing, Licensor provides the Work (and each + Contributor provides its Contributions) on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or + implied, including, without limitation, any warranties or conditions + of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A + PARTICULAR PURPOSE. You are solely responsible for determining the + appropriateness of using or redistributing the Work and assume any + risks associated with Your exercise of permissions under this License. + + 8. Limitation of Liability. In no event and under no legal theory, + whether in tort (including negligence), contract, or otherwise, + unless required by applicable law (such as deliberate and grossly + negligent acts) or agreed to in writing, shall any Contributor be + liable to You for damages, including any direct, indirect, special, + incidental, or consequential damages of any character arising as a + result of this License or out of the use or inability to use the + Work (including but not limited to damages for loss of goodwill, + work stoppage, computer failure or malfunction, or any and all + other commercial damages or losses), even if such Contributor + has been advised of the possibility of such damages. + + 9. Accepting Warranty or Additional Liability. While redistributing + the Work or Derivative Works thereof, You may choose to offer, + and charge a fee for, acceptance of support, warranty, indemnity, + or other liability obligations and/or rights consistent with this + License. However, in accepting such obligations, You may act only + on Your own behalf and on Your sole responsibility, not on behalf + of any other Contributor, and only if You agree to indemnify, + defend, and hold each Contributor harmless for any liability + incurred by, or claims asserted against, such Contributor by reason + of your accepting any such warranty or additional liability. + + END OF TERMS AND CONDITIONS + + APPENDIX: How to apply the Apache License to your work. + + To apply the Apache License to your work, attach the following + boilerplate notice, with the fields enclosed by brackets "[]" + replaced with your own identifying information. (Don't include + the brackets!) The text should be enclosed in the appropriate + comment syntax for the file format. We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] Samsung Research + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/ldm_patched/pfn/architecture/LaMa.py b/ldm_patched/pfn/architecture/LaMa.py new file mode 100644 index 0000000000000000000000000000000000000000..a781f3e4dda789c06493fcf35a9803ee61efce73 --- /dev/null +++ b/ldm_patched/pfn/architecture/LaMa.py @@ -0,0 +1,694 @@ +# pylint: skip-file +""" +Model adapted from advimman's lama project: https://github.com/advimman/lama +""" + +# Fast Fourier Convolution NeurIPS 2020 +# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py +# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf + +from typing import List + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torchvision.transforms.functional import InterpolationMode, rotate + + +class LearnableSpatialTransformWrapper(nn.Module): + def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True): + super().__init__() + self.impl = impl + self.angle = torch.rand(1) * angle_init_range + if train_angle: + self.angle = nn.Parameter(self.angle, requires_grad=True) + self.pad_coef = pad_coef + + def forward(self, x): + if torch.is_tensor(x): + return self.inverse_transform(self.impl(self.transform(x)), x) + elif isinstance(x, tuple): + x_trans = tuple(self.transform(elem) for elem in x) + y_trans = self.impl(x_trans) + return tuple( + self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x) + ) + else: + raise ValueError(f"Unexpected input type {type(x)}") + + def transform(self, x): + height, width = x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode="reflect") + x_padded_rotated = rotate( + x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0 + ) + + return x_padded_rotated + + def inverse_transform(self, y_padded_rotated, orig_x): + height, width = orig_x.shape[2:] + pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef) + + y_padded = rotate( + y_padded_rotated, + -self.angle.to(y_padded_rotated), + InterpolationMode.BILINEAR, + fill=0, + ) + y_height, y_width = y_padded.shape[2:] + y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w] + return y + + +class SELayer(nn.Module): + def __init__(self, channel, reduction=16): + super(SELayer, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction, bias=False), + nn.ReLU(inplace=True), + nn.Linear(channel // reduction, channel, bias=False), + nn.Sigmoid(), + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + res = x * y.expand_as(x) + return res + + +class FourierUnit(nn.Module): + def __init__( + self, + in_channels, + out_channels, + groups=1, + spatial_scale_factor=None, + spatial_scale_mode="bilinear", + spectral_pos_encoding=False, + use_se=False, + se_kwargs=None, + ffc3d=False, + fft_norm="ortho", + ): + # bn_layer not used + super(FourierUnit, self).__init__() + self.groups = groups + + self.conv_layer = torch.nn.Conv2d( + in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0), + out_channels=out_channels * 2, + kernel_size=1, + stride=1, + padding=0, + groups=self.groups, + bias=False, + ) + self.bn = torch.nn.BatchNorm2d(out_channels * 2) + self.relu = torch.nn.ReLU(inplace=True) + + # squeeze and excitation block + self.use_se = use_se + if use_se: + if se_kwargs is None: + se_kwargs = {} + self.se = SELayer(self.conv_layer.in_channels, **se_kwargs) + + self.spatial_scale_factor = spatial_scale_factor + self.spatial_scale_mode = spatial_scale_mode + self.spectral_pos_encoding = spectral_pos_encoding + self.ffc3d = ffc3d + self.fft_norm = fft_norm + + def forward(self, x): + half_check = False + if x.type() == "torch.cuda.HalfTensor": + # half only works on gpu anyway + half_check = True + + batch = x.shape[0] + + if self.spatial_scale_factor is not None: + orig_size = x.shape[-2:] + x = F.interpolate( + x, + scale_factor=self.spatial_scale_factor, + mode=self.spatial_scale_mode, + align_corners=False, + ) + + # (batch, c, h, w/2+1, 2) + fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1) + if half_check == True: + ffted = torch.fft.rfftn( + x.float(), dim=fft_dim, norm=self.fft_norm + ) # .type(torch.cuda.HalfTensor) + else: + ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm) + + ffted = torch.stack((ffted.real, ffted.imag), dim=-1) + ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1) + ffted = ffted.view( + ( + batch, + -1, + ) + + ffted.size()[3:] + ) + + if self.spectral_pos_encoding: + height, width = ffted.shape[-2:] + coords_vert = ( + torch.linspace(0, 1, height)[None, None, :, None] + .expand(batch, 1, height, width) + .to(ffted) + ) + coords_hor = ( + torch.linspace(0, 1, width)[None, None, None, :] + .expand(batch, 1, height, width) + .to(ffted) + ) + ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1) + + if self.use_se: + ffted = self.se(ffted) + + if half_check == True: + ffted = self.conv_layer(ffted.half()) # (batch, c*2, h, w/2+1) + else: + ffted = self.conv_layer( + ffted + ) # .type(torch.cuda.FloatTensor) # (batch, c*2, h, w/2+1) + + ffted = self.relu(self.bn(ffted)) + # forcing to be always float + ffted = ffted.float() + + ffted = ( + ffted.view( + ( + batch, + -1, + 2, + ) + + ffted.size()[2:] + ) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) # (batch,c, t, h, w/2+1, 2) + + ffted = torch.complex(ffted[..., 0], ffted[..., 1]) + + ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:] + output = torch.fft.irfftn( + ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm + ) + + if half_check == True: + output = output.half() + + if self.spatial_scale_factor is not None: + output = F.interpolate( + output, + size=orig_size, + mode=self.spatial_scale_mode, + align_corners=False, + ) + + return output + + +class SpectralTransform(nn.Module): + def __init__( + self, + in_channels, + out_channels, + stride=1, + groups=1, + enable_lfu=True, + separable_fu=False, + **fu_kwargs, + ): + # bn_layer not used + super(SpectralTransform, self).__init__() + self.enable_lfu = enable_lfu + if stride == 2: + self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2) + else: + self.downsample = nn.Identity() + + self.stride = stride + self.conv1 = nn.Sequential( + nn.Conv2d( + in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False + ), + nn.BatchNorm2d(out_channels // 2), + nn.ReLU(inplace=True), + ) + fu_class = FourierUnit + self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs) + if self.enable_lfu: + self.lfu = fu_class(out_channels // 2, out_channels // 2, groups) + self.conv2 = torch.nn.Conv2d( + out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False + ) + + def forward(self, x): + x = self.downsample(x) + x = self.conv1(x) + output = self.fu(x) + + if self.enable_lfu: + _, c, h, _ = x.shape + split_no = 2 + split_s = h // split_no + xs = torch.cat( + torch.split(x[:, : c // 4], split_s, dim=-2), dim=1 + ).contiguous() + xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous() + xs = self.lfu(xs) + xs = xs.repeat(1, 1, split_no, split_no).contiguous() + else: + xs = 0 + + output = self.conv2(x + output + xs) + + return output + + +class FFC(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=False, + enable_lfu=True, + padding_type="reflect", + gated=False, + **spectral_kwargs, + ): + super(FFC, self).__init__() + + assert stride == 1 or stride == 2, "Stride should be 1 or 2." + self.stride = stride + + in_cg = int(in_channels * ratio_gin) + in_cl = in_channels - in_cg + out_cg = int(out_channels * ratio_gout) + out_cl = out_channels - out_cg + # groups_g = 1 if groups == 1 else int(groups * ratio_gout) + # groups_l = 1 if groups == 1 else groups - groups_g + + self.ratio_gin = ratio_gin + self.ratio_gout = ratio_gout + self.global_in_num = in_cg + + module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d + self.convl2l = module( + in_cl, + out_cl, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d + self.convl2g = module( + in_cl, + out_cg, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d + self.convg2l = module( + in_cg, + out_cl, + kernel_size, + stride, + padding, + dilation, + groups, + bias, + padding_mode=padding_type, + ) + module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform + self.convg2g = module( + in_cg, + out_cg, + stride, + 1 if groups == 1 else groups // 2, + enable_lfu, + **spectral_kwargs, + ) + + self.gated = gated + module = ( + nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d + ) + self.gate = module(in_channels, 2, 1) + + def forward(self, x): + x_l, x_g = x if type(x) is tuple else (x, 0) + out_xl, out_xg = 0, 0 + + if self.gated: + total_input_parts = [x_l] + if torch.is_tensor(x_g): + total_input_parts.append(x_g) + total_input = torch.cat(total_input_parts, dim=1) + + gates = torch.sigmoid(self.gate(total_input)) + g2l_gate, l2g_gate = gates.chunk(2, dim=1) + else: + g2l_gate, l2g_gate = 1, 1 + + if self.ratio_gout != 1: + out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate + if self.ratio_gout != 0: + out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g) + + return out_xl, out_xg + + +class FFC_BN_ACT(nn.Module): + def __init__( + self, + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride=1, + padding=0, + dilation=1, + groups=1, + bias=False, + norm_layer=nn.BatchNorm2d, + activation_layer=nn.Identity, + padding_type="reflect", + enable_lfu=True, + **kwargs, + ): + super(FFC_BN_ACT, self).__init__() + self.ffc = FFC( + in_channels, + out_channels, + kernel_size, + ratio_gin, + ratio_gout, + stride, + padding, + dilation, + groups, + bias, + enable_lfu, + padding_type=padding_type, + **kwargs, + ) + lnorm = nn.Identity if ratio_gout == 1 else norm_layer + gnorm = nn.Identity if ratio_gout == 0 else norm_layer + global_channels = int(out_channels * ratio_gout) + self.bn_l = lnorm(out_channels - global_channels) + self.bn_g = gnorm(global_channels) + + lact = nn.Identity if ratio_gout == 1 else activation_layer + gact = nn.Identity if ratio_gout == 0 else activation_layer + self.act_l = lact(inplace=True) + self.act_g = gact(inplace=True) + + def forward(self, x): + x_l, x_g = self.ffc(x) + x_l = self.act_l(self.bn_l(x_l)) + x_g = self.act_g(self.bn_g(x_g)) + return x_l, x_g + + +class FFCResnetBlock(nn.Module): + def __init__( + self, + dim, + padding_type, + norm_layer, + activation_layer=nn.ReLU, + dilation=1, + spatial_transform_kwargs=None, + inline=False, + **conv_kwargs, + ): + super().__init__() + self.conv1 = FFC_BN_ACT( + dim, + dim, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs, + ) + self.conv2 = FFC_BN_ACT( + dim, + dim, + kernel_size=3, + padding=dilation, + dilation=dilation, + norm_layer=norm_layer, + activation_layer=activation_layer, + padding_type=padding_type, + **conv_kwargs, + ) + if spatial_transform_kwargs is not None: + self.conv1 = LearnableSpatialTransformWrapper( + self.conv1, **spatial_transform_kwargs + ) + self.conv2 = LearnableSpatialTransformWrapper( + self.conv2, **spatial_transform_kwargs + ) + self.inline = inline + + def forward(self, x): + if self.inline: + x_l, x_g = ( + x[:, : -self.conv1.ffc.global_in_num], + x[:, -self.conv1.ffc.global_in_num :], + ) + else: + x_l, x_g = x if type(x) is tuple else (x, 0) + + id_l, id_g = x_l, x_g + + x_l, x_g = self.conv1((x_l, x_g)) + x_l, x_g = self.conv2((x_l, x_g)) + + x_l, x_g = id_l + x_l, id_g + x_g + out = x_l, x_g + if self.inline: + out = torch.cat(out, dim=1) + return out + + +class ConcatTupleLayer(nn.Module): + def forward(self, x): + assert isinstance(x, tuple) + x_l, x_g = x + assert torch.is_tensor(x_l) or torch.is_tensor(x_g) + if not torch.is_tensor(x_g): + return x_l + return torch.cat(x, dim=1) + + +class FFCResNetGenerator(nn.Module): + def __init__( + self, + input_nc, + output_nc, + ngf=64, + n_downsampling=3, + n_blocks=18, + norm_layer=nn.BatchNorm2d, + padding_type="reflect", + activation_layer=nn.ReLU, + up_norm_layer=nn.BatchNorm2d, + up_activation=nn.ReLU(True), + init_conv_kwargs={}, + downsample_conv_kwargs={}, + resnet_conv_kwargs={}, + spatial_transform_layers=None, + spatial_transform_kwargs={}, + max_features=1024, + out_ffc=False, + out_ffc_kwargs={}, + ): + assert n_blocks >= 0 + super().__init__() + """ + init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False} + downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False} + resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False} + spatial_transform_kwargs = {} + out_ffc_kwargs = {} + """ + """ + print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer, + padding_type, activation_layer, + up_norm_layer, up_activation, + spatial_transform_layers, + add_out_act, max_features, out_ffc, file=sys.stderr) + + 4 3 64 3 18 + reflect + + ReLU(inplace=True) + None sigmoid 1024 False + """ + init_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} + downsample_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False} + resnet_conv_kwargs = { + "ratio_gin": 0.75, + "ratio_gout": 0.75, + "enable_lfu": False, + } + spatial_transform_kwargs = {} + out_ffc_kwargs = {} + + model = [ + nn.ReflectionPad2d(3), + FFC_BN_ACT( + input_nc, + ngf, + kernel_size=7, + padding=0, + norm_layer=norm_layer, + activation_layer=activation_layer, + **init_conv_kwargs, + ), + ] + + ### downsample + for i in range(n_downsampling): + mult = 2**i + if i == n_downsampling - 1: + cur_conv_kwargs = dict(downsample_conv_kwargs) + cur_conv_kwargs["ratio_gout"] = resnet_conv_kwargs.get("ratio_gin", 0) + else: + cur_conv_kwargs = downsample_conv_kwargs + model += [ + FFC_BN_ACT( + min(max_features, ngf * mult), + min(max_features, ngf * mult * 2), + kernel_size=3, + stride=2, + padding=1, + norm_layer=norm_layer, + activation_layer=activation_layer, + **cur_conv_kwargs, + ) + ] + + mult = 2**n_downsampling + feats_num_bottleneck = min(max_features, ngf * mult) + + ### resnet blocks + for i in range(n_blocks): + cur_resblock = FFCResnetBlock( + feats_num_bottleneck, + padding_type=padding_type, + activation_layer=activation_layer, + norm_layer=norm_layer, + **resnet_conv_kwargs, + ) + if spatial_transform_layers is not None and i in spatial_transform_layers: + cur_resblock = LearnableSpatialTransformWrapper( + cur_resblock, **spatial_transform_kwargs + ) + model += [cur_resblock] + + model += [ConcatTupleLayer()] + + ### upsample + for i in range(n_downsampling): + mult = 2 ** (n_downsampling - i) + model += [ + nn.ConvTranspose2d( + min(max_features, ngf * mult), + min(max_features, int(ngf * mult / 2)), + kernel_size=3, + stride=2, + padding=1, + output_padding=1, + ), + up_norm_layer(min(max_features, int(ngf * mult / 2))), + up_activation, + ] + + if out_ffc: + model += [ + FFCResnetBlock( + ngf, + padding_type=padding_type, + activation_layer=activation_layer, + norm_layer=norm_layer, + inline=True, + **out_ffc_kwargs, + ) + ] + + model += [ + nn.ReflectionPad2d(3), + nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0), + ] + model.append(nn.Sigmoid()) + self.model = nn.Sequential(*model) + + def forward(self, image, mask): + return self.model(torch.cat([image, mask], dim=1)) + + +class LaMa(nn.Module): + def __init__(self, state_dict) -> None: + super(LaMa, self).__init__() + self.model_arch = "LaMa" + self.sub_type = "Inpaint" + self.in_nc = 4 + self.out_nc = 3 + self.scale = 1 + + self.min_size = None + self.pad_mod = 8 + self.pad_to_square = False + + self.model = FFCResNetGenerator(self.in_nc, self.out_nc) + self.state = { + k.replace("generator.model", "model.model"): v + for k, v in state_dict.items() + } + + self.supports_fp16 = False + self.support_bf16 = True + + self.load_state_dict(self.state, strict=False) + + def forward(self, img, mask): + masked_img = img * (1 - mask) + inpainted_mask = mask * self.model.forward(masked_img, mask) + result = inpainted_mask + (1 - mask) * img + return result diff --git a/ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py b/ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py new file mode 100644 index 0000000000000000000000000000000000000000..f4d52aa1e063d274b7aec7bd1ace77b19eb2ca61 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/ChannelAttention.py @@ -0,0 +1,110 @@ +import math + +import torch.nn as nn + + +class CA_layer(nn.Module): + def __init__(self, channel, reduction=16): + super(CA_layer, self).__init__() + # global average pooling + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Sequential( + nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False), + nn.GELU(), + nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False), + # nn.Sigmoid() + ) + + def forward(self, x): + y = self.fc(self.gap(x)) + return x * y.expand_as(x) + + +class Simple_CA_layer(nn.Module): + def __init__(self, channel): + super(Simple_CA_layer, self).__init__() + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=channel, + out_channels=channel, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + def forward(self, x): + return x * self.fc(self.gap(x)) + + +class ECA_layer(nn.Module): + """Constructs a ECA module. + Args: + channel: Number of channels of the input feature map + k_size: Adaptive selection of kernel size + """ + + def __init__(self, channel): + super(ECA_layer, self).__init__() + + b = 1 + gamma = 2 + k_size = int(abs(math.log(channel, 2) + b) / gamma) + k_size = k_size if k_size % 2 else k_size + 1 + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv = nn.Conv1d( + 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False + ) + # self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x: input features with shape [b, c, h, w] + # b, c, h, w = x.size() + + # feature descriptor on the global spatial information + y = self.avg_pool(x) + + # Two different branches of ECA module + y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) + + # Multi-scale information fusion + # y = self.sigmoid(y) + + return x * y.expand_as(x) + + +class ECA_MaxPool_layer(nn.Module): + """Constructs a ECA module. + Args: + channel: Number of channels of the input feature map + k_size: Adaptive selection of kernel size + """ + + def __init__(self, channel): + super(ECA_MaxPool_layer, self).__init__() + + b = 1 + gamma = 2 + k_size = int(abs(math.log(channel, 2) + b) / gamma) + k_size = k_size if k_size % 2 else k_size + 1 + self.max_pool = nn.AdaptiveMaxPool2d(1) + self.conv = nn.Conv1d( + 1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False + ) + # self.sigmoid = nn.Sigmoid() + + def forward(self, x): + # x: input features with shape [b, c, h, w] + # b, c, h, w = x.size() + + # feature descriptor on the global spatial information + y = self.max_pool(x) + + # Two different branches of ECA module + y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) + + # Multi-scale information fusion + # y = self.sigmoid(y) + + return x * y.expand_as(x) diff --git a/ldm_patched/pfn/architecture/OmniSR/LICENSE b/ldm_patched/pfn/architecture/OmniSR/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..261eeb9e9f8b2b4b0d119366dda99c6fd7d35c64 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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/dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/OSA.py @@ -0,0 +1,577 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OSA.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:07:42 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn.functional as F +from einops import rearrange, repeat +from einops.layers.torch import Rearrange, Reduce +from torch import einsum, nn + +from .layernorm import LayerNorm2d + +# helpers + + +def exists(val): + return val is not None + + +def default(val, d): + return val if exists(val) else d + + +def cast_tuple(val, length=1): + return val if isinstance(val, tuple) else ((val,) * length) + + +# helper classes + + +class PreNormResidual(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = nn.LayerNorm(dim) + self.fn = fn + + def forward(self, x): + return self.fn(self.norm(x)) + x + + +class Conv_PreNormResidual(nn.Module): + def __init__(self, dim, fn): + super().__init__() + self.norm = LayerNorm2d(dim) + self.fn = fn + + def forward(self, x): + return self.fn(self.norm(x)) + x + + +class FeedForward(nn.Module): + def __init__(self, dim, mult=2, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + self.net = nn.Sequential( + nn.Linear(dim, inner_dim), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(inner_dim, dim), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Conv_FeedForward(nn.Module): + def __init__(self, dim, mult=2, dropout=0.0): + super().__init__() + inner_dim = int(dim * mult) + self.net = nn.Sequential( + nn.Conv2d(dim, inner_dim, 1, 1, 0), + nn.GELU(), + nn.Dropout(dropout), + nn.Conv2d(inner_dim, dim, 1, 1, 0), + nn.Dropout(dropout), + ) + + def forward(self, x): + return self.net(x) + + +class Gated_Conv_FeedForward(nn.Module): + def __init__(self, dim, mult=1, bias=False, dropout=0.0): + super().__init__() + + hidden_features = int(dim * mult) + + self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias) + + self.dwconv = nn.Conv2d( + hidden_features * 2, + hidden_features * 2, + kernel_size=3, + stride=1, + padding=1, + groups=hidden_features * 2, + bias=bias, + ) + + self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias) + + def forward(self, x): + x = self.project_in(x) + x1, x2 = self.dwconv(x).chunk(2, dim=1) + x = F.gelu(x1) * x2 + x = self.project_out(x) + return x + + +# MBConv + + +class SqueezeExcitation(nn.Module): + def __init__(self, dim, shrinkage_rate=0.25): + super().__init__() + hidden_dim = int(dim * shrinkage_rate) + + self.gate = nn.Sequential( + Reduce("b c h w -> b c", "mean"), + nn.Linear(dim, hidden_dim, bias=False), + nn.SiLU(), + nn.Linear(hidden_dim, dim, bias=False), + nn.Sigmoid(), + Rearrange("b c -> b c 1 1"), + ) + + def forward(self, x): + return x * self.gate(x) + + +class MBConvResidual(nn.Module): + def __init__(self, fn, dropout=0.0): + super().__init__() + self.fn = fn + self.dropsample = Dropsample(dropout) + + def forward(self, x): + out = self.fn(x) + out = self.dropsample(out) + return out + x + + +class Dropsample(nn.Module): + def __init__(self, prob=0): + super().__init__() + self.prob = prob + + def forward(self, x): + device = x.device + + if self.prob == 0.0 or (not self.training): + return x + + keep_mask = ( + torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_() + > self.prob + ) + return x * keep_mask / (1 - self.prob) + + +def MBConv( + dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0 +): + hidden_dim = int(expansion_rate * dim_out) + stride = 2 if downsample else 1 + + net = nn.Sequential( + nn.Conv2d(dim_in, hidden_dim, 1), + # nn.BatchNorm2d(hidden_dim), + nn.GELU(), + nn.Conv2d( + hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim + ), + # nn.BatchNorm2d(hidden_dim), + nn.GELU(), + SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate), + nn.Conv2d(hidden_dim, dim_out, 1), + # nn.BatchNorm2d(dim_out) + ) + + if dim_in == dim_out and not downsample: + net = MBConvResidual(net, dropout=dropout) + + return net + + +# attention related classes +class Attention(nn.Module): + def __init__( + self, + dim, + dim_head=32, + dropout=0.0, + window_size=7, + with_pe=True, + ): + super().__init__() + assert ( + dim % dim_head + ) == 0, "dimension should be divisible by dimension per head" + + self.heads = dim // dim_head + self.scale = dim_head**-0.5 + self.with_pe = with_pe + + self.to_qkv = nn.Linear(dim, dim * 3, bias=False) + + self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) + + self.to_out = nn.Sequential( + nn.Linear(dim, dim, bias=False), nn.Dropout(dropout) + ) + + # relative positional bias + if self.with_pe: + self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads) + + pos = torch.arange(window_size) + grid = torch.stack(torch.meshgrid(pos, pos)) + grid = rearrange(grid, "c i j -> (i j) c") + rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange( + grid, "j ... -> 1 j ..." + ) + rel_pos += window_size - 1 + rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum( + dim=-1 + ) + + self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False) + + def forward(self, x): + batch, height, width, window_height, window_width, _, device, h = ( + *x.shape, + x.device, + self.heads, + ) + + # flatten + + x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d") + + # project for queries, keys, values + + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + + # split heads + + q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v)) + + # scale + + q = q * self.scale + + # sim + + sim = einsum("b h i d, b h j d -> b h i j", q, k) + + # add positional bias + if self.with_pe: + bias = self.rel_pos_bias(self.rel_pos_indices) + sim = sim + rearrange(bias, "i j h -> h i j") + + # attention + + attn = self.attend(sim) + + # aggregate + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + + # merge heads + + out = rearrange( + out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width + ) + + # combine heads out + + out = self.to_out(out) + return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width) + + +class Block_Attention(nn.Module): + def __init__( + self, + dim, + dim_head=32, + bias=False, + dropout=0.0, + window_size=7, + with_pe=True, + ): + super().__init__() + assert ( + dim % dim_head + ) == 0, "dimension should be divisible by dimension per head" + + self.heads = dim // dim_head + self.ps = window_size + self.scale = dim_head**-0.5 + self.with_pe = with_pe + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + + self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout)) + + self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + # project for queries, keys, values + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + q, k, v = qkv.chunk(3, dim=1) + + # split heads + + q, k, v = map( + lambda t: rearrange( + t, + "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d", + h=self.heads, + w1=self.ps, + w2=self.ps, + ), + (q, k, v), + ) + + # scale + + q = q * self.scale + + # sim + + sim = einsum("b h i d, b h j d -> b h i j", q, k) + + # attention + attn = self.attend(sim) + + # aggregate + + out = einsum("b h i j, b h j d -> b h i d", attn, v) + + # merge heads + out = rearrange( + out, + "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)", + x=h // self.ps, + y=w // self.ps, + head=self.heads, + w1=self.ps, + w2=self.ps, + ) + + out = self.to_out(out) + return out + + +class Channel_Attention(nn.Module): + def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): + super(Channel_Attention, self).__init__() + self.heads = heads + + self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) + + self.ps = window_size + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + qkv = qkv.chunk(3, dim=1) + + q, k, v = map( + lambda t: rearrange( + t, + "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)", + ph=self.ps, + pw=self.ps, + head=self.heads, + ), + qkv, + ) + + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + out = attn @ v + + out = rearrange( + out, + "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)", + h=h // self.ps, + w=w // self.ps, + ph=self.ps, + pw=self.ps, + head=self.heads, + ) + + out = self.project_out(out) + + return out + + +class Channel_Attention_grid(nn.Module): + def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7): + super(Channel_Attention_grid, self).__init__() + self.heads = heads + + self.temperature = nn.Parameter(torch.ones(heads, 1, 1)) + + self.ps = window_size + + self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias) + self.qkv_dwconv = nn.Conv2d( + dim * 3, + dim * 3, + kernel_size=3, + stride=1, + padding=1, + groups=dim * 3, + bias=bias, + ) + self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias) + + def forward(self, x): + b, c, h, w = x.shape + + qkv = self.qkv_dwconv(self.qkv(x)) + qkv = qkv.chunk(3, dim=1) + + q, k, v = map( + lambda t: rearrange( + t, + "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)", + ph=self.ps, + pw=self.ps, + head=self.heads, + ), + qkv, + ) + + q = F.normalize(q, dim=-1) + k = F.normalize(k, dim=-1) + + attn = (q @ k.transpose(-2, -1)) * self.temperature + attn = attn.softmax(dim=-1) + out = attn @ v + + out = rearrange( + out, + "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)", + h=h // self.ps, + w=w // self.ps, + ph=self.ps, + pw=self.ps, + head=self.heads, + ) + + out = self.project_out(out) + + return out + + +class OSA_Block(nn.Module): + def __init__( + self, + channel_num=64, + bias=True, + ffn_bias=True, + window_size=8, + with_pe=False, + dropout=0.0, + ): + super(OSA_Block, self).__init__() + + w = window_size + + self.layer = nn.Sequential( + MBConv( + channel_num, + channel_num, + downsample=False, + expansion_rate=1, + shrinkage_rate=0.25, + ), + Rearrange( + "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w + ), # block-like attention + PreNormResidual( + channel_num, + Attention( + dim=channel_num, + dim_head=channel_num // 4, + dropout=dropout, + window_size=window_size, + with_pe=with_pe, + ), + ), + Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + # channel-like attention + Conv_PreNormResidual( + channel_num, + Channel_Attention( + dim=channel_num, heads=4, dropout=dropout, window_size=window_size + ), + ), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + Rearrange( + "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w + ), # grid-like attention + PreNormResidual( + channel_num, + Attention( + dim=channel_num, + dim_head=channel_num // 4, + dropout=dropout, + window_size=window_size, + with_pe=with_pe, + ), + ), + Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + # channel-like attention + Conv_PreNormResidual( + channel_num, + Channel_Attention_grid( + dim=channel_num, heads=4, dropout=dropout, window_size=window_size + ), + ), + Conv_PreNormResidual( + channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout) + ), + ) + + def forward(self, x): + out = self.layer(x) + return out diff --git a/ldm_patched/pfn/architecture/OmniSR/OSAG.py b/ldm_patched/pfn/architecture/OmniSR/OSAG.py new file mode 100644 index 0000000000000000000000000000000000000000..477e81f9da4eb1db9b5ec418549d75dd591209ec --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/OSAG.py @@ -0,0 +1,60 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OSAG.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:08:49 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + + +import torch.nn as nn + +from .esa import ESA +from .OSA import OSA_Block + + +class OSAG(nn.Module): + def __init__( + self, + channel_num=64, + bias=True, + block_num=4, + ffn_bias=False, + window_size=0, + pe=False, + ): + super(OSAG, self).__init__() + + # print("window_size: %d" % (window_size)) + # print("with_pe", pe) + # print("ffn_bias: %d" % (ffn_bias)) + + # block_script_name = kwargs.get("block_script_name", "OSA") + # block_class_name = kwargs.get("block_class_name", "OSA_Block") + + # script_name = "." + block_script_name + # package = __import__(script_name, fromlist=True) + block_class = OSA_Block # getattr(package, block_class_name) + group_list = [] + for _ in range(block_num): + temp_res = block_class( + channel_num, + bias, + ffn_bias=ffn_bias, + window_size=window_size, + with_pe=pe, + ) + group_list.append(temp_res) + group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias)) + self.residual_layer = nn.Sequential(*group_list) + esa_channel = max(channel_num // 4, 16) + self.esa = ESA(esa_channel, channel_num) + + def forward(self, x): + out = self.residual_layer(x) + out = out + x + return self.esa(out) diff --git a/ldm_patched/pfn/architecture/OmniSR/OmniSR.py b/ldm_patched/pfn/architecture/OmniSR/OmniSR.py new file mode 100644 index 0000000000000000000000000000000000000000..1e1c3f35e657fb972d4209456719a61163831385 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/OmniSR.py @@ -0,0 +1,143 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: OmniSR.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Sunday, 23rd April 2023 3:06:36 pm +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .OSAG import OSAG +from .pixelshuffle import pixelshuffle_block + + +class OmniSR(nn.Module): + def __init__( + self, + state_dict, + **kwargs, + ): + super(OmniSR, self).__init__() + self.state = state_dict + + bias = True # Fine to assume this for now + block_num = 1 # Fine to assume this for now + ffn_bias = True + pe = True + + num_feat = state_dict["input.weight"].shape[0] or 64 + num_in_ch = state_dict["input.weight"].shape[1] or 3 + num_out_ch = num_in_ch # we can just assume this for now. pixelshuffle smh + + pixelshuffle_shape = state_dict["up.0.weight"].shape[0] + up_scale = math.sqrt(pixelshuffle_shape / num_out_ch) + if up_scale - int(up_scale) > 0: + print( + "out_nc is probably different than in_nc, scale calculation might be wrong" + ) + up_scale = int(up_scale) + res_num = 0 + for key in state_dict.keys(): + if "residual_layer" in key: + temp_res_num = int(key.split(".")[1]) + if temp_res_num > res_num: + res_num = temp_res_num + res_num = res_num + 1 # zero-indexed + + residual_layer = [] + self.res_num = res_num + + if ( + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + in state_dict.keys() + ): + rel_pos_bias_weight = state_dict[ + "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight" + ].shape[0] + self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2) + else: + self.window_size = 8 + + self.up_scale = up_scale + + for _ in range(res_num): + temp_res = OSAG( + channel_num=num_feat, + bias=bias, + block_num=block_num, + ffn_bias=ffn_bias, + window_size=self.window_size, + pe=pe, + ) + residual_layer.append(temp_res) + self.residual_layer = nn.Sequential(*residual_layer) + self.input = nn.Conv2d( + in_channels=num_in_ch, + out_channels=num_feat, + kernel_size=3, + stride=1, + padding=1, + bias=bias, + ) + self.output = nn.Conv2d( + in_channels=num_feat, + out_channels=num_feat, + kernel_size=3, + stride=1, + padding=1, + bias=bias, + ) + self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias) + + # self.tail = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias) + + # for m in self.modules(): + # if isinstance(m, nn.Conv2d): + # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + # m.weight.data.normal_(0, sqrt(2. / n)) + + # chaiNNer specific stuff + self.model_arch = "OmniSR" + self.sub_type = "SR" + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.scale = up_scale + + self.supports_fp16 = True # TODO: Test this + self.supports_bfp16 = True + self.min_size_restriction = 16 + + self.load_state_dict(state_dict, strict=False) + + def check_image_size(self, x): + _, _, h, w = x.size() + # import pdb; pdb.set_trace() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect') + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0) + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + residual = self.input(x) + out = self.residual_layer(residual) + + # origin + out = torch.add(self.output(out), residual) + out = self.up(out) + + out = out[:, :, : H * self.up_scale, : W * self.up_scale] + return out diff --git a/ldm_patched/pfn/architecture/OmniSR/esa.py b/ldm_patched/pfn/architecture/OmniSR/esa.py new file mode 100644 index 0000000000000000000000000000000000000000..f9ce7f7a60bfe20b3737eaa2e3110fd460a2d104 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/esa.py @@ -0,0 +1,294 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: esa.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Thursday, 20th April 2023 9:28:06 am +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .layernorm import LayerNorm2d + + +def moment(x, dim=(2, 3), k=2): + assert len(x.size()) == 4 + mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1) + mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim) + return mk + + +class ESA(nn.Module): + """ + Modification of Enhanced Spatial Attention (ESA), which is proposed by + `Residual Feature Aggregation Network for Image Super-Resolution` + Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes + are deleted. + """ + + def __init__(self, esa_channels, n_feats, conv=nn.Conv2d): + super(ESA, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0) + self.conv3 = conv(f, f, kernel_size=3, padding=1) + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.conv1(x) + c1 = self.conv2(c1_) + v_max = F.max_pool2d(c1, kernel_size=7, stride=3) + c3 = self.conv3(v_max) + c3 = F.interpolate( + c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False + ) + cf = self.conv_f(c1_) + c4 = self.conv4(c3 + cf) + m = self.sigmoid(c4) + return x * m + + +class LK_ESA(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(LK_ESA, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.vec_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=2, + bias=bias, + ) + self.vec_conv3x1 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, 3), + padding=(0, 1), + groups=2, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=2, + bias=bias, + ) + self.hor_conv1x3 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(3, 1), + padding=(1, 0), + groups=2, + bias=bias, + ) + + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.conv1(x) + + res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) + res = self.hor_conv(res) + self.hor_conv1x3(res) + + cf = self.conv_f(c1_) + c4 = self.conv4(res + cf) + m = self.sigmoid(c4) + return x * m + + +class LK_ESA_LN(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(LK_ESA_LN, self).__init__() + f = esa_channels + self.conv1 = conv(n_feats, f, kernel_size=1) + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.norm = LayerNorm2d(n_feats) + + self.vec_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=2, + bias=bias, + ) + self.vec_conv3x1 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(1, 3), + padding=(0, 1), + groups=2, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=2, + bias=bias, + ) + self.hor_conv1x3 = nn.Conv2d( + in_channels=f * kernel_expand, + out_channels=f * kernel_expand, + kernel_size=(3, 1), + padding=(1, 0), + groups=2, + bias=bias, + ) + + self.conv4 = conv(f, n_feats, kernel_size=1) + self.sigmoid = nn.Sigmoid() + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + c1_ = self.norm(x) + c1_ = self.conv1(c1_) + + res = self.vec_conv(c1_) + self.vec_conv3x1(c1_) + res = self.hor_conv(res) + self.hor_conv1x3(res) + + cf = self.conv_f(c1_) + c4 = self.conv4(res + cf) + m = self.sigmoid(c4) + return x * m + + +class AdaGuidedFilter(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(AdaGuidedFilter, self).__init__() + + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=n_feats, + out_channels=1, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + self.r = 5 + + def box_filter(self, x, r): + channel = x.shape[1] + kernel_size = 2 * r + 1 + weight = 1.0 / (kernel_size**2) + box_kernel = weight * torch.ones( + (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device + ) + output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel) + return output + + def forward(self, x): + _, _, H, W = x.shape + N = self.box_filter( + torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r + ) + + # epsilon = self.fc(self.gap(x)) + # epsilon = torch.pow(epsilon, 2) + epsilon = 1e-2 + + mean_x = self.box_filter(x, self.r) / N + var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x + + A = var_x / (var_x + epsilon) + b = (1 - A) * mean_x + m = A * x + b + + # mean_A = self.box_filter(A, self.r) / N + # mean_b = self.box_filter(b, self.r) / N + # m = mean_A * x + mean_b + return x * m + + +class AdaConvGuidedFilter(nn.Module): + def __init__( + self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True + ): + super(AdaConvGuidedFilter, self).__init__() + f = esa_channels + + self.conv_f = conv(f, f, kernel_size=1) + + kernel_size = 17 + kernel_expand = kernel_expand + padding = kernel_size // 2 + + self.vec_conv = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=(1, kernel_size), + padding=(0, padding), + groups=f, + bias=bias, + ) + + self.hor_conv = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=(kernel_size, 1), + padding=(padding, 0), + groups=f, + bias=bias, + ) + + self.gap = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Conv2d( + in_channels=f, + out_channels=f, + kernel_size=1, + padding=0, + stride=1, + groups=1, + bias=True, + ) + + def forward(self, x): + y = self.vec_conv(x) + y = self.hor_conv(y) + + sigma = torch.pow(y, 2) + epsilon = self.fc(self.gap(y)) + + weight = sigma / (sigma + epsilon) + + m = weight * x + (1 - weight) + + return x * m diff --git a/ldm_patched/pfn/architecture/OmniSR/layernorm.py b/ldm_patched/pfn/architecture/OmniSR/layernorm.py new file mode 100644 index 0000000000000000000000000000000000000000..731a25f7542d45757a284648055d7c6ffad4c3fd --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/layernorm.py @@ -0,0 +1,70 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: layernorm.py +# Created Date: Tuesday April 28th 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Thursday, 20th April 2023 9:28:20 am +# Modified By: Chen Xuanhong +# Copyright (c) 2020 Shanghai Jiao Tong University +############################################################# + +import torch +import torch.nn as nn + + +class LayerNormFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, x, weight, bias, eps): + ctx.eps = eps + N, C, H, W = x.size() + mu = x.mean(1, keepdim=True) + var = (x - mu).pow(2).mean(1, keepdim=True) + y = (x - mu) / (var + eps).sqrt() + ctx.save_for_backward(y, var, weight) + y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1) + return y + + @staticmethod + def backward(ctx, grad_output): + eps = ctx.eps + + N, C, H, W = grad_output.size() + y, var, weight = ctx.saved_variables + g = grad_output * weight.view(1, C, 1, 1) + mean_g = g.mean(dim=1, keepdim=True) + + mean_gy = (g * y).mean(dim=1, keepdim=True) + gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g) + return ( + gx, + (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0), + grad_output.sum(dim=3).sum(dim=2).sum(dim=0), + None, + ) + + +class LayerNorm2d(nn.Module): + def __init__(self, channels, eps=1e-6): + super(LayerNorm2d, self).__init__() + self.register_parameter("weight", nn.Parameter(torch.ones(channels))) + self.register_parameter("bias", nn.Parameter(torch.zeros(channels))) + self.eps = eps + + def forward(self, x): + return LayerNormFunction.apply(x, self.weight, self.bias, self.eps) + + +class GRN(nn.Module): + """GRN (Global Response Normalization) layer""" + + def __init__(self, dim): + super().__init__() + self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1)) + self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1)) + + def forward(self, x): + Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True) + Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6) + return self.gamma * (x * Nx) + self.beta + x diff --git a/ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py b/ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py new file mode 100644 index 0000000000000000000000000000000000000000..4260fb7c9d8d912e34899ce7877595b617f9bb02 --- /dev/null +++ b/ldm_patched/pfn/architecture/OmniSR/pixelshuffle.py @@ -0,0 +1,31 @@ +#!/usr/bin/env python3 +# -*- coding:utf-8 -*- +############################################################# +# File: pixelshuffle.py +# Created Date: Friday July 1st 2022 +# Author: Chen Xuanhong +# Email: chenxuanhongzju@outlook.com +# Last Modified: Friday, 1st July 2022 10:18:39 am +# Modified By: Chen Xuanhong +# Copyright (c) 2022 Shanghai Jiao Tong University +############################################################# + +import torch.nn as nn + + +def pixelshuffle_block( + in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False +): + """ + Upsample features according to `upscale_factor`. + """ + padding = kernel_size // 2 + conv = nn.Conv2d( + in_channels, + out_channels * (upscale_factor**2), + kernel_size, + padding=1, + bias=bias, + ) + pixel_shuffle = nn.PixelShuffle(upscale_factor) + return nn.Sequential(*[conv, pixel_shuffle]) diff --git a/ldm_patched/pfn/architecture/RRDB.py b/ldm_patched/pfn/architecture/RRDB.py new file mode 100644 index 0000000000000000000000000000000000000000..8d318b90b865fecd0b88adc6daf2c6d2e29860a3 --- /dev/null +++ b/ldm_patched/pfn/architecture/RRDB.py @@ -0,0 +1,296 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import functools +import math +import re +from collections import OrderedDict + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from . import block as B + + +# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/esrgan.py +# Which enhanced stuff that was already here +class RRDBNet(nn.Module): + def __init__( + self, + state_dict, + norm=None, + act: str = "leakyrelu", + upsampler: str = "upconv", + mode: B.ConvMode = "CNA", + ) -> None: + """ + ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks. + By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao, + and Chen Change Loy. + This is old-arch Residual in Residual Dense Block Network and is not + the newest revision that's available at github.com/xinntao/ESRGAN. + This is on purpose, the newest Network has severely limited the + potential use of the Network with no benefits. + This network supports model files from both new and old-arch. + Args: + norm: Normalization layer + act: Activation layer + upsampler: Upsample layer. upconv, pixel_shuffle + mode: Convolution mode + """ + super(RRDBNet, self).__init__() + self.model_arch = "ESRGAN" + self.sub_type = "SR" + + self.state = state_dict + self.norm = norm + self.act = act + self.upsampler = upsampler + self.mode = mode + + self.state_map = { + # currently supports old, new, and newer RRDBNet arch models + # ESRGAN, BSRGAN/RealSR, Real-ESRGAN + "model.0.weight": ("conv_first.weight",), + "model.0.bias": ("conv_first.bias",), + "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"), + "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"), + r"model.1.sub.\1.RDB\2.conv\3.0.\4": ( + r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)", + r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)", + ), + } + if "params_ema" in self.state: + self.state = self.state["params_ema"] + # self.model_arch = "RealESRGAN" + self.num_blocks = self.get_num_blocks() + self.plus = any("conv1x1" in k for k in self.state.keys()) + if self.plus: + self.model_arch = "ESRGAN+" + + self.state = self.new_to_old_arch(self.state) + + self.key_arr = list(self.state.keys()) + + self.in_nc: int = self.state[self.key_arr[0]].shape[1] + self.out_nc: int = self.state[self.key_arr[-1]].shape[0] + + self.scale: int = self.get_scale() + self.num_filters: int = self.state[self.key_arr[0]].shape[0] + + c2x2 = False + if self.state["model.0.weight"].shape[-2] == 2: + c2x2 = True + self.scale = round(math.sqrt(self.scale / 4)) + self.model_arch = "ESRGAN-2c2" + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + # Detect if pixelunshuffle was used (Real-ESRGAN) + if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in ( + self.in_nc / 4, + self.in_nc / 16, + ): + self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc)) + else: + self.shuffle_factor = None + + upsample_block = { + "upconv": B.upconv_block, + "pixel_shuffle": B.pixelshuffle_block, + }.get(self.upsampler) + if upsample_block is None: + raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found") + + if self.scale == 3: + upsample_blocks = upsample_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + upscale_factor=3, + act_type=self.act, + c2x2=c2x2, + ) + else: + upsample_blocks = [ + upsample_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + act_type=self.act, + c2x2=c2x2, + ) + for _ in range(int(math.log(self.scale, 2))) + ] + + self.model = B.sequential( + # fea conv + B.conv_block( + in_nc=self.in_nc, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + B.ShortcutBlock( + B.sequential( + # rrdb blocks + *[ + B.RRDB( + nf=self.num_filters, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=self.norm, + act_type=self.act, + mode="CNA", + plus=self.plus, + c2x2=c2x2, + ) + for _ in range(self.num_blocks) + ], + # lr conv + B.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=self.norm, + act_type=None, + mode=self.mode, + c2x2=c2x2, + ), + ) + ), + *upsample_blocks, + # hr_conv0 + B.conv_block( + in_nc=self.num_filters, + out_nc=self.num_filters, + kernel_size=3, + norm_type=None, + act_type=self.act, + c2x2=c2x2, + ), + # hr_conv1 + B.conv_block( + in_nc=self.num_filters, + out_nc=self.out_nc, + kernel_size=3, + norm_type=None, + act_type=None, + c2x2=c2x2, + ), + ) + + # Adjust these properties for calculations outside of the model + if self.shuffle_factor: + self.in_nc //= self.shuffle_factor**2 + self.scale //= self.shuffle_factor + + self.load_state_dict(self.state, strict=False) + + def new_to_old_arch(self, state): + """Convert a new-arch model state dictionary to an old-arch dictionary.""" + if "params_ema" in state: + state = state["params_ema"] + + if "conv_first.weight" not in state: + # model is already old arch, this is a loose check, but should be sufficient + return state + + # add nb to state keys + for kind in ("weight", "bias"): + self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[ + f"model.1.sub./NB/.{kind}" + ] + del self.state_map[f"model.1.sub./NB/.{kind}"] + + old_state = OrderedDict() + for old_key, new_keys in self.state_map.items(): + for new_key in new_keys: + if r"\1" in old_key: + for k, v in state.items(): + sub = re.sub(new_key, old_key, k) + if sub != k: + old_state[sub] = v + else: + if new_key in state: + old_state[old_key] = state[new_key] + + # upconv layers + max_upconv = 0 + for key in state.keys(): + match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key) + if match is not None: + _, key_num, key_type = match.groups() + old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key] + max_upconv = max(max_upconv, int(key_num) * 3) + + # final layers + for key in state.keys(): + if key in ("HRconv.weight", "conv_hr.weight"): + old_state[f"model.{max_upconv + 2}.weight"] = state[key] + elif key in ("HRconv.bias", "conv_hr.bias"): + old_state[f"model.{max_upconv + 2}.bias"] = state[key] + elif key in ("conv_last.weight",): + old_state[f"model.{max_upconv + 4}.weight"] = state[key] + elif key in ("conv_last.bias",): + old_state[f"model.{max_upconv + 4}.bias"] = state[key] + + # Sort by first numeric value of each layer + def compare(item1, item2): + parts1 = item1.split(".") + parts2 = item2.split(".") + int1 = int(parts1[1]) + int2 = int(parts2[1]) + return int1 - int2 + + sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare)) + + # Rebuild the output dict in the right order + out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys) + + return out_dict + + def get_scale(self, min_part: int = 6) -> int: + n = 0 + for part in list(self.state): + parts = part.split(".")[1:] + if len(parts) == 2: + part_num = int(parts[0]) + if part_num > min_part and parts[1] == "weight": + n += 1 + return 2**n + + def get_num_blocks(self) -> int: + nbs = [] + state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + ( + r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)", + ) + for state_key in state_keys: + for k in self.state: + m = re.search(state_key, k) + if m: + nbs.append(int(m.group(1))) + if nbs: + break + return max(*nbs) + 1 + + def forward(self, x): + if self.shuffle_factor: + _, _, h, w = x.size() + mod_pad_h = ( + self.shuffle_factor - h % self.shuffle_factor + ) % self.shuffle_factor + mod_pad_w = ( + self.shuffle_factor - w % self.shuffle_factor + ) % self.shuffle_factor + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor) + x = self.model(x) + return x[:, :, : h * self.scale, : w * self.scale] + return self.model(x) diff --git a/ldm_patched/pfn/architecture/SCUNet.py b/ldm_patched/pfn/architecture/SCUNet.py new file mode 100644 index 0000000000000000000000000000000000000000..b8354a873085140e9ff7d582c43ba9818ed9524e --- /dev/null +++ b/ldm_patched/pfn/architecture/SCUNet.py @@ -0,0 +1,455 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278 +# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc +# ----------------------------------------------------------------------------------- + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from einops.layers.torch import Rearrange + +from .timm.drop import DropPath +from .timm.weight_init import trunc_normal_ + + +# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py +class WMSA(nn.Module): + """Self-attention module in Swin Transformer""" + + def __init__(self, input_dim, output_dim, head_dim, window_size, type): + super(WMSA, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + self.head_dim = head_dim + self.scale = self.head_dim**-0.5 + self.n_heads = input_dim // head_dim + self.window_size = window_size + self.type = type + self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True) + + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + # TODO recover + # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1)) + self.relative_position_params = nn.Parameter( + torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads) + ) + + self.linear = nn.Linear(self.input_dim, self.output_dim) + + trunc_normal_(self.relative_position_params, std=0.02) + self.relative_position_params = torch.nn.Parameter( + self.relative_position_params.view( + 2 * window_size - 1, 2 * window_size - 1, self.n_heads + ) + .transpose(1, 2) + .transpose(0, 1) + ) + + def generate_mask(self, h, w, p, shift): + """generating the mask of SW-MSA + Args: + shift: shift parameters in CyclicShift. + Returns: + attn_mask: should be (1 1 w p p), + """ + # supporting square. + attn_mask = torch.zeros( + h, + w, + p, + p, + p, + p, + dtype=torch.bool, + device=self.relative_position_params.device, + ) + if self.type == "W": + return attn_mask + + s = p - shift + attn_mask[-1, :, :s, :, s:, :] = True + attn_mask[-1, :, s:, :, :s, :] = True + attn_mask[:, -1, :, :s, :, s:] = True + attn_mask[:, -1, :, s:, :, :s] = True + attn_mask = rearrange( + attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)" + ) + return attn_mask + + def forward(self, x): + """Forward pass of Window Multi-head Self-attention module. + Args: + x: input tensor with shape of [b h w c]; + attn_mask: attention mask, fill -inf where the value is True; + Returns: + output: tensor shape [b h w c] + """ + if self.type != "W": + x = torch.roll( + x, + shifts=(-(self.window_size // 2), -(self.window_size // 2)), + dims=(1, 2), + ) + + x = rearrange( + x, + "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c", + p1=self.window_size, + p2=self.window_size, + ) + h_windows = x.size(1) + w_windows = x.size(2) + # square validation + # assert h_windows == w_windows + + x = rearrange( + x, + "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c", + p1=self.window_size, + p2=self.window_size, + ) + qkv = self.embedding_layer(x) + q, k, v = rearrange( + qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim + ).chunk(3, dim=0) + sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale + # Adding learnable relative embedding + sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q") + # Using Attn Mask to distinguish different subwindows. + if self.type != "W": + attn_mask = self.generate_mask( + h_windows, w_windows, self.window_size, shift=self.window_size // 2 + ) + sim = sim.masked_fill_(attn_mask, float("-inf")) + + probs = nn.functional.softmax(sim, dim=-1) + output = torch.einsum("hbwij,hbwjc->hbwic", probs, v) + output = rearrange(output, "h b w p c -> b w p (h c)") + output = self.linear(output) + output = rearrange( + output, + "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c", + w1=h_windows, + p1=self.window_size, + ) + + if self.type != "W": + output = torch.roll( + output, + shifts=(self.window_size // 2, self.window_size // 2), + dims=(1, 2), + ) + + return output + + def relative_embedding(self): + cord = torch.tensor( + np.array( + [ + [i, j] + for i in range(self.window_size) + for j in range(self.window_size) + ] + ) + ) + relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1 + # negative is allowed + return self.relative_position_params[ + :, relation[:, :, 0].long(), relation[:, :, 1].long() + ] + + +class Block(nn.Module): + def __init__( + self, + input_dim, + output_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer Block""" + super(Block, self).__init__() + self.input_dim = input_dim + self.output_dim = output_dim + assert type in ["W", "SW"] + self.type = type + if input_resolution <= window_size: + self.type = "W" + + self.ln1 = nn.LayerNorm(input_dim) + self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type) + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.ln2 = nn.LayerNorm(input_dim) + self.mlp = nn.Sequential( + nn.Linear(input_dim, 4 * input_dim), + nn.GELU(), + nn.Linear(4 * input_dim, output_dim), + ) + + def forward(self, x): + x = x + self.drop_path(self.msa(self.ln1(x))) + x = x + self.drop_path(self.mlp(self.ln2(x))) + return x + + +class ConvTransBlock(nn.Module): + def __init__( + self, + conv_dim, + trans_dim, + head_dim, + window_size, + drop_path, + type="W", + input_resolution=None, + ): + """SwinTransformer and Conv Block""" + super(ConvTransBlock, self).__init__() + self.conv_dim = conv_dim + self.trans_dim = trans_dim + self.head_dim = head_dim + self.window_size = window_size + self.drop_path = drop_path + self.type = type + self.input_resolution = input_resolution + + assert self.type in ["W", "SW"] + if self.input_resolution <= self.window_size: + self.type = "W" + + self.trans_block = Block( + self.trans_dim, + self.trans_dim, + self.head_dim, + self.window_size, + self.drop_path, + self.type, + self.input_resolution, + ) + self.conv1_1 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + self.conv1_2 = nn.Conv2d( + self.conv_dim + self.trans_dim, + self.conv_dim + self.trans_dim, + 1, + 1, + 0, + bias=True, + ) + + self.conv_block = nn.Sequential( + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + nn.ReLU(True), + nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False), + ) + + def forward(self, x): + conv_x, trans_x = torch.split( + self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1 + ) + conv_x = self.conv_block(conv_x) + conv_x + trans_x = Rearrange("b c h w -> b h w c")(trans_x) + trans_x = self.trans_block(trans_x) + trans_x = Rearrange("b h w c -> b c h w")(trans_x) + res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1)) + x = x + res + + return x + + +class SCUNet(nn.Module): + def __init__( + self, + state_dict, + in_nc=3, + config=[4, 4, 4, 4, 4, 4, 4], + dim=64, + drop_path_rate=0.0, + input_resolution=256, + ): + super(SCUNet, self).__init__() + self.model_arch = "SCUNet" + self.sub_type = "SR" + + self.num_filters: int = 0 + + self.state = state_dict + self.config = config + self.dim = dim + self.head_dim = 32 + self.window_size = 8 + + self.in_nc = in_nc + self.out_nc = self.in_nc + self.scale = 1 + self.supports_fp16 = True + + # drop path rate for each layer + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))] + + self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)] + + begin = 0 + self.m_down1 = [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[0]) + ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)] + + begin += config[0] + self.m_down2 = [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[1]) + ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)] + + begin += config[1] + self.m_down3 = [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[2]) + ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)] + + begin += config[2] + self.m_body = [ + ConvTransBlock( + 4 * dim, + 4 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 8, + ) + for i in range(config[3]) + ] + + begin += config[3] + self.m_up3 = [ + nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + 2 * dim, + 2 * dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 4, + ) + for i in range(config[4]) + ] + + begin += config[4] + self.m_up2 = [ + nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim, + dim, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution // 2, + ) + for i in range(config[5]) + ] + + begin += config[5] + self.m_up1 = [ + nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), + ] + [ + ConvTransBlock( + dim // 2, + dim // 2, + self.head_dim, + self.window_size, + dpr[i + begin], + "W" if not i % 2 else "SW", + input_resolution, + ) + for i in range(config[6]) + ] + + self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)] + + self.m_head = nn.Sequential(*self.m_head) + self.m_down1 = nn.Sequential(*self.m_down1) + self.m_down2 = nn.Sequential(*self.m_down2) + self.m_down3 = nn.Sequential(*self.m_down3) + self.m_body = nn.Sequential(*self.m_body) + self.m_up3 = nn.Sequential(*self.m_up3) + self.m_up2 = nn.Sequential(*self.m_up2) + self.m_up1 = nn.Sequential(*self.m_up1) + self.m_tail = nn.Sequential(*self.m_tail) + # self.apply(self._init_weights) + self.load_state_dict(state_dict, strict=True) + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (64 - h % 64) % 64 + mod_pad_w = (64 - w % 64) % 64 + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward(self, x0): + h, w = x0.size()[-2:] + x0 = self.check_image_size(x0) + + x1 = self.m_head(x0) + x2 = self.m_down1(x1) + x3 = self.m_down2(x2) + x4 = self.m_down3(x3) + x = self.m_body(x4) + x = self.m_up3(x + x4) + x = self.m_up2(x + x3) + x = self.m_up1(x + x2) + x = self.m_tail(x + x1) + + x = x[:, :, :h, :w] + return x + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) diff --git a/ldm_patched/pfn/architecture/SPSR.py b/ldm_patched/pfn/architecture/SPSR.py new file mode 100644 index 0000000000000000000000000000000000000000..c3cefff190292a63cf61fe3fa9c28131dac4f369 --- /dev/null +++ b/ldm_patched/pfn/architecture/SPSR.py @@ -0,0 +1,383 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from . import block as B + + +class Get_gradient_nopadding(nn.Module): + def __init__(self): + super(Get_gradient_nopadding, self).__init__() + kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]] + kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]] + kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0) + kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0) + self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False) # type: ignore + + self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False) # type: ignore + + def forward(self, x): + x_list = [] + for i in range(x.shape[1]): + x_i = x[:, i] + x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1) + x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1) + x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6) + x_list.append(x_i) + + x = torch.cat(x_list, dim=1) + + return x + + +class SPSRNet(nn.Module): + def __init__( + self, + state_dict, + norm=None, + act: str = "leakyrelu", + upsampler: str = "upconv", + mode: B.ConvMode = "CNA", + ): + super(SPSRNet, self).__init__() + self.model_arch = "SPSR" + self.sub_type = "SR" + + self.state = state_dict + self.norm = norm + self.act = act + self.upsampler = upsampler + self.mode = mode + + self.num_blocks = self.get_num_blocks() + + self.in_nc: int = self.state["model.0.weight"].shape[1] + self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0] + + self.scale = self.get_scale(4) + self.num_filters: int = self.state["model.0.weight"].shape[0] + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + n_upscale = int(math.log(self.scale, 2)) + if self.scale == 3: + n_upscale = 1 + + fea_conv = B.conv_block( + self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None + ) + rb_blocks = [ + B.RRDB( + self.num_filters, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + for _ in range(self.num_blocks) + ] + LR_conv = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=norm, + act_type=None, + mode=mode, + ) + + if upsampler == "upconv": + upsample_block = B.upconv_block + elif upsampler == "pixelshuffle": + upsample_block = B.pixelshuffle_block + else: + raise NotImplementedError(f"upsample mode [{upsampler}] is not found") + if self.scale == 3: + a_upsampler = upsample_block( + self.num_filters, self.num_filters, 3, act_type=act + ) + else: + a_upsampler = [ + upsample_block(self.num_filters, self.num_filters, act_type=act) + for _ in range(n_upscale) + ] + self.HR_conv0_new = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + self.HR_conv1_new = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.model = B.sequential( + fea_conv, + B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)), + *a_upsampler, + self.HR_conv0_new, + ) + + self.get_g_nopadding = Get_gradient_nopadding() + + self.b_fea_conv = B.conv_block( + self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None + ) + + self.b_concat_1 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_1 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_2 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_2 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_3 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_3 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_concat_4 = B.conv_block( + 2 * self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + self.b_block_4 = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.b_LR_conv = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=norm, + act_type=None, + mode=mode, + ) + + if upsampler == "upconv": + upsample_block = B.upconv_block + elif upsampler == "pixelshuffle": + upsample_block = B.pixelshuffle_block + else: + raise NotImplementedError(f"upsample mode [{upsampler}] is not found") + if self.scale == 3: + b_upsampler = upsample_block( + self.num_filters, self.num_filters, 3, act_type=act + ) + else: + b_upsampler = [ + upsample_block(self.num_filters, self.num_filters, act_type=act) + for _ in range(n_upscale) + ] + + b_HR_conv0 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + b_HR_conv1 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1) + + self.conv_w = B.conv_block( + self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None + ) + + self.f_concat = B.conv_block( + self.num_filters * 2, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=None, + ) + + self.f_block = B.RRDB( + self.num_filters * 2, + kernel_size=3, + gc=32, + stride=1, + bias=True, + pad_type="zero", + norm_type=norm, + act_type=act, + mode="CNA", + ) + + self.f_HR_conv0 = B.conv_block( + self.num_filters, + self.num_filters, + kernel_size=3, + norm_type=None, + act_type=act, + ) + self.f_HR_conv1 = B.conv_block( + self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None + ) + + self.load_state_dict(self.state, strict=False) + + def get_scale(self, min_part: int = 4) -> int: + n = 0 + for part in list(self.state): + parts = part.split(".") + if len(parts) == 3: + part_num = int(parts[1]) + if part_num > min_part and parts[0] == "model" and parts[2] == "weight": + n += 1 + return 2**n + + def get_num_blocks(self) -> int: + nb = 0 + for part in list(self.state): + parts = part.split(".") + n_parts = len(parts) + if n_parts == 5 and parts[2] == "sub": + nb = int(parts[3]) + return nb + + def forward(self, x): + x_grad = self.get_g_nopadding(x) + x = self.model[0](x) + + x, block_list = self.model[1](x) + + x_ori = x + for i in range(5): + x = block_list[i](x) + x_fea1 = x + + for i in range(5): + x = block_list[i + 5](x) + x_fea2 = x + + for i in range(5): + x = block_list[i + 10](x) + x_fea3 = x + + for i in range(5): + x = block_list[i + 15](x) + x_fea4 = x + + x = block_list[20:](x) + # short cut + x = x_ori + x + x = self.model[2:](x) + x = self.HR_conv1_new(x) + + x_b_fea = self.b_fea_conv(x_grad) + x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1) + + x_cat_1 = self.b_block_1(x_cat_1) + x_cat_1 = self.b_concat_1(x_cat_1) + + x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1) + + x_cat_2 = self.b_block_2(x_cat_2) + x_cat_2 = self.b_concat_2(x_cat_2) + + x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1) + + x_cat_3 = self.b_block_3(x_cat_3) + x_cat_3 = self.b_concat_3(x_cat_3) + + x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1) + + x_cat_4 = self.b_block_4(x_cat_4) + x_cat_4 = self.b_concat_4(x_cat_4) + + x_cat_4 = self.b_LR_conv(x_cat_4) + + # short cut + x_cat_4 = x_cat_4 + x_b_fea + x_branch = self.b_module(x_cat_4) + + # x_out_branch = self.conv_w(x_branch) + ######## + x_branch_d = x_branch + x_f_cat = torch.cat([x_branch_d, x], dim=1) + x_f_cat = self.f_block(x_f_cat) + x_out = self.f_concat(x_f_cat) + x_out = self.f_HR_conv0(x_out) + x_out = self.f_HR_conv1(x_out) + + ######### + # return x_out_branch, x_out, x_grad + return x_out diff --git a/ldm_patched/pfn/architecture/SRVGG.py b/ldm_patched/pfn/architecture/SRVGG.py new file mode 100644 index 0000000000000000000000000000000000000000..7a8ec37ae5dc4effd0ba688cf4c3a51801e1f2c9 --- /dev/null +++ b/ldm_patched/pfn/architecture/SRVGG.py @@ -0,0 +1,114 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +import math + +import torch.nn as nn +import torch.nn.functional as F + + +class SRVGGNetCompact(nn.Module): + """A compact VGG-style network structure for super-resolution. + It is a compact network structure, which performs upsampling in the last layer and no convolution is + conducted on the HR feature space. + Args: + num_in_ch (int): Channel number of inputs. Default: 3. + num_out_ch (int): Channel number of outputs. Default: 3. + num_feat (int): Channel number of intermediate features. Default: 64. + num_conv (int): Number of convolution layers in the body network. Default: 16. + upscale (int): Upsampling factor. Default: 4. + act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu. + """ + + def __init__( + self, + state_dict, + act_type: str = "prelu", + ): + super(SRVGGNetCompact, self).__init__() + self.model_arch = "SRVGG (RealESRGAN)" + self.sub_type = "SR" + + self.act_type = act_type + + self.state = state_dict + + if "params" in self.state: + self.state = self.state["params"] + + self.key_arr = list(self.state.keys()) + + self.in_nc = self.get_in_nc() + self.num_feat = self.get_num_feats() + self.num_conv = self.get_num_conv() + self.out_nc = self.in_nc # :( + self.pixelshuffle_shape = None # Defined in get_scale() + self.scale = self.get_scale() + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + self.body = nn.ModuleList() + # the first conv + self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1)) + # the first activation + if act_type == "relu": + activation = nn.ReLU(inplace=True) + elif act_type == "prelu": + activation = nn.PReLU(num_parameters=self.num_feat) + elif act_type == "leakyrelu": + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) # type: ignore + + # the body structure + for _ in range(self.num_conv): + self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1)) + # activation + if act_type == "relu": + activation = nn.ReLU(inplace=True) + elif act_type == "prelu": + activation = nn.PReLU(num_parameters=self.num_feat) + elif act_type == "leakyrelu": + activation = nn.LeakyReLU(negative_slope=0.1, inplace=True) + self.body.append(activation) # type: ignore + + # the last conv + self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1)) # type: ignore + # upsample + self.upsampler = nn.PixelShuffle(self.scale) + + self.load_state_dict(self.state, strict=False) + + def get_num_conv(self) -> int: + return (int(self.key_arr[-1].split(".")[1]) - 2) // 2 + + def get_num_feats(self) -> int: + return self.state[self.key_arr[0]].shape[0] + + def get_in_nc(self) -> int: + return self.state[self.key_arr[0]].shape[1] + + def get_scale(self) -> int: + self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0] + # Assume out_nc is the same as in_nc + # I cant think of a better way to do that + self.out_nc = self.in_nc + scale = math.sqrt(self.pixelshuffle_shape / self.out_nc) + if scale - int(scale) > 0: + print( + "out_nc is probably different than in_nc, scale calculation might be wrong" + ) + scale = int(scale) + return scale + + def forward(self, x): + out = x + for i in range(0, len(self.body)): + out = self.body[i](out) + + out = self.upsampler(out) + # add the nearest upsampled image, so that the network learns the residual + base = F.interpolate(x, scale_factor=self.scale, mode="nearest") + out += base + return out diff --git a/ldm_patched/pfn/architecture/SwiftSRGAN.py b/ldm_patched/pfn/architecture/SwiftSRGAN.py new file mode 100644 index 0000000000000000000000000000000000000000..dbb7725b08dc2462661b7ba45db605a06fadacb9 --- /dev/null +++ b/ldm_patched/pfn/architecture/SwiftSRGAN.py @@ -0,0 +1,161 @@ +# From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py + +import torch +from torch import nn + + +class SeperableConv2d(nn.Module): + def __init__( + self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True + ): + super(SeperableConv2d, self).__init__() + self.depthwise = nn.Conv2d( + in_channels, + in_channels, + kernel_size=kernel_size, + stride=stride, + groups=in_channels, + bias=bias, + padding=padding, + ) + self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias) + + def forward(self, x): + return self.pointwise(self.depthwise(x)) + + +class ConvBlock(nn.Module): + def __init__( + self, + in_channels, + out_channels, + use_act=True, + use_bn=True, + discriminator=False, + **kwargs, + ): + super(ConvBlock, self).__init__() + + self.use_act = use_act + self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn) + self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity() + self.act = ( + nn.LeakyReLU(0.2, inplace=True) + if discriminator + else nn.PReLU(num_parameters=out_channels) + ) + + def forward(self, x): + return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x)) + + +class UpsampleBlock(nn.Module): + def __init__(self, in_channels, scale_factor): + super(UpsampleBlock, self).__init__() + + self.conv = SeperableConv2d( + in_channels, + in_channels * scale_factor**2, + kernel_size=3, + stride=1, + padding=1, + ) + self.ps = nn.PixelShuffle( + scale_factor + ) # (in_channels * 4, H, W) -> (in_channels, H*2, W*2) + self.act = nn.PReLU(num_parameters=in_channels) + + def forward(self, x): + return self.act(self.ps(self.conv(x))) + + +class ResidualBlock(nn.Module): + def __init__(self, in_channels): + super(ResidualBlock, self).__init__() + + self.block1 = ConvBlock( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + self.block2 = ConvBlock( + in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False + ) + + def forward(self, x): + out = self.block1(x) + out = self.block2(out) + return out + x + + +class Generator(nn.Module): + """Swift-SRGAN Generator + Args: + in_channels (int): number of input image channels. + num_channels (int): number of hidden channels. + num_blocks (int): number of residual blocks. + upscale_factor (int): factor to upscale the image [2x, 4x, 8x]. + Returns: + torch.Tensor: super resolution image + """ + + def __init__( + self, + state_dict, + ): + super(Generator, self).__init__() + self.model_arch = "Swift-SRGAN" + self.sub_type = "SR" + self.state = state_dict + if "model" in self.state: + self.state = self.state["model"] + + self.in_nc: int = self.state["initial.cnn.depthwise.weight"].shape[0] + self.out_nc: int = self.state["final_conv.pointwise.weight"].shape[0] + self.num_filters: int = self.state["initial.cnn.pointwise.weight"].shape[0] + self.num_blocks = len( + set([x.split(".")[1] for x in self.state.keys() if "residual" in x]) + ) + self.scale: int = 2 ** len( + set([x.split(".")[1] for x in self.state.keys() if "upsampler" in x]) + ) + + in_channels = self.in_nc + num_channels = self.num_filters + num_blocks = self.num_blocks + upscale_factor = self.scale + + self.supports_fp16 = True + self.supports_bfp16 = True + self.min_size_restriction = None + + self.initial = ConvBlock( + in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False + ) + self.residual = nn.Sequential( + *[ResidualBlock(num_channels) for _ in range(num_blocks)] + ) + self.convblock = ConvBlock( + num_channels, + num_channels, + kernel_size=3, + stride=1, + padding=1, + use_act=False, + ) + self.upsampler = nn.Sequential( + *[ + UpsampleBlock(num_channels, scale_factor=2) + for _ in range(upscale_factor // 2) + ] + ) + self.final_conv = SeperableConv2d( + num_channels, in_channels, kernel_size=9, stride=1, padding=4 + ) + + self.load_state_dict(self.state, strict=False) + + def forward(self, x): + initial = self.initial(x) + x = self.residual(initial) + x = self.convblock(x) + initial + x = self.upsampler(x) + return (torch.tanh(self.final_conv(x)) + 1) / 2 diff --git a/ldm_patched/pfn/architecture/Swin2SR.py b/ldm_patched/pfn/architecture/Swin2SR.py new file mode 100644 index 0000000000000000000000000000000000000000..cb57ecfc4ada45a6b087247017732437b1af0fcc --- /dev/null +++ b/ldm_patched/pfn/architecture/Swin2SR.py @@ -0,0 +1,1377 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345 +# Written by Conde and Choi et al. +# From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py +# ----------------------------------------------------------------------------------- + +import math +import re + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +# Originally from the timm package +from .timm.drop import DropPath +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, H // window_size, W // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + pretrained_window_size (tuple[int]): The height and width of the window in pre-training. + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + attn_drop=0.0, + proj_drop=0.0, + pretrained_window_size=[0, 0], + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.pretrained_window_size = pretrained_window_size + self.num_heads = num_heads + + self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) # type: ignore + + # mlp to generate continuous relative position bias + self.cpb_mlp = nn.Sequential( + nn.Linear(2, 512, bias=True), + nn.ReLU(inplace=True), + nn.Linear(512, num_heads, bias=False), + ) + + # get relative_coords_table + relative_coords_h = torch.arange( + -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32 + ) + relative_coords_w = torch.arange( + -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32 + ) + relative_coords_table = ( + torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) + .permute(1, 2, 0) + .contiguous() + .unsqueeze(0) + ) # 1, 2*Wh-1, 2*Ww-1, 2 + if pretrained_window_size[0] > 0: + relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1 + else: + relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1 + relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1 + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = ( + torch.sign(relative_coords_table) + * torch.log2(torch.abs(relative_coords_table) + 1.0) + / np.log2(8) + ) + + self.register_buffer("relative_coords_table", relative_coords_table) + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(dim)) # type: ignore + self.v_bias = nn.Parameter(torch.zeros(dim)) # type: ignore + else: + self.q_bias = None + self.v_bias = None + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) # type: ignore + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + # cosine attention + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + logit_scale = torch.clamp( + self.logit_scale, + max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device), + ).exp() + attn = attn * logit_scale + + relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view( + -1, self.num_heads + ) + relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view( # type: ignore + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + relative_position_bias = 16 * torch.sigmoid(relative_position_bias) + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, window_size={self.window_size}, " + f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}" + ) + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r"""Swin Transformer Block. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + pretrained_window_size (int): Window size in pre-training. + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + pretrained_window_size=0, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + attn_drop=attn_drop, + proj_drop=drop, + pretrained_window_size=to_2tuple(pretrained_window_size), + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn( + x_windows, mask=self.attn_mask + ) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn( + x_windows, mask=self.calculate_mask(x_size).to(x.device) + ) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + x = shortcut + self.drop_path(self.norm1(x)) + + # FFN + x = x + self.drop_path(self.norm2(self.mlp(x))) + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(2 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.reduction(x) + x = self.norm(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + flops += H * W * self.dim // 2 + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + pretrained_window_size (int): Local window size in pre-training. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + pretrained_window_size=0, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + pretrained_window_size=pretrained_window_size, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() # type: ignore + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + def _init_respostnorm(self): + for blk in self.blocks: + nn.init.constant_(blk.norm1.bias, 0) # type: ignore + nn.init.constant_(blk.norm1.weight, 0) # type: ignore + nn.init.constant_(blk.norm2.bias, 0) # type: ignore + nn.init.constant_(blk.norm2.weight, 0) # type: ignore + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + self.proj = nn.Conv2d( + in_chans, embed_dim, kernel_size=patch_size, stride=patch_size # type: ignore + ) + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + B, C, H, W = x.shape + # FIXME look at relaxing size constraints + # assert H == self.img_size[0] and W == self.img_size[1], + # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + Ho, Wo = self.patches_resolution + flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1]) # type: ignore + if self.norm is not None: + flops += Ho * Wo * self.embed_dim + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=dim, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=dim, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size): + return ( + self.patch_embed( + self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) + ) + + x + ) + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]] # type: ignore + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class Upsample_hf(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample_hf, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution # type: ignore + flops = H * W * self.num_feat * 3 * 9 + return flops + + +class Swin2SR(nn.Module): + r"""Swin2SR + A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(Swin2SR, self).__init__() + + # Defaults + img_size = 128 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = [6, 6, 6, 6] + num_heads = [6, 6, 6, 6] + window_size = 7 + mlp_ratio = 4.0 + qkv_bias = True + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + num_in_ch = in_chans + num_out_ch = in_chans + num_feat = 64 + + self.model_arch = "Swin2SR" + self.sub_type = "SR" + self.state = state_dict + if "params_ema" in self.state: + self.state = self.state["params_ema"] + elif "params" in self.state: + self.state = self.state["params"] + + state_keys = self.state.keys() + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_aux.weight" in state_keys: + upsampler = "pixelshuffle_aux" + elif "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + self.state.get("conv_before_upsample.0.weight", None).shape[1] + if self.state.get("conv_before_upsample.weight", None) + else 64 + ) + + num_in_ch = self.state["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = self.state["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle" or upsampler == "pixelshuffle_aux": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + embed_dim = self.state["conv_first.weight"].shape[0] + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int( + math.sqrt( + self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_index" + ].shape[0] + ) + ) + + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + # The JPEG models are the only ones with window-size 7, and they also use this range + img_range = 255.0 if window_size == 7 else 1.0 + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + ## END AUTO DETECTION + + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) # type: ignore + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + + if self.upsampler == "pixelshuffle_hf": + self.layers_hf = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])], # type: ignore # no impact on SR results # type: ignore + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers_hf.append(layer) + + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffle_aux": + self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_after_aux = nn.Sequential( + nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == "pixelshuffle_hf": + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.upsample_hf = Upsample_hf(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.conv_first_hf = nn.Sequential( + nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + self.conv_before_upsample_hf = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, + embed_dim, + num_out_ch, + (patches_resolution[0], patches_resolution[1]), + ) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + assert self.upscale == 4, "only support x4 now." + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + + self.load_state_dict(state_dict) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward_features_hf(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers_hf: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffle_aux": + bicubic = F.interpolate( + x, + size=(H * self.upscale, W * self.upscale), + mode="bicubic", + align_corners=False, + ) + bicubic = self.conv_bicubic(bicubic) + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + aux = self.conv_aux(x) # b, 3, LR_H, LR_W + x = self.conv_after_aux(aux) + x = ( + self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale] + + bicubic[:, :, : H * self.upscale, : W * self.upscale] + ) + x = self.conv_last(x) + aux = aux / self.img_range + self.mean + elif self.upsampler == "pixelshuffle_hf": + # for classical SR with HF + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x_before = self.conv_before_upsample(x) + x_out = self.conv_last(self.upsample(x_before)) + + x_hf = self.conv_first_hf(x_before) + x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf + x_hf = self.conv_before_upsample_hf(x_hf) + x_hf = self.conv_last_hf(self.upsample_hf(x_hf)) + x = x_out + x_hf + x_hf = x_hf / self.img_range + self.mean + + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == "nearest+conv": + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu( + self.conv_up1( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.lrelu( + self.conv_up2( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") + ) + ) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + if self.upsampler == "pixelshuffle_aux": + # NOTE: I removed an "aux" output here. not sure what that was for + return x[:, :, : H * self.upscale, : W * self.upscale] # type: ignore + + elif self.upsampler == "pixelshuffle_hf": + x_out = x_out / self.img_range + self.mean # type: ignore + return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale] # type: ignore + + else: + return x[:, :, : H * self.upscale, : W * self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() # type: ignore + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() # type: ignore + return flops diff --git a/ldm_patched/pfn/architecture/SwinIR.py b/ldm_patched/pfn/architecture/SwinIR.py new file mode 100644 index 0000000000000000000000000000000000000000..439dcbcb2b12f7ff27a01490f4c2ae7b6e4eab9e --- /dev/null +++ b/ldm_patched/pfn/architecture/SwinIR.py @@ -0,0 +1,1224 @@ +# pylint: skip-file +# ----------------------------------------------------------------------------------- +# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257 +# Originally Written by Ze Liu, Modified by Jingyun Liang. +# ----------------------------------------------------------------------------------- + +import math +import re + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as checkpoint + +# Originally from the timm package +from .timm.drop import DropPath +from .timm.helpers import to_2tuple +from .timm.weight_init import trunc_normal_ + + +class Mlp(nn.Module): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + drop=0.0, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop(x) + x = self.fc2(x) + x = self.drop(x) + return x + + +def window_partition(x, window_size): + """ + Args: + x: (B, H, W, C) + window_size (int): window size + + Returns: + windows: (num_windows*B, window_size, window_size, C) + """ + B, H, W, C = x.shape + x = x.view(B, H // window_size, window_size, W // window_size, window_size, C) + windows = ( + x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C) + ) + return windows + + +def window_reverse(windows, window_size, H, W): + """ + Args: + windows: (num_windows*B, window_size, window_size, C) + window_size (int): Window size + H (int): Height of image + W (int): Width of image + + Returns: + x: (B, H, W, C) + """ + B = int(windows.shape[0] / (H * W / window_size / window_size)) + x = windows.view( + B, H // window_size, W // window_size, window_size, window_size, -1 + ) + x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1) + return x + + +class WindowAttention(nn.Module): + r"""Window based multi-head self attention (W-MSA) module with relative position bias. + It supports both of shifted and non-shifted window. + + Args: + dim (int): Number of input channels. + window_size (tuple[int]): The height and width of the window. + num_heads (int): Number of attention heads. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set + attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0 + proj_drop (float, optional): Dropout ratio of output. Default: 0.0 + """ + + def __init__( + self, + dim, + window_size, + num_heads, + qkv_bias=True, + qk_scale=None, + attn_drop=0.0, + proj_drop=0.0, + ): + super().__init__() + self.dim = dim + self.window_size = window_size # Wh, Ww + self.num_heads = num_heads + head_dim = dim // num_heads + self.scale = qk_scale or head_dim**-0.5 + + # define a parameter table of relative position bias + self.relative_position_bias_table = nn.Parameter( # type: ignore + torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads) + ) # 2*Wh-1 * 2*Ww-1, nH + + # get pair-wise relative position index for each token inside the window + coords_h = torch.arange(self.window_size[0]) + coords_w = torch.arange(self.window_size[1]) + coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww + coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww + relative_coords = ( + coords_flatten[:, :, None] - coords_flatten[:, None, :] + ) # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute( + 1, 2, 0 + ).contiguous() # Wh*Ww, Wh*Ww, 2 + relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0 + relative_coords[:, :, 1] += self.window_size[1] - 1 + relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1 + relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww + self.register_buffer("relative_position_index", relative_position_index) + + self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(dim, dim) + + self.proj_drop = nn.Dropout(proj_drop) + + trunc_normal_(self.relative_position_bias_table, std=0.02) + self.softmax = nn.Softmax(dim=-1) + + def forward(self, x, mask=None): + """ + Args: + x: input features with shape of (num_windows*B, N, C) + mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None + """ + B_, N, C = x.shape + qkv = ( + self.qkv(x) + .reshape(B_, N, 3, self.num_heads, C // self.num_heads) + .permute(2, 0, 3, 1, 4) + ) + q, k, v = ( + qkv[0], + qkv[1], + qkv[2], + ) # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + relative_position_bias = self.relative_position_bias_table[ + self.relative_position_index.view(-1) # type: ignore + ].view( + self.window_size[0] * self.window_size[1], + self.window_size[0] * self.window_size[1], + -1, + ) # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.permute( + 2, 0, 1 + ).contiguous() # nH, Wh*Ww, Wh*Ww + attn = attn + relative_position_bias.unsqueeze(0) + + if mask is not None: + nW = mask.shape[0] + attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze( + 1 + ).unsqueeze(0) + attn = attn.view(-1, self.num_heads, N, N) + attn = self.softmax(attn) + else: + attn = self.softmax(attn) + + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B_, N, C) + x = self.proj(x) + x = self.proj_drop(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}" + + def flops(self, N): + # calculate flops for 1 window with token length of N + flops = 0 + # qkv = self.qkv(x) + flops += N * self.dim * 3 * self.dim + # attn = (q @ k.transpose(-2, -1)) + flops += self.num_heads * N * (self.dim // self.num_heads) * N + # x = (attn @ v) + flops += self.num_heads * N * N * (self.dim // self.num_heads) + # x = self.proj(x) + flops += N * self.dim * self.dim + return flops + + +class SwinTransformerBlock(nn.Module): + r"""Swin Transformer Block. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resulotion. + num_heads (int): Number of attention heads. + window_size (int): Window size. + shift_size (int): Shift size for SW-MSA. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float, optional): Stochastic depth rate. Default: 0.0 + act_layer (nn.Module, optional): Activation layer. Default: nn.GELU + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__( + self, + dim, + input_resolution, + num_heads, + window_size=7, + shift_size=0, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.num_heads = num_heads + self.window_size = window_size + self.shift_size = shift_size + self.mlp_ratio = mlp_ratio + if min(self.input_resolution) <= self.window_size: + # if window size is larger than input resolution, we don't partition windows + self.shift_size = 0 + self.window_size = min(self.input_resolution) + assert ( + 0 <= self.shift_size < self.window_size + ), "shift_size must in 0-window_size" + + self.norm1 = norm_layer(dim) + self.attn = WindowAttention( + dim, + window_size=to_2tuple(self.window_size), + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + ) + + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp( + in_features=dim, + hidden_features=mlp_hidden_dim, + act_layer=act_layer, + drop=drop, + ) + + if self.shift_size > 0: + attn_mask = self.calculate_mask(self.input_resolution) + else: + attn_mask = None + + self.register_buffer("attn_mask", attn_mask) + + def calculate_mask(self, x_size): + # calculate attention mask for SW-MSA + H, W = x_size + img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1 + h_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + w_slices = ( + slice(0, -self.window_size), + slice(-self.window_size, -self.shift_size), + slice(-self.shift_size, None), + ) + cnt = 0 + for h in h_slices: + for w in w_slices: + img_mask[:, h, w, :] = cnt + cnt += 1 + + mask_windows = window_partition( + img_mask, self.window_size + ) # nW, window_size, window_size, 1 + mask_windows = mask_windows.view(-1, self.window_size * self.window_size) + attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2) + attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill( + attn_mask == 0, float(0.0) + ) + + return attn_mask + + def forward(self, x, x_size): + H, W = x_size + B, L, C = x.shape + # assert L == H * W, "input feature has wrong size" + + shortcut = x + x = self.norm1(x) + x = x.view(B, H, W, C) + + # cyclic shift + if self.shift_size > 0: + shifted_x = torch.roll( + x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2) + ) + else: + shifted_x = x + + # partition windows + x_windows = window_partition( + shifted_x, self.window_size + ) # nW*B, window_size, window_size, C + x_windows = x_windows.view( + -1, self.window_size * self.window_size, C + ) # nW*B, window_size*window_size, C + + # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size + if self.input_resolution == x_size: + attn_windows = self.attn( + x_windows, mask=self.attn_mask + ) # nW*B, window_size*window_size, C + else: + attn_windows = self.attn( + x_windows, mask=self.calculate_mask(x_size).to(x.device) + ) + + # merge windows + attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C) + shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C + + # reverse cyclic shift + if self.shift_size > 0: + x = torch.roll( + shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2) + ) + else: + x = shifted_x + x = x.view(B, H * W, C) + + # FFN + x = shortcut + self.drop_path(x) + x = x + self.drop_path(self.mlp(self.norm2(x))) + + return x + + def extra_repr(self) -> str: + return ( + f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " + f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}" + ) + + def flops(self): + flops = 0 + H, W = self.input_resolution + # norm1 + flops += self.dim * H * W + # W-MSA/SW-MSA + nW = H * W / self.window_size / self.window_size + flops += nW * self.attn.flops(self.window_size * self.window_size) + # mlp + flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio + # norm2 + flops += self.dim * H * W + return flops + + +class PatchMerging(nn.Module): + r"""Patch Merging Layer. + + Args: + input_resolution (tuple[int]): Resolution of input feature. + dim (int): Number of input channels. + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + """ + + def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm): + super().__init__() + self.input_resolution = input_resolution + self.dim = dim + self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False) + self.norm = norm_layer(4 * dim) + + def forward(self, x): + """ + x: B, H*W, C + """ + H, W = self.input_resolution + B, L, C = x.shape + assert L == H * W, "input feature has wrong size" + assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even." + + x = x.view(B, H, W, C) + + x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C + x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C + x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C + x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C + x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C + x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C + + x = self.norm(x) + x = self.reduction(x) + + return x + + def extra_repr(self) -> str: + return f"input_resolution={self.input_resolution}, dim={self.dim}" + + def flops(self): + H, W = self.input_resolution + flops = H * W * self.dim + flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim + return flops + + +class BasicLayer(nn.Module): + """A basic Swin Transformer layer for one stage. + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + ): + super().__init__() + self.dim = dim + self.input_resolution = input_resolution + self.depth = depth + self.use_checkpoint = use_checkpoint + + # build blocks + self.blocks = nn.ModuleList( + [ + SwinTransformerBlock( + dim=dim, + input_resolution=input_resolution, + num_heads=num_heads, + window_size=window_size, + shift_size=0 if (i % 2 == 0) else window_size // 2, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path[i] + if isinstance(drop_path, list) + else drop_path, + norm_layer=norm_layer, + ) + for i in range(depth) + ] + ) + + # patch merging layer + if downsample is not None: + self.downsample = downsample( + input_resolution, dim=dim, norm_layer=norm_layer + ) + else: + self.downsample = None + + def forward(self, x, x_size): + for blk in self.blocks: + if self.use_checkpoint: + x = checkpoint.checkpoint(blk, x, x_size) + else: + x = blk(x, x_size) + if self.downsample is not None: + x = self.downsample(x) + return x + + def extra_repr(self) -> str: + return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}" + + def flops(self): + flops = 0 + for blk in self.blocks: + flops += blk.flops() # type: ignore + if self.downsample is not None: + flops += self.downsample.flops() + return flops + + +class RSTB(nn.Module): + """Residual Swin Transformer Block (RSTB). + + Args: + dim (int): Number of input channels. + input_resolution (tuple[int]): Input resolution. + depth (int): Number of blocks. + num_heads (int): Number of attention heads. + window_size (int): Local window size. + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. + qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set. + drop (float, optional): Dropout rate. Default: 0.0 + attn_drop (float, optional): Attention dropout rate. Default: 0.0 + drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0 + norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm + downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False. + img_size: Input image size. + patch_size: Patch size. + resi_connection: The convolutional block before residual connection. + """ + + def __init__( + self, + dim, + input_resolution, + depth, + num_heads, + window_size, + mlp_ratio=4.0, + qkv_bias=True, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + norm_layer=nn.LayerNorm, + downsample=None, + use_checkpoint=False, + img_size=224, + patch_size=4, + resi_connection="1conv", + ): + super(RSTB, self).__init__() + + self.dim = dim + self.input_resolution = input_resolution + + self.residual_group = BasicLayer( + dim=dim, + input_resolution=input_resolution, + depth=depth, + num_heads=num_heads, + window_size=window_size, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop, + attn_drop=attn_drop, + drop_path=drop_path, + norm_layer=norm_layer, + downsample=downsample, + use_checkpoint=use_checkpoint, + ) + + if resi_connection == "1conv": + self.conv = nn.Conv2d(dim, dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv = nn.Sequential( + nn.Conv2d(dim, dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(dim // 4, dim, 3, 1, 1), + ) + + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=0, + embed_dim=dim, + norm_layer=None, + ) + + def forward(self, x, x_size): + return ( + self.patch_embed( + self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size)) + ) + + x + ) + + def flops(self): + flops = 0 + flops += self.residual_group.flops() + H, W = self.input_resolution + flops += H * W * self.dim * self.dim * 9 + flops += self.patch_embed.flops() + flops += self.patch_unembed.flops() + + return flops + + +class PatchEmbed(nn.Module): + r"""Image to Patch Embedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + if norm_layer is not None: + self.norm = norm_layer(embed_dim) + else: + self.norm = None + + def forward(self, x): + x = x.flatten(2).transpose(1, 2) # B Ph*Pw C + if self.norm is not None: + x = self.norm(x) + return x + + def flops(self): + flops = 0 + H, W = self.img_size + if self.norm is not None: + flops += H * W * self.embed_dim # type: ignore + return flops + + +class PatchUnEmbed(nn.Module): + r"""Image to Patch Unembedding + + Args: + img_size (int): Image size. Default: 224. + patch_size (int): Patch token size. Default: 4. + in_chans (int): Number of input image channels. Default: 3. + embed_dim (int): Number of linear projection output channels. Default: 96. + norm_layer (nn.Module, optional): Normalization layer. Default: None + """ + + def __init__( + self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None + ): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + patches_resolution = [ + img_size[0] // patch_size[0], # type: ignore + img_size[1] // patch_size[1], # type: ignore + ] + self.img_size = img_size + self.patch_size = patch_size + self.patches_resolution = patches_resolution + self.num_patches = patches_resolution[0] * patches_resolution[1] + + self.in_chans = in_chans + self.embed_dim = embed_dim + + def forward(self, x, x_size): + B, HW, C = x.shape + x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C + return x + + def flops(self): + flops = 0 + return flops + + +class Upsample(nn.Sequential): + """Upsample module. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + """ + + def __init__(self, scale, num_feat): + m = [] + if (scale & (scale - 1)) == 0: # scale = 2^n + for _ in range(int(math.log(scale, 2))): + m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(2)) + elif scale == 3: + m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1)) + m.append(nn.PixelShuffle(3)) + else: + raise ValueError( + f"scale {scale} is not supported. " "Supported scales: 2^n and 3." + ) + super(Upsample, self).__init__(*m) + + +class UpsampleOneStep(nn.Sequential): + """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle) + Used in lightweight SR to save parameters. + + Args: + scale (int): Scale factor. Supported scales: 2^n and 3. + num_feat (int): Channel number of intermediate features. + + """ + + def __init__(self, scale, num_feat, num_out_ch, input_resolution=None): + self.num_feat = num_feat + self.input_resolution = input_resolution + m = [] + m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1)) + m.append(nn.PixelShuffle(scale)) + super(UpsampleOneStep, self).__init__(*m) + + def flops(self): + H, W = self.input_resolution # type: ignore + flops = H * W * self.num_feat * 3 * 9 + return flops + + +class SwinIR(nn.Module): + r"""SwinIR + A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer. + + Args: + img_size (int | tuple(int)): Input image size. Default 64 + patch_size (int | tuple(int)): Patch size. Default: 1 + in_chans (int): Number of input image channels. Default: 3 + embed_dim (int): Patch embedding dimension. Default: 96 + depths (tuple(int)): Depth of each Swin Transformer layer. + num_heads (tuple(int)): Number of attention heads in different layers. + window_size (int): Window size. Default: 7 + mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4 + qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True + qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None + drop_rate (float): Dropout rate. Default: 0 + attn_drop_rate (float): Attention dropout rate. Default: 0 + drop_path_rate (float): Stochastic depth rate. Default: 0.1 + norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm. + ape (bool): If True, add absolute position embedding to the patch embedding. Default: False + patch_norm (bool): If True, add normalization after patch embedding. Default: True + use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False + upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction + img_range: Image range. 1. or 255. + upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None + resi_connection: The convolutional block before residual connection. '1conv'/'3conv' + """ + + def __init__( + self, + state_dict, + **kwargs, + ): + super(SwinIR, self).__init__() + + # Defaults + img_size = 64 + patch_size = 1 + in_chans = 3 + embed_dim = 96 + depths = [6, 6, 6, 6] + num_heads = [6, 6, 6, 6] + window_size = 7 + mlp_ratio = 4.0 + qkv_bias = True + qk_scale = None + drop_rate = 0.0 + attn_drop_rate = 0.0 + drop_path_rate = 0.1 + norm_layer = nn.LayerNorm + ape = False + patch_norm = True + use_checkpoint = False + upscale = 2 + img_range = 1.0 + upsampler = "" + resi_connection = "1conv" + num_feat = 64 + num_in_ch = in_chans + num_out_ch = in_chans + supports_fp16 = True + self.start_unshuffle = 1 + + self.model_arch = "SwinIR" + self.sub_type = "SR" + self.state = state_dict + if "params_ema" in self.state: + self.state = self.state["params_ema"] + elif "params" in self.state: + self.state = self.state["params"] + + state_keys = self.state.keys() + + if "conv_before_upsample.0.weight" in state_keys: + if "conv_up1.weight" in state_keys: + upsampler = "nearest+conv" + else: + upsampler = "pixelshuffle" + supports_fp16 = False + elif "upsample.0.weight" in state_keys: + upsampler = "pixelshuffledirect" + else: + upsampler = "" + + num_feat = ( + self.state.get("conv_before_upsample.0.weight", None).shape[1] + if self.state.get("conv_before_upsample.weight", None) + else 64 + ) + + if "conv_first.1.weight" in self.state: + self.state["conv_first.weight"] = self.state.pop("conv_first.1.weight") + self.state["conv_first.bias"] = self.state.pop("conv_first.1.bias") + self.start_unshuffle = round(math.sqrt(self.state["conv_first.weight"].shape[1] // 3)) + + num_in_ch = self.state["conv_first.weight"].shape[1] + in_chans = num_in_ch + if "conv_last.weight" in state_keys: + num_out_ch = self.state["conv_last.weight"].shape[0] + else: + num_out_ch = num_in_ch + + upscale = 1 + if upsampler == "nearest+conv": + upsample_keys = [ + x for x in state_keys if "conv_up" in x and "bias" not in x + ] + + for upsample_key in upsample_keys: + upscale *= 2 + elif upsampler == "pixelshuffle": + upsample_keys = [ + x + for x in state_keys + if "upsample" in x and "conv" not in x and "bias" not in x + ] + for upsample_key in upsample_keys: + shape = self.state[upsample_key].shape[0] + upscale *= math.sqrt(shape // num_feat) + upscale = int(upscale) + elif upsampler == "pixelshuffledirect": + upscale = int( + math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch) + ) + + max_layer_num = 0 + max_block_num = 0 + for key in state_keys: + result = re.match( + r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key + ) + if result: + layer_num, block_num = result.groups() + max_layer_num = max(max_layer_num, int(layer_num)) + max_block_num = max(max_block_num, int(block_num)) + + depths = [max_block_num + 1 for _ in range(max_layer_num + 1)] + + if ( + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + in state_keys + ): + num_heads_num = self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_bias_table" + ].shape[-1] + num_heads = [num_heads_num for _ in range(max_layer_num + 1)] + else: + num_heads = depths + + embed_dim = self.state["conv_first.weight"].shape[0] + + mlp_ratio = float( + self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0] + / embed_dim + ) + + # TODO: could actually count the layers, but this should do + if "layers.0.conv.4.weight" in state_keys: + resi_connection = "3conv" + else: + resi_connection = "1conv" + + window_size = int( + math.sqrt( + self.state[ + "layers.0.residual_group.blocks.0.attn.relative_position_index" + ].shape[0] + ) + ) + + if "layers.0.residual_group.blocks.1.attn_mask" in state_keys: + img_size = int( + math.sqrt( + self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0] + ) + * window_size + ) + + # The JPEG models are the only ones with window-size 7, and they also use this range + img_range = 255.0 if window_size == 7 else 1.0 + + self.in_nc = num_in_ch + self.out_nc = num_out_ch + self.num_feat = num_feat + self.embed_dim = embed_dim + self.num_heads = num_heads + self.depths = depths + self.window_size = window_size + self.mlp_ratio = mlp_ratio + self.scale = upscale / self.start_unshuffle + self.upsampler = upsampler + self.img_size = img_size + self.img_range = img_range + self.resi_connection = resi_connection + + self.supports_fp16 = False # Too much weirdness to support this at the moment + self.supports_bfp16 = True + self.min_size_restriction = 16 + + self.img_range = img_range + if in_chans == 3: + rgb_mean = (0.4488, 0.4371, 0.4040) + self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1) + else: + self.mean = torch.zeros(1, 1, 1, 1) + self.upscale = upscale + self.upsampler = upsampler + self.window_size = window_size + + ##################################################################################################### + ################################### 1, shallow feature extraction ################################### + self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1) + + ##################################################################################################### + ################################### 2, deep feature extraction ###################################### + self.num_layers = len(depths) + self.embed_dim = embed_dim + self.ape = ape + self.patch_norm = patch_norm + self.num_features = embed_dim + self.mlp_ratio = mlp_ratio + + # split image into non-overlapping patches + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + num_patches = self.patch_embed.num_patches + patches_resolution = self.patch_embed.patches_resolution + self.patches_resolution = patches_resolution + + # merge non-overlapping patches into image + self.patch_unembed = PatchUnEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=embed_dim, + embed_dim=embed_dim, + norm_layer=norm_layer if self.patch_norm else None, + ) + + # absolute position embedding + if self.ape: + self.absolute_pos_embed = nn.Parameter( # type: ignore + torch.zeros(1, num_patches, embed_dim) + ) + trunc_normal_(self.absolute_pos_embed, std=0.02) + + self.pos_drop = nn.Dropout(p=drop_rate) + + # stochastic depth + dpr = [ + x.item() for x in torch.linspace(0, drop_path_rate, sum(depths)) + ] # stochastic depth decay rule + + # build Residual Swin Transformer blocks (RSTB) + self.layers = nn.ModuleList() + for i_layer in range(self.num_layers): + layer = RSTB( + dim=embed_dim, + input_resolution=(patches_resolution[0], patches_resolution[1]), + depth=depths[i_layer], + num_heads=num_heads[i_layer], + window_size=window_size, + mlp_ratio=self.mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[ + sum(depths[:i_layer]) : sum(depths[: i_layer + 1]) # type: ignore + ], # no impact on SR results + norm_layer=norm_layer, + downsample=None, + use_checkpoint=use_checkpoint, + img_size=img_size, + patch_size=patch_size, + resi_connection=resi_connection, + ) + self.layers.append(layer) + self.norm = norm_layer(self.num_features) + + # build the last conv layer in deep feature extraction + if resi_connection == "1conv": + self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1) + elif resi_connection == "3conv": + # to save parameters and memory + self.conv_after_body = nn.Sequential( + nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1), + ) + + ##################################################################################################### + ################################ 3, high quality image reconstruction ################################ + if self.upsampler == "pixelshuffle": + # for classical SR + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.upsample = Upsample(upscale, num_feat) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR (to save parameters) + self.upsample = UpsampleOneStep( + upscale, + embed_dim, + num_out_ch, + (patches_resolution[0], patches_resolution[1]), + ) + elif self.upsampler == "nearest+conv": + # for real-world SR (less artifacts) + self.conv_before_upsample = nn.Sequential( + nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True) + ) + self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + if self.upscale == 4: + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + elif self.upscale == 8: + self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1) + self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) + self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) + else: + # for image denoising and JPEG compression artifact reduction + self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1) + + self.apply(self._init_weights) + self.load_state_dict(self.state, strict=False) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore # type: ignore + def no_weight_decay(self): + return {"absolute_pos_embed"} + + @torch.jit.ignore # type: ignore + def no_weight_decay_keywords(self): + return {"relative_position_bias_table"} + + def check_image_size(self, x): + _, _, h, w = x.size() + mod_pad_h = (self.window_size - h % self.window_size) % self.window_size + mod_pad_w = (self.window_size - w % self.window_size) % self.window_size + x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect") + return x + + def forward_features(self, x): + x_size = (x.shape[2], x.shape[3]) + x = self.patch_embed(x) + if self.ape: + x = x + self.absolute_pos_embed + x = self.pos_drop(x) + + for layer in self.layers: + x = layer(x, x_size) + + x = self.norm(x) # B L C + x = self.patch_unembed(x, x_size) + + return x + + def forward(self, x): + H, W = x.shape[2:] + x = self.check_image_size(x) + + self.mean = self.mean.type_as(x) + x = (x - self.mean) * self.img_range + + if self.start_unshuffle > 1: + x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle) + + if self.upsampler == "pixelshuffle": + # for classical SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.conv_last(self.upsample(x)) + elif self.upsampler == "pixelshuffledirect": + # for lightweight SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.upsample(x) + elif self.upsampler == "nearest+conv": + # for real-world SR + x = self.conv_first(x) + x = self.conv_after_body(self.forward_features(x)) + x + x = self.conv_before_upsample(x) + x = self.lrelu( + self.conv_up1( + torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest") # type: ignore + ) + ) + if self.upscale == 4: + x = self.lrelu( + self.conv_up2( + torch.nn.functional.interpolate( # type: ignore + x, scale_factor=2, mode="nearest" + ) + ) + ) + elif self.upscale == 8: + x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest'))) + x = self.conv_last(self.lrelu(self.conv_hr(x))) + else: + # for image denoising and JPEG compression artifact reduction + x_first = self.conv_first(x) + res = self.conv_after_body(self.forward_features(x_first)) + x_first + x = x + self.conv_last(res) + + x = x / self.img_range + self.mean + + return x[:, :, : H * self.upscale, : W * self.upscale] + + def flops(self): + flops = 0 + H, W = self.patches_resolution + flops += H * W * 3 * self.embed_dim * 9 + flops += self.patch_embed.flops() + for i, layer in enumerate(self.layers): + flops += layer.flops() # type: ignore + flops += H * W * 3 * self.embed_dim * self.embed_dim + flops += self.upsample.flops() # type: ignore + return flops diff --git a/ldm_patched/pfn/architecture/__init__.py b/ldm_patched/pfn/architecture/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/ldm_patched/pfn/architecture/block.py b/ldm_patched/pfn/architecture/block.py new file mode 100644 index 0000000000000000000000000000000000000000..d7bc5d227008a73c40f9087da1ee3ae2ca25a896 --- /dev/null +++ b/ldm_patched/pfn/architecture/block.py @@ -0,0 +1,546 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- + +from __future__ import annotations + +from collections import OrderedDict +try: + from typing import Literal +except ImportError: + from typing_extensions import Literal + +import torch +import torch.nn as nn + +#################### +# Basic blocks +#################### + + +def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1): + # helper selecting activation + # neg_slope: for leakyrelu and init of prelu + # n_prelu: for p_relu num_parameters + act_type = act_type.lower() + if act_type == "relu": + layer = nn.ReLU(inplace) + elif act_type == "leakyrelu": + layer = nn.LeakyReLU(neg_slope, inplace) + elif act_type == "prelu": + layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope) + else: + raise NotImplementedError( + "activation layer [{:s}] is not found".format(act_type) + ) + return layer + + +def norm(norm_type: str, nc: int): + # helper selecting normalization layer + norm_type = norm_type.lower() + if norm_type == "batch": + layer = nn.BatchNorm2d(nc, affine=True) + elif norm_type == "instance": + layer = nn.InstanceNorm2d(nc, affine=False) + else: + raise NotImplementedError( + "normalization layer [{:s}] is not found".format(norm_type) + ) + return layer + + +def pad(pad_type: str, padding): + # helper selecting padding layer + # if padding is 'zero', do by conv layers + pad_type = pad_type.lower() + if padding == 0: + return None + if pad_type == "reflect": + layer = nn.ReflectionPad2d(padding) + elif pad_type == "replicate": + layer = nn.ReplicationPad2d(padding) + else: + raise NotImplementedError( + "padding layer [{:s}] is not implemented".format(pad_type) + ) + return layer + + +def get_valid_padding(kernel_size, dilation): + kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1) + padding = (kernel_size - 1) // 2 + return padding + + +class ConcatBlock(nn.Module): + # Concat the output of a submodule to its input + def __init__(self, submodule): + super(ConcatBlock, self).__init__() + self.sub = submodule + + def forward(self, x): + output = torch.cat((x, self.sub(x)), dim=1) + return output + + def __repr__(self): + tmpstr = "Identity .. \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +class ShortcutBlock(nn.Module): + # Elementwise sum the output of a submodule to its input + def __init__(self, submodule): + super(ShortcutBlock, self).__init__() + self.sub = submodule + + def forward(self, x): + output = x + self.sub(x) + return output + + def __repr__(self): + tmpstr = "Identity + \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +class ShortcutBlockSPSR(nn.Module): + # Elementwise sum the output of a submodule to its input + def __init__(self, submodule): + super(ShortcutBlockSPSR, self).__init__() + self.sub = submodule + + def forward(self, x): + return x, self.sub + + def __repr__(self): + tmpstr = "Identity + \n|" + modstr = self.sub.__repr__().replace("\n", "\n|") + tmpstr = tmpstr + modstr + return tmpstr + + +def sequential(*args): + # Flatten Sequential. It unwraps nn.Sequential. + if len(args) == 1: + if isinstance(args[0], OrderedDict): + raise NotImplementedError("sequential does not support OrderedDict input.") + return args[0] # No sequential is needed. + modules = [] + for module in args: + if isinstance(module, nn.Sequential): + for submodule in module.children(): + modules.append(submodule) + elif isinstance(module, nn.Module): + modules.append(module) + return nn.Sequential(*modules) + + +ConvMode = Literal["CNA", "NAC", "CNAC"] + + +# 2x2x2 Conv Block +def conv_block_2c2( + in_nc, + out_nc, + act_type="relu", +): + return sequential( + nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1), + nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0), + act(act_type) if act_type else None, + ) + + +def conv_block( + in_nc: int, + out_nc: int, + kernel_size, + stride=1, + dilation=1, + groups=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type: str | None = "relu", + mode: ConvMode = "CNA", + c2x2=False, +): + """ + Conv layer with padding, normalization, activation + mode: CNA --> Conv -> Norm -> Act + NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16) + """ + + if c2x2: + return conv_block_2c2(in_nc, out_nc, act_type=act_type) + + assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode) + padding = get_valid_padding(kernel_size, dilation) + p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None + padding = padding if pad_type == "zero" else 0 + + c = nn.Conv2d( + in_nc, + out_nc, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + groups=groups, + ) + a = act(act_type) if act_type else None + if mode in ("CNA", "CNAC"): + n = norm(norm_type, out_nc) if norm_type else None + return sequential(p, c, n, a) + elif mode == "NAC": + if norm_type is None and act_type is not None: + a = act(act_type, inplace=False) + # Important! + # input----ReLU(inplace)----Conv--+----output + # |________________________| + # inplace ReLU will modify the input, therefore wrong output + n = norm(norm_type, in_nc) if norm_type else None + return sequential(n, a, p, c) + else: + assert False, f"Invalid conv mode {mode}" + + +#################### +# Useful blocks +#################### + + +class ResNetBlock(nn.Module): + """ + ResNet Block, 3-3 style + with extra residual scaling used in EDSR + (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17) + """ + + def __init__( + self, + in_nc, + mid_nc, + out_nc, + kernel_size=3, + stride=1, + dilation=1, + groups=1, + bias=True, + pad_type="zero", + norm_type=None, + act_type="relu", + mode: ConvMode = "CNA", + res_scale=1, + ): + super(ResNetBlock, self).__init__() + conv0 = conv_block( + in_nc, + mid_nc, + kernel_size, + stride, + dilation, + groups, + bias, + pad_type, + norm_type, + act_type, + mode, + ) + if mode == "CNA": + act_type = None + if mode == "CNAC": # Residual path: |-CNAC-| + act_type = None + norm_type = None + conv1 = conv_block( + mid_nc, + out_nc, + kernel_size, + stride, + dilation, + groups, + bias, + pad_type, + norm_type, + act_type, + mode, + ) + # if in_nc != out_nc: + # self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \ + # None, None) + # print('Need a projecter in ResNetBlock.') + # else: + # self.project = lambda x:x + self.res = sequential(conv0, conv1) + self.res_scale = res_scale + + def forward(self, x): + res = self.res(x).mul(self.res_scale) + return x + res + + +class RRDB(nn.Module): + """ + Residual in Residual Dense Block + (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks) + """ + + def __init__( + self, + nf, + kernel_size=3, + gc=32, + stride=1, + bias: bool = True, + pad_type="zero", + norm_type=None, + act_type="leakyrelu", + mode: ConvMode = "CNA", + _convtype="Conv2D", + _spectral_norm=False, + plus=False, + c2x2=False, + ): + super(RRDB, self).__init__() + self.RDB1 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB2 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + self.RDB3 = ResidualDenseBlock_5C( + nf, + kernel_size, + gc, + stride, + bias, + pad_type, + norm_type, + act_type, + mode, + plus=plus, + c2x2=c2x2, + ) + + def forward(self, x): + out = self.RDB1(x) + out = self.RDB2(out) + out = self.RDB3(out) + return out * 0.2 + x + + +class ResidualDenseBlock_5C(nn.Module): + """ + Residual Dense Block + style: 5 convs + The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18) + Modified options that can be used: + - "Partial Convolution based Padding" arXiv:1811.11718 + - "Spectral normalization" arXiv:1802.05957 + - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. + {Rakotonirina} and A. {Rasoanaivo} + + Args: + nf (int): Channel number of intermediate features (num_feat). + gc (int): Channels for each growth (num_grow_ch: growth channel, + i.e. intermediate channels). + convtype (str): the type of convolution to use. Default: 'Conv2D' + gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new + trainable parameters) + plus (bool): enable the additional residual paths from ESRGAN+ + (adds trainable parameters) + """ + + def __init__( + self, + nf=64, + kernel_size=3, + gc=32, + stride=1, + bias: bool = True, + pad_type="zero", + norm_type=None, + act_type="leakyrelu", + mode: ConvMode = "CNA", + plus=False, + c2x2=False, + ): + super(ResidualDenseBlock_5C, self).__init__() + + ## + + self.conv1x1 = conv1x1(nf, gc) if plus else None + ## + + + self.conv1 = conv_block( + nf, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv2 = conv_block( + nf + gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv3 = conv_block( + nf + 2 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + self.conv4 = conv_block( + nf + 3 * gc, + gc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + mode=mode, + c2x2=c2x2, + ) + if mode == "CNA": + last_act = None + else: + last_act = act_type + self.conv5 = conv_block( + nf + 4 * gc, + nf, + 3, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=last_act, + mode=mode, + c2x2=c2x2, + ) + + def forward(self, x): + x1 = self.conv1(x) + x2 = self.conv2(torch.cat((x, x1), 1)) + if self.conv1x1: + # pylint: disable=not-callable + x2 = x2 + self.conv1x1(x) # + + x3 = self.conv3(torch.cat((x, x1, x2), 1)) + x4 = self.conv4(torch.cat((x, x1, x2, x3), 1)) + if self.conv1x1: + x4 = x4 + x2 # + + x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) + return x5 * 0.2 + x + + +def conv1x1(in_planes, out_planes, stride=1): + return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False) + + +#################### +# Upsampler +#################### + + +def pixelshuffle_block( + in_nc: int, + out_nc: int, + upscale_factor=2, + kernel_size=3, + stride=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type="relu", +): + """ + Pixel shuffle layer + (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional + Neural Network, CVPR17) + """ + conv = conv_block( + in_nc, + out_nc * (upscale_factor**2), + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=None, + act_type=None, + ) + pixel_shuffle = nn.PixelShuffle(upscale_factor) + + n = norm(norm_type, out_nc) if norm_type else None + a = act(act_type) if act_type else None + return sequential(conv, pixel_shuffle, n, a) + + +def upconv_block( + in_nc: int, + out_nc: int, + upscale_factor=2, + kernel_size=3, + stride=1, + bias=True, + pad_type="zero", + norm_type: str | None = None, + act_type="relu", + mode="nearest", + c2x2=False, +): + # Up conv + # described in https://distill.pub/2016/deconv-checkerboard/ + upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode) + conv = conv_block( + in_nc, + out_nc, + kernel_size, + stride, + bias=bias, + pad_type=pad_type, + norm_type=norm_type, + act_type=act_type, + c2x2=c2x2, + ) + return sequential(upsample, conv) diff --git a/ldm_patched/pfn/architecture/face/LICENSE-GFPGAN b/ldm_patched/pfn/architecture/face/LICENSE-GFPGAN new file mode 100644 index 0000000000000000000000000000000000000000..5ac273fd509e328f396e6e4444673a3b051a4968 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/LICENSE-GFPGAN @@ -0,0 +1,351 @@ +Tencent is pleased to support the open source community by making GFPGAN available. + +Copyright (C) 2021 THL A29 Limited, a Tencent company. 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Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +In the event that redistribution and/or use for commercial purpose in +source or binary forms, with or without modification is required, +please contact the contributor(s) of the work. diff --git a/ldm_patched/pfn/architecture/face/arcface_arch.py b/ldm_patched/pfn/architecture/face/arcface_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b548af059a71b38c6c18cd35cbfed7bae7e55441 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/arcface_arch.py @@ -0,0 +1,265 @@ +import torch.nn as nn + + +def conv3x3(inplanes, outplanes, stride=1): + """A simple wrapper for 3x3 convolution with padding. + + Args: + inplanes (int): Channel number of inputs. + outplanes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + """ + return nn.Conv2d( + inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False + ) + + +class BasicBlock(nn.Module): + """Basic residual block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class IRBlock(nn.Module): + """Improved residual block (IR Block) used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + expansion = 1 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True): + super(IRBlock, self).__init__() + self.bn0 = nn.BatchNorm2d(inplanes) + self.conv1 = conv3x3(inplanes, inplanes) + self.bn1 = nn.BatchNorm2d(inplanes) + self.prelu = nn.PReLU() + self.conv2 = conv3x3(inplanes, planes, stride) + self.bn2 = nn.BatchNorm2d(planes) + self.downsample = downsample + self.stride = stride + self.use_se = use_se + if self.use_se: + self.se = SEBlock(planes) + + def forward(self, x): + residual = x + out = self.bn0(x) + out = self.conv1(out) + out = self.bn1(out) + out = self.prelu(out) + + out = self.conv2(out) + out = self.bn2(out) + if self.use_se: + out = self.se(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.prelu(out) + + return out + + +class Bottleneck(nn.Module): + """Bottleneck block used in the ResNetArcFace architecture. + + Args: + inplanes (int): Channel number of inputs. + planes (int): Channel number of outputs. + stride (int): Stride in convolution. Default: 1. + downsample (nn.Module): The downsample module. Default: None. + """ + + expansion = 4 # output channel expansion ratio + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d( + planes, planes, kernel_size=3, stride=stride, padding=1, bias=False + ) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d( + planes, planes * self.expansion, kernel_size=1, bias=False + ) + self.bn3 = nn.BatchNorm2d(planes * self.expansion) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class SEBlock(nn.Module): + """The squeeze-and-excitation block (SEBlock) used in the IRBlock. + + Args: + channel (int): Channel number of inputs. + reduction (int): Channel reduction ration. Default: 16. + """ + + def __init__(self, channel, reduction=16): + super(SEBlock, self).__init__() + self.avg_pool = nn.AdaptiveAvgPool2d( + 1 + ) # pool to 1x1 without spatial information + self.fc = nn.Sequential( + nn.Linear(channel, channel // reduction), + nn.PReLU(), + nn.Linear(channel // reduction, channel), + nn.Sigmoid(), + ) + + def forward(self, x): + b, c, _, _ = x.size() + y = self.avg_pool(x).view(b, c) + y = self.fc(y).view(b, c, 1, 1) + return x * y + + +class ResNetArcFace(nn.Module): + """ArcFace with ResNet architectures. + + Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition. + + Args: + block (str): Block used in the ArcFace architecture. + layers (tuple(int)): Block numbers in each layer. + use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True. + """ + + def __init__(self, block, layers, use_se=True): + if block == "IRBlock": + block = IRBlock + self.inplanes = 64 + self.use_se = use_se + super(ResNetArcFace, self).__init__() + + self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.prelu = nn.PReLU() + self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.bn4 = nn.BatchNorm2d(512) + self.dropout = nn.Dropout() + self.fc5 = nn.Linear(512 * 8 * 8, 512) + self.bn5 = nn.BatchNorm1d(512) + + # initialization + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + nn.init.constant_(m.bias, 0) + + def _make_layer(self, block, planes, num_blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False, + ), + nn.BatchNorm2d(planes * block.expansion), + ) + layers = [] + layers.append( + block(self.inplanes, planes, stride, downsample, use_se=self.use_se) + ) + self.inplanes = planes + for _ in range(1, num_blocks): + layers.append(block(self.inplanes, planes, use_se=self.use_se)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.prelu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.bn4(x) + x = self.dropout(x) + x = x.view(x.size(0), -1) + x = self.fc5(x) + x = self.bn5(x) + + return x diff --git a/ldm_patched/pfn/architecture/face/codeformer.py b/ldm_patched/pfn/architecture/face/codeformer.py new file mode 100644 index 0000000000000000000000000000000000000000..a0e2e985e8f4487547779574c6b210da412a8e71 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/codeformer.py @@ -0,0 +1,790 @@ +""" +Modified from https://github.com/sczhou/CodeFormer +VQGAN code, adapted from the original created by the Unleashing Transformers authors: +https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py +This version of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me. +""" +import math +from typing import Optional + +import torch +import torch.nn as nn +import torch.nn.functional as F +import logging as logger +from torch import Tensor + + +class VectorQuantizer(nn.Module): + def __init__(self, codebook_size, emb_dim, beta): + super(VectorQuantizer, self).__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.beta = beta # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + self.embedding = nn.Embedding(self.codebook_size, self.emb_dim) + self.embedding.weight.data.uniform_( + -1.0 / self.codebook_size, 1.0 / self.codebook_size + ) + + def forward(self, z): + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.emb_dim) + + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + d = ( + (z_flattened**2).sum(dim=1, keepdim=True) + + (self.embedding.weight**2).sum(1) + - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + ) + + mean_distance = torch.mean(d) + # find closest encodings + # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1) + min_encoding_scores, min_encoding_indices = torch.topk( + d, 1, dim=1, largest=False + ) + # [0-1], higher score, higher confidence + min_encoding_scores = torch.exp(-min_encoding_scores / 10) + + min_encodings = torch.zeros( + min_encoding_indices.shape[0], self.codebook_size + ).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # compute loss for embedding + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( + (z_q - z.detach()) ** 2 + ) + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return ( + z_q, + loss, + { + "perplexity": perplexity, + "min_encodings": min_encodings, + "min_encoding_indices": min_encoding_indices, + "min_encoding_scores": min_encoding_scores, + "mean_distance": mean_distance, + }, + ) + + def get_codebook_feat(self, indices, shape): + # input indices: batch*token_num -> (batch*token_num)*1 + # shape: batch, height, width, channel + indices = indices.view(-1, 1) + min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices) + min_encodings.scatter_(1, indices, 1) + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: # reshape back to match original input shape + z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous() + + return z_q + + +class GumbelQuantizer(nn.Module): + def __init__( + self, + codebook_size, + emb_dim, + num_hiddens, + straight_through=False, + kl_weight=5e-4, + temp_init=1.0, + ): + super().__init__() + self.codebook_size = codebook_size # number of embeddings + self.emb_dim = emb_dim # dimension of embedding + self.straight_through = straight_through + self.temperature = temp_init + self.kl_weight = kl_weight + self.proj = nn.Conv2d( + num_hiddens, codebook_size, 1 + ) # projects last encoder layer to quantized logits + self.embed = nn.Embedding(codebook_size, emb_dim) + + def forward(self, z): + hard = self.straight_through if self.training else True + + logits = self.proj(z) + + soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard) + + z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight) + + # + kl divergence to the prior loss + qy = F.softmax(logits, dim=1) + diff = ( + self.kl_weight + * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean() + ) + min_encoding_indices = soft_one_hot.argmax(dim=1) + + return z_q, diff, {"min_encoding_indices": min_encoding_indices} + + +class Downsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + return x + + +class Upsample(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.conv = nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = F.interpolate(x, scale_factor=2.0, mode="nearest") + x = self.conv(x) + + return x + + +class AttnBlock(nn.Module): + def __init__(self, in_channels): + super().__init__() + self.in_channels = in_channels + + self.norm = normalize(in_channels) + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x): + h_ = x + h_ = self.norm(h_) + q = self.q(h_) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, c, h * w) + q = q.permute(0, 2, 1) + k = k.reshape(b, c, h * w) + w_ = torch.bmm(q, k) + w_ = w_ * (int(c) ** (-0.5)) + w_ = F.softmax(w_, dim=2) + + # attend to values + v = v.reshape(b, c, h * w) + w_ = w_.permute(0, 2, 1) + h_ = torch.bmm(v, w_) + h_ = h_.reshape(b, c, h, w) + + h_ = self.proj_out(h_) + + return x + h_ + + +class Encoder(nn.Module): + def __init__( + self, + in_channels, + nf, + out_channels, + ch_mult, + num_res_blocks, + resolution, + attn_resolutions, + ): + super().__init__() + self.nf = nf + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.attn_resolutions = attn_resolutions + + curr_res = self.resolution + in_ch_mult = (1,) + tuple(ch_mult) + + blocks = [] + # initial convultion + blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1)) + + # residual and downsampling blocks, with attention on smaller res (16x16) + for i in range(self.num_resolutions): + block_in_ch = nf * in_ch_mult[i] + block_out_ch = nf * ch_mult[i] + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + if curr_res in attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != self.num_resolutions - 1: + blocks.append(Downsample(block_in_ch)) + curr_res = curr_res // 2 + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore + blocks.append(AttnBlock(block_in_ch)) # type: ignore + blocks.append(ResBlock(block_in_ch, block_in_ch)) # type: ignore + + # normalise and convert to latent size + blocks.append(normalize(block_in_ch)) # type: ignore + blocks.append( + nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1) # type: ignore + ) + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class Generator(nn.Module): + def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim): + super().__init__() + self.nf = nf + self.ch_mult = ch_mult + self.num_resolutions = len(self.ch_mult) + self.num_res_blocks = res_blocks + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.in_channels = emb_dim + self.out_channels = 3 + block_in_ch = self.nf * self.ch_mult[-1] + curr_res = self.resolution // 2 ** (self.num_resolutions - 1) + + blocks = [] + # initial conv + blocks.append( + nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1) + ) + + # non-local attention block + blocks.append(ResBlock(block_in_ch, block_in_ch)) + blocks.append(AttnBlock(block_in_ch)) + blocks.append(ResBlock(block_in_ch, block_in_ch)) + + for i in reversed(range(self.num_resolutions)): + block_out_ch = self.nf * self.ch_mult[i] + + for _ in range(self.num_res_blocks): + blocks.append(ResBlock(block_in_ch, block_out_ch)) + block_in_ch = block_out_ch + + if curr_res in self.attn_resolutions: + blocks.append(AttnBlock(block_in_ch)) + + if i != 0: + blocks.append(Upsample(block_in_ch)) + curr_res = curr_res * 2 + + blocks.append(normalize(block_in_ch)) + blocks.append( + nn.Conv2d( + block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1 + ) + ) + + self.blocks = nn.ModuleList(blocks) + + def forward(self, x): + for block in self.blocks: + x = block(x) + + return x + + +class VQAutoEncoder(nn.Module): + def __init__( + self, + img_size, + nf, + ch_mult, + quantizer="nearest", + res_blocks=2, + attn_resolutions=[16], + codebook_size=1024, + emb_dim=256, + beta=0.25, + gumbel_straight_through=False, + gumbel_kl_weight=1e-8, + model_path=None, + ): + super().__init__() + self.in_channels = 3 + self.nf = nf + self.n_blocks = res_blocks + self.codebook_size = codebook_size + self.embed_dim = emb_dim + self.ch_mult = ch_mult + self.resolution = img_size + self.attn_resolutions = attn_resolutions + self.quantizer_type = quantizer + self.encoder = Encoder( + self.in_channels, + self.nf, + self.embed_dim, + self.ch_mult, + self.n_blocks, + self.resolution, + self.attn_resolutions, + ) + if self.quantizer_type == "nearest": + self.beta = beta # 0.25 + self.quantize = VectorQuantizer( + self.codebook_size, self.embed_dim, self.beta + ) + elif self.quantizer_type == "gumbel": + self.gumbel_num_hiddens = emb_dim + self.straight_through = gumbel_straight_through + self.kl_weight = gumbel_kl_weight + self.quantize = GumbelQuantizer( + self.codebook_size, + self.embed_dim, + self.gumbel_num_hiddens, + self.straight_through, + self.kl_weight, + ) + self.generator = Generator( + nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim + ) + + if model_path is not None: + chkpt = torch.load(model_path, map_location="cpu") + if "params_ema" in chkpt: + self.load_state_dict( + torch.load(model_path, map_location="cpu")["params_ema"] + ) + logger.info(f"vqgan is loaded from: {model_path} [params_ema]") + elif "params" in chkpt: + self.load_state_dict( + torch.load(model_path, map_location="cpu")["params"] + ) + logger.info(f"vqgan is loaded from: {model_path} [params]") + else: + raise ValueError("Wrong params!") + + def forward(self, x): + x = self.encoder(x) + quant, codebook_loss, quant_stats = self.quantize(x) + x = self.generator(quant) + return x, codebook_loss, quant_stats + + +def calc_mean_std(feat, eps=1e-5): + """Calculate mean and std for adaptive_instance_normalization. + Args: + feat (Tensor): 4D tensor. + eps (float): A small value added to the variance to avoid + divide-by-zero. Default: 1e-5. + """ + size = feat.size() + assert len(size) == 4, "The input feature should be 4D tensor." + b, c = size[:2] + feat_var = feat.view(b, c, -1).var(dim=2) + eps + feat_std = feat_var.sqrt().view(b, c, 1, 1) + feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1) + return feat_mean, feat_std + + +def adaptive_instance_normalization(content_feat, style_feat): + """Adaptive instance normalization. + Adjust the reference features to have the similar color and illuminations + as those in the degradate features. + Args: + content_feat (Tensor): The reference feature. + style_feat (Tensor): The degradate features. + """ + size = content_feat.size() + style_mean, style_std = calc_mean_std(style_feat) + content_mean, content_std = calc_mean_std(content_feat) + normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand( + size + ) + return normalized_feat * style_std.expand(size) + style_mean.expand(size) + + +class PositionEmbeddingSine(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one + used by the Attention is all you need paper, generalized to work on images. + """ + + def __init__( + self, num_pos_feats=64, temperature=10000, normalize=False, scale=None + ): + super().__init__() + self.num_pos_feats = num_pos_feats + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, x, mask=None): + if mask is None: + mask = torch.zeros( + (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool + ) + not_mask = ~mask # pylint: disable=invalid-unary-operand-type + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + eps = 1e-6 + y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale + + dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device) + dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 + ).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +def _get_activation_fn(activation): + """Return an activation function given a string""" + if activation == "relu": + return F.relu + if activation == "gelu": + return F.gelu + if activation == "glu": + return F.glu + raise RuntimeError(f"activation should be relu/gelu, not {activation}.") + + +class TransformerSALayer(nn.Module): + def __init__( + self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu" + ): + super().__init__() + self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout) + # Implementation of Feedforward model - MLP + self.linear1 = nn.Linear(embed_dim, dim_mlp) + self.dropout = nn.Dropout(dropout) + self.linear2 = nn.Linear(dim_mlp, embed_dim) + + self.norm1 = nn.LayerNorm(embed_dim) + self.norm2 = nn.LayerNorm(embed_dim) + self.dropout1 = nn.Dropout(dropout) + self.dropout2 = nn.Dropout(dropout) + + self.activation = _get_activation_fn(activation) + + def with_pos_embed(self, tensor, pos: Optional[Tensor]): + return tensor if pos is None else tensor + pos + + def forward( + self, + tgt, + tgt_mask: Optional[Tensor] = None, + tgt_key_padding_mask: Optional[Tensor] = None, + query_pos: Optional[Tensor] = None, + ): + # self attention + tgt2 = self.norm1(tgt) + q = k = self.with_pos_embed(tgt2, query_pos) + tgt2 = self.self_attn( + q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask + )[0] + tgt = tgt + self.dropout1(tgt2) + + # ffn + tgt2 = self.norm2(tgt) + tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2)))) + tgt = tgt + self.dropout2(tgt2) + return tgt + + +def normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +@torch.jit.script # type: ignore +def swish(x): + return x * torch.sigmoid(x) + + +class ResBlock(nn.Module): + def __init__(self, in_channels, out_channels=None): + super(ResBlock, self).__init__() + self.in_channels = in_channels + self.out_channels = in_channels if out_channels is None else out_channels + self.norm1 = normalize(in_channels) + self.conv1 = nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore + ) + self.norm2 = normalize(out_channels) + self.conv2 = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 # type: ignore + ) + if self.in_channels != self.out_channels: + self.conv_out = nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 # type: ignore + ) + + def forward(self, x_in): + x = x_in + x = self.norm1(x) + x = swish(x) + x = self.conv1(x) + x = self.norm2(x) + x = swish(x) + x = self.conv2(x) + if self.in_channels != self.out_channels: + x_in = self.conv_out(x_in) + + return x + x_in + + +class Fuse_sft_block(nn.Module): + def __init__(self, in_ch, out_ch): + super().__init__() + self.encode_enc = ResBlock(2 * in_ch, out_ch) + + self.scale = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), + ) + + self.shift = nn.Sequential( + nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1), + ) + + def forward(self, enc_feat, dec_feat, w=1): + enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1)) + scale = self.scale(enc_feat) + shift = self.shift(enc_feat) + residual = w * (dec_feat * scale + shift) + out = dec_feat + residual + return out + + +class CodeFormer(VQAutoEncoder): + def __init__(self, state_dict): + dim_embd = 512 + n_head = 8 + n_layers = 9 + codebook_size = 1024 + latent_size = 256 + connect_list = ["32", "64", "128", "256"] + fix_modules = ["quantize", "generator"] + + # This is just a guess as I only have one model to look at + position_emb = state_dict["position_emb"] + dim_embd = position_emb.shape[1] + latent_size = position_emb.shape[0] + + try: + n_layers = len( + set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x]) + ) + except: + pass + + codebook_size = state_dict["quantize.embedding.weight"].shape[0] + + # This is also just another guess + n_head_exp = ( + state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd + ) + n_head = 2**n_head_exp + + in_nc = state_dict["encoder.blocks.0.weight"].shape[1] + + self.model_arch = "CodeFormer" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = in_nc + self.out_nc = in_nc + + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 16 + + super(CodeFormer, self).__init__( + 512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size + ) + + if fix_modules is not None: + for module in fix_modules: + for param in getattr(self, module).parameters(): + param.requires_grad = False + + self.connect_list = connect_list + self.n_layers = n_layers + self.dim_embd = dim_embd + self.dim_mlp = dim_embd * 2 + + self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd)) # type: ignore + self.feat_emb = nn.Linear(256, self.dim_embd) + + # transformer + self.ft_layers = nn.Sequential( + *[ + TransformerSALayer( + embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0 + ) + for _ in range(self.n_layers) + ] + ) + + # logits_predict head + self.idx_pred_layer = nn.Sequential( + nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False) + ) + + self.channels = { + "16": 512, + "32": 256, + "64": 256, + "128": 128, + "256": 128, + "512": 64, + } + + # after second residual block for > 16, before attn layer for ==16 + self.fuse_encoder_block = { + "512": 2, + "256": 5, + "128": 8, + "64": 11, + "32": 14, + "16": 18, + } + # after first residual block for > 16, before attn layer for ==16 + self.fuse_generator_block = { + "16": 6, + "32": 9, + "64": 12, + "128": 15, + "256": 18, + "512": 21, + } + + # fuse_convs_dict + self.fuse_convs_dict = nn.ModuleDict() + for f_size in self.connect_list: + in_ch = self.channels[f_size] + self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch) + + self.load_state_dict(state_dict) + + def _init_weights(self, module): + if isinstance(module, (nn.Linear, nn.Embedding)): + module.weight.data.normal_(mean=0.0, std=0.02) + if isinstance(module, nn.Linear) and module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.LayerNorm): + module.bias.data.zero_() + module.weight.data.fill_(1.0) + + def forward(self, x, weight=0.5, **kwargs): + detach_16 = True + code_only = False + adain = True + # ################### Encoder ##################### + enc_feat_dict = {} + out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list] + for i, block in enumerate(self.encoder.blocks): + x = block(x) + if i in out_list: + enc_feat_dict[str(x.shape[-1])] = x.clone() + + lq_feat = x + # ################# Transformer ################### + # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat) + pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1) + # BCHW -> BC(HW) -> (HW)BC + feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1)) + query_emb = feat_emb + # Transformer encoder + for layer in self.ft_layers: + query_emb = layer(query_emb, query_pos=pos_emb) + + # output logits + logits = self.idx_pred_layer(query_emb) # (hw)bn + logits = logits.permute(1, 0, 2) # (hw)bn -> b(hw)n + + if code_only: # for training stage II + # logits doesn't need softmax before cross_entropy loss + return logits, lq_feat + + # ################# Quantization ################### + # if self.training: + # quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight]) + # # b(hw)c -> bc(hw) -> bchw + # quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape) + # ------------ + soft_one_hot = F.softmax(logits, dim=2) + _, top_idx = torch.topk(soft_one_hot, 1, dim=2) + quant_feat = self.quantize.get_codebook_feat( + top_idx, shape=[x.shape[0], 16, 16, 256] # type: ignore + ) + # preserve gradients + # quant_feat = lq_feat + (quant_feat - lq_feat).detach() + + if detach_16: + quant_feat = quant_feat.detach() # for training stage III + if adain: + quant_feat = adaptive_instance_normalization(quant_feat, lq_feat) + + # ################## Generator #################### + x = quant_feat + fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list] + + for i, block in enumerate(self.generator.blocks): + x = block(x) + if i in fuse_list: # fuse after i-th block + f_size = str(x.shape[-1]) + if weight > 0: + x = self.fuse_convs_dict[f_size]( + enc_feat_dict[f_size].detach(), x, weight + ) + out = x + # logits doesn't need softmax before cross_entropy loss + # return out, logits, lq_feat + return out, logits diff --git a/ldm_patched/pfn/architecture/face/fused_act.py b/ldm_patched/pfn/architecture/face/fused_act.py new file mode 100644 index 0000000000000000000000000000000000000000..7ed526547b4644ac6341947a801b76d9ed798f26 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/fused_act.py @@ -0,0 +1,81 @@ +# pylint: skip-file +# type: ignore +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501 + +import torch +from torch import nn +from torch.autograd import Function + +fused_act_ext = None + + +class FusedLeakyReLUFunctionBackward(Function): + @staticmethod + def forward(ctx, grad_output, out, negative_slope, scale): + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + empty = grad_output.new_empty(0) + + grad_input = fused_act_ext.fused_bias_act( + grad_output, empty, out, 3, 1, negative_slope, scale + ) + + dim = [0] + + if grad_input.ndim > 2: + dim += list(range(2, grad_input.ndim)) + + grad_bias = grad_input.sum(dim).detach() + + return grad_input, grad_bias + + @staticmethod + def backward(ctx, gradgrad_input, gradgrad_bias): + (out,) = ctx.saved_tensors + gradgrad_out = fused_act_ext.fused_bias_act( + gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale + ) + + return gradgrad_out, None, None, None + + +class FusedLeakyReLUFunction(Function): + @staticmethod + def forward(ctx, input, bias, negative_slope, scale): + empty = input.new_empty(0) + out = fused_act_ext.fused_bias_act( + input, bias, empty, 3, 0, negative_slope, scale + ) + ctx.save_for_backward(out) + ctx.negative_slope = negative_slope + ctx.scale = scale + + return out + + @staticmethod + def backward(ctx, grad_output): + (out,) = ctx.saved_tensors + + grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply( + grad_output, out, ctx.negative_slope, ctx.scale + ) + + return grad_input, grad_bias, None, None + + +class FusedLeakyReLU(nn.Module): + def __init__(self, channel, negative_slope=0.2, scale=2**0.5): + super().__init__() + + self.bias = nn.Parameter(torch.zeros(channel)) + self.negative_slope = negative_slope + self.scale = scale + + def forward(self, input): + return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale) + + +def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5): + return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale) diff --git a/ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py b/ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..b6e820e006f52936c3399d3d37fdf571f2385dcb --- /dev/null +++ b/ldm_patched/pfn/architecture/face/gfpgan_bilinear_arch.py @@ -0,0 +1,389 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn + +from .gfpganv1_arch import ResUpBlock +from .stylegan2_bilinear_arch import ( + ConvLayer, + EqualConv2d, + EqualLinear, + ResBlock, + ScaledLeakyReLU, + StyleGAN2GeneratorBilinear, +) + + +class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorBilinearSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorBilinearSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class GFPGANBilinear(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for + deployment. It can be easily converted to the clean version: GFPGANv1Clean. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False, + ): + super(GFPGANBilinear, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + self.min_size_restriction = 512 + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer( + 3, channels[f"{first_out_size}"], 1, bias=True, activate=True + ) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append(ResBlock(in_channels, out_channels)) + in_channels = out_channels + + self.final_conv = ConvLayer( + in_channels, channels["4"], 3, bias=True, activate=True + ) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append( + EqualConv2d( + channels[f"{2**i}"], + 3, + 1, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ) + ) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels["4"] * 4 * 4, + linear_out_channel, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=1, + ), + ) + ) + self.condition_shift.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ) + ) + + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True): + """Forward function for GFPGANBilinear. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs diff --git a/ldm_patched/pfn/architecture/face/gfpganv1_arch.py b/ldm_patched/pfn/architecture/face/gfpganv1_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..72d72fc865ec35b2ccd23f13b3d8ef0be5dbaf7a --- /dev/null +++ b/ldm_patched/pfn/architecture/face/gfpganv1_arch.py @@ -0,0 +1,566 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU +from .stylegan2_arch import ( + ConvLayer, + EqualConv2d, + EqualLinear, + ResBlock, + ScaledLeakyReLU, + StyleGAN2Generator, +) + + +class StyleGAN2GeneratorSFT(StyleGAN2Generator): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ConvUpLayer(nn.Module): + """Convolutional upsampling layer. It uses bilinear upsampler + Conv. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + activate=True, + ): + super(ConvUpLayer, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + # self.scale is used to scale the convolution weights, which is related to the common initializations. + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + + if bias and not activate: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + # activation + if activate: + if bias: + self.activation = FusedLeakyReLU(out_channels) + else: + self.activation = ScaledLeakyReLU(0.2) + else: + self.activation = None + + def forward(self, x): + # bilinear upsample + out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) + # conv + out = F.conv2d( + out, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + # activation + if self.activation is not None: + out = self.activation(out) + return out + + +class ResUpBlock(nn.Module): + """Residual block with upsampling. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels): + super(ResUpBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvUpLayer( + in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True + ) + self.skip = ConvUpLayer( + in_channels, out_channels, 1, bias=False, activate=False + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out + + +class GFPGANv1(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be + applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1). + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + channel_multiplier=1, + resample_kernel=(1, 3, 3, 1), + decoder_load_path=None, + fix_decoder=True, + # for stylegan decoder + num_mlp=8, + lr_mlp=0.01, + input_is_latent=False, + different_w=False, + narrow=1, + sft_half=False, + ): + super(GFPGANv1, self).__init__() + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = ConvLayer( + 3, channels[f"{first_out_size}"], 1, bias=True, activate=True + ) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append( + ResBlock(in_channels, out_channels, resample_kernel) + ) + in_channels = out_channels + + self.final_conv = ConvLayer( + in_channels, channels["4"], 3, bias=True, activate=True + ) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResUpBlock(in_channels, out_channels)) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append( + EqualConv2d( + channels[f"{2**i}"], + 3, + 1, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ) + ) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = EqualLinear( + channels["4"] * 4 * 4, + linear_out_channel, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + resample_kernel=resample_kernel, + lr_mlp=lr_mlp, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=1, + ), + ) + ) + self.condition_shift.append( + nn.Sequential( + EqualConv2d( + out_channels, + out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ScaledLeakyReLU(0.2), + EqualConv2d( + out_channels, + sft_out_channels, + 3, + stride=1, + padding=1, + bias=True, + bias_init_val=0, + ), + ) + ) + + def forward( + self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs + ): + """Forward function for GFPGANv1. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = self.conv_body_first(x) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + + feat = self.final_conv(feat) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs + + +class FacialComponentDiscriminator(nn.Module): + """Facial component (eyes, mouth, noise) discriminator used in GFPGAN.""" + + def __init__(self): + super(FacialComponentDiscriminator, self).__init__() + # It now uses a VGG-style architectrue with fixed model size + self.conv1 = ConvLayer( + 3, + 64, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv2 = ConvLayer( + 64, + 128, + 3, + downsample=True, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv3 = ConvLayer( + 128, + 128, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv4 = ConvLayer( + 128, + 256, + 3, + downsample=True, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.conv5 = ConvLayer( + 256, + 256, + 3, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ) + self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False) + + def forward(self, x, return_feats=False, **kwargs): + """Forward function for FacialComponentDiscriminator. + Args: + x (Tensor): Input images. + return_feats (bool): Whether to return intermediate features. Default: False. + """ + feat = self.conv1(x) + feat = self.conv3(self.conv2(feat)) + rlt_feats = [] + if return_feats: + rlt_feats.append(feat.clone()) + feat = self.conv5(self.conv4(feat)) + if return_feats: + rlt_feats.append(feat.clone()) + out = self.final_conv(feat) + + if return_feats: + return out, rlt_feats + else: + return out, None diff --git a/ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py b/ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..16470d6345f71ed1517ff26f65b9cd125d80d99e --- /dev/null +++ b/ldm_patched/pfn/architecture/face/gfpganv1_clean_arch.py @@ -0,0 +1,370 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .stylegan2_clean_arch import StyleGAN2GeneratorClean + + +class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean): + """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform). + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + narrow=1, + sft_half=False, + ): + super(StyleGAN2GeneratorCSFT, self).__init__( + out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + ) + self.sft_half = sft_half + + def forward( + self, + styles, + conditions, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorCSFT. + Args: + styles (list[Tensor]): Sample codes of styles. + conditions (list[Tensor]): SFT conditions to generators. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + + # the conditions may have fewer levels + if i < len(conditions): + # SFT part to combine the conditions + if self.sft_half: # only apply SFT to half of the channels + out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1) + out_sft = out_sft * conditions[i - 1] + conditions[i] + out = torch.cat([out_same, out_sft], dim=1) + else: # apply SFT to all the channels + out = out * conditions[i - 1] + conditions[i] + + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ResBlock(nn.Module): + """Residual block with bilinear upsampling/downsampling. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + mode (str): Upsampling/downsampling mode. Options: down | up. Default: down. + """ + + def __init__(self, in_channels, out_channels, mode="down"): + super(ResBlock, self).__init__() + + self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1) + self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1) + self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False) + if mode == "down": + self.scale_factor = 0.5 + elif mode == "up": + self.scale_factor = 2 + + def forward(self, x): + out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) + # upsample/downsample + out = F.interpolate( + out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) + # skip + x = F.interpolate( + x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False + ) + skip = self.skip(x) + out = out + skip + return out + + +class GFPGANv1Clean(nn.Module): + """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT. + It is the clean version without custom compiled CUDA extensions used in StyleGAN2. + Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None. + fix_decoder (bool): Whether to fix the decoder. Default: True. + num_mlp (int): Layer number of MLP style layers. Default: 8. + input_is_latent (bool): Whether input is latent style. Default: False. + different_w (bool): Whether to use different latent w for different layers. Default: False. + narrow (float): The narrow ratio for channels. Default: 1. + sft_half (bool): Whether to apply SFT on half of the input channels. Default: False. + """ + + def __init__( + self, + state_dict, + ): + super(GFPGANv1Clean, self).__init__() + + out_size = 512 + num_style_feat = 512 + channel_multiplier = 2 + decoder_load_path = None + fix_decoder = False + num_mlp = 8 + input_is_latent = True + different_w = True + narrow = 1 + sft_half = True + + self.model_arch = "GFPGAN" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = 3 + self.out_nc = 3 + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 512 + + self.input_is_latent = input_is_latent + self.different_w = different_w + self.num_style_feat = num_style_feat + + unet_narrow = narrow * 0.5 # by default, use a half of input channels + channels = { + "4": int(512 * unet_narrow), + "8": int(512 * unet_narrow), + "16": int(512 * unet_narrow), + "32": int(512 * unet_narrow), + "64": int(256 * channel_multiplier * unet_narrow), + "128": int(128 * channel_multiplier * unet_narrow), + "256": int(64 * channel_multiplier * unet_narrow), + "512": int(32 * channel_multiplier * unet_narrow), + "1024": int(16 * channel_multiplier * unet_narrow), + } + + self.log_size = int(math.log(out_size, 2)) + first_out_size = 2 ** (int(math.log(out_size, 2))) + + self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1) + + # downsample + in_channels = channels[f"{first_out_size}"] + self.conv_body_down = nn.ModuleList() + for i in range(self.log_size, 2, -1): + out_channels = channels[f"{2**(i - 1)}"] + self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down")) + in_channels = out_channels + + self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1) + + # upsample + in_channels = channels["4"] + self.conv_body_up = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up")) + in_channels = out_channels + + # to RGB + self.toRGB = nn.ModuleList() + for i in range(3, self.log_size + 1): + self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1)) + + if different_w: + linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat + else: + linear_out_channel = num_style_feat + + self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel) + + # the decoder: stylegan2 generator with SFT modulations + self.stylegan_decoder = StyleGAN2GeneratorCSFT( + out_size=out_size, + num_style_feat=num_style_feat, + num_mlp=num_mlp, + channel_multiplier=channel_multiplier, + narrow=narrow, + sft_half=sft_half, + ) + + # load pre-trained stylegan2 model if necessary + if decoder_load_path: + self.stylegan_decoder.load_state_dict( + torch.load( + decoder_load_path, map_location=lambda storage, loc: storage + )["params_ema"] + ) + # fix decoder without updating params + if fix_decoder: + for _, param in self.stylegan_decoder.named_parameters(): + param.requires_grad = False + + # for SFT modulations (scale and shift) + self.condition_scale = nn.ModuleList() + self.condition_shift = nn.ModuleList() + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + if sft_half: + sft_out_channels = out_channels + else: + sft_out_channels = out_channels * 2 + self.condition_scale.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), + ) + ) + self.condition_shift.append( + nn.Sequential( + nn.Conv2d(out_channels, out_channels, 3, 1, 1), + nn.LeakyReLU(0.2, True), + nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1), + ) + ) + self.load_state_dict(state_dict) + + def forward( + self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs + ): + """Forward function for GFPGANv1Clean. + Args: + x (Tensor): Input images. + return_latents (bool): Whether to return style latents. Default: False. + return_rgb (bool): Whether return intermediate rgb images. Default: True. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + """ + conditions = [] + unet_skips = [] + out_rgbs = [] + + # encoder + feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2) + for i in range(self.log_size - 2): + feat = self.conv_body_down[i](feat) + unet_skips.insert(0, feat) + feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2) + + # style code + style_code = self.final_linear(feat.view(feat.size(0), -1)) + if self.different_w: + style_code = style_code.view(style_code.size(0), -1, self.num_style_feat) + + # decode + for i in range(self.log_size - 2): + # add unet skip + feat = feat + unet_skips[i] + # ResUpLayer + feat = self.conv_body_up[i](feat) + # generate scale and shift for SFT layers + scale = self.condition_scale[i](feat) + conditions.append(scale.clone()) + shift = self.condition_shift[i](feat) + conditions.append(shift.clone()) + # generate rgb images + if return_rgb: + out_rgbs.append(self.toRGB[i](feat)) + + # decoder + image, _ = self.stylegan_decoder( + [style_code], + conditions, + return_latents=return_latents, + input_is_latent=self.input_is_latent, + randomize_noise=randomize_noise, + ) + + return image, out_rgbs diff --git a/ldm_patched/pfn/architecture/face/restoreformer_arch.py b/ldm_patched/pfn/architecture/face/restoreformer_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..4492260291d6d74b2c0d38130f7aa8b50ba2fc11 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/restoreformer_arch.py @@ -0,0 +1,776 @@ +# pylint: skip-file +# type: ignore +"""Modified from https://github.com/wzhouxiff/RestoreFormer +""" +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class VectorQuantizer(nn.Module): + """ + see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py + ____________________________________________ + Discretization bottleneck part of the VQ-VAE. + Inputs: + - n_e : number of embeddings + - e_dim : dimension of embedding + - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2 + _____________________________________________ + """ + + def __init__(self, n_e, e_dim, beta): + super(VectorQuantizer, self).__init__() + self.n_e = n_e + self.e_dim = e_dim + self.beta = beta + + self.embedding = nn.Embedding(self.n_e, self.e_dim) + self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e) + + def forward(self, z): + """ + Inputs the output of the encoder network z and maps it to a discrete + one-hot vector that is the index of the closest embedding vector e_j + z (continuous) -> z_q (discrete) + z.shape = (batch, channel, height, width) + quantization pipeline: + 1. get encoder input (B,C,H,W) + 2. flatten input to (B*H*W,C) + """ + # reshape z -> (batch, height, width, channel) and flatten + z = z.permute(0, 2, 3, 1).contiguous() + z_flattened = z.view(-1, self.e_dim) + # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z + + d = ( + torch.sum(z_flattened**2, dim=1, keepdim=True) + + torch.sum(self.embedding.weight**2, dim=1) + - 2 * torch.matmul(z_flattened, self.embedding.weight.t()) + ) + + # could possible replace this here + # #\start... + # find closest encodings + + min_value, min_encoding_indices = torch.min(d, dim=1) + + min_encoding_indices = min_encoding_indices.unsqueeze(1) + + min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z) + min_encodings.scatter_(1, min_encoding_indices, 1) + + # dtype min encodings: torch.float32 + # min_encodings shape: torch.Size([2048, 512]) + # min_encoding_indices.shape: torch.Size([2048, 1]) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape) + # .........\end + + # with: + # .........\start + # min_encoding_indices = torch.argmin(d, dim=1) + # z_q = self.embedding(min_encoding_indices) + # ......\end......... (TODO) + + # compute loss for embedding + loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean( + (z_q - z.detach()) ** 2 + ) + + # preserve gradients + z_q = z + (z_q - z).detach() + + # perplexity + + e_mean = torch.mean(min_encodings, dim=0) + perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10))) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d) + + def get_codebook_entry(self, indices, shape): + # shape specifying (batch, height, width, channel) + # TODO: check for more easy handling with nn.Embedding + min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices) + min_encodings.scatter_(1, indices[:, None], 1) + + # get quantized latent vectors + z_q = torch.matmul(min_encodings.float(), self.embedding.weight) + + if shape is not None: + z_q = z_q.view(shape) + + # reshape back to match original input shape + z_q = z_q.permute(0, 3, 1, 2).contiguous() + + return z_q + + +# pytorch_diffusion + derived encoder decoder +def nonlinearity(x): + # swish + return x * torch.sigmoid(x) + + +def Normalize(in_channels): + return torch.nn.GroupNorm( + num_groups=32, num_channels=in_channels, eps=1e-6, affine=True + ) + + +class Upsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, x): + x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") + if self.with_conv: + x = self.conv(x) + return x + + +class Downsample(nn.Module): + def __init__(self, in_channels, with_conv): + super().__init__() + self.with_conv = with_conv + if self.with_conv: + # no asymmetric padding in torch conv, must do it ourselves + self.conv = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) + + def forward(self, x): + if self.with_conv: + pad = (0, 1, 0, 1) + x = torch.nn.functional.pad(x, pad, mode="constant", value=0) + x = self.conv(x) + else: + x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2) + return x + + +class ResnetBlock(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout, + temb_channels=512 + ): + super().__init__() + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + + self.norm1 = Normalize(in_channels) + self.conv1 = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if temb_channels > 0: + self.temb_proj = torch.nn.Linear(temb_channels, out_channels) + self.norm2 = Normalize(out_channels) + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = torch.nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + self.conv_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=3, stride=1, padding=1 + ) + else: + self.nin_shortcut = torch.nn.Conv2d( + in_channels, out_channels, kernel_size=1, stride=1, padding=0 + ) + + def forward(self, x, temb): + h = x + h = self.norm1(h) + h = nonlinearity(h) + h = self.conv1(h) + + if temb is not None: + h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None] + + h = self.norm2(h) + h = nonlinearity(h) + h = self.dropout(h) + h = self.conv2(h) + + if self.in_channels != self.out_channels: + if self.use_conv_shortcut: + x = self.conv_shortcut(x) + else: + x = self.nin_shortcut(x) + + return x + h + + +class MultiHeadAttnBlock(nn.Module): + def __init__(self, in_channels, head_size=1): + super().__init__() + self.in_channels = in_channels + self.head_size = head_size + self.att_size = in_channels // head_size + assert ( + in_channels % head_size == 0 + ), "The size of head should be divided by the number of channels." + + self.norm1 = Normalize(in_channels) + self.norm2 = Normalize(in_channels) + + self.q = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.k = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.v = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.proj_out = torch.nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) + self.num = 0 + + def forward(self, x, y=None): + h_ = x + h_ = self.norm1(h_) + if y is None: + y = h_ + else: + y = self.norm2(y) + + q = self.q(y) + k = self.k(h_) + v = self.v(h_) + + # compute attention + b, c, h, w = q.shape + q = q.reshape(b, self.head_size, self.att_size, h * w) + q = q.permute(0, 3, 1, 2) # b, hw, head, att + + k = k.reshape(b, self.head_size, self.att_size, h * w) + k = k.permute(0, 3, 1, 2) + + v = v.reshape(b, self.head_size, self.att_size, h * w) + v = v.permute(0, 3, 1, 2) + + q = q.transpose(1, 2) + v = v.transpose(1, 2) + k = k.transpose(1, 2).transpose(2, 3) + + scale = int(self.att_size) ** (-0.5) + q.mul_(scale) + w_ = torch.matmul(q, k) + w_ = F.softmax(w_, dim=3) + + w_ = w_.matmul(v) + + w_ = w_.transpose(1, 2).contiguous() # [b, h*w, head, att] + w_ = w_.view(b, h, w, -1) + w_ = w_.permute(0, 3, 1, 2) + + w_ = self.proj_out(w_) + + return x + w_ + + +class MultiHeadEncoder(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + double_z=True, + enable_mid=True, + head_size=1, + **ignore_kwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.enable_mid = enable_mid + + # downsampling + self.conv_in = torch.nn.Conv2d( + in_channels, self.ch, kernel_size=3, stride=1, padding=1 + ) + + curr_res = resolution + in_ch_mult = (1,) + tuple(ch_mult) + self.down = nn.ModuleList() + for i_level in range(self.num_resolutions): + block = nn.ModuleList() + attn = nn.ModuleList() + block_in = ch * in_ch_mult[i_level] + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + down = nn.Module() + down.block = block + down.attn = attn + if i_level != self.num_resolutions - 1: + down.downsample = Downsample(block_in, resamp_with_conv) + curr_res = curr_res // 2 + self.down.append(down) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, + 2 * z_channels if double_z else z_channels, + kernel_size=3, + stride=1, + padding=1, + ) + + def forward(self, x): + hs = {} + # timestep embedding + temb = None + + # downsampling + h = self.conv_in(x) + hs["in"] = h + for i_level in range(self.num_resolutions): + for i_block in range(self.num_res_blocks): + h = self.down[i_level].block[i_block](h, temb) + if len(self.down[i_level].attn) > 0: + h = self.down[i_level].attn[i_block](h) + + if i_level != self.num_resolutions - 1: + # hs.append(h) + hs["block_" + str(i_level)] = h + h = self.down[i_level].downsample(h) + + # middle + # h = hs[-1] + if self.enable_mid: + h = self.mid.block_1(h, temb) + hs["block_" + str(i_level) + "_atten"] = h + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + hs["mid_atten"] = h + + # end + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + # hs.append(h) + hs["out"] = h + + return hs + + +class MultiHeadDecoder(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z): + # assert z.shape[1:] == self.z_shape[1:] + self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block](h) + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class MultiHeadDecoderTransformer(nn.Module): + def __init__( + self, + ch, + out_ch, + ch_mult=(1, 2, 4, 8), + num_res_blocks=2, + attn_resolutions=(16,), + dropout=0.0, + resamp_with_conv=True, + in_channels=3, + resolution=512, + z_channels=256, + give_pre_end=False, + enable_mid=True, + head_size=1, + **ignorekwargs + ): + super().__init__() + self.ch = ch + self.temb_ch = 0 + self.num_resolutions = len(ch_mult) + self.num_res_blocks = num_res_blocks + self.resolution = resolution + self.in_channels = in_channels + self.give_pre_end = give_pre_end + self.enable_mid = enable_mid + + # compute in_ch_mult, block_in and curr_res at lowest res + block_in = ch * ch_mult[self.num_resolutions - 1] + curr_res = resolution // 2 ** (self.num_resolutions - 1) + self.z_shape = (1, z_channels, curr_res, curr_res) + print( + "Working with z of shape {} = {} dimensions.".format( + self.z_shape, np.prod(self.z_shape) + ) + ) + + # z to block_in + self.conv_in = torch.nn.Conv2d( + z_channels, block_in, kernel_size=3, stride=1, padding=1 + ) + + # middle + if self.enable_mid: + self.mid = nn.Module() + self.mid.block_1 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size) + self.mid.block_2 = ResnetBlock( + in_channels=block_in, + out_channels=block_in, + temb_channels=self.temb_ch, + dropout=dropout, + ) + + # upsampling + self.up = nn.ModuleList() + for i_level in reversed(range(self.num_resolutions)): + block = nn.ModuleList() + attn = nn.ModuleList() + block_out = ch * ch_mult[i_level] + for i_block in range(self.num_res_blocks + 1): + block.append( + ResnetBlock( + in_channels=block_in, + out_channels=block_out, + temb_channels=self.temb_ch, + dropout=dropout, + ) + ) + block_in = block_out + if curr_res in attn_resolutions: + attn.append(MultiHeadAttnBlock(block_in, head_size)) + up = nn.Module() + up.block = block + up.attn = attn + if i_level != 0: + up.upsample = Upsample(block_in, resamp_with_conv) + curr_res = curr_res * 2 + self.up.insert(0, up) # prepend to get consistent order + + # end + self.norm_out = Normalize(block_in) + self.conv_out = torch.nn.Conv2d( + block_in, out_ch, kernel_size=3, stride=1, padding=1 + ) + + def forward(self, z, hs): + # assert z.shape[1:] == self.z_shape[1:] + # self.last_z_shape = z.shape + + # timestep embedding + temb = None + + # z to block_in + h = self.conv_in(z) + + # middle + if self.enable_mid: + h = self.mid.block_1(h, temb) + h = self.mid.attn_1(h, hs["mid_atten"]) + h = self.mid.block_2(h, temb) + + # upsampling + for i_level in reversed(range(self.num_resolutions)): + for i_block in range(self.num_res_blocks + 1): + h = self.up[i_level].block[i_block](h, temb) + if len(self.up[i_level].attn) > 0: + h = self.up[i_level].attn[i_block]( + h, hs["block_" + str(i_level) + "_atten"] + ) + # hfeature = h.clone() + if i_level != 0: + h = self.up[i_level].upsample(h) + + # end + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nonlinearity(h) + h = self.conv_out(h) + return h + + +class RestoreFormer(nn.Module): + def __init__( + self, + state_dict, + ): + super(RestoreFormer, self).__init__() + + n_embed = 1024 + embed_dim = 256 + ch = 64 + out_ch = 3 + ch_mult = (1, 2, 2, 4, 4, 8) + num_res_blocks = 2 + attn_resolutions = (16,) + dropout = 0.0 + in_channels = 3 + resolution = 512 + z_channels = 256 + double_z = False + enable_mid = True + fix_decoder = False + fix_codebook = True + fix_encoder = False + head_size = 8 + + self.model_arch = "RestoreFormer" + self.sub_type = "Face SR" + self.scale = 8 + self.in_nc = 3 + self.out_nc = out_ch + self.state = state_dict + + self.supports_fp16 = False + self.supports_bf16 = True + self.min_size_restriction = 16 + + self.encoder = MultiHeadEncoder( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + double_z=double_z, + enable_mid=enable_mid, + head_size=head_size, + ) + self.decoder = MultiHeadDecoderTransformer( + ch=ch, + out_ch=out_ch, + ch_mult=ch_mult, + num_res_blocks=num_res_blocks, + attn_resolutions=attn_resolutions, + dropout=dropout, + in_channels=in_channels, + resolution=resolution, + z_channels=z_channels, + enable_mid=enable_mid, + head_size=head_size, + ) + + self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25) + + self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1) + self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1) + + if fix_decoder: + for _, param in self.decoder.named_parameters(): + param.requires_grad = False + for _, param in self.post_quant_conv.named_parameters(): + param.requires_grad = False + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + elif fix_codebook: + for _, param in self.quantize.named_parameters(): + param.requires_grad = False + + if fix_encoder: + for _, param in self.encoder.named_parameters(): + param.requires_grad = False + + self.load_state_dict(state_dict) + + def encode(self, x): + hs = self.encoder(x) + h = self.quant_conv(hs["out"]) + quant, emb_loss, info = self.quantize(h) + return quant, emb_loss, info, hs + + def decode(self, quant, hs): + quant = self.post_quant_conv(quant) + dec = self.decoder(quant, hs) + + return dec + + def forward(self, input, **kwargs): + quant, diff, info, hs = self.encode(input) + dec = self.decode(quant, hs) + + return dec, None diff --git a/ldm_patched/pfn/architecture/face/stylegan2_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..1eb0e9f15f706e2b9759bde4d0244d424c3ae76f --- /dev/null +++ b/ldm_patched/pfn/architecture/face/stylegan2_arch.py @@ -0,0 +1,865 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU, fused_leaky_relu +from .upfirdn2d import upfirdn2d + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + + Args: + x (Tensor): Style codes with shape (b, c). + + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +def make_resample_kernel(k): + """Make resampling kernel for UpFirDn. + + Args: + k (list[int]): A list indicating the 1D resample kernel magnitude. + + Returns: + Tensor: 2D resampled kernel. + """ + k = torch.tensor(k, dtype=torch.float32) + if k.ndim == 1: + k = k[None, :] * k[:, None] # to 2D kernel, outer product + # normalize + k /= k.sum() + return k + + +class UpFirDnUpsample(nn.Module): + """Upsample, FIR filter, and downsample (upsampole version). + + References: + 1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html # noqa: E501 + 2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html # noqa: E501 + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Upsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnUpsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) * (factor**2) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2 + factor - 1, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad) + return out + + def __repr__(self): + return f"{self.__class__.__name__}(factor={self.factor})" + + +class UpFirDnDownsample(nn.Module): + """Upsample, FIR filter, and downsample (downsampole version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + factor (int): Downsampling scale factor. Default: 2. + """ + + def __init__(self, resample_kernel, factor=2): + super(UpFirDnDownsample, self).__init__() + self.kernel = make_resample_kernel(resample_kernel) + self.factor = factor + + pad = self.kernel.shape[0] - factor + self.pad = ((pad + 1) // 2, pad // 2) + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad) + return out + + def __repr__(self): + return f"{self.__class__.__name__}(factor={self.factor})" + + +class UpFirDnSmooth(nn.Module): + """Upsample, FIR filter, and downsample (smooth version). + + Args: + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. + upsample_factor (int): Upsampling scale factor. Default: 1. + downsample_factor (int): Downsampling scale factor. Default: 1. + kernel_size (int): Kernel size: Default: 1. + """ + + def __init__( + self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1 + ): + super(UpFirDnSmooth, self).__init__() + self.upsample_factor = upsample_factor + self.downsample_factor = downsample_factor + self.kernel = make_resample_kernel(resample_kernel) + if upsample_factor > 1: + self.kernel = self.kernel * (upsample_factor**2) + + if upsample_factor > 1: + pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1) + self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1) + elif downsample_factor > 1: + pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1) + self.pad = ((pad + 1) // 2, pad // 2) + else: + raise NotImplementedError + + def forward(self, x): + out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(upsample_factor={self.upsample_factor}" + f", downsample_factor={self.downsample_factor})" + ) + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ["fused_lrelu", None]: + raise ValueError( + f"Wrong activation value in EqualLinear: {activation}" + "Supported ones are: ['fused_lrelu', None]." + ) + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == "fused_lrelu": + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, bias={self.bias is not None})" + ) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + + There is no bias in ModulatedConv2d. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1), + eps=1e-8, + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + if self.sample_mode == "upsample": + self.smooth = UpFirDnSmooth( + resample_kernel, + upsample_factor=2, + downsample_factor=1, + kernel_size=kernel_size, + ) + elif self.sample_mode == "downsample": + self.smooth = UpFirDnSmooth( + resample_kernel, + upsample_factor=1, + downsample_factor=2, + kernel_size=kernel_size, + ) + elif self.sample_mode is None: + pass + else: + raise ValueError( + f"Wrong sample mode {self.sample_mode}, " + "supported ones are ['upsample', 'downsample', None]." + ) + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, + in_channels, + bias=True, + bias_init_val=1, + lr_mul=1, + activation=None, + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + if self.sample_mode == "upsample": + x = x.view(1, b * c, h, w) + weight = weight.view( + b, self.out_channels, c, self.kernel_size, self.kernel_size + ) + weight = weight.transpose(1, 2).reshape( + b * c, self.out_channels, self.kernel_size, self.kernel_size + ) + out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + out = self.smooth(out) + elif self.sample_mode == "downsample": + x = self.smooth(x) + x = x.view(1, b * c, *x.shape[2:4]) + out = F.conv2d(x, weight, padding=0, stride=2, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + else: + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=(1, 3, 3, 1), + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + resample_kernel=resample_kernel, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. Default: (1, 3, 3, 1). + """ + + def __init__( + self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1) + ): + super(ToRGB, self).__init__() + if upsample: + self.upsample = UpFirDnUpsample(resample_kernel, factor=2) + else: + self.upsample = None + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = self.upsample(skip) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2Generator(nn.Module): + """StyleGAN2 Generator. + + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + resample_kernel (list[int]): A list indicating the 1D resample kernel + magnitude. A cross production will be applied to extent 1D resample + kernel to 2D resample kernel. Default: (1, 3, 3, 1). + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + resample_kernel=(1, 3, 3, 1), + lr_mlp=0.01, + narrow=1, + ): + super(StyleGAN2Generator, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, + num_style_feat, + bias=True, + bias_init_val=0, + lr_mul=lr_mlp, + activation="fused_lrelu", + ) + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel, + ) + self.to_rgb1 = ToRGB( + channels["4"], + num_style_feat, + upsample=False, + resample_kernel=resample_kernel, + ) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + resample_kernel=resample_kernel, + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + resample_kernel=resample_kernel, + ) + ) + self.to_rgbs.append( + ToRGB( + out_channels, + num_style_feat, + upsample=True, + resample_kernel=resample_kernel, + ) + ) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2Generator. + + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}," + f" stride={self.stride}, padding={self.padding}, " + f"bias={self.bias is not None})" + ) + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + downsample=False, + resample_kernel=(1, 3, 3, 1), + bias=True, + activate=True, + ): + layers = [] + # downsample + if downsample: + layers.append( + UpFirDnSmooth( + resample_kernel, + upsample_factor=1, + downsample_factor=2, + kernel_size=kernel_size, + ) + ) + stride = 2 + self.padding = 0 + else: + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=self.padding, + bias=bias and not activate, + ) + ) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + resample_kernel (list[int]): A list indicating the 1D resample + kernel magnitude. A cross production will be applied to + extent 1D resample kernel to 2D resample kernel. + Default: (1, 3, 3, 1). + """ + + def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, + out_channels, + 3, + downsample=True, + resample_kernel=resample_kernel, + bias=True, + activate=True, + ) + self.skip = ConvLayer( + in_channels, + out_channels, + 1, + downsample=True, + resample_kernel=resample_kernel, + bias=False, + activate=False, + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out diff --git a/ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..601f8cc4b33bdbb371d710a2bb0656e8ce102e26 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/stylegan2_bilinear_arch.py @@ -0,0 +1,709 @@ +# pylint: skip-file +# type: ignore +import math +import random + +import torch +from torch import nn +from torch.nn import functional as F + +from .fused_act import FusedLeakyReLU, fused_leaky_relu + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + Args: + x (Tensor): Style codes with shape (b, c). + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class EqualLinear(nn.Module): + """Equalized Linear as StyleGAN2. + Args: + in_channels (int): Size of each sample. + out_channels (int): Size of each output sample. + bias (bool): If set to ``False``, the layer will not learn an additive + bias. Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + lr_mul (float): Learning rate multiplier. Default: 1. + activation (None | str): The activation after ``linear`` operation. + Supported: 'fused_lrelu', None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + bias=True, + bias_init_val=0, + lr_mul=1, + activation=None, + ): + super(EqualLinear, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.lr_mul = lr_mul + self.activation = activation + if self.activation not in ["fused_lrelu", None]: + raise ValueError( + f"Wrong activation value in EqualLinear: {activation}" + "Supported ones are: ['fused_lrelu', None]." + ) + self.scale = (1 / math.sqrt(in_channels)) * lr_mul + + self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul)) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + if self.bias is None: + bias = None + else: + bias = self.bias * self.lr_mul + if self.activation == "fused_lrelu": + out = F.linear(x, self.weight * self.scale) + out = fused_leaky_relu(out, bias) + else: + out = F.linear(x, self.weight * self.scale, bias=bias) + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, bias={self.bias is not None})" + ) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + There is no bias in ModulatedConv2d. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. + Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + eps (float): A value added to the denominator for numerical stability. + Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8, + interpolation_mode="bilinear", + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + # modulation inside each modulated conv + self.modulation = EqualLinear( + num_style_feat, + in_channels, + bias=True, + bias_init_val=1, + lr_mul=1, + activation=None, + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.scale * self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + if self.sample_mode == "upsample": + x = F.interpolate( + x, + scale_factor=2, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + elif self.sample_mode == "downsample": + x = F.interpolate( + x, + scale_factor=0.5, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, " + f"demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. + Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode="bilinear", + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + interpolation_mode=interpolation_mode, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.activate = FusedLeakyReLU(out_channels) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # activation (with bias) + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB from features. + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__( + self, in_channels, num_style_feat, upsample=True, interpolation_mode="bilinear" + ): + super(ToRGB, self).__init__() + self.upsample = upsample + self.interpolation_mode = interpolation_mode + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate( + skip, + scale_factor=2, + mode=self.interpolation_mode, + align_corners=self.align_corners, + ) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2GeneratorBilinear(nn.Module): + """StyleGAN2 Generator. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of + StyleGAN2. Default: 2. + lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, + out_size, + num_style_feat=512, + num_mlp=8, + channel_multiplier=2, + lr_mlp=0.01, + narrow=1, + interpolation_mode="bilinear", + ): + super(StyleGAN2GeneratorBilinear, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.append( + EqualLinear( + num_style_feat, + num_style_feat, + bias=True, + bias_init_val=0, + lr_mul=lr_mlp, + activation="fused_lrelu", + ) + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + self.to_rgb1 = ToRGB( + channels["4"], + num_style_feat, + upsample=False, + interpolation_mode=interpolation_mode, + ) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + interpolation_mode=interpolation_mode, + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + interpolation_mode=interpolation_mode, + ) + ) + self.to_rgbs.append( + ToRGB( + out_channels, + num_style_feat, + upsample=True, + interpolation_mode=interpolation_mode, + ) + ) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2Generator. + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. + Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is + False. Default: True. + truncation (float): TODO. Default: 1. + truncation_latent (Tensor | None): TODO. Default: None. + inject_index (int | None): The injection index for mixing noise. + Default: None. + return_latents (bool): Whether to return style latents. + Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latent with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None + + +class ScaledLeakyReLU(nn.Module): + """Scaled LeakyReLU. + Args: + negative_slope (float): Negative slope. Default: 0.2. + """ + + def __init__(self, negative_slope=0.2): + super(ScaledLeakyReLU, self).__init__() + self.negative_slope = negative_slope + + def forward(self, x): + out = F.leaky_relu(x, negative_slope=self.negative_slope) + return out * math.sqrt(2) + + +class EqualConv2d(nn.Module): + """Equalized Linear as StyleGAN2. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + stride (int): Stride of the convolution. Default: 1 + padding (int): Zero-padding added to both sides of the input. + Default: 0. + bias (bool): If ``True``, adds a learnable bias to the output. + Default: ``True``. + bias_init_val (float): Bias initialized value. Default: 0. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + stride=1, + padding=0, + bias=True, + bias_init_val=0, + ): + super(EqualConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.stride = stride + self.padding = padding + self.scale = 1 / math.sqrt(in_channels * kernel_size**2) + + self.weight = nn.Parameter( + torch.randn(out_channels, in_channels, kernel_size, kernel_size) + ) + if bias: + self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val)) + else: + self.register_parameter("bias", None) + + def forward(self, x): + out = F.conv2d( + x, + self.weight * self.scale, + bias=self.bias, + stride=self.stride, + padding=self.padding, + ) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, " + f"out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}," + f" stride={self.stride}, padding={self.padding}, " + f"bias={self.bias is not None})" + ) + + +class ConvLayer(nn.Sequential): + """Conv Layer used in StyleGAN2 Discriminator. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Kernel size. + downsample (bool): Whether downsample by a factor of 2. + Default: False. + bias (bool): Whether with bias. Default: True. + activate (bool): Whether use activateion. Default: True. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + downsample=False, + bias=True, + activate=True, + interpolation_mode="bilinear", + ): + layers = [] + self.interpolation_mode = interpolation_mode + # downsample + if downsample: + if self.interpolation_mode == "nearest": + self.align_corners = None + else: + self.align_corners = False + + layers.append( + torch.nn.Upsample( + scale_factor=0.5, + mode=interpolation_mode, + align_corners=self.align_corners, + ) + ) + stride = 1 + self.padding = kernel_size // 2 + # conv + layers.append( + EqualConv2d( + in_channels, + out_channels, + kernel_size, + stride=stride, + padding=self.padding, + bias=bias and not activate, + ) + ) + # activation + if activate: + if bias: + layers.append(FusedLeakyReLU(out_channels)) + else: + layers.append(ScaledLeakyReLU(0.2)) + + super(ConvLayer, self).__init__(*layers) + + +class ResBlock(nn.Module): + """Residual block used in StyleGAN2 Discriminator. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + """ + + def __init__(self, in_channels, out_channels, interpolation_mode="bilinear"): + super(ResBlock, self).__init__() + + self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True) + self.conv2 = ConvLayer( + in_channels, + out_channels, + 3, + downsample=True, + interpolation_mode=interpolation_mode, + bias=True, + activate=True, + ) + self.skip = ConvLayer( + in_channels, + out_channels, + 1, + downsample=True, + interpolation_mode=interpolation_mode, + bias=False, + activate=False, + ) + + def forward(self, x): + out = self.conv1(x) + out = self.conv2(out) + skip = self.skip(x) + out = (out + skip) / math.sqrt(2) + return out diff --git a/ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py b/ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py new file mode 100644 index 0000000000000000000000000000000000000000..c48de9af6904b8d1891a84efa8e4d76104d5d710 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/stylegan2_clean_arch.py @@ -0,0 +1,453 @@ +# pylint: skip-file +# type: ignore +import math + +import torch +from torch import nn +from torch.nn import functional as F +from torch.nn import init +from torch.nn.modules.batchnorm import _BatchNorm + + +@torch.no_grad() +def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs): + """Initialize network weights. + Args: + module_list (list[nn.Module] | nn.Module): Modules to be initialized. + scale (float): Scale initialized weights, especially for residual + blocks. Default: 1. + bias_fill (float): The value to fill bias. Default: 0 + kwargs (dict): Other arguments for initialization function. + """ + if not isinstance(module_list, list): + module_list = [module_list] + for module in module_list: + for m in module.modules(): + if isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, nn.Linear): + init.kaiming_normal_(m.weight, **kwargs) + m.weight.data *= scale + if m.bias is not None: + m.bias.data.fill_(bias_fill) + elif isinstance(m, _BatchNorm): + init.constant_(m.weight, 1) + if m.bias is not None: + m.bias.data.fill_(bias_fill) + + +class NormStyleCode(nn.Module): + def forward(self, x): + """Normalize the style codes. + Args: + x (Tensor): Style codes with shape (b, c). + Returns: + Tensor: Normalized tensor. + """ + return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8) + + +class ModulatedConv2d(nn.Module): + """Modulated Conv2d used in StyleGAN2. + There is no bias in ModulatedConv2d. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether to demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + eps (float): A value added to the denominator for numerical stability. Default: 1e-8. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + eps=1e-8, + ): + super(ModulatedConv2d, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.kernel_size = kernel_size + self.demodulate = demodulate + self.sample_mode = sample_mode + self.eps = eps + + # modulation inside each modulated conv + self.modulation = nn.Linear(num_style_feat, in_channels, bias=True) + # initialization + default_init_weights( + self.modulation, + scale=1, + bias_fill=1, + a=0, + mode="fan_in", + nonlinearity="linear", + ) + + self.weight = nn.Parameter( + torch.randn(1, out_channels, in_channels, kernel_size, kernel_size) + / math.sqrt(in_channels * kernel_size**2) + ) + self.padding = kernel_size // 2 + + def forward(self, x, style): + """Forward function. + Args: + x (Tensor): Tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + Returns: + Tensor: Modulated tensor after convolution. + """ + b, c, h, w = x.shape # c = c_in + # weight modulation + style = self.modulation(style).view(b, 1, c, 1, 1) + # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1) + weight = self.weight * style # (b, c_out, c_in, k, k) + + if self.demodulate: + demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) + weight = weight * demod.view(b, self.out_channels, 1, 1, 1) + + weight = weight.view( + b * self.out_channels, c, self.kernel_size, self.kernel_size + ) + + # upsample or downsample if necessary + if self.sample_mode == "upsample": + x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False) + elif self.sample_mode == "downsample": + x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False) + + b, c, h, w = x.shape + x = x.view(1, b * c, h, w) + # weight: (b*c_out, c_in, k, k), groups=b + out = F.conv2d(x, weight, padding=self.padding, groups=b) + out = out.view(b, self.out_channels, *out.shape[2:4]) + + return out + + def __repr__(self): + return ( + f"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, " + f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})" + ) + + +class StyleConv(nn.Module): + """Style conv used in StyleGAN2. + Args: + in_channels (int): Channel number of the input. + out_channels (int): Channel number of the output. + kernel_size (int): Size of the convolving kernel. + num_style_feat (int): Channel number of style features. + demodulate (bool): Whether demodulate in the conv layer. Default: True. + sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None. + """ + + def __init__( + self, + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=True, + sample_mode=None, + ): + super(StyleConv, self).__init__() + self.modulated_conv = ModulatedConv2d( + in_channels, + out_channels, + kernel_size, + num_style_feat, + demodulate=demodulate, + sample_mode=sample_mode, + ) + self.weight = nn.Parameter(torch.zeros(1)) # for noise injection + self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1)) + self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True) + + def forward(self, x, style, noise=None): + # modulate + out = self.modulated_conv(x, style) * 2**0.5 # for conversion + # noise injection + if noise is None: + b, _, h, w = out.shape + noise = out.new_empty(b, 1, h, w).normal_() + out = out + self.weight * noise + # add bias + out = out + self.bias + # activation + out = self.activate(out) + return out + + +class ToRGB(nn.Module): + """To RGB (image space) from features. + Args: + in_channels (int): Channel number of input. + num_style_feat (int): Channel number of style features. + upsample (bool): Whether to upsample. Default: True. + """ + + def __init__(self, in_channels, num_style_feat, upsample=True): + super(ToRGB, self).__init__() + self.upsample = upsample + self.modulated_conv = ModulatedConv2d( + in_channels, + 3, + kernel_size=1, + num_style_feat=num_style_feat, + demodulate=False, + sample_mode=None, + ) + self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1)) + + def forward(self, x, style, skip=None): + """Forward function. + Args: + x (Tensor): Feature tensor with shape (b, c, h, w). + style (Tensor): Tensor with shape (b, num_style_feat). + skip (Tensor): Base/skip tensor. Default: None. + Returns: + Tensor: RGB images. + """ + out = self.modulated_conv(x, style) + out = out + self.bias + if skip is not None: + if self.upsample: + skip = F.interpolate( + skip, scale_factor=2, mode="bilinear", align_corners=False + ) + out = out + skip + return out + + +class ConstantInput(nn.Module): + """Constant input. + Args: + num_channel (int): Channel number of constant input. + size (int): Spatial size of constant input. + """ + + def __init__(self, num_channel, size): + super(ConstantInput, self).__init__() + self.weight = nn.Parameter(torch.randn(1, num_channel, size, size)) + + def forward(self, batch): + out = self.weight.repeat(batch, 1, 1, 1) + return out + + +class StyleGAN2GeneratorClean(nn.Module): + """Clean version of StyleGAN2 Generator. + Args: + out_size (int): The spatial size of outputs. + num_style_feat (int): Channel number of style features. Default: 512. + num_mlp (int): Layer number of MLP style layers. Default: 8. + channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2. + narrow (float): Narrow ratio for channels. Default: 1.0. + """ + + def __init__( + self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1 + ): + super(StyleGAN2GeneratorClean, self).__init__() + # Style MLP layers + self.num_style_feat = num_style_feat + style_mlp_layers = [NormStyleCode()] + for i in range(num_mlp): + style_mlp_layers.extend( + [ + nn.Linear(num_style_feat, num_style_feat, bias=True), + nn.LeakyReLU(negative_slope=0.2, inplace=True), + ] + ) + self.style_mlp = nn.Sequential(*style_mlp_layers) + # initialization + default_init_weights( + self.style_mlp, + scale=1, + bias_fill=0, + a=0.2, + mode="fan_in", + nonlinearity="leaky_relu", + ) + + # channel list + channels = { + "4": int(512 * narrow), + "8": int(512 * narrow), + "16": int(512 * narrow), + "32": int(512 * narrow), + "64": int(256 * channel_multiplier * narrow), + "128": int(128 * channel_multiplier * narrow), + "256": int(64 * channel_multiplier * narrow), + "512": int(32 * channel_multiplier * narrow), + "1024": int(16 * channel_multiplier * narrow), + } + self.channels = channels + + self.constant_input = ConstantInput(channels["4"], size=4) + self.style_conv1 = StyleConv( + channels["4"], + channels["4"], + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + ) + self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False) + + self.log_size = int(math.log(out_size, 2)) + self.num_layers = (self.log_size - 2) * 2 + 1 + self.num_latent = self.log_size * 2 - 2 + + self.style_convs = nn.ModuleList() + self.to_rgbs = nn.ModuleList() + self.noises = nn.Module() + + in_channels = channels["4"] + # noise + for layer_idx in range(self.num_layers): + resolution = 2 ** ((layer_idx + 5) // 2) + shape = [1, 1, resolution, resolution] + self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape)) + # style convs and to_rgbs + for i in range(3, self.log_size + 1): + out_channels = channels[f"{2**i}"] + self.style_convs.append( + StyleConv( + in_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode="upsample", + ) + ) + self.style_convs.append( + StyleConv( + out_channels, + out_channels, + kernel_size=3, + num_style_feat=num_style_feat, + demodulate=True, + sample_mode=None, + ) + ) + self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True)) + in_channels = out_channels + + def make_noise(self): + """Make noise for noise injection.""" + device = self.constant_input.weight.device + noises = [torch.randn(1, 1, 4, 4, device=device)] + + for i in range(3, self.log_size + 1): + for _ in range(2): + noises.append(torch.randn(1, 1, 2**i, 2**i, device=device)) + + return noises + + def get_latent(self, x): + return self.style_mlp(x) + + def mean_latent(self, num_latent): + latent_in = torch.randn( + num_latent, self.num_style_feat, device=self.constant_input.weight.device + ) + latent = self.style_mlp(latent_in).mean(0, keepdim=True) + return latent + + def forward( + self, + styles, + input_is_latent=False, + noise=None, + randomize_noise=True, + truncation=1, + truncation_latent=None, + inject_index=None, + return_latents=False, + ): + """Forward function for StyleGAN2GeneratorClean. + Args: + styles (list[Tensor]): Sample codes of styles. + input_is_latent (bool): Whether input is latent style. Default: False. + noise (Tensor | None): Input noise or None. Default: None. + randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True. + truncation (float): The truncation ratio. Default: 1. + truncation_latent (Tensor | None): The truncation latent tensor. Default: None. + inject_index (int | None): The injection index for mixing noise. Default: None. + return_latents (bool): Whether to return style latents. Default: False. + """ + # style codes -> latents with Style MLP layer + if not input_is_latent: + styles = [self.style_mlp(s) for s in styles] + # noises + if noise is None: + if randomize_noise: + noise = [None] * self.num_layers # for each style conv layer + else: # use the stored noise + noise = [ + getattr(self.noises, f"noise{i}") for i in range(self.num_layers) + ] + # style truncation + if truncation < 1: + style_truncation = [] + for style in styles: + style_truncation.append( + truncation_latent + truncation * (style - truncation_latent) + ) + styles = style_truncation + # get style latents with injection + if len(styles) == 1: + inject_index = self.num_latent + + if styles[0].ndim < 3: + # repeat latent code for all the layers + latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + else: # used for encoder with different latent code for each layer + latent = styles[0] + elif len(styles) == 2: # mixing noises + if inject_index is None: + inject_index = random.randint(1, self.num_latent - 1) + latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) + latent2 = ( + styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) + ) + latent = torch.cat([latent1, latent2], 1) + + # main generation + out = self.constant_input(latent.shape[0]) + out = self.style_conv1(out, latent[:, 0], noise=noise[0]) + skip = self.to_rgb1(out, latent[:, 1]) + + i = 1 + for conv1, conv2, noise1, noise2, to_rgb in zip( + self.style_convs[::2], + self.style_convs[1::2], + noise[1::2], + noise[2::2], + self.to_rgbs, + ): + out = conv1(out, latent[:, i], noise=noise1) + out = conv2(out, latent[:, i + 1], noise=noise2) + skip = to_rgb(out, latent[:, i + 2], skip) # feature back to the rgb space + i += 2 + + image = skip + + if return_latents: + return image, latent + else: + return image, None diff --git a/ldm_patched/pfn/architecture/face/upfirdn2d.py b/ldm_patched/pfn/architecture/face/upfirdn2d.py new file mode 100644 index 0000000000000000000000000000000000000000..4ea4541513f27e3c9dddcee864cfeb87efddadb7 --- /dev/null +++ b/ldm_patched/pfn/architecture/face/upfirdn2d.py @@ -0,0 +1,194 @@ +# pylint: skip-file +# type: ignore +# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501 + +import os + +import torch +from torch.autograd import Function +from torch.nn import functional as F + +upfirdn2d_ext = None + + +class UpFirDn2dBackward(Function): + @staticmethod + def forward( + ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size + ): + up_x, up_y = up + down_x, down_y = down + g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad + + grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1) + + grad_input = upfirdn2d_ext.upfirdn2d( + grad_output, + grad_kernel, + down_x, + down_y, + up_x, + up_y, + g_pad_x0, + g_pad_x1, + g_pad_y0, + g_pad_y1, + ) + grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3]) + + ctx.save_for_backward(kernel) + + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + ctx.up_x = up_x + ctx.up_y = up_y + ctx.down_x = down_x + ctx.down_y = down_y + ctx.pad_x0 = pad_x0 + ctx.pad_x1 = pad_x1 + ctx.pad_y0 = pad_y0 + ctx.pad_y1 = pad_y1 + ctx.in_size = in_size + ctx.out_size = out_size + + return grad_input + + @staticmethod + def backward(ctx, gradgrad_input): + (kernel,) = ctx.saved_tensors + + gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1) + + gradgrad_out = upfirdn2d_ext.upfirdn2d( + gradgrad_input, + kernel, + ctx.up_x, + ctx.up_y, + ctx.down_x, + ctx.down_y, + ctx.pad_x0, + ctx.pad_x1, + ctx.pad_y0, + ctx.pad_y1, + ) + # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], + # ctx.out_size[1], ctx.in_size[3]) + gradgrad_out = gradgrad_out.view( + ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1] + ) + + return gradgrad_out, None, None, None, None, None, None, None, None + + +class UpFirDn2d(Function): + @staticmethod + def forward(ctx, input, kernel, up, down, pad): + up_x, up_y = up + down_x, down_y = down + pad_x0, pad_x1, pad_y0, pad_y1 = pad + + kernel_h, kernel_w = kernel.shape + _, channel, in_h, in_w = input.shape + ctx.in_size = input.shape + + input = input.reshape(-1, in_h, in_w, 1) + + ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1])) + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + ctx.out_size = (out_h, out_w) + + ctx.up = (up_x, up_y) + ctx.down = (down_x, down_y) + ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1) + + g_pad_x0 = kernel_w - pad_x0 - 1 + g_pad_y0 = kernel_h - pad_y0 - 1 + g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1 + g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1 + + ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1) + + out = upfirdn2d_ext.upfirdn2d( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 + ) + # out = out.view(major, out_h, out_w, minor) + out = out.view(-1, channel, out_h, out_w) + + return out + + @staticmethod + def backward(ctx, grad_output): + kernel, grad_kernel = ctx.saved_tensors + + grad_input = UpFirDn2dBackward.apply( + grad_output, + kernel, + grad_kernel, + ctx.up, + ctx.down, + ctx.pad, + ctx.g_pad, + ctx.in_size, + ctx.out_size, + ) + + return grad_input, None, None, None, None + + +def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)): + if input.device.type == "cpu": + out = upfirdn2d_native( + input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1] + ) + else: + out = UpFirDn2d.apply( + input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]) + ) + + return out + + +def upfirdn2d_native( + input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1 +): + _, channel, in_h, in_w = input.shape + input = input.reshape(-1, in_h, in_w, 1) + + _, in_h, in_w, minor = input.shape + kernel_h, kernel_w = kernel.shape + + out = input.view(-1, in_h, 1, in_w, 1, minor) + out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1]) + out = out.view(-1, in_h * up_y, in_w * up_x, minor) + + out = F.pad( + out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)] + ) + out = out[ + :, + max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0), + max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0), + :, + ] + + out = out.permute(0, 3, 1, 2) + out = out.reshape( + [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1] + ) + w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w) + out = F.conv2d(out, w) + out = out.reshape( + -1, + minor, + in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, + in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1, + ) + out = out.permute(0, 2, 3, 1) + out = out[:, ::down_y, ::down_x, :] + + out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1 + out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1 + + return out.view(-1, channel, out_h, out_w) diff --git a/ldm_patched/pfn/architecture/timm/LICENSE b/ldm_patched/pfn/architecture/timm/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..b4e9438bd1e07e17abf58cfd86e536ec880348a3 --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2019 Ross Wightman + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/ldm_patched/pfn/architecture/timm/drop.py b/ldm_patched/pfn/architecture/timm/drop.py new file mode 100644 index 0000000000000000000000000000000000000000..14f0da914b2a198af7e6124cd90bad6adaf8a84e --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/drop.py @@ -0,0 +1,223 @@ +""" DropBlock, DropPath + +PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers. + +Papers: +DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890) + +Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382) + +Code: +DropBlock impl inspired by two Tensorflow impl that I liked: + - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74 + - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py + +Hacked together by / Copyright 2020 Ross Wightman +""" +import torch +import torch.nn as nn +import torch.nn.functional as F + + +def drop_block_2d( + x, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, + batchwise: bool = False, +): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf + + DropBlock with an experimental gaussian noise option. This layer has been tested on a few training + runs with success, but needs further validation and possibly optimization for lower runtime impact. + """ + _, C, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) + # seed_drop_rate, the gamma parameter + gamma = ( + gamma_scale + * drop_prob + * total_size + / clipped_block_size**2 + / ((W - block_size + 1) * (H - block_size + 1)) + ) + + # Forces the block to be inside the feature map. + w_i, h_i = torch.meshgrid( + torch.arange(W).to(x.device), torch.arange(H).to(x.device) + ) + valid_block = ( + (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2) + ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2)) + valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype) + + if batchwise: + # one mask for whole batch, quite a bit faster + uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device) + else: + uniform_noise = torch.rand_like(x) + block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype) + block_mask = -F.max_pool2d( + -block_mask, + kernel_size=clipped_block_size, # block_size, + stride=1, + padding=clipped_block_size // 2, + ) + + if with_noise: + normal_noise = ( + torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) + if batchwise + else torch.randn_like(x) + ) + if inplace: + x.mul_(block_mask).add_(normal_noise * (1 - block_mask)) + else: + x = x * block_mask + normal_noise * (1 - block_mask) + else: + normalize_scale = ( + block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7) + ).to(x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale + return x + + +def drop_block_fast_2d( + x: torch.Tensor, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, +): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf + + DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid + block mask at edges. + """ + _, _, H, W = x.shape + total_size = W * H + clipped_block_size = min(block_size, min(W, H)) + gamma = ( + gamma_scale + * drop_prob + * total_size + / clipped_block_size**2 + / ((W - block_size + 1) * (H - block_size + 1)) + ) + + block_mask = torch.empty_like(x).bernoulli_(gamma) + block_mask = F.max_pool2d( + block_mask.to(x.dtype), + kernel_size=clipped_block_size, + stride=1, + padding=clipped_block_size // 2, + ) + + if with_noise: + normal_noise = torch.empty_like(x).normal_() + if inplace: + x.mul_(1.0 - block_mask).add_(normal_noise * block_mask) + else: + x = x * (1.0 - block_mask) + normal_noise * block_mask + else: + block_mask = 1 - block_mask + normalize_scale = ( + block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6) + ).to(dtype=x.dtype) + if inplace: + x.mul_(block_mask * normalize_scale) + else: + x = x * block_mask * normalize_scale + return x + + +class DropBlock2d(nn.Module): + """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf""" + + def __init__( + self, + drop_prob: float = 0.1, + block_size: int = 7, + gamma_scale: float = 1.0, + with_noise: bool = False, + inplace: bool = False, + batchwise: bool = False, + fast: bool = True, + ): + super(DropBlock2d, self).__init__() + self.drop_prob = drop_prob + self.gamma_scale = gamma_scale + self.block_size = block_size + self.with_noise = with_noise + self.inplace = inplace + self.batchwise = batchwise + self.fast = fast # FIXME finish comparisons of fast vs not + + def forward(self, x): + if not self.training or not self.drop_prob: + return x + if self.fast: + return drop_block_fast_2d( + x, + self.drop_prob, + self.block_size, + self.gamma_scale, + self.with_noise, + self.inplace, + ) + else: + return drop_block_2d( + x, + self.drop_prob, + self.block_size, + self.gamma_scale, + self.with_noise, + self.inplace, + self.batchwise, + ) + + +def drop_path( + x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True +): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). + + This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, + the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... + See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for + changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use + 'survival rate' as the argument. + + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * ( + x.ndim - 1 + ) # work with diff dim tensors, not just 2D ConvNets + random_tensor = x.new_empty(shape).bernoulli_(keep_prob) + if keep_prob > 0.0 and scale_by_keep: + random_tensor.div_(keep_prob) + return x * random_tensor + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + self.scale_by_keep = scale_by_keep + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) + + def extra_repr(self): + return f"drop_prob={round(self.drop_prob,3):0.3f}" diff --git a/ldm_patched/pfn/architecture/timm/helpers.py b/ldm_patched/pfn/architecture/timm/helpers.py new file mode 100644 index 0000000000000000000000000000000000000000..cdafee0709165dd992118e3b09b8d26f70ea8a2a --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/helpers.py @@ -0,0 +1,31 @@ +""" Layer/Module Helpers +Hacked together by / Copyright 2020 Ross Wightman +""" +import collections.abc +from itertools import repeat + + +# From PyTorch internals +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): + return x + return tuple(repeat(x, n)) + + return parse + + +to_1tuple = _ntuple(1) +to_2tuple = _ntuple(2) +to_3tuple = _ntuple(3) +to_4tuple = _ntuple(4) +to_ntuple = _ntuple + + +def make_divisible(v, divisor=8, min_value=None, round_limit=0.9): + min_value = min_value or divisor + new_v = max(min_value, int(v + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than 10%. + if new_v < round_limit * v: + new_v += divisor + return new_v diff --git a/ldm_patched/pfn/architecture/timm/weight_init.py b/ldm_patched/pfn/architecture/timm/weight_init.py new file mode 100644 index 0000000000000000000000000000000000000000..b0169774657d86c1946008e746f2f4f7e833a44c --- /dev/null +++ b/ldm_patched/pfn/architecture/timm/weight_init.py @@ -0,0 +1,128 @@ +import math +import warnings + +import torch +from torch.nn.init import _calculate_fan_in_and_fan_out + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_( + tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 +) -> torch.Tensor: + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are + applied while sampling the normal with mean/std applied, therefore a, b args + should be adjusted to match the range of mean, std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def trunc_normal_tf_( + tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0 +) -> torch.Tensor: + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + + NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the + bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 + and the result is subsquently scaled and shifted by the mean and std args. + + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + _no_grad_trunc_normal_(tensor, 0, 1.0, a, b) + with torch.no_grad(): + tensor.mul_(std).add_(mean) + return tensor + + +def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + if mode == "fan_in": + denom = fan_in + elif mode == "fan_out": + denom = fan_out + elif mode == "fan_avg": + denom = (fan_in + fan_out) / 2 + + variance = scale / denom # type: ignore + + if distribution == "truncated_normal": + # constant is stddev of standard normal truncated to (-2, 2) + trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) + elif distribution == "normal": + tensor.normal_(std=math.sqrt(variance)) + elif distribution == "uniform": + bound = math.sqrt(3 * variance) + # pylint: disable=invalid-unary-operand-type + tensor.uniform_(-bound, bound) + else: + raise ValueError(f"invalid distribution {distribution}") + + +def lecun_normal_(tensor): + variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") diff --git a/ldm_patched/pfn/model_loading.py b/ldm_patched/pfn/model_loading.py new file mode 100644 index 0000000000000000000000000000000000000000..e000871c1bfe66a07dc13b51ad709cb0de092a41 --- /dev/null +++ b/ldm_patched/pfn/model_loading.py @@ -0,0 +1,99 @@ +import logging as logger + +from .architecture.DAT import DAT +from .architecture.face.codeformer import CodeFormer +from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean +from .architecture.face.restoreformer_arch import RestoreFormer +from .architecture.HAT import HAT +from .architecture.LaMa import LaMa +from .architecture.OmniSR.OmniSR import OmniSR +from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet +from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 +from .architecture.SwiftSRGAN import Generator as SwiftSRGAN +from .architecture.Swin2SR import Swin2SR +from .architecture.SwinIR import SwinIR +from .types import PyTorchModel + + +class UnsupportedModel(Exception): + pass + + +def load_state_dict(state_dict) -> PyTorchModel: + logger.debug(f"Loading state dict into pytorch model arch") + + state_dict_keys = list(state_dict.keys()) + + if "params_ema" in state_dict_keys: + state_dict = state_dict["params_ema"] + elif "params-ema" in state_dict_keys: + state_dict = state_dict["params-ema"] + elif "params" in state_dict_keys: + state_dict = state_dict["params"] + + state_dict_keys = list(state_dict.keys()) + # SRVGGNet Real-ESRGAN (v2) + if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys: + model = RealESRGANv2(state_dict) + # SPSR (ESRGAN with lots of extra layers) + elif "f_HR_conv1.0.weight" in state_dict: + model = SPSR(state_dict) + # Swift-SRGAN + elif ( + "model" in state_dict_keys + and "initial.cnn.depthwise.weight" in state_dict["model"].keys() + ): + model = SwiftSRGAN(state_dict) + # SwinIR, Swin2SR, HAT + elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys: + if ( + "layers.0.residual_group.blocks.0.conv_block.cab.0.weight" + in state_dict_keys + ): + model = HAT(state_dict) + elif "patch_embed.proj.weight" in state_dict_keys: + model = Swin2SR(state_dict) + else: + model = SwinIR(state_dict) + # GFPGAN + elif ( + "toRGB.0.weight" in state_dict_keys + and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys + ): + model = GFPGANv1Clean(state_dict) + # RestoreFormer + elif ( + "encoder.conv_in.weight" in state_dict_keys + and "encoder.down.0.block.0.norm1.weight" in state_dict_keys + ): + model = RestoreFormer(state_dict) + elif ( + "encoder.blocks.0.weight" in state_dict_keys + and "quantize.embedding.weight" in state_dict_keys + ): + model = CodeFormer(state_dict) + # LaMa + elif ( + "model.model.1.bn_l.running_mean" in state_dict_keys + or "generator.model.1.bn_l.running_mean" in state_dict_keys + ): + model = LaMa(state_dict) + # Omni-SR + elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys: + model = OmniSR(state_dict) + # SCUNet + elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys: + model = SCUNet(state_dict) + # DAT + elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys: + model = DAT(state_dict) + # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1 + else: + try: + model = ESRGAN(state_dict) + except: + # pylint: disable=raise-missing-from + raise UnsupportedModel + return model diff --git a/ldm_patched/pfn/types.py b/ldm_patched/pfn/types.py new file mode 100644 index 0000000000000000000000000000000000000000..193333b9e8049d9558ca2ea253d41ee44b0b294b --- /dev/null +++ b/ldm_patched/pfn/types.py @@ -0,0 +1,69 @@ +from typing import Union + +from .architecture.DAT import DAT +from .architecture.face.codeformer import CodeFormer +from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean +from .architecture.face.restoreformer_arch import RestoreFormer +from .architecture.HAT import HAT +from .architecture.LaMa import LaMa +from .architecture.OmniSR.OmniSR import OmniSR +from .architecture.RRDB import RRDBNet as ESRGAN +from .architecture.SCUNet import SCUNet +from .architecture.SPSR import SPSRNet as SPSR +from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2 +from .architecture.SwiftSRGAN import Generator as SwiftSRGAN +from .architecture.Swin2SR import Swin2SR +from .architecture.SwinIR import SwinIR + +PyTorchSRModels = ( + RealESRGANv2, + SPSR, + SwiftSRGAN, + ESRGAN, + SwinIR, + Swin2SR, + HAT, + OmniSR, + SCUNet, + DAT, +) +PyTorchSRModel = Union[ + RealESRGANv2, + SPSR, + SwiftSRGAN, + ESRGAN, + SwinIR, + Swin2SR, + HAT, + OmniSR, + SCUNet, + DAT, +] + + +def is_pytorch_sr_model(model: object): + return isinstance(model, PyTorchSRModels) + + +PyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer) +PyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer] + + +def is_pytorch_face_model(model: object): + return isinstance(model, PyTorchFaceModels) + + +PyTorchInpaintModels = (LaMa,) +PyTorchInpaintModel = Union[LaMa] + + +def is_pytorch_inpaint_model(model: object): + return isinstance(model, PyTorchInpaintModels) + + +PyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels) +PyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel] + + +def is_pytorch_model(model: object): + return isinstance(model, PyTorchModels) diff --git a/ldm_patched/t2ia/adapter.py b/ldm_patched/t2ia/adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..e9a606b1cd67fd9a955a0ea0a86d1bd5498d85e5 --- /dev/null +++ b/ldm_patched/t2ia/adapter.py @@ -0,0 +1,293 @@ +#taken from https://github.com/TencentARC/T2I-Adapter +import torch +import torch.nn as nn +from collections import OrderedDict + + +def conv_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D convolution module. + """ + if dims == 1: + return nn.Conv1d(*args, **kwargs) + elif dims == 2: + return nn.Conv2d(*args, **kwargs) + elif dims == 3: + return nn.Conv3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +def avg_pool_nd(dims, *args, **kwargs): + """ + Create a 1D, 2D, or 3D average pooling module. + """ + if dims == 1: + return nn.AvgPool1d(*args, **kwargs) + elif dims == 2: + return nn.AvgPool2d(*args, **kwargs) + elif dims == 3: + return nn.AvgPool3d(*args, **kwargs) + raise ValueError(f"unsupported dimensions: {dims}") + + +class Downsample(nn.Module): + """ + A downsampling layer with an optional convolution. + :param channels: channels in the inputs and outputs. + :param use_conv: a bool determining if a convolution is applied. + :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then + downsampling occurs in the inner-two dimensions. + """ + + def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.dims = dims + stride = 2 if dims != 3 else (1, 2, 2) + if use_conv: + self.op = conv_nd( + dims, self.channels, self.out_channels, 3, stride=stride, padding=padding + ) + else: + assert self.channels == self.out_channels + self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride) + + def forward(self, x): + assert x.shape[1] == self.channels + if not self.use_conv: + padding = [x.shape[2] % 2, x.shape[3] % 2] + self.op.padding = padding + + x = self.op(x) + return x + + +class ResnetBlock(nn.Module): + def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True): + super().__init__() + ps = ksize // 2 + if in_c != out_c or sk == False: + self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + # print('n_in') + self.in_conv = None + self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps) + if sk == False: + self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps) + else: + self.skep = None + + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=use_conv) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + if self.in_conv is not None: # edit + x = self.in_conv(x) + + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + if self.skep is not None: + return h + self.skep(x) + else: + return h + x + + +class Adapter(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True): + super(Adapter, self).__init__() + self.unshuffle_amount = 8 + resblock_no_downsample = [] + resblock_downsample = [3, 2, 1] + self.xl = xl + if self.xl: + self.unshuffle_amount = 16 + resblock_no_downsample = [1] + resblock_downsample = [2] + + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + for i in range(len(channels)): + for j in range(nums_rb): + if (i in resblock_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv)) + elif (i in resblock_no_downsample) and (j == 0): + self.body.append( + ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + else: + self.body.append( + ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv)) + self.body = nn.ModuleList(self.body) + self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + x = self.conv_in(x) + for i in range(len(self.channels)): + for j in range(self.nums_rb): + idx = i * self.nums_rb + j + x = self.body[idx](x) + if self.xl: + features.append(None) + if i == 0: + features.append(None) + features.append(None) + if i == 2: + features.append(None) + else: + features.append(None) + features.append(None) + features.append(x) + + return features + + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential( + OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model))])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class StyleAdapter(nn.Module): + + def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4): + super().__init__() + + scale = width ** -0.5 + self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)]) + self.num_token = num_token + self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale) + self.ln_post = LayerNorm(width) + self.ln_pre = LayerNorm(width) + self.proj = nn.Parameter(scale * torch.randn(width, context_dim)) + + def forward(self, x): + # x shape [N, HW+1, C] + style_embedding = self.style_embedding + torch.zeros( + (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device) + x = torch.cat([x, style_embedding], dim=1) + x = self.ln_pre(x) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer_layes(x) + x = x.permute(1, 0, 2) # LND -> NLD + + x = self.ln_post(x[:, -self.num_token:, :]) + x = x @ self.proj + + return x + + +class ResnetBlock_light(nn.Module): + def __init__(self, in_c): + super().__init__() + self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1) + self.act = nn.ReLU() + self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1) + + def forward(self, x): + h = self.block1(x) + h = self.act(h) + h = self.block2(h) + + return h + x + + +class extractor(nn.Module): + def __init__(self, in_c, inter_c, out_c, nums_rb, down=False): + super().__init__() + self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0) + self.body = [] + for _ in range(nums_rb): + self.body.append(ResnetBlock_light(inter_c)) + self.body = nn.Sequential(*self.body) + self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0) + self.down = down + if self.down == True: + self.down_opt = Downsample(in_c, use_conv=False) + + def forward(self, x): + if self.down == True: + x = self.down_opt(x) + x = self.in_conv(x) + x = self.body(x) + x = self.out_conv(x) + + return x + + +class Adapter_light(nn.Module): + def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64): + super(Adapter_light, self).__init__() + self.unshuffle_amount = 8 + self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount) + self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount) + self.channels = channels + self.nums_rb = nums_rb + self.body = [] + self.xl = False + + for i in range(len(channels)): + if i == 0: + self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False)) + else: + self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True)) + self.body = nn.ModuleList(self.body) + + def forward(self, x): + # unshuffle + x = self.unshuffle(x) + # extract features + features = [] + for i in range(len(self.channels)): + x = self.body[i](x) + features.append(None) + features.append(None) + features.append(x) + + return features diff --git a/ldm_patched/taesd/taesd.py b/ldm_patched/taesd/taesd.py new file mode 100644 index 0000000000000000000000000000000000000000..0b4b885f74bd9d7f1cd2560b9f07daa579e634a3 --- /dev/null +++ b/ldm_patched/taesd/taesd.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python3 +""" +Tiny AutoEncoder for Stable Diffusion +(DNN for encoding / decoding SD's latent space) +""" +import torch +import torch.nn as nn + +import ldm_patched.modules.utils +import ldm_patched.modules.ops + +def conv(n_in, n_out, **kwargs): + return ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs) + +class Clamp(nn.Module): + def forward(self, x): + return torch.tanh(x / 3) * 3 + +class Block(nn.Module): + def __init__(self, n_in, n_out): + super().__init__() + self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out)) + self.skip = ldm_patched.modules.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity() + self.fuse = nn.ReLU() + def forward(self, x): + return self.fuse(self.conv(x) + self.skip(x)) + +def Encoder(): + return nn.Sequential( + conv(3, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64), + conv(64, 4), + ) + +def Decoder(): + return nn.Sequential( + Clamp(), conv(4, 64), nn.ReLU(), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False), + Block(64, 64), conv(64, 3), + ) + +class TAESD(nn.Module): + latent_magnitude = 3 + latent_shift = 0.5 + + def __init__(self, encoder_path=None, decoder_path=None): + """Initialize pretrained TAESD on the given device from the given checkpoints.""" + super().__init__() + self.taesd_encoder = Encoder() + self.taesd_decoder = Decoder() + self.vae_scale = torch.nn.Parameter(torch.tensor(1.0)) + if encoder_path is not None: + self.taesd_encoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(encoder_path, safe_load=True)) + if decoder_path is not None: + self.taesd_decoder.load_state_dict(ldm_patched.modules.utils.load_torch_file(decoder_path, safe_load=True)) + + @staticmethod + def scale_latents(x): + """raw latents -> [0, 1]""" + return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1) + + @staticmethod + def unscale_latents(x): + """[0, 1] -> raw latents""" + return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude) + + def decode(self, x): + x_sample = self.taesd_decoder(x * self.vae_scale) + x_sample = x_sample.sub(0.5).mul(2) + return x_sample + + def encode(self, x): + return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale diff --git a/ldm_patched/unipc/uni_pc.py b/ldm_patched/unipc/uni_pc.py new file mode 100644 index 0000000000000000000000000000000000000000..08bf0fc9e6787aec84500b4e3d24a4c8d253b433 --- /dev/null +++ b/ldm_patched/unipc/uni_pc.py @@ -0,0 +1,894 @@ +#code taken from: https://github.com/wl-zhao/UniPC and modified + +import torch +import torch.nn.functional as F +import math + +from tqdm.auto import trange, tqdm + + +class NoiseScheduleVP: + def __init__( + self, + schedule='discrete', + betas=None, + alphas_cumprod=None, + continuous_beta_0=0.1, + continuous_beta_1=20., + ): + """Create a wrapper class for the forward SDE (VP type). + + *** + Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t. + We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images. + *** + + The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ). + We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper). + Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have: + + log_alpha_t = self.marginal_log_mean_coeff(t) + sigma_t = self.marginal_std(t) + lambda_t = self.marginal_lambda(t) + + Moreover, as lambda(t) is an invertible function, we also support its inverse function: + + t = self.inverse_lambda(lambda_t) + + =============================================================== + + We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]). + + 1. For discrete-time DPMs: + + For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by: + t_i = (i + 1) / N + e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1. + We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3. + + Args: + betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details) + alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details) + + Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`. + + **Important**: Please pay special attention for the args for `alphas_cumprod`: + The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that + q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ). + Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have + alpha_{t_n} = \sqrt{\hat{alpha_n}}, + and + log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}). + + + 2. For continuous-time DPMs: + + We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise + schedule are the default settings in DDPM and improved-DDPM: + + Args: + beta_min: A `float` number. The smallest beta for the linear schedule. + beta_max: A `float` number. The largest beta for the linear schedule. + cosine_s: A `float` number. The hyperparameter in the cosine schedule. + cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule. + T: A `float` number. The ending time of the forward process. + + =============================================================== + + Args: + schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs, + 'linear' or 'cosine' for continuous-time DPMs. + Returns: + A wrapper object of the forward SDE (VP type). + + =============================================================== + + Example: + + # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', betas=betas) + + # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1): + >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod) + + # For continuous-time DPMs (VPSDE), linear schedule: + >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.) + + """ + + if schedule not in ['discrete', 'linear', 'cosine']: + raise ValueError("Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(schedule)) + + self.schedule = schedule + if schedule == 'discrete': + if betas is not None: + log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0) + else: + assert alphas_cumprod is not None + log_alphas = 0.5 * torch.log(alphas_cumprod) + self.total_N = len(log_alphas) + self.T = 1. + self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1)) + self.log_alpha_array = log_alphas.reshape((1, -1,)) + else: + self.total_N = 1000 + self.beta_0 = continuous_beta_0 + self.beta_1 = continuous_beta_1 + self.cosine_s = 0.008 + self.cosine_beta_max = 999. + self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.)) + self.schedule = schedule + if schedule == 'cosine': + # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T. + # Note that T = 0.9946 may be not the optimal setting. However, we find it works well. + self.T = 0.9946 + else: + self.T = 1. + + def marginal_log_mean_coeff(self, t): + """ + Compute log(alpha_t) of a given continuous-time label t in [0, T]. + """ + if self.schedule == 'discrete': + return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device), self.log_alpha_array.to(t.device)).reshape((-1)) + elif self.schedule == 'linear': + return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0 + elif self.schedule == 'cosine': + log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.)) + log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0 + return log_alpha_t + + def marginal_alpha(self, t): + """ + Compute alpha_t of a given continuous-time label t in [0, T]. + """ + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + """ + Compute sigma_t of a given continuous-time label t in [0, T]. + """ + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + + def inverse_lambda(self, lamb): + """ + Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t. + """ + if self.schedule == 'linear': + tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + Delta = self.beta_0**2 + tmp + return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0) + elif self.schedule == 'discrete': + log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb) + t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]), torch.flip(self.t_array.to(lamb.device), [1])) + return t.reshape((-1,)) + else: + log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb)) + t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (1. + self.cosine_s) / math.pi - self.cosine_s + t = t_fn(log_alpha) + return t + + +def model_wrapper( + model, + noise_schedule, + model_type="noise", + model_kwargs={}, + guidance_type="uncond", + condition=None, + unconditional_condition=None, + guidance_scale=1., + classifier_fn=None, + classifier_kwargs={}, +): + """Create a wrapper function for the noise prediction model. + + DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to + firstly wrap the model function to a noise prediction model that accepts the continuous time as the input. + + We support four types of the diffusion model by setting `model_type`: + + 1. "noise": noise prediction model. (Trained by predicting noise). + + 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0). + + 3. "v": velocity prediction model. (Trained by predicting the velocity). + The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2]. + + [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models." + arXiv preprint arXiv:2202.00512 (2022). + [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models." + arXiv preprint arXiv:2210.02303 (2022). + + 4. "score": marginal score function. (Trained by denoising score matching). + Note that the score function and the noise prediction model follows a simple relationship: + ``` + noise(x_t, t) = -sigma_t * score(x_t, t) + ``` + + We support three types of guided sampling by DPMs by setting `guidance_type`: + 1. "uncond": unconditional sampling by DPMs. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier. + The input `model` has the following format: + `` + model(x, t_input, **model_kwargs) -> noise | x_start | v | score + `` + + The input `classifier_fn` has the following format: + `` + classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond) + `` + + [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis," + in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794. + + 3. "classifier-free": classifier-free guidance sampling by conditional DPMs. + The input `model` has the following format: + `` + model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score + `` + And if cond == `unconditional_condition`, the model output is the unconditional DPM output. + + [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance." + arXiv preprint arXiv:2207.12598 (2022). + + + The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999) + or continuous-time labels (i.e. epsilon to T). + + We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise: + `` + def model_fn(x, t_continuous) -> noise: + t_input = get_model_input_time(t_continuous) + return noise_pred(model, x, t_input, **model_kwargs) + `` + where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver. + + =============================================================== + + Args: + model: A diffusion model with the corresponding format described above. + noise_schedule: A noise schedule object, such as NoiseScheduleVP. + model_type: A `str`. The parameterization type of the diffusion model. + "noise" or "x_start" or "v" or "score". + model_kwargs: A `dict`. A dict for the other inputs of the model function. + guidance_type: A `str`. The type of the guidance for sampling. + "uncond" or "classifier" or "classifier-free". + condition: A pytorch tensor. The condition for the guided sampling. + Only used for "classifier" or "classifier-free" guidance type. + unconditional_condition: A pytorch tensor. The condition for the unconditional sampling. + Only used for "classifier-free" guidance type. + guidance_scale: A `float`. The scale for the guided sampling. + classifier_fn: A classifier function. Only used for the classifier guidance. + classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function. + Returns: + A noise prediction model that accepts the noised data and the continuous time as the inputs. + """ + + def get_model_input_time(t_continuous): + """ + Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. + For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N]. + For continuous-time DPMs, we just use `t_continuous`. + """ + if noise_schedule.schedule == 'discrete': + return (t_continuous - 1. / noise_schedule.total_N) * 1000. + else: + return t_continuous + + def noise_pred_fn(x, t_continuous, cond=None): + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + t_input = get_model_input_time(t_continuous) + output = model(x, t_input, **model_kwargs) + if model_type == "noise": + return output + elif model_type == "x_start": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims) + elif model_type == "v": + alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x + elif model_type == "score": + sigma_t = noise_schedule.marginal_std(t_continuous) + dims = x.dim() + return -expand_dims(sigma_t, dims) * output + + def cond_grad_fn(x, t_input): + """ + Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t). + """ + with torch.enable_grad(): + x_in = x.detach().requires_grad_(True) + log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs) + return torch.autograd.grad(log_prob.sum(), x_in)[0] + + def model_fn(x, t_continuous): + """ + The noise predicition model function that is used for DPM-Solver. + """ + if t_continuous.reshape((-1,)).shape[0] == 1: + t_continuous = t_continuous.expand((x.shape[0])) + if guidance_type == "uncond": + return noise_pred_fn(x, t_continuous) + elif guidance_type == "classifier": + assert classifier_fn is not None + t_input = get_model_input_time(t_continuous) + cond_grad = cond_grad_fn(x, t_input) + sigma_t = noise_schedule.marginal_std(t_continuous) + noise = noise_pred_fn(x, t_continuous) + return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad + elif guidance_type == "classifier-free": + if guidance_scale == 1. or unconditional_condition is None: + return noise_pred_fn(x, t_continuous, cond=condition) + else: + x_in = torch.cat([x] * 2) + t_in = torch.cat([t_continuous] * 2) + c_in = torch.cat([unconditional_condition, condition]) + noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2) + return noise_uncond + guidance_scale * (noise - noise_uncond) + + assert model_type in ["noise", "x_start", "v"] + assert guidance_type in ["uncond", "classifier", "classifier-free"] + return model_fn + + +class UniPC: + def __init__( + self, + model_fn, + noise_schedule, + predict_x0=True, + thresholding=False, + max_val=1., + variant='bh1', + noise_mask=None, + masked_image=None, + noise=None, + ): + """Construct a UniPC. + + We support both data_prediction and noise_prediction. + """ + self.model = model_fn + self.noise_schedule = noise_schedule + self.variant = variant + self.predict_x0 = predict_x0 + self.thresholding = thresholding + self.max_val = max_val + self.noise_mask = noise_mask + self.masked_image = masked_image + self.noise = noise + + def dynamic_thresholding_fn(self, x0, t=None): + """ + The dynamic thresholding method. + """ + dims = x0.dim() + p = self.dynamic_thresholding_ratio + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.thresholding_max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + return x0 + + def noise_prediction_fn(self, x, t): + """ + Return the noise prediction model. + """ + if self.noise_mask is not None: + return self.model(x, t) * self.noise_mask + else: + return self.model(x, t) + + def data_prediction_fn(self, x, t): + """ + Return the data prediction model (with thresholding). + """ + noise = self.noise_prediction_fn(x, t) + dims = x.dim() + alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t) + x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims) + if self.thresholding: + p = 0.995 # A hyperparameter in the paper of "Imagen" [1]. + s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1) + s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims) + x0 = torch.clamp(x0, -s, s) / s + if self.noise_mask is not None: + x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image + return x0 + + def model_fn(self, x, t): + """ + Convert the model to the noise prediction model or the data prediction model. + """ + if self.predict_x0: + return self.data_prediction_fn(x, t) + else: + return self.noise_prediction_fn(x, t) + + def get_time_steps(self, skip_type, t_T, t_0, N, device): + """Compute the intermediate time steps for sampling. + """ + if skip_type == 'logSNR': + lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device)) + lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device)) + logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device) + return self.noise_schedule.inverse_lambda(logSNR_steps) + elif skip_type == 'time_uniform': + return torch.linspace(t_T, t_0, N + 1).to(device) + elif skip_type == 'time_quadratic': + t_order = 2 + t = torch.linspace(t_T**(1. / t_order), t_0**(1. / t_order), N + 1).pow(t_order).to(device) + return t + else: + raise ValueError("Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type)) + + def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device): + """ + Get the order of each step for sampling by the singlestep DPM-Solver. + """ + if order == 3: + K = steps // 3 + 1 + if steps % 3 == 0: + orders = [3,] * (K - 2) + [2, 1] + elif steps % 3 == 1: + orders = [3,] * (K - 1) + [1] + else: + orders = [3,] * (K - 1) + [2] + elif order == 2: + if steps % 2 == 0: + K = steps // 2 + orders = [2,] * K + else: + K = steps // 2 + 1 + orders = [2,] * (K - 1) + [1] + elif order == 1: + K = steps + orders = [1,] * steps + else: + raise ValueError("'order' must be '1' or '2' or '3'.") + if skip_type == 'logSNR': + # To reproduce the results in DPM-Solver paper + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device) + else: + timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[torch.cumsum(torch.tensor([0,] + orders), 0).to(device)] + return timesteps_outer, orders + + def denoise_to_zero_fn(self, x, s): + """ + Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. + """ + return self.data_prediction_fn(x, s) + + def multistep_uni_pc_update(self, x, model_prev_list, t_prev_list, t, order, **kwargs): + if len(t.shape) == 0: + t = t.view(-1) + if 'bh' in self.variant: + return self.multistep_uni_pc_bh_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + else: + assert self.variant == 'vary_coeff' + return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs) + + def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True): + print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)') + ns = self.noise_schedule + assert order <= len(model_prev_list) + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_t = ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = (lambda_prev_i - lambda_prev_0) / h + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + K = len(rks) + # build C matrix + C = [] + + col = torch.ones_like(rks) + for k in range(1, K + 1): + C.append(col) + col = col * rks / (k + 1) + C = torch.stack(C, dim=1) + + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + C_inv_p = torch.linalg.inv(C[:-1, :-1]) + A_p = C_inv_p + + if use_corrector: + print('using corrector') + C_inv = torch.linalg.inv(C) + A_c = C_inv + + hh = -h if self.predict_x0 else h + h_phi_1 = torch.expm1(hh) + h_phi_ks = [] + factorial_k = 1 + h_phi_k = h_phi_1 + for k in range(1, K + 2): + h_phi_ks.append(h_phi_k) + h_phi_k = h_phi_k / hh - 1 / factorial_k + factorial_k *= (k + 1) + + model_t = None + if self.predict_x0: + x_t_ = ( + sigma_t / sigma_prev_0 * x + - alpha_t * h_phi_1 * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - alpha_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - alpha_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + else: + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + x_t_ = ( + (torch.exp(log_alpha_t - log_alpha_prev_0)) * x + - (sigma_t * h_phi_1) * model_prev_0 + ) + # now predictor + x_t = x_t_ + if len(D1s) > 0: + # compute the residuals for predictor + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_p[k]) + # now corrector + if use_corrector: + model_t = self.model_fn(x_t, t) + D1_t = (model_t - model_prev_0) + x_t = x_t_ + k = 0 + for k in range(K - 1): + x_t = x_t - sigma_t * h_phi_ks[k + 1] * torch.einsum('bkchw,k->bchw', D1s, A_c[k][:-1]) + x_t = x_t - sigma_t * h_phi_ks[K] * (D1_t * A_c[k][-1]) + return x_t, model_t + + def multistep_uni_pc_bh_update(self, x, model_prev_list, t_prev_list, t, order, x_t=None, use_corrector=True): + # print(f'using unified predictor-corrector with order {order} (solver type: B(h))') + ns = self.noise_schedule + assert order <= len(model_prev_list) + dims = x.dim() + + # first compute rks + t_prev_0 = t_prev_list[-1] + lambda_prev_0 = ns.marginal_lambda(t_prev_0) + lambda_t = ns.marginal_lambda(t) + model_prev_0 = model_prev_list[-1] + sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t) + log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t) + alpha_t = torch.exp(log_alpha_t) + + h = lambda_t - lambda_prev_0 + + rks = [] + D1s = [] + for i in range(1, order): + t_prev_i = t_prev_list[-(i + 1)] + model_prev_i = model_prev_list[-(i + 1)] + lambda_prev_i = ns.marginal_lambda(t_prev_i) + rk = ((lambda_prev_i - lambda_prev_0) / h)[0] + rks.append(rk) + D1s.append((model_prev_i - model_prev_0) / rk) + + rks.append(1.) + rks = torch.tensor(rks, device=x.device) + + R = [] + b = [] + + hh = -h[0] if self.predict_x0 else h[0] + h_phi_1 = torch.expm1(hh) # h\phi_1(h) = e^h - 1 + h_phi_k = h_phi_1 / hh - 1 + + factorial_i = 1 + + if self.variant == 'bh1': + B_h = hh + elif self.variant == 'bh2': + B_h = torch.expm1(hh) + else: + raise NotImplementedError() + + for i in range(1, order + 1): + R.append(torch.pow(rks, i - 1)) + b.append(h_phi_k * factorial_i / B_h) + factorial_i *= (i + 1) + h_phi_k = h_phi_k / hh - 1 / factorial_i + + R = torch.stack(R) + b = torch.tensor(b, device=x.device) + + # now predictor + use_predictor = len(D1s) > 0 and x_t is None + if len(D1s) > 0: + D1s = torch.stack(D1s, dim=1) # (B, K) + if x_t is None: + # for order 2, we use a simplified version + if order == 2: + rhos_p = torch.tensor([0.5], device=b.device) + else: + rhos_p = torch.linalg.solve(R[:-1, :-1], b[:-1]) + else: + D1s = None + + if use_corrector: + # print('using corrector') + # for order 1, we use a simplified version + if order == 1: + rhos_c = torch.tensor([0.5], device=b.device) + else: + rhos_c = torch.linalg.solve(R, b) + + model_t = None + if self.predict_x0: + x_t_ = ( + expand_dims(sigma_t / sigma_prev_0, dims) * x + - expand_dims(alpha_t * h_phi_1, dims)* model_prev_0 + ) + + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + else: + x_t_ = ( + expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x + - expand_dims(sigma_t * h_phi_1, dims) * model_prev_0 + ) + if x_t is None: + if use_predictor: + pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s) + else: + pred_res = 0 + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * pred_res + + if use_corrector: + model_t = self.model_fn(x_t, t) + if D1s is not None: + corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s) + else: + corr_res = 0 + D1_t = (model_t - model_prev_0) + x_t = x_t_ - expand_dims(sigma_t * B_h, dims) * (corr_res + rhos_c[-1] * D1_t) + return x_t, model_t + + + def sample(self, x, timesteps, t_start=None, t_end=None, order=3, skip_type='time_uniform', + method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver', + atol=0.0078, rtol=0.05, corrector=False, callback=None, disable_pbar=False + ): + # t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end + # t_T = self.noise_schedule.T if t_start is None else t_start + device = x.device + steps = len(timesteps) - 1 + if method == 'multistep': + assert steps >= order + # timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device) + assert timesteps.shape[0] - 1 == steps + # with torch.no_grad(): + for step_index in trange(steps, disable=disable_pbar): + if self.noise_mask is not None: + x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index])) + if step_index == 0: + vec_t = timesteps[0].expand((x.shape[0])) + model_prev_list = [self.model_fn(x, vec_t)] + t_prev_list = [vec_t] + elif step_index < order: + init_order = step_index + # Init the first `order` values by lower order multistep DPM-Solver. + # for init_order in range(1, order): + vec_t = timesteps[init_order].expand(x.shape[0]) + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, init_order, use_corrector=True) + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list.append(model_x) + t_prev_list.append(vec_t) + else: + extra_final_step = 0 + if step_index == (steps - 1): + extra_final_step = 1 + for step in range(step_index, step_index + 1 + extra_final_step): + vec_t = timesteps[step].expand(x.shape[0]) + if lower_order_final: + step_order = min(order, steps + 1 - step) + else: + step_order = order + # print('this step order:', step_order) + if step == steps: + # print('do not run corrector at the last step') + use_corrector = False + else: + use_corrector = True + x, model_x = self.multistep_uni_pc_update(x, model_prev_list, t_prev_list, vec_t, step_order, use_corrector=use_corrector) + for i in range(order - 1): + t_prev_list[i] = t_prev_list[i + 1] + model_prev_list[i] = model_prev_list[i + 1] + t_prev_list[-1] = vec_t + # We do not need to evaluate the final model value. + if step < steps: + if model_x is None: + model_x = self.model_fn(x, vec_t) + model_prev_list[-1] = model_x + if callback is not None: + callback(step_index, model_prev_list[-1], x, steps) + else: + raise NotImplementedError() + # if denoise_to_zero: + # x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0) + return x + + +############################################################# +# other utility functions +############################################################# + +def interpolate_fn(x, xp, yp): + """ + A piecewise linear function y = f(x), using xp and yp as keypoints. + We implement f(x) in a differentiable way (i.e. applicable for autograd). + The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.) + + Args: + x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver). + xp: PyTorch tensor with shape [C, K], where K is the number of keypoints. + yp: PyTorch tensor with shape [C, K]. + Returns: + The function values f(x), with shape [N, C]. + """ + N, K = x.shape[0], xp.shape[1] + all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2) + sorted_all_x, x_indices = torch.sort(all_x, dim=2) + x_idx = torch.argmin(x_indices, dim=2) + cand_start_idx = x_idx - 1 + start_idx = torch.where( + torch.eq(x_idx, 0), + torch.tensor(1, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1) + start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2) + end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2) + start_idx2 = torch.where( + torch.eq(x_idx, 0), + torch.tensor(0, device=x.device), + torch.where( + torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx, + ), + ) + y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1) + start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2) + end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2) + cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x) + return cand + + +def expand_dims(v, dims): + """ + Expand the tensor `v` to the dim `dims`. + + Args: + `v`: a PyTorch tensor with shape [N]. + `dim`: a `int`. + Returns: + a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`. + """ + return v[(...,) + (None,)*(dims - 1)] + + +class SigmaConvert: + schedule = "" + def marginal_log_mean_coeff(self, sigma): + return 0.5 * torch.log(1 / ((sigma * sigma) + 1)) + + def marginal_alpha(self, t): + return torch.exp(self.marginal_log_mean_coeff(t)) + + def marginal_std(self, t): + return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t))) + + def marginal_lambda(self, t): + """ + Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T]. + """ + log_mean_coeff = self.marginal_log_mean_coeff(t) + log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff)) + return log_mean_coeff - log_std + +def predict_eps_sigma(model, input, sigma_in, **kwargs): + sigma = sigma_in.view(sigma_in.shape[:1] + (1,) * (input.ndim - 1)) + input = input * ((sigma ** 2 + 1.0) ** 0.5) + return (input - model(input, sigma_in, **kwargs)) / sigma + + +def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'): + timesteps = sigmas.clone() + if sigmas[-1] == 0: + timesteps = sigmas[:] + timesteps[-1] = 0.001 + else: + timesteps = sigmas.clone() + ns = SigmaConvert() + + if image is not None: + img = image * ns.marginal_alpha(timesteps[0]) + if max_denoise: + noise_mult = 1.0 + else: + noise_mult = ns.marginal_std(timesteps[0]) + img += noise * noise_mult + else: + img = noise + + model_type = "noise" + + model_fn = model_wrapper( + lambda input, sigma, **kwargs: predict_eps_sigma(model, input, sigma, **kwargs), + ns, + model_type=model_type, + guidance_type="uncond", + model_kwargs=extra_args, + ) + + order = min(3, len(timesteps) - 2) + uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant) + x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable) + x /= ns.marginal_alpha(timesteps[-1]) + return x diff --git a/ldm_patched/utils/latent_visualization.py b/ldm_patched/utils/latent_visualization.py new file mode 100644 index 0000000000000000000000000000000000000000..a1ad403a1d77fd1effe0ecd153d18ae3059ddae4 --- /dev/null +++ b/ldm_patched/utils/latent_visualization.py @@ -0,0 +1,97 @@ +import torch +from PIL import Image +import struct +import numpy as np +from ldm_patched.modules.args_parser import args, LatentPreviewMethod +from ldm_patched.taesd.taesd import TAESD +import ldm_patched.utils.path_utils +import ldm_patched.modules.utils + +MAX_PREVIEW_RESOLUTION = 512 + +class LatentPreviewer: + def decode_latent_to_preview(self, x0): + pass + + def decode_latent_to_preview_image(self, preview_format, x0): + preview_image = self.decode_latent_to_preview(x0) + return ("JPEG", preview_image, MAX_PREVIEW_RESOLUTION) + +class TAESDPreviewerImpl(LatentPreviewer): + def __init__(self, taesd): + self.taesd = taesd + + def decode_latent_to_preview(self, x0): + x_sample = self.taesd.decode(x0[:1])[0].detach() + x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0) + x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2) + x_sample = x_sample.astype(np.uint8) + + preview_image = Image.fromarray(x_sample) + return preview_image + + +class Latent2RGBPreviewer(LatentPreviewer): + def __init__(self, latent_rgb_factors): + self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu") + + def decode_latent_to_preview(self, x0): + latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors + + latents_ubyte = (((latent_image + 1) / 2) + .clamp(0, 1) # change scale from -1..1 to 0..1 + .mul(0xFF) # to 0..255 + .byte()).cpu() + + return Image.fromarray(latents_ubyte.numpy()) + + +def get_previewer(device, latent_format): + previewer = None + method = args.preview_option + if method != LatentPreviewMethod.NoPreviews: + # TODO previewer methods + taesd_decoder_path = None + if latent_format.taesd_decoder_name is not None: + taesd_decoder_path = next( + (fn for fn in ldm_patched.utils.path_utils.get_filename_list("vae_approx") + if fn.startswith(latent_format.taesd_decoder_name)), + "" + ) + taesd_decoder_path = ldm_patched.utils.path_utils.get_full_path("vae_approx", taesd_decoder_path) + + if method == LatentPreviewMethod.Auto: + method = LatentPreviewMethod.Latent2RGB + if taesd_decoder_path: + method = LatentPreviewMethod.TAESD + + if method == LatentPreviewMethod.TAESD: + if taesd_decoder_path: + taesd = TAESD(None, taesd_decoder_path).to(device) + previewer = TAESDPreviewerImpl(taesd) + else: + print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name)) + + if previewer is None: + if latent_format.latent_rgb_factors is not None: + previewer = Latent2RGBPreviewer(latent_format.latent_rgb_factors) + return previewer + +def prepare_callback(model, steps, x0_output_dict=None): + preview_format = "JPEG" + if preview_format not in ["JPEG", "PNG"]: + preview_format = "JPEG" + + previewer = get_previewer(model.load_device, model.model.latent_format) + + pbar = ldm_patched.modules.utils.ProgressBar(steps) + def callback(step, x0, x, total_steps): + if x0_output_dict is not None: + x0_output_dict["x0"] = x0 + + preview_bytes = None + if previewer: + preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) + pbar.update_absolute(step + 1, total_steps, preview_bytes) + return callback + diff --git a/ldm_patched/utils/path_utils.py b/ldm_patched/utils/path_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..6cae149b47c5efb959cdcb815aafb28f37307b9c --- /dev/null +++ b/ldm_patched/utils/path_utils.py @@ -0,0 +1,262 @@ +import os +import time + +supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors']) + +folder_names_and_paths = {} + +base_path = os.getcwd() +models_dir = os.path.join(base_path, "models") +folder_names_and_paths["checkpoints"] = ([os.path.join(models_dir, "checkpoints")], supported_pt_extensions) +folder_names_and_paths["configs"] = ([os.path.join(models_dir, "configs")], [".yaml"]) + +folder_names_and_paths["loras"] = ([os.path.join(models_dir, "loras")], supported_pt_extensions) +folder_names_and_paths["vae"] = ([os.path.join(models_dir, "vae")], supported_pt_extensions) +folder_names_and_paths["clip"] = ([os.path.join(models_dir, "clip")], supported_pt_extensions) +folder_names_and_paths["unet"] = ([os.path.join(models_dir, "unet")], supported_pt_extensions) +folder_names_and_paths["clip_vision"] = ([os.path.join(models_dir, "clip_vision")], supported_pt_extensions) +folder_names_and_paths["style_models"] = ([os.path.join(models_dir, "style_models")], supported_pt_extensions) +folder_names_and_paths["embeddings"] = ([os.path.join(models_dir, "embeddings")], supported_pt_extensions) +folder_names_and_paths["diffusers"] = ([os.path.join(models_dir, "diffusers")], ["folder"]) +folder_names_and_paths["vae_approx"] = ([os.path.join(models_dir, "vae_approx")], supported_pt_extensions) + +folder_names_and_paths["controlnet"] = ([os.path.join(models_dir, "controlnet"), os.path.join(models_dir, "t2i_adapter")], supported_pt_extensions) +folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], supported_pt_extensions) + +folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions) + +folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], []) + +folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions) + +folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions) + +folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""}) + +output_directory = os.path.join(os.getcwd(), "output") +temp_directory = os.path.join(os.getcwd(), "temp") +input_directory = os.path.join(os.getcwd(), "input") +user_directory = os.path.join(os.getcwd(), "user") + +filename_list_cache = {} + +if not os.path.exists(input_directory): + try: + pass # os.makedirs(input_directory) + except: + print("Failed to create input directory") + +def set_output_directory(output_dir): + global output_directory + output_directory = output_dir + +def set_temp_directory(temp_dir): + global temp_directory + temp_directory = temp_dir + +def set_input_directory(input_dir): + global input_directory + input_directory = input_dir + +def get_output_directory(): + global output_directory + return output_directory + +def get_temp_directory(): + global temp_directory + return temp_directory + +def get_input_directory(): + global input_directory + return input_directory + + +#NOTE: used in http server so don't put folders that should not be accessed remotely +def get_directory_by_type(type_name): + if type_name == "output": + return get_output_directory() + if type_name == "temp": + return get_temp_directory() + if type_name == "input": + return get_input_directory() + return None + + +# determine base_dir rely on annotation if name is 'filename.ext [annotation]' format +# otherwise use default_path as base_dir +def annotated_filepath(name): + if name.endswith("[output]"): + base_dir = get_output_directory() + name = name[:-9] + elif name.endswith("[input]"): + base_dir = get_input_directory() + name = name[:-8] + elif name.endswith("[temp]"): + base_dir = get_temp_directory() + name = name[:-7] + else: + return name, None + + return name, base_dir + + +def get_annotated_filepath(name, default_dir=None): + name, base_dir = annotated_filepath(name) + + if base_dir is None: + if default_dir is not None: + base_dir = default_dir + else: + base_dir = get_input_directory() # fallback path + + return os.path.join(base_dir, name) + + +def exists_annotated_filepath(name): + name, base_dir = annotated_filepath(name) + + if base_dir is None: + base_dir = get_input_directory() # fallback path + + filepath = os.path.join(base_dir, name) + return os.path.exists(filepath) + + +def add_model_folder_path(folder_name, full_folder_path): + global folder_names_and_paths + if folder_name in folder_names_and_paths: + folder_names_and_paths[folder_name][0].append(full_folder_path) + else: + folder_names_and_paths[folder_name] = ([full_folder_path], set()) + +def get_folder_paths(folder_name): + return folder_names_and_paths[folder_name][0][:] + +def recursive_search(directory, excluded_dir_names=None): + if not os.path.isdir(directory): + return [], {} + + if excluded_dir_names is None: + excluded_dir_names = [] + + result = [] + dirs = {} + + # Attempt to add the initial directory to dirs with error handling + try: + dirs[directory] = os.path.getmtime(directory) + except FileNotFoundError: + print(f"Warning: Unable to access {directory}. Skipping this path.") + + for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True): + subdirs[:] = [d for d in subdirs if d not in excluded_dir_names] + for file_name in filenames: + relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory) + result.append(relative_path) + + for d in subdirs: + path = os.path.join(dirpath, d) + try: + dirs[path] = os.path.getmtime(path) + except FileNotFoundError: + print(f"Warning: Unable to access {path}. Skipping this path.") + continue + return result, dirs + +def filter_files_extensions(files, extensions): + return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files))) + + + +def get_full_path(folder_name, filename): + global folder_names_and_paths + if folder_name not in folder_names_and_paths: + return None + folders = folder_names_and_paths[folder_name] + filename = os.path.relpath(os.path.join("/", filename), "/") + for x in folders[0]: + full_path = os.path.join(x, filename) + if os.path.isfile(full_path): + return full_path + + return None + +def get_filename_list_(folder_name): + global folder_names_and_paths + output_list = set() + folders = folder_names_and_paths[folder_name] + output_folders = {} + for x in folders[0]: + files, folders_all = recursive_search(x, excluded_dir_names=[".git"]) + output_list.update(filter_files_extensions(files, folders[1])) + output_folders = {**output_folders, **folders_all} + + return (sorted(list(output_list)), output_folders, time.perf_counter()) + +def cached_filename_list_(folder_name): + global filename_list_cache + global folder_names_and_paths + if folder_name not in filename_list_cache: + return None + out = filename_list_cache[folder_name] + + for x in out[1]: + time_modified = out[1][x] + folder = x + if os.path.getmtime(folder) != time_modified: + return None + + folders = folder_names_and_paths[folder_name] + for x in folders[0]: + if os.path.isdir(x): + if x not in out[1]: + return None + + return out + +def get_filename_list(folder_name): + out = cached_filename_list_(folder_name) + if out is None: + out = get_filename_list_(folder_name) + global filename_list_cache + filename_list_cache[folder_name] = out + return list(out[0]) + +def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0): + def map_filename(filename): + prefix_len = len(os.path.basename(filename_prefix)) + prefix = filename[:prefix_len + 1] + try: + digits = int(filename[prefix_len + 1:].split('_')[0]) + except: + digits = 0 + return (digits, prefix) + + def compute_vars(input, image_width, image_height): + input = input.replace("%width%", str(image_width)) + input = input.replace("%height%", str(image_height)) + return input + + filename_prefix = compute_vars(filename_prefix, image_width, image_height) + + subfolder = os.path.dirname(os.path.normpath(filename_prefix)) + filename = os.path.basename(os.path.normpath(filename_prefix)) + + full_output_folder = os.path.join(output_dir, subfolder) + + if os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) != output_dir: + err = "**** ERROR: Saving image outside the output folder is not allowed." + \ + "\n full_output_folder: " + os.path.abspath(full_output_folder) + \ + "\n output_dir: " + output_dir + \ + "\n commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) + print(err) + raise Exception(err) + + try: + counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1 + except ValueError: + counter = 1 + except FileNotFoundError: + os.makedirs(full_output_folder, exist_ok=True) + counter = 1 + return full_output_folder, filename, counter, subfolder, filename_prefix diff --git a/models/checkpoints/put_checkpoints_here b/models/checkpoints/put_checkpoints_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip/put_clip_or_text_encoder_models_here b/models/clip/put_clip_or_text_encoder_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip_vision/put_clip_vision_models_here b/models/clip_vision/put_clip_vision_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/clip_vision/wd-v1-4-moat-tagger-v2.csv b/models/clip_vision/wd-v1-4-moat-tagger-v2.csv new file mode 100644 index 0000000000000000000000000000000000000000..71796801c13109547bc017d40fc6f5b89bfd9cc0 --- /dev/null +++ b/models/clip_vision/wd-v1-4-moat-tagger-v2.csv @@ -0,0 +1,9084 @@ +tag_id,name,category,count +9999999,general,9,807858 +9999998,sensitive,9,3771700 +9999997,questionable,9,769899 +9999996,explicit,9,560281 +470575,1girl,0,4225150 +212816,solo,0,3515897 +13197,long_hair,0,2982517 +8601,breasts,0,2323580 +469576,looking_at_viewer,0,2089971 +3389,blush,0,2040471 +1815,smile,0,1903619 +15080,short_hair,0,1568265 +11906,open_mouth,0,1565950 +16751,bangs,0,1516840 +10959,blue_eyes,0,1225129 +566835,multiple_girls,0,1120328 +429,skirt,0,1100620 +87788,blonde_hair,0,1098200 +403247,large_breasts,0,1083979 +412368,simple_background,0,1074818 +16867,brown_hair,0,1072209 +12590,shirt,0,1001030 +13200,black_hair,0,981413 +380350,hair_ornament,0,939495 +8526,red_eyes,0,897316 +1882,thighhighs,0,890813 +5735,gloves,0,886283 +383159,long_sleeves,0,883900 +540830,1boy,0,881194 +2373,hat,0,879697 +515193,white_background,0,874291 +2241,dress,0,838290 +4563,bow,0,795194 +464575,ribbon,0,793922 +9294,navel,0,786293 +375387,holding,0,732899 +1821,2girls,0,729721 +6126,animal_ears,0,722334 +4607,cleavage,0,693321 +658573,hair_between_eyes,0,692014 +376054,bare_shoulders,0,656975 +1709,twintails,0,648384 +16578,brown_eyes,0,645874 +16613,jewelry,0,644654 +667868,medium_breasts,0,642525 +12289,sitting,0,630993 +417660,very_long_hair,0,622920 +572080,closed_mouth,0,618062 +464906,underwear,0,610036 +8889,nipples,0,591774 +16509,school_uniform,0,585679 +10960,green_eyes,0,584783 +10953,blue_hair,0,564360 +15675,standing,0,551783 +15654,purple_eyes,0,536623 +466499,collarbone,0,520875 +391,panties,0,506334 +3843,jacket,0,493731 +15674,tail,0,487490 +1681,monochrome,0,478584 +444,swimsuit,0,467619 +608813,full_body,0,463008 +465619,closed_eyes,0,455512 +464561,hair_ribbon,0,449452 +89189,yellow_eyes,0,447582 +376766,white_shirt,0,435867 +547463,upper_body,0,434670 +2355,ponytail,0,431021 +11449,weapon,0,430315 +11429,pink_hair,0,427100 +16442,purple_hair,0,426385 +8101,ass,0,423113 +4334,braid,0,417832 +464559,flower,0,411874 +63,comic,0,411524 +3522,ahoge,0,408654 +16581,white_hair,0,407226 +472154,short_sleeves,0,389311 +384553,:d,0,387533 +622137,hetero,0,384576 +374844,hair_bow,0,381335 +513837,greyscale,0,377967 +16580,grey_hair,0,375103 +1300281,male_focus,0,371503 +2750,heart,0,361897 +2363,pantyhose,0,356177 +484168,sidelocks,0,354421 +6539,bikini,0,349809 +3870,thighs,0,348316 +2365,nude,0,341086 +5403,red_hair,0,338682 +390728,multicolored_hair,0,336863 +660909,cowboy_shot,0,336374 +4569,sweat,0,334084 +383282,pleated_skirt,0,332323 +2376,hairband,0,329485 +13804,earrings,0,328755 +465265,small_breasts,0,325542 +5827,boots,0,320845 +13879,outdoors,0,320641 +301022,lying,0,312125 +4352,censored,0,310189 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+1305340,morgana_(persona_5),4,694 +1533069,rhea_(fire_emblem),4,694 +1555021,sei_shounagon_(fate),4,694 +1386133,ogata_hyakunosuke,4,693 +1755030,fujimaru_ritsuka_(female)_(polar_chaldea_uniform),4,691 +1276392,otokura_yuuki,4,689 +1401541,izayoi_liko,4,689 +1459805,yukoku_kiriko,4,689 +559713,black_heart,4,687 +1346345,takimoto_hifumi,4,687 +1441569,yuri_(doki_doki_literature_club),4,686 +1644984,fine_motion_(umamusume),4,686 +1644976,admire_vega_(umamusume),4,686 +1630689,grecale_(kancolle),4,686 +1571004,shibuya_kanon,4,686 +1452677,semiramis_(fate),4,685 +1627797,wu_zetian_(fate),4,685 +1607938,getou_suguru,4,684 +376441,arle_nadja,4,683 +664614,ryuzaki_kaoru,4,683 +1797173,hyakumantenbara_salome,4,683 +1619673,puru-see_(hoshizuki_(seigetsu)),4,682 +1152270,ibuki_tsubasa,4,681 +1227600,mochizuki_anna,4,681 +1275005,ousaka_shizuku,4,681 +1782428,bb_(swimsuit_mooncancer)_(third_ascension)_(fate),4,679 +1693037,le_malin_(listless_lapin)_(azur_lane),4,679 +1768854,mysterious_heroine_x_alter_(first_ascension)_(fate),4,678 +1631348,shinshuu_maru_(kancolle),4,678 +480982,asbel_lhant,4,677 +1316228,nakagawa_natsuki,4,677 +1667164,g41_(girls'_frontline),4,677 +404920,sage_(dq3),4,676 +1331206,kawakami_mai,4,676 +1423942,eris_greyrat,4,676 +1560517,utage_(arknights),4,676 +1405059,sand_cat_(kemono_friends),4,675 +1680295,mika_(blue_archive),4,675 +664407,himekawa_yuki,4,674 +1631118,kishinami_(kancolle),4,674 +1285963,alena_(dq4),4,672 +1645008,smart_falcon_(umamusume),4,672 +497689,cheria_barnes,4,671 +1631097,kasuga_maru_(kancolle),4,671 +1432300,yatadera_narumi,4,669 +1387122,mordred_(swimsuit_rider)_(fate),4,667 +1529378,sakata_kintoki_(fate),4,666 +1408392,golden_snub-nosed_monkey_(kemono_friends),4,666 +1780213,hina_(swimsuit)_(blue_archive),4,666 +1875916,tiki_(adult)_(fire_emblem),4,665 +1386436,amami_rantarou,4,664 +548087,hasegawa_kobato,4,663 +1257848,hoshimiya_kate,4,663 +1335159,sugimoto_saichi,4,663 +596743,fukawa_touko,4,662 +1476820,yozora_mel,4,662 +1789595,saijo_juri,4,661 +1327917,yuel_(granblue_fantasy),4,660 +1460549,nekomiya_hinata,4,660 +688953,igarashi_kyoko,4,658 +1639090,maya_kai_ni_(kancolle),4,658 +1588338,tomoe_gozen_(swimsuit_saber)_(fate),4,658 +1657407,blue_poison_(shoal_beat)_(arknights),4,658 +1856336,hasumi_(gym_uniform)_(blue_archive),4,656 +1598223,kayoko_(blue_archive),4,655 +1434774,uehara_himari,4,654 +1344209,roxy_migurdia,4,653 +1837736,saren_(summer)_(princess_connect!),4,653 +1485459,jean_bart_(azur_lane),4,653 +713347,katagiri_sanae,4,652 +1440285,queen_elizabeth_(azur_lane),4,652 +1667205,9a-91_(girls'_frontline),4,651 +1479271,rengoku_kyoujurou,4,650 +1386290,keebo,4,649 +1599618,whislash_(arknights),4,646 +971934,vampy,4,645 +1639099,naganami_kai_ni_(kancolle),4,644 +1326283,jeanne_d'arc_(granblue_fantasy),4,643 +1644991,matikanefukukitaru_(umamusume),4,643 +1645010,sweep_tosho_(umamusume),4,642 +879608,toyokawa_fuka,4,641 +1387119,kiyohime_(swimsuit_lancer)_(fate),4,641 +1836895,karyl_(summer)_(princess_connect!),4,641 +1596254,razor_(genshin_impact),4,640 +1480768,siro_(dennou_shoujo_youtuber_siro),4,639 +1491126,tsuyuri_kanao,4,637 +1667169,negev_(girls'_frontline),4,636 +1440310,eldridge_(azur_lane),4,636 +1284942,midway_princess,4,635 +1420829,matsubara_kanon,4,633 +1541801,magallan_(arknights),4,633 +1782915,ijichi_nijika,4,632 +1411715,okusawa_misaki,4,628 +1875918,tiki_(young)_(fire_emblem),4,627 +1790615,spicy_nun_(diva),4,625 +1067206,hayasaka_mirei,4,623 +1781831,jeanne_d'arc_(swimsuit_archer)_(second_ascension)_(fate),4,623 +1420746,udagawa_tomoe,4,621 +1445024,shirasagi_chisato,4,621 +1678878,la_pluma_(arknights),4,619 +1265247,sato_shin,4,617 +1491428,takamiya_rion,4,612 +1623487,crypto_(apex_legends),4,612 +1533772,uzuki_sayaka,4,607 +1533771,kumada_masaru,4,600 diff --git a/models/configs/anything_v3.yaml b/models/configs/anything_v3.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/anything_v3.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference.yaml b/models/configs/v1-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d4effe569e897369918625f9d8be5603a0e6a0d6 --- /dev/null +++ b/models/configs/v1-inference.yaml @@ -0,0 +1,70 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inference_clip_skip_2.yaml b/models/configs/v1-inference_clip_skip_2.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8bcfe584ae73d60e2c7a6f89b3f7befbd487ea34 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2.yaml @@ -0,0 +1,73 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_clip_skip_2_fp16.yaml b/models/configs/v1-inference_clip_skip_2_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..7eca31c7b5e571c2b1348e94ed9d69978ebd2d52 --- /dev/null +++ b/models/configs/v1-inference_clip_skip_2_fp16.yaml @@ -0,0 +1,74 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + params: + layer: "hidden" + layer_idx: -2 diff --git a/models/configs/v1-inference_fp16.yaml b/models/configs/v1-inference_fp16.yaml new file mode 100644 index 0000000000000000000000000000000000000000..147f42b17b835cc839338156f99e8f971df5c1aa --- /dev/null +++ b/models/configs/v1-inference_fp16.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 1.0e-04 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 10000 ] + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder diff --git a/models/configs/v1-inpainting-inference.yaml b/models/configs/v1-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..45f3f82d461cd8c6109f26ec3b1da75366eda0b0 --- /dev/null +++ b/models/configs/v1-inpainting-inference.yaml @@ -0,0 +1,71 @@ +model: + base_learning_rate: 7.5e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false # Note: different from the one we trained before + conditioning_key: hybrid # important + monitor: val/loss_simple_ema + scale_factor: 0.18215 + finetune_keys: null + + scheduler_config: # 10000 warmup steps + target: ldm.lr_scheduler.LambdaLinearScheduler + params: + warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch + cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases + f_start: [ 1.e-6 ] + f_max: [ 1. ] + f_min: [ 1. ] + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + image_size: 32 # unused + in_channels: 9 # 4 data + 4 downscaled image + 1 mask + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_heads: 8 + use_spatial_transformer: True + transformer_depth: 1 + context_dim: 768 + use_checkpoint: True + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenCLIPEmbedder + diff --git a/models/configs/v2-inference-v.yaml b/models/configs/v2-inference-v.yaml new file mode 100644 index 0000000000000000000000000000000000000000..8ec8dfbfefe94ae8522c93017668fea78d580acf --- /dev/null +++ b/models/configs/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference-v_fp32.yaml b/models/configs/v2-inference-v_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..d5c9b9cb29ca162ade44a7c922f59e75d7d57813 --- /dev/null +++ b/models/configs/v2-inference-v_fp32.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference.yaml b/models/configs/v2-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..152c4f3c2b36c3b246a9cb10eb8166134b0d2e1c --- /dev/null +++ b/models/configs/v2-inference.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inference_fp32.yaml b/models/configs/v2-inference_fp32.yaml new file mode 100644 index 0000000000000000000000000000000000000000..0d03231f3f2c2e8ef8fbe0d781e5f3d65409ef3a --- /dev/null +++ b/models/configs/v2-inference_fp32.yaml @@ -0,0 +1,67 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: False + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" diff --git a/models/configs/v2-inpainting-inference.yaml b/models/configs/v2-inpainting-inference.yaml new file mode 100644 index 0000000000000000000000000000000000000000..32a9471d71b828c51bcbbabfe34c5f6c8282c803 --- /dev/null +++ b/models/configs/v2-inpainting-inference.yaml @@ -0,0 +1,158 @@ +model: + base_learning_rate: 5.0e-05 + target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + scale_factor: 0.18215 + monitor: val/loss_simple_ema + finetune_keys: null + use_ema: False + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 9 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + + +data: + target: ldm.data.laion.WebDataModuleFromConfig + params: + tar_base: null # for concat as in LAION-A + p_unsafe_threshold: 0.1 + filter_word_list: "data/filters.yaml" + max_pwatermark: 0.45 + batch_size: 8 + num_workers: 6 + multinode: True + min_size: 512 + train: + shards: + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-0/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-1/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-2/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-3/{00000..18699}.tar -" + - "pipe:aws s3 cp s3://stability-aws/laion-a-native/part-4/{00000..18699}.tar -" #{00000-94333}.tar" + shuffle: 10000 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.RandomCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + # NOTE use enough shards to avoid empty validation loops in workers + validation: + shards: + - "pipe:aws s3 cp s3://deep-floyd-s3/datasets/laion_cleaned-part5/{93001..94333}.tar - " + shuffle: 0 + image_key: jpg + image_transforms: + - target: torchvision.transforms.Resize + params: + size: 512 + interpolation: 3 + - target: torchvision.transforms.CenterCrop + params: + size: 512 + postprocess: + target: ldm.data.laion.AddMask + params: + mode: "512train-large" + p_drop: 0.25 + +lightning: + find_unused_parameters: True + modelcheckpoint: + params: + every_n_train_steps: 5000 + + callbacks: + metrics_over_trainsteps_checkpoint: + params: + every_n_train_steps: 10000 + + image_logger: + target: main.ImageLogger + params: + enable_autocast: False + disabled: False + batch_frequency: 1000 + max_images: 4 + increase_log_steps: False + log_first_step: False + log_images_kwargs: + use_ema_scope: False + inpaint: False + plot_progressive_rows: False + plot_diffusion_rows: False + N: 4 + unconditional_guidance_scale: 5.0 + unconditional_guidance_label: [""] + ddim_steps: 50 # todo check these out for depth2img, + ddim_eta: 0.0 # todo check these out for depth2img, + + trainer: + benchmark: True + val_check_interval: 5000000 + num_sanity_val_steps: 0 + accumulate_grad_batches: 1 diff --git a/models/controlnet/put_controlnets_and_t2i_here b/models/controlnet/put_controlnets_and_t2i_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/diffusers/put_diffusers_models_here b/models/diffusers/put_diffusers_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/embeddings/put_embeddings_or_textual_inversion_concepts_here b/models/embeddings/put_embeddings_or_textual_inversion_concepts_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/gligen/put_gligen_models_here b/models/gligen/put_gligen_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/hypernetworks/put_hypernetworks_here b/models/hypernetworks/put_hypernetworks_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/inpaint/put_inpaint_here b/models/inpaint/put_inpaint_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/loras/put_loras_here b/models/loras/put_loras_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/prompt_expansion/fooocus_expansion/config.json b/models/prompt_expansion/fooocus_expansion/config.json new file mode 100644 index 0000000000000000000000000000000000000000..c1a0538454bb7abd99b40426a090f93dd47bde11 --- /dev/null +++ b/models/prompt_expansion/fooocus_expansion/config.json @@ -0,0 +1,40 @@ +{ + "_name_or_path": "gpt2", + "activation_function": "gelu_new", + "architectures": [ + "GPT2LMHeadModel" + ], + "attn_pdrop": 0.1, + "bos_token_id": 50256, + "embd_pdrop": 0.1, + "eos_token_id": 50256, + "pad_token_id": 50256, + "initializer_range": 0.02, + "layer_norm_epsilon": 1e-05, + "model_type": "gpt2", + "n_ctx": 1024, + "n_embd": 768, + "n_head": 12, + "n_inner": null, + "n_layer": 12, + "n_positions": 1024, + "reorder_and_upcast_attn": false, + "resid_pdrop": 0.1, + "scale_attn_by_inverse_layer_idx": false, + "scale_attn_weights": true, + "summary_activation": null, + "summary_first_dropout": 0.1, + "summary_proj_to_labels": true, + "summary_type": "cls_index", + "summary_use_proj": true, + "task_specific_params": { + "text-generation": { + "do_sample": true, + "max_length": 50 + } + }, + "torch_dtype": "float32", + "transformers_version": "4.23.0.dev0", + "use_cache": true, + "vocab_size": 50257 +} diff --git a/models/prompt_expansion/fooocus_expansion/merges.txt b/models/prompt_expansion/fooocus_expansion/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956 --- /dev/null +++ b/models/prompt_expansion/fooocus_expansion/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T 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16329, + "Ġteenage": 16330, + "ĠPhysical": 16331, + "ĠLang": 16332, + "ĠCelt": 16333, + "Ġsued": 16334, + "ividually": 16335, + "Ġpatience": 16336, + "chair": 16337, + "regular": 16338, + "Ġaug": 16339, + "inv": 16340, + "except": 16341, + "ĠLil": 16342, + "Ġnest": 16343, + "fd": 16344, + "sum": 16345, + "ĠChase": 16346, + "Russia": 16347, + "ĠJennifer": 16348, + "Ġoffseason": 16349, + "Overall": 16350, + "Fore": 16351, + "Ġriot": 16352, + "Aud": 16353, + "former": 16354, + "Ġdefenders": 16355, + "ĠCT": 16356, + "iotic": 16357, + "ribly": 16358, + "Ġautomated": 16359, + "Ġpenis": 16360, + "Ġinsist": 16361, + "Ġdiagram": 16362, + "ĠSQL": 16363, + "ĠGarc": 16364, + "Ġwitch": 16365, + "client": 16366, + "ierra": 16367, + "ambers": 16368, + "Ġrecount": 16369, + "far": 16370, + "Very": 16371, + "osterone": 16372, + "Ġappreciated": 16373, + "ĠPerfect": 16374, + "Section": 16375, + "Ġdoses": 16376, + "ocaust": 16377, + "Ġcostly": 16378, + "Ġgrams": 16379, + "ĠShi": 16380, + "Ġwrestling": 16381, + "Ġ1971": 16382, + "Ġtrophy": 16383, + "Ġnerve": 16384, + "ĠKaz": 16385, + "ĠExperience": 16386, + "Ġpledged": 16387, + "Ġplayback": 16388, + "Ġcreativity": 16389, + "bye": 16390, + "Ġattackers": 16391, + "Ġholders": 16392, + "ĠCoach": 16393, + "ĠPhD": 16394, + "Ġtransfers": 16395, + "Ġcolored": 16396, + "ĠHindu": 16397, + "Ġdrown": 16398, + "Ġlistened": 16399, + "ĠWA": 16400, + "iasm": 16401, + "PO": 16402, + "Ġappealing": 16403, + "Ġdisclosed": 16404, + "ĠChicken": 16405, + "agging": 16406, + "Ġpleaded": 16407, + "Ġnavigation": 16408, + "ĠReturns": 16409, + "Ġ[[": 16410, + "ROR": 16411, + "EA": 16412, + "Ġphotographer": 16413, + "ĠRider": 16414, + "ippers": 16415, + "Ġslice": 16416, + "Ġerect": 16417, + "Ġhed": 16418, + "issance": 16419, + "ĠVikings": 16420, + "urious": 16421, + "Ġappet": 16422, + "oubtedly": 16423, + "Child": 16424, + "Ġauthentic": 16425, + "oos": 16426, + "ĠMaking": 16427, + "Ġannouncing": 16428, + "Ġbod": 16429, + "Ġmeter": 16430, + "ĠNine": 16431, + 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16482, + "ĠWoods": 16483, + "anga": 16484, + "squ": 16485, + "Ġmaj": 16486, + "amon": 16487, + "Ġaxis": 16488, + "Ġ1974": 16489, + "ĠDenmark": 16490, + "Ġwarrior": 16491, + "ĠPand": 16492, + "Ġoutlined": 16493, + "ĠBO": 16494, + "insula": 16495, + "zilla": 16496, + "ebook": 16497, + "Ġdare": 16498, + "Ġsearched": 16499, + "Ġnavigate": 16500, + "Sn": 16501, + "writing": 16502, + "Ġunited": 16503, + "Japan": 16504, + "ĠHebrew": 16505, + "Ġflame": 16506, + "Ġrelies": 16507, + "Ġcatching": 16508, + "ĠSho": 16509, + "Ġimprisonment": 16510, + "Ġpockets": 16511, + "Ġclosure": 16512, + "ĠFam": 16513, + "tim": 16514, + "adequ": 16515, + "Activity": 16516, + "Ġrecruiting": 16517, + "ĠWATCH": 16518, + "ĠArgentina": 16519, + "dest": 16520, + "Ġapologize": 16521, + "oro": 16522, + "Ġlacks": 16523, + "Ġtuned": 16524, + "ĠGriffin": 16525, + "Ġinfamous": 16526, + "Ġcelebrity": 16527, + "sson": 16528, + "Ġ----------------------------------------------------------------": 16529, + "ĠIsis": 16530, + 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"ppo": 16634, + "Bo": 16635, + "Ġfounding": 16636, + "Ġundermine": 16637, + "Ġeasiest": 16638, + "ĠCzech": 16639, + "ĠMack": 16640, + "Ġsexuality": 16641, + "ĠNixon": 16642, + "Win": 16643, + "ĠArn": 16644, + "ĠKin": 16645, + "ãĤ£": 16646, + "icer": 16647, + "Ġfortun": 16648, + "Ġsurfaces": 16649, + "aghd": 16650, + "Ġcarriers": 16651, + "ĠPART": 16652, + "ĠTib": 16653, + "Ġinterval": 16654, + "Ġfrustrating": 16655, + "ĠShip": 16656, + "ĠArmed": 16657, + "ffe": 16658, + "Ġboats": 16659, + "ĠAbraham": 16660, + "inis": 16661, + "Ġsuited": 16662, + "thread": 16663, + "iov": 16664, + "abul": 16665, + "ĠVenezuela": 16666, + "Ġtom": 16667, + "super": 16668, + "Ġcastle": 16669, + "although": 16670, + "ioxide": 16671, + "eches": 16672, + "Ġevolutionary": 16673, + "Ġnegotiate": 16674, + "Ġconfronted": 16675, + "Remember": 16676, + "Ġ170": 16677, + "Such": 16678, + "Ġ911": 16679, + "mult": 16680, + "ĠAbyss": 16681, + "urry": 16682, + "kees": 16683, + "spec": 16684, + "ĠBarbara": 16685, + "Ġbelonging": 16686, + "Ġvillain": 16687, + "istani": 16688, + "Ġaccountable": 16689, + "Ġportions": 16690, + "ĠDecl": 16691, + "Ur": 16692, + "ĠKate": 16693, + "gre": 16694, + "Ġmagazines": 16695, + "UCK": 16696, + "Ġregulate": 16697, + "omon": 16698, + "ĠAlmost": 16699, + "Ġoverview": 16700, + "Ġscram": 16701, + "Ġloot": 16702, + "ĠFitz": 16703, + "Ġcharacteristic": 16704, + "ĠSnake": 16705, + "say": 16706, + "ĠRico": 16707, + "Ġtrait": 16708, + "ĠJoined": 16709, + "aucus": 16710, + "Ġadaptation": 16711, + "ĠAirlines": 16712, + "Ġarchae": 16713, + "ĠIde": 16714, + "Ġbikes": 16715, + "Ġliterary": 16716, + "Ġinfluences": 16717, + "ĠUsed": 16718, + "Creat": 16719, + "Ġplea": 16720, + "ĠDefence": 16721, + "ĠAssass": 16722, + "Ġpond": 16723, + "ULT": 16724, + ")\"": 16725, + "Ġevaluated": 16726, + "Ġobtaining": 16727, + "Ġdemographic": 16728, + "Ġvigil": 16729, + "aley": 16730, + "Ġspouse": 16731, + "ĠSeahawks": 16732, + "respons": 16733, + "ĠBelt": 16734, + "umatic": 16735, + "Ġrises": 16736, + "runner": 16737, + "ĠMichelle": 16738, + "Ġpotent": 16739, + "race": 16740, + "ĠPAC": 16741, + "Find": 16742, + "olesterol": 16743, + "ISS": 16744, + "ĠIntroduced": 16745, + "resses": 16746, + "ignment": 16747, + "Os": 16748, + "ĠTu": 16749, + "ĠDex": 16750, + "icides": 16751, + "Ġsparked": 16752, + "ĠLaura": 16753, + "ĠBryant": 16754, + "Ġsmiling": 16755, + "ĠNexus": 16756, + "Ġdefendants": 16757, + "ĠCatal": 16758, + "Ġdishes": 16759, + "shaped": 16760, + "Ġprolong": 16761, + "mt": 16762, + "($": 16763, + "ãĢĤ": 16764, + "Ġcalculations": 16765, + "ĠSame": 16766, + "Ġpiv": 16767, + "HH": 16768, + "Ġcancelled": 16769, + "Ġgrin": 16770, + "Ġterritories": 16771, + "istically": 16772, + "Come": 16773, + "ĠParent": 16774, + "Project": 16775, + "Ġneglig": 16776, + "ĠPrivacy": 16777, + "Ġammo": 16778, + "LECT": 16779, + "olutely": 16780, + "ĠEpic": 16781, + "Ġmisunder": 16782, + "wal": 16783, + "April": 16784, + "mos": 16785, + "pathy": 16786, + "ĠCarson": 16787, + "Ġalbums": 16788, 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16840, + "official": 16841, + "Ġlifting": 16842, + "е": 16843, + "Requ": 16844, + "itutes": 16845, + "Ġpackaging": 16846, + "ĠWorkers": 16847, + "hran": 16848, + "ĠLebanon": 16849, + "olesc": 16850, + "Ġpunished": 16851, + "ĠJuan": 16852, + "Ġjam": 16853, + "ĠDocument": 16854, + "Ġmapping": 16855, + "icates": 16856, + "Ġinevitably": 16857, + "Ġvanilla": 16858, + "ĠTon": 16859, + "Ġwatches": 16860, + "Ġleagues": 16861, + "Ġinitiated": 16862, + "degree": 16863, + "portion": 16864, + "Ġrecalls": 16865, + "Ġruin": 16866, + "Ġmelt": 16867, + "IAN": 16868, + "Ġhem": 16869, + "Exp": 16870, + "Ġbaking": 16871, + "ĠColomb": 16872, + "atible": 16873, + "Ġradius": 16874, + "plug": 16875, + "ĠIF": 16876, + "etically": 16877, + "Ġfict": 16878, + "HER": 16879, + "ĠTap": 16880, + "atinum": 16881, + "Ġink": 16882, + "Ġcoh": 16883, + "ĠWizard": 16884, + "both": 16885, + "tex": 16886, + "Ġspends": 16887, + "ĠCurrently": 16888, + "ĠPit": 16889, + "Ġneurons": 16890, + "ignt": 16891, + "Ġrall": 16892, + 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"mill": 17805, + "Ġvaries": 17806, + "ĠPakistani": 17807, + "Dragon": 17808, + "Ġune": 17809, + "Ġhorizon": 17810, + "³³³³³³³³": 17811, + "Ġprovinces": 17812, + "Ġfrankly": 17813, + "Ġenacted": 17814, + "notes": 17815, + "['": 17816, + "Ġ192": 17817, + "ocracy": 17818, + "Ġendorsement": 17819, + "Ġovertime": 17820, + "True": 17821, + "Lab": 17822, + "licted": 17823, + "ĠDNC": 17824, + "Ġbeats": 17825, + "ĠJamie": 17826, + "152": 17827, + "ĠINT": 17828, + "Contact": 17829, + "Ġaccounted": 17830, + "hash": 17831, + "ĠPackers": 17832, + "pires": 17833, + "Ġlesbian": 17834, + "Ġamendments": 17835, + "Ġhopeful": 17836, + "ĠFinland": 17837, + "Ġspotlight": 17838, + "Ġconfigured": 17839, + "Ġtroubled": 17840, + "Ġgaze": 17841, + "ĠCalgary": 17842, + "Ġreliability": 17843, + "Ġinsurg": 17844, + "swer": 17845, + "buy": 17846, + "ĠSkin": 17847, + "Ġpixels": 17848, + "Ġhandgun": 17849, + "Ġparas": 17850, + "Ġcategor": 17851, + "ĠEL": 17852, + "ĠRex": 17853, + "Indeed": 17854, + "Ġkinda": 17855, + "Ġconjunction": 17856, + "ĠBryan": 17857, + "ĠManufact": 17858, + "yang": 17859, + "Plus": 17860, + "SQL": 17861, + "ishment": 17862, + "Ġdominate": 17863, + "Ġnail": 17864, + "Ġoath": 17865, + "Ġerupt": 17866, + "ĠFine": 17867, + "itbart": 17868, + "ĠChip": 17869, + "ĠAbd": 17870, + "ĠNam": 17871, + "Ġbuyer": 17872, + "Ġdissent": 17873, + "Leaks": 17874, + "Contin": 17875, + "Ġrider": 17876, + "ĠSomeone": 17877, + "Ġillusion": 17878, + "cin": 17879, + "ĠBoeing": 17880, + "Ġinadequ": 17881, + "ovation": 17882, + "iants": 17883, + "Ġrebuild": 17884, + "450": 17885, + "ĠDestiny": 17886, + "SW": 17887, + "ĠTill": 17888, + "Hit": 17889, + "iaz": 17890, + "ĠBangl": 17891, + "achers": 17892, + "ĠReform": 17893, + "Ġsegments": 17894, + "Ġsystematic": 17895, + "dc": 17896, + "ĠConservatives": 17897, + "Ġportal": 17898, + "hor": 17899, + "ĠDragonbound": 17900, + "Ġdragged": 17901, + "omo": 17902, + "Ġthee": 17903, + "advert": 17904, + "ĠReports": 17905, + "ĠEt": 17906, + "Ġbarrels": 17907, + "August": 17908, + "Ġcomparisons": 17909, + "Ġhex": 17910, + "Ġanthrop": 17911, + "\"[": 17912, + "borough": 17913, + "abi": 17914, + "Ġpictured": 17915, + "playing": 17916, + "ĠAddress": 17917, + "ĠMirror": 17918, + "Smith": 17919, + "Ġtires": 17920, + "ĠNPR": 17921, + "AAAA": 17922, + "Ġclassification": 17923, + "ĠThan": 17924, + "ĠHarm": 17925, + "ĠRA": 17926, + "Ġrejection": 17927, + "mination": 17928, + "Ġranged": 17929, + "ĠFalls": 17930, + "DI": 17931, + "Host": 17932, + "ãĤ´": 17933, + "ĠExample": 17934, + "listed": 17935, + "thirds": 17936, + "Ġsafegu": 17937, + "brand": 17938, + "Ġprobable": 17939, + "Canada": 17940, + "ITION": 17941, + "ĠQaeda": 17942, + "Ġchick": 17943, + "Ġimports": 17944, + "hit": 17945, + "loc": 17946, + "WW": 17947, + "Ġblew": 17948, + "Ġanytime": 17949, + "Ġwholes": 17950, + "iked": 17951, + "Ġcalculation": 17952, + "create": 17953, + "ĠOri": 17954, + "Ġupgraded": 17955, + "Ġappar": 17956, + "utory": 17957, + "ĠMol": 17958, + "Brit": 17959, + "ĠJong": 17960, + "INAL": 17961, + "ĠStarting": 17962, + "Ġdice": 17963, + "urtle": 17964, + "Ġrelying": 17965, + "closure": 17966, + "Ġprofitable": 17967, + "Ġslaughter": 17968, + "ĠManual": 17969, + "caster": 17970, + "Ġ\"$": 17971, + "Ġfeather": 17972, + "ĠSimply": 17973, + "ieves": 17974, + "Ġdeterior": 17975, + "ĠPCI": 17976, + "Ġstamp": 17977, + "Ġflaws": 17978, + "Ġshade": 17979, + "hammer": 17980, + "Ġpassport": 17981, + "Ġconting": 17982, + "amel": 17983, + "Ġobservers": 17984, + "Ġneglect": 17985, + "ĠRB": 17986, + "ĠBrotherhood": 17987, + "Ġskeptical": 17988, + "family": 17989, + "usk": 17990, + "Ġemotionally": 17991, + "âĻ": 17992, + "ĠBeta": 17993, + "asonable": 17994, + "idity": 17995, + "ĠMul": 17996, + "Ġkicking": 17997, + "ĠCarm": 17998, + "ollah": 17999, + "VERTIS": 18000, + "ĠAthen": 18001, + "Ġladder": 18002, + "ĠBullet": 18003, + "å£": 18004, + "0001": 18005, + "ĠWildlife": 18006, + "ĠMask": 18007, + "ĠNan": 18008, + "Rev": 18009, + "Ġunacceptable": 18010, + "legal": 18011, + "Ġcrowded": 18012, + "agi": 18013, + "ĠCox": 18014, + "je": 18015, + "Ġmorality": 18016, + "Ġfuels": 18017, + "Ġcables": 18018, + "Ġmankind": 18019, + "ĠCaribbean": 18020, + "Ġanchor": 18021, + "Ġbyte": 18022, + "ĠOften": 18023, + "ĠOz": 18024, + "Ġcrafted": 18025, + "Ġhistorian": 18026, + "ĠWu": 18027, + "Ġtowers": 18028, + "ĠCitizens": 18029, + "Ġhelm": 18030, + "Ġcredentials": 18031, + "Ġsingular": 18032, + "ĠJesse": 18033, + "Ġtackles": 18034, + "Ġcontempt": 18035, + "Ġafore": 18036, + "ĠShadows": 18037, + "Ġnil": 18038, + "Ġurgent": 18039, + "apple": 18040, + "blood": 18041, + "Ġvon": 18042, + "Ġoffline": 18043, + "Ġbreathe": 18044, + "Ġjumps": 18045, + "Ġirrelevant": 18046, + "oxic": 18047, + "omal": 18048, + "important": 18049, + "Jim": 18050, + "Ġgloves": 18051, + "arming": 18052, + "depth": 18053, + "Ġtalents": 18054, + "ookie": 18055, + "ĠSB": 18056, + "Ġpalm": 18057, + "uffs": 18058, + "esta": 18059, + "IGH": 18060, + "Ġcanon": 18061, + "ĠVerizon": 18062, + "ĠPle": 18063, + "Ġcoupled": 18064, + "velt": 18065, + "Ġfundraising": 18066, + "ĠGetting": 18067, + "ĠDLC": 18068, + "Ġmathematical": 18069, + "ĠHS": 18070, + "ĠCardinals": 18071, + "telling": 18072, + "Ġsponsors": 18073, + "ĠÏ": 18074, + "ĠBulls": 18075, + "option": 18076, + "Ġpropose": 18077, + "Ġmemorable": 18078, + "Ġembraced": 18079, + "Ġdeclining": 18080, + "Health": 18081, + "eda": 18082, + "Ġ};": 18083, + "Ġspam": 18084, + "mile": 18085, + "Ġpitcher": 18086, + "ĠEight": 18087, + "Ġcaring": 18088, + "utic": 18089, + "role": 18090, + "Ġairline": 18091, + "ernandez": 18092, + "ĠAthlet": 18093, + "Ġcertification": 18094, + "uxe": 18095, + "riger": 18096, + "Ġempir": 18097, + "Ġsensation": 18098, + "Ġdism": 18099, + "Ġbolt": 18100, + "Ġevolve": 18101, + "House": 18102, + "Ġconsultation": 18103, + "ĠDuty": 18104, + "Ġtouches": 18105, + "ĠNathan": 18106, + "Ġfaint": 18107, + "had": 18108, + "\"(": 18109, + "ĠConsumer": 18110, + "ĠExtreme": 18111, + "Ġ127": 18112, + "ĠHerm": 18113, + "ĠSacrament": 18114, + "izoph": 18115, + "Ġanxious": 18116, + "ulously": 18117, + "Ġsocially": 18118, + "ĠUTC": 18119, + "Ġsolving": 18120, + "ĠLetter": 18121, + "History": 18122, + "educ": 18123, + "Price": 18124, + "));": 18125, + "Ġreload": 18126, + "amic": 18127, + "Ġpork": 18128, + "Ġdiscourse": 18129, + "Ġtournaments": 18130, + "airo": 18131, + "ĠKur": 18132, + "ĠCosta": 18133, + "Ġviolating": 18134, + "Ġinterfere": 18135, + "Ġrecreational": 18136, + "uffle": 18137, + "Ġspeeches": 18138, + "Ġneeding": 18139, + "Ġremembers": 18140, + "Ġcredited": 18141, + "nia": 18142, + "focused": 18143, + "amera": 18144, + "Ġbru": 18145, + "umbs": 18146, + "ĠCuban": 18147, + "Ġpreceding": 18148, + "Ġnonsense": 18149, + "acial": 18150, + "Ġsmartphones": 18151, + "ĠStories": 18152, + "Sports": 18153, + "ĠEmergency": 18154, + "ouncing": 18155, + "efined": 18156, + "Ġber": 18157, + "Ġconsulting": 18158, + "Ġmasters": 18159, + "heastern": 18160, + ".\"[": 18161, + "ĠRunning": 18162, + "Ġsuscept": 18163, + "ĠFeng": 18164, + "America": 18165, + "prises": 18166, + "stitial": 18167, + "ĠWeekly": 18168, + "ĠGreater": 18169, + "modules": 18170, + "ifter": 18171, + "Graphics": 18172, + "uler": 18173, + "Ġwholly": 18174, + "Ġsuppress": 18175, + "Ġconcealed": 18176, + "Ġhappily": 18177, + "Ġaccepts": 18178, + "ĠEnjoy": 18179, + "Ġrivers": 18180, + "ĠExcept": 18181, + "225": 18182, + "ĠNHS": 18183, + "ĠMcConnell": 18184, + "Ġpussy": 18185, + "ferred": 18186, + "utable": 18187, + "Ġattain": 18188, + "Ġ>=": 18189, + "Ġdeposits": 18190, + "rophic": 18191, + "Ġnotorious": 18192, + "ĠShaw": 18193, + "ilitation": 18194, + "Ġepidemic": 18195, + "allic": 18196, + "Ġsmallest": 18197, + "ovich": 18198, + "Ġaccessories": 18199, + "perties": 18200, + "Ġsurplus": 18201, + "ĠMech": 18202, + "Ġambig": 18203, + "ĠImmigration": 18204, + "Ġchim": 18205, + "eval": 18206, + "Ġpracticing": 18207, + "ĠMystery": 18208, + "Ġdomains": 18209, + "ĠSilicon": 18210, + "apps": 18211, + "Ġkilometers": 18212, + "ea": 18213, + "ĠSmash": 18214, + "Ġwarranty": 18215, + "Ġnost": 18216, + "sil": 18217, + "rev": 18218, + "Jon": 18219, + "ĠDublin": 18220, + "Ġtastes": 18221, + "Ġbout": 18222, + "great": 18223, + "error": 18224, + "Ġswitches": 18225, + "ĠBapt": 18226, + "DO": 18227, + "oki": 18228, + "Ġsourced": 18229, + "produ": 18230, + "Ġattachment": 18231, + "ĠIssue": 18232, + "ĠQuestion": 18233, + "Join": 18234, + "Ġfitted": 18235, + "Ġunlawful": 18236, + "^^": 18237, + "erek": 18238, + "Ġauthentication": 18239, + "Ġstole": 18240, + "Ġaccountability": 18241, + "label": 18242, + "Search": 18243, + "Ġalbeit": 18244, + "atican": 18245, + "funded": 18246, + "ĠAdding": 18247, + "ĠIQ": 18248, + "Ġsubmar": 18249, + "lit": 18250, + "aque": 18251, + "ĠLearning": 18252, + "Ġinteger": 18253, + "Master": 18254, + "ĠChrom": 18255, + "Ġpremier": 18256, + "Op": 18257, + "ĠLiu": 18258, + "Ġblessed": 18259, + "ĠGlobe": 18260, + "ĠResponse": 18261, + "Ġlegitim": 18262, + "ĠMerkel": 18263, + "Ġdisposal": 18264, + "´": 18265, + "Ġgauge": 18266, + "peat": 18267, + "Ġinduced": 18268, + "Ġquestionable": 18269, + "arthy": 18270, + "ĠVit": 18271, + "ĠFeed": 18272, + "Until": 18273, + "Ut": 18274, + "worthy": 18275, + "RY": 18276, + "ĠHerald": 18277, + "ĠHammer": 18278, + "Ġmedal": 18279, + "ĠRivers": 18280, + "ĠHack": 18281, + "Ġclarify": 18282, + "Ġtracked": 18283, + "Ġautonomous": 18284, + "Ġtenant": 18285, + "ĠQatar": 18286, + "erie": 18287, + "Ġgrim": 18288, + "ĠMonitor": 18289, + "Ġresistant": 18290, + "ĠSpec": 18291, + "ĠWells": 18292, + "NAS": 18293, + "148": 18294, + "Ġminers": 18295, + "iotics": 18296, + "Ġmisses": 18297, + "116": 18298, + "gian": 18299, + "git": 18300, + "ĠEyes": 18301, + "pres": 18302, + "Ġgraduated": 18303, + "Ġangel": 18304, + "Ġsynchron": 18305, + "Ġefficiently": 18306, + "Ġtransmitted": 18307, + "Harry": 18308, + "Ġglobally": 18309, + "ENCE": 18310, + "ĠMontana": 18311, + "raged": 18312, + "ĠPrevention": 18313, + "Ġpiss": 18314, + "ĠLl": 18315, + "Ġshelf": 18316, + "ĠBJP": 18317, + "ĠTestament": 18318, + "ĠLate": 18319, + "iker": 18320, + "ĠHapp": 18321, + "ĠJulian": 18322, + "hall": 18323, + "Ġspont": 18324, + "Ġshutdown": 18325, + "Ġinconsistent": 18326, + "Ġsubscribers": 18327, + "Ġskeleton": 18328, + "ĠNebraska": 18329, + "Ġinspire": 18330, + "ĠVoid": 18331, + "Feed": 18332, + "Ġangles": 18333, + "ĠSprings": 18334, + "Ġbenchmark": 18335, + "Ġvaccines": 18336, + "izophren": 18337, + "sexual": 18338, + "uffed": 18339, + "Ġshine": 18340, + "ĠKath": 18341, + "Ġgesture": 18342, + "inea": 18343, + "Ġrip": 18344, + "Ġoppression": 18345, + "Ġconscience": 18346, + "bt": 18347, + "ĠLum": 18348, + "Ġincidence": 18349, + "ĠFa": 18350, + "wr": 18351, + "Ġmineral": 18352, + "ĠSpurs": 18353, + "alky": 18354, + "Ġthunder": 18355, + "Ġopio": 18356, + "Being": 18357, + "ĠPalm": 18358, + "Ġwasted": 18359, + "Ġlb": 18360, + "iaries": 18361, + "ĠInitiative": 18362, + "Ġcurric": 18363, + "Ġmarker": 18364, + "ĠMcL": 18365, + "Ġextensions": 18366, + "ĠPv": 18367, + "ĠArms": 18368, + "Ġofferings": 18369, + "Ġdefenses": 18370, + "Ġvendor": 18371, + "Ġcontradict": 18372, + "ĠColin": 18373, + "Ġreddit": 18374, + "Ġperipher": 18375, + "122": 18376, + "Ġsins": 18377, + "Edit": 18378, + "ICT": 18379, + "Soft": 18380, + "ĠShah": 18381, + "Ġadministrator": 18382, + "ĠTrip": 18383, + "Ġpornography": 18384, + "Ġtuition": 18385, + "inence": 18386, + "ĠProgress": 18387, + "Ġcatalog": 18388, + "Ġsuite": 18389, + "Ġhike": 18390, + "Ġreproductive": 18391, + "engine": 18392, + "Ġdrought": 18393, + "ĠNoah": 18394, + "Ġ230": 18395, + "Ġdude": 18396, + "Ġrelaxed": 18397, + "Ġpartition": 18398, + "Ġparticipant": 18399, + "Ġtelesc": 18400, + "Ġfeas": 18401, + "ĠFF": 18402, + "owner": 18403, + "Ġsweeping": 18404, + "Ġlenses": 18405, + "Ġmatchup": 18406, + "ĠRepl": 18407, + "ournals": 18408, + "Ġcredible": 18409, + "Ġgrandmother": 18410, + "Ġthermal": 18411, + "Ġsubscribing": 18412, + "Ġidentities": 18413, + "colm": 18414, + "UCT": 18415, + "Ġreluctant": 18416, + "users": 18417, + "ĠCort": 18418, + "Ġassisted": 18419, + "OSS": 18420, + "ATIONS": 18421, + "ISH": 18422, + "Ġpharmaceutical": 18423, + "icable": 18424, + "adian": 18425, + "ĠSonic": 18426, + "ĠFury": 18427, + "ĠMong": 18428, + "AH": 18429, + "ĠPsychology": 18430, + "Ġphosph": 18431, + "Ġtreats": 18432, + "ŃĶ": 18433, + "Ġsteadily": 18434, + "ĠHello": 18435, + "Ġrelates": 18436, + "Ġclue": 18437, + "Expl": 18438, + "auth": 18439, + "Ġrevision": 18440, + "Ġeld": 18441, + "osion": 18442, + "Ġbron": 18443, + "144": 18444, + "rikes": 18445, + "Ġmines": 18446, + "Ġblanket": 18447, + "ĠFail": 18448, + "eled": 18449, + "ĠImagine": 18450, + "ĠPlanned": 18451, + "aic": 18452, + "Request": 18453, + "Mad": 18454, + "ĠHorse": 18455, + "ĠEagle": 18456, + "Ġcapac": 18457, + "157": 18458, + "Ġling": 18459, + "ĠNice": 18460, + "ĠParenthood": 18461, + "minster": 18462, + "ogs": 18463, + "ensitive": 18464, + "Nothing": 18465, + "Ġcarn": 18466, + "Fin": 18467, + "ĠPE": 18468, + "Ġrifles": 18469, + "ĠLP": 18470, + "Sand": 18471, + "ĠguiActive": 18472, + "Ġtourist": 18473, + "CNN": 18474, + "Ġunveiled": 18475, + "Ġpredecessor": 18476, + "}{": 18477, + "uber": 18478, + "Ġoffshore": 18479, + "Ġoptical": 18480, + "ĠRot": 18481, + "ĠPearl": 18482, + "eton": 18483, + "Ġstared": 18484, + "Ġfarther": 18485, + "atility": 18486, + "contin": 18487, + "ĠGy": 18488, + "ĠFoster": 18489, + "ĠCoc": 18490, + "rients": 18491, + "Ġdesigning": 18492, + "ĠEconomy": 18493, + "ONG": 18494, + "Women": 18495, + "ĠNancy": 18496, + "erver": 18497, + "Ġmascul": 18498, + "Ġcasualties": 18499, + "Ġ225": 18500, + "ĠSullivan": 18501, + "ĠChoice": 18502, + "Ġaster": 18503, + "ws": 18504, + "Ġhotels": 18505, + "Ġconsiderations": 18506, + "Ġcouch": 18507, + "ĠStrip": 18508, + "ĠGn": 18509, + "Ġmanipulate": 18510, + "lied": 18511, + "Ġsynthetic": 18512, + "Ġassaulted": 18513, + "Ġoffenses": 18514, + "ĠDrake": 18515, + "Ġimpe": 18516, + "October": 18517, + "ĠHeritage": 18518, + "hl": 18519, + "ĠBlair": 18520, + "Unlike": 18521, + "Ġgrief": 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"ĠLomb": 28503, + "Ġchoke": 28504, + "ĠWorm": 28505, + "Ġadmiration": 28506, + "Ġswung": 28507, + "ensibly": 28508, + "Ġrash": 28509, + "ĠGoals": 28510, + "ĠImportant": 28511, + "Shot": 28512, + "ĠRas": 28513, + "Ġtrainers": 28514, + "ĠBun": 28515, + "Working": 28516, + "Ġharmed": 28517, + "ĠPandora": 28518, + "ĠLTE": 28519, + "Ġmushroom": 28520, + "ĠCHAR": 28521, + "ĠFee": 28522, + "ĠMoy": 28523, + "Born": 28524, + "oliberal": 28525, + "ĠMartial": 28526, + "Ġgentlemen": 28527, + "Ġlingering": 28528, + "Official": 28529, + "Ġgraffiti": 28530, + "ĠNames": 28531, + "Der": 28532, + "Ġquint": 28533, + "istrate": 28534, + "azeera": 28535, + "ĠNOTICE": 28536, + "ĠFlorence": 28537, + "Ġpayable": 28538, + "Ġdepicts": 28539, + "ĠSpecies": 28540, + "Heart": 28541, + "âĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢâĶĢ": 28542, + "Ġenclosed": 28543, + "Increases": 28544, + "Daily": 28545, + "ĠLis": 28546, + "Ġenactment": 28547, + "ĠBacon": 28548, + "ĠSteele": 28549, + "demand": 28550, + "Ġ183": 28551, + "Ġmouths": 28552, + 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"Environment": 31441, + "Alright": 31442, + "Rain": 31443, + "IPP": 31444, + "ĠShiite": 31445, + "Ġmound": 31446, + "ĠAbilities": 31447, + "condition": 31448, + "Staff": 31449, + "Ġcompetence": 31450, + "ĠMoor": 31451, + "ĠDiablo": 31452, + "Ġwithheld": 31453, + "Ġostensibly": 31454, + "ĠBrom": 31455, + "Ġmsg": 31456, + "Ġdenomin": 31457, + "ĠReferences": 31458, + "ĠFP": 31459, + "Ġplunged": 31460, + "Ġpamph": 31461, + "moving": 31462, + "central": 31463, + "Ġdownright": 31464, + "Ġfading": 31465, + "Tal": 31466, + "Typ": 31467, + "ĠThy": 31468, + "ukes": 31469, + "ithe": 31470, + "Ġove": 31471, + "Ġbattled": 31472, + "Ġseafood": 31473, + "Ġfigur": 31474, + "ĠRD": 31475, + "crop": 31476, + "Ġsquads": 31477, + "{\\": 31478, + "à¹": 31479, + "ĠEh": 31480, + "Ġinterviewing": 31481, + "ĠQin": 31482, + "Ġaspiring": 31483, + "PLIC": 31484, + "Ġclauses": 31485, + "ĠGast": 31486, + "ĠNir": 31487, + "Ġluggage": 31488, + "Ġhose": 31489, + "Ġsystemd": 31490, + "Ġdescending": 31491, + "ĠRevised": 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31544, + "Ġintolerance": 31545, + "ĠSnapchat": 31546, + "ĠSharks": 31547, + "Ġdummy": 31548, + "ĠDiagn": 31549, + "ĠGuitar": 31550, + "imeters": 31551, + "403": 31552, + "REG": 31553, + "Ax": 31554, + "Ġseparates": 31555, + "ĠMahm": 31556, + "Ġtv": 31557, + "jah": 31558, + "OOL": 31559, + "Circ": 31560, + "ĠWindsor": 31561, + "ussian": 31562, + "Ġintuition": 31563, + "Ġdisdain": 31564, + "ĠDonovan": 31565, + "Ġ221": 31566, + "Emb": 31567, + "Ġcondemning": 31568, + "Ġgenerosity": 31569, + "zzy": 31570, + "Ġpanties": 31571, + "ĠPrevent": 31572, + "ActionCode": 31573, + "ANA": 31574, + "342": 31575, + "externalActionCode": 31576, + "Ġspecifying": 31577, + "Ġcrystall": 31578, + "Jere": 31579, + "Ġrupt": 31580, + "ĠApprentice": 31581, + "Ġprofiling": 31582, + "к": 31583, + "Strike": 31584, + "Ġsideline": 31585, + "Ġobligated": 31586, + "Ġoccult": 31587, + "Ġbureaucratic": 31588, + "antically": 31589, + "rupted": 31590, + "negative": 31591, + "ĠEthiopia": 31592, + "ĠCivic": 31593, + "Ġinsiders": 31594, + "eligible": 31595, + "ĠTVs": 31596, + "ĠBAR": 31597, + "ĠTI": 31598, + "iologist": 31599, + "ĠAIR": 31600, + "Ġsubstituted": 31601, + "Arab": 31602, + "ĠSaul": 31603, + "ĠYog": 31604, + "prem": 31605, + "Ġbuilders": 31606, + "Ġstationary": 31607, + "Ġdoubtful": 31608, + "Ġvigorously": 31609, + "Ġthrilling": 31610, + "Physical": 31611, + "ĠCarey": 31612, + "ĠHydra": 31613, + "geoning": 31614, + "ĠSly": 31615, + "yton": 31616, + "Ġborrowers": 31617, + "ĠParkinson": 31618, + "Ġë": 31619, + "ĠJamaica": 31620, + "Ġsatir": 31621, + "Ġinsurgents": 31622, + "ĠFirm": 31623, + "Ġisot": 31624, + "ĠKarn": 31625, + "ourning": 31626, + "akens": 31627, + "docs": 31628, + "little": 31629, + "ĠMonaco": 31630, + "CLASS": 31631, + "Turkey": 31632, + "Ly": 31633, + "ĠConan": 31634, + "assic": 31635, + "Ġstarred": 31636, + "ĠPacers": 31637, + "eties": 31638, + "Ġtipping": 31639, + "Moon": 31640, + "ĠRw": 31641, + "same": 31642, + "Ġcavity": 31643, + "Ġgoof": 31644, + "ĠZo": 31645, + "Shock": 31646, + "ummer": 31647, + "Ġemphasizes": 31648, + "Ġregrett": 31649, + "Ġnovelty": 31650, + "Ġenvy": 31651, + "ĠPassive": 31652, + "rw": 31653, + "505": 31654, + "Ġindifferent": 31655, + "ĠRica": 31656, + "ĠHimself": 31657, + "ĠFreddie": 31658, + "Ġadip": 31659, + "ä¸Ģ": 31660, + "Ġbreakout": 31661, + "Ġhurried": 31662, + "ĠHuang": 31663, + "ĠDisk": 31664, + "Ġroaming": 31665, + "?????-?????-": 31666, + "UV": 31667, + "ĠRicky": 31668, + "ĠSigma": 31669, + "Ġmarginalized": 31670, + "Ġedits": 31671, + "Ġ304": 31672, + "memory": 31673, + "Ġspecimen": 31674, + "293": 31675, + "ãģ¯": 31676, + "Ġvertically": 31677, + "Ġaudition": 31678, + "ĠHeck": 31679, + "Ġcaster": 31680, + "ĠHoldings": 31681, + "adal": 31682, + "ĠCron": 31683, + "ĠLiam": 31684, + "Ġdeflect": 31685, + "Pick": 31686, + "ĠDebug": 31687, + "REF": 31688, + "Ġversatility": 31689, + "othes": 31690, + "classified": 31691, + "ĠMahar": 31692, + "ĠHort": 31693, + "Counter": 31694, + "stasy": 31695, + "noticed": 31696, + "331": 31697, + "ĠShim": 31698, + "fuck": 31699, + "ĠBie": 31700, + "Ġairing": 31701, + "ĠProtein": 31702, + "ĠHolding": 31703, + "Ġspectators": 31704, + "iliated": 31705, + "ĠThatcher": 31706, + "nosis": 31707, + "ãĥ¼ãĥ³": 31708, + "Tele": 31709, + "Boston": 31710, + "ĠTempl": 31711, + "stay": 31712, + "Ġdeclarations": 31713, + "479": 31714, + "Volume": 31715, + "ĠDesigner": 31716, + "ĠOverwatch": 31717, + "idae": 31718, + "Ġonwards": 31719, + "Ġnets": 31720, + "ĠManila": 31721, + "particularly": 31722, + "Ġpolitic": 31723, + "oother": 31724, + "Ġportraits": 31725, + "Ġpavement": 31726, + "cffff": 31727, + "Ġsaints": 31728, + "Ġbeginners": 31729, + "ESPN": 31730, + "Ġshortcomings": 31731, + "âķIJâķIJ": 31732, + "Ġcomet": 31733, + "ĠOrganic": 31734, + "quel": 31735, + "Ġhospitalized": 31736, + "Break": 31737, + "Ġpeel": 31738, + "dylib": 31739, + "aspx": 31740, + "urances": 31741, + "ĠTIM": 31742, + "Pg": 31743, + "Ġreadable": 31744, + "ĠMalik": 31745, + "Ġmuzzle": 31746, + "Ġbenchmarks": 31747, + "dal": 31748, + "ĠVacc": 31749, + "ĠHicks": 31750, + "609": 31751, + "ĠBiblical": 31752, + "heng": 31753, + "Ġoverload": 31754, + "ĠCivilization": 31755, + "Ġimmoral": 31756, + "Ġfries": 31757, + "ãĤĴ": 31758, + "Ġreproduced": 31759, + "Ġformulation": 31760, + "jug": 31761, + "irez": 31762, + "gear": 31763, + "Ġcoached": 31764, + "MpServer": 31765, + "ĠSJ": 31766, + "ĠKw": 31767, + "Init": 31768, + "deal": 31769, + "ĠOro": 31770, + "ĠLoki": 31771, + "ĠSongs": 31772, + "Ġ232": 31773, + "ĠLouise": 31774, + "asionally": 31775, + "Ġuncond": 31776, + "ollywood": 31777, + "Ġprogressives": 31778, + "ĠEnough": 31779, + "ĠDoe": 31780, + "Ġwreckage": 31781, + "Ġbrushed": 31782, + "ĠBaseType": 31783, + "Ġzoning": 31784, + "ishable": 31785, + "hetically": 31786, + "ĠCaucus": 31787, + "ĠHue": 31788, + "Ġkarma": 31789, + "ĠSporting": 31790, + "Ġtrader": 31791, + "Ġseeming": 31792, + "ĠCapture": 31793, + "430": 31794, + "bish": 31795, + "Ġtunes": 31796, + "Ġindoors": 31797, + "ĠSphere": 31798, + "ĠDancing": 31799, + "TERN": 31800, + "Ġnob": 31801, + "ĠGST": 31802, + "maps": 31803, + "Ġpeppers": 31804, + "Fit": 31805, + "Ġoversees": 31806, + "ĠRabbi": 31807, + "ĠRuler": 31808, + "vertising": 31809, + "office": 31810, + "xxx": 31811, + "Ġraft": 31812, + "Changed": 31813, + "Ġtextbooks": 31814, + "Links": 31815, + "ĠOmn": 31816, + "ãĢij": 31817, + "Ġinconvenience": 31818, + "ĠDonetsk": 31819, + "=~": 31820, + "Ġimplicitly": 31821, + "Ġboosts": 31822, + "ĠBones": 31823, + "ĠBoom": 31824, + "Courtesy": 31825, + "Ġsensational": 31826, + "ANY": 31827, + "Ġgreedy": 31828, + "eden": 31829, + "Ġinexper": 31830, + "ĠLer": 31831, + "ĠVale": 31832, + "Ġtighten": 31833, + "ĠEAR": 31834, + "ĠNum": 31835, + "Ġancestor": 31836, + "Sent": 31837, + "ĠHorde": 31838, + "urgical": 31839, + "allah": 31840, + "Ġsap": 31841, + "amba": 31842, + "ĠSpread": 31843, + "twitch": 31844, + "Ġgrandson": 31845, + "Ġfracture": 31846, + "Ġmoderator": 31847, + "ĠSeventh": 31848, + "ĠReverse": 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"ĠAngry": 31900, + "Ġdisqual": 31901, + "ĠFrequency": 31902, + "ĠEthernet": 31903, + "Ġhelper": 31904, + "Percent": 31905, + "Ġhorrifying": 31906, + "Ġail": 31907, + "ĠAllan": 31908, + "EEE": 31909, + "ĠCrossing": 31910, + "449": 31911, + "Ġholog": 31912, + "ĠPuzzles": 31913, + "ĠGoes": 31914, + "erenn": 31915, + "604": 31916, + "ãģı": 31917, + "ĠRafael": 31918, + "Ġatten": 31919, + "ĠEmanuel": 31920, + "Ġupro": 31921, + "ĠSusp": 31922, + "Psych": 31923, + "ĠTrainer": 31924, + "ĠNES": 31925, + "ĠHunts": 31926, + "becue": 31927, + "Ġcounselor": 31928, + "Rule": 31929, + "Ġtoxins": 31930, + "Ġbanners": 31931, + "rifice": 31932, + "Ġgreeting": 31933, + "Ġfrenzy": 31934, + "Ġallocate": 31935, + "Ġ*)": 31936, + "expr": 31937, + "503": 31938, + "ĠChick": 31939, + "ĠTorn": 31940, + "Ġconsolidation": 31941, + "ĠFletcher": 31942, + "switch": 31943, + "frac": 31944, + "clips": 31945, + "ĠMcKin": 31946, + "ĠLunar": 31947, + "Month": 31948, + "ITCH": 31949, + "Ġscholarly": 31950, + "raped": 31951, + "398": 31952, + "Ġ1910": 31953, + "Ġegreg": 31954, + "Ġinsecure": 31955, + "Ġvictorious": 31956, + "cffffcc": 31957, + "Ġsingled": 31958, + "Ġelves": 31959, + "ĠWond": 31960, + "burst": 31961, + "Ġcamoufl": 31962, + "ĠBLACK": 31963, + "Ġconditioned": 31964, + "çī": 31965, + "answered": 31966, + "Ġcompulsory": 31967, + "ascist": 31968, + "Ġpodcasts": 31969, + "ĠFrankfurt": 31970, + "bnb": 31971, + "Ġneoliberal": 31972, + "ĠKeyboard": 31973, + "ĠBelle": 31974, + "warm": 31975, + "Ġtrusts": 31976, + "Ġinsured": 31977, + "ĠBucc": 31978, + "usable": 31979, + "607": 31980, + "ĠPlains": 31981, + "Ġ1890": 31982, + "Ġsabotage": 31983, + "Ġlodged": 31984, + "felt": 31985, + "Ġga": 31986, + "ĠNarc": 31987, + "ĠSalem": 31988, + "Ġseventy": 31989, + "ĠBlank": 31990, + "pocket": 31991, + "Ġwhisper": 31992, + "Ġmating": 31993, + "omics": 31994, + "ĠSalman": 31995, + "ĠKad": 31996, + "Ġangered": 31997, + "Ġcollisions": 31998, + "Ġextraordinarily": 31999, + "Ġcoercion": 32000, + "Ghost": 32001, + "birds": 32002, + "èĢ": 32003, + "kok": 32004, + "Ġpermissible": 32005, + "avorable": 32006, + "Ġpointers": 32007, + "Ġdissip": 32008, + "aci": 32009, + "Ġtheatrical": 32010, + "ĠCosmic": 32011, + "Ġforgetting": 32012, + "Ġfinalized": 32013, + "大": 32014, + "yout": 32015, + "library": 32016, + "Ġbooming": 32017, + "ĠBelieve": 32018, + "ĠTeacher": 32019, + "ĠLiv": 32020, + "ĠGOODMAN": 32021, + "ĠDominican": 32022, + "ORED": 32023, + "ĠParties": 32024, + "Ġprecipitation": 32025, + "ĠSlot": 32026, + "Roy": 32027, + "ĠCombined": 32028, + "Ġintegrating": 32029, + "Ġchrome": 32030, + "Ġintestinal": 32031, + "ĠRebell": 32032, + "Ġmatchups": 32033, + "Ġblockbuster": 32034, + "ĠLoren": 32035, + "ĠLevy": 32036, + "Ġpreaching": 32037, + "ĠSending": 32038, + "ĠPurpose": 32039, + "rax": 32040, + "fif": 32041, + "Ġauthoritative": 32042, + "ĠPET": 32043, + "astical": 32044, + "Ġdishon": 32045, + "Ġchatting": 32046, + "Ġ\"$:/": 32047, + "Connection": 32048, + "Ġrecreate": 32049, + "Ġdelinqu": 32050, + "Ġbroth": 32051, + "ĠDirty": 32052, + "ĠAdmin": 32053, + "zman": 32054, + "Ġscholarships": 32055, + "Ġ253": 32056, + "contact": 32057, + "alsa": 32058, + "767": 32059, + "creen": 32060, + "abbage": 32061, + "Ġ1915": 32062, + "Ġblended": 32063, + "Ġalarmed": 32064, + "Language": 32065, + "356": 32066, + "Ġblends": 32067, + "ĠChanged": 32068, + "Wolf": 32069, + "Ġhepat": 32070, + "Creating": 32071, + "Ġpersecut": 32072, + "Ġsweetness": 32073, + "arte": 32074, + "Ġforfeiture": 32075, + "ĠRoberto": 32076, + "impro": 32077, + "NFL": 32078, + "ĠMagnet": 32079, + "Detailed": 32080, + "Ġinsignificant": 32081, + "ĠPOLIT": 32082, + "ĠBBQ": 32083, + "ĠCPS": 32084, + "Ġseaw": 32085, + "aminer": 32086, + "mL": 32087, + "endif": 32088, + "finals": 32089, + "Ġ265": 32090, + "uish": 32091, + "Ġ})": 32092, + "ĠProblems": 32093, + "Ġemblem": 32094, + "Ġseriousness": 32095, + "Ġparsing": 32096, + "Ġsubstitution": 32097, + "Ġpressured": 32098, + "Ġrecycled": 32099, + "aleb": 32100, + "Ruby": 32101, + 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35838, + "majority": 35839, + "ĠNeville": 35840, + "ĠFreeBSD": 35841, + "Ġtart": 35842, + "panic": 35843, + "690": 35844, + "Ġcocoa": 35845, + "Ġsampled": 35846, + "Ġlookup": 35847, + "Indust": 35848, + "Ġinjections": 35849, + "genre": 35850, + "Ġau": 35851, + "Ġroadway": 35852, + "Ġgenitals": 35853, + "Kind": 35854, + "ĠExaminer": 35855, + "ĠYaz": 35856, + "Fresh": 35857, + "Ġparalysis": 35858, + "ĠAluminum": 35859, + "Ġreap": 35860, + "oké": 35861, + "Ġsloppy": 35862, + "ĠTunnel": 35863, + "posium": 35864, + "nery": 35865, + "enic": 35866, + "Ġherbal": 35867, + "ĠOuter": 35868, + "ĠBuilder": 35869, + "Ġincur": 35870, + "Ġideologies": 35871, + "Ġbackups": 35872, + "consuming": 35873, + "ĠDetect": 35874, + "deck": 35875, + "ĠKNOW": 35876, + "ĠGret": 35877, + "ĠMIC": 35878, + "Ġtoughness": 35879, + "ĠExhibit": 35880, + "Ġhive": 35881, + "Les": 35882, + "ĠSCHOOL": 35883, + "ĠAtari": 35884, + "alde": 35885, + "ĠNull": 35886, + "andestine": 35887, + "mouse": 35888, + "Ġbrigade": 35889, + 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"028": 46957, + "Ġheadlined": 46958, + "Ġprimed": 46959, + "Ġhoard": 46960, + "ĠShad": 46961, + "ĠENTER": 46962, + "Ġtriangular": 46963, + "Ġcapit": 46964, + "lik": 46965, + "ĠAncients": 46966, + "Ġlash": 46967, + "Ġconvol": 46968, + "Ġcolonel": 46969, + "enemy": 46970, + "Gra": 46971, + "Ġpubs": 46972, + "utters": 46973, + "Ġassigns": 46974, + "ĠPenet": 46975, + "ĠMonstrous": 46976, + "ĠBowen": 46977, + "ilver": 46978, + "Haunted": 46979, + "ĠDing": 46980, + "started": 46981, + "plin": 46982, + "Ġcontaminants": 46983, + "ĠDOE": 46984, + "ffen": 46985, + "ĠTechnician": 46986, + "Ry": 46987, + "Ġrobbers": 46988, + "Ġhotline": 46989, + "ĠGuardiola": 46990, + "ĠKaufman": 46991, + "rower": 46992, + "ĠDresden": 46993, + "ĠAlpine": 46994, + "Elf": 46995, + "Ġfmt": 46996, + "ĠSard": 46997, + "urses": 46998, + "gpu": 46999, + "Unix": 47000, + "Ġunequivocally": 47001, + "ĠCitizenship": 47002, + "quad": 47003, + "mire": 47004, + "ĠSweeney": 47005, + "Battery": 47006, + "615": 47007, + 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47058, + "Ġscanners": 47059, + "Ġdeviations": 47060, + "ĠRobotics": 47061, + "ĠCompton": 47062, + "Ġsorcerer": 47063, + "Ġendogenous": 47064, + "Ġemulation": 47065, + "ĠPiercing": 47066, + "ĠAph": 47067, + "ĠSocket": 47068, + "Ġbould": 47069, + "ĠOU": 47070, + "ĠBorderlands": 47071, + "Ġ1863": 47072, + "Gordon": 47073, + "ĠWTO": 47074, + "Ġrestricts": 47075, + "Ġmosaic": 47076, + "Ġmelodies": 47077, + "çĦ": 47078, + "Tar": 47079, + "Ġdisson": 47080, + "ĠProvides": 47081, + "Ġ......": 47082, + "bek": 47083, + "FIX": 47084, + "Ġbroom": 47085, + "anship": 47086, + "Doctors": 47087, + "Ġnerds": 47088, + "ĠRegions": 47089, + "naissance": 47090, + "Ġmete": 47091, + "Ġcrept": 47092, + "plings": 47093, + "Ġgirlfriends": 47094, + "knit": 47095, + "igent": 47096, + "owe": 47097, + "Ġushered": 47098, + "ĠBaz": 47099, + "Mobil": 47100, + "434": 47101, + "ĠPresents": 47102, + "origin": 47103, + "Ġinsomnia": 47104, + "ĠAux": 47105, + "439": 47106, + "ĠChili": 47107, + "irsch": 47108, + "GAME": 47109, + "Ġgestation": 47110, + "algia": 47111, + "romising": 47112, + "$,": 47113, + "crow": 47114, + "ĠInspection": 47115, + "atomic": 47116, + "Relations": 47117, + "JOHN": 47118, + "roman": 47119, + "ĠClockwork": 47120, + "ĠBakr": 47121, + "mone": 47122, + "MET": 47123, + "Ġthirsty": 47124, + "Ġbc": 47125, + "Ġfaculties": 47126, + "Rum": 47127, + "Ġnuance": 47128, + "ĠDarius": 47129, + "pleting": 47130, + "fters": 47131, + "etchup": 47132, + "Registration": 47133, + "ĠKE": 47134, + "Rah": 47135, + "Ġpreferential": 47136, + "ĠLash": 47137, + "ĠHH": 47138, + "Valid": 47139, + "ĠNAV": 47140, + "Ġstarve": 47141, + "ĠGong": 47142, + "zynski": 47143, + "ĠActress": 47144, + "Ġwik": 47145, + "Ġunaccompanied": 47146, + "lvl": 47147, + "Bride": 47148, + "ADS": 47149, + "ĠCommando": 47150, + "ĠVaughn": 47151, + "Wallet": 47152, + "Ġhopping": 47153, + "ĠVie": 47154, + "Ġcaveats": 47155, + "Ġalas": 47156, + "ifled": 47157, + "abuse": 47158, + "661": 47159, + "Ġibn": 47160, + "Ġgul": 47161, + "Ġrobbing": 47162, + "til": 47163, + "ILA": 47164, + "Ġmitigating": 47165, + "Ġaptly": 47166, + "Ġtyrant": 47167, + "Ġmidday": 47168, + "ĠGilmore": 47169, + "ĠDecker": 47170, + "Ġ§§": 47171, + "partial": 47172, + "Exactly": 47173, + "Ġphenotype": 47174, + "Ġ[+]": 47175, + "ĠPlex": 47176, + "ĠIps": 47177, + "versions": 47178, + "Ġebook": 47179, + "Ġchic": 47180, + "gross": 47181, + "\":\"\"},{\"": 47182, + "ĠSurprisingly": 47183, + "Morgan": 47184, + "Ġresidues": 47185, + "ĠConfederation": 47186, + "infeld": 47187, + "Ġlyr": 47188, + "moderate": 47189, + "Ġperpendicular": 47190, + "VK": 47191, + "Ġsynchronized": 47192, + "Ġrefreshed": 47193, + "Ġadore": 47194, + "ĠTorment": 47195, + "olina": 47196, + "Ġ2600": 47197, + "ItemTracker": 47198, + "Ġpies": 47199, + "ĠFAT": 47200, + "ĠRHP": 47201, + "048": 47202, + "ĠRESP": 47203, + "ĠBJ": 47204, + "allows": 47205, + "Pand": 47206, + "Ġunwelcome": 47207, + "ĠVoc": 47208, + "ĠBastard": 47209, + "ĠOW": 47210, + "ĠLAR": 47211, + "ĠHealer": 47212, 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"wat": 47261, + "Ġsuperhuman": 47262, + "Ġridiculed": 47263, + "Ġholiest": 47264, + "ombo": 47265, + "Ġinterns": 47266, + "Ġhone": 47267, + "ĠParagu": 47268, + "URI": 47269, + "Ġdangling": 47270, + "ãĤ»": 47271, + "sov": 47272, + "ictional": 47273, + "availability": 47274, + "Ġrevocation": 47275, + "Ġdow": 47276, + "inic": 47277, + "ĠTHEIR": 47278, + "Ġiso": 47279, + "Ġoutings": 47280, + "ĠLethal": 47281, + "Ġ)))": 47282, + "Ġinaccur": 47283, + "Ġoutlandish": 47284, + "Ġanus": 47285, + "letico": 47286, + "idon": 47287, + "lol": 47288, + "Ġunregulated": 47289, + "Ġsuccumbed": 47290, + "Ġcuff": 47291, + "ĠWasteland": 47292, + "letal": 47293, + "Ġsubstr": 47294, + "Ġcoffers": 47295, + "Ġautomakers": 47296, + "ovi": 47297, + "ĠXue": 47298, + "ĠDaytona": 47299, + "Ġjarring": 47300, + "Ġfumes": 47301, + "Ġdisbanded": 47302, + "zik": 47303, + "itton": 47304, + "Ġstrikingly": 47305, + "Ġspores": 47306, + "Adapter": 47307, + ".):": 47308, + "ĠLyndon": 47309, + "ivalry": 47310, + "Ġorally": 47311, + "Ġtumultuous": 47312, + "Ġdispleasure": 47313, + "Ġcones": 47314, + "orrect": 47315, + "Ġappease": 47316, + "Ġderby": 47317, + "ĠTripoli": 47318, + "ĠAless": 47319, + "Ġpoked": 47320, + "ĠGuilty": 47321, + "vP": 47322, + "Enough": 47323, + "Ġoriginals": 47324, + "699": 47325, + "Ġrabbi": 47326, + "Ġproverbial": 47327, + "Ġpostpone": 47328, + "elope": 47329, + "ĠMisty": 47330, + "Ġstaffed": 47331, + "ĠUnemployment": 47332, + "reditary": 47333, + "Ġdiligent": 47334, + "recomm": 47335, + "measures": 47336, + "asin": 47337, + "825": 47338, + "Ġponds": 47339, + "Ġmmol": 47340, + "ĠSAR": 47341, + "ĠCARE": 47342, + "Ġ371": 47343, + "Ġclenched": 47344, + "ĠCorsair": 47345, + "Ġcaricature": 47346, + "zn": 47347, + "attach": 47348, + "ĠSchro": 47349, + "speak": 47350, + "painted": 47351, + "ĠSuc": 47352, + "ĠENT": 47353, + "Ġcellul": 47354, + "ĠPaid": 47355, + "diagn": 47356, + "WHERE": 47357, + "Ġtexted": 47358, + "Barn": 47359, + "Ġretracted": 47360, + "ĠReferred": 47361, + "Sav": 47362, + "Ġupkeep": 47363, + "Ġworkplaces": 47364, + "ĠTokens": 47365, + "Ġamplify": 47366, + "clinical": 47367, + "Ġmultic": 47368, + "mberg": 47369, + "Ġconvoluted": 47370, + "Region": 47371, + "565": 47372, + "ĠTopic": 47373, + "Ġsnail": 47374, + "Ġsaline": 47375, + "Ġinsurrection": 47376, + "ĠPetr": 47377, + "forts": 47378, + "BAT": 47379, + "ĠNavajo": 47380, + "Ġrudimentary": 47381, + "ĠLaksh": 47382, + "ONDON": 47383, + "Measure": 47384, + "Ġtransformer": 47385, + "ĠGoddard": 47386, + "Ġcoincides": 47387, + "irin": 47388, + "Rex": 47389, + "ĠBok": 47390, + "quit": 47391, + "Ġshotguns": 47392, + "Ġproletarian": 47393, + "Ġscorp": 47394, + "ĠAda": 47395, + "514": 47396, + "Ġslander": 47397, + "recorded": 47398, + "Ġembell": 47399, + "risome": 47400, + "Ġapologizing": 47401, + "ĠMulcair": 47402, + "ĠGibraltar": 47403, + "Cla": 47404, + "Ġallot": 47405, + "ĠAttention": 47406, + "Ġ433": 47407, + "leave": 47408, + "Ġwhine": 47409, + "ĠIssa": 47410, + "ĠFaust": 47411, + "ĠBarron": 47412, + "heny": 47413, + "Ġvictimized": 47414, + "Jews": 47415, + "Ġnurturing": 47416, + "ettel": 47417, + "Winged": 47418, + "ĠSubtle": 47419, + "Ġflavorful": 47420, + "ĠReps": 47421, + "enged": 47422, + "callback": 47423, + "Ġdirectional": 47424, + "Ġclasp": 47425, + "ĠDirections": 47426, + "planet": 47427, + "iculture": 47428, + "Helper": 47429, + "icion": 47430, + "acia": 47431, + "Ġç¥ŀ": 47432, + "Ġsurges": 47433, + "Ġcanoe": 47434, + "ĠPremiership": 47435, + "been": 47436, + "Ġdefied": 47437, + "ĠTrooper": 47438, + "Ġtripod": 47439, + "Ġgasp": 47440, + "ĠEuph": 47441, + "ĠAds": 47442, + "vernight": 47443, + "highly": 47444, + "Role": 47445, + "Ġentangled": 47446, + "ĠZeit": 47447, + "618": 47448, + "ĠRusty": 47449, + "Ġhavens": 47450, + "ĠVaughan": 47451, + "HAEL": 47452, + "ĠSERVICE": 47453, + "/,": 47454, + "Ġstricken": 47455, + "Ġdelusions": 47456, + "Ġbis": 47457, + "ĠHaf": 47458, + "Ġgratification": 47459, + "Ġenticing": 47460, + "UNCH": 47461, + "Adams": 47462, + "ĠOLED": 47463, + "ĠBeetle": 47464, + "Ġ1899": 47465, + "ĠSOFTWARE": 47466, + "ategor": 47467, + "VL": 47468, + "ĠTotem": 47469, + "ĠGators": 47470, + "ATURES": 47471, + "Ġimpedance": 47472, + "Registered": 47473, + "ĠCary": 47474, + "ĠAerial": 47475, + "onne": 47476, + "enium": 47477, + "Ġdred": 47478, + "ĠBeg": 47479, + "Ġconcurrently": 47480, + "Ġsuperpower": 47481, + "ĠXan": 47482, + "jew": 47483, + "imester": 47484, + "ĠDickinson": 47485, + "âĶģ": 47486, + "Fla": 47487, + "Ġpree": 47488, + "ĠRollins": 47489, + "©¶æ": 47490, + "Ġdenomination": 47491, + "ĠLana": 47492, + "516": 47493, + "Ġinciting": 47494, + "scribed": 47495, + "juries": 47496, + "ĠWonders": 47497, + "approximately": 47498, + "Ġsuspending": 47499, + "Ġmountainous": 47500, + "ĠLaugh": 47501, + "oidal": 47502, + "Ns": 47503, + "Detect": 47504, + ")=": 47505, + "ĠLuthor": 47506, + "ĠSchwarzenegger": 47507, + "ĠMuller": 47508, + "ĠDevi": 47509, + "ecycle": 47510, + "Jar": 47511, + "613": 47512, + "ĠLongh": 47513, + "Bah": 47514, + "ĠSPORTS": 47515, + "nw": 47516, + "Ġrefinement": 47517, + "Ġwaterways": 47518, + "Ġdiner": 47519, + "Blade": 47520, + "683": 47521, + "Fac": 47522, + "Ġinitials": 47523, + "Ġrog": 47524, + "Ġparanormal": 47525, + "BUT": 47526, + "Ġ[(": 47527, + "ĠSwanson": 47528, + "ĠMesh": 47529, + "âĸ¬": 47530, + "Improve": 47531, + "ĠRadiation": 47532, + "ĠEsther": 47533, + "ĠEsk": 47534, + "ĠAly": 47535, + "iky": 47536, + "Ġirrad": 47537, + "ĠBuckingham": 47538, + "Ġrefill": 47539, + "Ġ._": 47540, + "Repe": 47541, + "CONCLUS": 47542, + "Ġdifferentiated": 47543, + "Ġchirop": 47544, + "ĠAtkins": 47545, + "Pattern": 47546, + "Ġexcise": 47547, + "Ġcabal": 47548, + "NSA": 47549, + "ĠSTA": 47550, + "ĠSIL": 47551, + "ĠParaly": 47552, + "Ġrye": 47553, + "ĠHowell": 47554, + "ĠCountdown": 47555, + "nesses": 47556, + "alysed": 47557, + "Ġresize": 47558, + "ãĤ½": 47559, + "Ġbudgetary": 47560, + "ĠStras": 47561, + "wang": 47562, + "Ġapiece": 47563, + "Ġprecincts": 47564, + "Ġpeach": 47565, + "Ġskyline": 47566, + "Ġ353": 47567, + "popular": 47568, + "Appearances": 47569, + "ĠMechanics": 47570, + "ĠDevOnline": 47571, + "Sullivan": 47572, + "Zen": 47573, + "Ġpu": 47574, + "opolis": 47575, + "544": 47576, + "Ġdeform": 47577, + "Ġcounteract": 47578, + "ĠLange": 47579, + "Ġ417": 47580, + "Console": 47581, + "774": 47582, + "Ġnodding": 47583, + "Ġpopulism": 47584, + "Ġhep": 47585, + "Ġcounselling": 47586, + "compliance": 47587, + "UFF": 47588, + "Ġundeniably": 47589, + "Ġrailing": 47590, + "ĠHorowitz": 47591, + "ĠSimone": 47592, + "ĠBungie": 47593, + "Ġak": 47594, + "ĠTalks": 47595, + "xff": 47596, + "flake": 47597, + "Crash": 47598, + "Ġsweaty": 47599, + "Ġbanquet": 47600, + "ĠOFFIC": 47601, + "Ġinventive": 47602, + "Ġastronomer": 47603, + "ĠStamford": 47604, + "ĠScare": 47605, + "ĠGREEN": 47606, + "olicited": 47607, + "Ġrusher": 47608, + "Ġcentrist": 47609, + "ighting": 47610, + "Ġsubclass": 47611, + "Ġdisav": 47612, + "Ġdefund": 47613, + "ĠNanto": 47614, + "ociate": 47615, + "mast": 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ed", + "1 3", + "Ġdes ign", + "Ġch ange", + "Ġch ang", + "Ġb o", + "Ġv is", + "em ber", + "Ġb ook", + "read y", + "Ġk ill", + "2 5", + "pp ed", + "Ġa way", + "Ġab le", + "Ġcount ry", + "Ġcon st", + "ar n", + "Ġor der", + "A R", + "i or", + "i um", + "or th", + "1 8", + "ail able", + "Ġs w", + "Ġm illion", + "Ġ1 3", + "at ic", + "t ed", + "ĠG o", + "Ġo per", + "en g", + "Ġth ing", + "aj or", + "con om", + "ĠCom m", + "Ġwh y", + "u red", + "ur al", + "Ġs chool", + "b y", + "ĠM ar", + "Ġa ff", + "Ġd ays", + "Ġan n", + "us h", + "an e", + "I f", + "e g", + "Ġpro f", + "Ġhe alth", + "ou th", + "B ut", + "ion al", + ". ,", + "Ġs ol", + "Ġal ready", + "Ġ3 0", + "Ġchar act", + "H e", + "Ġf riend", + "E S", + "i ans", + "ic le", + "' d", + "ĠO n", + "Ġle ast", + "Ġp rom", + "Ġd r", + "Ġh ist", + "it her", + "Ġ est", + "i qu", + "1 7", + "s on", + "Ġte ll", + "Ġt alk", + "oh n", + "o int", + "le ction", + "A N", + "Ġunt il", + "au gh", + "Ġl ater", + "Ġ ve", + "Ġv iew", + "end ing", + "iv ed", + "Ġwor d", + "w are", + "Ġc ost", + "Ġen ough", + "Ġg ive", + "ĠUn ited", + "Ġte chn", + "are nt", + "O R", + "Ġp ar", + "ĠD r", + "Ġ201 6", + "r ist", + "er ing", + "Ġ Â", + "Ġl arge", + "s ide", + "ac y", + "cc ess", + "Ġw in", + "Ġimport ant", + "Ġ19 9", + "Ġdoes n", + "Ġ1 7", + "Ġbus iness", + "Ġcle ar", + "Ġre se", + "\" ,", + "ur y", + "Ġe qu", + "as ter", + "al f", + "ĠAmeric an", + "n ect", + "Ġex pect", + "ivers ity", + "Ġo cc", + "ĠF l", + "Ġk ind", + "Ġme an", + "Ġp ast", + "Ġde v", + "Ġb as", + "le t", + "ra ft", + "Ġor gan", + "Ġde l", + "Ġper form", + "Ġst ory", + "Ġse ason", + "ĠC ol", + "Ġcl aim", + "Ġc ame", + "Ġwith in", + "Ġl ine", + "Ġpro ject", + "ĠA t", + "Ġcontro l", + "end ed", + "ĠS y", + "Ġa ir", + "iz ation", + "Ġ *", + "le y", + "Ġm oney", + "id d", + "Y ou", + "f or", + "Ġfam ily", + "Ġm aking", + "Ġb it", + "Ġpol ice", + "Ġhapp en", + "Ġ vers", + "on y", + "u ff", + "ĠW hen", + "Ġs it", + "ide o", + "l f", + "is on", + "Ġsu re", + "g in", + "Ġapp ear", + "Ġl ight", + "Ġ es", + "o f", + "Ġw ater", + "Ġt imes", + "n ot", + "Ġg row", + "Ġcomp any", + "ĠT e", + "ow s", + "Ġm ar", + "our ce", + "i ol", + "ar m", + "b r", + "Ġex ample", + "Ġcon c", + "Ġf ore", + "ĠT o", + "p ro", + "E N", + "ri es", + "Ġ2 5", + "ĠC an", + "ne y", + "Ġact ually", + "Ġe ver", + "ur ity", + "ak en", + "ap s", + "Ġt ax", + "Ġm ajor", + "am a", + "Ġof ten", + "er al", + "Ġhum an", + "Ġj ob", + "is ter", + "Ġav ailable", + "oc r", + "en n", + "a id", + "iv id", + "Ġrec ord", + "? \"", + "Ġs ing", + "ĠA m", + "id ence", + "Ġnew s", + "st er", + "Ġe conom", + "Ġfollow ing", + "ĠB r", + "is ing", + "Ġh our", + "m ost", + "um ent", + "Ġse x", + "Ġdes c", + "Ġbec ome", + "ĠE d", + "Ġto ok", + "Ġha ving", + "Ġprodu ct", + "a ult", + "A s", + "ar ing", + "Ġme ans", + "Ġh op", + "un e", + "Ġch o", + "Ġc ertain", + "Ġn on", + "Ġde al", + "2 4", + "le ment", + "oc i", + "en e", + "Ġs ide", + "ĠP r", + "ĠM ay", + "Ġre ason", + "u ed", + "c hed", + "ul ation", + "Ġe lect", + "Ġoffic ial", + "Ġposs ible", + "Ġh old", + "and s", + "ot s", + "Ġc ity", + "or ies", + "Ġse ver", + "Ġchild ren", + "Ġon ce", + "Ġact iv", + "l er", + "Ġn ight", + "it ions", + "ĠJ ohn", + "a pe", + "pl ay", + "Ġd one", + "Ġl im", + "Ġwork ing", + "ĠP res", + "or ld", + "e b", + "ĠC o", + "Ġb ody", + "ail s", + "ut es", + "ĠM r", + "Ġwhe ther", + "Ġa uthor", + "ro p", + "Ġpro per", + "Ġse en", + ") ;", + "Ġf ac", + "ĠS u", + "Ġcon d", + "it ing", + "Ġcour se", + "Ġ }", + "-------- --------", + "a ign", + "Ġev ent", + "Ġen g", + "Ġp ot", + "Ġin tern", + "i am", + "Ġsh ort", + "em pt", + "ã Ĥ", + "ĠG od", + "il ar", + "8 0", + "Ġor ig", + "I S", + "our n", + "ab ility", + "it ive", + "Ġd am", + "Ġ1 00", + "Ġp ress", + "Ġdo ing", + "Ġprot ect", + "r ing", + "Ġthough t", + "Ġquest ion", + "re w", + "ĠW ar", + "Ġsever al", + "ĠSt ate", + "Ġg iven", + "Ġf und", + "ĠT w", + "Ġw ent", + "an ces", + "w ork", + "p or", + "m y", + "4 0", + "Ġar g", + "art ment", + "ust om", + "Ġpol ic", + "Ġme et", + "Ġc reat", + "2 2", + "ĠSt ates", + "Ġg ames", + "ra w", + "ut ure", + "Ġunder stand", + "ur s", + "ĠO b", + "l ish", + "s y", + "Ġm akes", + "Ġw on", + "ag on", + "Ġh tt", + "Ġl ove", + "ent ial", + "Ġcomple te", + "p ar", + "ĠI m", + "A L", + "Ġacc ount", + " ł", + "ore d", + "ver t", + "Ġ ident", + "Ġ201 5", + "Ġother s", + "ĠM in", + "i ber", + "ver age", + "The re", + "ition al", + "d d", + "Ġpro b", + "Ġyou ng", + "Ġal ong", + "Ġacc ording", + "Ġy et", + "Ġmem bers", + "ĠWh at", + "o id", + "ĠM an", + "A nd", + "Ġam ong", + "a i", + "Ġem ploy", + "ĠR es", + "Ġ >", + "Ġinv ol", + "Ġl ow", + "a f", + "ĠC ar", + "Ġh ig", + "ĠO ne", + "ĠS ec", + "in ation", + "Ġlike ly", + "Ġan t", + "ag ed", + "ĠR uss", + "Ġb en", + "Ġre le", + "F or", + "b ack", + "ĠN ot", + "Ġpres ident", + "b all", + "Ġacc ess", + "ivid ual", + "ĠD em", + "ĠE uro", + "6 0", + "Ġkn own", + "ir l", + "ĠG r", + "Ġear ly", + "u se", + "iet y", + "âĢ ĵ", + "Ġf ight", + "Ġs ent", + "Ġto day", + "Ġmark et", + "\" .", + "Ġb ased", + "Ġstr ong", + "ur ther", + "Ġde b", + "m ber", + "Ġproble m", + "Ġde ath", + "Ġsoc ial", + "im ate", + "A S", + "ort un", + "Ġcamp aign", + "er y", + "C h", + "Ġe y", + "i ally", + "Ġm us", + "w h", + "p os", + "Ġ er", + "Ġsa f", + "Ġmonth s", + "ir on", + "Ġv iol", + "Ġf ive", + "Ġst re", + "Ġplay ers", + "in c", + "al d", + "y ear", + "a un", + "Ġsu ccess", + "Ġpres ent", + "ere nce", + "Ġ201 4", + "Ġsu gg", + "Ġpartic ular", + "Ġtr y", + "Ġsugg est", + "ĠCh rist", + "on es", + "Ġpri v", + "2 3", + "Ġc rit", + "Ġl and", + "Ġloc al", + "if y", + "2 9", + "Ġa ut", + "E D", + "ĠG u", + "Ġm ult", + "Ġpolit ical", + "Ġask ed", + "Ġfor mer", + "it ter", + "ri pt", + "Ġcl ose", + "Ġp ract", + "ĠY ork", + "Ġget ting", + "Ġac ross", + "Ġcom b", + "Ġbelie ve", + "Ġ z", + "Ġto get", + "Ġtoget her", + "ĠC ent", + "ir c", + "Ġind ividual", + "ĠM c", + "2 7", + "is k", + "ĠE ng", + "Ġf ace", + "Ġ2 4", + "Ġval ue", + "Ġare a", + "e v", + "Ġw rit", + "ĠPres ident", + "Ġv ot", + "Ġke y", + "Ġm om", + "p ut", + "Ġany thing", + "Ġexper ience", + "att le", + "Ġm ind", + "a ff", + "om m", + "Ġf uture", + "g ed", + "Ġc ut", + "Ġto t", + "it ch", + "Ġv ideo", + "Ġinvest ig", + "Ġn et", + "ĠM y", + "r ict", + "i en", + ". )", + "Ġimp ro", + "th ough", + "ward s", + "Ġcon nect", + "ĠM ed", + "sel ves", + "ens ive", + "m b", + "o ber", + "at ors", + "A n", + "Ġ5 0", + "Ġre du", + "res ent", + "Ġab ove", + "Ġf re", + "ĠEuro pe", + "s w", + "Ġam ount", + "ĠA pp", + "Ġe ither", + "Ġmil it", + "Ġan al", + "Ġf ail", + "ĠE n", + "al es", + "Ġspec ial", + "Ġbl ack", + "I T", + "c her", + "Ġlook ing", + "Ġf ire", + "y n", + "Ġal most", + "o on", + "Ġstud y", + "Ġm iss", + "c hes", + "ro wn", + "Ġt re", + "Ġcommun ity", + "Ġmed ia", + "Ġf ood", + "Ġcom es", + "ĠUn iversity", + "Ġsing le", + "Wh at", + "u ly", + "Ġh alf", + "ag ue", + "h od", + "ĠRep ublic", + "Ġstart ed", + "Ġqu ick", + "ot o", + "b ook", + "Ġiss ue", + "it or", + "Ġel se", + "Ġcons ider", + "2 6", + "ro du", + "Ġt aken", + "2 8", + "9 9", + "ĠW ith", + "Ġtr ue", + "Ġw a", + "Ġtr ad", + "Ġag o", + "Ġm ess", + "ie f", + "Ġadd ed", + "o ke", + "Ġb ad", + "Ġf av", + "3 3", + "Ġsim ilar", + "as k", + "ĠD on", + "Ġcharact er", + "ort s", + "ĠH ouse", + "Ġreport ed", + "Ġty pe", + "v al", + "i od", + "ĠHow ever", + "Ġt arg", + "Ġent ire", + "pp ing", + "Ġhist ory", + "Ġl ive", + "ff ic", + ".... ....", + "ed eral", + "Ġtr ying", + "Ġdisc uss", + "ĠH ar", + "ac es", + "l ished", + "Ġse lf", + "os p", + "re st", + "Ġro om", + "el t", + "Ġf all", + "ol ution", + "Ġe t", + "Ġ x", + "Ġis n", + "Ġide a", + "b o", + "Ġs ound", + "ĠD ep", + "Ġsome one", + "ci ally", + "ull y", + "Ġf oc", + "Ġob ject", + "if t", + "ap er", + "Ġplay er", + "Ġr ather", + "Ġserv ice", + "as hing", + "ĠD o", + "ĠP art", + "ru g", + "m on", + "p ly", + "Ġm or", + "Ġnot hing", + "Ġprov ide", + "I C", + "un g", + "Ġpart y", + "Ġex ist", + "Ġm ag", + "7 0", + "Ġr ul", + "Ġh ouse", + "Ġbeh ind", + "Ġhow ever", + "ĠW orld", + "Ġs um", + "Ġapp lic", + "Ġ ;", + "Ġfun ction", + "g r", + "ĠP ol", + "Ġfr ont", + "2 00", + "Ġser ies", + "Ġt em", + "Ġty p", + "ill s", + "Ġo pt", + "Ġpoint s", + "Ġbel ow", + "itt ed", + "Ġspec ific", + "Ġ201 7", + "um b", + "Ġr a", + "Ġpre vious", + "Ġpre t", + "re me", + "Ġc ustom", + "Ġcour t", + "ĠM e", + "Ġre pl", + "Ġwho le", + "g o", + "c er", + "Ġt reat", + "ĠA ct", + "Ġprob ably", + "Ġle arn", + "end er", + "ĠA ss", + "Ġvers ion", + "n ow", + "Ġche ck", + "ĠC al", + "R E", + "min ist", + "O n", + "our ces", + "Ġben ef", + "Ġd oc", + "Ġdet er", + "Ġen c", + "Ġsu per", + "Ġadd ress", + "Ġv ict", + "Ġ201 3", + "Ġme as", + "t r", + "Ġf ield", + "W hen", + "Ġsign ific", + "u ge", + "Ġfe at", + "Ġcomm on", + "l oad", + "Ġbe gin", + "Ġbr ing", + "Ġa ction", + "er man", + "Ġdesc rib", + "Ġind ust", + "Ġwant ed", + "ri ed", + "m ing", + "Ġatt empt", + "4 5", + "f er", + "Ġd ue", + "ress ion", + "# #", + "Ġsh all", + "Ġs ix", + "o o", + "Ġst ep", + "Ġp ub", + "Ġhim self", + "Ġ2 3", + "Ġc op", + "Ġd est", + "Ġst op", + "A C", + "ib ility", + "Ġl ab", + "ic ult", + "Ġhour s", + "Ġcre ate", + "Ġf urther", + "ĠAmeric a", + "ĠC ity", + "Ġd ou", + "he ad", + "S T", + "ĠN orth", + "c ing", + "Ġn ational", + "u le", + "ĠIn st", + "Ġt aking", + "ĠQ u", + "ir t", + "Ġre d", + "Ġrese arch", + "v iron", + "ĠG e", + "Ġbre ak", + "an a", + "Ġsp ace", + "ater ial", + "Ġrec ent", + "ĠA b", + "Ġgener al", + "Ġh it", + "Ġper iod", + "Ġevery thing", + "ive ly", + "Ġph ys", + "Ġsay ing", + "an ks", + "Ġc ou", + "Ġc ult", + "ac ed", + "e al", + "u ation", + "Ġc oun", + "l u", + "Ġinclud e", + "Ġpos ition", + "ĠA fter", + "ĠCan ad", + "ĠE m", + "Ġim m", + "ĠR ed", + "Ġp ick", + "Ġcom pl", + "Ġm atter", + "re g", + "e xt", + "ang u", + "is c", + "o le", + "a ut", + "Ġcomp et", + "e ed", + "f ect", + "Ġ2 1", + "ĠS en", + "ĠThe se", + "as ing", + "Ġcan not", + "Ġin it", + "Ġrel ations", + "ac hed", + "Ġb ar", + "Ġ4 0", + "ĠT H", + "Ġ201 2", + "Ġv ol", + "Ġg round", + "Ġsec urity", + "Ġup d", + "il t", + "3 5", + "Ġconc ern", + "ĠJ ust", + "Ġwh ite", + "Ġseem s", + "ĠH er", + "pe cially", + "i ents", + "Ġann oun", + "Ġf ig", + "ight s", + "Ġst ri", + "l ike", + "id s", + "Ġs us", + "Ġw atch", + "Ġ â", + "Ġw ind", + "ĠC ont", + "Ġit self", + "Ġm ass", + "A l", + "y le", + "iqu e", + "ĠN ational", + "Ġab s", + "Ġp ack", + "Ġout side", + "Ġan im", + "Ġp ain", + "et er", + "Ġman ag", + "du ct", + "og n", + "Ġ ]", + "ĠSe pt", + "se c", + "o ff", + "ĠJ an", + "Ġf oot", + "ad es", + "Ġth ird", + "Ġm ot", + "Ġev idence", + "int on", + "Ġth reat", + "a pt", + "pl es", + "c le", + "Ġl o", + "Ġde cl", + "Ġit em", + "med i", + "Ġrep resent", + "om b", + "am er", + "Ġsignific ant", + "og raph", + "s u", + "Ġc al", + "i res", + "00 00", + "I D", + "A M", + "Ġsim ply", + "Ġlong er", + "Ġf ile", + "O T", + "c he", + "S o", + "ate g", + "or g", + "ĠH is", + "Ġen er", + "Ġd om", + "Ġup on", + "il i", + "\": \"", + "Ġthem selves", + "Ġcom ing", + "Ġqu ite", + "Ġdiff icult", + "ĠB ar", + "il ities", + "re l", + "end s", + "c ial", + "6 4", + "Ġwom an", + "ra p", + "y r", + "Ġne cess", + "ip s", + "Ġte xt", + "Ġrequ ire", + "Ġmilit ary", + "Ġre view", + "Ġresp ons", + "7 5", + "Ġsub ject", + "Ġinst ead", + "Ġiss ues", + "Ġg en", + "\" ,\"", + "Ġmin utes", + "Ġwe ap", + "r ay", + "am ed", + "t ime", + "b l", + "H ow", + "Ġc ode", + "ĠS m", + "Ġhig her", + "ĠSt e", + "r is", + "Ġp age", + "Ġstud ents", + "ĠIn tern", + "Ġmet hod", + "ĠA ug", + "ĠP er", + "ĠA g", + "Ġpolic y", + "ĠS w", + "Ġex ec", + "Ġac cept", + "um e", + "rib ut", + "Ġword s", + "Ġfin al", + "Ġchang es", + "ĠDem ocr", + "Ġfriend s", + "Ġres pect", + "Ġe p", + "Ġcomp an", + "iv il", + "Ġdam age", + "** **", + "og le", + "viron ment", + "Ġne g", + "ent al", + "Ġa p", + "Ġtot al", + "iv al", + "! \"", + "l im", + "Ġneed s", + "Ġag re", + "Ġdevelop ment", + "Ġa ge", + "ip le", + "2 1", + "Ġresult s", + "ĠA f", + "S h", + "Ġg un", + "ĠOb ama", + "ro ll", + "Ġ @", + "Ġright s", + "ĠB rit", + "Ġrun ning", + "Ġwas n", + "Ġp ort", + "Ġr ate", + "Ġpret ty", + "Ġtarg et", + "Ġsa w", + "Ġc irc", + "Ġwor ks", + "ic ro", + "al t", + "o ver", + "ww w", + "Th at", + "l ier", + "Ġevery one", + "ud e", + "Ġp ie", + "idd le", + "ra el", + "Ġr ad", + "Ġbl ock", + "Ġw alk", + "T o", + "ã ģ", + "n es", + "ĠA ust", + "a ul", + "ro te", + "ĠS outh", + "ess ion", + "op h", + "Ġshow s", + "Ġs ite", + "Ġj o", + "Ġr isk", + "cl us", + "l t", + "Ġin j", + "id ing", + "ĠS pe", + "Ġch all", + "ir m", + "Ġ2 2", + "itt ing", + "st r", + "Ġh y", + "L E", + "ke y", + "Ġbe gan", + "at ur", + "ashing ton", + "l am", + "ĠD av", + "b it", + "Ġs ize", + "ĠP ar", + "3 8", + "ourn al", + "f ace", + "Ġdec ision", + "Ġl arg", + "Ġj ud", + "re ct", + "Ġcontin ue", + "ĠO ct", + "ove red", + "ĠI nt", + "==== ====", + "Ġp arent", + "ĠW ill", + "Ġeas y", + "Ġd rug", + "ang er", + "Ġs ense", + "Ġd i", + "id ay", + "Ġener gy", + "ist ic", + "Ġass oci", + "ar ter", + "ob al", + "e ks", + "ĠE l", + "ur ch", + "Ġg irl", + "o e", + "it le", + "Ġ2 8", + "ĠC he", + "Ġrequ est", + "Ġso on", + "Ġh ost", + "k y", + "Ġst ates", + "om es", + "Ġm aterial", + "le x", + "Ġmom ent", + "Ġan sw", + "on se", + "Ġes pecially", + "Ġn orm", + "Ġserv ices", + "p ite", + "r an", + "Ġro le", + "4 4", + ") :", + "Ġc red", + "C l", + "____ ____", + "Ġm at", + "Ġl og", + "ĠCl inton", + "O U", + "Ġoff ice", + "Ġ2 6", + "Ġch arg", + "Ġtr ack", + "m a", + "Ġhe art", + "Ġb all", + "Ġperson al", + "Ġbuild ing", + "n a", + "s et", + "b ody", + "ĠBl ack", + "Ġincre ase", + "itt en", + "Ġneed ed", + "3 6", + "3 2", + "= \"", + "Ġl ost", + "Ġbec ame", + "Ġgrou ps", + "ĠM us", + "Ġw rote", + "ĠP e", + "Ġpro p", + "j oy", + "à ©", + "ĠWh ite", + "Ġde ad", + ". '", + "Ġhtt p", + "Ġwe bs", + "O S", + "Ġins ide", + "Ġwr ong", + "Ġstat ement", + "Ġ ...", + "y l", + "Ġfil m", + "Ġmus ic", + "Ġsh are", + "ific ation", + "Ġre lease", + "Ġfor ward", + "Ġst ay", + "Ġcomp ut", + "it te", + "s er", + "Ġorig inal", + "Ġc ard", + "Ġc and", + "Ġd iv", + "at ural", + "Ġfav or", + "O M", + "Ġc ases", + "us es", + "Ġse ction", + "Ġle ave", + "g ing", + "ov ed", + "ĠW ashington", + "3 9", + "ĠG l", + "Ġrequ ired", + "act ion", + "ap an", + "o or", + "it er", + "ĠK ing", + "Ġcount ries", + "ĠG erman", + "ll ing", + "Ġ2 7", + "3 4", + "Ġquest ions", + "Ġpr im", + "Ġc ell", + "Ġsh oot", + "Ġany one", + "ĠW est", + "Ġaff ect", + "ep end", + "Ġon line", + "ĠIs rael", + "ĠSept ember", + "Ġab ility", + "Ġcont ent", + "is es", + "Ġre ve", + "Ġl aun", + "Ġind ic", + "Ġfor ce", + "c ast", + "Ġso ld", + "av ing", + "f l", + "Ġso ft", + "Ġcompan ies", + "ce ed", + "Ġart icle", + "Ġa ud", + "Ġre v", + "Ġed uc", + "Ġplay ing", + "0 5", + "Ġhe ld", + "ct or", + "Ġrele ased", + "Ġf ederal", + "3 7", + "Ġad minist", + "Ġinter view", + "Ġinst all", + "Ġrece ived", + "Ġs ource", + "u k", + "P h", + "Ġser ious", + "Ġcre ated", + "Ġc ause", + "Ġim medi", + "Ġdef in", + "u el", + "ĠDep artment", + "ct ions", + "ĠC our", + "ĠN ow", + "z e", + "it es", + "it ution", + "Ġl ate", + "Ġspe ak", + "n ers", + "Ġleg al", + "ar i", + "ĠC or", + "Ġwe eks", + "Ġmod el", + "Ġp red", + "Ġex act", + "B C", + "ĠB y", + "IN G", + "os ing", + "Ġt akes", + "Ġreg ard", + "Ġopp ortun", + "Ġpr ice", + "Ġ19 8", + "ĠA pr", + "f ully", + "Ġor d", + "Ġproble ms", + "ru ction", + "h am", + "ĠC ount", + "le ge", + "Ġlead ers", + "E T", + "le v", + "Ġde ep", + "olog ical", + "es e", + "h aps", + "ĠS ome", + "Ġp ers", + "Ġcont ract", + "Ġrelations hip", + "s p", + "ou d", + "Ġb ase", + "4 8", + "m it", + "A d", + "anc ial", + "Ġcons um", + "Ġpot ential", + "Ġl angu", + "re m", + "et h", + "Ġrel ig", + "ress ed", + "6 6", + "Ġl ink", + "Ġl ower", + "ay er", + "ĠJ une", + "Ġf em", + "un t", + "er c", + "ur d", + "Ġcont act", + "Ġ ill", + "Ġm other", + "Ġest ab", + "h tt", + "ĠM arch", + "ĠB ro", + "ĠCh ina", + "Ġ2 9", + "Ġs qu", + "Ġprov ided", + "Ġa verage", + "as ons", + "Ġ201 1", + "Ġex am", + "l in", + "5 5", + "n ed", + "Ġper fect", + "Ġt ou", + "al se", + "u x", + "Ġbu y", + "Ġsh ot", + "Ġcol lect", + "Ġph ot", + "Ġplay ed", + "Ġsur pr", + "Ġofficial s", + "Ġsim ple", + "av y", + "Ġindust ry", + "Ġhand s", + "g round", + "Ġp ull", + "Ġr ound", + "Ġus er", + "Ġr ange", + "u ary", + "Ġpriv ate", + "op s", + "e es", + "Ġw ays", + "ĠM ich", + "Ġve h", + "Ġex cept", + "Ġter ms", + "im um", + "pp er", + "I ON", + "ore s", + "ĠDr agon", + "ou l", + "Ġd en", + "Ġperform ance", + "Ġb ill", + "c il", + "4 7", + "Ġen vironment", + "Ġex c", + "ad d", + "Ġwor th", + "Ġp ict", + "Ġch ance", + "Ġ201 8", + "b or", + "Ġspe ed", + "ict ion", + "Ġal leg", + "ĠJ apan", + "at ory", + "re et", + "Ġm atch", + "ĠI I", + "Ġst ru", + "ord er", + "Ġst e", + "Ġl iving", + "Ġst ruct", + "in o", + "Ġse par", + "her n", + "Ġresp onse", + "Ġen joy", + "Ġv ia", + "A D", + "um ents", + "ace book", + "Ġmem ber", + "ib r", + "iz ing", + "Ġto ol", + "ĠM on", + "ĠWh ile", + "h ood", + "ĠA ng", + "ĠD ef", + "Ġoff er", + "T r", + "a ur", + "Ġturn ed", + "ĠJ uly", + "d own", + "an ced", + "Ġrec ently", + "ĠE ar", + "Ġc e", + "ĠSt ar", + "ĠC ong", + "rough t", + "Ġbl ood", + "Ġhop e", + "Ġcom ment", + "ain t", + "Ġar ri", + "il es", + "Ġpartic ip", + "ough t", + "ri ption", + "0 8", + "4 9", + "Ġg ave", + "Ġse lect", + "Ġkill ed", + "sy ch", + "Ġgo es", + "i j", + "Ġc oll", + "Ġimp act", + "at ives", + "ĠS er", + "0 9", + "ĠAug ust", + "Ġb oy", + "d e", + "ĠD es", + "Ġf elt", + "U S", + "Ġexpect ed", + "Ġim age", + "ĠM ark", + "cc ording", + "o ice", + "E C", + "ĠM ag", + "en ed", + "h old", + "ĠP ost", + "Ġpre vent", + "N o", + "Ġinvol ved", + "Ġey es", + "Ġquick ly", + "A t", + "un k", + "Ġbeh av", + "Ġ ur", + "Ġl ed", + "c ome", + "e y", + "Ġcand id", + "Ġear lier", + "Ġfoc us", + "et y", + "P ro", + "led ge", + "ix ed", + "ill ed", + "Ġpop ular", + "A P", + "Ġset t", + "l ight", + "Ġvar ious", + "in ks", + "Ġlevel s", + "Ġro ad", + "ell ig", + "ab les", + "he l", + "itte e", + "ĠG ener", + "y pe", + "Ġhe ard", + "ic les", + "Ġm is", + "Ġus ers", + "ĠS an", + "Ġimpro ve", + "Ġf ather", + "Ġse arch", + "The y", + "v il", + "Ġprof ess", + "Ġkn ew", + "Ġl oss", + "Ġev ents", + "6 5", + "Ġb illion", + "0 7", + "0 2", + "ĠNew s", + "ĠA M", + "Ġco ver", + "w here", + "ens ion", + "Ġb ott", + "Ġare as", + "en ces", + "op e", + "ĠTw itter", + "a el", + "Ġget s", + "ĠGo ogle", + "Ġs n", + "i ant", + "Ġv ote", + "Ġnear ly", + "Ġinclud ed", + "Ġrec ogn", + "z z", + "m m", + "al ed", + "Ġhappen ed", + "0 4", + "Ġh ot", + "Ġwho se", + "Ġc ivil", + "Ġsu ff", + "o es", + "it iz", + "ĠSy ri", + "Ġresp ond", + "Ġh on", + "Ġfeat ures", + "Ġeconom ic", + "ĠApr il", + "r im", + "Ġtechn ology", + "Ġo ption", + "ag ing", + "Ġpur ch", + "R e", + "Ġl at", + "ch ie", + "is l", + "Ġrec omm", + "u f", + "Ġtr aining", + "Ġeffect s", + "Ġf ast", + "Ġ201 0", + "Ġocc ur", + "Ġwebs ite", + "Ġem ail", + "Ġs ens", + "e ch", + "Ġo il", + "Ġinf lu", + "Ġcurrent ly", + "ĠS ch", + "ĠAd d", + "Ġgo al", + "Ġsc ient", + "Ġcon v", + "1 00", + "em y", + "Ġdec ided", + "Ġtra vel", + "Ġm ention", + "L L", + "0 3", + "Ġe lection", + "Ġph one", + "Ġlook s", + "Ġsit uation", + "Ġc y", + "Ġh or", + "b ed", + "ĠCour t", + "a ily", + "av es", + "Ġqu ality", + "ĠCom p", + "w ise", + "Ġt able", + "Ġst aff", + "ĠW ind", + "et t", + "Ġtri ed", + "ide red", + "Ġadd ition", + "Ġb ox", + "Ġl ack", + "ar ily", + "Ġw ide", + "Ġm id", + "Ġbo ard", + "ys is", + "Ġant i", + "h a", + "Ġd ig", + "en ing", + "Ġd ro", + "C on", + "6 8", + "Ġsl ow", + "b ased", + "se qu", + "Ġp ath", + "E x", + "ak er", + "Ġwork ed", + "Ġp en", + "Ġeng ine", + "Ġlook ed", + "ĠSu per", + "ĠS erv", + "Ġvict im", + "U n", + "Ġproper ty", + "Ġint rodu", + "Ġexec ut", + "ĠP M", + "L e", + "Ġcol or", + "ĠM ore", + "Ġ6 0", + "Ġnet work", + "Ġd ate", + "c ul", + "id ge", + "Ġext ra", + "3 1", + "Ġs le", + "6 7", + "Ġw ond", + "Ġreport s", + "j ust", + "ĠAust ral", + "Ġcap ital", + "Ġen s", + "Ġcomm and", + "Ġallow ed", + "Ġpre p", + "Ġca pt", + "h ib", + "Ġnum bers", + "ch an", + "Ġf air", + "m p", + "om s", + "Ġre ach", + "W ith", + "t ain", + "Ġbro ad", + "Ġcou ple", + "ec ause", + "ly ing", + "ĠF eb", + "Ġsc reen", + "Ġl ives", + "Ġpri or", + "ĠCong ress", + "A r", + "Ġappro ach", + "Ġe mer", + "ar ies", + "ĠD is", + "s erv", + "ĠN e", + "Ġbu ilt", + "c ies", + "Ġre pe", + "Ġrul es", + "for ce", + "ĠP al", + "Ġfin ancial", + "Ġcons idered", + "ĠCh ar", + "n ces", + "ĠI S", + "Ġb rought", + "Ġb i", + "i ers", + "ĠS im", + "O P", + "Ġproduct s", + "Ġvis it", + "Ġdoc ument", + "Ġcon duct", + "Ġcomplete ly", + "in ing", + "ĠCal if", + "ib ly", + "Ġwr itten", + "ĠT V", + "em ents", + "Ġd raw", + "O ne", + "Ġpub lished", + "Ġsec ret", + "r ain", + "he t", + "ĠF acebook", + "ond ay", + "ĠU p", + "Ġsex ual", + "Ġth ous", + "ĠP at", + "Ġ ess", + "Ġstand ard", + "Ġar m", + "g es", + "ect ion", + "Ġf ell", + "Ġfore ign", + "an i", + "ĠFr iday", + "Ġreg ular", + "in ary", + "Ġincre ased", + "Ġus ually", + "Ġdem on", + "Ġd ark", + "Ġadd itional", + "ro l", + "ĠO f", + "Ġprodu ction", + "! !", + "und red", + "Ġintern ational", + "id ents", + "ĠF ree", + "rou p", + "Ġr ace", + "Ġm ach", + "Ġh uge", + "A ll", + "le ar", + "ove mber", + "Ġto wn", + "Ġatt ention", + "ĠO ff", + "y ond", + "ĠThe n", + "f ield", + "Ġter ror", + "ra z", + "ĠB o", + "Ġmeet ing", + "ĠP ark", + "Ġar rest", + "Ġf ear", + "Ġa w", + "ĠV al", + "or ing", + "' ,", + "Ġext reme", + "ar r", + "Ġwork ers", + "A fter", + "Ġ3 1", + "n et", + "am ent", + "Ġdirect ly", + "Ġpop ulation", + "ub e", + "ĠOct ober", + "ĠI N", + "ĠJan uary", + "5 9", + "ĠDav id", + "Ġc ross", + "ce mber", + "ĠF irst", + "Ġmess age", + "ir it", + "Ġn ation", + "Ġp oll", + "is ions", + "Ġansw er", + "n y", + "is ode", + "Ġcar ry", + "ĠRuss ia", + "Ġhe ar", + "eng th", + "ro y", + "Ġn atural", + "in ally", + "Ġdo g", + "m itted", + "Ġtr ade", + "Ġsub st", + "Ġmult iple", + "ĠAf ric", + "Ġf ans", + "Ġs ort", + "Ġgl obal", + "ic ation", + "ĠW ed", + "ar a", + "Ġa chie", + "Ġlangu age", + "ve y", + "Ġt al", + "Ġnecess ary", + "Ġdet ails", + "Ġs en", + "ĠS und", + "ĠRe g", + "ĠR ec", + "0 6", + "Ġs il", + "ress ive", + "Ġmed ical", + "un ch", + "orn ia", + "Ġu nd", + "f ort", + "oc ks", + "ĠM onday", + "ues day", + "c raft", + "7 7", + "ur t", + "Ġ ver", + "ĠH ill", + "Ġrece ive", + "Ġmor ning", + "es tern", + "Ġb ank", + "Ġs at", + "ir th", + "ĠH igh", + "Ġdev ice", + "ĠTH E", + "ĠCent er", + "Ġsaf e", + "Ġp le", + "ĠCanad a", + "Ġsystem s", + "Ġass ist", + "Ġsur v", + "Ġb attle", + "ĠS oc", + "vert is", + "S he", + "Ġp aper", + "Ġgrow th", + "Ġc ast", + "S c", + "Ġpl ans", + "ll ed", + "Ġpart s", + "Ġw all", + "Ġmove ment", + "Ġpract ice", + "im ately", + "Ġdis play", + "Ġsomet imes", + "om p", + "ĠP aul", + "ĠY es", + "k ing", + "5 8", + "o ly", + "Ġs on", + "Ġav oid", + "ok es", + "ĠJ ew", + "Ġto wards", + "as c", + "Ġ //", + "ĠK ore", + "Ġtalk ing", + "Ġcor rect", + "Ġsp ent", + "ic ks", + "i able", + "e ared", + "Ġter m", + "Ġwant s", + "om ing", + "Ġ ut", + "Ġdou b", + "Ġfor ces", + "Ġp lease", + "6 9", + "ĠN ovember", + "at form", + "ond on", + "Ġon es", + "Ġimmedi ately", + "ĠRuss ian", + "ĠM et", + "Ġde g", + "Ġparent s", + "C H", + "ĠAmeric ans", + "al y", + "ĠM od", + "Ġsh own", + "Ġcond itions", + "Ġst uff", + "Ġre b", + "ĠY our", + "Ġinclud es", + "n own", + "ĠS am", + "Ġexper ien", + "m ission", + "ĠE ven", + "augh t", + "Ġannoun ced", + "ĠRepublic an", + "Ġdeter min", + "Ġdescrib ed", + "ĠCount y", + "( )", + "Ġdo or", + "Ġchang ed", + "Ġne igh", + "ĠH ere", + "Ġcle an", + "Ġp an", + "ĠDe cember", + "ĠEurope an", + "ir ing", + "ap ter", + "Ġcl ub", + "ĠT uesday", + "Ġp aid", + "ĠN et", + "Ġattack s", + "Ġcharact ers", + "Ġal one", + "Ġdirect or", + "d om", + "Ġ3 5", + "Ġl oad", + "Ġr out", + "ĠCalif ornia", + "Ġfin ally", + "Ġr ac", + "Ġcont r", + "Ġexact ly", + "res h", + "p ri", + "ĠIs lam", + "Ġn ature", + "Ġcare er", + "Ġlat est", + "Ġcon vers", + "ĠS l", + "p ose", + "ci ent", + "ĠIn c", + "iv ity", + "8 8", + "ĠA tt", + "ĠM or", + "nes day", + "Ġwe ight", + "k en", + "Ġnot e", + "Ġteam s", + "Ġ \\", + "air s", + "ĠG reen", + "Ġh undred", + "on ent", + "Ġstre ng", + "Ġcons ist", + "ic ated", + "Ġreg ul", + "Ġl ic", + "ast ic", + "Ġt en", + "urs day", + "ellig ence", + "ous ly", + "ĠU K", + "B I", + "Ġcost s", + "Ġind epend", + "ĠA P", + "Ġnorm al", + "Ġh om", + "Ġob vious", + "Ġs we", + "Ġst ar", + "Ġread y", + "ac her", + "Ġimp lement", + "g est", + "Ġs ong", + "ĠG et", + "ĠL ab", + "Ġinterest ing", + "us ing", + "Ġg iving", + "ĠSund ay", + "Ġet c", + "Ġm iddle", + "Ġrem ember", + "r ight", + "os ition", + "ut ions", + "Ġm ax", + "4 6", + "Ġyour self", + "Ġdem and", + "Ġtreat ment", + "Ġd anger", + "ĠC ons", + "Ġgu y", + "ĠBrit ish", + "Ġphys ical", + "Ġrel ated", + "Ġrem ain", + "Ġcould n", + "Ġref er", + "Ġc itiz", + "b ox", + "EN T", + "bo ard", + "Ġin n", + "I G", + "er o", + "ĠSt reet", + "osp ital", + "ren ch", + "cher s", + "Ġst ra", + "O L", + "ag er", + "ĠA N", + "Ġeas ily", + "I A", + "en ge", + "in y", + "Ġcl os", + "ock ed", + "Ġus es", + "ĠC oun", + "I m", + "u ild", + "? ?", + "m ore", + "Ġan g", + "Ġwr ite", + "ol ute", + "5 7", + "Ġlead er", + "Ġread ing", + "< /", + "Ġaut om", + "est s", + "4 3", + "Ġleg isl", + "ĠG old", + "Ġdesign ed", + "ĠS T", + "ĠLe g", + "a res", + "Ġbe aut", + "ĠT ex", + "Ġappear s", + "Ġstru gg", + "ĠR om", + "Ġ 00", + "Ġcho ice", + "Ġparticular ly", + "ĠF rom", + "op er", + "ĠL ondon", + "ann ed", + "Ġallow s", + "ob ile", + "Ġdiffere nce", + "âĢ ¢", + "ĠV iew", + "ĠWed nesday", + "Ġal though", + "Ġrel ative", + "Ġapplic ation", + "ate ver", + "Ġare n", + "Ġmy self", + "Ġim ag", + "Ġdis e", + "Ġsoc iety", + "Ġfre qu", + "ĠEng lish", + "Ġpo or", + "ĠD ay", + "Ġwrit ing", + "Ġse ven", + "Ġstart ing", + "Ġb ud", + "Ġpr int", + "ĠTr ans", + "uf act", + "ĠSt ud", + "n ew", + "Ġcr im", + "Ġg ives", + "Ġco ol", + "a e", + "i ance", + "ĠGener al", + "Ġthink ing", + "Ġsa ve", + "Ġlim ited", + "ĠPart y", + "Ġmean ing", + "p en", + "ow ers", + "ĠJ ack", + "E M", + "Ġn ice", + "ru pt", + "Ġg as", + "Ġe ight", + "Ġfe et", + "Ġeff ort", + "Ġ ign", + "ic it", + "B l", + "co in", + "Ġop in", + "Ġbr ain", + "Wh ile", + "he st", + "ĠTh ursday", + "Ġwould n", + "augh ter", + "Ġtou ch", + "le ments", + "Ġstud ies", + "Ġcent er", + "c ont", + "or ge", + "Ġcomput er", + "Ġinvestig ation", + "P l", + "or ks", + "Ġ200 8", + "Ġincre asing", + "Ġst ore", + "Ġcom ments", + "Ġb al", + "m en", + "Ġdo ll", + "Ġl iber", + "Ġw ife", + "Ġlaw s", + "atur day", + "it ness", + "Ġmod ern", + "ĠS k", + "Ġadminist ration", + "Ġopportun ity", + "Ġs al", + "Ġpower ful", + "M y", + "Ġclaim s", + "ĠEar th", + "ord s", + "Ġt itle", + "Ġes c", + "n ame", + "N ot", + "om en", + "Ġbe yond", + "Ġc amer", + "Ġse ll", + "it ute", + "ear ch", + "Ġapp l", + "im ent", + "4 2", + "ĠAr t", + "Ġun f", + "Ġviol ence", + "ur g", + "ĠE ast", + "Ġcomp ared", + "Ġopt ions", + "Ġthrough out", + "Ġv s", + "ig r", + ". [", + "ac hes", + "7 8", + "Ġfil es", + "F L", + "E L", + "ar ian", + "ĠJ ames", + "ĠA ir", + "an ch", + "Ġdet ail", + "Ġpie ce", + "P S", + "Ġn amed", + "Ġeduc ation", + "Ġdri ve", + "Ġitem s", + "Ġstud ent", + "ic ed", + ": :", + "ic o", + "Ġth row", + "Ġsc ene", + "Ġcomple x", + "Ġ200 9", + "Ġpre c", + "ĠB re", + "7 9", + "Ġcon cept", + "Ġstat us", + "am ing", + "Ġd ied", + "Ġknow ledge", + "Ġbegin ning", + "O D", + "ru ary", + "Ġcertain ly", + "Ġgu ys", + "Ġsl ight", + "in n", + "ound s", + "Ġf ine", + "Ġf at", + "ic ations", + "Ġper haps", + "ĠA nt", + "Ġinc ome", + "Ġhtt ps", + "Ġmajor ity", + "port s", + "st on", + "Ġgreat er", + "Ġfe ed", + "ent ially", + "Ġsaf ety", + "Ġun ique", + "and om", + "Ġg one", + "Ġshow ed", + "Ġhist or", + "Ġcoun ter", + "i us", + "id a", + "Ġlead ing", + "i pe", + "Ġs end", + "ĠDon ald", + "er ve", + "Ġdef ense", + "ines e", + "Ġy es", + "ĠF ire", + "ĠMus lim", + "ra q", + "Ġcontin ued", + "os h", + "Ġprov ides", + "Ġpr ison", + "ĠP re", + "Ġhapp y", + "Ġeconom y", + "Ġtr ust", + "ag s", + "ĠG ame", + "Ġweap ons", + "um an", + "ĠC le", + "it ation", + "Ġanal ysis", + "ĠT imes", + "Ġsc ience", + "- >", + "Ġfig ure", + "Ġdis app", + "ent y", + "Ġsoft ware", + "Ġu lt", + "Ġoffic ers", + "N ew", + "I s", + "Ġrem ains", + "ĠInd ia", + "Ġp sych", + "ri ef", + "Ġc at", + "es c", + "Ġob serv", + "Ġst age", + "ĠD ark", + "Ġent er", + "ch ange", + "Ġpass ed", + "Ġdes pite", + "ĠO ut", + "Ġmov ie", + "r s", + "Ġv oice", + "m ine", + "ĠPl ay", + "Ġto ward", + "ĠT er", + "Ġreg ion", + "Ġval ues", + "or ters", + "Ġm ount", + "Ġoffic er", + "ĠO ther", + "b an", + "Ġh ous", + "w ood", + "ro om", + "I V", + "ĠS un", + "se e", + "ĠO ver", + "ro g", + "9 0", + "Ġl ay", + "ĠT ur", + "a wn", + "Ġpress ure", + "ĠS ub", + "Ġbook s", + "ed om", + "ĠS and", + "A A", + "ag o", + "Ġre asons", + "f ord", + "Ġactiv ity", + "U T", + "N ow", + "ĠSen ate", + "ce ll", + "n ight", + "Ġcall s", + "in ter", + "Ġlet ter", + "ĠR ob", + "ĠJ e", + "Ġcho ose", + "ĠL aw", + "G et", + "B e", + "Ġro b", + "Ġtyp es", + "Ġpl atform", + "Ġqu arter", + "R A", + "ĠT ime", + "Ġmay be", + "ĠC r", + "9 5", + "p re", + "Ġmov ing", + "Ġl if", + "Ġgo ld", + "Ġs om", + "Ġpat ients", + "Ġtr uth", + "ĠK e", + "ur ance", + "ant ly", + "m ar", + "Ġchar ge", + "ĠG reat", + "Ġce le", + "---------------- ----------------", + "Ġro ck", + "ro id", + "an cy", + "Ġcred it", + "a ud", + "B y", + "ĠE very", + "Ġmov ed", + "ing er", + "rib ution", + "Ġn ames", + "Ġstra ight", + "ĠHe alth", + "ĠW ell", + "Ġfe ature", + "Ġr ule", + "Ġsc he", + "in ated", + "ĠMich ael", + "ber g", + "4 1", + "il ed", + "b and", + "Ġcl ick", + "ĠAng el", + "on ents", + " Ń", + "ĠI raq", + "ĠS aturday", + "Ġa ware", + "p art", + "Ġpat tern", + "O W", + "ĠL et", + "Ġgr ad", + "ign ed", + "Ġassoci ated", + "Ġst yle", + "n o", + "i ation", + "a ith", + "il ies", + "Ġst ories", + "ur ation", + "Ġindividual s", + "ĠâĢ ¦", + "m iss", + "ĠAss oci", + "ish ing", + "ab y", + "Ġsum mer", + "ĠB en", + "Ġ3 2", + "Ġar ch", + "ut y", + "ĠTex as", + "h ol", + "Ġfull y", + "Ġm ill", + "Ġfollow ed", + "ĠB ill", + "ĠInd ian", + "ĠSec ret", + "ĠB el", + "ĠFeb ruary", + "Ġjob s", + "Ġseem ed", + "ĠGo vern", + "i pped", + "Ġreal ity", + "Ġl ines", + "Ġp ark", + "Ġmeas ure", + "ĠO ur", + "I M", + "Ġbro ther", + "Ġgrow ing", + "Ġb an", + "Ġest im", + "Ġc ry", + "ĠS chool", + "Ġme chan", + "ĠO F", + "ĠWind ows", + "Ġr ates", + "ĠO h", + "Ġpos itive", + "Ġcult ure", + "ist ics", + "ic a", + "Ġh ar", + "y a", + "ite ly", + "i pp", + "Ġm ap", + "en cies", + "ĠWill iam", + "I I", + "ak ers", + "5 6", + "ĠM art", + "ĠR em", + "Ġal tern", + "it ude", + "Ġco ach", + "row d", + "D on", + "Ġk ids", + "Ġj ournal", + "Ġcor por", + "Ġf alse", + "Ġwe b", + "Ġsle ep", + "Ġcont ain", + "Ġst o", + "Ġb ed", + "iver se", + "ĠR ich", + "ĠCh inese", + "Ġp un", + "Ġme ant", + "k nown", + "Ġnot ice", + "Ġfavor ite", + "a ven", + "Ġcond ition", + "Ġpur pose", + ") )", + "Ġorgan ization", + "Ġchall eng", + "Ġman ufact", + "Ġsus p", + "ĠA c", + "Ġcrit ic", + "un es", + "uc lear", + "Ġm er", + "vent ion", + "Ġ8 0", + "Ġm ist", + "ĠU s", + "ĠT or", + "htt p", + "ol f", + "Ġlarg er", + "Ġadv ant", + "Ġrese ar", + "Ġact ions", + "m l", + "Ġke pt", + "Ġa im", + ", '", + "c ol", + "Ġbenef its", + "if ying", + "Ġact ual", + "ĠIntern ational", + "Ġveh icle", + "Ġch ief", + "Ġeff orts", + "ĠLe ague", + "ĠM ost", + "Ġwa it", + "Ġad ult", + "Ġover all", + "Ġspe ech", + "Ġhigh ly", + "Ġfem ale", + "Ġer ror", + "Ġeffect ive", + "5 4", + "Ġenc our", + "w ell", + "Ġfail ed", + "Ġcons erv", + "Ġprogram s", + "Ġt rou", + "Ġa head", + "5 00", + "vertis ement", + "I P", + "ĠF ound", + "p ir", + "Ġ %", + "Ġcr ime", + "and er", + "Ġloc ation", + "ĠI ran", + "Ġbehav ior", + "az ing", + "Ġr are", + "Ġem b", + "Ġca used", + "Ġsh ip", + "Ġact ive", + "Ġcont ribut", + "Ġg reen", + "Ġac qu", + "Ġref lect", + "ven ue", + "Ġf irm", + "Ġb irth", + "] .", + "Ġclear ly", + "Ġem ot", + "Ġag ency", + "ri age", + "Ġmem ory", + "9 8", + "S A", + "ĠSe e", + "ac ing", + "C C", + "Ġbig gest", + "Ġr ap", + "Ġbas ic", + "Ġb and", + "e at", + "Ġsus pect", + "ĠM ac", + "Ġ9 0", + "m ark", + "ist an", + "Ġsp read", + "am s", + "k i", + "as y", + "ra v", + "ĠR ober", + "Ġdemon str", + "r ated", + "Ġabs olute", + "Ġpl aces", + "Ġim pl", + "ibr ary", + "Ġc ards", + "Ġdest roy", + "Ġv irt", + "ve re", + "Ġapp eared", + "y an", + "p oint", + "Ġbe g", + "Ġtem per", + "s pe", + "ant ed", + "ear s", + "ĠD irect", + "Ġl ength", + "Ġbl og", + "am b", + "Ġint eg", + "Ġres ources", + "ac c", + "if ul", + "Ġsp ot", + "Ġfor ced", + "Ġthous ands", + "ĠMin ister", + "Ġqu al", + "ĠF rench", + "at ically", + "Ġgener ally", + "Ġdr ink", + "Ġth us", + "I L", + "od es", + "Ġappro pri", + "ĠRe ad", + "Ġwh om", + "Ġey e", + "Ġcol lege", + "Ġ4 5", + "ire ction", + "Ġens ure", + "Ġapp arent", + "id ers", + "Ġrelig ious", + "Ġmin or", + "ol ic", + "Ġt ro", + "ĠWh y", + "rib ute", + "m et", + "Ġprim ary", + "Ġdevelop ed", + "Ġpe ace", + "Ġsk in", + "st e", + "av a", + "Ġbl ue", + "Ġfam ilies", + "Ġ ir", + "Ġapp ly", + "Ġin form", + "ĠSm ith", + "C T", + "i i", + "Ġlim it", + "Ġres ist", + "........ ........", + "um n", + "Ġconf lic", + "Ġtw e", + "ud d", + "ĠT om", + "Ġl iter", + "qu e", + "b on", + "Ġha ir", + "Ġevent ually", + "Ġp us", + "Ġhelp ed", + "Ġag g", + "or ney", + "ĠApp le", + "Ġf it", + "ĠS ur", + "Ġpre m", + "Ġs ales", + "Ġsecond s", + "Ġstreng th", + "Ġfeel ing", + "¿ ½", + "Ġt our", + "Ġknow s", + "o om", + "Ġex erc", + "Ġsom ew", + "ï ¿½", + "> >", + "Ġsp okes", + "Ġide as", + "Ġreg ist", + "so ft", + "ĠD el", + "ĠP C", + "Ġpro pos", + "Ġlaun ch", + "Ġbott om", + "T H", + "ĠP lease", + "v est", + "it z", + "ĠIn ter", + "Ġsc ript", + "Ġr at", + "ar ning", + "Ġ il", + "ĠJ er", + "ĠA re", + "Ġwh atever", + "ok en", + "ci ence", + "Ġmod e", + "Ġag ree", + "Ġs ources", + "Ġinit ial", + "Ġrest rict", + "Ġwond er", + "us ion", + "## ##", + "ĠS il", + "vil le", + "Ġb urn", + "t w", + "as ion", + "Ġ £", + "Ġn or", + "u ing", + "Ġre ached", + "Ġs un", + "Ġc ateg", + "ig ration", + "Ġc ook", + "Ġprom ot", + "Ġm ale", + "Ġcl imate", + "Ġf ix", + "Ġalleg ed", + "U R", + "all ed", + "Ġim ages", + "C ont", + "ot a", + "Ġschool s", + "i os", + "Ġd rop", + "Ġst ream", + "ĠM o", + "Ġprevious ly", + "al ing", + "Ġp et", + "Ġdou ble", + "Ġ( @", + "ann el", + "Ġdef ault", + "t ies", + "Ġr ank", + "ĠD ec", + "ĠCoun cil", + "Ġweap on", + "Ġst ock", + "Ġanal y", + "ĠSt r", + "Ġpict ure", + "ĠPol ice", + "f erence", + "Ġcent ury", + "Ġcitiz ens", + "Ġon to", + "Ġexp and", + "Ġhe ro", + "ĠS ol", + "Ġw ild", + "Ġupd ate", + "Ġcustom ers", + "r ont", + "d ef", + "Ġl ik", + "Ġcrim inal", + "ĠChrist ian", + "S P", + "7 6", + "Ġle aving", + "Ġother wise", + "ĠD ist", + "Ġbas is", + "5 2", + "5 3", + "ic ip", + "ĠB er", + "Ġrecomm end", + "Ġfl oor", + "Ġc rowd", + "ol es", + "Ġ7 0", + "Ġcent ral", + "ĠE v", + "Ġd ream", + "Ġdown load", + "Ġconf ir", + "ĠTh om", + "Ġwind ow", + "Ġhapp ens", + "Ġun it", + "Ġt end", + "Ġs pl", + "Ġbec omes", + "Ġfight ing", + "Ġpred ict", + "ĠP ress", + "ĠP ower", + "Ġhe avy", + "ak ed", + "Ġf an", + "or ter", + "ate gy", + "B A", + "iz es", + "Ġsp end", + "H ere", + "Ġ200 7", + "Ġad op", + "ĠH am", + "Ġfoot ball", + "ĠP ort", + "od ay", + "5 1", + "amp ions", + "Ġtrans fer", + "h t", + "Ġ3 8", + "ter m", + "ac ity", + "Ġb ur", + "] ,", + "tern al", + "r ig", + "b ut", + "Ġthere fore", + "ĠB ecause", + "res p", + "re y", + "Ġm ission", + "S ome", + "Ġnot ed", + "Ġass um", + "Ġdise ase", + "Ġed it", + "Ġprog ress", + "r d", + "ĠB rown", + "oc al", + "Ġadd ing", + "Ġra ised", + "ĠAn y", + "Ġt ick", + "Ġsee ing", + "ĠPe ople", + "Ġagre ement", + "Ġser ver", + "Ġw at", + "Ġdeb ate", + "Ġsupp osed", + "il ing", + "Ġlarg est", + "Ġsuccess ful", + "ĠP ri", + "ĠDemocr atic", + "Ġj ump", + "ĠSyri a", + "Ġown ers", + "Ġoff ers", + "Ġshoot ing", + "Ġeff ic", + "se y", + "Ġha ven", + "ver se", + "te red", + "ĠL ight", + "im al", + "ĠB ig", + "Ġdef end", + "Ġbe at", + "Ġrecord s", + "% )", + "Ġsc en", + "Ġemploy ees", + "Ġdev ices", + "he m", + "Ġcom mer", + "ĠM ex", + "Ġbenef it", + "ĠPro f", + "Ġil leg", + "Ġsur face", + "ĠAl so", + "Ġh arm", + "ing ly", + "w ide", + "ĠA lex", + "Ġsh ut", + "ĠC ur", + "Ġl ose", + "p m", + "Ġchall enge", + "se mb", + "Ġst ation", + "Ġint elligence", + "Ġacc ur", + "ĠFl or", + "Ġrequ ires", + "ĠM al", + "b um", + "Ġh ospital", + "Ġsp irit", + "Ġoff ered", + "Ġprodu ce", + "ĠComm un", + "Ġcreat ing", + "Ġcr is", + "s pect", + "Ġend ed", + "Ġd aily", + "Ġvot ers", + "land s", + "i as", + "i h", + "on a", + "Ġsm art", + "ĠOff ice", + "ĠL ord", + "ri al", + "ĠIntern et", + "Ġcirc um", + "Ġextreme ly", + "' .", + "Ġopin ion", + "ĠM il", + "Ġg ain", + "B S", + "ĠF in", + "y p", + "Ġuse ful", + "Ġbud get", + "Ġcom fort", + "is f", + "Ġback ground", + "el ine", + "Ġep isode", + "Ġen emy", + "Ġtri al", + "Ġestab lish", + "d ate", + "ĠC ap", + "Ġcontin ues", + "Ġshow ing", + "ĠUn ion", + "w ith", + "Ġpost ed", + "ĠSy stem", + "Ġe at", + "ri an", + "Ġr ise", + "ĠGerman y", + "il s", + "Ġsign ed", + "Ġv ill", + "Ġgr and", + "m or", + "ĠEng land", + "Ġproject s", + "um ber", + "Ġconf erence", + "z a", + "Ġrespons ible", + "ĠAr ab", + "Ġlearn ed", + "âĢĶ âĢĶ", + "i pping", + "ĠGe orge", + "O C", + "Ġreturn ed", + "ĠAustral ia", + "Ġb rief", + "Q u", + "Ġbr and", + "ill ing", + "ab led", + "Ġhig hest", + "Ġtr ain", + "ĠComm ission", + "wh ile", + "Ġn om", + "cept ion", + "Ġm ut", + "ĠBl ue", + "Ġinc ident", + "v ant", + "8 6", + "ĠI D", + "Ġn uclear", + "7 4", + "ĠL ike", + "ĠR E", + "ĠM icro", + "l i", + "m ail", + "Ġcharg es", + "8 9", + "Ġad just", + "ad o", + "Ġear th", + "N A", + "Ġpr ices", + "P A", + "Ġd raft", + "Ġrun s", + "Ġcandid ate", + "ens es", + "Ġmanag ement", + "ĠPh il", + "ĠM iss", + "Ġte ach", + "g ram", + "Ġunderstand ing", + "a it", + "ic ago", + "A dd", + "ĠE p", + "sec ut", + "Ġsepar ate", + "Ġinst ance", + "Ġe th", + "Ġun less", + "**** ****", + "ĠF ore", + "in ate", + "Ġoper ations", + "S p", + "Ġf aith", + "g ar", + "ĠCh urch", + "ron ic", + "Ġconf ig", + "os ure", + "Ġactiv ities", + "Ġtrad itional", + "Ġ3 6", + "Ġd irection", + "Ġmach ine", + "Ġsur round", + "Ġp ush", + "un ction", + "ĠE U", + "Ġeas ier", + "Ġarg ument", + "G B", + "Ġm icro", + "Ġsp ending", + "iz ations", + "Ġthe ory", + "ad ow", + "Ġcall ing", + "ĠL ast", + "Ġd er", + "Ġinflu ence", + "Ġcomm it", + "Ġph oto", + "Ġun c", + "ist ry", + "g n", + "ast e", + "ack s", + "Ġdis p", + "ad y", + "d o", + "ĠG ood", + "Ġ `", + "Ġw ish", + "Ġreve aled", + "Âł Âł", + "l ig", + "Ġen force", + "ĠComm ittee", + "Ġche m", + "Ġmil es", + "Ġinterest ed", + "Ġsol ution", + "ic y", + "in ct", + "Ġ- >", + "ĠD et", + "Ġrem oved", + "Ġcomp ar", + "e ah", + "Ġpl ant", + "ĠS ince", + "Ġachie ve", + "Ġadvant age", + "Ġslight ly", + "b ing", + "Ġpl aced", + "u nder", + "201 5", + "ĠM ad", + "Ġt im", + "os es", + "Ġc ru", + "ĠR ock", + "Ġmost ly", + "Ġneg ative", + "Ġset ting", + "Ġprodu ced", + "Ġm ur", + "Ġconnect ion", + "ĠM er", + "Ġdri ver", + "Ġexecut ive", + "Ġass ault", + "Ġb orn", + "ĠV er", + "t ained", + "Ġstruct ure", + "Ġredu ce", + "Ġdec ades", + "Ġd ed", + "u ke", + "ĠM any", + "idd en", + "Ġle ague", + "S e", + "Ġjo in", + "Ġdis co", + "Ġd ie", + "c ks", + "act ions", + "Ġass ess", + "ag n", + "Ġgo als", + "our s", + "I R", + "Ġsen ior", + "ill er", + "m od", + "ip ment", + "oc ol", + "u y", + "ĠQ ue", + "Ġpart ies", + "ir gin", + "Ġle arning", + "it able", + "Ġstre et", + "Ġcamer a", + "A pp", + "Ġsk ills", + "b re", + "c ious", + "Ġcele br", + "ĠFr anc", + "Ġexist ing", + "Ġwill ing", + "l or", + "Ġ id", + "ĠSp ace", + "Ġcrit ical", + "ĠL a", + "ortun ately", + "Ġser ve", + "Ġc old", + "Ġspec ies", + "T S", + "Ġanim als", + "ĠB ay", + "Ġold er", + "ĠU nder", + "est ic", + "ĠT re", + "Ġte acher", + "Ġpre fer", + "v is", + "Ġth read", + "ĠM att", + "Ġmanag er", + "ãĥ »", + "Ġprofess ional", + "ĠV ol", + "Ġnot es", + "The se", + "ul a", + "Ġf resh", + "ent ed", + "u zz", + "ed y", + "clus ion", + "ĠR el", + "Ġdoub t", + "E O", + "Ġopen ed", + "ĠB it", + "Ad vertisement", + "Ġgu ess", + "ĠU N", + "Ġse qu", + "Ġexpl ain", + "ott en", + "Ġatt ract", + "ak s", + "Ġstr ing", + "Ġcont ext", + "oss ible", + "ĠRepublic ans", + "Ġsol id", + "Ġc ities", + "Ġask ing", + "Ġr andom", + "u ps", + "ur ies", + "ar ant", + "dd en", + "g l", + "ĠFlor ida", + "Ġdep end", + "ĠSc ott", + "Ġ3 3", + "Ġi T", + "ic on", + "Ġmention ed", + "Ġ2 000", + "Ġclaim ed", + "Ġdefin itely", + "ul f", + "Ġc ore", + "Ġopen ing", + "ĠCon st", + "wh ich", + "ĠT ra", + "A G", + "7 2", + "Ġbelie ved", + "ad a", + "Ġ4 8", + "ĠSec urity", + "yr ight", + "ĠP et", + "ĠL ou", + "Ġhold ing", + "======== ========", + "Ġ ice", + "Ġb row", + "Ġauthor ities", + "h ost", + "w ord", + "Ġsc ore", + "ĠD iv", + "Ġcell s", + "Ġtrans l", + "Ġneigh bor", + "Ġrem ove", + "u ct", + "Ġdist rict", + "ĠA ccording", + "Ġwor se", + "Ġconcern s", + "Ġpresident ial", + "Ġpolic ies", + "ĠH all", + "7 3", + "Ġh us", + "A Y", + "Ġ200 6", + "ĠJ ud", + "Ġindepend ent", + "ĠJust ice", + "ili ar", + "pr int", + "igh ter", + "Ġprotect ion", + "z en", + "Ġsu dden", + "h ouse", + "ĠJ es", + "P R", + "ĠIn f", + "Ġb ul", + "Ġ _", + "ĠServ ice", + "ĠP R", + "Ġstr ategy", + "ff ect", + "Ġgirl s", + "Ġmiss ing", + "oy al", + "ĠTe am", + "ul ated", + "Ġd at", + "Ġpolit ics", + "ab or", + "A ccording", + "Ġspe ll", + "Ġg raph", + "ort hern", + "T C", + "A b", + "Ġlab or", + "is her", + "Ġk ick", + "ĠiT unes", + "Ġstep s", + "pos es", + "Ġsmall er", + "E n", + "ber t", + "Ġro ll", + "Ġresear chers", + "Ġcl osed", + "Ġtrans port", + "Ġlaw y", + "________ ________", + "ĠCh icago", + "Ġas pect", + "Ġn one", + "Ġmar riage", + "9 6", + "Ġe lements", + "ĠF re", + "ĠS al", + "Ġd ram", + "F C", + "t op", + "e qu", + "Ġhe aring", + "Ġsupport ed", + "Ġtest ing", + "co hol", + "Ġmass ive", + "Ġst ick", + "Ġgu ard", + "is co", + "ph one", + "F rom", + "How ever", + "Ġb order", + "Ġcop y", + "ograph y", + "l ist", + "7 1", + "Ġown er", + "cl ass", + "ru it", + "r ate", + "ĠO nce", + "Ġdig ital", + "Ġt ask", + "ER S", + "Ġinc red", + "t es", + "+ +", + "ĠFr ance", + "Ġb reat", + "ow l", + "Ġiss ued", + "ĠW estern", + "Ġdet ect", + "Ġpart ners", + "Ġsh ared", + "ĠC all", + "Ġcan cer", + "ac he", + "rib e", + "Ġexpl ained", + "Ġhe at", + "{ \"", + "Ġinvest ment", + "ĠB ook", + "Ġw ood", + "Ġtool s", + "ĠAl though", + "Ġbelie f", + "Ġcris is", + "Ġg e", + "ĠM P", + "Ġoper ation", + "ty pe", + "~ ~", + "g a", + "Ġcont ains", + "ant a", + "Ġexp ress", + "ĠG roup", + "ĠJ ournal", + "k a", + "Ġam b", + "ĠUS A", + "Ġfind ing", + "Ġfund ing", + "h ow", + "Ġestab lished", + "ide os", + "Ġdeg ree", + "Ġdanger ous", + "ang ing", + "Ġfre edom", + "pp ort", + "out hern", + "Ġch urch", + "Ġc atch", + "ĠTw o", + "Ġpres ence", + "ĠGu ard", + "U p", + "Ġauthor ity", + "ĠPro ject", + "Ġbut ton", + "Ġcon sequ", + "Ġval id", + "Ġwe ak", + "Ġstart s", + "Ġref erence", + "ĠM em", + "\" )", + "U N", + "or age", + "ĠO pen", + "Ġcol lection", + "y m", + "g ency", + "Ġbeaut iful", + "ro s", + "Ġtell s", + "Ġwa iting", + "n el", + "Ġprov iding", + "ĠDemocr ats", + "Ġd aughter", + "Ġm aster", + "Ġpur poses", + "ĠJapan ese", + "Ġequ al", + "Ġturn s", + "Ġdoc uments", + "Ġwatch ing", + "R es", + "Ġr an", + "201 4", + "Ġre ject", + "ĠKore a", + "Ġvictim s", + "Le vel", + "ere nces", + "Ġw itness", + "Ġ3 4", + "Ġre form", + "com ing", + "Ġocc up", + "Ġc aught", + "Ġtra ffic", + "ad ing", + "Ġmod els", + "ar io", + "Ġserv ed", + "Ġb atter", + "u ate", + "ĠSecret ary", + "Ġagre ed", + "Ġtr uly", + "yn am", + "ĠR et", + "Ġun its", + "ĠRes earch", + "h and", + "az ine", + "ĠM ike", + "Ġvar iety", + "ot al", + "Ġam azing", + "Ġconfir med", + "Ġentire ly", + "Ġpurch ase", + "Ġe lement", + "Ġc ash", + "Ġdeter mine", + "D e", + "Ġc ars", + "ĠW all", + "â ĸ", + "Ġview s", + "Ġdrug s", + "Ġdep artment", + "ĠSt ep", + "u it", + "Ġ3 9", + "as ure", + "ĠCl ass", + "Ġc overed", + "ĠB ank", + "Ġme re", + "u ana", + "Ġmult i", + "Ġm ix", + "Ġun like", + "lev ision", + "Ġsto pped", + "Ġs em", + "ĠG al", + "ul es", + "Ġwe l", + "ĠJohn son", + "l a", + "Ġsk ill", + "Ġbec oming", + "ri e", + "Ġappropri ate", + "f e", + "ell ow", + "ĠPro t", + "ul ate", + "oc ation", + "Ġweek end", + "od ies", + "Ġsit es", + "Ġanim al", + "ĠT im", + "Ġsc ale", + "Ġcharg ed", + "Ġinst ruct", + "ill a", + "Ġmethod s", + "Ġc ert", + "Ġjud ge", + "ĠH el", + "Ġdoll ars", + "Ġstand ing", + "ĠS qu", + "Ġdeb t", + "l iam", + "Ġdri ving", + "ĠS um", + "ĠEd ition", + "Ġal bum", + "and on", + "I F", + "ĠU k", + "6 3", + "ad er", + "Ġcommer cial", + "es h", + "ĠGovern ment", + "Ġdisc overed", + "Ġout put", + "ĠHill ary", + "ĠCar ol", + "Ġ200 5", + "Ġab use", + "anc ing", + "Ġsw itch", + "Ġann ual", + "T w", + "Ġst ated", + "ag ement", + "in ner", + "Ġdem ocr", + "Ġres idents", + "Ġallow ing", + "Ġfact ors", + "od d", + "Ġf uck", + "em ies", + "Ġoccur red", + "ot i", + "Ġn orth", + "ĠP ublic", + "Ġinj ury", + "Ġins urance", + "C L", + "oll y", + "ã Ģ", + "Ġrepe ated", + "Ġar ms", + "ang ed", + "Ġconst ruction", + "Ġf le", + "P U", + "ic ians", + "Ġfor ms", + "ĠMc C", + "ant ic", + "Ġm ental", + "p ire", + "Ġequ ipment", + "Ġf ant", + "Ġdiscuss ion", + "Ġregard ing", + "k in", + "ar p", + "Ġch air", + "og ue", + "Ġpro ceed", + "ĠI d", + "O ur", + "Ġmur der", + "M an", + "Ġ4 9", + "as p", + "Ġsupp ly", + "Ġin put", + "Ġwe alth", + "liam ent", + "Ġpro ced", + "or ial", + "ĠSt at", + "ĠN FL", + "hen s", + "ĠInst itute", + "Ġput ting", + "ourn ament", + "et ic", + "Ġloc ated", + "Ġk id", + "er ia", + "r un", + "Ġpr inc", + "Ġ !", + "go ing", + "ĠB et", + "Ġcl ot", + "Ġtell ing", + "Ġprop osed", + "i ot", + "or ry", + "Ġfund s", + "g ment", + "ĠL ife", + "Ġb aby", + "ĠB ack", + "Ġsp oke", + "Im age", + "Ġear n", + "ĠA T", + "g u", + "Ġex change", + "ĠL in", + "ov ing", + "Ġp air", + "M ore", + "az on", + "Ġarrest ed", + "Ġkill ing", + "c an", + "ĠC ard", + "y d", + "Ġident ified", + "Ġm obile", + "Ġthan ks", + "ony m", + "ĠF orm", + "Ġhundred s", + "ĠCh ris", + "ĠC at", + "Ġtre nd", + "h at", + "ĠA v", + "om an", + "Ġelect ric", + "ĠW il", + "S E", + "O f", + "Ġrest aur", + "ot ed", + "Ġtr ig", + "Ġn ine", + "Ġb omb", + "Wh y", + " ¯", + "Ġco verage", + "Ġapp eal", + "ĠRober t", + "ĠS up", + "Ġfin ished", + "Ġfl ow", + "Ġdel iver", + "Ġcal cul", + "Ġphot os", + "Ġph il", + "Ġpie ces", + "Ġapp re", + "k es", + "Ġr ough", + "D o", + "Ġpart ner", + "Ġconcern ed", + "Ġ3 7", + "ĠG en", + "C ol", + "ct ors", + "Ġ= >", + "st ate", + "Ġsuggest ed", + "ĠFor ce", + "C E", + "Ġher self", + "ĠPl an", + "w orks", + "o oth", + "ren cy", + "Ġcor ner", + "Ġhus band", + "Ġintern et", + "ĠA ut", + "em s", + "os en", + "ĠAt l", + "g en", + "Ġbal ance", + "6 2", + "Ġsound s", + "te xt", + "Ġar r", + "ov es", + "Ġmill ions", + "Ġrad io", + "Ġsat isf", + "ĠD am", + "M r", + "G o", + "S pe", + "Ġcomb at", + "r ant", + "ĠG ree", + "Ġf uel", + "Ġdist ance", + "Ġtest s", + "Ġdec re", + "ĠE r", + "Ġman aged", + "D S", + "Ġt it", + "Ġmeas ures", + "ĠL iber", + "Ġatt end", + "as hed", + "ĠJ ose", + "ĠN ight", + "d it", + "ĠN ov", + "ĠE nd", + "out s", + "Ġgener ation", + "Ġadv oc", + "y th", + "Ġconvers ation", + "ĠS ky", + "act ive", + "ce l", + "ri er", + "ĠFr ank", + "Ġg ender", + "Ġcon cent", + "Ġcar ried", + "and a", + "ĠV irgin", + "Ġarri ved", + "ic ide", + "ad ed", + "Ġfail ure", + "Ġmin imum", + "le ts", + "Ġwor st", + "Ġkeep ing", + "Ġint ended", + "Ġilleg al", + "Ġsub sc", + "Ġdetermin ed", + "Ġtri p", + "Y es", + "Ġra ise", + "Ġ ~", + "Ġfeel s", + "Ġpack age", + "ĠJ o", + "h i", + "201 6", + "re al", + "Ġf ra", + "Ġsy mb", + "M e", + "uck y", + "p ret", + "ĠK h", + "ĠEd it", + "ĠWe b", + "em ic", + "ĠCol or", + "Ġjust ice", + "I nt", + "Ġfar m", + "ck now", + "\" >", + "el ess", + "Ġredu ced", + "Ġ5 00", + "x x", + "ĠR ad", + "ĠW ood", + "Ġcl in", + "Ġhy p", + "il er", + "ur a", + "k ins", + "8 5", + "6 1", + "ĠThe ir", + "ĠM ary", + "Ġs an", + "Ġno vel", + "ĠWh o", + "Ġcap acity", + "Ġimp ossible", + "Ġpl ays", + "Ġmin ister", + "ij uana", + "ic ate", + "ĠS et", + "Ġf ram", + "Ġ ing", + "Ġcommun ities", + "ĠF BI", + "it a", + "Ġb on", + "Ġstr ateg", + "Ġinterest s", + "l ock", + "g ers", + "m as", + "ĠAN D", + "Ġconflic t", + "Ġrequire ments", + "Ġs ac", + "Ġoper ating", + "in i", + "rel ated", + "Ġcomm itted", + "Ġrelative ly", + "Ġs outh", + "¯ ¯", + "Ġaff ord", + "Ġident ity", + "Ġdec isions", + "Ġacc used", + "pl ace", + "Ġvict ory", + "o ch", + "i at", + "N ame", + "C om", + "t ion", + "ed s", + "Ġsee k", + "Ġt ight", + "ĠIm ages", + "Ġinit i", + "Ġhum ans", + "Ġfam iliar", + "Ġaud ience", + "Ġintern al", + "vent ure", + "Ġs ides", + "ĠT O", + "Ġd im", + "Ġcon clud", + "Ġapp oint", + "Ġenforce ment", + "ĠJ im", + "ĠAssoci ation", + "Ġcircum st", + "ĠCanad ian", + "Ġjo ined", + "Ġdiffere nces", + "ĠL os", + "Ġprot est", + "Ġtw ice", + "w in", + "Ġgl ass", + "ars h", + "ĠAr my", + "Ġexp ression", + "Ġdec ide", + "Ġplan ning", + "an ia", + "Ġhand le", + "ĠMicro soft", + "ĠN or", + "Ġmax imum", + "ĠRe v", + "Ġse a", + "Ġev al", + "Ġhel ps", + "re f", + "Ġb ound", + "Ġm outh", + "Ġstand ards", + "Ġcl im", + "ĠC amp", + "ĠF ox", + "cl es", + "Ġar my", + "ĠTe chn", + "ack ing", + "x y", + "S S", + "Ġ4 2", + "Ġbu g", + "ĠUk rain", + "ĠM ax", + "ĠJ ones", + "ĠSh ow", + "l o", + "Ġplan et", + "Ġ7 5", + "Ġwin ning", + "Ġf aster", + "Ġspe ct", + "Ġbro ken", + "T R", + "Ġdef ined", + "Ġhealth y", + "Ġcompet ition", + "htt ps", + "ĠIs land", + "ĠF e", + "Ġannoun ce", + "ĠC up", + "ĠInst ead", + "Ġcl ient", + "Ġposs ibly", + "se ction", + "ock et", + "l ook", + "Ġfin ish", + "Ġcre w", + "Ġres erv", + "Ġed itor", + "Ġh ate", + "Ġs ale", + "Ġcontro vers", + "Ġp ages", + "w ing", + "Ġnum er", + "Ġopp osition", + "Ġ200 4", + "Ġref uge", + "Ġfl ight", + "Ġap art", + "ĠL at", + "A meric", + "ĠAfric a", + "Ġapplic ations", + "ĠPal est", + "ĠB ur", + "Ġg ar", + "ĠSoc ial", + "Ġup gr", + "Ġsh ape", + "Ġspe aking", + "ans ion", + "a o", + "ĠS n", + "Ġwor ry", + "ĠBrit ain", + "P lease", + "rou d", + "Ġh un", + "Ġintrodu ced", + "Ġd iet", + "I nd", + "ĠSec ond", + "Ġfun ctions", + "ut s", + "ĠE ach", + "ĠJe ff", + "Ġst ress", + "Ġaccount s", + "Ġgu arant", + "ĠAn n", + "ed ia", + "Ġhon est", + "Ġt ree", + "ĠAfric an", + "ĠB ush", + "} ,", + "Ġs ch", + "ĠOn ly", + "Ġf if", + "ig an", + "Ġexerc ise", + "ĠEx p", + "Ġscient ists", + "Ġlegisl ation", + "ĠW ork", + "ĠS pr", + "à Ĥ", + "ĠH uman", + "Ġ è", + "Ġsur vey", + "Ġr ich", + "ri p", + "Ġmain tain", + "Ġfl o", + "Ġleaders hip", + "st ream", + "ĠIslam ic", + "Ġ 01", + "ĠCol lege", + "Ġmag ic", + "ĠPr ime", + "Ġfig ures", + "201 7", + "ind er", + "x ual", + "ĠDe ad", + "Ġabsolute ly", + "Ġfour th", + "Ġpresent ed", + "resp ond", + "rib le", + "Ġal cohol", + "at o", + "ĠD E", + "por ary", + "Ġgr ab", + "Ġvar i", + "Ġqu ant", + "ĠPh oto", + "Ġpl us", + "r ick", + "ar ks", + "Ġaltern ative", + "Ġp il", + "Ġappro x", + "th at", + "Ġobject s", + "ĠR o", + "ĠAnd roid", + "Ġsignificant ly", + "ĠR oad", + "k ay", + "R ead", + "av or", + "Ġa cknow", + "ĠH D", + "ĠS ing", + "O r", + "ĠM ont", + "Ġun s", + "pro f", + "Ġneg oti", + "ĠAr ch", + "ik i", + "Ġte levision", + "ĠJew ish", + "Ġcomm ittee", + "Ġmot or", + "Ġappear ance", + "Ġs itting", + "Ġstri ke", + "ĠD own", + "com p", + "ĠH ist", + "Ġf old", + "ac ement", + "ĠLou is", + "Ġbel ong", + "ĠâĢ ¢", + "Ġm ort", + "Ġprep ared", + "Ġ6 4", + "ĠM aster", + "Ġind eed", + "ĠD en", + "Ġre nt", + "T A", + "our ney", + "ar c", + "S u", + "9 7", + "Ġadv ice", + "Ġchang ing", + "Ġlist ed", + "Ġlaun ched", + "is ation", + "ĠP eter", + "is hes", + "Ġl ived", + "ĠM el", + "ĠSup reme", + "ĠF ederal", + "Ġ) ;", + "ruct ure", + "Ġset s", + "Ġphil os", + "u ous", + "Ġ ł", + "Ġappl ied", + "ĠN OT", + "Ġhous ing", + "ĠM ount", + "Ġo dd", + "Ġsu st", + "D A", + "ffic ient", + "Ġ ?", + "ol ved", + "Ġp owers", + "Ġth r", + "Ġrem aining", + "ĠW ater", + "L C", + "Ġca uses", + "ãģ ®", + "Ġman ner", + "ad s", + "Ġsuggest s", + "Ġend s", + "stand ing", + "f ig", + "ĠD un", + "id th", + "Ġg ay", + "Ġter min", + "ĠAngel es", + "M S", + "Ġscient ific", + "Ġco al", + "ap ers", + "b ar", + "ĠThom as", + "Ġsy m", + "ĠR un", + "th is", + "P C", + "igr ants", + "Ġmin ute", + "ĠDist rict", + "cell ent", + "Ġle aves", + "Ġcomple ted", + "am in", + "Ġfoc used", + "Ġmon itor", + "Ġveh icles", + "M A", + "ĠM ass", + "ĠGr and", + "Ġaffect ed", + "itution al", + "Ġconst ruct", + "Ġfollow s", + "Ġt on", + "re ens", + "Ġh omes", + "ĠE xt", + "ĠLe vel", + "r ast", + "ĠI r", + "Ġel im", + "Ġlarge ly", + "ĠJ oe", + "Ġvot es", + "all s", + "Ġbusiness es", + "ĠFound ation", + "ĠCent ral", + "Ġy ards", + "Ġmaterial s", + "ul ner", + "Ġgu ide", + "Ġclos er", + "um s", + "Ġsp orts", + "ed er", + "J ust", + "Ġtax es", + "8 4", + "ĠO ld", + "Ġdec ade", + "ol a", + "Ġv ir", + "Ġdro pped", + "Ġdel ay", + "it ect", + "Ġsec ure", + "ste in", + "le vel", + "Ġtre ated", + "Ġfil ed", + "ain e", + "Ġv an", + "Ġm ir", + "Ġcol umn", + "ict ed", + "e per", + "Ġro t", + "Ġcons ult", + "Ġent ry", + "Ġmar ijuana", + "ĠD ou", + "Ġapparent ly", + "ok ing", + "clus ive", + "Ġincre ases", + "an o", + "Ġspecific ally", + "Ġte le", + "ens ions", + "Ġrelig ion", + "ab ilities", + "Ġfr ame", + "ĠN ote", + "ĠLe e", + "Ġhelp ing", + "Ġed ge", + "ost on", + "Ġorgan izations", + "à ĥ", + "ĠB oth", + "hip s", + "Ġbig ger", + "Ġbo ost", + "ĠSt and", + "Ġro w", + "ul s", + "ab ase", + "Ġr id", + "L et", + "are n", + "ra ve", + "Ġst ret", + "P D", + "Ġv ision", + "Ġwe aring", + "Ġappre ci", + "Ġa ward", + "ĠU se", + "Ġfact or", + "w ar", + "ul ations", + ") (", + "Ġg od", + "Ġter rit", + "Ġpar am", + "ast s", + "8 7", + "Ġen emies", + "ĠG ames", + "F F", + "Ġacc ident", + "W ell", + "ĠMart in", + "T ER", + "Ġat h", + "ĠHe ll", + "Ġfor g", + "Ġve ter", + "ĠMed ic", + "f ree", + "Ġst ars", + "Ġexp ensive", + "Ġac ad", + "ra wn", + "ĠW he", + "Ġl ock", + "Ġform at", + "Ġsold iers", + "s m", + "Ġag ent", + "Ġrespons ibility", + "or a", + "ĠS cience", + "Ġrap id", + "Ġt ough", + "ĠJes us", + "Ġbelie ves", + "M L", + "Ġwe ar", + "le te", + "Ãĥ ÃĤ", + "ĠD ri", + "Ġcomm ission", + "ĠB ob", + "O h", + "ap ed", + "Ġwar m", + "ÃĥÃĤ ÃĥÃĤ", + "Ġ200 3", + "ort ion", + "Ġhas n", + "ust er", + "Ġun ivers", + "ĠI ll", + "Ġk ing", + "olog ies", + "9 4", + "ĠT em", + "ĠM os", + "Ġpat ient", + "ĠMex ico", + "ce an", + "ĠDe ath", + "ĠSand ers", + "y ou", + "ĠC ast", + "ĠComp any", + "pt y", + "Ġhappen ing", + "F P", + "ĠB attle", + "Ġb ought", + "A m", + "M od", + "U s", + "ut ers", + "ĠC re", + "ĠTh ose", + "Ġ4 4", + "is er", + "Ġs oul", + "ĠT op", + "ĠHar ry", + "ĠA w", + "Ġse at", + "ff ee", + "Ġrev olution", + "Ġ( \"", + "ĠD uring", + "et te", + "Ġr ing", + "Ġoff ensive", + "Ġreturn s", + "Ġv ideos", + "Ġdis cl", + "Ġfam ous", + "en ced", + "ĠS ign", + "ĠR iver", + "Ġ3 00", + "P M", + "ĠB us", + "ĠC H", + "Ġcandid ates", + "ard en", + "Ġpercent age", + "Ġvis ual", + "Ġthan k", + "Ġtrou ble", + "ner gy", + "Ġ200 1", + "Ġpro ve", + "ash ion", + "Ġen h", + "ĠL ong", + "U M", + "Ġconnect ed", + "Ġposs ibility", + "O ver", + "Ġexper t", + "Ġl ibrary", + "art s", + "ĠDirect or", + "Ġfell ow", + "9 2", + "ir ty", + "Ġd ry", + "Ġsign s", + "ĠL ove", + "Ġqu iet", + "f oot", + "Ġp ure", + "ĠH un", + "Ġf illed", + "ph as", + "ĠE lect", + "end ment", + "ĠEx pl", + "Ġun able", + "n s", + "m o", + "Ġv ast", + "ob e", + "Ġident ify", + "app ing", + "ĠCarol ina", + "g ress", + "Ġpro te", + "Ġf ish", + "Ġcircumst ances", + "raz y", + "ĠPh ot", + "Ġb odies", + "ĠM ur", + "Ġdevelop ing", + "ĠA R", + "Ġexperien ced", + "Ġsubst ant", + "ĠBo ard", + "es ome", + "Ġdom estic", + "Ġcomb ined", + "ĠP ut", + "Ġchem ical", + "ĠCh ild", + "Ġpo ol", + "ĠC y", + "Ġe gg", + "c ons", + "st ers", + "Ġh urt", + "Ġmark ets", + "Ġconserv ative", + "Ġsupp orters", + "Ġag encies", + "id el", + "O b", + "ur b", + "Ġ4 3", + "ĠDef ense", + "y e", + "ĠA p", + "du le", + "Ġtemper ature", + "Ġconduct ed", + "ĠCh ief", + "Ġpull ed", + "Ġf ol", + "L ast", + "ont o", + "os is", + "V ER", + "D es", + "ĠP an", + "F irst", + "Ġadv ance", + "Ġlic ense", + "r ors", + "ĠJ on", + "Ġimag ine", + "Ġhe ll", + "Ġf ixed", + "Ġinc or", + "os ite", + "ĠL og", + "ick en", + "] :", + "Ġsurpr ise", + "h ab", + "Ġc raft", + "ol t", + "ĠJ ul", + "Ġd ial", + "Ġrele vant", + "Ġent ered", + "Ġlead s", + "ĠA D", + "ĠCle an", + "Ġpict ures", + "ess or", + "Ġal t", + "Ġpay ing", + "P er", + "ĠMark et", + "Ġupd ates", + "am ily", + "ĠT ype", + "ĠH ome", + "Ġ5 5", + "semb ly", + "rom e", + "8 3", + "Ġgreat est", + "Ġhe ight", + "Ġhe av", + "ain ts", + "Ġlist en", + "as er", + "ĠS H", + "Ġcap able", + "ac le", + "Ġpers pect", + "in ating", + "Ġoff ering", + "ry pt", + "ĠDe velop", + "ab in", + "r c", + "Ġbr ight", + "al ty", + "ar row", + "Ġsupp l", + "ind ing", + "ack ed", + "gy pt", + "ĠAn other", + "p g", + "ĠVirgin ia", + "ĠL u", + "Ġpl anned", + "Ġp it", + "Ġswe et", + "T ype", + "ĠD i", + "Ġtyp ically", + "ĠFranc isco", + "Ġpro spect", + "ĠD an", + "Ġte en", + "re es", + "Ġsc hed", + "Ġh ol", + "Ġsc r", + "Ġlot s", + "l ife", + "Ġnews p", + "Ġfor get", + "ĠN one", + "ĠM iddle", + "ĠR yan", + "ed d", + "Ġse vere", + "Ġsu it", + "ll er", + "9 3", + "Ġcor respond", + "Ġexpl os", + "u ations", + "Ġfl ag", + "g ame", + "r id", + "Ġpr in", + "ĠD ata", + "Ġde ploy", + "ĠEn ter", + "su it", + "gh an", + "ĠM en", + "Ġthough ts", + "Ġmat ters", + "Ġad apt", + "ĠA ri", + "Ġf ill", + "Ġfor th", + "Ġs am", + "Ġ4 1", + "Ġpay ment", + "ĠH or", + "Ġsp ring", + "du c", + "Ġl osing", + "Ġbring ing", + "F O", + "al a", + "Ġdist ribution", + "he red", + "b our", + "ĠIsrael i", + "om a", + "Ġcomb ination", + "Ġpl enty", + "V E", + "C an", + "ĠH aw", + "Ġper man", + "ĠSpe cial", + "Ġto w", + "Ġsee king", + "Ġexam ples", + "Ġclass es", + "c r", + "Ġbe er", + "Ġmov es", + "ĠI P", + "ĠK n", + "Ġpan el", + "E ven", + "Ġproper ly", + "Ġr is", + "Ġpl ug", + "Ġestim ated", + "E very", + "Ġdef ensive", + "ag raph", + "Ġpre gn", + "Ġinst it", + "ĠV ict", + "Ġvol ume", + "Ġpos itions", + "Ġl inks", + "ĠPro gram", + "ĠWe ek", + "ag ues", + "Ġtrans form", + "k er", + "ĠC EO", + "Ġc as", + "Ġopp onent", + "Ġtwe et", + "ĠC ode", + "Ġsh op", + "Ġf ly", + "Ġtal ks", + "Ġb ag", + "Ph one", + "Ġa id", + "Ġpl ants", + "Ġ6 5", + "Ġatt orney", + "ar ters", + "qu est", + "ĠMag ic", + "Ġbeg ins", + "Ġmy ster", + "Ġenvironment al", + "Ġst orage", + "N N", + "Ġm arg", + "Ġs ke", + "Ġmet al", + "ell y", + "Ġord ered", + "Ġrem ained", + "Ġl oved", + "Ġprom pt", + "Ġupd ated", + "Ġexper ts", + "Ġwalk ing", + "Ġan cient", + "Ġperform ed", + "AT E", + "Ġne ither", + "i ency", + "Ġmanufact ure", + "ĠP ak", + "Ġselect ed", + "Ġm ine", + "Ġult imately", + "Ġexpl an", + "Ġlab el", + "ĠServ ices", + "ribut ed", + "Tr ump", + "Ġsy n", + "ĠU lt", + "S C", + "Ġme at", + "Ġg iant", + "ĠW ars", + "ĠO N", + "Ġad m", + "Ġinter pret", + "Ġeven ing", + "Ġev il", + "ĠB oston", + "ĠW ild", + "Ġ Ã", + "ĠBit coin", + "ĠAm azon", + "D r", + "ĠIn formation", + "Ġobvious ly", + "Ġadv anced", + "Ph oto", + "ol ar", + "Ġwe ather", + "Ġsymb ol", + "Ġso le", + "Ġpot entially", + "ost er", + "Ġorig inally", + "m un", + "3 00", + "az e", + "ess ions", + "Ġde ck", + "Ġst ood", + "Ġyou th", + "ĠB ern", + "R ep", + "ĠT est", + "Ġbas ically", + "ot ic", + "Ġinvol ve", + "ol it", + "ly n", + "S ee", + "Ġair craft", + "Ġconf irm", + "E W", + "Ġmess ages", + "ĠRich ard", + "Ġk it", + "Ġpro hib", + "Ġv ulner", + "is ters", + "Ġexist ence", + "Ġturn ing", + "ĠS P", + "Ġdes ire", + "Ġfl at", + "Ġm ent", + "se ason", + "ang es", + "Ġneighbor hood", + "ĠL ake", + "AT ION", + "Ġpoint ed", + "b ur", + "Ġinn ov", + "uc ks", + "U L", + "Ġprofess or", + "Ġexp ressed", + "A B", + "ic ious", + "Ġ200 2", + "ĠDe v", + "Ġs ession", + "Ġb are", + "s en", + "Ġdis s", + "ĠC ath", + "ĠP ass", + "ĠP oint", + "Ġdo ctor", + "or row", + "ail ed", + "ĠR ub", + "ĠD C", + "ĠChar l", + "p erson", + "Ġwrit er", + "igh ters", + "ure au", + "Ġob lig", + "Ġrecord ed", + "Ġbro ke", + "Ġord ers", + "il ty", + "Ġmot ion", + "in ity", + "l aw", + "ad ium", + "Ġimm igration", + "Ġcontr ast", + "Ġb att", + "Ġex cellent", + "Ġtechn ical", + "am i", + "Ġt un", + "Ġcl oud", + "ĠY ear", + "ge on", + "Ġcre ation", + "Ġstr ange", + "Ġa uth", + "Ġfor t", + "b orn", + "Ġext ent", + "ĠT oday", + "ĠCl ub", + "Ġr ain", + "Ġs ample", + "Ġaccept ed", + "Ġt act", + "Ġf ired", + "ĠS on", + "Ġstand s", + "Ġb oot", + "Ġ4 7", + "Ġstat ements", + "Ġvers ions", + "Ġse lling", + "ound ed", + "Ġ199 0", + "Ġwere n", + "ĠW atch", + "Ġexper iment", + "P ost", + "Ġret ail", + "ul ed", + "In st", + "un te", + "ãĥ ¼", + "Ġdep art", + "Ġb ond", + "i very", + "om pl", + "Ġre action", + "ĠSyri an", + "ĠP ac", + "app ed", + "ani el", + "D P", + "Ġres olution", + "Ġre act", + "Ġappro ved", + "on om", + "m ond", + "ĠO ffic", + "-- -", + "Ġrepl ace", + "Ġt ack", + "Ġsp ort", + "Ġch ain", + "Ġemer gency", + "r ad", + "ĠPalest in", + "Ġ4 6", + "Ġautom atically", + "Ġrout e", + "Ġp al", + "Ġb anks", + "ĠPar is", + "ĠMed ia", + "ro ad", + "ic ing", + "i xt", + "ist ed", + "Ġg rew", + "Ġco ord", + "ĠW here", + "om in", + "Ġsub s", + "� �", + "Ġ ±", + "Ġcorpor ate", + "Ġse lection", + "n oon", + "ĠRep ort", + "c s", + "clud ing", + "ord ers", + "anc he", + "ĠIt s", + "Ġslow ly", + "ĠE gypt", + "ĠA cc", + "Ġcol le", + "iqu es", + "E X", + "Ġattempt s", + "ur l", + "ĠC ross", + "Ġfind ings", + "ĠS C", + "ĠO R", + "Ġind ex", + "ens ity", + "ĠW ay", + "ĠL and", + "Ġsh ock", + "d is", + "Ġd ynam", + "Ġc art", + "m osp", + "S ince", + "i est", + "ĠB oy", + "Ġst orm", + "ĠCont in", + "201 3", + "he w", + "il it", + "Ġess ential", + "iqu id", + "O ther", + "ive red", + "Ġreason able", + "A ct", + "Ġsub sequ", + "ĠP ack", + "ĠF ort", + "Ġconsider ing", + "Ġun iversity", + "l og", + "Ġmar ried", + "Ġill ust", + "ĠTr ue", + "£ ı", + "Ġnumer ous", + "rast ructure", + "Ġserious ly", + "Ġrefer red", + "u a", + "Ġconsist ent", + "on na", + "ĠRe al", + "ru ption", + "ci ples", + "Ġfact s", + "9 1", + "ot es", + "er g", + "The n", + "Ġacc ompl", + "N ote", + "Ġre venue", + "Ġpass ing", + "Ġm al", + "e en", + "ĠY et", + "Ġg ather", + "ter day", + "ew ork", + "ĠA uthor", + "P e", + "Ġopt im", + "Ġr ub", + "Ġè £ı", + "Ġun known", + "st one", + "Ġun ion", + "ol ve", + "Ġopportun ities", + "Ġbrow ser", + "ĠW al", + "ĠC ost", + "Ġreport ing", + "st s", + "p et", + "Ġs and", + "Ġsudden ly", + "Ġsurpr ising", + "ĠV R", + "Ġsomew hat", + "ĠB as", + "ult ure", + "iz z", + "ĠC D", + "Ġchalleng es", + "Ġsett ings", + "Ġexperien ces", + "ĠF ull", + "Ġcan n", + "Ġrece iving", + "ES T", + "Ġj oint", + "Ġcult ural", + "Ġa st", + "8 2", + "as tern", + "ce ived", + "ĠC ru", + "Ġb ull", + "p ired", + "am m", + "Ġfac ing", + "p ower", + "Ġb oss", + "ĠH ol", + "Ġinst r", + "Ġincreasing ly", + "Ġsh ift", + "Ġstre ets", + "ĠWilliam s", + "ab b", + "Ġl ie", + "Ġl augh", + "ĠC a", + "P L", + "Ġadult s", + "Ġcustom er", + "Ġob tained", + "Ġsupport ing", + "ht ml", + "f ire", + "Ġdetail ed", + "Ġpick ed", + "ĠR ight", + "ld er", + "E E", + "st ood", + "ĠK im", + "Ġw ire", + "Ġs ight", + "Ġdevelop ers", + "Ġpers ons", + "Ġs ad", + "Ġc up", + "Ġwar ning", + "Ġboy s", + "l ong", + "Ġb ird", + "f o", + "Ġw al", + "Ġobserv ed", + "Ġz one", + "iven ess", + "Ġch annel", + "c ript", + "Ġref used", + "ĠAg ain", + "Ġsu c", + "Ġspokes man", + "ĠRe f", + "r ite", + "ou ston", + "ãĥ ³", + "ĠS her", + "Ġact s", + "ĠN ame", + "Ġstrugg le", + "ar ry", + "omet imes", + "Ġdisc rim", + "H T", + "Ġcateg ory", + "Ġreal ize", + "Ġemploy ee", + "ĠAf ghan", + "en ger", + "Ġgun s", + "ĠSte ve", + "ĠM ot", + "ĠO l", + "ok ed", + "Ġth ick", + "Ġfair ly", + "ill y", + "Ġsur ve", + "ĠM at", + "we ight", + "â Ķ", + "Ġtro ops", + "Ġag ents", + "Ġbatter y", + "Ġmot iv", + "à ¡", + "S ec", + "d en", + "o very", + "L S", + "Ġfl u", + "Ġconf ident", + "ĠO per", + "Ġem pty", + "Ġp hen", + "Ġse ctor", + "Ġexc ited", + "Ġrem ote", + "ap h", + "o en", + "Ġdestroy ed", + "Ġmor al", + "ĠH P", + "ĠR on", + "Ġd ress", + "ĠB at", + "Ġl it", + "ĠM S", + "Ġa f", + "H L", + "r um", + "is ms", + "Ġshould n", + "Ġsym pt", + "ĠTor onto", + "het ic", + "Ġcar bon", + "Ġinstall ed", + "Ġviol ent", + "Ġsol ar", + "j a", + "Ġpract ices", + "Ġr ide", + "ĠP enn", + "Ġimpro ved", + "Ġaud io", + "Ġbehav i", + "ĠP S", + "Ġe ating", + "D ata", + "ĠRe view", + "p ass", + "cl aim", + "u ated", + "ang ers", + "c hen", + "Ġproper ties", + "Ġany where", + "An other", + "Ġbl ow", + "ĠJack son", + "Ġp roud", + "Ġplan e", + "l ines", + "Ġsqu are", + "Ġpro of", + "ans as", + "Ġtalk ed", + "m akers", + "Ġs ister", + "Ġhold s", + "Ġres ident", + "Ġ= =", + "Ġresist ance", + "Ġspl it", + "Ġpro secut", + "Ġconf idence", + "res ents", + "Ġcut s", + "Ġexcept ion", + "Ġz ero", + "Get ty", + "Ġcop yright", + "Ġtot ally", + "orm al", + "ific ations", + "ĠAustral ian", + "Ġs ick", + "Ġ1 50", + "Ġhouse hold", + "Ġfe es", + "Ġdri vers", + "og en", + "ĠN Y", + "Ġnecess arily", + "Ġregul ations", + "ear ing", + "s l", + "Ġperspect ive", + "c are", + "ic ial", + "H is", + "Ġesc ape", + "Ġsurpr ised", + "ĠV an", + "ur rent", + "Ġv ac", + "8 1", + "ĠTh us", + "Ġem phas", + "ĠCh ampions", + "ĠI ce", + "Ġn arr", + "Ġhead s", + "Ġca using", + "b el", + "f ortunately", + "ĠM a", + "Ġtarg ets", + "ci pl", + "Ġafter noon", + "Ġadd s", + "ĠMay be", + "ĠF our", + "ess ed", + "ple te", + "Ġus ual", + "ch o", + "ing u", + "Ġwith d", + "ĠE nergy", + "ĠE conom", + "O O", + "Ġart icles", + "Ġinj ured", + "Ġman age", + "Ġexpl ains", + "Ġdi agn", + "R ec", + "at ures", + "Ġlink ed", + "Ġdiscuss ed", + "Ġexpl o", + "Ġocc asion", + "ath an", + "Ġopp osite", + "Ġfac es", + "Ġden ied", + "ĠK night", + "Ġn ut", + "Ġapprox imately", + "Ġdisapp oint", + "onym ous", + "ĠB est", + "ĠL o", + "ĠH y", + "ĠA ff", + "Ġvot ing", + "an while", + "ĠII I", + "Ġinstit utions", + "ag ram", + "ĠD aily", + "Ġdr ag", + "Ġnear by", + "Ġgu ilty", + "Ġcon ver", + "P re", + "s hip", + "Ġre ward", + "Ġphilos oph", + "ĠS S", + "u gh", + "Ġapp s", + "f riend", + "Ġu pper", + "Ġad vert", + "Ġs now", + "Ġfr ust", + "Ġour selves", + "F r", + "ĠD ie", + "amp ion", + "Ġdis miss", + "Ġc ere", + "Ġsign al", + "f rom", + "Ġ ).", + "Ġ5 2", + "Ġcr imes", + "it ors", + "est ival", + "use um", + "Ġcoun cil", + "ĠS aud", + "M ay", + "ĠG un", + "ic ian", + "et her", + "Ġsu fficient", + "ĠH en", + "so le", + "Ġhistor ical", + "ĠF ar", + "ĠT urn", + "Ġp in", + "Ġsuc ceed", + "m at", + "ly mp", + "Ġtrad ition", + "ĠO k", + "Ġc ro", + "Ġdesc ription", + "al le", + "Ġsk y", + "T e", + "Ġwide ly", + "Ġw ave", + "Ġdefin ition", + "ĠJew s", + "Ġcy cle", + "Ġref ere", + "Ġbr ings", + "us al", + "Ġal ive", + "Ġfrequ ently", + "Ġint ention", + "ĠCont rol", + "l v", + "y stem", + "Ġpriv acy", + "g ent", + "ren ce", + "ĠQu est", + "ĠChrist mas", + "Ġr ail", + "Ġco oper", + "Ġtest ed", + "ĠC apt", + "as ks", + "Ġcomfort able", + "Ġdel ivered", + "sc ape", + "Ġdep th", + "ĠG OP", + "Ġwrit es", + "Ġass ets", + "Ġsa v", + "im ents", + "Ġtrans ition", + "Ġart ist", + "ĠL ook", + "Ġl ob", + "Ġcomp onents", + "ar ity", + "Ġwalk ed", + "Ġro ot", + "Ġparticip ants", + "Ġnot iced", + "Ġres c", + "Ġn av", + "ĠAd minist", + "d a", + "ut ral", + "pl ate", + "Ġimport ance", + "Ġass ert", + "ious ly", + "c ription", + "Ġinj uries", + "ĠChe ck", + "Ġregist ered", + "Ġint ent", + "Ġmiss ed", + "ograph ic", + "Ġsent ence", + "oun ter", + "Ġassist ance", + "ev in", + "Ġdat abase", + "Ġbuild ings", + "Ġclass ic", + "Ġth inks", + "ĠOh io", + "P r", + "ug g", + "Ġfe e", + "p an", + "Ġeffect ively", + "Ġfac ility", + "Ġbe ar", + "Ġch apter", + "Ġdog s", + "ĠCol umb", + "Ġl atter", + "it ial", + "Ġad mitted", + "T V", + "ĠGe org", + "Ġpost s", + "\\ \\", + "Ġlawy er", + "Ġequ ival", + "Ġm and", + "Ġcontro lled", + "ĠW alk", + "ĠAnd rew", + "Ġmen u", + "am ental", + "Ġprotect ed", + "v a", + "Ġadminist r", + "or al", + "Ġre in", + "ĠS ar", + "Ġamount s", + "Ġn ative", + "ĠM oon", + "Ġrep resents", + "Ġab andon", + "Ġcarry ing", + "Ġt ank", + "m ary", + "Ġdecl ared", + "T ube", + "Ġh at", + "Ġpun ish", + "el lect", + "m es", + "Ġun iverse", + "ĠR od", + "ph y", + "Ġinf rastructure", + "Ġ5 1", + "Ġopp osed", + "ow nt", + "c a", + "ĠM ake", + "Ġhard ware", + "Ġco ffee", + "R el", + "b al", + "w orld", + "ĠS af", + "ĠSe a", + "in als", + "Ġown ed", + "Ġh all", + "ers ion", + "Ġdescrib e", + "ĠP ot", + "Ġport ion", + "Ġat mosp", + "Ġgovern ments", + "Ġdep ending", + "Ġoff ense", + "Ġtr ick", + "aw a", + "ĠL ine", + "ĠV is", + "ĠH ard", + "ĠOr ig", + "ĠCl ick", + "Ġdes k", + "ĠVal ley", + "ĠS ov", + "Ġmov ies", + "Ġrem ark", + "Ġm ail", + "Ġcons cious", + "Ġrul ing", + "ĠR ights", + "Ġmed ic", + "he nt", + "ĠW omen", + "> <", + "Ġrepl aced", + "ĠP rem", + "ĠTh anks", + "Ġre new", + "ĠB all", + "if orm", + "Ġsh ots", + "C omm", + "Ġar med", + "Ġconst ant", + "Ġt aste", + "Ġreal ized", + "Ġbu ff", + "Ġm o", + "Ġeffic ient", + "M ost", + "or ation", + "if ies", + "Ġcommun ication", + "Ġfl ood", + "Ġconsequ ences", + "Ġany way", + "ig g", + "ĠG M", + "ĠTh ank", + "Ġ iron", + "Ġev olution", + "ĠC op", + "tw itter", + "Ġ9 5", + "Ġrelationship s", + "ad el", + "ĠYou ng", + "Ġpropos al", + "ay ers", + "uild ing", + "ĠH ot", + "OR E", + "c os", + "Ġcoll abor", + "P G", + "ax y", + "Ġknow ing", + "Ġsupport s", + "ow ed", + "Ġcontrol s", + "Ġmere ly", + "um er", + "Ġath let", + "Ġf ashion", + "p ath", + "Ġg ift", + "Ġer a", + "AN D", + "Ġkind s", + "ĠKore an", + "Ġleg it", + "ul ous", + "Ġess entially", + "Ġthe rap", + "n ic", + "Ġsuff ered", + "Ġh ur", + "Ġprom ise", + "Ġex cess", + "Ġover w", + "Ġpr ime", + "ĠH ouston", + "er ry", + "ĠM s", + "R S", + "201 2", + "Ġst ores", + "ĠO lymp", + "Ġj ourney", + "Al though", + "S ub", + "ĠE duc", + "ĠCh apter", + "Ġrequest s", + "Ġconsum ers", + "Ġt iny", + "Ġis ol", + "ĠF air", + "b a", + "ĠY OU", + "Ġcr ash", + "ce ler", + "Ġemot ional", + "Ġgood s", + "Ġelect ed", + "Ġmod er", + "ĠLin ux", + "Ġbl ocks", + "Ġis land", + "ĠSoc iety", + "Ġelect ions", + "Ġbroad cast", + "Ġche ap", + "Ġn ations", + "Ġse asons", + "4 00", + "Ġwas te", + "ĠS at", + "Ġfield s", + "em ploy", + "Ġprof ile", + "Ġauth ors", + "AL L", + "ĠG ra", + "w est", + "ĠT y", + "Ġdeath s", + "Ġv acc", + "Ġfor med", + "Ġd u", + "Ġon going", + "ĠMuslim s", + "el f", + "ig ure", + "Ġass ume", + "ĠUkrain e", + "w ater", + "Ġco ast", + "Ġvot ed", + "g or", + "ĠA S", + "ĠMich igan", + "az a", + "ĠAr m", + "i ro", + "Ġf lex", + "as ters", + "' '", + "Ġwel come", + "ar l", + "Ġloc ations", + "ig ation", + "ĠF il", + "Ġbu ying", + "Ġarch itect", + "Ġhard er", + "ĠC ub", + "Ġinter face", + "Ġrestaur ant", + "Ġdisco ver", + "Ġex ceed", + "Ġfav our", + "ger y", + "Ġd uty", + "Ġp itch", + "ad or", + "ĠM ach", + "b oy", + "Ġrespond ed", + "Ġext ended", + "her s", + "M any", + "ra id", + "if er", + "ĠIn s", + "S er", + "Ġmed ium", + "s he", + "ĠS ports", + "Ġmag azine", + "ut ation", + "Ġlim its", + "ĠG all", + "Ġex ternal", + "raz il", + "Ġyoung er", + "t le", + "Ġrem ind", + "ĠC ON", + "Ġimmedi ate", + "Ġh idden", + "Ġvol unte", + "Ġsim pl", + "od cast", + "Ġph ase", + "d r", + "Ġpl ot", + "Ġexp osure", + "R I", + "og rap", + "v in", + "an ish", + "ĠAc ad", + "ĠEng ine", + "Ġexp ansion", + "ĠP ay", + "Y our", + "Ġpus hed", + "ĠE ll", + "ĠHe ad", + "Ġmarket ing", + "ĠA C", + "k et", + "Ġh its", + "Ġg ro", + "ĠA ge", + "ĠSc ot", + "] [", + "Ġst im", + "Ġi Phone", + "Ī Ĵ", + "Ġn arrow", + "ĠGet ty", + "ĠTur key", + "Ġperfect ly", + "Ġen able", + "ut ch", + "Ġprec ise", + "Ġreg ime", + "Ġsh if", + "Ġcomp ens", + "g un", + "d iv", + "Ġch osen", + "ĠK en", + "An y", + "Ġtre es", + "Ġrecomm ended", + "ĠR en", + "u able", + "ĠH T", + "F ollow", + "E G", + "ĠH and", + "ĠK enn", + "Ġarg uments", + "Ġex ists", + "Ġb ike", + "ĠCons erv", + "Ġbre aking", + "ĠG ar", + "Ġc razy", + "Ġvirt ual", + "ay lor", + "ix el", + "Ġ19 80", + "Ġper mission", + "ĠSer ies", + "Ġconsum er", + "Ġclose ly", + "c alled", + "Ġ5 4", + "Ġhop es", + "Ġar ray", + "ĠW in", + "ĠLab our", + "Ġsp ons", + "ĠI re", + "Ġp ow", + "Ġread ers", + "Ġemploy ment", + "Ġcreat ure", + "Ġresult ing", + "Ġaccur ate", + "Ġmom ents", + "Ġarg ued", + "Ġp ed", + "D uring", + "Ġ5 3", + "ĠT al", + "Ġs ought", + "Ġsuff ering", + "Ġ icon", + "le e", + "Ġ( $", + "al ian", + " °", + "Ġp ra", + "Ġbon us", + "( \"", + "k o", + "Ġact ing", + "D E", + "f all", + "Ġcompar ison", + "Ġsm ooth", + "ĠN AS", + "u pp", + "ĠJose ph", + "ep ing", + "ĠT ake", + "ĠM id", + "Ġs ending", + "f ast", + "ĠF all", + "Ġdeal ing", + "us er", + "ĠOr gan", + "C o", + "Ġatt ached", + "Ġse es", + "% .", + "Ġtyp ical", + "AR T", + "Ġfind s", + "ĠAs ia", + "um in", + "ĠC ore", + "ĠE nt", + "in ent", + "u ce", + "ĠBl ood", + "ĠN ever", + "Ġem ails", + "Ġhigh light", + "Ġconf ront", + "at us", + "ut ed", + "Ġun us", + "Ġtop ic", + "ĠAd am", + "Ġb le", + "at i", + "Ġunder stood", + "S et", + "st ruct", + "T P", + "Ġm ob", + "a a", + "ĠSt art", + "pect ed", + "se ll", + "Ġded icated", + "ĠC A", + "u an", + "Ġsong s", + "esc ription", + "Ġte ch", + "Ġr ape", + "Ġas ide", + "Ġgr ant", + "Ġ5 6", + "s ub", + "Ġarg ue", + "Ġcont aining", + "Ġsche dule", + "Ġliber al", + "Ġpublic ly", + "Ġheav ily", + "ĠU t", + "in er", + "ĠS ection", + "ĠC are", + "we et", + "l s", + "D is", + "âĶ Ģ", + "ĠF ollow", + "B ack", + "ĠI T", + "Ġb es", + "j i", + "ĠH it", + "est ed", + "Ġevery body", + "ĠSw ed", + "Ġfem in", + "Ġfac ilities", + "Ġcon ven", + "C omp", + "ĠO S", + "c ore", + "Ġan x", + "Ġdiv ision", + "ĠC am", + "ĠSt an", + "m ates", + "Ġexpl ore", + "pl om", + "Ġsh ares", + "pl oad", + "an es", + "Ġide al", + "et ers", + "ĠB ase", + "Ġpl astic", + "Ġdist inct", + "ĠNet work", + "ĠSe attle", + "Ġtrad ing", + "ens us", + "int end", + "Ġex hib", + "Ġinit ially", + "ĠF ood", + "Ġthous and", + "ĠBus iness", + "act er", + "Ġpar agraph", + "Ġrough ly", + "Ġw ww", + "Ġcreat ive", + "ĠCon f", + "Ġconsum ption", + "Ġfil ms", + "ag an", + "Ġob tain", + "Ġt all", + "Ġt or", + "Ġacknow led", + "Ġg rown", + "al o", + "K E", + "Ġ4 00", + "end ers", + "t aining", + "U G", + "Ġsu icide", + "Ġwat ched", + "ĠL ist", + "al i", + "re hens", + "Ġsurround ing", + "Ġp ip", + "Ġf lying", + "ĠJ ava", + "ord an", + "Ġserv ing", + "in ations", + "p ost", + "Ġsh o", + "A v", + "Ġj ail", + "z y", + "Ġ199 9", + "Ġ< /", + "Ġliter ally", + "ĠS ir", + "Ġexp osed", + "Ġl ies", + "st ar", + "Ġb at", + "Ġear ned", + "ĠD ig", + "Ġspec ified", + "ĠSe ason", + "Ġdeg rees", + "Don ald", + "Ġcent re", + "Ġsh aring", + "Ġwin ter", + "ĠC O", + "C he", + "Ġ Î", + "M P", + "Ġun w", + "Ġfew er", + "ĠM ir", + "Ġsomew here", + "ĠK ey", + "Ġattack ed", + "ĠK ir", + "Ġdom ain", + "Ġstrong er", + "Ġ9 9", + "Ġpen alty", + "I d", + "Sc ript", + "Ġdecl ined", + "Ġne ck", + "Ġfra ud", + "Ġcur rency", + "Ġr ising", + "R C", + "âĢ¦ âĢ¦", + "H z", + "Ġt ab", + "Ġtal ent", + "n am", + "ĠN BA", + "Ġvill age", + "Ġleg s", + "ĠN ext", + "E d", + "Ġac id", + "Ġhy d", + "8 00", + "Ġinvol ving", + "ĠIm age", + "ĠBe fore", + "F l", + "Ġyes terday", + "S ource", + "Ġterror ist", + "Ġsu p", + "Ġsy nt", + "ĠSaud i", + "Ġw est", + "Ġr u", + "b urg", + "Ġvis ible", + "Ġstru ck", + "r ison", + "Ġaw esome", + "Ġd rawn", + "Ġansw ers", + "ĠG irl", + "ĠR am", + "Ġthreat s", + "Ġdef eat", + "os it", + "Ġv ent", + "atur ally", + "Americ an", + "end a", + "ĠH oly", + "Ġr um", + "% ,", + "c ase", + "ĠHist ory", + "ĠYou Tube", + "Ġsit uations", + "ĠD NA", + "S te", + "Ġsa ved", + "It em", + "Ġrec ip", + "olog ist", + "Ġfac ed", + "Ġel ig", + "O nce", + "ĠL i", + "u h", + "Ġmist ake", + "ĠDiv ision", + "ĠB ell", + "Ġsympt oms", + " ®", + "Ġdom in", + "Ġfall ing", + "Ġend ing", + "as hes", + "Ġmat ches", + "ĠOn line", + "Ġexplan ation", + "D ef", + "red it", + "Ġany more", + "ĠT otal", + "ĠF OR", + "us hed", + "Ġlet ters", + "Ġris ks", + "ĠO K", + "Ġreported ly", + ": \\", + "Ġpl ate", + "Ġsubject s", + "Ġattempt ed", + "if ier", + "ian a", + "Ġunlike ly", + "ĠTh ough", + "um a", + "ĠIn vest", + "ĠPr in", + "ic an", + "ĠD ar", + "ĠColor ado", + "au g", + "Ġve get", + "a os", + "ri a", + "Ġshe l", + "Ġmark ed", + "Ġ( )", + "Ġsp r", + "p o", + "ĠL ink", + "Ġdef e", + "ĠJ r", + "Ġthem e", + "Ġpass ion", + "ĠP en", + "Ġinf o", + "iz er", + "Ġsh it", + "ĠC ivil", + "ap se", + "c re", + "Ġpo ly", + "Ġcomp onent", + "ĠChar les", + "ĠIre land", + "ĠPro v", + "Ġdo ctors", + "Ġgr anted", + "Ġpain t", + "Ġhon or", + "Ġsm oke", + "Ġpay ments", + "Ġprim arily", + "ĠKing dom", + "r ich", + "ate ll", + "Ġde als", + "Ġsched uled", + "Ġfund amental", + "Ġprote in", + "Ġnewsp aper", + "Ġcl ients", + "yth on", + "ĠD ate", + "h us", + "Ġfeed back", + "Ġstret ch", + "Ġc ock", + "Ġhot el", + "ĠQue en", + "Ġsu gar", + "Ġj u", + "Ġmil k", + "Ġappro val", + "ĠL ive", + "Ġequival ent", + "ef ully", + "Ġins ert", + "z ona", + "Ġext ension", + "d ri", + "J ohn", + "Ġacc omp", + "S m", + "ĠF und", + "Ġconst antly", + "Ġ` `", + "Ġgener ated", + "ĠA ction", + "ĠP sych", + "ĠT ri", + "Ġrecogn ize", + "Ġv ary", + "ph a", + "ĠR a", + "d f", + "et ch", + "ĠSov iet", + "Tw o", + "Ġpattern s", + "Ġprof ession", + "an ing", + "T ime", + "ĠL im", + "Ġcol ors", + "ĠA z", + "ĠT R", + "Ġinf ect", + "Ġphen omen", + "Ġshe ll", + "Al so", + "Ġput s", + "Ġdel ivery", + "Ġbro wn", + "Ġprocess ing", + "Ġlight s", + "ess age", + "ĠBro ok", + "ĠA ud", + "l ation", + "Ġindust rial", + "L ike", + "ĠB razil", + "rou s", + "ES S", + "ĠL uc", + "Ġsome how", + "Ġ8 5", + "Ġpro port", + "Ġpolit icians", + "Ġindic ate", + "Ġh ole", + "Ġtechn iques", + "Ġcompet itive", + "Ġph r", + "Ġv o", + "ist ent", + "ĠD ream", + "Ġcamp us", + "Ġaspect s", + "Ġhelp ful", + "Ġsh ield", + "or se", + "Ġtrig ger", + "m al", + "Ġ5 8", + "Ġt ort", + "Ġperson ally", + "Ġt ag", + "Ġkeep s", + "ĠV ideo", + "Ġben ch", + "Ġg ap", + "a ire", + "Ġe ast", + "Ġrec overy", + "per ial", + "Ġprof it", + "ĠM ic", + "Ġ5 7", + "Ġcol on", + "Ġstrong ly", + "st yle", + "Ġalleg ations", + "h an", + "Ġrep orters", + "j o", + "r ine", + "arg et", + "and al", + "Ġ0 3", + "Ġfl ash", + "tr ans", + "Ġstr ict", + "Ġpark ing", + "ĠPak istan", + "Ġl i", + "Ġwe ird", + "ĠE ric", + "Ġreg ions", + "ĠJ un", + "Ġint ellect", + "ĠW H", + "od ing", + "rib utes", + "up id", + "ĠT it", + "Ġf inger", + "or ia", + "Ġe lev", + "ĠF ield", + "Ġcon clusion", + "; ;", + "Ġfeel ings", + "Ġext ensive", + "Ġm ixed", + "Ġne uro", + "v y", + "Ġhar ass", + "ĠC irc", + "ou ch", + "Ġterrit ory", + "Ġsuccess fully", + "M ar", + "Ġing red", + "Ġoverw hel", + "Ġl ayer", + "V iew", + "Ġall ies", + "ill ance", + "ĠTh ree", + "Ġb unch", + "Ġnorm ally", + "Ġnet works", + "Ġsac r", + "ĠC IA", + "b les", + "Ġch ose", + "Ġopp onents", + "Ġregard less", + "Ġfr anch", + "Ġpre f", + "ĠP o", + "Ġbr idge", + "ann a", + "ĠSil ver", + "Ġw age", + "p age", + "ri or", + "Ġrad ical", + "ĠL ittle", + "Ġman ip", + "Ġsecret ary", + "Ġg ang", + "D R", + "F A", + "Ġdec ent", + "ĠSp irit", + "Ġun cle", + "ĠDevelop ment", + "Ġinvest ors", + "Ġwall s", + "Ġpub lish", + "Ġgener ate", + "iss ions", + "c ar", + "Ġprom ote", + "Ġcut ting", + "Ġche st", + "Ġdrink ing", + "Ġcollect ed", + "Ġ7 2", + "Ġhop ing", + "Ġem br", + "gor ith", + "Ġwar ned", + "Ġinstruct ions", + "O G", + "ĠD id", + "ĠAg ency", + "Ġg ear", + "Ġcritic ism", + "ĠF urther", + "Ġut il", + "ann y", + "R ed", + "Ġcoun sel", + "ĠAs ian", + "Ġredu ction", + "p ool", + "Ġteach ing", + "Ġdeep ly", + "i y", + "Ġestim ates", + "Ġcho ices", + "Ġperman ent", + "in em", + "ke l", + "Ġf asc", + "p se", + "f ile", + "ĠL ow", + "ĠP erson", + "Ġt ournament", + "st al", + "Ġm el", + "U ST", + "ĠR ay", + "az i", + "V al", + "Ġcont ained", + "ĠH olly", + "Ġw ake", + "Ġreve al", + "Ġprocess es", + "ĠIS IS", + "Ġ0 9", + "Ġbl ind", + "Ġste el", + "ĠB ad", + "Ġcare fully", + "app y", + "ro it", + "Ġg aming", + "Ġhous es", + "ĠC oll", + "Ġtr uck", + "er m", + "Ġsc ored", + "Ġocc as", + "ret urn", + "b ound", + "v ar", + "Ġsh arp", + "Ġaf raid", + "ĠE X", + "am ber", + "c ific", + "Ġsche me", + "N C", + "ĠPol it", + "Ġdecl ine", + "Ġ199 8", + "Ġpus hing", + "Ġposs ession", + "Ġpriv ile", + "Ġteacher s", + "Ġy ield", + "H A", + "ĠDav is", + "it led", + "#### ####", + "Ġr ig", + "ĠD aniel", + "ac on", + "Ġh ide", + "ut en", + "Ġcolle agues", + "Ġprin ciples", + "Ġl oud", + "Ġs in", + "ĠDem on", + "Ġst one", + "Ġ0 2", + "Ġt aught", + "Ġter rible", + "Ġst uck", + "ĠPol icy", + "te en", + "Ġimplement ation", + "ĠB BC", + "ĠAP I", + "Ġwhe el", + "all as", + "Ġch ampions", + "ol ars", + "play er", + "Ġrepeated ly", + "ĠSt ill", + "Ġlik es", + "ast y", + "es ter", + "ĠCath olic", + "R L", + "Ġb ath", + "Ġno ise", + "t itle", + "Ġn orthern", + "P art", + "Ġmag n", + "Ġf ab", + "ĠAs h", + "Ġdis pl", + "Ġtick et", + "Ġm urd", + "Ġalong side", + "ĠMus ic", + "Ġr iver", + "ĠSte el", + "ĠC L", + "ĠPl ayer", + "ĠM ult", + "ow ing", + "re p", + "s ize", + "Ġt ur", + "ĠGeorg ia", + "isc al", + "ra ction", + "Ġc able", + "Ġ5 9", + "Ġw ins", + "Ġup coming", + "Ġsurv ive", + "Ġins pired", + "ĠEduc ation", + "Ġstat istics", + "ĠF oot", + "iam i", + "Ġy ellow", + "ĠP age", + ". -", + "ĠH as", + "Ġur ban", + "Ġa x", + "es sel", + "\\ \"", + "Ġquarter back", + "Ġreg ister", + "ĠLab or", + "Ġab ilities", + "ĠF amily", + "Ġvar iable", + "ĠPr ice", + "Ġcont em", + "Ġth in", + "ĠE qu", + "d ata", + "Ġg otten", + "Ġconst it", + "Ġas ks", + "Ġt ail", + "Ġexc iting", + "ĠE ffect", + "ĠSp anish", + "Ġencour age", + "ins on", + "ĠA h", + "Ġcommit ment", + "C S", + "Ġr ally", + "Ġ: :", + "Ġsubs id", + "Ġsp in", + "Ġcapt ured", + "201 8", + "Ġinn oc", + "Ġalleged ly", + "ĠC ome", + "Ġart ists", + "ĠN umber", + "Ġelect ronic", + "Ġreg ional", + "ap es", + "Ġw ra", + "Ġmy th", + "pr ise", + "ĠM iller", + "ĠC reat", + "ĠEp isode", + "b ell", + "Ġdirect ed", + "Ġext ract", + "Ġs orry", + "Ġv ice", + "ag ger", + "ĠSu pport", + "Ġ6 6", + "ĠI ron", + "Ġwonder ful", + "Ġg ra", + "N et", + "ion e", + "E ng", + "Ġsh ips", + "ik es", + "ĠK evin", + "it ar", + "Ġactiv ists", + "tr ue", + "ĠAri zona", + "ent h", + "ĠDes pite", + "ĠS E", + "Ġha bit", + "ern el", + "Ġin qu", + "Ġab ortion", + "Ġv oid", + "Ġexpl icit", + "Ġeng aged", + "Ġang ry", + "Ġr ating", + "Ġfr ag", + "b ro", + "ick ing", + "d ev", + "Ġwor ried", + "Ġob ser", + "Ġap artment", + "ĠG T", + "Ġest ate", + "ĠConst itution", + "em on", + "ĠS now", + "Ġcount y", + "Ġdis ag", + "ĠStep hen", + "Ġimm igrants", + "w ind", + "ĠN ations", + "Ġfol ks", + "O ut", + "Ġg all", + "Ġtarget ed", + "Ġst ead", + "ĠB on", + "ĠL ib", + "Ġinform ed", + "Ġ12 0", + "ch ain", + "idel ines", + "or ough", + "Ġdri ven", + "Ġregular ly", + "Ġbas ket", + "Ġprinc iple", + "oc ument", + "Ġst un", + "ib ilities", + "ĠRom an", + "ĠAb out", + "Ġal ert", + "Ġdemocr acy", + "Ġrepresent ed", + "H S", + "c ers", + "p arent", + "Ar t", + "p ack", + "Ġdi plom", + "re ts", + "ĠN O", + "Ġcapt ure", + "ĠAd v", + "Ħ ¢", + "Ġannounce ment", + "ĠL ear", + "Ġh ook", + "Ġpur s", + "ĠS uch", + "ĠC amer", + "Ġrefuge es", + "ĠV e", + "P ol", + "Ġrecogn ized", + "l ib", + "Ġhad n", + "A ss", + "Ġpil ot", + "us hing", + "Ġreturn ing", + "Ġtra il", + "ĠSt one", + "Ġrout ine", + "Ġcour ts", + "Ġdes per", + "Ġfriend ly", + "ĠIt aly", + "Ġpl ed", + "Ġbreat h", + "Ġstud io", + "N S", + "Ġimp ressive", + "ĠAfghan istan", + "Ġf ing", + "Ġd ownt", + "ink ing", + "ĠR og", + "i ary", + "col or", + "se x", + "ar on", + "Ġf ault", + "ĠN ick", + "D own", + "ĠR ose", + "ĠS outhern", + "X X", + "is odes", + "L ist", + "6 00", + "Ġout come", + "er r", + "Ġelse where", + "Ġret ire", + "Ġp ounds", + "ĠGl obal", + "Pe ople", + "Ġcommun ications", + "Ġlo an", + "Ġrat io", + "ĠEm pire", + "Ġg onna", + "Ġinv ent", + "D F", + "Ġ19 70", + "ĠComm on", + "p at", + "Ġprom ised", + "Ġd inner", + "ĠH om", + "Ġcreat es", + "Ġoper ate", + "ver ty", + "ĠJ ordan", + "et ime", + "Ġsust ain", + "R eg", + "Ġincred ible", + "im a", + "Ġwar rant", + "Ġm m", + "A tt", + "Ġlaw suit", + "Ġreview s", + "it ure", + "ĠS ource", + "l ights", + "ĠF ord", + "Ġ6 3", + "g roup", + "st ore", + "Ġfeat ured", + "Ġfore ver", + "Ġpo verty", + "ĠP op", + "ĠC NN", + "az z", + "ab is", + "ach ing", + "Ġl aid", + "ĠSu pp", + "Ġfil ter", + "en a", + "ĠCommun ity", + "Ġcreat ures", + "u ction", + "ĠR oyal", + "Ġassoci ation", + "ĠCon nect", + "ĠBr ad", + "âĸ Ī", + "l ers", + "the re", + "ĠG i", + "Ġval uable", + "AC K", + "ĠT aylor", + "Ġl iquid", + "ĠAtt orney", + "ĠCar l", + "ĠF inal", + "ag a", + "ĠWil son", + "B ecause", + "ĠProf essor", + "ak a", + "Ġincred ibly", + "r ance", + "! )", + "R ef", + "s k", + "Ġsol utions", + "Ġatmosp here", + "Ġbl ame", + "um es", + "ĠN ob", + "C A", + "um ps", + "r ical", + "ĠPut in", + "ĠD est", + "or ic", + "ĠP A", + "Ġrespect ively", + "w an", + "Ġfif th", + "â Ħ¢", + "ĠC ry", + "Ġgovern or", + "res ident", + "Ġpurch ased", + "Ġh ack", + "Ġint ense", + "ob s", + "Ġorig in", + "Ġdef ine", + "Ġcare ful", + "** *", + "Ġshould er", + "Cl ick", + "Ġt ied", + "Ġdest ruction", + "ou red", + "Ġno body", + "Ġh o", + "ĠEx per", + "Ġt ip", + "\" ;", + "Ġtechn ique", + "Ġj ur", + "ĠP ok", + "b ow", + "Ġleg end", + "Ġacc ord", + "Ġbus y", + "ĠInt el", + "Ġh ang", + "ak i", + ". ]", + "âĢĶâĢĶ âĢĶâĢĶ", + "Ġsur gery", + "Ġrep rodu", + "Ġun iform", + "Ġscen es", + "c ode", + "Ġ6 2", + "l isher", + "ĠH ave", + "ph ia", + "Ġcry pt", + "Ġrec on", + "Ġsc ream", + "Ġadop ted", + "Ġsc ores", + "N e", + "ĠIt alian", + "in cluding", + "B O", + "Ġindic ated", + "Ġent ertain", + "G u", + "T ext", + "i el", + "Ġtw enty", + "Ġeng age", + "off s", + "ĠPac ific", + "Ġsm ile", + "Ġperson nel", + "Ġto ler", + "Ġdo ors", + "Ġt one", + "Ġmach ines", + "Ġent ering", + "ten ance", + "C O", + "ĠJer sey", + "Ġfore st", + "Ġhor se", + "Ġcompl aint", + "ĠSpr ing", + "y o", + "ĠPl us", + "ed ing", + "ĠRet urn", + "qu arters", + "ial s", + "c ow", + "Ġacad emic", + "Ġf ruit", + "Ġ199 6", + "og ether", + "Ġw ine", + "Ġpur su", + "ĠSte ven", + "Ġlic ens", + "Wh o", + "Ġclot hes", + "re ction", + "Ġsqu ad", + "Ġst able", + "Ġr aw", + "z ens", + "St ar", + "ut ies", + "anc er", + "Ġke ys", + "ĠM u", + "Ġcompl icated", + "ig er", + "ĠTe xt", + "Ġabs or", + "Ġ6 8", + "Ġfun ny", + "Ġrel ief", + "ĠL ew", + "ĠC ook", + "Ġch art", + "Ġdraw ing", + "G E", + "Ġmod ule", + "ĠB ull", + "I LL", + "Ġs alt", + "0000 0000", + "il le", + "Ġres ource", + "aw ay", + "adel phia", + "ĠB ru", + "Ġ6 7", + "Ġsome body", + "Ġparticip ate", + "Ġro se", + "we red", + "Ġmus cle", + "Ġcons ent", + "Ġcontin uing", + "ĠGuard ian", + "ĠOr der", + "reg on", + "Ġre ar", + "Ġprov ision", + "Ġlik ed", + "ri ent", + "Ġb ra", + "Tr ans", + "Ġmeet ings", + "Ġto x", + "Ġcon vent", + "Ġaut o", + "Ġrec ording", + "ĠSo ft", + "00 1", + "ĠR oll", + "Ġprogram ming", + "Ġp ic", + "Ġprov ed", + "Ġst ab", + "ĠA st", + "Ġca ption", + "ul ating", + "ĠAtt ack", + "Ġnew ly", + "Ġ199 7", + "f r", + "Ġdis cipl", + "ĠGree k", + "Ġed ition", + "ĠDo es", + "ĠB ox", + "if le", + "ack et", + "Ġpass es", + "Ġgu est", + "Ġac celer", + "it als", + "U D", + "Ġaut hent", + "ĠR est", + "ov al", + "t a", + "u ine", + "Ġarm or", + "ĠT own", + "Ġcomp at", + "Ġinc hes", + "Des pite", + "Ġass ign", + "he rent", + "Ġprep are", + "ĠM eg", + "oc key", + "Ġdep ends", + "Ġtrack s", + "w atch", + "Ġl ists", + "ĠN orthern", + "Ġal ter", + "re c", + "ĠE astern", + "Ġcond em", + "Ġevery where", + "? '", + "Ġaff ili", + "Ġf ought", + "\": {\"", + "Ġm ac", + "it arian", + "Ġsc ope", + "ĠA L", + "aw s", + "ar ms", + "Ġqu e", + "Ġenjoy ed", + "nes ota", + "Ġagg ressive", + "ĠSt ory", + "ĠI V", + "Ġrec ipe", + "Ġrare ly", + "ĠMed ical", + "val ue", + "ang el", + "ay ing", + "omet hing", + "Ġsub section", + "Ġs outhern", + "Ġfrequ ency", + "re te", + "roll ed", + "ult s", + "ĠN ic", + "Ġbeh alf", + "Ġsequ ence", + "ab et", + "Ġcontrovers ial", + "Ġcomp rom", + "Ġwork er", + "Ġmain ly", + "Ġal gorith", + "ĠM ajor", + "or ce", + "g ender", + "Ġorgan ized", + "Ġf ake", + "Ġconclud ed", + "ĠE D", + "ĠEx ec", + "r age", + "Ġch ances", + "ber ry", + "ĠTr ad", + "Ġconfig uration", + "Ġwithd raw", + "Ġf ro", + "ud es", + "ĠBro ther", + "ĠB rian", + "Ġtri es", + "Ġsam ples", + "Ġb id", + "ĠGold en", + "Ġphot ograph", + "if est", + "ĠD O", + "ĠPar liament", + "******** ********", + "R em", + "Ġcont est", + "Ġsign ing", + "p x", + "ĠZ eal", + "âĶĢ âĶĢ", + "E ar", + "Ġex it", + "Be fore", + "ĠCor por", + "n ull", + "mon th", + "Ġrac ial", + "ott ed", + "ĠV eg", + "ĠRe uters", + "Ġsw ord", + "ps on", + "ĠRom ney", + "a ed", + "Ġt rib", + "Ġin ner", + "Ġprot ocol", + "ĠB i", + "ĠM iami", + "ever al", + "p ress", + "Ġsh ipping", + "ĠAm endment", + "ĠHow ard", + "con nect", + "ĠD isc", + "ĠJ ac", + "iam ond", + "ĠThere fore", + "s es", + "ĠPrin cess", + "ĠUS B", + "ĠAn th", + "Ġsurve illance", + "Ġap olog", + "Ġ6 1", + "ow a", + "Ġf ulf", + "j s", + "Ġl uck", + "ust ed", + "Ġ §", + "n i", + "Ġant icip", + "em an", + "Ġwin ner", + "Ġsil ver", + "ll a", + "ic ity", + "Ġunus ual", + "Ġcr ack", + "Ġt ies", + "e z", + "Ġpract ical", + "Ġprov ince", + "ĠPl ace", + "Ġprior ity", + "IC E", + "Ġdescrib es", + "Ġbr anch", + "F orm", + "ask a", + "miss ions", + "b i", + "Ġp orn", + "ĠTur k", + "Ġent hus", + "Ġf ighters", + "Ġ0 8", + "ĠDet roit", + "Ġfound ation", + "av id", + "A re", + "Ġjud gment", + "cl ing", + "Ġsol ve", + "ĠDes ign", + "W here", + "hes is", + "ĠT ro", + "a fter", + "Ġne utral", + "ĠPalestin ian", + "ĠHolly wood", + "Ġadv is", + "ĠN on", + "y es", + "ol is", + "Ġrep utation", + "Ġsm ell", + "Ġb read", + "ĠB ul", + "ĠBe ach", + "Ġclaim ing", + "Ġgen etic", + "Ġtechn ologies", + "Ġupgr ade", + "row s", + "Ġdevelop er", + "ĠJ osh", + "ĠDis ney", + "erv ed", + "ip al", + "Ġun ex", + "Ġbare ly", + "t hen", + "ĠP ub", + "Ġill ness", + "et ary", + "ĠB al", + "Ġp atch", + "Ġbut t", + "Ġst upid", + "ĠD og", + "ĠD allas", + "f ront", + "ie ce", + "Ġprot ests", + "Ġch at", + "oen ix", + "Ġw ing", + "Ġpar liament", + "Ġ7 7", + "ose xual", + "Ġre nder", + "pt ions", + "ĠCo ast", + "os a", + "ĠG reg", + "h op", + "ĠMan agement", + "Ġbit coin", + "Ġrec over", + "Ġincor por", + "or ne", + "ĠUs ing", + "Ġpre ced", + "Ġthreat ened", + "Ġspirit ual", + "ĠE vent", + "ĠF red", + "Ġadvert ising", + "Ġimprove ments", + "ĠC ustom", + "Ġer rors", + "Ġsens itive", + "ĠN avy", + "Ġcre am", + "L ook", + "Ġex clusive", + "Ġcomp rehens", + "Ġde leg", + "Ġcon ce", + "Ġrem em", + "Ġstruct ures", + "Ġst ored", + "N D", + "Ġ1 000", + "U P", + "ĠB udd", + "A F", + "w oman", + "ĠAcad emy", + "ð Ł", + "se a", + "Ġtem porary", + "Ab out", + "es ters", + "Ġtick ets", + "Ġposs ess", + "in ch", + "o z", + "Ġl a", + "Ġcontract s", + "Ġun p", + "Ġc ig", + "ĠK at", + "ult ural", + "as m", + "Ġmount ain", + "ĠCapt ain", + "St ep", + "m aking", + "ĠSp ain", + "Ġequ ally", + "Ġl ands", + "at ers", + "Ġreject ed", + "er a", + "im m", + "ri x", + "C D", + "Ġtrans action", + "g ener", + "less ly", + "Ġ| |", + "Ġc os", + "ĠHen ry", + "Ġprov isions", + "Ġg ained", + "Ġdirect ory", + "Ġra ising", + "ĠS ep", + "ol en", + "ond er", + "Ġcon sole", + "in st", + "Ġb om", + "Ġunc ertain", + "1 50", + "ock ing", + "Ġmeas ured", + "Ġpl ain", + "Ġse ats", + "Ġd ict", + "S L", + "af e", + "Ġest imate", + "iz on", + "at hered", + "Ġcontribut ed", + "Ġep isodes", + "omm od", + "G r", + "AN T", + "Ġ6 9", + "G ener", + "Ġ2 50", + "vious ly", + "rog en", + "Ġterror ism", + "Ġmove ments", + "ent le", + "oun ce", + "ĠS oul", + "Ġpre v", + "ĠT able", + "act s", + "ri ors", + "t ab", + "Ġsuff er", + "Ġn erv", + "Ġmain stream", + "ĠW olf", + "Ġfranch ise", + "b at", + "Ġdem ands", + "Ġag enda", + "Ġdo zen", + "Ġclin ical", + "iz ard", + "ĠO p", + "t d", + "Ġvis ited", + "ĠPer haps", + "Ġact or", + "Ġde lic", + "Ġcont ribute", + "Ġin ject", + "ĠE s", + "ac co", + "Ġlist ening", + "Ġcon gress", + "epend ent", + "Ġprem ium", + "Ġ7 6", + "ĠIr ish", + "Ġass igned", + "ĠPh ys", + "Ġworld wide", + "Ġnarr ative", + "ot ype", + "m ont", + "b ase", + "ĠB owl", + "ĠAdminist ration", + "Ġrel ation", + "ĠE V", + "C P", + "Ġco vers", + "Ġ7 8", + "Ġcert ific", + "Ġgr ass", + "Ġ0 4", + "pir acy", + "ir a", + "Ġengine ering", + "ĠM ars", + "Ġun employ", + "ĠFore ign", + "st ract", + "Ġv en", + "Ġst eal", + "Ġrepl ied", + "Ġult imate", + "Ġtit les", + "d ated", + "Ġj oy", + "a us", + "Ġhy per", + "ak u", + "Ġoffic ially", + "ĠPro duct", + "Ġdifficult y", + "per or", + "Ġresult ed", + "rib ed", + "l ink", + "wh o", + "~~ ~~", + "ĠSpe ed", + "ĠV iet", + "W ind", + "ĠBar ack", + "Ġrestrict ions", + "ĠSh are", + "Ġ199 5", + "ition ally", + "Ġbeaut y", + "op t", + "Ġm aps", + "ĠC R", + "ĠN ation", + "ĠCru z", + "W ill", + "Ġelectric ity", + "Ġor g", + "Ġb urd", + "Ġviol ation", + "Ġus age", + "Ġper mit", + "ĠCh ron", + "ĠF ant", + "Ġn aturally", + "Ġ0 7", + "Ġth rown", + "ĠAw oken", + "Ġal ien", + "ĠHer o", + "ĠK ent", + "ĠR ick", + "ri ke", + "Ġp ace", + "}, {\"", + "G L", + "Ġpo ison", + "ĠT ower", + "Ġform al", + "al ysis", + "Ġgen uine", + "Ġk il", + "a ver", + "Ġproced ure", + "ĠPro p", + "intend o", + "ĠM ain", + "as ant", + "Ġtr ained", + "G ame", + "ĠL oad", + "ĠM A", + "Ġcru cial", + "Ġle ts", + "ĠF R", + "Ġch ampion", + "1 01", + "ĠCon ference", + "Ġwrit ers", + "Ġconnect ions", + "Ġo kay", + "ir ms", + "ĠR and", + "Ġenc ounter", + "ĠB uff", + "Ġachie ved", + "Ġche cks", + "isc ons", + "Ġassist ant", + "Ġwhen ever", + "ĠA ccess", + "ĠU r", + "b in", + "Ġcl ock", + "is p", + "op her", + "Ġb orrow", + "Ġm ad", + "Ġperson ality", + "on ly", + "IS T", + "ab ama", + "Ġg ains", + "Ġcommon ly", + "Ġter r", + "Ġhyp ot", + "Ġre ly", + "Ġt iss", + "iscons in", + "Ġrid ic", + "f unction", + "ĠO regon", + "Ġun com", + "r ating", + "el and", + "ĠN C", + "Ġm oon", + "ann on", + "Ġvulner able", + "ut ive", + "³³ ³³", + "ĠRad io", + "Ġw estern", + "se ct", + "ĠT ony", + "Ġocc urs", + "ĠO s", + "ĠH on", + "à Ń", + "Ġv essel", + "ĠScot land", + "Ġdiscrim ination", + "Ġsubsequ ent", + "st ring", + "Ġfant asy", + "ĠSh adow", + "Ġtest im", + "W E", + "it i", + "r as", + "Ġbo at", + "Ġmar ks", + "Ġord inary", + "Ġre n", + "Ġrepresent ative", + "Ġpet ition", + "Ġ7 3", + "Ġad venture", + "Ġign ore", + "ĠPhil adelphia", + "ĠS av", + "V P", + "Ġfact ory", + "Ġt asks", + "Ġdep ression", + "z ed", + "................ ................", + "ĠSt orm", + "Ġc ogn", + "Ġelig ible", + "Ġredu cing", + "v ia", + "Ġ0 5", + "Ġstri king", + "Ġdoll ar", + "h o", + "O V", + "Ġinstr ument", + "Ġphilosoph y", + "ĠMo ore", + "ĠA venue", + "Ġrul ed", + "ĠFr ont", + "IN E", + "ĠM ah", + "Ġscen ario", + "ĠNAS A", + "Ġen orm", + "Ġdeb ut", + "Ġte a", + "T oday", + "Ġabs ence", + "S im", + "Ġh am", + "le ep", + "Ġt ables", + "ĠHe art", + "M I", + "K e", + "re qu", + "V D", + "m ap", + "Ġchair man", + "Ġp ump", + "Ġrapid ly", + "v i", + "Ġsubstant ial", + "E P", + "d es", + "ch ant", + "ili pp", + "ĠS anta", + "ri ers", + "anche ster", + "L oad", + "ĠC ase", + "Ġsa ving", + "Ġ7 4", + "ĠA FP", + "er ning", + "oun ced", + "ĠMin nesota", + "ĠW as", + "Ġrec ru", + "Ġassess ment", + "ĠB ron", + "U E", + "Ġdynam ic", + "Ġf urn", + "ul ator", + "Ġprop ag", + "h igh", + "Ġacc ommod", + "Ġst ack", + "ĠS us", + "w rit", + "Ġre ven", + "ĠGod d", + "ĠZeal and", + "ab s", + "Ġbr ut", + "Ġper pet", + "h ot", + "Ġhard ly", + "ĠB urn", + "ãĤ ¹", + "Ġst y", + "Ġtrans actions", + "Ġg ate", + "Ġsc reens", + "Ġsub mitted", + "Ġ1 01", + "Ġlangu ages", + "ugh t", + "em en", + "Ġfall s", + "Ġc oc", + "Ĥ ¬", + "Ġstri kes", + "p a", + "Ġdel iber", + "ĠI M", + "Ġrel ax", + "ann els", + "ĠSen ator", + "Ġext rem", + "Ġ} ,", + "ĠDe b", + "Ġbe ll", + "Ġdis order", + "c ut", + "Ġi OS", + "Ġl ocked", + "Ġem issions", + "Ġshort ly", + "\" ]", + "ĠJud ge", + "ĠS ometimes", + "Ġr ival", + "Ġd ust", + "Ġreach ing", + "F ile", + "¯¯ ¯¯", + "ino is", + "ĠJ ason", + "Ġs atell", + "are t", + "Ġst ations", + "Ġag ric", + "ĠTechn ology", + "com es", + "ĠUn fortunately", + "ĠChild ren", + "Ġappl ies", + "ast ed", + "Ġan ger", + "ail ability", + "ĠDam age", + "Ġcomp are", + "ĠStand ard", + "Ġaim ed", + "ĠB a", + "angu age", + "Ġreg ulation", + "Ġj ury", + "Ġair port", + "Ġse ctions", + "ĠPr ince", + "em ed", + "Ġmedic ine", + "Ġh itting", + "Ġsp ark", + "ol ves", + "Ġad s", + "St ate", + "Ġfood s", + "Ġrepl acement", + "Ġch icken", + "Ġlow est", + "Ġmind s", + "Ġinvol ves", + "u i", + "Ġarr ang", + "Ġproced ures", + "ĠWh ich", + "ivers ary", + "Ġb ills", + "Ġimprove ment", + "Ġin ev", + "Ġexpect ations", + "Ġintellect ual", + "Ġsp aces", + "Ġmechan ism", + "2 50", + "bre ak", + "ĠZ e", + "ĠT enn", + "ĠB alt", + "Ġbar rel", + "Ġstat ic", + "man n", + "Pol ice", + "Ġt ips", + "Ġhand ling", + "c us", + "od ed", + "il ton", + "ir y", + "Ġjournal ists", + "our se", + "Ġcom ic", + "Ġnom ine", + "IT Y", + "Ġvers us", + "Ġlo op", + "Ġsur f", + "ĠInd ust", + "ĠHun ter", + "Ġbelief s", + "is an", + "Ġset up", + "Ġbre w", + "im age", + "Ġcomput ers", + "f ol", + "} ,\"", + "ĠMed al", + "Ġtax p", + "Ġdisplay ed", + "Ġg rav", + "Ġf iscal", + "M on", + "ĠMos cow", + "ĠK ong", + "ĠCent re", + "Ġcamer as", + "ĠMr s", + "ĠH ay", + "Ġa ver", + "ĠK elly", + "p y", + "Ġrequire ment", + "Ġent itled", + "omb ie", + "Ġsh adow", + "ag ic", + "ĠA k", + "Ġel ite", + "Ġdiv ided", + "Ġhead ing", + "Ġcop ies", + "Ġloss es", + "Ġv it", + "k ed", + "ĠB ry", + "Ġan s", + "ĠSte am", + "Ġrep orter", + "he im", + "ĠIt em", + "Ġsuper ior", + "d on", + "ere nt", + "à ¶", + "Ġtherap y", + "Ġpe ak", + "ĠMod el", + "Ġl ying", + "Ġg am", + "z er", + "r itten", + "Ġrespons es", + "Ġconsider ation", + "ĠB ible", + "Ġl oyal", + "Ġinst ant", + "Ġp m", + "ĠFore st", + "à ¼", + "Ġext end", + "Ġconv icted", + "Ġfound er", + "Ġconv in", + "ĠO ak", + "che ck", + "Ġsch olars", + "p ed", + "Ġover se", + "T op", + "c ount", + "ĠAr k", + " ·", + "Ġ0 6", + "ĠL A", + "m d", + "ĠLat in", + "im ental", + "ĠC PU", + "Ġsubst ance", + "Ġminor ity", + "Ġmanufact uring", + "E r", + "ocol ate", + "Ġatt ended", + "ĠMan ager", + "r ations", + "Ġappreci ate", + "om y", + "GB T", + "id ency", + "B L", + "Ġguarant ee", + "pos ition", + "Ġo cean", + "clud e", + "Ġhead ed", + "Ġt ape", + "Ġlo ose", + "Ġlog ic", + "Ġpro ven", + "Ġsp ir", + "Ġad mit", + "is a", + "Ġinvestig ate", + "Ġ199 4", + "sy lv", + "ĠL ost", + "c est", + "Ġ7 1", + "Ġrequest ed", + "Ġwind ows", + "ĠPok é", + "ĠWith out", + "M et", + "Ġbehavi our", + "Ġread er", + "Ġh ung", + "ĠKe ep", + "Ġro les", + "Ġimplement ed", + "Ġbl ank", + "Ġserv es", + "ĠJ ay", + "Ġc ited", + "ĠF riend", + "prof it", + "ap on", + "Ġrep air", + "it em", + "arr ass", + "Ġcrit ics", + "ad i", + "ĠF ather", + "Ġsh out", + "Ġf ool", + "Ġ8 8", + "Ġprodu cing", + "Ġl ib", + "Ġround s", + "Ġcirc le", + "Ġpre par", + "Ġsub mit", + "Ġn ic", + "mor row", + "ãĥ «", + "U nder", + "Ġv ital", + "ater n", + "Ġpass word", + "Ġpublic ation", + "Ġprom inent", + "Ġspeak s", + "Ġb ars", + "Ġde eper", + "ĠM ill", + "port ed", + "Ġw id", + "Ġbut ter", + "Ġsm oking", + "Ġindic ates", + "K ey", + "rop ri", + "ĠF ile", + "all ing", + "ast ing", + "ĠR us", + "Ġad j", + "Ġ7 9", + "av al", + "Ġpres um", + "bur gh", + "on ic", + "Ġf ur", + "Ġpoll s", + "ik a", + "Ġsecond ary", + "Ġmon ster", + "ig s", + "ĠCur rent", + "E vent", + "Ġowners hip", + "end ar", + "Ġarri ve", + "ĠT ax", + "Ġn ull", + "ĠPri v", + "Ġth ro", + "Ġk iss", + "c at", + "Ġup set", + "ang le", + "it ches", + "ect or", + "olog ists", + "ĠGal axy", + "Ġcor ruption", + "Ġh int", + "ent er", + "ĠH ospital", + "Ġgreat ly", + "Ġbeg un", + "es y", + "Ġso il", + "ĠAnt on", + "Ġmain tenance", + "ãĥ ©", + "Ġdo zens", + "Ġhuman ity", + "ĠAl abama", + "Ġr om", + "w orth", + "ap ing", + "sylv ania", + "l ah", + "Ġg athered", + "G A", + "Ġattack ing", + "f ound", + "ĠSqu are", + "Ġar bit", + "ict ions", + "ĠW isconsin", + "Ġd ance", + "ĠS aint", + "arch y", + "Ġbase ball", + "Ġcontribut ions", + "Ġliter ature", + "Ġex ha", + "per ty", + "t est", + "Ġb ab", + "Ġcontain er", + "let ter", + "Ġfall en", + "Ġwebs ites", + "Ġbott le", + "ĠS ac", + "Ġbre ast", + "ĠP L", + "Ġveter an", + "Ġinterview s", + "ĠA le", + "Ġb anned", + "eng ers", + "ĠRev olution", + "in th", + "Ġconc erning", + "IV E", + "Ġexp enses", + "ĠMatt hew", + "ĠColumb ia", + "d s", + "ist ance", + "Ġent ity", + ".. .\"", + "Ġrel iable", + "Ġpar alle", + "ĠChrist ians", + "Ġopin ions", + "Ġin du", + "l ow", + "Ġcompet e", + "Ġth orough", + "Ġemploy ed", + "Ġestablish ment", + "ig en", + "ĠC ro", + "Ġlawy ers", + "ĠSt ation", + "T E", + "ĠL ind", + "ĠP ur", + "it ary", + "Ġeffic iency", + "âĢ IJ", + "ĠL y", + "Ġm ask", + "Ġdis aster", + "Ġag es", + "ER E", + "es is", + "ĠH old", + "Ġcas ual", + "b led", + "Ġen abled", + "ĠEn vironment", + "ĠInt elligence", + "i per", + "ĠM ap", + "ĠB E", + "Ġemer ged", + "is dom", + "Ġc abin", + "Ġregist ration", + "Ġfing ers", + "Ġro ster", + "Ġfram ework", + "ĠDo ctor", + "et ts", + "Ġtransport ation", + "Ġaware ness", + "H er", + "Ġattempt ing", + "O ff", + "ĠSt ore", + "ÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤ", + "ĠK now", + "Ġdef ence", + "Ġsc an", + "ĠT en", + "ĠCh air", + "ĠP H", + "ĠAtl anta", + "Ġfuck ing", + "Ġans wered", + "b n", + "ĠK ar", + "Ġcateg ories", + "Ġr ational", + "Ġc ust", + "Ġrob ot", + "Ġcorrect ly", + "Ġg if", + "Ġgraph ics", + "m ic", + "Ġground s", + "ĠO pp", + "i ate", + "Ġdist ributed", + "Ġsan ctions", + "Ġchalleng ing", + "ut o", + "Ġingred ients", + "Ġinv ited", + "Ġfound ed", + "ĠRe qu", + "d ed", + "Ġb owl", + "Ġbrother s", + "ĠH a", + "I O", + "Ġw ages", + "im ore", + "oc ial", + "Ġse ed", + "ative ly", + "Ġaddress es", + "ĠI owa", + "ab eth", + "Ġatt itude", + "is d", + "ch ild", + "Ġm ole", + "Ġdisco very", + "y ard", + "B r", + "Ġ8 2", + "Ġsuppl ies", + "ell ing", + "Ġdist ingu", + "C R", + "Ġre cept", + "Ġ vert", + "Ġsw im", + "b ec", + "d oor", + "ĠY eah", + "Ġg al", + "Ġinter act", + "ĠE SP", + "ĠC S", + "amp s", + "Ġconvin ced", + "Ġobject ive", + "Ġdis h", + "ĠPhot os", + "l ad", + "Ġdownt own", + "o il", + "in ction", + "Ġto morrow", + "ĠC OM", + "Ġsurv ival", + "sh ot", + "Ġsett lement", + "C ons", + "ĠX box", + "int erest", + "ĠS M", + "arg o", + "en ess", + "Ġeth nic", + "b ered", + "M in", + "ĠT ok", + "Ġinc ent", + "ĠComm and", + "Ġmain tained", + "Ġbreak s", + "br idge", + "at ar", + "ag g", + "ĠF inally", + "un icip", + "ĠO nt", + "le ft", + "Ġrecogn ition", + "Ġ* /", + "ĠP ers", + "Ġwe lf", + "Ġaddress ed", + "ĠK ansas", + "Ġvir us", + "Ġwhere as", + "Ġp apers", + "ram s", + "ĠMin istry", + "Ġple asure", + "Ġacqu ired", + "Ġd uration", + "j pg", + "Ġcal m", + "ĠN HL", + "Ġburn ing", + "Ġfold er", + "ick ed", + "ĠP y", + "ĠIll inois", + "Cl ass", + "ĠGodd ess", + "Ġperform ing", + "Ġwelf are", + "j ar", + "In ter", + "Ġl in", + "Ġenh ance", + "Ġnot ion", + "f are", + "yp es", + "ĠAre a", + "Ġcann abis", + "ĠDie go", + "f s", + "ĠM anchester", + "com m", + "in ite", + "Ġcover ing", + "ĠS ound", + "Ġ19 60", + "Ġ8 4", + "e lect", + "z ing", + "Ġcitiz en", + "Ġph ones", + "Ġr aid", + "Ġign ored", + "ĠOb ject", + "Ġu pload", + "c ard", + "Ġmod ified", + "Ġroom s", + "ia h", + "r ange", + "he ast", + "ach us", + "Ġsuggest ing", + "âĢ ĭ", + "gr ade", + "E l", + "Ġclot hing", + "Ġr h", + "ĠH an", + "un ity", + "en cing", + "ĠAust in", + "sec ution", + "t ra", + "d em", + "ĠQ ual", + "Ġhe aven", + "Ġst ages", + "Ġw edd", + "pl us", + "ific ial", + "ĠIm m", + "ĠH o", + "iet ies", + "Ġphr ase", + "Ġbr ill", + "act ory", + "Ġprov iders", + "Ġsil ence", + "Ġa er", + "ĠA I", + "ĠAd venture", + "Ġplatform s", + "Ġdemonstr ated", + "Ġinter f", + "ing ton", + "Ġr aces", + "Ġgr ade", + "ult ane", + "ĠTh rough", + "f alse", + "Ġb ow", + "ĠA B", + "Ġfl avor", + "Ġhistor ic", + "g ov", + "Ġcol our", + "Ġview ed", + "ĠEm ail", + "el come", + "Ġinter vention", + "Ġd iversity", + "Ġperiod s", + "Ġre verse", + "ĠV ery", + "Ġqu ote", + "ĠLe ft", + "th rough", + "Ġsc rew", + "Ġland ing", + "Ġp ill", + "Ġw et", + "Ġprot esters", + "Ġrepe at", + "av ed", + "er k", + "Ġsal ary", + "ĠPenn sylvania", + "St ill", + "Ġmay or", + "Ġkit chen", + "Ġfeat uring", + "ĠM useum", + "ĠT ournament", + "ĠF al", + "Ġser vers", + "U C", + "Ġany body", + "im g", + "ĠTr ade", + "ixt ure", + "the less", + "Ġfin ance", + "Ġcl osing", + "ĠPat ri", + "i ac", + "ab el", + "Ġ> >", + "or ous", + "Ġf irms", + "sc reen", + "un a", + "Ġemb arrass", + "ul se", + "Ġlet ting", + "Ġth rew", + "ile y", + "Ġch annels", + "l an", + "ĠVeg as", + "Ġse ar", + "Ġfant astic", + "ar re", + "uzz le", + "ĠD er", + "Th ose", + "Ġsw ing", + "Ġshe et", + "ind ex", + "co ver", + "og an", + "Ġvari ables", + "ĠTe ch", + "Ġsp oken", + "ac hel", + "ĠD a", + "ĠMount ain", + "Ġload ed", + "Ġfoot age", + "vers ion", + "Ġun l", + "ĠPh oenix", + "Ġthrow ing", + "Ġf iring", + "Ġtrack ing", + "Ġw idth", + "Ġstrugg ling", + "ro oms", + "ot ion", + "Ġmonth ly", + "ĠSer ver", + "Ġegg s", + "op en", + "M C", + "Ġ199 3", + "Ġh ired", + "Ġstay ed", + "ĠAll en", + "Ġst ro", + "Ġ9 8", + "st ep", + "ĠTurk ish", + "Ġfab ric", + "ist ing", + "ĠD om", + "Ġd ates", + "Ġpr on", + "Ġbasket ball", + "Ġl ucky", + "ĠArab ia", + "Ġassum ed", + "est y", + "Ġaff airs", + "Ġgl ad", + "ĠInd eed", + "ĠF A", + "ĠW ord", + "Ġjo ining", + "if ice", + "p read", + "ir ts", + "ĠSe lect", + "Ġpop ulations", + "aw are", + "Ġn ose", + "Ġcompl aints", + "st art", + "Ġsc oring", + "Th anks", + "Ġmin ing", + "Ġvisit ors", + "S H", + "Ġdam aged", + "Ġcharacter istics", + "ĠP ent", + "D C", + "Ġ8 3", + "ĠS ix", + "r ates", + "Ġfl ags", + "ĠB rew", + "d og", + "M ark", + "// //", + "Ġexec ution", + "Ġj oke", + "ph ones", + "Ġtestim ony", + "Ġob st", + "Q L", + "ĠC ut", + "Ġstud ied", + "ĠN intendo", + "ick et", + "ĠN BC", + "Ġl ad", + "ĠB ra", + "ĠM oh", + "Ġk ernel", + "Ġoverwhel ming", + "Ġag ed", + "Ġapplic able", + "ĠC ond", + "Ġroad s", + "ĠBl ock", + "m ade", + "od ge", + "Ġcomm ands", + "Ġoff ices", + "vel and", + "Ġt ut", + "Ġrece iver", + "ĠF ro", + "Ġsho pping", + "Ġi P", + "ĠSt re", + "ĠA BC", + "Ġentertain ment", + "ĠB ow", + "ort ed", + "M c", + "Ġread s", + "gr ad", + "ĠCol lect", + "Ġâ ĪĴ", + "ĠCap ital", + "eder ation", + "Ġemploy er", + "Ġinvolve ment", + "Ġanx iety", + "al ia", + "Ġro of", + "ĠAm ong", + "ĠDemocr at", + "Ġstat s", + "ĠV ill", + "Ġconst itutional", + "Ġrefer ring", + "itt y", + "Ġtack le", + "out ube", + "Ġback ed", + "ĠH ong", + "ĠBro ad", + "Ġe le", + "ĠO tt", + "Ġ199 2", + "h our", + "achus etts", + "C al", + "Ġdefe ated", + "Ġ8 1", + "es p", + "Ġseem ingly", + "w as", + "ĠJ enn", + "ĠK urd", + "Ġg ene", + "Ġdisc ount", + "R et", + "EC T", + "( );", + "Ġclub s", + "Ġs id", + "ĠM arsh", + "Che ck", + "Ġp p", + "ĠE ag", + "ides pread", + "Ġbe ings", + "F T", + "Ġintrodu ction", + "ĠCh ange", + "AR D", + "Ġ1 10", + "ad ows", + "ier ce", + "Ġme al", + "a uthor", + "ĠB ang", + "lah oma", + "Ġr anks", + "201 1", + "?? ??", + "m ax", + "Ġcoll apse", + "Ġop ens", + "Ġe cho", + "Ġs oph", + "Ġrac ist", + "Ġenorm ous", + "Ġw aves", + "Ġt ap", + "Ġcomprehens ive", + ". --", + "ĠR oy", + "Ġfarm ers", + "Rel ated", + "a ired", + "ron es", + "ĠC rim", + "Ġproport ion", + "Ġdesign s", + "Ġnegoti ations", + "Ġvirt ually", + "ĠBat man", + "Ġwar n", + "Ġlegit imate", + "m ate", + "Ġcon vention", + ", ,", + "net ic", + "ĠS D", + "Ġconsist ently", + "Ġcompens ation", + "Ġpunish ment", + "Ġy e", + "Ġt ie", + "ĠB ureau", + "ir lf", + "ĠB u", + "ĠA ren", + "ĠPh ilipp", + "Ġkn ife", + "Ġmem ories", + "ĠR oss", + "Ġang le", + "Ġ8 6", + "ĠTh under", + "Ġre nd", + "ĠT our", + "Ġcount s", + "s ung", + "ĠIm p", + "Ġeduc ational", + "Ġaccess ible", + "C OM", + "Ġd rew", + "y er", + "G l", + "am ine", + "OR T", + "O B", + "I B", + "m aster", + "Ġtri als", + "og y", + "h ar", + "ĠTr ust", + "Ġprefer red", + "irlf riend", + "ĠN ev", + "Ġb in", + "Ġc ow", + "P age", + "Ġsign ature", + "ĠB L", + "7 00", + "Ġret ired", + "Ġby tes", + "Ġneigh b", + "ĠLeg end", + "Ġdev ast", + "Ġsuspect ed", + "is ons", + "ĠPoké mon", + "sc ale", + "Ġcap abilities", + "Ġre vel", + "Ġche ese", + "d y", + "igr ant", + "Ġfail ing", + "b its", + "ĠHer oes", + "ĠG host", + "ĠS cient", + "Ġappoint ed", + "ur i", + "Ġinst itution", + "Ġexpand ed", + "g reg", + "Ġmonitor ing", + "Ġp odcast", + "Ġcoal ition", + "Ġ9 6", + "J o", + "Ġst olen", + "ĠS ab", + "Ġstop s", + "Ġhol iday", + "Ġint r", + "C ar", + "Bl ack", + "ĠL GBT", + "Ġwar ming", + "ĠAnd erson", + "Ġ8 9", + "Ġprodu cer", + "M ed", + "Ġaccur acy", + "ĠMar vel", + "iz abeth", + "ĠPat rick", + "m ony", + "Ġmin i", + "ac les", + "Ġover t", + "the y", + "Ġmembers hip", + "ĠV en", + "Ġex ch", + "Ġrem oval", + "ĠD ave", + "T Y", + "m ad", + "ĠF ind", + "Ġad equ", + "Ġe c", + "Ġte eth", + "Ġemot ion", + "Ġper m", + "Ġsole ly", + "d b", + "Ġextra ord", + "IG HT", + "c al", + "Ġgu idelines", + "Ġd ying", + "Ġsusp ended", + "ĠPrem ier", + "ĠAnth ony", + "el ve", + "Ġd ad", + "ĠE th", + "ĠFoot ball", + "Ġabandon ed", + "Ġ< <", + "Ġm arch", + "Ġhor ror", + "âĢ¦ \"", + "Ġchild hood", + "Ġcampaign s", + "Ġl unch", + "ĠAl bert", + "bl ock", + "âĸĪ âĸĪ", + "ound ing", + "Ġb one", + "or gan", + "ad ers", + "ĠFl ash", + "ĠDri ve", + "Ġton ight", + "Ġw ars", + "ĠF L", + "Ġform ation", + "con st", + "New s", + "Ġcom pe", + "or ious", + "ĠSt aff", + "Ġdiscuss ions", + "ĠProt ection", + "ĠJ am", + "Ġcrit eria", + "Ġinstall ation", + "Ġaccompl ish", + "iz za", + "Ġpub lisher", + "Ġresc ue", + "ĠT ry", + "U LL", + "ĠS om", + "ĠH op", + "ore t", + "th s", + "ord on", + "Ġp ocket", + "ĠIn v", + "Down load", + "ĠCr ime", + "Ġb ene", + "ĠGu ide", + "ĠAs sembly", + "Ġparam eters", + "I E", + "ĠAlex ander", + "Ġconc ert", + "ĠSc he", + "Ġsh oes", + "Ġvis iting", + "Ġrec all", + "Ġb ub", + "Ġr ural", + "Ġconc rete", + "ĠR os", + "N ext", + "R uss", + "Ġlo ans", + "ĠSh ield", + "Ġtre m", + "hem at", + "k g", + "ĠHar ris", + "is ition", + "ĠM ove", + "ĠF C", + "Ġf ate", + "ĠCh o", + "Ġt ired", + "Ġprinc ipal", + "h ist", + "ien ces", + "ath y", + "Ġse vent", + "Ġm ood", + "Ġstrateg ic", + "Ġdise ases", + "Ġfor um", + "Ġtem por", + "Ġhead quarters", + "P ar", + "ig e", + "fl ix", + "Ġgu itar", + "Ġ9 4", + "On ly", + "Ġrele ases", + "ro ph", + "================ ================", + "Ġ6 00", + "ĠContin ue", + "ig ate", + "ĠC rit", + "sy stem", + "Ġdis abled", + "Ġunex pected", + "ith ub", + "Ġuncle ar", + "ĠE st", + "Ġcontr ad", + "Ġstrateg ies", + "vent ures", + "Ġpass age", + "AM E", + "Ġimpro ving", + "Ġreve als", + "Ġdecre ase", + "ov a", + "Ġann oy", + "ĠSh ort", + "ĠL ibrary", + "Ġcy ber", + "n ell", + "ĠH ur", + "ĠC B", + "Ġphot ograp", + "U I", + "Ġs ed", + "G e", + "Ġ8 7", + "Ġd iverse", + "Ġencour aged", + "Ġcons piracy", + "Ġbird s", + "Ġoper ator", + "Ġhand ful", + "Ġclass ified", + "? )", + "Ġdram atic", + "Ġinvestig ators", + "it o", + "Ġw idespread", + "ĠR oom", + "-------------------------------- --------------------------------", + "Ġcollect ive", + "Ġjournal ist", + "St ring", + "Ġtemper atures", + "il a", + "Ġgu id", + "Ġins pect", + "Ġmiss ile", + "ĠMay or", + "Ġman ual", + "Ġsim ultane", + "Ġrat ings", + "Ġsu ck", + "Ġ9 7", + "Ġunivers al", + "Ġph arm", + "Ġdis rupt", + "ian o", + "A V", + "Ġf t", + "Ġstat ist", + "old s", + "ĠWalk er", + "ph p", + "Ġunder t", + "ĠL as", + "ish op", + "nt il", + "res hold", + "ĠWhe ther", + "M s", + "Ġden y", + "ĠCl oud", + "Ġprov ider", + "Ġsurv iv", + "ĠUp date", + "h as", + "Ġmist akes", + "ch arge", + "pl ed", + "r ity", + "Ġn ode", + "ĠMass achusetts", + "ool s", + "lic ation", + "Ġf ails", + "em ale", + "or i", + "back s", + "Ġsh irt", + "Ġ' '", + "ĠN AT", + "Ġwat ers", + "els on", + "Ġe ase", + "Ġsc ar", + "Ġcont ents", + "m ind", + "Ġcont ribution", + "Ġsh r", + "Ġhand ed", + "Ġst ability", + "Ġtra ve", + "E m", + "Ġmir ror", + "12 3", + "Ġwe igh", + "Ġf iction", + "ou ver", + "ist ant", + "r ition", + "ĠF ed", + "Ġphys ically", + "Ġst ake", + "ĠArt icle", + "ĠAr c", + "ĠLew is", + "ĠM ind", + "Ġdemonstr ate", + "Ġprof its", + "v ision", + "om ic", + "ol id", + "Ġbatt les", + "Ġdri ves", + "Ġeas tern", + "ĠS ony", + "!! !", + "ar ation", + "v ard", + "ĠG L", + "port ation", + "Ġ9 2", + "Ġlaw makers", + "Ġprotect ing", + "ĠE PA", + "Ġy eah", + "Ġsh ame", + "ol ph", + "e ven", + "x it", + "Ġatt ach", + "Ġrepresent ing", + "Ġob s", + "ĠUt ah", + "iff s", + "ĠFre edom", + "à ³", + "A K", + "Ġinc idents", + "it age", + "Ġview ers", + "c d", + "Ġm ouse", + "Ġcl ar", + "Ġaccord ance", + "Ġb ot", + "c or", + "ĠSum mer", + "he ld", + "Ġinnoc ent", + "Ġiniti ative", + "ol s", + "________________ ________________", + "Ġsp ots", + "p ace", + "Ġconvent ional", + "Ġcorpor ations", + "Ġblock ed", + "H D", + "at tered", + "Ġref ers", + "Ġbu ck", + "ĠDig ital", + "12 0", + "Ġtop ics", + "T F", + "Ä ģ", + "br id", + "re ement", + "Ġunder lying", + "ĠM ember", + "Ġinvestig ating", + "Ġpregn ancy", + "Ġtouch down", + "ĠB and", + "ĠCall er", + "Ġinst ances", + "P P", + "w a", + "G ood", + "Ġ199 1", + "ĠC old", + "Ġfear s", + "Ġrem arks", + "Ĩ Ĵ", + "at al", + "Ġm it", + "Ġexper iments", + "i pt", + "Col or", + "ind u", + "Up date", + "Ġ9 3", + "A g", + "Ġ å", + "anc ouver", + "B oth", + "Ġjud ges", + "Ob ject", + "Ġst ere", + "umb n", + "Ġparticip ation", + "ĠSt ars", + "ĠJ ere", + "Ġweek ly", + "ĠB an", + "Ġconvers ations", + "ĠP itt", + "u z", + "ĠIndian a", + "ĠK ick", + "Ġinf ection", + "Ġhero es", + "Ġsett led", + "Ġstri p", + "Ġh al", + "Ġd ump", + "ĠS ci", + "Ġl es", + "Ġref erences", + "ĠU RL", + "ĠBr idge", + "Ġwant ing", + "For ce", + "Ġex clus", + "Me anwhile", + "m n", + "Ġg entle", + "m aker", + "sen al", + "ĠG ro", + "ou ri", + "ĠR ain", + "ĠAll iance", + "Ġl ift", + "el a", + "S D", + "ĠCle veland", + "Ġrank ed", + "Ġst adium", + "Ġdead ly", + "ä ¸", + "Ġr iding", + "ar ia", + "ĠAr mor", + "Ġdocument ation", + "ĠGree ce", + "ree k", + "Ġl ens", + "ĠS a", + "Ġg ross", + "ĠE mer", + "ag ers", + "ĠD ub", + "ĠR h", + "ĠAM D", + "Ġarri val", + "Ġdes ert", + "Ġsupp lement", + "ĠRes p", + "Ġkn ee", + "Ġmarg in", + "f ont", + "og g", + "201 0", + "ĠP ir", + "ĠP rom", + "iv als", + "Ġint ake", + "Ġdifferent ly", + "ug s", + "Ġb its", + "clud ed", + "Ġsearch ing", + "ĠD u", + "um ble", + "Ġfunction al", + "ĠBalt imore", + "ĠC ould", + "Ġdes ired", + "Ġcirc uit", + "ĠL yn", + "ĠG O", + "ĠF alse", + "re pre", + "' :", + "alt ies", + "Ġmin im", + "Ġdro ve", + "ĠSh ould", + "Ġh ip", + "Ġpro s", + "Ġut ility", + "ĠN ature", + "ĠM ode", + "P resident", + "o pp", + "r at", + "form ance", + "Ġconcent ration", + "Ġf ont", + "ĠB ud", + "Ġam id", + "Ġre vers", + "ĠM L", + "B ar", + "Ġinter action", + "Ġjur isd", + "Ġspell s", + "d ep", + "f il", + "Ġcivil ians", + "ut ter", + "ĠCo oper", + "ĠBel ow", + "Ġent rance", + "Ġcon vert", + "Ġcontrovers y", + "ow ered", + "Ġcontr ary", + "Ġar c", + "ĠExec utive", + "ĠOffic er", + "Ġpack ages", + "Ġprog ressive", + "w idth", + "Ġreserv ed", + "v ol", + "ĠSam sung", + "Ġprint ed", + "Ġcent ers", + "Ġintrodu ce", + "ĠKenn edy", + "Ġodd s", + "Ġsure ly", + "Ġindepend ence", + "Ġpass engers", + "repre ne", + "ĠBe h", + "Ġl oves", + "ĠESP N", + "Ġfac ilit", + "Ġident ical", + "Ġdo ct", + "Ġpartners hip", + "con f", + "ĠH ide", + "Ġconf used", + "ĠC ow", + "M en", + "Ġw rest", + "ĠIraq i", + "Ġh oles", + "ĠStud ies", + "Ġpregn ant", + "h ard", + "Ġsign als", + "I X", + "Ġpull ing", + "Ġgrad uate", + "Ġnomine e", + "D ate", + "Ġper mitted", + "Ġâ Ĥ¬", + "ĠOk lahoma", + "St art", + "Ġauthor ized", + "Ġal arm", + "ĠC os", + "v an", + "Ġgener ations", + "c ular", + "Ġdr agon", + "ĠSoft ware", + "ĠEd ward", + "Ġcontro ller", + "S en", + "ge red", + "ĠV ik", + "Ġappro ached", + "Th ank", + "Ġcan ce", + "Ġform ula", + "ĠSm all", + "Ġweak ness", + "Ġr amp", + "it udes", + "j ud", + "Ġbrill iant", + "Ġacc us", + "s ource", + "Ġ8 00", + "ĠE vil", + "S w", + "Ġhom eless", + "we ek", + "i ens", + "r ics", + "ĠTh ird", + "T O", + "Ġorgan ic", + "Ġpresent ation", + "ag h", + "ĠDown load", + "v ation", + "Ġas sembly", + "or able", + "hold ers", + "ĠBern ie", + "ĠHel p", + "Ġt ong", + "ĠF ight", + "Ġbe ach", + "B ook", + "ĠL ic", + "Ġr ush", + "ĠR ound", + "ou p", + "ĠMar x", + "Ġcalcul ated", + "ĠDe vil", + "ĠSar ah", + "Ġoccasion ally", + "Ġbul let", + "Av ailable", + "g ate", + "Ġ9 1", + "Ġh osp", + "Ġprom ises", + "ĠH IV", + "ĠSt adium", + "ĠSt ock", + "ĠCorpor ation", + "g age", + "N G", + "ĠC redit", + "Ġs ne", + "ib l", + "Ġacc um", + "s uch", + "Ġterror ists", + "Ġconscious ness", + "ĠZ h", + "Ġdram a", + "ool a", + "pir ation", + "Ġlab our", + "ĠN in", + "Ġut ter", + "Ġdemocr atic", + "Ġass ass", + "il ation", + "Ġg est", + "Ġab road", + "Ġmet ab", + "Ġs orts", + "Ġfl av", + "U B", + "Ġm g", + "ĠNot hing", + "ĠO d", + "Ġmus ical", + "200 9", + "Ġdro ps", + "oc ated", + "ater al", + "0000 00", + "Ġg re", + "Ġequ ality", + "Ġburd en", + "Ġv ig", + "ĠLe ader", + "-------- ----", + "Ġcere mony", + "Ġf ighter", + "Ġact ors", + "Ġ æ", + "am an", + "F i", + "Ġal ign", + "put er", + "Ġe lder", + "ĠN SA", + "Ġrepresent ation", + "ĠOnt ario", + "IT H", + "usal em", + "Ġharass ment", + "itz er", + "Ġsy mp", + "Ġbox es", + "ĠD R", + "Ġman ifest", + "at re", + "Ġ ^", + "Ġd ies", + "le ton", + "Ġmiss ions", + "et he", + "Ġres olve", + "Ġfollow ers", + "Ġas c", + "Ġk m", + "l ord", + "am med", + "Ġsil ent", + "ĠAssoci ated", + "Ġtim ing", + "Ġprison ers", + "ĠK ings", + "ĠF ive", + "Ġtow er", + "Ġappro aches", + "Ġprecise ly", + "Ġb ureau", + "ĠM other", + "ĠI ss", + "Ġkey board", + "it ual", + "Ġfund ed", + "Ġstay ing", + "Ġpsych ological", + "Ġm ile", + "ĠLe on", + "ĠBar b", + "w ill", + "Ġw ider", + "ĠAtl antic", + "Ġt ill", + "ĠR ome", + "ro t", + "Ġaccomp an", + "Ġfl our", + "ac o", + "W orld", + "ĠExp ress", + "ĠY u", + "C or", + "Ġple ased", + "part y", + "Ġpoint ing", + "Ġinf lation", + "Ġro y", + "Ġ ),", + "ain er", + "Ġwedd ing", + "orm on", + "Ġrequ iring", + "Ġqual ified", + "Ġse gment", + "EN D", + "Ġs izes", + "e als", + "Ġcor rupt", + "ass ador", + "Ġcele b", + "Ġdream s", + "ĠM ess", + "Ġcheck ing", + "ĠV ersion", + "Ġprep aring", + "Ġact ively", + "ĠD iff", + "Ġl ux", + "ĠW inter", + "act eria", + "ĠN E", + "Ġdep uty", + "Ġtrans gender", + "Ġsum mary", + "Ġin her", + "er ies", + "ch ar", + "ĠY an", + "Ġkn ock", + "ĠP ath", + "Ġl ip", + "roll er", + "Ġimp ression", + "Ġcelebr ate", + "Ġsl ide", + "Ġgu ests", + "Ġcl ip", + "F S", + "Ġsav ings", + "Ġcapt ain", + "Ġleg acy", + "ĠDen ver", + "Ġw ounded", + "tab oola", + "AC T", + "Ġpurs ue", + "Ġo xy", + "Ġ q", + "Ġsem i", + "ĠN eed", + "ĠAff airs", + "Ġob sc", + "Ġcheck ed", + "Ġd ual", + "C ode", + "ĠM D", + "le m", + "ult y", + "Ġ ©", + "ĠEl izabeth", + "Ġcent uries", + "ard ed", + "s rc", + "Ġev ident", + "enn is", + "at in", + "Ġunemploy ment", + "ĠMar io", + "Ġint im", + "Ch rist", + "Ġbi ological", + "Ġsold ier", + "ĠAdd ed", + "Ġm ath", + "ĠG il", + "Ġbi as", + "Ġd ating", + "ĠO cean", + "Ġm ice", + "M us", + "h ire", + "ĠT es", + "Ser ver", + "lim ited", + "S ize", + "Ġmet ers", + "Ġrock et", + "es see", + "Ġcertific ate", + "ĠIran ian", + "AS S", + "Ġgr id", + "D ec", + "Ġro lling", + "com mun", + "ĠSwed en", + "b ury", + "Ġtiss ue", + "Ġrac ism", + "ĠL ocal", + "Ġmyster y", + "Ġexam ine", + "Ġst em", + "Ġs its", + "Ġhop ed", + "ot ing", + "Ġdial ogue", + "Ġpers u", + "W atch", + "l ay", + "M AN", + "Ġch ronic", + "ĠPort land", + "mark et", + "ĠS EC", + "Ġparalle l", + "Ġsc andal", + "Ġcar ries", + "Ġphenomen on", + "h uman", + "ack er", + "ĠO x", + "Ġretire ment", + "tain ment", + "ov ie", + "ĠG ear", + "Ġd uties", + "Ġdo se", + "Ġsc roll", + "M B", + "in f", + "Ġsa uce", + "Ġland scape", + "red dit", + "ĠChampions hip", + "ĠRed dit", + "al id", + "Ġco in", + "Ġover s", + "Ġpost ing", + "ab out", + "Ġf el", + "and y", + "Ġb old", + "Ġfocus ing", + "e ffect", + "G R", + "Ġde emed", + "Ġrecommend ations", + "Ġste pped", + "Ġvot er", + "ĠDe ep", + "ĠInst agram", + "Ġmoder ate", + "ĠMary land", + "Ġrestrict ed", + "ĠM B", + "ĠCh all", + "Ġto b", + "Ġc ir", + "ĠO cc", + "ĠE ver", + "Ġcoll aps", + "IN FO", + "= -", + "ĠP ict", + "ĠAcc ount", + "n c", + "Ġo ught", + "Ġex port", + "Ġdr unk", + "( '", + "Ġw ise", + "ĠM ort", + "ne cess", + "Ġan cest", + "ĠInc re", + "Ġfrequ ent", + "m ir", + "Ġinterpret ation", + "Ġdepend ent", + "Ġco ins", + "ĠB ol", + "V ideo", + "ĠJust in", + "Ġfat al", + "Ġcook ing", + "Ġconf usion", + "ip her", + "Ġcust ody", + "ĠMor gan", + "om ach", + "ĠGovern or", + "Ġrestaur ants", + "el ing", + "Ġacknowled ged", + "Ġthe r", + "Ġgen es", + "ch ing", + "He y", + "Ġtact ics", + "ĠMex ican", + "Ġv end", + "Ġhe s", + "qu er", + "Ġnot ing", + "ĠCamer on", + "Ġtarget ing", + "ro ck", + "Ġcred its", + "Ġemot ions", + "Ġrepresent atives", + "new s", + "Ġlegisl ative", + "Ġrem oving", + "Ġtweet ed", + "ĠCar ter", + "ĠF ixed", + "Ġfor cing", + "Ġspeak er", + "Ġm ales", + "ĠViet nam", + "l ined", + "Ġconcept s", + "Ġvo ices", + "o ir", + "ĠT rib", + "W he", + "ĠJer usalem", + "ĠS ant", + "Ġc ul", + "Ġl ady", + "ĠHaw ai", + "Ġar ts", + "ĠIn n", + "ĠMach ine", + "ĠEm peror", + "Ġsl ot", + "g ly", + "ĠPro cess", + "II I", + "Ġathlet es", + "ĠTem ple", + "ĠRep resent", + "Ġpres c", + "Ġt ons", + "Ġgold en", + "Ġp unch", + "ĠG R", + "iver pool", + "Ġen act", + "Ġlob by", + "Ġm os", + "Ġpick ing", + "Ġlif etime", + "Ġcogn itive", + "E ach", + "z o", + "Ġd ub", + "Ġcons ists", + "ol n", + "Ġf estival", + "am ous", + "Ġint ellig", + "w ords", + "ĠSm art", + "Ġde le", + "Ġl apt", + "Ġmag ical", + "ĠS in", + "b us", + "ur ities", + "igh th", + "ĠRub y", + "ĠS ure", + "ol ving", + "Ġj un", + "O ST", + "Ġimp osed", + "Ġast ron", + "Ġcor rel", + "ĠN S", + "ĠK it", + "ĠF uture", + "b urn", + "Ġimm une", + "oc us", + "Ġcour ses", + "ĠSt ring", + "Ġle an", + "Ġg host", + "Ġout comes", + "Ġexp ense", + "Ġevery day", + "Ġaccept able", + "A h", + "Ġequ ipped", + "Ġor ange", + "F R", + "ĠD utch", + "Th ough", + "ĠR ank", + "Q U", + "ĠRober ts", + "wh at", + "re nd", + "Ġdisapp ear", + "Ġsp awn", + "ĠL am", + "o is", + "Ġdes erve", + "Ġmin imal", + "Ġnerv ous", + "ĠW ould", + "Ġro ok", + "ĠV ancouver", + "Ġres ign", + "sh ire", + "ĠW orks", + "ĠB uild", + "Ġafford able", + "ĠG ary", + "ĠAren a", + "Ġh anging", + "Ġimpl ications", + "ĠS ong", + "Ġmain taining", + "Ġgu ards", + "C ON", + "Ġder ived", + "Ġexecut ed", + "Ġthe ories", + "Ġqu oted", + "ĠAnd re", + "og a", + "sel ess", + "in fo", + "ĠBel g", + "Ġt ears", + "ĠSur v", + "Ġbirth day", + "ig ious", + "im mer", + "Ġspect rum", + "Ġarchitect ure", + "Ġrec ruit", + "arm a", + "T able", + "Ġmon sters", + "ĠG ov", + "Ġdest ination", + "Ġattract ive", + "Ġf oss", + "ĠMore over", + "Ġpres ents", + "TH E", + "Ġrep ly", + "pt on", + "Ġc um", + "Ġdel ight", + "Ġaffect s", + "Ġdon ations", + "ĠT oy", + "ĠH im", + "M ENT", + "Ġover come", + "it ched", + "ĠFant asy", + "ĠH at", + "ĠBe ast", + "b ott", + "Ġinvestig ations", + "R un", + "Ġhun ting", + "d i", + "f und", + "Ġs essions", + "est yle", + "Ġport ray", + "oid s", + "Y eah", + "Ġcommun icate", + "Ġcom edy", + "ĠY ang", + "Ġbel t", + "ĠMar ine", + "Ġpredict ed", + "Pl ay", + "Ġimportant ly", + "Ġremark able", + "Ġelim inate", + "D avid", + "Ġb ind", + "V ID", + "Ġadvoc ates", + "ĠG aza", + "im p", + "D B", + "ĠN a", + "ĠSim ilar", + "I ES", + "Ġchar ity", + "v as", + "m ath", + "Ġâ ĸ", + "ok er", + "nd um", + "Ġcap s", + "ĠH al", + "2 000", + "e an", + "Ġfle et", + "Ġrec re", + "R ight", + "Ġsleep ing", + "ij ing", + "k ind", + "Ġdesign ated", + "à ¤", + "Ġanim ation", + "ke e", + "ĠInt rodu", + "Ġ/ >", + "Ġdelay ed", + "Ġtrem end", + "Ġcur ious", + "U se", + "Ġle ct", + "d am", + "Ġinnov ation", + "ĠPoint s", + "Ġload ing", + "Ġdisp ute", + "ct ic", + "ird s", + "ĠB Y", + "Ġn urs", + "ĠVal ue", + "ION S", + "ĠH um", + "Ġtem plate", + "m ers", + "Ġappear ances", + "ĠEnter tainment", + "Ġtransl ation", + "Ġsa ke", + "Ġbene ath", + "Ġin hib", + "Ġe uro", + "abet es", + "Ġstud ying", + "ĠM as", + "Ġper ceived", + "Ġexam ined", + "Ġe ager", + "Ġco aches", + "Ġim per", + "ch i", + "Ġprodu ces", + "\" ).", + "ĠEvery one", + "Ġm unicip", + "Ġg irlfriend", + "Ġh ire", + "ĠV ice", + "Ġsu itable", + "op y", + "Ġin equ", + "ĠD uke", + "f ish", + "f irst", + "ĠO bs", + "Ġinter ior", + "ĠBru ce", + "ĠR y", + "Ġanal ys", + "Ġconsider able", + "Ġfore cast", + "Ġf ert", + "ors hip", + "ĠD rug", + "ĠA LL", + ": \"", + "th ur", + "ĠM ail", + "Ġball ot", + "Ġinst antly", + "ĠCh annel", + "Ġp icks", + "Ġ198 9", + "Ġt ent", + "ol i", + "Ġcivil ian", + "b ling", + "ell o", + "b u", + "Ġin ch", + "Ġlog o", + "Ġcooper ation", + "Ġwal ks", + "Ġinvest ments", + "Ġimp rison", + "ĠF estival", + "ĠK y", + "Ġleg ally", + "Ġg ri", + "ch arg", + "S l", + "Ġthreat ening", + "du ction", + "fl ow", + "Ġdismiss ed", + "ibr aries", + "c ap", + "e le", + "ĠMc G", + "ĠHar vard", + "ĠConserv ative", + "ĠC BS", + "p ng", + "Ġro ots", + "ĠH aving", + "umb led", + "ĠF un", + "\\ /", + "ĠS earch", + "ple x", + "Ġdiscuss ing", + "Ġcontin u", + "ĠT ai", + "ĠW ik", + "F ree", + "f it", + "Ġref use", + "Ġmanag ing", + "Ġsy nd", + "ip edia", + "w alk", + "Ġprofession als", + "Ġguid ance", + "Ġunivers ities", + "Ġas semb", + "unt u", + "F inally", + "AS E", + "ĠAut o", + "ĠH ad", + "Ġann iversary", + "L D", + "ĠD ur", + "ĠUlt imate", + "ih ad", + "pro duct", + "Ġtrans it", + "Ġrest ore", + "Ġexpl aining", + "Ġass et", + "Ġtransfer red", + "Ġbur st", + "ap olis", + "ĠMag azine", + "ĠC ra", + "ĠB R", + "gg ed", + "ĠH E", + "M ich", + "b et", + "ĠL ady", + "yl um", + "erv es", + "Ġme ets", + "wh ite", + "L og", + "Ġcorrespond ing", + "Ġins isted", + "G G", + "Ġsurround ed", + "Ġt ens", + "Ġl ane", + "Ġco inc", + "h ome", + "Ġexist ed", + "ect ed", + "ĠDou ble", + "lam m", + "Ġske pt", + "ex p", + "Ġper ception", + "ie v", + "ĠBe ing", + "o ft", + "Ġadop t", + ". :", + "] ;", + "Wind ows", + "Ġsatell ite", + "AS H", + "Ġinf ant", + "d escription", + "ĠMe anwhile", + "c m", + "oc a", + "ĠT reat", + "act or", + "Ġtob acco", + "ĠN orm", + "em ption", + "Ġfl esh", + "Ġj e", + "o op", + "ĠHe aven", + "Ġbe ating", + "an im", + "Ġgather ing", + "Ġcult iv", + "G O", + "ab e", + "ĠJon athan", + "ĠSaf ety", + "Ġbad ly", + "pro t", + "Ġcho osing", + "Ġcontact ed", + "Ġqu it", + "Ġdist ur", + "Ġst ir", + "Ġto ken", + "D et", + "ĠP a", + "Ġfunction ality", + "00 3", + "s ome", + "Ġlimit ations", + "Ġmet h", + "b uild", + "con fig", + "N T", + "re ll", + "ble m", + "ĠM om", + "Ġveter ans", + "ĠH u", + "Ġtrend s", + "are r", + "ĠG iven", + "ĠCa ption", + "m ay", + "AS T", + "Ġwond ering", + "ĠCl ark", + "n ormal", + "Ġsepar ated", + "Ġdes p", + "st ic", + "b rew", + "Ġrel ating", + "ĠN ik", + "ĠF arm", + "Ġenthus i", + "g ood", + "d eb", + "Ġactiv ist", + "Ġm art", + "Ġexplos ion", + "ĠEconom ic", + "L ink", + "Ġins ight", + "Ġconven ient", + "Ġcounter part", + "su pport", + "ĠV irt", + "ag en", + "ĠTenn essee", + "ĠSim on", + "ĠA ward", + "OC K", + "ĠF igure", + "Ġoverse as", + "Ġpr ide", + "ĠC as", + "n ote", + "m g", + "C urrent", + "Ġdispl ays", + "cont ent", + "Ġtravel ing", + "Ġhosp itals", + "ĠFin ancial", + "ĠP ast", + "Ġdefend ant", + "Ġstream ing", + "m ble", + "ĠBer lin", + "uk i", + "Ġdist ribut", + "Ġant ib", + "Ġch ocolate", + "ĠCast le", + "Ġinter rupt", + "ĠR ow", + "Ġconvers ion", + "Ġbug s", + "ĠR ather", + "li est", + "L Y", + "ĠJe an", + "com mon", + "ak h", + "Ġ1 30", + "ot ton", + "ĠDe an", + "Ġam endment", + "Ġgame play", + "ĠWar ren", + "od a", + "Ġhigh lights", + "Ġir re", + "ĠNAT O", + "Ġball s", + "Ġdemand ing", + "U RE", + "ĠL uke", + "F igure", + "st op", + "on ia", + "z one", + "iz ers", + "ĠW R", + "Ġaward ed", + "Ġregul atory", + "ĠH art", + "ĠS N", + "pl ing", + "Ġs our", + "ĠP ixel", + "us ive", + "Ġf et", + "ĠS ent", + "Ġautom atic", + "Ġf er", + "vern ment", + "ĠKh an", + "T ON", + "f ather", + "Ġextraord inary", + "th rop", + "ĠP ython", + "ĠG PU", + "Ġsex ually", + "Ġdesk top", + "it ivity", + "ĠAnton io", + "Ġo rient", + "Ġe ars", + "ob by", + "ous es", + "vertis ements", + "Ġmanufacture rs", + "ic ient", + "min ute", + "Ġconv iction", + "Ġg arden", + "p ublic", + "Ġsatisf ied", + "f old", + "O K", + "Ġin hab", + "ĠTh ink", + "Ġprogram me", + "Ġst omach", + "Ġcoord in", + "Ġh oly", + "Ġth reshold", + "Ġr het", + "Ġser ial", + "Ġemploy ers", + "ĠEvery thing", + "ra h", + "Ġb other", + "Ġbr ands", + "Val ue", + "ĠT ed", + "ĠPlan et", + "Ġp ink", + "ĠFurther more", + "s a", + "P E", + "re ck", + "ĠUS D", + "ot te", + "Ġ& &", + "Ġland ed", + "g ets", + "Ġprodu cers", + "Ġhealth care", + "Ġdomin ant", + "Ġdest ro", + "Ġam ended", + "ch ron", + "Ġf its", + "ĠSy d", + "ĠAuthor ity", + "AT CH", + "Ġfight s", + "ĠL LC", + "Ġ-- -", + "ĠCor p", + "Ġtox ic", + "spe cific", + "ĠC orn", + "ĠChe l", + "Ġtele phone", + "ĠP ant", + "Ġmyster ious", + "aun ch", + "od ox", + "med ia", + "Ġwitness es", + "ag u", + "Ġquestion ed", + "ĠBre xit", + "ĠRem ember", + "ene z", + "Ġend orse", + "iat ric", + "ĠId ent", + "Ġridic ulous", + "1 10", + "Ġpr ayer", + "Ġscient ist", + "Ġ19 50", + "ĠA qu", + "Ġunder ground", + "ĠU FC", + "m are", + "ĠL ater", + "w ich", + "Ġsubsc rib", + "Ġhost s", + "Ġer r", + "Ġgr ants", + "ant om", + "Ġsum mon", + "ear ly", + "ĠC lear", + "ĠPr im", + "Ġsusp ension", + "Ġguarant eed", + "app er", + "Ġr ice", + "ĠSe an", + "ĠSh in", + "Ġrefere ndum", + "Ġfl ed", + "r ust", + "Ġ3 60", + "ter y", + "Ġsh ocked", + "B R", + "ĠO il", + "ĠAll ah", + "Ġpart ly", + "Ġign or", + "Ġtrans mission", + "Ġhom osexual", + "ivers al", + "Ġhop efully", + "ãĤ ¤", + "Ġless on", + "L eg", + "Ġ ..", + "Y et", + "t able", + "app ropri", + "re tt", + "Ġbo ards", + "Ġincor rect", + "Ġb acteria", + "ar u", + "am ac", + "Ġsn ap", + ".' \"", + "Ġpar ad", + "t em", + "he art", + "Ġav ailability", + "Ġw isdom", + "Ġ( +", + "Ġpri est", + "ĠÂł ĠÂł", + "O pen", + "Ġsp an", + "Ġparam eter", + "Ġconv ince", + "Ġ( %)", + "r ac", + "Ġf o", + "Ġsafe ly", + "Ġconver ted", + "ĠOlymp ic", + "Ġres erve", + "Ġhe aling", + "ĠM ine", + "M ax", + "Ġin herent", + "ĠGra ham", + "Ġinteg rated", + "D em", + "Ġpip eline", + "Ġapp lying", + "Ġem bed", + "ĠCharl ie", + "Ġc ave", + "200 8", + "Ġcons ensus", + "Ġre wards", + "P al", + "ĠHT ML", + "Ġpopular ity", + "look ing", + "ĠSw ord", + "ĠAr ts", + "' )", + "Ġelect ron", + "clus ions", + "Ġinteg rity", + "Ġexclus ively", + "Ġgr ace", + "Ġtort ure", + "Ġburn ed", + "tw o", + "Ġ18 0", + "P rodu", + "Ġent reprene", + "raph ics", + "Ġg ym", + "ric ane", + "ĠT am", + "Ġadministr ative", + "Ġmanufacture r", + "Ġ vel", + "ĠN i", + "Ġisol ated", + "ĠMedic ine", + "Ġback up", + "Ġpromot ing", + "Ġcommand er", + "Ġfle e", + "ĠRus sell", + "Ġforg otten", + "ĠMiss ouri", + "Ġres idence", + "m ons", + "Ġrese mb", + "Ġw and", + "Ġmeaning ful", + "P T", + "Ġb ol", + "Ġhe lic", + "Ġwealth y", + "Ġr ifle", + "str ong", + "row ing", + "pl an", + "as ury", + "âĢ¦ .", + "Ġexpand ing", + "ĠHam ilton", + "Ġrece ives", + "S I", + "eat ures", + "ĠAn im", + "RE E", + "P ut", + "Ġbrief ly", + "ri ve", + "Ġstim ul", + "Ġ`` (", + "Ġ __", + "Ġch ip", + "Ġha z", + "Ġpri ze", + "ĠTh ings", + "AC E", + "ul in", + "d ict", + "ok u", + "Ġassoci ate", + "ock ets", + "y outube", + "St ory", + "ateg ory", + "Ġm ild", + "ail ing", + "ĠY e", + "O rig", + "ĠK a", + "or ig", + "Ġpropag anda", + "Ġan onymous", + "Ġstrugg led", + "Ġout rage", + "AT ED", + "ĠBe ijing", + "r ary", + "Ġle ather", + "Ġworld s", + "Ġbroad er", + "12 5", + "id al", + "ĠBet ter", + "Ġt ear", + "E xt", + "Ġpropos als", + "Ġit er", + "ĠSqu ad", + "Ġvol unt", + "m i", + "D id", + "ĠP u", + "p in", + "Ġspeak ers", + "Ġb orders", + "Ġfig ured", + "= '", + "Ġsimultane ously", + "aed a", + "Ġcharg ing", + "Ġur ged", + "Ġcon j", + "25 6", + "ĠG ordon", + "mer ce", + "Ġdocument ary", + "Sh are", + "it ol", + "ON E", + "ĠG arden", + "h att", + "ĠThom pson", + "ane ous", + "ap ore", + "Ġt anks", + "Ġless ons", + "tr ack", + "Ġout standing", + "Ġvolunte ers", + "Ġsp ray", + "Ġmanag ers", + "l arge", + "Ġcamp s", + "Ġart ificial", + "ĠR u", + "Ġb ags", + "th al", + "Ġcompat ible", + "ĠBl ade", + "Ġf ed", + "Ġarg ues", + "F I", + "Ġunf air", + "Ġcor n", + "Ġoff set", + "Ġdirect ions", + "Ġdisappoint ed", + "ĠCon vention", + "Ġview ing", + "M E", + "oc ity", + "Ġtown s", + "Ġlay ers", + "Ġro lled", + "Ġjump ed", + "Ġatt ribute", + "Ġun necess", + "inc oln", + "Ġsupp ose", + "ĠNet her", + "ch a", + "Ġbur ied", + "Ġsix th", + "B en", + "ress ing", + "OU R", + "Ġw ound", + "Ġcy cl", + "Ġmechan isms", + "Ġcongress ional", + "ĠE lement", + "Ġagre ements", + "Ġdec or", + "Ġclos est", + "ĠM it", + "Go ogle", + "} }", + "Ġm ixture", + "Ġflu id", + "S ign", + "ĠSch olar", + "Ġp ist", + "ask et", + "ab ling", + "Ġrac ing", + "he ro", + "ri el", + "ass y", + "Ġche aper", + "b en", + "Ġvert ical", + "amac are", + "ĠRead ing", + "g ments", + "Ġhelic op", + "Ġsacr ifice", + "ay a", + "p aren", + "V A", + "ĠL es", + "ĠStud io", + "Ġviol ations", + "ĠAn na", + "ac er", + "é ¾", + "ĠR at", + "ĠBe ck", + "ĠD ick", + "ĠA CT", + "Ġcomp osition", + "Ġtext ure", + "ĠO wn", + "Ġsmart phone", + "ĠN A", + "Ġfor b", + "im port", + "Ġdef ending", + "il st", + "re r", + "Ġo h", + "ĠJere my", + "Ġbank ing", + "cept ions", + "Ġrespect ive", + "/ .", + "Ġdr inks", + "ĠW i", + "Ġb ands", + "ĠL iverpool", + "Ġg rip", + "ĠB uy", + "Ġopen ly", + "Ġreview ed", + "per t", + "Ġver ify", + "ĠCo le", + "ĠW ales", + "M O", + "Ġun pre", + "Ġshel ter", + "ĠIm perial", + "Ġgu i", + "ĠD ak", + "Ġsuggest ions", + "Ġexplicit ly", + "Ġsl ave", + "Ġblock chain", + "Ġcompet ing", + "Ġprom ising", + "S ON", + "Ġsoc cer", + "Ġconst itution", + "4 29", + "Ġdist ract", + "ĠU ser", + "es ides", + "ĠMet hod", + "ĠTok yo", + "Ġaccompan ied", + "Cl ient", + "s ur", + "al og", + "Ġident ification", + "Ġinv asion", + "as ma", + "Ġindust ries", + "pp ers", + "Ġsub tle", + "ĠUn it", + "n atural", + "Ġsurv ived", + "Ġfl aw", + "ĺ ħ", + "ĠH oll", + "Ġdef icit", + "Ġtut orial", + "ĠCh ance", + "Ġarg uing", + "Ġcontem porary", + "Ġinteg ration", + "for ward", + "Ġt um", + "it is", + "Ġh iding", + "ĠD omin", + "ĠT an", + "ĠB uilding", + "ĠV in", + "Ġspokes person", + "ĠNot es", + "Ġemer ging", + "Ġprepar ation", + "Ġpro st", + "Ġsuspect s", + "Ġaut onom", + "D escription", + "Ġdeal t", + "ĠP ear", + "Ġstead y", + "Ġdecre ased", + "Ġso vere", + "ĠCl in", + "Ġgrad ually", + "ors es", + "ĠW AR", + "S erv", + "ãĤ ¢", + "h r", + "Ġd irty", + "ĠB arn", + "ĠB C", + "Ġd il", + "Ġcal endar", + "Ġcompl iance", + "Ġch amber", + "b b", + "Ġpass enger", + "ate ful", + "ĠT itle", + "ĠSyd ney", + "ĠG ot", + "Ġdark ness", + "Ġdef ect", + "Ġpack ed", + "ass ion", + "Ġgod s", + "Ġh arsh", + "IC K", + "le ans", + "Ġalgorith m", + "Ġoxy gen", + "Ġvis its", + "Ġbl ade", + "Ġkil omet", + "ĠKent ucky", + "Ġkill er", + "P ack", + "enn y", + "Ġdiv ine", + "Ġnom ination", + "be ing", + "Ġeng ines", + "Ġc ats", + "Ġbuff er", + "ĠPh ill", + "Ġtra ff", + "AG E", + "Ġtong ue", + "Ġrad iation", + "ere r", + "m em", + "ĠExpl icit", + "é¾ į", + "Ġcou ples", + "Ġphys ics", + "ĠMc K", + "Ġpolit ically", + "aw ks", + "ĠBl oom", + "Ġwor ship", + "e ger", + "ut er", + "ĠF O", + "Ġmat hemat", + "Ġsent enced", + "Ġdis k", + "ĠM arg", + "Ġ/ *", + "P I", + "Ġoption al", + "Ġbab ies", + "Ġse eds", + "ĠScott ish", + "Ġth y", + "] ]", + "ĠHit ler", + "P H", + "ng th", + "Ġrec overed", + "ing e", + "Ġpow der", + "Ġl ips", + "Ġdesign er", + "Ġdis orders", + "Ġcour age", + "Ġch aos", + "\" },{\"", + "Ġcar rier", + "b ably", + "H igh", + "ĠR T", + "es ity", + "l en", + "Ġrout es", + "u ating", + "F il", + "N OT", + "w all", + "s burgh", + "Ġeng aging", + "ĠJava Script", + "ore r", + "li hood", + "Ġun ions", + "ĠF ederation", + "ĠTes la", + "Ġcomple tion", + "ĠT a", + "Ġprivile ge", + "ĠOr ange", + "Ġne ur", + "paren cy", + "Ġb ones", + "Ġtit led", + "Ġprosecut ors", + "ĠM E", + "Ġengine er", + "ĠUn iverse", + "ĠH ig", + "n ie", + "o ard", + "Ġheart s", + "ĠG re", + "uss ion", + "Ġmin istry", + "Ġpen et", + "ĠN ut", + "ĠO w", + "ĠX P", + "in stein", + "Ġbul k", + "S ystem", + "ic ism", + "ĠMarket able", + "Ġpre val", + "Ġpost er", + "Ġatt ending", + "ur able", + "Ġlicens ed", + "ĠG h", + "et ry", + "ĠTrad able", + "Ġbl ast", + "à ¤", + "ĠTit an", + "ell ed", + "d ie", + "H ave", + "ĠFl ame", + "Ġprof ound", + "Ġparticip ating", + "Ġan ime", + "ĠE ss", + "Ġspec ify", + "Ġregard ed", + "ĠSpe ll", + "Ġs ons", + "own ed", + "Ġm erc", + "Ġexper imental", + "land o", + "h s", + "ĠDun geon", + "in os", + "Ġcomp ly", + "ĠSystem s", + "ar th", + "Ġse ized", + "l ocal", + "ĠGirl s", + "ud o", + "on ed", + "ĠF le", + "Ġconstruct ed", + "Ġhost ed", + "Ġsc ared", + "act ic", + "ĠIs lands", + "ĠM ORE", + "Ġbl ess", + "Ġblock ing", + "Ġch ips", + "Ġev ac", + "P s", + "Ġcorpor ation", + "Ġo x", + "Ġlight ing", + "Ġneighb ors", + "ĠU b", + "ar o", + "Ġbe ef", + "ĠU ber", + "F acebook", + "ar med", + "it ate", + "ĠR ating", + "ĠQu ick", + "Ġoccup ied", + "Ġaim s", + "ĠAdd itionally", + "ĠInt erest", + "Ġdram atically", + "Ġhe al", + "Ġpain ting", + "Ġengine ers", + "M M", + "ĠM ust", + "Ġquant ity", + "P aul", + "Ġearn ings", + "ĠPost s", + "st ra", + "ãĥ¼ ãĥ", + "Ġst ance", + "Ġdro pping", + "sc ript", + "Ġd ressed", + "M ake", + "Ġjust ify", + "ĠL td", + "Ġprompt ed", + "Ġscr ut", + "Ġspeed s", + "ĠGi ants", + "om er", + "ĠEd itor", + "Ġdescrib ing", + "ĠL ie", + "ment ed", + "Ġnow here", + "oc aly", + "Ġinst ruction", + "fort able", + "Ġent ities", + "Ġc m", + "ĠN atural", + "Ġinqu iry", + "Ġpress ed", + "iz ont", + "for ced", + "Ġra ises", + "ĠNet flix", + "ĠS ide", + "Ġout er", + "Ġamong st", + "im s", + "ows ki", + "Ġclim b", + "ne ver", + "Ġcomb ine", + "d ing", + "Ġcomp r", + "Ġsignific ance", + "Ġremem bered", + "ĠNev ada", + "ĠT el", + "ĠSc ar", + "ĠWar riors", + "ĠJ ane", + "Ġcou p", + "b as", + "Ġtermin al", + ", -", + "O H", + "Ġt ension", + "Ġw ings", + "ĠMy ster", + "�� ��", + "ĠUn like", + "val id", + "viron ments", + "ĠAl i", + "Ġn aked", + "book s", + "ĠM un", + "ĠG ulf", + "Ġd ensity", + "Ġdim in", + "Ġdesper ate", + "Ġpres idency", + "Ġ198 6", + "h y", + "IN D", + "Ġun lock", + "im ens", + "Ġhand led", + "ĠE b", + "Ġdisapp eared", + "Ġgen re", + "Ġ198 8", + "Ġdetermin ation", + "St ream", + "ik o", + "ap ters", + "Ġacknow ledge", + "J an", + "Ġcapital ism", + "P at", + "Ġ20 20", + "Ġpain ful", + "Ġcur ve", + "Ġbom bs", + "st orm", + "ĠMet al", + "en cer", + "ĠF ig", + "ĠA aron", + "anc hes", + "Ġins piration", + "Ġexha ust", + "t ains", + "ash i", + "Ġdesc ript", + "Ġr itual", + "ĠChel sea", + "Ġpromot ion", + "ĠH ung", + "ĠW ard", + "iv a", + "ĠE T", + "Ġto ss", + "all ow", + "ĠFranc is", + "D ep", + "Ġhapp iness", + "ĠGl ass", + "Ġbet a", + "Ġstreng then", + "N E", + "o a", + "Ġbutt ons", + "ĠMur ray", + "Ġkick ed", + "Qu est", + "ĠT alk", + "ĠS everal", + "ĠZ ero", + "Ġdr one", + "ul k", + "Ġc am", + "ĠM obile", + "Ġprevent ing", + "Ġret ro", + "ĠA x", + "Ġcru el", + "Ġflo at", + ". ),", + "Ġfil ing", + "ĠGr ant", + "ĠB or", + "Ġr ib", + "Ġchampions hip", + "ĠM erc", + "Ġsty les", + "Ġc ake", + "Ġbuild s", + "ĠS elf", + "io x", + "Ġep ic", + "oy d", + "B el", + "ĠSt ew", + ". (", + "ah u", + "ĠBe yond", + "Ġout s", + "Ġsol o", + "ĠT ree", + "Ġpres erve", + "Ġt ub", + "AR E", + "ro c", + "ĠIm pro", + "ĠW right", + "Ġbu nd", + "Ġtr aged", + "Ġoccas ional", + "b ian", + "Sec ond", + "r ons", + "Ġinter actions", + "form ed", + "s ing", + "Ġown s", + "Ġh ockey", + "Gener al", + "Ġlog ical", + "Ġexp end", + "Ġesc al", + "ĠGr iff", + "ĠC rown", + "ĠRes erve", + "Ġsto pping", + "Ġexc use", + "sec ond", + "Ġoper ated", + "Ġre aches", + "ĠMal ays", + "Ġpoll ution", + "ĠBrook lyn", + "Ġde lete", + "Ġhas h", + "Bl ock", + "ah a", + "âĢ ³", + "Ġsh orter", + "p iece", + "> >>", + "ĠM ormon", + "t or", + "Ġpartic les", + "ĠB art", + "ry ption", + "Ġad min", + "Ġsqu ee", + "VID IA", + "Ġcreat or", + "iam eter", + "ic ular", + "N BC", + "Ġgrab bed", + "Ġn odd", + "Ġr ated", + "Ġrot ation", + "Ġgr asp", + "Ġexcess ive", + "ĠE C", + "ĠWh it", + "Ġinvent ory", + "ault s", + "ĠF B", + "Ġe cosystem", + "Ġbill ions", + "Ġvent ure", + "n amed", + "Ġdef ender", + "out e", + "Inst ead", + "ir able", + "W ar", + "Ġassum ption", + "Ġb ite", + "Ġearth qu", + "t ail", + "sp ace", + "Ġgif ts", + "boy s", + "Ġinev itable", + "Ġstruct ural", + "Ġbenef icial", + "Ġcompe lling", + "h ole", + "erv ation", + "Ġco at", + "o j", + "inc arn", + "ĠY ears", + "Ġdetermin ing", + "Ġrhet oric", + "Ġbound aries", + "Ġwh ites", + "A nt", + "add y", + ") -", + "ra ham", + "eter min", + "Ġhar vest", + "ĠCon c", + "Ġlapt op", + "ĠM atch", + "Ġenjoy ing", + "cc a", + "oll ar", + "Ġtri ps", + "Ġadd iction", + "ĠS ak", + "Ġpow ered", + "Ġc ous", + "ĠRuss ians", + "ie re", + "Ġret rie", + "qu ality", + "Ġdiff er", + "Ġking dom", + "ĠL aur", + "ĠCap itol", + "Ġcon clusions", + "ĠAl tern", + "ĠN av", + "Ġtrans parent", + "B ER", + "G roup", + "ĠCom plete", + "Ġinf er", + "Ġint rig", + "Ġins ane", + "R O", + "oph ob", + "is en", + "qu al", + "Mich ael", + "Ġm useum", + "ĠP ope", + "Ġres et", + "r ative", + "f ive", + "Ġagg reg", + "itte es", + "osit ory", + "Ġcar b", + "ĠRec ord", + "Ġdec ides", + "ĠF ix", + "Ġexcept ions", + "ĠCommission er", + "un s", + "ĠEnvironment al", + "Ġlegend ary", + "ist ence", + "Ġtun nel", + "k m", + "Ġins ult", + "Ġt roll", + "Ġsh ake", + "Ġdet ention", + "qu es", + "ĠCh rome", + "ĠF iles", + "Ġsub t", + "Ġprospect s", + "Ġpro l", + "re nder", + "pro of", + "Ġperform ances", + "St r", + "Ġh ref", + "ern ame", + "Ġachieve ment", + "Ġf ut", + "F ull", + "ĠLe ban", + "go ogle", + "ãĥ Ī", + "amp a", + "May be", + "Ġproject ed", + "ĠE mb", + "Ġcol leg", + "Ġa wards", + "Ġâ Ķ", + "G old", + "ĠBl ake", + "ĠR aj", + "if ting", + "Ġp ending", + "Ġinst inct", + "Ġdevelop ments", + "Con nect", + "ĠM and", + "ĠW ITH", + "ĠPhilipp ines", + "prof ile", + "Ġalt ogether", + "ĠB und", + "ĠT D", + "oo oo", + "amp ed", + "ip h", + "Ġste am", + "Ġold est", + "Ġdet ection", + "ul pt", + "Ġ ç", + "ĠWay ne", + "200 6", + "f a", + "Ġcir cles", + "ĠF u", + "Ġdon ors", + "appropri ate", + "ĠDak ota", + "j amin", + "Ġmotiv ated", + "Ġpurch ases", + "ĠLouis iana", + "ĠS pl", + "Ġgl obe", + "Ġ10 5", + "z ip", + "c all", + "Ġdepart ments", + "Ġsustain able", + "10 5", + "ĠO P", + "if iers", + "Ġprevent ed", + "Ġinc omp", + "ĠComm ander", + "Ġdom inated", + "Ġ »", + "Ġinvest ed", + "Ġcomplex ity", + "Ġin cl", + "Ġens uring", + "Ġreal m", + "yn c", + "ĠInd ependent", + "r ained", + "ĠJ en", + "ĠFl ight", + "Ġat he", + "Ġspec ulation", + "ĠT E", + "oc ate", + "t ic", + "Ġpl aint", + "her ry", + "Ġto y", + "Ġ1 11", + "Ġpl ates", + "st atus", + "ĠIs a", + "Ġdev oted", + "C op", + "ĠE S", + "25 5", + "ur rency", + "M ain", + "Ġsl aves", + "Ġpe pper", + "Ġqu otes", + "Ġce iling", + "ĠF ish", + "Ġtrans formation", + "Ġfra ction", + "Ġadvant ages", + "Ġto ile", + "Ġstun ning", + "Ġmo ist", + "bre aking", + "s i", + "ĠL ocation", + "ĠMed ium", + "Ġtext s", + "Ġu gly", + "Ġb io", + ". âĢĶ", + "ĠB ased", + "Ġtr ains", + "ĠW ing", + "ĠAn cient", + "ĠRec ords", + "ĠH ope", + "Spe cial", + "ades h", + "ob i", + "[ /", + "Ġtempor arily", + "V er", + "h u", + "os er", + "Ġover night", + "Ġm amm", + "ĠTre asury", + "ĠV enezuel", + "ĠMeg a", + "Ġt ar", + "Ġexpect s", + "bl ack", + "or ph", + "\\\\ \\\\", + "Ġaccept ance", + "Ġrad ar", + "s is", + "Ġjun ior", + "Ġfram es", + "Ġobserv ation", + "ac ies", + "P ower", + "ĠAdv anced", + "M ag", + "olog ically", + "ĠMe chan", + "Ġsent ences", + "Ġanaly sts", + "augh ters", + "force ment", + "Ġv ague", + "Ġcl ause", + "Ġdirect ors", + "Ġeval uate", + "Ġcabin et", + "M att", + "ĠClass ic", + "A ng", + "Ġcl er", + "ĠB uck", + "Ġresear cher", + "Ġ16 0", + "Ġpoor ly", + "Ġexperien cing", + "ĠP ed", + "ĠMan hattan", + "Ġfre ed", + "Ġthem es", + "ad vant", + "Ġn in", + "Ġpra ise", + "10 4", + "ĠLib ya", + "b est", + "Ġtrust ed", + "Ġce ase", + "Ġd ign", + "D irect", + "Ġbomb ing", + "Ġm igration", + "ĠSci ences", + "Ġmunicip al", + "ĠA verage", + "Ġgl ory", + "Ġreve aling", + "Ġare na", + "Ġuncertain ty", + "Ġbattle field", + "ia o", + "G od", + "Ġc inem", + "ra pe", + "el le", + "ap ons", + "Ġlist ing", + "Ġwa ited", + "Ġsp otted", + "ke ley", + "ĠAud io", + "e or", + "ard ing", + "idd ing", + "ig ma", + "ĠN eg", + "Ġl one", + "Ġ ----", + "ex e", + "d eg", + "Ġtrans f", + "Ġwas h", + "Ġsl avery", + "Ġexpl oring", + "ĠW W", + "ats on", + "Ġen cl", + "l ies", + "ĠC reek", + "Ġwood en", + "Man ager", + "ĠBr and", + "um my", + "ĠAr thur", + "Ġbureau cr", + "Ġbl end", + "ar ians", + "F urther", + "Ġsupposed ly", + "Ġwind s", + "Ġ19 79", + "Ġgrav ity", + "Ġanalys es", + "ĠTra vel", + "ĠV eter", + "Ġd umb", + "Ġaltern ate", + "g al", + "Ġconsum ed", + "Ġeffect iveness", + ".' '", + "Ġpath s", + "ond a", + "L A", + "ĠStr ong", + "Ġen ables", + "Ġesc aped", + "Ġ\" \"", + "Ġ1 12", + "Ġ198 3", + "Ġsm iled", + "Ġtend ency", + "F ire", + "Ġp ars", + "ĠR oc", + "Ġl ake", + "Ġf itness", + "ĠA th", + "ĠH orn", + "Ġh ier", + "Ġimp ose", + "m other", + "Ġp ension", + "ic ut", + "bor ne", + "ic iary", + ". _", + "ĠS U", + "Ġpol ar", + "is y", + "eng u", + "itial ized", + "AT A", + "w rite", + "Ġexerc ises", + "ĠD iamond", + "ot ypes", + "Ġharm ful", + "on z", + "Ġprint ing", + "st ory", + "Ġexpert ise", + "ĠG er", + "Ġtraged y", + "ĠF ly", + "Ġd ivid", + "amp ire", + "st ock", + "M em", + "Ġre ign", + "Ġun ve", + "Ġam end", + "ĠProp het", + "Ġmut ual", + "ĠF ac", + "Ġrepl acing", + "H ar", + "ĠCirc uit", + "Ġthro at", + "ĠSh ot", + "Ġbatter ies", + "Ġto ll", + "Ġaddress ing", + "ĠMedic aid", + "Ġp upp", + "ĠN ar", + "ol k", + "Ġequ ity", + "M R", + "ĠHis pan", + "ĠL arge", + "m id", + "D ev", + "Ġexp ed", + "Ġdem o", + "ĠMarsh all", + "erg us", + "Ġf iber", + "Ġdiv orce", + "ĠCre ate", + "Ġsl ower", + "ĠPark er", + "ĠStud ent", + "ĠTr aining", + "Ret urn", + "ĠT ru", + "Ġc ub", + "ĠRe ached", + "Ġpan ic", + "Ġqu arters", + "Ġre ct", + "Ġtreat ing", + "Ġr ats", + "ĠChristian ity", + "ol er", + "Ġsac red", + "Ġdecl are", + "ul ative", + "et ing", + "Ġdeliver ing", + "est one", + "Ġt el", + "ĠL arry", + "Ġmet a", + "ac cept", + "art z", + "ĠRog er", + "hand ed", + "Ġhead er", + "Ġtra pped", + "ĠCent ury", + "Ġkn ocked", + "ĠOx ford", + "Ġsurviv ors", + "b ot", + "Ġdemon stration", + "Ġd irt", + "Ġass ists", + "OM E", + "ĠD raft", + "ortun ate", + "fol io", + "pe red", + "ust ers", + "g t", + "ĠL ock", + "Ġjud icial", + "ver ted", + "Ġsec ured", + "out ing", + "ĠBook s", + "Ġhost ing", + "Ġlif ted", + "l ength", + "Ġj er", + "Ġwhe els", + "ĠR ange", + "umbn ails", + "Ġdiagn osis", + "te ch", + "ĠStew art", + "ĠP ract", + "Ġnation wide", + "Ġde ar", + "Ġoblig ations", + "Ġgrow s", + "Ġmand atory", + "Ġsusp icious", + "! '", + "A pr", + "G reat", + "Ġmort gage", + "Ġprosecut or", + "Ġeditor ial", + "ĠK r", + "Ġprocess ed", + "ung le", + "Ġflex ibility", + "Ear lier", + "ĠC art", + "ĠS ug", + "Ġfoc uses", + "Ġstart up", + "Ġbre ach", + "ĠT ob", + "cy cle", + "ãĢ Į", + "ro se", + "Ġb izarre", + "ãĢ į", + "Ġveget ables", + "$ $", + "Ġret reat", + "osh i", + "ĠSh op", + "ĠG round", + "ĠSt op", + "ĠHawai i", + "ĠA y", + "Per haps", + "ĠBe aut", + "uff er", + "enn a", + "Ġproduct ivity", + "F ixed", + "cont rol", + "Ġabs ent", + "ĠCamp aign", + "G reen", + "Ġident ifying", + "Ġreg ret", + "Ġpromot ed", + "ĠSe ven", + "Ġer u", + "ne ath", + "aug hed", + "ĠP in", + "ĠL iving", + "C ost", + "om atic", + "me ga", + "ĠN ig", + "oc y", + "Ġin box", + "Ġem pire", + "Ġhor izont", + "Ġbr anches", + "Ġmet aph", + "Act ive", + "ed i", + "ĠFil m", + "ĠS omething", + "Ġmod s", + "inc ial", + "ĠOrig inal", + "G en", + "Ġspir its", + "Ġear ning", + "H ist", + "Ġr iders", + "Ġsacr ific", + "M T", + "ĠV A", + "ĠS alt", + "Ġoccup ation", + "ĠM i", + "Ġdis g", + "lic t", + "Ġn it", + "Ġn odes", + "e em", + "ĠP ier", + "Ġhat red", + "ps y", + "ãĥ ī", + "Ġthe ater", + "Ġsophistic ated", + "Ġdef ended", + "Ġbes ides", + "Ġthorough ly", + "ĠMedic are", + "Ġbl amed", + "arent ly", + "Ġcry ing", + "F OR", + "pri v", + "Ġsing ing", + "ĠI l", + "Ġc ute", + "o ided", + "olit ical", + "ĠNe uro", + "å ¤", + "Ġdon ation", + "ĠEag les", + "ĠG ive", + "T om", + "Ġsubstant ially", + "ĠLic ense", + "ĠJ a", + "Ġg rey", + "ĠAn imal", + "ĠE R", + "ĠU nd", + "Ġke en", + "Ġconclud e", + "ĠMississ ippi", + "Eng ine", + "ĠStud ios", + "P ress", + "o vers", + "ll ers", + "Ġ3 50", + "ĠR angers", + "Ġr ou", + "ert o", + "E p", + "iss a", + "iv an", + "Ġse al", + "ĠReg ist", + "dis play", + "Ġwe aken", + "u um", + "ĠComm ons", + "ĠS ay", + "Ġcult ures", + "Ġl aughed", + "Ġsl ip", + "Ġtreat ments", + "iz able", + "m art", + "ĠR ice", + "Ġbe ast", + "Ġob esity", + "ĠLa ure", + "ig a", + "Wh ich", + "hold er", + "Ġelder ly", + "Ġp ays", + "Ġcompl ained", + "Ġc rop", + "Ġpro c", + "Ġexplos ive", + "ĠF an", + "ĠAr senal", + "A uthor", + "ef ul", + "Ġme als", + "Ġ( -", + "id ays", + "Ġimag ination", + "Ġann ually", + "Ġm s", + "as ures", + "H ead", + "ik h", + "m atic", + "Ġboy friend", + "ĠCom puter", + "Ġb ump", + "Ġsur ge", + "ĠCra ig", + "ĠKir k", + "D el", + "medi ate", + "Ġscen arios", + "ĠM ut", + "ĠSt ream", + "Ġcompet itors", + "Ù Ħ", + "ĠStan ford", + "ĠRes ources", + "az ed", + "b age", + "Ġorgan is", + "ĠRe lease", + "Ġsepar ately", + "Ġha bits", + "Ġmeasure ments", + "ĠCl ose", + "Ġaccomp any", + "Ġg ly", + "Ġt ang", + "ĠR ou", + "Ġplug in", + "Ġcon vey", + "ĠChall enge", + "oot s", + "j an", + "Ġcur s", + "ĠRel ations", + "ke eper", + "Ġapproach ing", + "p ing", + "Spe aking", + "Ġarrang ement", + "ĠV I", + "are ttes", + "Ġaffect ing", + "Ġperm its", + "b ecause", + "Ġu seless", + "ĠH us", + "!! !!", + "Ġdestro ying", + "Un fortunately", + "Ġfasc inating", + "S em", + "Ġelect oral", + "Ġtrans parency", + "ĠCh aos", + "Ġvolunte er", + "Ġstatist ical", + "Ġactiv ated", + "ro x", + "We b", + "H E", + "ĠHamp shire", + "is ive", + "M ap", + "Ġtr ash", + "ĠLaw rence", + "st ick", + "C r", + "Ġr ings", + "EX T", + "Ġoper ational", + "op es", + "D oes", + "ĠEv ans", + "Ġwitness ed", + "P ort", + "Ġlaunch ing", + "ec onom", + "w ear", + "ĠPart icip", + "um m", + "cul es", + "ĠR AM", + "ĠT un", + "Ġass ured", + "Ġb inary", + "Ġbet ray", + "Ġexpl oration", + "ĠF el", + "Ġad mission", + "it ated", + "S y", + "Ġav oided", + "ĠSim ulator", + "Ġcelebr ated", + "ĠElect ric", + "¥ ŀ", + "Ġcl uster", + "itzer land", + "he alth", + "L ine", + "ĠN ash", + "at on", + "Ġsp are", + "Ġenter prise", + "ĠD IS", + "clud es", + "Ġfl ights", + "Ġreg ards", + "Ġà Ĺ", + "h alf", + "Ġtr ucks", + "Ġcontact s", + "Ġunc ons", + "ĠCl imate", + "Ġimm ense", + "N EW", + "oc c", + "ect ive", + "Ġemb od", + "Ġpat rol", + "Ġbes ide", + "Ġv iable", + "Ġcre ep", + "Ġtrig gered", + "ver ning", + "Ġcompar able", + "q l", + "Ġg aining", + "ass es", + "Ġ( );", + "ĠG rey", + "ĠM LS", + "s ized", + "Ġpros per", + "\" ?", + "Ġpoll ing", + "Ġsh ar", + "ĠR C", + "Ġfire arm", + "or ient", + "Ġf ence", + "Ġvari ations", + "g iving", + "ĠP i", + "osp el", + "Ġpled ge", + "Ġc ure", + "Ġsp y", + "Ġviol ated", + "Ġr ushed", + "Ġstro ke", + "ĠBl og", + "sel s", + "ĠE c", + ",' '", + "Ġp ale", + "ĠColl ins", + "ter ror", + "ĠCanad ians", + "Ġt une", + "Ġlabor atory", + "Ġn ons", + "t arian", + "Ġdis ability", + "ĠG am", + "Ġsing er", + "al g", + "ĠSen ior", + "Ġtrad ed", + "ĠWar rior", + "Ġinf ring", + "ĠFrank lin", + "Ġstr ain", + "ĠSwed ish", + "Ġsevent h", + "ĠB enn", + "ĠT ell", + "Ġsynd rome", + "Ġwond ered", + "id en", + "++ ++", + "ig o", + "Ġpur ple", + "Ġjournal ism", + "Ġreb el", + "Ġf u", + "bl og", + "Ġinv ite", + "ren cies", + "ĠCont act", + "Is rael", + "ĠCont ent", + "Ġche er", + "Ġbed room", + "ĠEngine ering", + "ĠQue ens", + "Ġd well", + "ĠPlay Station", + "ĠD im", + "ĠCol on", + "l r", + "Ġoper ates", + "Ġmotiv ation", + "US A", + "ast ered", + "C ore", + "ĠTr uth", + "ol o", + "OS E", + "ĠMem ory", + "Ġpred ec", + "Ġan arch", + "Ġ19 20", + "ĠY am", + "à ¨", + "b id", + "Ġgr ateful", + "Ġexc itement", + "Ġtre asure", + "Ġlong est", + "ct ive", + "Ġdes erves", + "Ġreserv es", + "Ġcop s", + "ĠOtt awa", + "ĠEgypt ian", + "ank ed", + "Ġart if", + "Ġhypot hesis", + ": /", + "Ġpurch asing", + "Ġlove ly", + "H P", + "Ġdiv ide", + "Ġstrict ly", + "Ġquestion ing", + "Ġtaxp ayers", + "ĠJ oy", + "Ġroll s", + "ĠHe avy", + "Ġp orts", + "Ġmag netic", + "Ġinf lamm", + "Ġbr ush", + "t ics", + "â ĪĴ", + "Ġbott les", + "pp y", + "Ġp add", + "ãĤ ¯", + "m illion", + "Ġdevast ating", + "Ġcomp iled", + "Ġmed ication", + "Ġtw elve", + "ĠPer ry", + "Sp ace", + "im b", + "y our", + "Ġle aked", + "ĠT ar", + "Ġun ity", + "Ġinfect ed", + "Ġtravel ed", + "ID E", + "ĠMc Donald", + "t xt", + "ĠPr inc", + "Ġinter ven", + "ĠTai wan", + "ĠP ow", + "Ġbe aring", + "ĠTh read", + "Ġz ones", + "iz ards", + "un ks", + "Ch apter", + "ll or", + "Ġ ·", + "Ġw ounds", + "Ġdisc retion", + "Ġsucceed ed", + "ik ing", + "Ġicon ic", + "C all", + "Ġscreen ing", + "ĠM is", + "ict s", + "Ġmin isters", + "Ġsepar ation", + "Pl ayer", + "Ġb ip", + "Ġbel oved", + "Ġcount ing", + "ĠE ye", + "ar ound", + "ing ing", + "Ġtable t", + "Ġoff ence", + "in ance", + "h ave", + "ĠInf o", + "ĠNin ja", + "Ġprotect ive", + "ĠC ass", + "M ac", + "ĠQual ity", + "N orth", + "Ġ ic", + "ĠCub a", + "ĠChron icle", + "ĠPro perty", + "Ġfast est", + "ot os", + "ĠG erm", + "OW N", + "Ġbo om", + "ĠStan ley", + "ergus on", + "Ġcle ver", + "Ġent ers", + "m ode", + "ter ior", + "ĠS ens", + "Ġlin ear", + "AR K", + "Ġcomp aring", + "Ġpure ly", + "Ġsaf er", + "ĠPot ter", + "Ġc ups", + "R T", + "Ġgl uc", + "Ġatt ributed", + "Ġdu pl", + "ĠP ap", + "Ġprec ious", + "Ġp a", + "iction ary", + "ĠT ig", + "ĠTo o", + "ol utions", + "st an", + "Ġrob ots", + "Ġlob b", + "Ġstat ute", + "Ġprevent ion", + "w estern", + "16 0", + "ĠAct ive", + "ĠMar ia", + "h al", + "N one", + "ell ar", + "ĠK B", + "ĠPart ners", + "ĠSing le", + "ĠFollow ing", + "ang o", + "ac ious", + "Ġth ou", + "Ġk g", + "Ġinflu ential", + "ĠFriend s", + "S ur", + "ain ted", + "Ġfor ums", + "Ġst arter", + "Ġcitizens hip", + "ĠE lection", + "on ge", + "ot ation", + "os ph", + ";; ;;", + "ut ical", + "p ur", + "ere n", + "Ġaccus ations", + "bit ious", + "ab bit", + "ĠOr d", + "Post ed", + "ir k", + "Ġsens itivity", + "ic he", + "ĠAm y", + "ĠF ab", + "Ġsum mit", + "Ġped est", + "Ġrub ber", + "Ġagric ultural", + "Ġcan cel", + "A E", + "Ġin aug", + "Ġcont am", + "Ġfirm ly", + "i w", + "st age", + "ĠK an", + "Ġt ier", + "Ġinv ention", + "Ġtransl ated", + "ĠR ules", + "B ox", + "Tw itter", + "ID S", + "Ġp izza", + "Ġdeb ug", + "ĠD rop", + "v s", + "Ġh orses", + "b ig", + "Ġb oring", + "Ġh ood", + "ĠMcC ain", + "at ched", + "ĠBro s", + "Ġsk ip", + "Ġess ay", + "st at", + "ĠLeg ends", + "Ġam munition", + "au c", + "Ġshoot er", + "Ġun h", + "Ġsuppl ied", + "Ġgener ic", + "ĠS K", + "ib an", + "yr ics", + "Ġ25 5", + "Ġclim bing", + "Form er", + "Ġfl ip", + "Ġjump ing", + "Ġfrust ration", + "ĠTer ry", + "Ġneighborhood s", + "Ġmed ian", + "be an", + "Ġbr ains", + "Follow ing", + "Ġsh aped", + "Ġdraw s", + "Ġal tered", + "J ack", + "Ġrecip es", + "Ġsk illed", + "we alth", + "ach i", + "e lection", + "Ġbehavi ors", + "de als", + "ĠU ntil", + "F e", + "Ġdecl aration", + "mar ks", + "ĠBet ween", + "cel ona", + "Ġres on", + "Ġbub ble", + "Am ong", + "Ġim perial", + "G S", + "Ġfemin ist", + "200 5", + "ĠK yle", + "Ġaccount ing", + "ĠTe le", + "ĠT yr", + "Ġconnect ing", + "Ġre hab", + "ĠP red", + "s im", + "Ġmeant ime", + "Ġphys ician", + "M W", + "ĠCamp bell", + "ĠBr andon", + "Ġcontribut ing", + "ĠR ule", + "ĠWe ight", + "ĠN ap", + "Ġinter active", + "Ġv ag", + "Ġhel met", + "ĠCom b", + "f our", + "Ġsh ipped", + "Ġcomple ting", + "ĠP D", + "PD ATE", + "Ġspread ing", + "Ġsc ary", + "erv ing", + "ĠG as", + "Ġfr ank", + "s chool", + "Ġrom antic", + "Ġstab il", + "R ob", + "Ġaccur ately", + "Ġac ute", + "ĠH ann", + "Ġsymbol s", + "Ġcivil ization", + "ĠA W", + "Ġlight ning", + "Ġcons iders", + "Ġven ue", + "Ġ ×", + "Ġo ven", + "ĠS F", + "h is", + "Ġn u", + "ĠLear n", + "Ġpe oples", + "Ġst d", + "Ġsle e", + "Ġs lic", + "ĠStat istics", + "Ġcor ners", + "ĠB aker", + "Ġ: )", + "ment ation", + "ol ver", + "Ġlaugh ing", + "ĠT odd", + "ond e", + "ĠH ills", + "Ġn uts", + "ĠW oman", + "pl ane", + "Ġl iver", + "ĠIn side", + "S orry", + "Ġagre es", + "Ġfund ament", + "ĠF isher", + "Ġa uction", + "Ġthread s", + "gl as", + "ĠBas ic", + "ĠN at", + "Ġlack ing", + "Ġceleb ration", + "j u", + "Ġs illy", + "E uro", + "Ġt att", + "ight y", + "cont rolled", + "T est", + "ĠSing h", + "Ġr age", + "Ġrh yth", + "o ffic", + "ĠPh antom", + "Ġhead lines", + "Ġrespond ing", + "ĠMor ning", + "Ġvit amin", + "Ġboot s", + "ĠS ite", + "al in", + "p i", + "Ġvir al", + "ĠU C", + "D ER", + "ĠSe x", + "Ġst ocks", + "c urrent", + "Ġch urches", + "ĠR are", + "ĠMur phy", + "Ġden ial", + "ĠG aming", + "Ġtou g", + "Ġn ick", + "Ġm akers", + "ĠRon ald", + "Ġgener ous", + "ĠD oc", + "ĠMor ris", + "Ġtransform ed", + "ĠN ormal", + "Ġ10 4", + "ĠKick starter", + "ĠUp on", + "On line", + "ĠI RS", + "Ġw rap", + "Ġl oving", + "Ġarri ves", + "ĠD ue", + "Ġhe ter", + "ĠM ade", + "Ġrent al", + "Ġbelong s", + "Ġatt orneys", + "Ġcro ps", + "Ġmat ched", + "ul um", + "ol ine", + "10 9", + "Ġdis par", + "Ġbuy ers", + "ĠCam bridge", + "Ġeth ics", + "rou ps", + "Ġjust ified", + "Ġmarg inal", + "Ġrespect ed", + "win ning", + "Ġnodd ed", + "ĠSer ge", + "ĠForm er", + "C raft", + "######## ########", + "ĠWar ner", + "Ġd ash", + "et e", + "Ġent ert", + "ĠE scape", + "out heast", + "Ġkn ees", + "ĠB omb", + "Ġr ug", + "P ass", + "Ġatt itudes", + "go vernment", + "ĠPri or", + "Ġqual ities", + "Ġnot ification", + "ĠPh one", + "l ie", + "Ġanticip ated", + "ĠCom bat", + "ĠBar ry", + "Ġ198 2", + "Us ers", + "on er", + "Ġcomput ing", + "ĠConnect icut", + "Ġless er", + "Ġpe ers", + "ĠC u", + "Ġtechn ically", + "Ġsub mission", + "ĠUn iversal", + "Ġman ually", + "our ge", + "Ġrespond ents", + "ĠB TC", + "ĠH ost", + "Ġf are", + "ĠB ird", + "Ġrece ipt", + "al so", + "Ġj ack", + "Ġagric ulture", + "Ġsk ull", + "Ġ! =", + "Ġpass ive", + "ĠC I", + "Ġsoc ieties", + "Ġremind ed", + "Ġinter ference", + "B uy", + "Ġâ ľ", + "g on", + "Ġscrut iny", + "ĠW itch", + "Ġconduct ing", + "Ġ ãĥ", + "Ġexch anges", + "ĠMit chell", + "Ġinhab it", + "Ġtw ist", + "B D", + "Ġwhere ver", + "group on", + "Ġj okes", + "ĠBen jamin", + "ĠR andom", + "fr ame", + "ĠL ions", + "Ġhighlight ed", + "ĠArk ansas", + "E nt", + "Ġp ile", + "Ġpre lim", + "g s", + "mind ed", + "Ġfel ony", + "ĠG A", + "ĠL uck", + "Ġpract ically", + "ĠB os", + "Ġact ress", + "D am", + "ĠB ou", + "Ġvis a", + "Ġembed ded", + "Ġhy brid", + "Ġear liest", + "Ġsoon er", + "s ocial", + "ĠH A", + "Ġste ep", + "Ġdis advant", + "Ġexplo it", + "ĠE gg", + "ĠUlt ra", + "Ġnecess ity", + "L ocal", + "ie ge", + "Ġd ated", + "Ġmass es", + "Ġsubsc ription", + "pl ess", + "Ġan onym", + "Ġpresum ably", + "Bl ue", + "The ir", + "asket ball", + "ĠPhil ip", + "Ġcom ed", + "load ed", + "r ane", + "Ġref lection", + "Ch ina", + "Ġext ends", + "Ġform ing", + "Ġund ers", + "200 1", + "Ġgr at", + "Ġconcent rations", + "Ġins ulin", + "Ġsec ular", + "Ġwh ilst", + "Ġwin ners", + "Ad vertisements", + "Ġdeliber ately", + "ĠWork ing", + "Ġs ink", + "et ics", + "d ale", + "Ġmand ate", + "Ġg ram", + "Ġvac ation", + "Ġwarn ings", + "ri pp", + "ĠTH AT", + "Ġcomment ary", + "Ġint u", + "Ġa est", + "Ġreason ing", + "Ġbreak down", + "ĠZ ombie", + "Ġ-- >", + "ĠPolit ical", + "c ott", + "Ġthr ust", + "Ġtechn ological", + "Ġdec iding", + "Ġtraff icking", + "L ong", + "W elcome", + "pr ising", + "ĠCommun ications", + "Ġend ors", + "Ġsw ift", + "Ġmetab ol", + "co ins", + "res a", + "ĠHT TP", + "Ġen roll", + "ĠH appy", + "us r", + "int age", + "Ġ[ \"", + "u ably", + "ĠM aterial", + "Ġrepe al", + "Se pt", + "k h", + "ĠMod i", + "Ġunder neath", + "ĠI L", + "sh ore", + "Ġdiagn osed", + "ace utical", + "Ġsh ower", + "au x", + "ĠSw itch", + "ĠStre ngth", + "Ġj ihad", + "n ational", + "Ġtra uma", + "uss y", + "on i", + "Ġcons olid", + "Ġcal ories", + "ĠF lynn", + "ag ged", + "16 8", + "ĠP ink", + "Ġfulf ill", + "Ġch ains", + "Ġnot ably", + "ĠA V", + "L ife", + "ĠCh uck", + "m us", + "ĠUr ban", + "ĠH end", + "Ġdep osit", + "ĠS ad", + "Ġaff air", + "OR K", + "ie val", + "ĠF DA", + "Ġt rop", + "ĠOver all", + "Ġvirt ue", + "Ġsatisf action", + "au nd", + "Ġl un", + "ĠSw itzerland", + "ĠOper ation", + "pro cess", + "Ġsh ook", + "Ġcount ies", + "le ased", + "ĠCharl otte", + "1 12", + "Ġtrans cript", + "Ġre dd", + "p ush", + "ĠHe y", + "ĠAn alysis", + "[ \"", + "Ġaltern atives", + "ard less", + "Ġele ph", + "Ġpre jud", + "ĠLe af", + "H aving", + "ĠH ub", + "Ġexpress ions", + "ĠVol ume", + "Ġshock ing", + "ĠRed s", + "Ġread ily", + "Ġplan ets", + "ad ata", + "Ġcollaps ed", + "ĠMad rid", + "Ġir rit", + "i pper", + "ĠEn c", + "ĠW ire", + "Ġbu zz", + "ĠG P", + "ash a", + "Ġaccident ally", + "ur u", + "Ġfrust rated", + "ĠS A", + "Ġhung ry", + "ĠH uff", + "Ġlab els", + "ant o", + "ĠE P", + "Ġbar riers", + ") |", + "ĠBer keley", + "ĠJ ets", + "Ġp airs", + "ĠL an", + "J ames", + "ĠB ear", + "Ġhum or", + "ĠLiber ty", + "Ġmagn itude", + "Ġag ing", + "ĠM ason", + "Ġfriends hip", + "umb ling", + "Ġemer ge", + "Ġnewsp apers", + "Ġam bitious", + "ĠRich ards", + "atern al", + "Ġ198 1", + "Ġcook ies", + "Ġsc ulpt", + "Ġpur suit", + "L ocation", + "Ġscript s", + "p c", + "Ġarrang ements", + "Ġd iameter", + "Ġl oses", + "am ation", + "Ġl iqu", + "ĠJ ake", + "aret te", + "Ġunderstand s", + "ĠZ en", + "v m", + "Ġappro ve", + "Ġw ip", + "Ġult ra", + "Ġint end", + "ĠD I", + "asc ular", + "Ġst ays", + "ĠK or", + "ĠK l", + "Ġinvest ing", + "L a", + "Ġbelie ving", + "b ad", + "m outh", + "Ġtaxp ayer", + "ãĥ ĥ", + "ĠQue bec", + "Ġl ap", + "ĠSw iss", + "d rop", + "Ġdr ain", + "ir i", + "et c", + "ft en", + "ĠN ex", + "Ġst raw", + "Ġscream ing", + "Ġcount ed", + "Ġdam aging", + "Ġamb assador", + "cent ury", + "Ġpro x", + "Ġarrest s", + "u v", + "il ateral", + "ĠCh arg", + "Ġpresc ribed", + "Ġindepend ently", + "Ġf ierce", + "ĠB aby", + "Ġb rave", + "Ġsu its", + "= >", + "Ġbas eline", + "ĠR ate", + "Ġis lands", + "Ġ( (", + "g reen", + "ix els", + "Ġname ly", + "ĠVill age", + "th an", + "am y", + "V ersion", + "g mail", + "ential s", + "ĠS ud", + "ĠMel bourne", + "Ġarri ving", + "Ġquant um", + "e ff", + "rop olitan", + "T ri", + "Ġfun eral", + "ĠI R", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "ĠC ob", + "it ably", + "Ġt urb", + "Ġcomb o", + "Re view", + "Ġdeploy ment", + "u ity", + "ĠB ott", + "Ġinv isible", + "Ġrender ing", + "Ġunl ocked", + "Ġa qu", + "ĠVlad imir", + "Ġp ad", + "ĠBr ain", + "ĠLeg acy", + "dr agon", + "ĠKurd ish", + "Ġsound ed", + "Ġdet ained", + "ĠD M", + "g ary", + "Ġd aughters", + "Ġdistur bing", + "uk a", + "ĠPar ad", + "Ġt ast", + "Ġunf ortunate", + "Ġu l", + "em in", + "Ġattend ance", + "tr l", + "Ġpar ks", + "ĠMem orial", + "ĠAl ice", + "oth y", + "gu ard", + "ĠD ise", + "ĠSh an", + "ĠFor um", + "R ich", + "Ġshif ted", + "ue z", + "Ġl ighter", + "ĠMag n", + "Ġc od", + "S ch", + "ham mad", + "P ub", + "3 50", + "ĠP okemon", + "Ġprot otype", + "Ġun re", + "B ase", + "ĠStud ents", + "ĠRep ly", + "ĠCommun ist", + "Ġg au", + "ĠTy ler", + "I Z", + "Ġparticip ated", + "Ġsup rem", + "ĠDet ails", + "Ġvessel s", + "ro d", + "Ġt ribe", + "ke ep", + "Ġassum ptions", + "Ġp ound", + "Ġcr ude", + "ĠAv ailable", + "Ġswim ming", + "Ġin clusion", + "Ġadv ances", + "c ulation", + "Ġconserv ation", + "Ġover d", + "ĠBuff alo", + "Art icle", + "ed ge", + "Ġaw a", + "ĠMad ison", + "Ġsid ew", + "Ġcat ast", + "ĠK rist", + "uc le", + "ĠHigh way", + "ĠTer ror", + "Ġactiv ation", + "Ġuncons cious", + "ĠSat an", + "ĠSus an", + "ill ery", + "Ġarr anged", + "i op", + "Ġrum ors", + "ur ring", + "th ink", + "ĠKe ith", + "ĠK ind", + "Ġavoid ing", + "by n", + "n ut", + "ĠSpe aker", + "r us", + "n ames", + "Ġgu ilt", + "ĠOlymp ics", + "Ġsa il", + "ĠM es", + "lev ant", + "ĠColumb us", + "a ft", + "C ity", + "S outh", + "ĠHar vey", + "ĠP un", + "S everal", + "Ġment ally", + "Ġimp ress", + "m ount", + "ĠUb untu", + "âĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ", + "ĠSuper man", + "ĠMP s", + "Ġintent ions", + "ĠR acing", + "Ġlike lihood", + "Ġ2 40", + "T otal", + "Ġto ys", + "ĠW atson", + "Ġur ge", + "L ear", + "ĠP aper", + "Ġoccur ring", + "ĠB eng", + "ĠC ert", + "Ġst ones", + "T im", + "ĠTw in", + "z b", + "ĠD ynam", + "Ġpolit ician", + "k ens", + "ĠEnter prise", + "UT ERS", + "Ġab ol", + "Ġref resh", + "Ġarbit rary", + "pe ction", + "Ġtrou bles", + "Ġ} );", + "t v", + "Ġpil ots", + "Ġdist ribute", + "Ġaud it", + "Ġp ause", + "orig inal", + "Ġr ivals", + " £", + "F ig", + "T L", + "ab il", + "ry ing", + "L in", + "ion ed", + "l on", + "Ġf ancy", + "Ġcr ashed", + "Ġt ract", + "Ġshe d", + "Ġcons ume", + "B ased", + "down load", + "in it", + "Ġvolt age", + "Int rodu", + "Ġcondem ned", + "ĠFin ance", + "res pect", + "Ġex cluded", + "Ġestablish ing", + "her ic", + "Ġher itage", + "Ġspect acular", + "Ġun st", + "ĠSnow den", + "ĠL ane", + "S an", + "Ġprotect ions", + "st ruction", + "inc inn", + "Ġmac ro", + "C ustom", + "ios ity", + "Ġes p", + "Ġfunction ing", + "Ġm ush", + "Ġp uzzle", + "Ġeth ical", + "M al", + "Ġgo verning", + "ĠF erguson", + "Ġrest ored", + "Ġst ressed", + "ĠCoun ter", + "ĠK as", + "cl ip", + "AN S", + "Ġse iz", + "U K", + "by ss", + "old own", + "ap i", + "Ġperman ently", + "oun ters", + "W est", + "Th rough", + "L ight", + "at oes", + "Ġne at", + "Ġc ord", + "ure r", + "Ġsevere ly", + "ĠA ven", + "Ġinter rog", + "Ġtri ple", + "G iven", + "N umber", + "Ġar ise", + "Ġs her", + "pl ant", + "Ġfl ower", + "ĠC ou", + "Ġat e", + "Ġnew er", + "b ul", + "Ġmean while", + "ĠL air", + "Ġadjust ment", + "ĠCop yright", + "Ġd ivers", + "i ological", + "Ġgam ers", + "o at", + "Ġhistor ically", + "Ġanal og", + "Ġlong time", + "Ġpres cription", + "ĠM ist", + "ĠHy per", + "ĠM aine", + "ĠDe ity", + "Ġmulti pl", + "ĠRe incarn", + "ĠH yd", + "ĠP ic", + "S il", + "r ants", + "ĠC ris", + ". ;", + "( {", + "epend ence", + "Ġrec y", + "ate ur", + "Ġqu ad", + "Ġgl ob", + "Ġcon ced", + "te am", + "Ġcapital ist", + "ĠL ot", + "Ġroy al", + "ĠCy ber", + "Ġblack s", + "met ic", + "ri v", + "ĠD anny", + "Ġsp o", + "ĠR O", + "Ġanim ated", + "rypt ed", + "ĠDep uty", + "Ġrend ered", + "F E", + "Ġstre ak", + "Ġcloud s", + "ĠDou g", + "~~~~ ~~~~", + "Ġdisc our", + "ĠVe h", + "Ġpsych ology", + "ĠJ ourney", + "Ġcry stal", + "ĠFro st", + "Ġsuspic ion", + "Ġrel ate", + "or us", + "ĠC rypt", + "ĠN VIDIA", + "com ed", + "ut ing", + "incinn ati", + "Ġvulner ability", + "ost ic", + "Ġisol ation", + "Ġcool ing", + "ĠCoal ition", + "Ġ1 19", + "F our", + "ĠDe al", + "Ġâ ī", + "se mble", + "ram ent", + "ĠBar celona", + "Ġ10 2", + "Ġcoc aine", + "ocaly pse", + "F eb", + "ogen ic", + "Ġmut ation", + "Ġcrypt oc", + "ĠK el", + "ĠG it", + "a is", + "Ġs isters", + "AN K", + "Ġactiv ate", + "T er", + "Ġd read", + "yl on", + "Ġprop ri", + "A ust", + "ĠDef ault", + "Ġout door", + "Ġshe er", + "ce ive", + "Ġg ently", + "Ð ¾", + "Pro gram", + "Ġâ ĨĴ", + "Ġve gan", + "ĠCr us", + "Ġrespons ibilities", + "ĠH R", + "OL D", + "Ġprev ents", + "Ġst iff", + "ĠW ere", + "Ġathlet ic", + "ĠSc ore", + "Ġ) :", + "Ġcolumn s", + "ĠL oc", + "av ailable", + "ĠF ram", + "ĠS essions", + "Ġcompan ion", + "Ġpack s", + "14 0", + "ĠKn ights", + "Ġf art", + "Ġstream s", + "Ġsh ore", + "Ġapp eals", + "ĠPer formance", + "h aul", + "ĠSt ra", + "ĠN ag", + "10 3", + "ĠTrans portation", + "B B", + "E v", + "z an", + "P ublic", + "Ġtw in", + "uls ion", + "M ult", + "Ġelect ro", + "Ġstat ue", + "ation ally", + "ĠN ort", + "Ġins pection", + "/ *", + "ig ue", + "Ġcomp assion", + "ĠT ales", + "ĠSte in", + "ĠSc reen", + "ĠB ug", + "ĠL ion", + "g irl", + "Ġwithdraw al", + "Ġobject ives", + "Ġblood y", + "Ġprelim inary", + "Ġj acket", + "Ġdim ensions", + "ĠC ool", + "ĠOcc up", + "Ġw reck", + "Ġdoub led", + "ank ing", + "Ġ19 75", + "Ġglass es", + "ĠW ang", + "pro v", + "P ath", + "connect ed", + "ĠMult i", + "ĠNor way", + "agon ist", + "Ġfe ared", + "Ġtouch ing", + "Ġarg uably", + "¯¯¯¯ ¯¯¯¯", + "ĠNC AA", + "che m", + "Ġsp at", + "ĠW WE", + "ĠC el", + "ig ger", + "Ġattack er", + "ĠJo in", + "ob ject", + "ett a", + "Ġelim inated", + "d et", + "Ġdest ruct", + "ĠLuc as", + "ct uary", + "18 0", + "ĠBr ady", + "ĠBl ues", + "B ay", + "au kee", + "Ġtim eline", + "Ġdeleg ates", + "w ritten", + "uff icient", + "Ġsh apes", + "Cop yright", + "ou ble", + "serv ice", + "Ġp ione", + "Ġcolleg es", + "Ġrow s", + "Ġsp ite", + "Ġassess ed", + "3 60", + "Ġle ase", + "Ġconfident ial", + "ck er", + "ĠMan ning", + "ĠV oice", + "Ġse aled", + "Ġcalcul ate", + "N O", + "ĠAss istant", + "Ġteen ager", + "ul ent", + "ather ine", + "Ġm ock", + "Ġd iamond", + "Ġf est", + "Ġsw itched", + "Ġres ume", + "ĠPu erto", + "Ġl anes", + "ir ation", + "ĠSimilar ly", + "Ġro d", + "ĠS el", + "ĠPal ace", + "ĠLim ited", + "e ous", + "Ġvar iant", + "Ġw ard", + "Ġ) )", + "Sh ow", + "OO K", + "A lex", + "ĠN ep", + "br is", + "ĠWik ipedia", + "Ġexcept ional", + "Ġman ages", + "ĠD raw", + "Ag ain", + "Ġco pper", + "ut t", + "Ġex ports", + "Ġport folio", + "Ġelev ated", + "R ated", + "ĠOther wise", + "ĠT act", + "ĠShe l", + "ĠT X", + "\" âĢĶ", + "Ġres ur", + "ĠW a", + "ven ant", + "Ġmon etary", + "pe ople", + "E mail", + "Ġfif ty", + "ĠS weet", + "ĠMalays ia", + "Ġconf using", + "ĠR io", + "ud a", + "uten ant", + "\" );", + "Ġpra ised", + "Ġvol umes", + "t urn", + "Ġm ature", + "Ġnon profit", + "Ġpassion ate", + "ĠPriv ate", + "Ġ10 3", + "Ġdesc end", + "ç ¥ŀ", + "uff y", + "head ed", + "Whe ther", + "ri en", + "ze ch", + "be it", + "Ġch rom", + "ĠMc M", + "Ġd ancing", + "Ġe leg", + "ĠNot iced", + "11 5", + "Ġadvoc acy", + "ENT S", + "amb ling", + "ĠMin or", + "ĠF inn", + "Ġprior ities", + "Ġthere of", + "ĠSt age", + "ĠRog ers", + "Ġsubst itute", + "ĠJ ar", + "ĠJeff erson", + "Ġlight ly", + "10 2", + "ĠL isa", + "u its", + "ys ical", + "Ġshif ts", + "Ġd rones", + "Ġwork place", + "Ġres id", + "ens ed", + "ah n", + "Ġpref erences", + "ser ver", + "Ġdeb ates", + "d oc", + "ĠGod s", + "Ġhelicop ter", + "Ġhon our", + "Ġconsider ably", + "ed ed", + "ĠF emale", + "ĠAn ne", + "Ġre un", + "ĠF ace", + "ĠHall ow", + "ĠBud get", + "Ġcondem n", + "Ġt ender", + "Pro f", + "ocr atic", + "ĠTurn er", + "ĠAg ric", + "Ġ19 76", + "Ġa pt", + "d isc", + "ĠF ighter", + "ĠA ur", + "Ġgar bage", + "in put", + "ĠK arl", + "ĠOl iver", + "ĠL anguage", + "k n", + "N on", + "ĠCl ar", + "Ġtrad itions", + "Ġad vertisement", + "ĠS or", + "Ġarch ive", + "Ġvill ages", + "7 50", + "Ġimplement ing", + "w aukee", + "Ġdiet ary", + "Ġswitch ing", + "Rep ublic", + "Ġvel ocity", + "Ġc it", + "ĠA wards", + "Ġfin ancing", + "Ġlast ed", + ") ]", + "Ġrem inder", + "P erson", + "Ġprec ision", + "Ġdesign ers", + "ĠF ried", + "ĠB order", + "Ġtr agic", + "Ġw ield", + "Ġiniti atives", + "ĠT ank", + "w er", + "Ġjo ins", + "R o", + "in ery", + "Ġar row", + "Ġgener ating", + "found er", + "Ġsear ches", + "Ġrandom ly", + "A ccess", + "Ġb atch", + "Ġp osed", + "l at", + "Ġpursu ing", + "as a", + "Ġtest ified", + "form ing", + "ĠSh ar", + "w iki", + "ĠE ither", + "S ometimes", + "Ġsen ators", + "ĠJohn ny", + "ĠTal iban", + "ĠG PS", + "\":\" /", + "ãģ® å", + "Ġanaly zed", + "ĠRub io", + "ĠMove ment", + "op ard", + "ii i", + "St and", + "f ight", + "Ġign oring", + "i ang", + "ĠG N", + "so ever", + "ĠST AT", + "Ġref using", + "Ġswe at", + "Ġb ay", + "P ORT", + "ir med", + "ak y", + "Ġdis pro", + "Ġlabel ed", + "Ġ10 8", + "H ello", + "Ġple asant", + "ab a", + "Ġtri umph", + "Ġab oard", + "Ġinc om", + "ĠC row", + "le tt", + "Ġfol k", + "Ġch ase", + "` `", + "ĠBr us", + "Ġte ens", + "c ue", + "Ġter rain", + "h yd", + "il ight", + "OR Y", + "Su pport", + "ew s", + "ll i", + "rain ts", + "ĠC and", + "Ġab used", + "ach ment", + "l arg", + "B as", + "ĠC ancer", + "Ġ19 78", + "Ġsupp orter", + "ac cess", + "ĠTer min", + "ĠT ampa", + "ĠAN Y", + "Ġnew est", + "ĠCrim inal", + "ed u", + "Ġ19 30", + "Ġadm its", + "Ġend e", + "Ġfail ures", + "ur ate", + "ful ness", + "cy cl", + "ĠSub ject", + "Ġinf inite", + "th ree", + "W A", + "p it", + "ĠInst all", + "R ad", + "ili ation", + "G M", + "Ġcontin ent", + "Ġaccommod ate", + "ĠCl ay", + "Ġp up", + "ĠF unction", + "Ġham mer", + "ĠAlbert a", + "Ġrev ised", + "Ġminor ities", + "Ġmeasure ment", + "Con nell", + "Ġdis able", + "ĠM ix", + "In cre", + "Ġfor k", + "ĠR osen", + "Ġimpl ies", + "umb lr", + "AN G", + "Ġprote ins", + "Ġagg ression", + "Ġfacilit ate", + "S N", + "Ġilleg ally", + "u er", + "Ġacad em", + "Ġp uzz", + "ĠSh ift", + "p ay", + "oll o", + "Ġaud iences", + "B uild", + "Ġno ble", + "Ġsynt ax", + "â ĺħ", + "Ġbe am", + "ĠB ed", + "ĠA ld", + "Ġorig ins", + "v ideo", + "Ġ19 77", + "ĠAss ault", + "Ġgar age", + "Te am", + "Ġver dict", + "Ġd war", + "ĠVirt ual", + "e vent", + "Ke ep", + "Ġsent iment", + "Ġwild life", + "sh irt", + "Ġb urg", + "Ġrecommend ation", + "rep resent", + "Ġgall ery", + "own ers", + "Ġsch olar", + "Ġconven ience", + "ĠSw ift", + "Ġconv inc", + "C ap", + "Ġwar fare", + "ĠVis ual", + "Ġconst itute", + "Ġab ort", + "ĠWe ather", + "ĠLook ing", + "ĠH em", + "Ġmart ial", + "Ġinc oming", + "et ition", + "Ġtoler ance", + "ĠCre ated", + "Ġfl ows", + "ĠE lder", + "Ġsoul s", + "Ġf oul", + "ĠP ain", + "ĠC AN", + "Ġ2 20", + "b c", + "he nd", + "Ġgen ius", + "R eal", + "ĠW r", + "omet er", + "p ad", + "Ġlim iting", + "ĠS i", + "ĠL ore", + "ĠAd ventures", + "Ġvar ied", + "D isc", + "f in", + "ĠPerson al", + "Ch ris", + "Ġinv ented", + "Ġd ive", + "ĠR ise", + "Ġo z", + "ĠCom ics", + "Ġexp ose", + "ĠRe b", + "let ters", + "s ite", + "im ated", + "Ġh acking", + "Ġeduc ated", + "ĠNob ody", + "Ġdep ri", + "Ġincent ive", + "ãĤ ·", + "Ġovers ight", + "Ġtrib es", + "ĠBelg ium", + "Ġlicens ing", + "our t", + "Produ ct", + "ah l", + "ĠG em", + "Ġspecial ist", + "Ġc ra", + "ann ers", + "ĠCor byn", + "Ġ19 73", + "RE AD", + "Ġsum mar", + "Ġover look", + "ĠApp lication", + "Ġin appropriate", + "Ġdownload ed", + "Q ue", + "ĠB ears", + "Ġth umb", + "ĠChar acter", + "ĠReincarn ated", + "ĠS id", + "Ġdemonstr ates", + "s ky", + "ĠBloom berg", + "ĠAr ray", + "ĠRes ults", + "ĠFour th", + "ĠED T", + "ĠO scar", + "c end", + "Ġ10 6", + "ĠN ULL", + "ĠH ERE", + "m atch", + "ĠBr un", + "Ġgluc ose", + "ie g", + "eg u", + "Ġcert ified", + "Ġrel ie", + "Ġhuman itarian", + "Ġpr ayers", + "K ing", + "Ġn an", + "h ou", + "10 8", + "ul u", + "Ġrenew able", + "Ġdistingu ish", + "Ġd ense", + "ĠV ent", + "ĠPack age", + "ĠB oss", + "Ġedit ors", + "Ġm igr", + "T ra", + "ĠPet ers", + "ĠAr ctic", + "200 4", + "ĠC ape", + "Ġloc ally", + "Ġlast ing", + "Ġhand y", + ". ).", + "P an", + "ĠR ES", + "Ind ex", + "Ġt ensions", + "Ġformer ly", + "Ġide ological", + "Ġsens ors", + "Ġdeal ers", + "Ġdef ines", + "S k", + "Ġproceed s", + "Ġpro xy", + "az ines", + "ĠB ash", + "ĠP ad", + "ĠC raft", + "eal ous", + "Ġshe ets", + "omet ry", + "J une", + "cl ock", + "T T", + "ĠThe atre", + "ĠB uzz", + "Ġch apters", + "Ġmill enn", + "Ġd ough", + "ĠCongress ional", + "Ġimag ined", + "av ior", + "Ġclin ic", + "Ġ19 45", + "Ġhold er", + "ro ot", + "oles ter", + "Ġrest art", + "B N", + "ĠHam as", + "ĠJ ob", + "Ġor b", + "Ġr am", + "Ġdiscl ose", + "Ġtransl ate", + "Ġimm igrant", + "Ġannoy ing", + "Ġtreat y", + "an ium", + "ĠTe a", + "ĠLeg ion", + "Ġcrowd s", + "ĠB ec", + "ĠA er", + "oh yd", + "B ro", + "Look ing", + "Ġl bs", + "Ġagg ress", + "Ġse am", + "Ġinter cept", + "ĠM I", + "mer cial", + "act iv", + "ĠC it", + "Ġdim ension", + "Ġconsist ency", + "Ġr ushing", + "ĠDou glas", + "Ġtr im", + "Inst all", + "ick er", + "Ġsh y", + "10 6", + "Ġment ions", + "pe lled", + "ĠT ak", + "c ost", + "Ġclass room", + "Ġfort une", + "dri ven", + "Ġun le", + "ĠWhe el", + "Ġinvest or", + "ĠM asters", + "k it", + "Ġassoci ations", + "ĠEv olution", + "op ing", + "us cript", + "Ġprov incial", + "ĠWal ter", + "av i", + "S O", + "Ġun limited", + "Eng lish", + "ĠC ards", + "ĠEb ola", + "ne red", + "Ġreven ge", + "Ġout right", + "um per", + "Ġf itting", + "ĠSol id", + "Ġform ally", + "Ġproblem atic", + "Ġhaz ard", + "Ġenc ryption", + "Ġstraight forward", + "ĠA K", + "Ġp se", + "ĠOr b", + "ĠCh amber", + "ĠM ak", + "Cont ents", + "Ġloyal ty", + "Ġl yrics", + "ĠSy m", + "Ġwel comed", + "Ġcook ed", + "Ġmon op", + "Ġn urse", + "Ġmis leading", + "Ġe ternal", + "Ġshif ting", + "Ġ+ =", + "V is", + "Ġinst itutional", + "ill ary", + "Ġp ant", + "VER T", + "ĠA CC", + "ĠEn h", + "Ġinc on", + "ĠRE UTERS", + "Ġdon ated", + "âĢ¦âĢ¦ âĢ¦âĢ¦", + "In tern", + "Ġexhib it", + "Ġt ire", + "ĠR ic", + "ĠCh ampion", + "ĠMu hammad", + "N ING", + "ĠSoc cer", + "Ġmob ility", + "Ġvary ing", + "ĠM ovie", + "Ġl ord", + "o ak", + "F ield", + "Ġve ctor", + "us ions", + "Ġsc rap", + "Ġen abling", + "m ake", + "T or", + ". *", + "| |", + "ĠWe bsite", + "ĠN PC", + "Ġsocial ist", + "ĠBill y", + "ĠAdd itional", + "Ġc argo", + "Ġfar ms", + "ĠSo on", + "ĠPri ze", + "Ġmid night", + "Ġ9 00", + "se en", + "ĠSp ot", + "Ġshe ep", + "Ġspons ored", + "ĠH i", + "ĠJ ump", + "Ġ19 67", + "Micro soft", + "ĠAg ent", + "Ġch arts", + "d ir", + "Ġadj acent", + "Ġtr icks", + "Ġman ga", + "Ġex agger", + "/ >", + "foot ball", + "ĠF CC", + "G C", + "ĠT ier", + "and ra", + "OU ND", + "% ),", + "Ġfru its", + "V C", + "ĠA A", + "R ober", + "Ġmid st", + "â Ĺ", + "ank a", + "Ġlegisl ature", + "ĠNe il", + "Ġtour ists", + "\" \"", + "ĠWar ning", + "ĠNever theless", + "ĠOffic ial", + "ĠWh atever", + "Ġm old", + "Ġdraft ed", + "Ġsubst ances", + "Ġbre ed", + "Ġt ags", + "ĠT ask", + "Ġver b", + "Ġmanufact ured", + "com ments", + "ĠPol ish", + "Pro v", + "Ġdetermin es", + "Ob ama", + "k ers", + "Ġutter ly", + "Ġse ct", + "sc he", + "ĠG ates", + "ĠCh ap", + "Ġal uminum", + "Ġz ombie", + "ĠT ouch", + "ĠU P", + "Ġsatisf y", + "Ġpred omin", + "asc ript", + "Ġelabor ate", + "Ġ19 68", + "Ġmeas uring", + "ĠV ari", + "any ahu", + "Ġs ir", + "ul ates", + "id ges", + "ick ets", + "ĠSp encer", + "T M", + "oub ted", + "Ġpre y", + "Ġinstall ing", + "ĠC ab", + "re ed", + "re ated", + "Su pp", + "Ġwr ist", + "ĠK erry", + "10 7", + "ĠK le", + "ĠR achel", + "Ġc otton", + "ĠA RE", + "ĠE le", + "Cont rol", + "Ġload s", + "ĠD od", + "an as", + "b one", + "Ġclass ical", + "ĠReg ional", + "ĠInt eg", + "V M", + "Ġdes ires", + "Ġaut ism", + "support ed", + "ĠM essage", + "Ġcomp act", + "writ er", + "Ġ10 9", + "ĠHur ricane", + "c ision", + "Ġcy cles", + "Ġdr ill", + "Ġcolle ague", + "Ġm aker", + "G erman", + "Ġmist aken", + "S un", + "ĠG ay", + "Ġwhat soever", + "Ġsell s", + "ĠA irl", + "l iv", + "ĠO ption", + "Ġsol ved", + "Ġse ctors", + "Ġhorizont al", + "Ġequ ation", + "ĠSk ill", + "ĠB io", + "g ement", + "ĠSn ap", + "ĠLeg al", + "Ġtradem ark", + "Ġmake up", + "Ġassemb led", + "Ġsa ves", + "ĠHallow een", + "ĠVer mont", + "ĠFR OM", + "Ġfar ming", + "ĠP odcast", + "accept able", + "ĠHig her", + "Ġas leep", + "ull ivan", + "Ġrefere n", + "ĠLe v", + "Ġbul lets", + "ok o", + "H C", + "Ġst airs", + "Ġmain tains", + "ĠL ower", + "ĠV i", + "Ġmar ine", + "Ġac res", + "Ġcoordin ator", + "ĠJ oh", + "Ġcounterpart s", + "ĠBrother s", + "Ġind ict", + "b ra", + "Ġch unk", + "Ġc ents", + "H ome", + "ĠMon th", + "Ġaccording ly", + "if les", + "ĠGerm ans", + "ĠSy n", + "H ub", + "Ġey eb", + "âĶĢâĶĢ âĶĢâĶĢ", + "Ġr anges", + "ĠHoll and", + "ĠRob ot", + "f c", + "M ike", + "Ġpl asma", + "Ġsw ap", + "Ġath lete", + "ĠR ams", + ",' \"", + "Ġinfect ions", + "Ġcor rid", + "Ġv ib", + "Ġpat ches", + "Ġtradition ally", + "Ġrevel ation", + "Ġswe ep", + "Ġgl ance", + "Ġin ex", + "200 3", + "ĠR aw", + "work ing", + "os ures", + "ĠD at", + "ĠLyn ch", + "Ġle verage", + "ĠRe id", + "Ġcorrel ation", + "ian ces", + "av ascript", + "Ġrep ository", + "ret ty", + "Ġ19 72", + "24 0", + "Ġo un", + "p ol", + "ĠRe ed", + "Ġtact ical", + "is ite", + "App le", + "ĠQu inn", + "Ġrap ed", + "ill o", + "Euro pe", + "Ġalgorith ms", + "ĠRod rig", + "i u", + "Ġill um", + "Ġf ame", + "Ġintrodu cing", + "Ġdel ays", + "ĠRaid ers", + "Ġwh istle", + "Ġnovel s", + "ĠRe ally", + "Ġder iv", + "Ġpublic ations", + "ĠNe ither", + "ĠCom merce", + "Ġa ston", + "l anguage", + "Not es", + "ĠR oth", + "ĠF ear", + "Ġm ate", + "Ġpar ade", + "ĠQ B", + "Ġman eu", + "ĠC incinnati", + "m itting", + "Ġwa ist", + "ĠR ew", + "Ġdisc ont", + "Ð °", + "Ġst aring", + "Ġal ias", + "Ġsec urities", + "Ġtoile t", + "ĠJ edi", + "Ġun law", + "v ised", + "//// ////", + "] (", + "ĠWe iss", + "Ġpre st", + "ĠComp an", + "Ġmem o", + "ĠGr ace", + "J uly", + "ĠEl ite", + "cent er", + "ĠSt ay", + "Ġgal axy", + "Ġto oth", + "ĠS ettings", + "Ġsubject ed", + "ãĤ ¦", + "Ġline back", + "Ġretail ers", + "ĠW ant", + "Ġd angers", + "A ir", + "Ġvolunt ary", + "ew ay", + "Ġinterpret ed", + "ot ine", + "à §", + "Ġp el", + "Serv ice", + "ĠEvent ually", + "Ġcare ers", + "Ġthreat en", + "Ġmem or", + "ĠBrad ley", + "anc ies", + "s n", + "ĠUn known", + "N ational", + "Ġsh adows", + "ail and", + "ĠD ash", + "Every one", + "izz ard", + "M arch", + "= (", + "Ġpull s", + "Ġstr anger", + "Ġback wards", + "ĠBern ard", + "imens ional", + "Ġch ron", + "Ġtheoret ical", + "k top", + "Ġw are", + "ĠInvest ig", + "ĠIn iti", + "ĠOper ations", + "o ven", + "oc ide", + "* /", + "Ġfl ames", + "ĠC ash", + "sh it", + "Ġc ab", + "ĠAn aly", + "ĠSe ah", + "Ġdefin ing", + "Ġorder ing", + "Ġimm un", + "Ġpers istent", + "AC H", + "Russ ian", + "m ans", + "Ġh ind", + "Ġphot ography", + " ©", + "Ġh ug", + "Ġ10 7", + "ĠH ence", + "i ots", + "ude au", + "Ġsubsid ies", + "Ġroutine ly", + "ĠDev ice", + "it ic", + "Ġdisg ust", + "land er", + "Ġ19 40", + "Ġassign ment", + "ĠB esides", + "w ick", + "ĠD ust", + "us c", + "struct ed", + "11 1", + "de velop", + "Ġf ond", + "Ġinter section", + "Ġdign ity", + "Ġcommission er", + "With out", + "re ach", + "Ġcart oon", + "Ġsc ales", + "ãĥ Ń", + "F IG", + "Ġsurve ys", + "ĠIndones ia", + "Ġart work", + "Ġun ch", + "Ġcy cling", + "un ct", + "au er", + "or ate", + "ĠOb viously", + "Ġcharacter ized", + "fe ld", + "Ġaff irm", + "Ġinn ings", + "Ġ é", + "Ġal iens", + "Ġcl oth", + "et ooth", + "ĠC ertain", + " §", + "Ġdig est", + "k now", + "ĠX L", + "Ġpredict ions", + "Ġd in", + "W AR", + "Ġafter math", + "Ex ample", + "ĠSu ccess", + "ĠTh r", + "IG N", + "Ġmin er", + "B us", + "Ġcl arity", + "heim er", + "ĠO UT", + "ĠS end", + "ĠCirc le", + "ĠD iet", + "Ġpron ounced", + "Ġcreat ors", + "Ġearthqu ake", + "atter y", + "ge ons", + "Ġo d", + "Ġlay ing", + "or p", + "U lt", + "pro ject", + "Ġunder min", + "Ġsequ el", + "S am", + "ĠDark ness", + "Ġre ception", + "b ull", + "Y S", + "ĠV ir", + "Ġsequ ences", + "ĠCo in", + "Ġout fit", + "ĠW ait", + "1 19", + "Ġdel ivers", + ".... ..", + "Ġbl own", + "ĠE sc", + "ĠM ath", + "per m", + "ĠU l", + "Ġgl im", + "Ġfac ial", + "Ġgreen house", + "Ġto kens", + "/ -", + "ĠAnn ual", + "ĠON E", + "Ġteen age", + "ĠPhys ical", + "ĠL ang", + "ĠC elt", + "Ġsu ed", + "ivid ually", + "Ġpat ience", + "ch air", + "reg ular", + "Ġa ug", + "in v", + "ex cept", + "ĠL il", + "Ġn est", + "f d", + "s um", + "ĠCh ase", + "Russ ia", + "ĠJenn ifer", + "Ġoff season", + "Over all", + "F ore", + "Ġr iot", + "A ud", + "form er", + "Ġdefend ers", + "ĠC T", + "iot ic", + "rib ly", + "Ġautom ated", + "Ġpen is", + "Ġins ist", + "Ġdi agram", + "ĠS QL", + "ĠG arc", + "Ġw itch", + "cl ient", + "ier ra", + "am bers", + "Ġrec ount", + "f ar", + "V ery", + "oster one", + "Ġappreci ated", + "ĠPer fect", + "S ection", + "Ġd oses", + "oca ust", + "Ġcost ly", + "Ġg rams", + "ĠSh i", + "Ġwrest ling", + "Ġ19 71", + "Ġtro phy", + "Ġn erve", + "ĠK az", + "ĠExper ience", + "Ġpled ged", + "Ġplay back", + "Ġcreat ivity", + "by e", + "Ġattack ers", + "Ġhold ers", + "ĠCo ach", + "ĠPh D", + "Ġtransf ers", + "Ġcol ored", + "ĠH indu", + "Ġd rown", + "Ġlist ened", + "ĠW A", + "ias m", + "P O", + "Ġappeal ing", + "Ġdiscl osed", + "ĠCh icken", + "ag ging", + "Ġple aded", + "Ġnav igation", + "ĠReturn s", + "Ġ[ [", + "R OR", + "E A", + "Ġphotograp her", + "ĠR ider", + "ipp ers", + "Ġsl ice", + "Ġe rect", + "Ġhe d", + "iss ance", + "ĠVik ings", + "ur ious", + "Ġapp et", + "oubted ly", + "Ch ild", + "Ġauthent ic", + "o os", + "ĠM aking", + "Ġannoun cing", + "Ġb od", + "Ġmet er", + "ĠN ine", + "ĠR ogue", + "Ġwork force", + "Ġrenew ed", + "Ġorganis ations", + "ac s", + "P LE", + "Sh ort", + "Ġcomp ounds", + "ĠVis it", + "Ġen velop", + "ear th", + "Ġsupport ive", + "gg le", + "ĠBrus sels", + "ĠGu ild", + "Cre ate", + "RE L", + "Ġaver aged", + "Ġ19 69", + "ri ages", + "Ġlength y", + "Ġforg ot", + "O kay", + "ĠE rd", + "Ġdeal er", + "Ġrec ession", + "D D", + "Ġdesper ately", + "Ġhun ger", + "Ġst icks", + "Ġm ph", + "ĠF aith", + "Ġintention ally", + "Ġdem ol", + "ue ller", + "ĠS ale", + "Ġde bris", + "s pring", + "Ġle ap", + ">> >>", + "Ġcontain ers", + "se lling", + "rane an", + "atter ing", + "Ġcomment ed", + "ĠC M", + "on ut", + "Ġwood s", + "es pecially", + "Ġorgan ize", + "iv ic", + "ĠWood s", + "ang a", + "s qu", + "Ġm aj", + "am on", + "Ġax is", + "Ġ19 74", + "ĠDen mark", + "Ġwar rior", + "ĠP and", + "Ġout lined", + "ĠB O", + "ins ula", + "z illa", + "eb ook", + "Ġd are", + "Ġsear ched", + "Ġnav igate", + "S n", + "writ ing", + "Ġun ited", + "J apan", + "ĠHe brew", + "Ġfl ame", + "Ġrel ies", + "Ġcatch ing", + "ĠSh o", + "Ġimprison ment", + "Ġp ockets", + "Ġclos ure", + "ĠF am", + "t im", + "ade qu", + "Act ivity", + "Ġrecru iting", + "ĠW ATCH", + "ĠArgent ina", + "d est", + "Ġapolog ize", + "or o", + "Ġlack s", + "Ġtun ed", + "ĠGriff in", + "Ġinf amous", + "Ġcelebr ity", + "ss on", + "Ġ ----------------------------------------------------------------", + "ĠIs is", + "ĠDis play", + "Ġcred ibility", + "Ġeconom ies", + "Ġhead line", + "ĠCow boys", + "Ġind ef", + "Ġl ately", + "Ġincent ives", + "but ton", + "ĠM ob", + "A ut", + "Ġres igned", + "ĠO m", + "c amp", + "Ġprof iles", + "Ġsche mes", + "olph ins", + "ay ed", + "Cl inton", + "en h", + "ĠY ahoo", + "Ġab st", + "Ġan k", + "su its", + "Ġw ished", + "ĠMar co", + "udd en", + "Ġsp here", + "ĠB ishop", + "Ġincorpor ated", + "ĠPl ant", + "11 4", + "Ġh ated", + "p ic", + "Ġdon ate", + "Ġl ined", + "Ġbe ans", + "Ġsteal ing", + "Ġcost ume", + "Ġsher iff", + "Ġfor ty", + "Ġint act", + "Ġadapt ed", + "Ġtrave lling", + "b art", + "Ġnice ly", + "Ġdri ed", + "Ġsc al", + "os ity", + "NOT E", + "ĠB h", + "ĠBron cos", + "ĠI gn", + "Ġint imate", + "Ġchem istry", + "Ġopt imal", + "D eb", + "ĠGener ation", + "Ġ] ,", + "ich i", + "ĠW ii", + "ĠYOU R", + "vent ions", + "W rite", + "Ġpop ul", + "un ning", + "ĠW or", + "V ol", + "Ġqu een", + "head s", + "K K", + "Ġanaly ze", + "op ic", + "ear chers", + "Ġd ot", + "leg raph", + "ast ically", + "Ġupgr ades", + "Ġca res", + "Ġext ending", + "Ġfree ze", + "Ġin ability", + "Ġorg ans", + "Ġpret end", + "Ġout let", + "11 3", + "ol an", + "ĠM all", + "ul ing", + "t alk", + "Ġexpress ing", + "ĠAl ways", + "ĠBe gin", + "f iles", + "Ġlic enses", + "% %", + "ĠM itt", + "Ġfil ters", + "ĠMil waukee", + "G N", + "Ġunf old", + "M o", + "Ġnut rition", + "pp o", + "B o", + "Ġfound ing", + "Ġunder mine", + "Ġeas iest", + "ĠC zech", + "ĠM ack", + "Ġsexual ity", + "ĠN ixon", + "W in", + "ĠAr n", + "ĠK in", + "ãĤ £", + "ic er", + "Ġfort un", + "Ġsurf aces", + "agh d", + "Ġcar riers", + "ĠP ART", + "ĠT ib", + "Ġinter val", + "Ġfrust rating", + "ĠSh ip", + "ĠAr med", + "ff e", + "Ġbo ats", + "ĠAb raham", + "in is", + "Ġsu ited", + "th read", + "i ov", + "ab ul", + "ĠVenezuel a", + "Ġto m", + "su per", + "Ġcast le", + "alth ough", + "iox ide", + "ec hes", + "Ġevolution ary", + "Ġnegoti ate", + "Ġconfront ed", + "Rem ember", + "Ġ17 0", + "S uch", + "Ġ9 11", + "m ult", + "ĠA byss", + "ur ry", + "ke es", + "spe c", + "ĠBarb ara", + "Ġbelong ing", + "Ġvill ain", + "ist ani", + "Ġaccount able", + "Ġport ions", + "ĠDe cl", + "U r", + "ĠK ate", + "g re", + "Ġmag azines", + "UC K", + "Ġregul ate", + "om on", + "ĠAl most", + "Ġover view", + "Ġsc ram", + "Ġl oot", + "ĠF itz", + "Ġcharacter istic", + "ĠSn ake", + "s ay", + "ĠR ico", + "Ġtra it", + "ĠJo ined", + "au cus", + "Ġadapt ation", + "ĠAirl ines", + "Ġarch ae", + "ĠI de", + "Ġb ikes", + "Ġliter ary", + "Ġinflu ences", + "ĠUs ed", + "C reat", + "Ġple a", + "ĠDef ence", + "ĠAss ass", + "Ġp ond", + "UL T", + ") \"", + "Ġeval uated", + "Ġob taining", + "Ġdem ographic", + "Ġvig il", + "ale y", + "Ġsp ouse", + "ĠSeah awks", + "resp ons", + "ĠB elt", + "um atic", + "Ġr ises", + "run ner", + "ĠMichel le", + "Ġpot ent", + "r ace", + "ĠP AC", + "F ind", + "olester ol", + "IS S", + "ĠIntrodu ced", + "ress es", + "ign ment", + "O s", + "ĠT u", + "ĠDe x", + "ic ides", + "Ġspark ed", + "ĠLaur a", + "ĠBry ant", + "Ġsm iling", + "ĠNex us", + "Ġdefend ants", + "ĠCat al", + "Ġdis hes", + "sh aped", + "Ġpro long", + "m t", + "( $", + "ãĢ Ĥ", + "Ġcalcul ations", + "ĠS ame", + "Ġp iv", + "H H", + "Ġcance lled", + "Ġgr in", + "Ġterrit ories", + "ist ically", + "C ome", + "ĠP arent", + "Pro ject", + "Ġneg lig", + "ĠPriv acy", + "Ġam mo", + "LE CT", + "olute ly", + "ĠEp ic", + "Ġmis under", + "w al", + "Apr il", + "m os", + "path y", + "ĠC arson", + "Ġalbum s", + "ĠE asy", + "Ġpist ol", + "< <", + "Ġ\\ (", + "t arget", + "hel p", + "Ġinter pre", + "cons cious", + "ĠH ousing", + "ĠJ oint", + "12 7", + "Ġbe ers", + "s cience", + "ĠFire fox", + "effect ive", + "ĠC abin", + "ĠO kay", + "ĠApp lic", + "Ġspace craft", + "ĠS R", + "ve t", + "ĠStr ange", + "S B", + "Ġcor ps", + "iber al", + "e fficient", + "Ġpreval ence", + "Ġeconom ists", + "11 8", + "Th read", + "ord able", + "OD E", + "ĠC ant", + "=- =-", + "if iable", + "ĠA round", + "Ġpo le", + "Ġwilling ness", + "CL A", + "ĠK id", + "Ġcomple ment", + "Ġsc attered", + "Ġin mates", + "Ġble eding", + "e very", + "Ġque ue", + "ĠTr ain", + "Ġh ij", + "Ġme lee", + "ple ted", + "Ġdig it", + "Ġg em", + "offic ial", + "Ġlif ting", + "Ð µ", + "Re qu", + "it utes", + "Ġpack aging", + "ĠWork ers", + "h ran", + "ĠLeban on", + "ol esc", + "Ġpun ished", + "ĠJ uan", + "Ġj am", + "ĠD ocument", + "Ġm apping", + "ic ates", + "Ġinev itably", + "Ġvan illa", + "ĠT on", + "Ġwat ches", + "Ġle agues", + "Ġiniti ated", + "deg ree", + "port ion", + "Ġrec alls", + "Ġru in", + "Ġm elt", + "I AN", + "Ġhe m", + "Ex p", + "Ġb aking", + "ĠCol omb", + "at ible", + "Ġrad ius", + "pl ug", + "ĠI F", + "et ically", + "Ġf ict", + "H ER", + "ĠT ap", + "atin um", + "Ġin k", + "Ġco h", + "ĠW izard", + "b oth", + "te x", + "Ġsp ends", + "ĠCurrent ly", + "ĠP it", + "Ġneur ons", + "ig nt", + "Ġr all", + "Ġbus es", + "b uilding", + "Ġadjust ments", + "Ġc ried", + "ibl ical", + "att ed", + "ĠZ ion", + "ĠM atter", + "Ġmed itation", + "ĠD ennis", + "Ġour s", + "ĠT ab", + "Ġrank ings", + "ort al", + "Ġad vers", + "Ġsur render", + "ĠG ob", + "ci um", + "om as", + "im eter", + "Ġmulti player", + "Ġhero in", + "Ġoptim istic", + "Ġindic ator", + "ĠBr ig", + "Ġgro cery", + "Ġapplic ant", + "ĠRock et", + "v id", + "Ex ception", + "p ent", + "Ġorgan izing", + "Ġenc ounters", + "ĠT OD", + "Ġjew el", + "S ave", + "ĠChrist ie", + "Ġhe ating", + "Ġl azy", + "ĠC P", + "Ġcous in", + "Con fig", + "Ġreg ener", + "Ġne arest", + "Ġachie ving", + "EN S", + "th row", + "ĠRich mond", + "ant le", + "200 2", + "Ġan ten", + "b ird", + "13 3", + "Ġn arc", + "r aint", + "un ny", + "ĠHispan ic", + "ourn aments", + "Ġprop he", + "ĠTh ailand", + "ĠT i", + "Ġinject ion", + "Ġinher it", + "rav is", + "Ġmed i", + "Ġwho ever", + "ĠDE BUG", + "G P", + "ĠH ud", + "C ard", + "p rom", + "Ġp or", + "Ġover head", + "L aw", + "Ġviol ate", + "Ġhe ated", + "Ġdescript ions", + "Ġachieve ments", + "ĠBe er", + "ĠQu ant", + "W as", + "Ġe ighth", + "ĠI v", + "Ġspecial ized", + "U PDATE", + "ĠD elta", + "P op", + "J ul", + "ĠAs k", + "oph y", + "Ġnews letters", + "ĠT ool", + "Ġg ard", + "ĠConf eder", + "ĠGM T", + "ĠAb bott", + "Ġimm unity", + "ĠV M", + "Is lam", + "Ġimpl icit", + "w d", + "Ġ19 44", + "rav ity", + "omet ric", + "Ġsurv iving", + "ur ai", + "ĠPr ison", + "Ġr ust", + "ĠSk etch", + "Ġbe es", + "ĠThe ory", + "Ġmer it", + "T ex", + "ch at", + "Ġm im", + "Ġpast e", + "ĠK och", + "Ġignor ance", + "ĠSh oot", + "Ġbas ement", + "Un ited", + "ĠAd vis", + "he ight", + "Ġf oster", + "Ġdet ain", + "in formation", + "Ġne ural", + "' ;", + "Ġprov es", + "all ery", + "Ġinv itation", + "um bers", + "Ġc attle", + "Ġbicy cle", + "z i", + "Ġconsult ant", + "Ġap ology", + "ĠT iger", + "Ġ12 3", + "99 9", + "Ġind ividually", + "r t", + "ig ion", + "ĠBrazil ian", + "Ġdist urb", + "Ġentreprene urs", + "Ġfore sts", + "cer pt", + "pl ates", + "p her", + "clip se", + "Ġtw itter", + "Ġac ids", + "ograph ical", + "h um", + "ĠB ald", + "if ully", + "Ġcomp iler", + "ĠD A", + "Ġdon or", + "as i", + "Ġtrib al", + "l ash", + "ĠCon fig", + "Ġapplic ants", + "Ġsal aries", + "13 5", + "Put in", + "ĠF ocus", + "ir s", + "Ġmisc onduct", + "ĠH az", + "Ġeat en", + "M obile", + "Mus lim", + "ĠMar cus", + "v iol", + "Ġfavor able", + "Ġst ub", + "ad in", + "ĠH ob", + "Ġfaith ful", + "Ġelectron ics", + "Ġvac uum", + "w ait", + "back ed", + "econom ic", + "d ist", + "Ġten ure", + "Ġsince re", + "ĠT ogether", + "ĠW ave", + "Ġprog ression", + "Ġden ying", + "Ġdist ress", + "br aska", + "th ird", + "Ġmix ing", + "Ġcolon ial", + "Ġpriv ately", + "Ġun rest", + "atern ity", + "Ġprem ises", + "ant i", + "greg ation", + "Ġlic ence", + "ĠH ind", + "ĠSam uel", + "Ġconvinc ing", + "ĠA ce", + "ĠR ust", + "ĠNet anyahu", + "Ġhand les", + "ĠP atch", + "orient ed", + "ah o", + "ĠG onz", + "Ġhack ers", + "claim er", + "Ġcustom s", + "ĠGr an", + "f ighters", + "Ġl uc", + "Ġman uscript", + "aren thood", + "Ġdev il", + "Ġwar riors", + "Ġoff enders", + "Will iam", + "Ġhol idays", + "Ġnight mare", + "Ġle ver", + "iff erent", + "St at", + "Ġexhib ition", + "put ed", + "ĠP ure", + "Ġal pha", + "Ġenthus iasm", + "ĠRepresent atives", + "E AR", + "ĠT yp", + "Ġwhe at", + "ĠAl f", + "Ġcor rection", + "Ġev angel", + "AT T", + "M iss", + "Ġs oup", + "Ġimpl ied", + "par am", + "Ġsex y", + "ĠL ux", + "Ġrep ublic", + "p atch", + "ab lish", + "Ġic ons", + "Ġfather s", + "ĠG ET", + "ĠCar ib", + "Ġregul ated", + "ĠCo hen", + "ĠBob by", + "Ġn er", + "Ġb ent", + "vent ory", + "ĠAl ong", + "ĠE ST", + "ĠWall ace", + "Ġmurd ers", + "r ise", + "ke ll", + "ĠCommon wealth", + "Ġn asty", + "et a", + "ĠM IT", + "Ġadminist ered", + "Ġgenuine ly", + "Ed itor", + "n ick", + "Ġhyd ro", + "**************** ****************", + "ĠB le", + "Ġfin es", + "Ġg orge", + "aus ible", + "r h", + "Ġapp le", + "ment ioned", + "Ġro pe", + "ot yp", + "H R", + "Ġdisappoint ing", + "Ġc age", + "n ik", + "Ġdoub ts", + "ĠF REE", + "print s", + "ĠM UST", + "Ġvend ors", + "ĠIn qu", + "Ġliber als", + "Ġcontract or", + "Ġup side", + "child ren", + "Ġtrick y", + "Ġregul ators", + "charg ed", + "l iter", + "Ġ ***", + "Ġreb ell", + "l ang", + "Ġloc als", + "Ġphys icians", + "Ġhe y", + "ar se", + "t m", + "ĠLe x", + "Ġbehavior al", + "success ful", + "F X", + "Ġbr ick", + "ov ic", + "Ġcon form", + "Ġreview ing", + "Ġins ights", + "Ġbi ology", + "ĠRem ove", + "ĠExt ra", + "Ġcomm itting", + "indu ced", + "ignt y", + "ig m", + "Ġat omic", + "Comm on", + "ĠE M", + "ĠP ere", + "ĠIt ems", + "e h", + "Ġpres erved", + "ĠH ood", + "Ġprison er", + "Ġbankrupt cy", + "Ġg ren", + "us hes", + "Ġexplo itation", + "Ġsign atures", + "Ġfin an", + "] ,\"", + "ĠM R", + "Ġme g", + "rem lin", + "Ġmusic ians", + "Ġselect ing", + "Ġexam ining", + "IN K", + "l ated", + "H i", + "Ġart ic", + "Ġp ets", + "Ġimp air", + "ĠM AN", + "Ġtable ts", + "in clude", + "R ange", + "Ġca ut", + "Ġlog s", + "Ġmount ing", + "Ġun aware", + "Ġdynam ics", + "ĠPalest ine", + "ĠQu arter", + "ĠPur ple", + "Ġm a", + "ĠIm port", + "Ġcollect ions", + "ci ation", + "Ġsuccess or", + "Ġcl one", + "Ġaim ing", + "Ġposs essed", + "Ġstick ing", + "Ġsh aking", + "Ġloc ate", + "ĠH ockey", + "T urn", + "17 0", + "Ġfif teen", + "ĠHar rison", + "Ġcontinu ously", + "ĠT C", + "ĠVal ent", + "ĠRes cue", + "Ġby pass", + "am ount", + "Ġm ast", + "Ġprotect s", + "Ġart istic", + "Ġsomet ime", + "Ġsh oe", + "Ġshout ed", + "ific ant", + "et itive", + "ĠReg ister", + "ĠJ in", + "Ġconcent rated", + "ling ton", + "on ies", + "Ġgener ator", + "yr im", + "ĠAr men", + "Ġclear ing", + "id o", + "ĠT W", + "al ph", + "Ġlad ies", + "H ard", + "Ġdial og", + "Ġinput s", + "æ ľ", + "Ġpos es", + "Ġsl ots", + "ĠPrem ium", + "Ġle aks", + "Ġboss es", + "Ġ11 3", + "c ourse", + "A cc", + "ĠNew ton", + "ĠAust ria", + "ĠM age", + "Ġte aches", + "ab ad", + "Ġwe ars", + "Ġc yl", + "Ġcur se", + "ĠS ales", + "ĠW ings", + "Ġp sy", + "Ġg aps", + "ĠIce land", + "ĠP interest", + "Ġland lord", + "Ġdefin itions", + "ĠK er", + "Ġsufficient ly", + "ĠP ence", + "ĠArch itect", + "Ġsur pass", + "Ġ11 4", + "Ġsuper hero", + "ĠDise ase", + "Ġpri ests", + "ĠC ulture", + "Ġdefin itive", + "Ġsecret ly", + "ĠD ance", + "inst all", + "ch ief", + "ĠJess ica", + "W ould", + "Up dated", + "Ġlock er", + "ĠK ay", + "Ġmem orial", + "è ¦", + "f at", + "Ġdis gu", + "Ġflav ors", + "ĠBase ball", + "ĠRes istance", + "Ġk icks", + "Ġen v", + "Ġteen agers", + "D ark", + "ĠC AR", + "Ġh alt", + "ĠL G", + "ĠGab riel", + "Ġfe ver", + "Ġs atur", + "Ġm all", + "Ġaffili ate", + "ĠS leep", + "ĠSpe cific", + "ĠV el", + "Ġj ar", + "ĠSac red", + "ĠEd wards", + "ĠA CL", + "Ġret ained", + "ĠG iant", + "Ġlim itation", + "in ces", + "Ġref usal", + "ĠT ale", + "ĠBut ler", + "Ġacc idents", + "ĠC SS", + "Ġimport ed", + "ĠCop y", + "Î ±", + "ER T", + "z el", + "Ġdiv isions", + "h ots", + "ĠAl b", + "ĠD S", + "Load er", + "W ashington", + "at isf", + "ĠCreat ive", + "\\ .", + "ĠAut om", + "red ict", + "Ġrecept or", + "ĠCarl os", + "Met hod", + "ok a", + "Ġmal icious", + "Ġste pping", + ", [", + "ĠD ad", + "Ġatt raction", + "ĠEffect s", + "ĠPir ate", + "ĠC er", + "ĠIndust ry", + "ĠR ud", + "Ġchar ter", + "Ġd ining", + "Ġins ists", + "Ġconfig ure", + "Ġ( #", + "ĠSim ple", + "ĠSc roll", + "UT C", + "17 5", + "ĠK on", + "Ġmarket place", + "Ġ ãĤ", + "Ġref res", + "Ġg ates", + "er red", + "ĠP od", + "Ġbeh ave", + "Fr ank", + "n ode", + "Ġendors ed", + "he tt", + "as ive", + "ĠHom eland", + "Ġr ides", + "ĠLe ave", + "er ness", + "Ġflood ing", + "A FP", + "Ġris en", + "Ġcontin ually", + "Ġun anim", + "ĠCont ract", + "ĠP as", + "Ġgu ided", + "ĠCh ile", + "b d", + "Ġsu cc", + "pt ic", + "Ġcomm ittees", + "ĠL uther", + "ĠAny one", + "Ġs ab", + "12 4", + "Ġp ixel", + "ĠB ak", + "ĠT ag", + "ĠBenn ett", + "En ter", + "sm all", + "ĠPresident ial", + "Ġp ul", + "Ġcontr ace", + "arch ive", + "Ġcoast al", + "ĠK ids", + "19 2", + "âĢ ²", + "ick y", + "ING TON", + "Ġw olf", + "ĠSt alin", + "T ur", + "id get", + "am as", + "ĠUn less", + "Ġspons or", + "Ġmor ph", + "ĠCho ose", + "Ġrun ner", + "Ġun bel", + "Ġm ud", + "ĠMan a", + "Ġdub bed", + "Ġg odd", + "ure rs", + "wind ow", + "Ġrel ied", + "Ġcelebr ating", + "os c", + "Ġ13 5", + "Ġlobb ying", + "Ġincom plete", + "Ġrestrict ion", + "Ġinc ap", + "it us", + "Ġexpect ation", + "ĠAp ollo", + "Ġint ens", + "Ġsyn c", + "G H", + "Ġmanip ulation", + "B Y", + "Ġspe ar", + "Ġbre asts", + "Ġvol can", + "il ia", + "M aterial", + "Ġform ats", + "ĠB ast", + "Ġparliament ary", + "Ġsn ake", + "Ġserv ants", + "ĠTr udeau", + "ĠGr im", + "ĠArab ic", + "ĠSC P", + "ĠBoy s", + "st ation", + "Ġprospect ive", + "ord e", + "in itialized", + "Ġb ored", + "AB LE", + "Ġaccess ed", + "Ġtax i", + "ĠShe ll", + "aid en", + "urs ed", + "in ates", + "ĠIns urance", + "ĠPet e", + "Sept ember", + "6 50", + "Ġad ventures", + "ĠCo ver", + "Ġt ribute", + "Ġsk etch", + "Ġem power", + "Ġ Ø", + "ĠGl enn", + "ĠD aw", + "= \\\"", + "ĠPolit ics", + "Ġgu ides", + "Ġd ioxide", + "ĠG ore", + "ĠBr ight", + "ĠS ierra", + "Ġval ued", + "c ond", + "Ġpo inter", + "Se lect", + "Ġrisk y", + "Ġabsor b", + "im ages", + "Ġref uses", + "Ġbon uses", + "__ _", + "Ġh ilar", + "ĠF eatures", + "2 20", + "ĠCollect or", + "F oot", + "Ġ19 64", + "cul us", + "Ġd awn", + "Ġwork out", + "ĠL O", + "Ġphilosoph ical", + "ĠSand y", + "ĠYou th", + "Ġl iable", + "A f", + "bl ue", + "Ġovert urn", + "less ness", + "ĠTrib une", + "ĠIn g", + "Ġfact ories", + "Ġcat ches", + "Ġpr one", + "Ġmat rix", + "Ġlog in", + "Ġin acc", + "Ġex ert", + "s ys", + "Ġneed le", + "ĠQ ur", + "Ġnot ified", + "ould er", + "t x", + "Ġremind s", + "Ġpublisher s", + "Ġn ort", + "Ġg it", + "Ġfl ies", + "ĠEm ily", + "Ġflow ing", + "ĠAl ien", + "ĠStr ateg", + "Ġhard est", + "Ġmod ification", + "AP I", + "ĠM Y", + "Ġcr ashes", + "st airs", + "n umber", + "Ġur ging", + "ch annel", + "ĠFal con", + "Ġinhabit ants", + "Ġterr ifying", + "Ġutil ize", + "Ġban ner", + "Ġcig arettes", + "Ġsens es", + "ĠHol mes", + "Ġpract ition", + "ĠPhill ips", + "ott o", + "Ġcomp ile", + "Mod el", + "ĠK o", + "Ġ[ ]", + "Americ ans", + "ĠTer ms", + "Ġmed ications", + "ĠAn a", + "Ġfundament ally", + "ĠNot ice", + "Ġwe aker", + "Ġ 0000", + "Ġgar lic", + "Ġout break", + "Ġeconom ist", + "ĠB irth", + "Ġobst acles", + "ar cer", + "ĠOr thodox", + "Ġplace bo", + "ĠC rew", + "asp berry", + "ĠAng els", + "Ġdis charge", + "Ġdestruct ive", + "11 7", + "ĠR ising", + "Ġd airy", + "l ate", + "Ġcoll ision", + "ĠTig ers", + "ean or", + "ocument ed", + "ĠIn valid", + "Ġd ont", + "ĠL iter", + "ĠV a", + "Ġhyd rogen", + "Ġvari ants", + "ĠBrown s", + "Ġ19 65", + "Ġind igenous", + "Ġtrad es", + "Ġremain der", + "Ġswe pt", + "ĠImp act", + "Ġred ist", + "Ġun int", + "grad uate", + "ãĥ ķ", + "ĠW ILL", + "ãģ® ç", + "ĠCrit ical", + "Ġf isher", + "Ġv icious", + "Ġrevers ed", + "Y ear", + "ĠS ox", + "Ġshoot ings", + "Ġfil ming", + "Ġtouchdown s", + "ai res", + "m el", + "Ġgrand father", + "Ġaffect ion", + "ing le", + "Ġover ly", + "Add itional", + "Ġsup reme", + "ĠGr ad", + "Ġsport ing", + "Ġmer cy", + "ĠBrook s", + "ount y", + "Ġperform s", + "Ġtight ly", + "Ġdem ons", + "Ġkill ings", + "Ġfact ion", + "ĠNov a", + "aut s", + "Ġund oubtedly", + "ar in", + "Ġunder way", + "ra k", + "Ġl iv", + "ĠReg ion", + "Ġbrief ing", + "s ers", + "cl oud", + "ĠM ik", + "us p", + "Ġpred iction", + "az or", + "Ġport able", + "ĠG and", + "Ġpresent ing", + "Ġ10 80", + " »", + "ush i", + "ĠSp ark", + "there um", + "Ġjust ification", + "ĠN y", + "Ġcontract ors", + "ming ham", + "ĠSt yle", + "å ħ", + "ĠChron icles", + "ĠPict ure", + "Ġprov ing", + "Ġw ives", + "set t", + "Ġmole cules", + "ĠFair y", + "Ġconsist ing", + "Ġp ier", + "al one", + "in ition", + "Ġn ucle", + "j son", + "Ġg otta", + "Ġmob il", + "Ġver bal", + "ar ium", + "Ġmon ument", + "uck ed", + "Ġ25 6", + "T ech", + "mine craft", + "ĠTr ack", + "Ġt ile", + "Ġcompat ibility", + "as is", + "Ġs add", + "Ġinstruct ed", + "ĠM ueller", + "Ġle thal", + "Ġhorm one", + "Ġor che", + "el se", + "Ġske let", + "Ġentert aining", + "Ġminim ize", + "ag ain", + "Ġunder go", + "Ġconst raints", + "Ġcig arette", + "ĠIslam ist", + "Ġtravel s", + "ĠPant hers", + "l ings", + "C are", + "Ġlaw suits", + "ur as", + "Ġcry st", + "Ġlow ered", + "Ġaer ial", + "Ġcomb inations", + "Ġha un", + "Ġch a", + "Ġv ine", + "Ġquant ities", + "Ġlink ing", + "b ank", + "Ġso y", + "B ill", + "ĠAngel a", + "Ġrecip ient", + "ĠProt est", + "Ġs ocket", + "Ġsolid arity", + "Ġâ Ĩ", + "m ill", + "Ġvar ies", + "ĠPak istani", + "Dr agon", + "Ġun e", + "Ġhor izon", + "³³³³ ³³³³", + "Ġprov inces", + "Ġfrank ly", + "Ġenact ed", + "not es", + "[ '", + "Ġ19 2", + "ocr acy", + "Ġendorse ment", + "Ġover time", + "Tr ue", + "L ab", + "lic ted", + "ĠD NC", + "Ġbe ats", + "ĠJam ie", + "15 2", + "ĠIN T", + "Cont act", + "Ġaccount ed", + "h ash", + "ĠPack ers", + "p ires", + "Ġles bian", + "Ġamend ments", + "Ġhop eful", + "ĠFin land", + "Ġspot light", + "Ġconfig ured", + "Ġtrou bled", + "Ġg aze", + "ĠCal gary", + "Ġrel iability", + "Ġins urg", + "sw er", + "b uy", + "ĠSk in", + "Ġp ixels", + "Ġhand gun", + "Ġpar as", + "Ġcateg or", + "ĠE L", + "ĠRe x", + "Ind eed", + "Ġkind a", + "Ġconj unction", + "ĠBry an", + "ĠMan ufact", + "y ang", + "Pl us", + "S QL", + "ish ment", + "Ġdom inate", + "Ġn ail", + "Ġo ath", + "Ġeru pt", + "ĠF ine", + "it bart", + "ĠCh ip", + "ĠAb d", + "ĠN am", + "Ġbuy er", + "Ġdiss ent", + "Le aks", + "Cont in", + "Ġr ider", + "ĠSome one", + "Ġill usion", + "c in", + "ĠBoe ing", + "Ġin adequ", + "ov ation", + "i ants", + "Ġreb uild", + "4 50", + "ĠDest iny", + "S W", + "ĠT ill", + "H it", + "ia z", + "ĠBang l", + "acher s", + "ĠRe form", + "Ġse gments", + "Ġsystem atic", + "d c", + "ĠConserv atives", + "Ġport al", + "h or", + "ĠDragon bound", + "Ġdrag ged", + "om o", + "Ġthe e", + "ad vert", + "ĠRep orts", + "ĠE t", + "Ġbarrel s", + "Aug ust", + "Ġcompar isons", + "Ġhe x", + "Ġan throp", + "\" [", + "bor ough", + "ab i", + "Ġpict ured", + "play ing", + "ĠAdd ress", + "ĠMir ror", + "Sm ith", + "Ġt ires", + "ĠN PR", + "AA AA", + "Ġclass ification", + "ĠTh an", + "ĠH arm", + "ĠR A", + "Ġreject ion", + "min ation", + "Ġr anged", + "ĠF alls", + "D I", + "H ost", + "ãĤ ´", + "ĠEx ample", + "list ed", + "th irds", + "Ġsaf egu", + "br and", + "Ġprob able", + "Can ada", + "IT ION", + "ĠQ aeda", + "Ġch ick", + "Ġimport s", + "h it", + "l oc", + "W W", + "Ġble w", + "Ġany time", + "Ġwh oles", + "ik ed", + "Ġcal culation", + "cre ate", + "ĠO ri", + "Ġupgr aded", + "Ġapp ar", + "ut ory", + "ĠM ol", + "B rit", + "ĠJ ong", + "IN AL", + "ĠStart ing", + "Ġd ice", + "urt le", + "Ġre lying", + "cl osure", + "Ġprof itable", + "Ġsl aughter", + "ĠMan ual", + "c aster", + "Ġ\" $", + "Ġfe ather", + "ĠSim ply", + "ie ves", + "Ġdeter ior", + "ĠPC I", + "Ġst amp", + "Ġfl aws", + "Ġsh ade", + "ham mer", + "Ġpass port", + "Ġcont ing", + "am el", + "Ġobser vers", + "Ġneg lect", + "ĠR B", + "ĠBrother hood", + "Ġskept ical", + "f amily", + "us k", + "Ġemotion ally", + "â Ļ", + "ĠBet a", + "ason able", + "id ity", + "ĠM ul", + "Ġkick ing", + "ĠC arm", + "oll ah", + "VERT IS", + "ĠAt hen", + "Ġlad der", + "ĠBul let", + "å £", + "00 01", + "ĠWild life", + "ĠM ask", + "ĠN an", + "R ev", + "Ġun acceptable", + "leg al", + "Ġcrowd ed", + "ag i", + "ĠC ox", + "j e", + "Ġmor ality", + "Ġfu els", + "Ġc ables", + "Ġman kind", + "ĠCarib bean", + "Ġanch or", + "Ġby te", + "ĠO ften", + "ĠO z", + "Ġcraft ed", + "Ġhistor ian", + "ĠW u", + "Ġtow ers", + "ĠCitiz ens", + "Ġhel m", + "Ġcred entials", + "Ġsing ular", + "ĠJes se", + "Ġtack les", + "Ġcont empt", + "Ġa fore", + "ĠSh adows", + "Ġn il", + "Ġur gent", + "app le", + "bl ood", + "Ġv on", + "Ġoff line", + "Ġbreat he", + "Ġj umps", + "Ġirre levant", + "ox ic", + "om al", + "import ant", + "J im", + "Ġgl oves", + "arm ing", + "dep th", + "Ġtal ents", + "ook ie", + "ĠS B", + "Ġpal m", + "uff s", + "est a", + "IG H", + "Ġcan on", + "ĠVer izon", + "ĠP le", + "Ġcou pled", + "vel t", + "Ġfundra ising", + "ĠGet ting", + "ĠD LC", + "Ġmathemat ical", + "ĠH S", + "ĠCard inals", + "te lling", + "Ġspons ors", + "Ġ Ï", + "ĠBull s", + "op tion", + "Ġprop ose", + "Ġmem orable", + "Ġembr aced", + "Ġdecl ining", + "He alth", + "ed a", + "Ġ} ;", + "Ġsp am", + "m ile", + "Ġpit cher", + "ĠE ight", + "Ġcar ing", + "ut ic", + "ro le", + "Ġair line", + "ernand ez", + "ĠAth let", + "Ġcert ification", + "ux e", + "rig er", + "Ġem pir", + "Ġsens ation", + "Ġdis m", + "Ġb olt", + "Ġev olve", + "H ouse", + "Ġconsult ation", + "ĠD uty", + "Ġtou ches", + "ĠN athan", + "Ġf aint", + "h ad", + "\" (", + "ĠCons umer", + "ĠExt reme", + "Ġ12 7", + "ĠHer m", + "ĠSac rament", + "iz oph", + "Ġanx ious", + "ul ously", + "Ġsoc ially", + "ĠU TC", + "Ġsol ving", + "ĠLet ter", + "Hist ory", + "ed uc", + "Pr ice", + ") );", + "Ġrel oad", + "am ic", + "Ġp ork", + "Ġdisc ourse", + "Ġt ournaments", + "ai ro", + "ĠK ur", + "ĠCost a", + "Ġviol ating", + "Ġinterf ere", + "Ġrecre ational", + "uff le", + "Ġspe eches", + "Ġneed ing", + "Ġremem bers", + "Ġcred ited", + "n ia", + "f ocused", + "amer a", + "Ġb ru", + "um bs", + "ĠCub an", + "Ġpreced ing", + "Ġnons ense", + "ac ial", + "Ġsmart phones", + "ĠSt ories", + "S ports", + "ĠEmer gency", + "oun cing", + "ef ined", + "Ġb er", + "Ġconsult ing", + "Ġm asters", + "he astern", + ".\" [", + "ĠRun ning", + "Ġsus cept", + "ĠF eng", + "Americ a", + "pr ises", + "st itial", + "ĠWeek ly", + "ĠGreat er", + "mod ules", + "if ter", + "G raphics", + "ul er", + "Ġwho lly", + "Ġsupp ress", + "Ġconce aled", + "Ġhapp ily", + "Ġaccept s", + "ĠEn joy", + "Ġr ivers", + "ĠEx cept", + "2 25", + "ĠN HS", + "ĠMc Connell", + "Ġp ussy", + "fer red", + "ut able", + "Ġatt ain", + "Ġ> =", + "Ġdepos its", + "roph ic", + "Ġnot orious", + "ĠSh aw", + "il itation", + "Ġepid emic", + "all ic", + "Ġsmall est", + "ov ich", + "Ġaccess ories", + "per ties", + "Ġsur plus", + "ĠMe ch", + "Ġamb ig", + "ĠImm igration", + "Ġch im", + "ev al", + "Ġpract icing", + "ĠMyster y", + "Ġdom ains", + "ĠSil icon", + "app s", + "Ġkilomet ers", + "e a", + "ĠSm ash", + "Ġwarrant y", + "Ġn ost", + "s il", + "re v", + "J on", + "ĠDub lin", + "Ġtast es", + "Ġb out", + "g reat", + "er ror", + "Ġsw itches", + "ĠB apt", + "D O", + "ok i", + "Ġsour ced", + "pro du", + "Ġattach ment", + "ĠIss ue", + "ĠQuest ion", + "Jo in", + "Ġf itted", + "Ġunlaw ful", + "^ ^", + "ere k", + "Ġauthent ication", + "Ġst ole", + "Ġaccount ability", + "l abel", + "S earch", + "Ġal beit", + "atic an", + "fund ed", + "ĠAdd ing", + "ĠI Q", + "Ġsub mar", + "l it", + "a que", + "ĠLear ning", + "Ġint eger", + "M aster", + "ĠCh rom", + "Ġprem ier", + "O p", + "ĠLi u", + "Ġbl essed", + "ĠGl obe", + "ĠResp onse", + "Ġlegit im", + "ĠMer kel", + "Ġdispos al", + " ´", + "Ġgau ge", + "pe at", + "Ġindu ced", + "Ġquestion able", + "arth y", + "ĠV it", + "ĠF eed", + "U ntil", + "U t", + "worth y", + "R Y", + "ĠH erald", + "ĠHam mer", + "Ġmed al", + "ĠR ivers", + "ĠH ack", + "Ġclar ify", + "Ġtrack ed", + "Ġautonom ous", + "Ġten ant", + "ĠQ atar", + "er ie", + "Ġgr im", + "ĠMon itor", + "Ġresist ant", + "ĠSpe c", + "ĠWell s", + "N AS", + "14 8", + "Ġmin ers", + "iot ics", + "Ġmiss es", + "11 6", + "g ian", + "g it", + "ĠE yes", + "p res", + "Ġgrad uated", + "Ġang el", + "Ġsyn chron", + "Ġefficient ly", + "Ġtrans mitted", + "H arry", + "Ġglob ally", + "EN CE", + "ĠMont ana", + "r aged", + "ĠPre vention", + "Ġp iss", + "ĠL l", + "Ġshe lf", + "ĠB JP", + "ĠTest ament", + "ĠL ate", + "ik er", + "ĠH app", + "ĠJul ian", + "h all", + "Ġsp ont", + "Ġshut down", + "Ġincons istent", + "Ġsubscrib ers", + "Ġske leton", + "ĠNe braska", + "Ġins pire", + "ĠV oid", + "F eed", + "Ġang les", + "ĠSpr ings", + "Ġbench mark", + "Ġvacc ines", + "izoph ren", + "se xual", + "uff ed", + "Ġsh ine", + "ĠK ath", + "Ġgest ure", + "ine a", + "Ġr ip", + "Ġopp ression", + "Ġcons cience", + "b t", + "ĠL um", + "Ġinc idence", + "ĠF a", + "w r", + "Ġmin eral", + "ĠSp urs", + "alk y", + "Ġth under", + "Ġop io", + "Be ing", + "ĠPal m", + "Ġwas ted", + "Ġl b", + "i aries", + "ĠIniti ative", + "Ġcur ric", + "Ġmark er", + "ĠMc L", + "Ġext ensions", + "ĠP v", + "ĠAr ms", + "Ġoffer ings", + "Ġdef enses", + "Ġvend or", + "Ġcontrad ict", + "ĠCol in", + "Ġredd it", + "Ġper ipher", + "12 2", + "Ġs ins", + "E dit", + "IC T", + "So ft", + "ĠSh ah", + "Ġadministr ator", + "ĠT rip", + "Ġporn ography", + "Ġtu ition", + "in ence", + "ĠPro gress", + "Ġcat alog", + "Ġsu ite", + "Ġh ike", + "Ġreprodu ctive", + "eng ine", + "Ġd rought", + "ĠNo ah", + "Ġ2 30", + "Ġd ude", + "Ġrelax ed", + "Ġpart ition", + "Ġparticip ant", + "Ġtel esc", + "Ġfe as", + "ĠF F", + "own er", + "Ġswe eping", + "Ġl enses", + "Ġmatch up", + "ĠRe pl", + "ourn als", + "Ġcred ible", + "Ġgrand mother", + "Ġther mal", + "Ġsubscrib ing", + "Ġident ities", + "col m", + "U CT", + "Ġreluct ant", + "us ers", + "ĠC ort", + "Ġassist ed", + "OS S", + "ATION S", + "IS H", + "Ġpharm aceutical", + "ic able", + "ad ian", + "ĠSon ic", + "ĠF ury", + "ĠM ong", + "A H", + "ĠPsych ology", + "Ġph osph", + "Ġtreat s", + "Ń Ķ", + "Ġstead ily", + "ĠHell o", + "Ġrel ates", + "Ġcl ue", + "Ex pl", + "a uth", + "Ġrev ision", + "Ġe ld", + "os ion", + "Ġbr on", + "14 4", + "ri kes", + "Ġmin es", + "Ġblank et", + "ĠF ail", + "el ed", + "ĠIm agine", + "ĠPl anned", + "a ic", + "Re quest", + "M ad", + "ĠHor se", + "ĠEag le", + "Ġcap ac", + "15 7", + "Ġl ing", + "ĠN ice", + "ĠP arenthood", + "min ster", + "og s", + "ens itive", + "Not hing", + "Ġcar n", + "F in", + "ĠP E", + "Ġr ifles", + "ĠL P", + "S and", + "Ġgui Active", + "Ġtour ist", + "C NN", + "Ġunve iled", + "Ġpredec essor", + "} {", + "u ber", + "Ġoff shore", + "Ġopt ical", + "ĠR ot", + "ĠPear l", + "et on", + "Ġst ared", + "Ġfart her", + "at ility", + "cont in", + "ĠG y", + "ĠF oster", + "ĠC oc", + "ri ents", + "Ġdesign ing", + "ĠEconom y", + "ON G", + "W omen", + "ĠN ancy", + "er ver", + "Ġmas cul", + "Ġcasual ties", + "Ġ2 25", + "ĠS ullivan", + "ĠCh oice", + "Ġa ster", + "w s", + "Ġhot els", + "Ġconsider ations", + "Ġcou ch", + "ĠSt rip", + "ĠG n", + "Ġmanip ulate", + "l ied", + "Ġsynt hetic", + "Ġassault ed", + "Ġoff enses", + "ĠDra ke", + "Ġim pe", + "Oct ober", + "ĠHer itage", + "h l", + "ĠBl air", + "Un like", + "Ġg rief", + "Ġ4 50", + "Ġopt ed", + "Ġresign ation", + "il o", + "Ġver se", + "ĠT omb", + "Ġu pt", + "Ġa ired", + "ĠH ook", + "ĠML B", + "Ġassum es", + "out ed", + "ĠV ers", + "Ġinfer ior", + "Ġbund le", + "ĠD NS", + "ograp her", + "Ġmult ip", + "ĠSoul s", + "Ġillust rated", + "Ġtact ic", + "Ġdress ing", + "Ġdu o", + "Con f", + "Ġrel ent", + "Ġc ant", + "Ġscar ce", + "Ġcand y", + "ĠC F", + "Ġaffili ated", + "Ġspr int", + "yl an", + "ĠGarc ia", + "Ġj unk", + "Pr int", + "ex ec", + "C rit", + "Ġport rait", + "ir ies", + "ĠOF F", + "Ġdisp utes", + "W R", + "L ove", + "ãģ Ħ", + "ĠRe yn", + "Ġh ipp", + "op ath", + "Ġflo ors", + "ĠFe el", + "Ġwor ries", + "Ġsett lements", + "ĠP os", + "Ġmos que", + "Ġfin als", + "Ġcr ushed", + "ĠPro bably", + "ĠB ot", + "ĠM ans", + "ĠPer iod", + "Ġsovere ignty", + "Ġsell er", + "Ġap ost", + "Ġam ateur", + "Ġd orm", + "Ġconsum ing", + "Ġarm our", + "ĠRo ose", + "Ġint ensive", + "Ġelim inating", + "ĠSun ni", + "ĠAle ppo", + "j in", + "Ġadv ise", + "p al", + "ĠH alo", + "Ġdes cent", + "Ġsimpl er", + "Ġbo oth", + "ST R", + "L ater", + "ĠC ave", + "== =", + "Ġm ol", + "Ġf ist", + "Ġshot gun", + "su pp", + "Ġrob bery", + "E ffect", + "Ġobsc ure", + "ĠProf essional", + "Ġemb assy", + "Ġmilit ant", + "Ġinc arcer", + "Ġgener ates", + "Ġlaun ches", + "Ġadministr ators", + "Ġsh aft", + "Ġcirc ular", + "Ġfresh man", + "ĠW es", + "ĠJo el", + "ĠD rew", + "ĠDun can", + "ĠApp arently", + "s ight", + "ĠIntern al", + "ĠInd ividual", + "ĠF E", + "Ġb ore", + "ĠM t", + "Ġbroad ly", + "ĠO ptions", + "ount ain", + "ip es", + "ĠV ideos", + "20 4", + "Ġh ills", + "Ġsim ulation", + "Ġdisappoint ment", + "it an", + "ĠLabor atory", + "Ġup ward", + "Ġbound ary", + "Ġdark er", + "h art", + "Ġdomin ance", + "C ong", + "ĠOr acle", + "ĠL ords", + "Ġscholars hip", + "ĠVin cent", + "ed e", + "ĠR ah", + "Ġencour ages", + "ro v", + "Ġqu o", + "Ġprem ise", + "ĠCris is", + "ĠHol ocaust", + "Ġrhyth m", + "Ġmet ric", + "cl ub", + "Ġtransport ed", + "Ġn od", + "ĠP ist", + "Ġancest ors", + "ĠFred er", + "th umbnails", + "ĠC E", + "ON D", + "Ph il", + "ven ge", + "ĠProduct s", + "cast le", + "Ġqual ifying", + "ĠK aren", + "VERTIS EMENT", + "Ġmight y", + "Ġexplan ations", + "Ġfix ing", + "D i", + "Ġdecl aring", + "Ġanonym ity", + "Ġju ven", + "ĠN ord", + "ĠDo om", + "ĠAct ually", + "O k", + "ph is", + "ĠDes ert", + "Ġ11 6", + "I K", + "ĠF M", + "Ġinc omes", + "V EL", + "ok ers", + "Ġpe cul", + "Ġlight weight", + "g ue", + "Ġacc ent", + "Ġincre ment", + "ĠCh an", + "Ġcompl aining", + "ĠB aghd", + "Ġmidfield er", + "Ġover haul", + "Pro cess", + "ĠH ollow", + "ĠTit ans", + "Sm all", + "man uel", + "ĠUn ity", + "ĠEv ents", + "S ty", + "Ġdispro portion", + "n esty", + "en es", + "ĠC od", + "Ġdemonstr ations", + "ĠCrim son", + "ĠO H", + "Ġen rolled", + "Ġc el", + "ĠBre tt", + "Ġa ide", + "Ġhe els", + "Ġbroad band", + "Ġmark ing", + "Ġw izard", + "ĠN J", + "ĠChief s", + "Ġingred ient", + "Ġd ug", + "ĠSh ut", + "urch ase", + "end or", + "Ġfar mer", + "ĠGold man", + "12 9", + "15 5", + "Or der", + "Ġl ion", + "i ably", + "Ġst ain", + "ar ray", + "ilit ary", + "ĠFA Q", + "Ġexpl oded", + "ĠMcC arthy", + "ĠT weet", + "ĠG reens", + "ek ing", + "l n", + "ens en", + "Ġmotor cycle", + "Ġpartic le", + "Ġch olesterol", + "B ron", + "Ġst air", + "Ġox id", + "Ġdes irable", + "ib les", + "Ġthe or", + "for cing", + "Ġpromot ional", + "ov o", + "b oot", + "ĠBon us", + "raw ling", + "Ġshort age", + "ĠP sy", + "Ġrecru ited", + "Ġinf ants", + "Ġtest osterone", + "Ġded uct", + "Ġdistinct ive", + "Ġfirm ware", + "bu ilt", + "14 5", + "Ġexpl ored", + "Ġfact ions", + "Ġv ide", + "Ġtatt oo", + "Ġfinan cially", + "Ġfat igue", + "Ġproceed ing", + "const itutional", + "Ġmis er", + "Ġch airs", + "gg ing", + "ipp le", + "Ġd ent", + "Ġdis reg", + "ç Ķ", + "st ant", + "ll o", + "b ps", + "aken ing", + "Ġab normal", + "ĠE RA", + "å£ «", + "ĠH BO", + "ĠM AR", + "Ġcon cess", + "Ġserv ant", + "Ġas pir", + "l av", + "ĠPan el", + "am o", + "Ġprec ip", + "Ġrecord ings", + "Ġproceed ed", + "Ġcol ony", + "ĠT ang", + "ab lo", + "Ġstri pped", + "Le ft", + "to o", + "Ġpot atoes", + "Ġfin est", + "% ).", + "Ġc rap", + "ĠZ ach", + "ab ases", + "ĠG oth", + "Ġbillion aire", + "w olf", + "Ġsan ction", + "S K", + "Ġlog ged", + "P o", + "ey ed", + "un al", + "Ġcr icket", + "Ġarm ies", + "Ġunc overed", + "Cl oud", + "ó n", + "Ġreb ounds", + "Ġm es", + "O per", + "P ac", + "Ġnation ally", + "Ġinsert ed", + "p ict", + "Ġgovern ance", + "Ð ¸", + "Ġprivile ges", + "G ET", + "Ġfavor ites", + "im ity", + "Ġlo ver", + "the m", + "em pl", + "Ġgorge ous", + "An n", + "Ġsl ipped", + "Ġve to", + "B ob", + "Ġsl im", + "u cc", + "ĠF ame", + "udden ly", + "Ġden ies", + "ĠM aur", + "Ġdist ances", + "Ġw anna", + "t ar", + "ĠS ER", + "Ġâ Ī", + "Ġle mon", + "at hetic", + "Ġlit eral", + "Ġdistingu ished", + "Ġansw ering", + "G I", + "Ġrelig ions", + "ĠPhil os", + "ĠL ay", + "Ġcomp os", + "ire ments", + "ĠK os", + "ine z", + "roll ing", + "Ġyoung est", + "and ise", + "ĠB orn", + "Ġalt ar", + "am ina", + "ĠB oot", + "v oc", + "Ġdig ging", + "Ġpress ures", + "Ġl en", + "26 4", + "Ġassass ination", + "ĠBir mingham", + "ĠMy th", + "Ġsovere ign", + "ĠArt ist", + "ĠPhot ograph", + "Ġdep icted", + "Ġdisp ens", + "orth y", + "Ġamb ul", + "int eg", + "ĠC ele", + "ĠTib et", + "Ġhier archy", + "Ġc u", + "Ġpre season", + "ĠPet erson", + "Ġcol ours", + "Ġworry ing", + "Ġback ers", + "ĠPal mer", + "ĠÎ ¼", + "Ġcontribut or", + "Ġhear ings", + "Ġur ine", + "Ġ Ù", + "ourge ois", + "Sim ilar", + "ĠZ immer", + "s omething", + "ĠUS C", + "Ġstrength s", + "ĠF I", + "Ġlog ging", + "As ked", + "ĠTh ai", + "in qu", + "ĠW alt", + "Ġcrew s", + "it ism", + "3 01", + "Ġshar ply", + "um ed", + "Ġred irect", + "r ators", + "In f", + "ĠWe apons", + "Ġte asp", + "19 99", + "L ive", + "ĠEs pecially", + "ĠS ter", + "ĠVeter ans", + "Ġint ro", + "other apy", + "Ġmal ware", + "Ġbre eding", + "Ġmole cular", + "ĠR oute", + "ĠCom ment", + "oc hem", + "Ġa in", + "Se ason", + "Ġlineback er", + "Ä «", + "ĠEconom ics", + "es ar", + "ĠL ives", + "ĠEm ma", + "Ġk in", + "ĠTer rit", + "Ġpl anted", + "ot on", + "ĠBut ter", + "ĠSp ons", + "P ER", + "Ġdun geon", + "Ġsymb olic", + "Ġfil med", + "Ġdi ets", + "Ġconclud es", + "Ġcertain ty", + "ĠForm at", + "Ġstr angers", + "form at", + "ĠPh ase", + "Ġcop ied", + "Ġmet res", + "ld a", + "ĠUs ers", + "Ġdeliber ate", + "Ġwas hed", + "ĠL ance", + "im ation", + "Ġimpro per", + "ĠGen esis", + "ick r", + "ĠK ush", + "Ġreal ise", + "Ġembarrass ing", + "alk ing", + "b ucks", + "Ġver ified", + "Ġout line", + "year s", + "ĠIn come", + "20 2", + "Ġz ombies", + "F inal", + "ĠMill enn", + "Ġmod ifications", + "ĠV ision", + "ĠM oses", + "ver b", + "iter ranean", + "ĠJ et", + "Ġnav al", + "ĠA gg", + "Ġur l", + "Ġvict ories", + "Ġnon etheless", + "Ġinj ust", + "ĠF act", + "ç ļ", + "Ġins ufficient", + "re view", + "face book", + "Ġnegoti ating", + "Ġguarant ees", + "im en", + "uten berg", + "Ġg ambling", + "Ġcon gr", + "Load ing", + "Ġnever theless", + "Ġpres idents", + "ĠIndust rial", + "Ġ11 8", + "Ġp oured", + "ĠT ory", + "Ġ17 5", + "Ġ: =", + "Sc ott", + "ange red", + "T ok", + "Ġorgan izers", + "M at", + "ĠG rowth", + "Ġad ul", + "Ġens ures", + "Ġ11 7", + "é¾į å", + "Ġmass acre", + "Ġgr ades", + "be fore", + "AD VERTISEMENT", + "ĠSl ow", + "ĠM MA", + "âĢĶ \"", + "ĠV atican", + "Q aeda", + "Ġo we", + "66 66", + "ĠS orry", + "ĠGr ass", + "Ġbackground s", + "Ġexha usted", + "Ġcl an", + "Ġcomprom ised", + "ĠE lf", + "ĠIsa ac", + "ens on", + "In vest", + "IF A", + "Ġinterrupt ed", + "ãĥī ãĥ©", + "Ġtw isted", + "ĠDrag ons", + "M ode", + "ĠK remlin", + "Ġfert il", + "he res", + "ph an", + "ĠN ode", + "f ed", + "ĠOr c", + "Ġunw illing", + "C ent", + "Ġprior it", + "Ġgrad uates", + "Ġsubject ive", + "Ġiss uing", + "ĠL t", + "Ġview er", + "Ġw oke", + "Th us", + "bro ok", + "Ġdep ressed", + "Ġbr acket", + "ĠG or", + "ĠFight ing", + "Ġstri ker", + "Rep ort", + "ĠPortug al", + "Ġne o", + "w ed", + "19 9", + "Ġflee ing", + "sh adow", + "ident ified", + "US E", + "Ste am", + "Ġstret ched", + "Ġrevel ations", + "art ed", + "ĠD w", + "Ġalign ment", + "est on", + "ĠJ ared", + "S ep", + "Ġblog s", + "up date", + "g om", + "r isk", + "Ġcl ash", + "ĠH our", + "Ġrun time", + "Ġunw anted", + "Ġsc am", + "Ġr ack", + "Ġen light", + "on est", + "ĠF err", + "Ġconv ictions", + "Ġp iano", + "Ġcirc ulation", + "ĠW elcome", + "Ġback lash", + "ĠW ade", + "Ġrece ivers", + "ot ive", + "J eff", + "Ġnetwork ing", + "ĠPre p", + "ĠExpl orer", + "Ġlect ure", + "Ġupload ed", + "ĠMe at", + "B LE", + "ĠNaz is", + "ĠSy nd", + "st ud", + "ro ots", + "ri ans", + "Ġportray ed", + "Ġ ??", + "ĠBudd ha", + "s un", + "Rober t", + "ĠCom plex", + "Ġover see", + "Ġste alth", + "T itle", + "ĠJ obs", + "ĠK um", + "Ġappreci ation", + "ĠM OD", + "Ġbas ics", + "Ġcl ips", + "Ġnurs ing", + "Ġpropos ition", + "Ġreal ised", + "ĠNY C", + "Ġall ocated", + "ri um", + "ar an", + "ĠPro duction", + "ĠV ote", + "Ġsm ugg", + "Ġhun ter", + "az er", + "ĠCh anges", + "Ġfl uct", + "y on", + "Ar ray", + "Ġk its", + "W ater", + "Ġuncom mon", + "Ġrest ing", + "ell s", + "w ould", + "Ġpurs ued", + "Ġassert ion", + "omet own", + "ĠMos ul", + "ĠPl atform", + "io let", + "Ġshare holders", + "Ġtra ils", + "P ay", + "ĠEn forcement", + "ty pes", + "ĠAn onymous", + "Ġsatisf ying", + "il ogy", + "Ġ( '", + "w ave", + "c ity", + "Ste ve", + "Ġconfront ation", + "ĠE ld", + "C apt", + "ah an", + "ht m", + "ĠC trl", + "ON S", + "2 30", + "if a", + "hold ing", + "Ġdelic ate", + "Ġj aw", + "ĠGo ing", + "or um", + "S al", + "Ġd ull", + "ĠB eth", + "Ġpr isons", + "Ġe go", + "ĠEl sa", + "avor ite", + "ĠG ang", + "ĠN uclear", + "Ġsp ider", + "ats u", + "Ġsam pling", + "Ġabsor bed", + "ĠPh arm", + "iet h", + "Ġbuck et", + "ĠRec omm", + "O F", + "ĠF actory", + "AN CE", + "Ġb acter", + "H as", + "ĠObs erv", + "12 1", + "Ġprem iere", + "De velop", + "Ġcur rencies", + "C ast", + "Ġaccompany ing", + "ĠNash ville", + "Ġfat ty", + "ĠBre nd", + "Ġloc ks", + "Ġcent ered", + "ĠU T", + "augh s", + "or ie", + "ĠAff ordable", + "v ance", + "D L", + "em et", + "Ġthr one", + "ĠBlu etooth", + "Ġn aming", + "if ts", + "AD E", + "Ġcorrect ed", + "Ġprompt ly", + "ĠST R", + "Ġgen ome", + "Ġcop e", + "Ġval ley", + "Ġround ed", + "ĠK end", + "al ion", + "p ers", + "Ġtour ism", + "Ġst ark", + "v l", + "Ġblow ing", + "ĠSche dule", + "st d", + "Ġunh appy", + "Ġlit igation", + "ced es", + "Ġand roid", + "Ġinteg ral", + "ere rs", + "ud ed", + "t ax", + "Ġre iter", + "ĠMot ors", + "oci ated", + "Ġwond ers", + "ĠAp ost", + "uck ing", + "ĠRoose velt", + "f ram", + "Ġyield s", + "Ġconstit utes", + "aw k", + "Int erest", + "Ġinter im", + "Ġbreak through", + "ĠC her", + "Ġpro sec", + "ĠD j", + "ĠM T", + "Res p", + "ĠP T", + "Ġs perm", + "ed it", + "B T", + "Lin ux", + "count ry", + "le ague", + "Ġd ick", + "Ġo ct", + "Ġinsert ing", + "Ġsc ra", + "ĠBrew ing", + "Ġ19 66", + "Ġrun ners", + "Ġpl un", + "id y", + "ĠD ian", + "Ġdys function", + "Ġex clusion", + "Ġdis gr", + "Ġincorpor ate", + "Ġrecon c", + "Ġnom inated", + "ĠAr cher", + "d raw", + "achel or", + "Ġwrit ings", + "Ġshall ow", + "Ġh ast", + "ĠB MW", + "ĠR S", + "Ġth igh", + "Ġ19 63", + "Ġl amb", + "Ġfav ored", + "ag le", + "Ġcool er", + "ĠH ours", + "ĠG U", + "ĠOrig in", + "Ġglim pse", + "---------------- ----", + "L im", + "Ġche ek", + "Ġj ealous", + "- '", + "Ġhar ness", + "ĠPo ison", + "Ġdis abilities", + "ne apolis", + "Ġout look", + "Ġnot ify", + "ĠIndian apolis", + "Ġab rupt", + "ns ic", + "Ġenc rypted", + "Ġfor fe", + "reat h", + "Ġr abb", + "Ġfound ations", + "Ġcompl iment", + "ĠInter view", + "ĠS we", + "Ġad olesc", + "Ġmon itors", + "ĠSacrament o", + "Ġtime ly", + "Ġcontem pl", + "Ġposition ed", + "Ġpost ers", + "ph ies", + "iov ascular", + "v oid", + "ĠFif th", + "Ġinvestig ative", + "OU N", + "Ġinteg rate", + "ĠIN C", + "ish a", + "ibl ings", + "ĠRe quest", + "ĠRodrig uez", + "Ġsl ides", + "ĠD X", + "Ġfemin ism", + "Ġdat as", + "Ġb end", + "ir us", + "ĠNig eria", + "F ox", + "Ch ange", + "Ġair plane", + "ĠLad en", + "Ġpublic ity", + "ixt y", + "Ġcommit ments", + "Ġaggreg ate", + "Ġdisplay ing", + "ĠAr row", + "Ġ12 2", + "Ġrespect s", + "and roid", + "s ix", + "ĠSh a", + "Ġrest oration", + ") \\", + "W S", + "oy s", + "Ġillust rate", + "with out", + "12 6", + "ĠâĶ Ĥ", + "Ġpick up", + "n els", + "Ġ ....", + "f ood", + "ĠF en", + ") ?", + "Ġphenomen a", + "Ġcompan ions", + "ĠW rite", + "Ġsp ill", + "Ġbr idges", + "ĠUp dated", + "ĠF o", + "Ġinsect s", + "ASH INGTON", + "Ġsc are", + "il tr", + "ĠZh ang", + "Ġsever ity", + "Ġind ul", + "14 9", + "ĠCo ffee", + "Ġnorm s", + "Ġp ulse", + "ĠF T", + "Ġhorr ific", + "ĠDest roy", + "ĠJ SON", + "Ġo live", + "Ġdiscuss es", + "R est", + "E lect", + "ĠW inn", + "ĠSurv iv", + "ĠH ait", + "S ure", + "op ed", + "Ġro oted", + "ĠS ke", + "ĠBron ze", + "Ġl ol", + "Def ault", + "Ġcommod ity", + "red ited", + "Ġliber tarian", + "Ġforb idden", + "Ġgr an", + "à ¨", + "Ġl ag", + "en z", + "dri ve", + "Ġmathemat ics", + "Ġw ires", + "Ġcrit ically", + "Ġcarb ohyd", + "ĠChance llor", + "ĠEd die", + "Ġban ning", + "ĠF ri", + "Ġcompl ications", + "et ric", + "ĠBangl adesh", + "Ġband width", + "St op", + "ĠOrig inally", + "Ġhalf way", + "yn asty", + "sh ine", + "Ġt ales", + "rit ies", + "av ier", + "Ġspin ning", + "ĠWH O", + "Ġneighbour hood", + "b ach", + "Ġcommer ce", + "ĠS le", + "B U", + "Ġentreprene ur", + "Ġpecul iar", + "ĠCom ments", + "f re", + "3 20", + "IC S", + "Ġimag ery", + "ĠCan on", + "ĠElect ronic", + "sh ort", + "( (", + "D ig", + "Ġcomm em", + "u ced", + "Ġincl ined", + "ĠSum mon", + "Ġcl iff", + "ĠMed iterranean", + "Ġpo etry", + "Ġprosper ity", + "ĠRe ce", + "Ġp ills", + "m ember", + "Ġfin ale", + "un c", + "ĠG ig", + "ä ½", + "Ġl od", + "Ġback ward", + "- +", + "ĠFor ward", + "Ġth ri", + "s ure", + "Ġso ap", + "ĠF X", + "R ES", + "ĠSe xual", + "oul os", + "Ġfool ish", + "Ġright eous", + "Ġco ff", + "terror ism", + "ust ain", + "ot er", + "Ġab uses", + "ne xt", + "Ġab usive", + "Ġthere after", + "Ġprohib ition", + "ĠS UP", + "Ġd ip", + "Ġr ipped", + "Ġinher ited", + "Ġb ats", + "st ru", + "G T", + "Ġflaw ed", + "ph abet", + "Ġf og", + "do ors", + "Ġim aging", + "Ġdig its", + "ĠHung ary", + "Ġar rog", + "Ġteach ings", + "Ġprotocol s", + "ĠB anks", + "à ¸", + "p ound", + "ĠC urt", + ".\" )", + ". /", + "Ġex emption", + "end ix", + "ĠM ull", + "Ġimpro ves", + "ĠG amer", + "d imensional", + "I con", + "ĠMarg aret", + "St atus", + "d ates", + "Ġint ends", + "Ġdep ict", + "Ġpark ed", + "J oe", + "ĠMar ines", + "chn ology", + "! ).", + "Ġjud ged", + "Ġwe ights", + "R ay", + "Ġapart ments", + "he ster", + "Ġrein force", + "Ġoff ender", + "occ up", + "Ġs ore", + "e pt", + "ĠPH P", + "ĠB row", + "Ġauthor ization", + "ĠR isk", + "ĠDel aware", + "ĠQ U", + "Ġnot ifications", + "Ġsun light", + "Ġex clude", + "d at", + "Ġm esh", + "ĠSud an", + "Ġbelong ed", + "Ġsub way", + "Ġno on", + "ĠInter ior", + "ol ics", + "ĠL akers", + "Ġc oding", + "Dis claimer", + "Cal if", + "O ld", + "Ġdis l", + "???? ?", + "Ġconfir ms", + "Ġrecruit ment", + "Ġhom icide", + "Cons ider", + "ĠJeff rey", + "ft y", + "} ;", + "Ġobject ion", + "do ing", + "ĠLe o", + "W ant", + "Ġgl ow", + "ĠClar ke", + "ĠNorm an", + "Ġver ification", + "Ġpack et", + "ĠForm ula", + "Ġpl ag", + "es ville", + "Ġshout ing", + "Ġo v", + "ĠR EC", + "ĠB ub", + "Ġn inth", + "Ġener g", + "Ġvalid ity", + "Ġup s", + "j ack", + "Ġneighbor ing", + "ĠN ec", + "ew orks", + "ĠH ab", + "are z", + "Ġsp ine", + "Ġevent ual", + "ĠLe aders", + "ĠC arn", + "Ġprob ation", + "Ġrom ance", + "ms g", + "ĠMechan ical", + "ER Y", + "R ock", + "Ġpart isan", + "N ode", + "ass ets", + "min ent", + "Ġforeign ers", + "Ġtest ify", + "ĠUs ually", + "l ords", + "ĠG ren", + "ĠPow ell", + "BI L", + "Ġs r", + "Ġadd ict", + "Ġshell s", + "Ġs igh", + "ĠY ale", + "tern ity", + "Ġ7 50", + "E U", + "ĠR ifle", + "Ġpat ron", + "em a", + "ĠB annon", + "an ity", + "Ġtrop ical", + "ĠV II", + "c ross", + "Every thing", + "ĠIS O", + "Ġhum ble", + "ass ing", + "ĠF IG", + "Ġupd ating", + "ys on", + "Ġcal cium", + "Ġcompet ent", + "Ġste ering", + "Pro t", + "ĠS Y", + "ĠFin als", + "ĠR ug", + "15 9", + "13 7", + "ĠG olf", + "Ġ12 6", + "Ġaccommod ation", + "ĠHug hes", + "Ġaest hetic", + "art isan", + "ĠTw ilight", + "Ġpr ince", + "ĠAgric ulture", + "ĠDis co", + "Ġpreced ent", + "Ġtyp ing", + "author ized", + "O ption", + "ĠA ub", + "l ishes", + "ach t", + "m ag", + "P eter", + "ĠU FO", + "mont on", + "ĠL ith", + "Ġa rom", + "Ġsec uring", + "Ġconf ined", + "priv ate", + "Ġsw ords", + "Ġmark ers", + "Ġmetab olic", + "se lect", + "ĠCur se", + "ĠO t", + "g ressive", + "Ġinc umb", + "ĠS aga", + "Ġpr iced", + "Ġclear ance", + "Cont ent", + "Ġdr illing", + "Ġnot ices", + "Ġb ourgeois", + "Ġv est", + "Ġcook ie", + "ĠGuard ians", + "ry s", + "in yl", + "Ġ12 4", + "Ġpl ausible", + "on gh", + "ĠOd in", + "Ġconcept ion", + "ĠY uk", + "ĠBaghd ad", + "ĠFl ag", + "Aust ral", + "ĠI BM", + "Ġintern ationally", + "ĠWiki Leaks", + "I ED", + "Ġc yn", + "Ġcho oses", + "ĠP ill", + "Ġcomb ining", + "Ġrad i", + "ĠMoh ammed", + "def ense", + "atch ing", + "Sub ject", + "ic iency", + "Fr ame", + "Ġ{ \"", + "Ġche ss", + "Ġtim er", + "19 0", + "Ġt in", + "Ġord inance", + "emet ery", + "Ġacc using", + "Ġnotice able", + "Ġcent res", + "Ġl id", + "ĠM ills", + "img ur", + "Ġz oom", + "erg ic", + "Ġcomp ression", + "pr im", + "f ind", + "Ġsur g", + "Ġp and", + "ĠK ee", + "ĠCh ad", + "cell ence", + "oy le", + "Ġsocial ism", + "ĠT ravis", + "ĠM Hz", + "Ġgu ild", + "ALL Y", + "ĠSub scribe", + "ĠRel ated", + "Ġoccur rence", + "itch ing", + "Ġfict ional", + "Ġcr ush", + "ĠE A", + "c od", + "m ix", + "ĠTri ple", + "Ġretrie ve", + "Ġstimul us", + "Ġpsych iat", + "ĠDo or", + "Ġhomosexual ity", + "Ġelement ary", + "Ġcell ular", + "id ian", + "ĠL aun", + "Ġintrig uing", + "Ġfo am", + "ĠB ass", + "id i", + "its u", + "Ġass ure", + "Ġcongr at", + "Ġbusiness man", + "ĠBo ost", + "cl ose", + "Ġl ied", + "Ġsc iences", + "ĠO mega", + "ĠG raphics", + "Ġ< =", + "sp oken", + "Ġconnect ivity", + "S aturday", + "ĠAven gers", + "Ġto ggle", + "Ġank le", + "Ġnational ist", + "mod el", + "ĠP ool", + "ophob ia", + "V ar", + "ĠM ons", + "ator ies", + "Ġaggress ively", + "C lear", + "For ge", + "act ers", + "Ġhed ge", + "Ġpip es", + "Ġbl unt", + "Ġs q", + "Ġremote ly", + "W ed", + "as ers", + "Ġref riger", + "Ġt iles", + "Ġresc ued", + "Ġcompr ised", + "ins ky", + "Ġman if", + "avan augh", + "Ġprol ifer", + "Ġal igned", + "x ml", + "Ġtri v", + "Ġcoord ination", + "ĠP ER", + "ĠQu ote", + "13 4", + "b f", + "ĠS aw", + "Ġtermin ation", + "Ġ19 0", + "Ġadd itions", + "Ġtri o", + "Ġproject ions", + "Ġpositive ly", + "Ġin clusive", + "Ġmem br", + "19 90", + "old er", + "Ġpract iced", + "ink le", + "Ar ch", + "Ġstar ters", + "ari us", + "Ġinter mediate", + "ĠBen ef", + "ĠK iller", + "Ġinter ventions", + "ĠK il", + "ĠF lying", + "In v", + "Ġprem ature", + "Ġpsych iatric", + "Ġind ie", + "Ġcoll ar", + "ĠRain bow", + "af i", + "Ġdis ruption", + "ĠFO X", + "cast ing", + "Ġmis dem", + "c ro", + "Ġw ipe", + "ard on", + "Ġb ast", + "ĠTom my", + "ĠRepresent ative", + "Ġbell y", + "ĠP O", + "ĠBre itbart", + "13 2", + "Ġmess aging", + "Sh ould", + "Ref erences", + "ĠG RE", + "ist ical", + "L P", + "ĠC av", + "ĠC razy", + "Ġintu itive", + "ke eping", + "ĠM oss", + "Ġdiscont in", + "ĠMod ule", + "Ġun related", + "ĠPract ice", + "ĠTrans port", + "Ġstatist ically", + "orn s", + "Ġs ized", + "p u", + "Ġca f", + "ĠWorld s", + "ĠRod gers", + "ĠL un", + "ĠCom ic", + "l iving", + "Ġc ared", + "Ġclim bed", + ") {", + "Ġconsist ed", + "Ġmed ieval", + "fol k", + "Ġh acked", + "Ġd ire", + "ĠHerm ione", + "Ġt ended", + "ce ans", + "D aniel", + "w ent", + "Ġlegisl ators", + "Ġred es", + "g ames", + "Ġg n", + "am iliar", + "Ġ+ +", + "gg y", + "th reat", + "Ġmag net", + "Ġper ceive", + "Ġz ip", + "Ġindict ment", + "Ġcrit ique", + "g ard", + "ĠSaf e", + "ĠC ream", + "Ġad vent", + "ob a", + "Ġv owed", + "ous ands", + "Ġsk i", + "Ġabort ions", + "u art", + "Ġstun ned", + "Ġadv ancing", + "Ġlack ed", + "Ġ\\ \"", + "Ġsch izophren", + "Ġeleg ant", + "Ġconf erences", + "Ġcance led", + "ĠHud son", + "ĠHop efully", + "Ġtr ump", + "Ġfrequ encies", + "Ġmet eor", + "ĠJun ior", + "ĠFle et", + "ĠMal colm", + "ĠT ools", + "Ġ ........", + "Ġh obby", + "ĠEurope ans", + "Ġ15 00", + "ĠInt o", + "Ġs way", + "ĠApp ro", + "ĠCom pl", + "Comm unity", + "Ġt ide", + "ĠSum mit", + "ä »", + "Ġinter vals", + "ĠE ther", + "Ġhabit at", + "ĠSteven s", + "lish ing", + "ĠDom ain", + "Ġtrig gers", + "Ġch asing", + "Ġchar m", + "ĠFl ower", + "it ored", + "Ġbless ing", + "Ġtext ures", + "F ive", + "Ġliqu or", + "R P", + "F IN", + "Ġ19 62", + "C AR", + "Un known", + "Ġres il", + "ĠL ily", + "Ġabund ance", + "Ġpredict able", + "r ar", + "Ġbull shit", + "le en", + "che t", + "M or", + "M uch", + "ä ¹", + "Ġemphas ized", + "Ġcr ust", + "Ġprim itive", + "Ġenjoy able", + "ĠPict ures", + "Ġteam mate", + "pl er", + "ĠT ol", + "ĠK ane", + "Ġsummon ed", + "th y", + "ram a", + "ĠH onda", + "Ġreal izing", + "Ġquick er", + "Ġconcent rate", + "cle ar", + "Ġ2 10", + "ĠErd ogan", + "ar is", + "Ġrespond s", + "ĠB I", + "Ġelig ibility", + "Ġpus hes", + "ĠId aho", + "Ġagg rav", + "Ġru ins", + "ur ations", + "Ġb ans", + "Ġan at", + "sh are", + "Ġgr ind", + "h in", + "um en", + "Ġut ilities", + "ĠYan kees", + "Ġdat abases", + "ĠD D", + "Ġdispl aced", + "Ġdepend encies", + "Ġstim ulation", + "h un", + "h ouses", + "ĠP retty", + "ĠRaven s", + "ĠTOD AY", + "Ġassoci ates", + "Ġthe rape", + "cl ed", + "Ġde er", + "Ġrep airs", + "rent ice", + "Ġrecept ors", + "Ġrem ed", + "ĠC e", + "Ġmar riages", + "Ġball ots", + "ĠSold ier", + "Ġhilar ious", + "op l", + "13 8", + "Ġinherent ly", + "Ġignor ant", + "Ġb ounce", + "ĠE aster", + "REL ATED", + "ĠCur rency", + "E V", + "ãĥ ŀ", + "ĠLe ad", + "Ġdece ased", + "B rien", + "ĠMus k", + "J S", + "Ġmer ge", + "heart ed", + "c reat", + "m itt", + "m und", + "ĠâĢ ĭ", + "ĠB ag", + "Ġproject ion", + "Ġj ava", + "ĠStand ards", + "ĠLeon ard", + "Ġcoc onut", + "ĠPop ulation", + "Ġtra ject", + "Ġimp ly", + "Ġcur iosity", + "ĠD B", + "ĠF resh", + "ĠP or", + "Ġheav ier", + "ne ys", + "gom ery", + "Ġdes erved", + "Ġphr ases", + "ĠG C", + "Ġye ast", + "d esc", + "De ath", + "Ġreb oot", + "Ġmet adata", + "IC AL", + "Ġrep ay", + "ĠInd ependence", + "Ġsubur ban", + "ical s", + "Ġat op", + "Ġall ocation", + "gener ation", + "ĠG ram", + "Ġmoist ure", + "Ġp ine", + "ĠLiber als", + "Ġa ides", + "Ġund erest", + "ĠBer ry", + "Ġcere mon", + "3 70", + "ast rous", + "ĠPir ates", + "Ġt ense", + "ĠIndust ries", + "ĠApp eals", + "ĠN ear", + "Ġè£ı ç", + "Ġlo vers", + "ĠC AP", + "ĠC raw", + "Ġg iants", + "Ġeffic acy", + "E lement", + "ĠBeh avior", + "ĠToy ota", + "Ġint est", + "P riv", + "A I", + "Ġmaneu ver", + "Ġperfect ion", + "Ġb ang", + "p aper", + "r ill", + "Ge orge", + "b order", + "in ters", + "ĠS eth", + "Ġcl ues", + "ĠLe vi", + "ĠRe venue", + "14 7", + "Ġv apor", + "Ġfortun ate", + "Ġthreat ens", + "Ġve t", + "Ġdepend ency", + "ers ed", + "art icle", + "ĠBl izzard", + "Ġch lor", + "Ġmin us", + "ĠB ills", + "Ġcryptoc urrency", + "Ġmetabol ism", + "ter ing", + "Ġp estic", + "step s", + "ĠTre asure", + "ract ed", + "ĠConst ant", + "Ġtem p", + "13 9", + "ĠDet ective", + "ur ally", + "Ġrecover ing", + "Ġcort ex", + "Ġ14 4", + "cl osed", + "Ġprejud ice", + "aun ted", + "Ġstorm s", + "ĠN OW", + "Ġmach inery", + "Add ress", + "Ġcompe lled", + "27 0", + "Ġdesp air", + "b ane", + "Ġveget able", + "Ġbed s", + "Lear n", + "Ġcolor ful", + "Ġsp ike", + "Ġmarg ins", + "Ġsymp athy", + "Ġworks hop", + "ĠC BC", + "S at", + "Ġburn s", + "ĠG ender", + "Ġ12 9", + "ĠC able", + "Ġdeb ts", + "ĠThe resa", + "Ġreflect ing", + "Ġa irst", + "Ġr im", + "ram id", + "Ġweakness es", + "W rit", + "ogg le", + "t i", + "ĠCh arge", + "Ġwe ighed", + "Ġ( .", + "Ġl aughter", + "Ġrou ter", + "ĠDemocr acy", + "D ear", + "Ġhas ht", + "Ġd y", + "Ġhint s", + "run ning", + "Ġfin ishes", + "ar us", + "M ass", + "res ult", + "asc us", + "Ġv intage", + "Ġcon qu", + "Ġwild ly", + "ac ist", + "Ġl ingu", + "Ġprot agonist", + "st rom", + "te enth", + "ĠSol o", + "m ac", + "f illed", + "Ġre nown", + "it ives", + "Ġmot ive", + "ĠAnt ar", + "ĠM ann", + "ĠAd just", + "Ġrock ets", + "Ġtrou bling", + "e i", + "Ġorgan isms", + "ass is", + "Christ ian", + "Ġ14 5", + "ĠH ass", + "Ġsw all", + "Ġw ax", + "ĠSurv ival", + "V S", + "ĠM urd", + "v d", + "stand ard", + "Ġdrag ons", + "Ġacceler ation", + "r ational", + "f inal", + "Ġp aired", + "ĠE thereum", + "Ġinterf aces", + "Ġres ent", + "Ġartif acts", + "Å «", + "are l", + "Ġcompet itor", + "ĠNich olas", + "ĠSur face", + "c pp", + "ĠT ot", + "Ġeconom ically", + "Ġorgan ised", + "Ġen forced", + "in ho", + "Ġvar ieties", + "Ġab dom", + "ĠBa iley", + "id av", + "ĠSal v", + "p aid", + "Ġalt itude", + "ess ert", + "ĠG utenberg", + "are a", + "op oulos", + "Ġprofess ors", + "igg s", + "ĠF ate", + "he y", + "Ġ3 000", + "D ist", + "Ġtw ins", + "c ill", + "ĠM aps", + "Ġtra ps", + "Ġwe ed", + "ĠK iss", + "Ġy oga", + "Ġrecip ients", + "ĠWest minster", + "Ġpool s", + "ĠWal mart", + "18 8", + "ĠSchool s", + "att ack", + "ĠAR M", + "par agraph", + "W arning", + "j l", + "Ġself ish", + "anche z", + "ĠHe ights", + "F re", + "ĠS oph", + "Ġ --------------------------------", + "t ml", + "33 3", + "Ġraid s", + "Ġsatell ites", + "KE Y", + "Ġlast s", + "Ñ Ĥ", + "In s", + "ĠD ame", + "Ġunp redict", + "// /", + "gh ai", + "Ġart illery", + "Ġcru ise", + "Ġg el", + "ĠCabin et", + "Ġbl ows", + "ĠE sp", + "Ġprox imity", + "ot he", + "ĠSk ills", + "ĠU pper", + "ob o", + "ĠN DP", + "Ġenjoy s", + "Ġrepe ating", + "ĠConst ruction", + "ĠQuest ions", + "H illary", + "Ġu int", + "Ġprocess ors", + "ĠGib son", + "ĠMult iple", + "q a", + "ĠB om", + "ĠM iles", + "vent ional", + "Ġhur ts", + "s kin", + "ĠA IDS", + "Ġadvis ers", + "ĠR oot", + "Ġmethod ology", + "ĠD ale", + "Ġdet on", + "ĠKnow ledge", + "sequ ently", + "Ġ12 1", + "Ġconnect s", + "C y", + "ĠD anger", + "Ġcontribut ors", + "ĠB ent", + "Ġbr ass", + "ĠGun s", + "int o", + "ĠFort une", + "Ġbro ker", + "bal ance", + "Ġlength s", + "Ġv ic", + "Ġaver aging", + "Ġappropri ately", + "ĠCamer a", + "Ġsand wich", + "ĠCD C", + "Ġcoord inate", + "Ġnav ig", + "Ġgood ness", + "l aim", + "Ġbra ke", + "Ġextrem ist", + "ĠW ake", + "ĠM end", + "ĠT iny", + "ĠC OL", + "ĠR F", + "ĠD ual", + "ĠW ine", + "C ase", + "Ġref ined", + "Ġl amp", + "L ead", + "Ġb apt", + "ĠCar b", + "ĠS add", + "ĠMin neapolis", + "PD F", + "Ear ly", + "ĠH idden", + "I ts", + "ĠT IME", + "Ġp ap", + "Ġcommission ed", + "ĠF ew", + "ĠCol ts", + "ĠB ren", + "Ġbot hered", + "Ġlike wise", + "Ex per", + "ĠSch w", + "c ry", + "n n", + "ĠM itch", + "im on", + "M G", + "b m", + "UM P", + "r ays", + "Ġregist ry", + "Ġ2 70", + "ach ine", + "re lla", + "ant ing", + "00 000", + "Ġru ined", + "sp ot", + "Ġt a", + "Ġmaxim ize", + "Ġincon ven", + "D ead", + "H uman", + "En abled", + "ĠMar ie", + "Ġch ill", + "ĠParad ise", + "Ġstar ring", + "ĠLat ino", + "ĠProt ocol", + "ĠE VER", + "Ġsuppl iers", + "m essage", + "ĠBro ck", + "Ġser um", + "âĸĪâĸĪ âĸĪâĸĪ", + "Ġen comp", + "Ġamb ition", + "ues e", + "Ġar rows", + "And rew", + "Ġanten na", + "Ġ19 61", + "ĠB ark", + "Ġb ool", + "ãĤ ª", + "ĠSt orage", + "Ġrail way", + "Ġtoug her", + "ĠC ad", + "Ġwas hing", + "P y", + "' ]", + "em bed", + "ĠMem phis", + "ack le", + "Ġfam ously", + "ĠF ortunately", + "ov ies", + "Ġmind set", + "Ġsne ak", + "ĠD h", + "RA W", + "ĠSim pson", + "Ġliv est", + "Ġland mark", + "Ġc ement", + "L ow", + "Ġthr illed", + "ĠCour se", + "in el", + "Ġch uck", + "id ate", + "gl obal", + "Ġwh it", + "Ġ �", + "ad ays", + "s ki", + "ĠS V", + "Ġvir uses", + "30 6", + "ĠResp ons", + "Ġthe aters", + "ĠBr anch", + "ĠGene va", + "ĠM K", + "Ġunbel iev", + "Ġcommun ist", + "Orig inal", + "ĠRe ceived", + "ĠTrans fer", + "ĠAr g", + "In put", + "ĠStr ategy", + "Ġpal ace", + "the ning", + "D ri", + "Ġsent encing", + "umbn ail", + "Ġp ins", + "re cy", + "Ġs iblings", + "Get ting", + "ĠB U", + "ĠNorth west", + "Ġprolong ed", + "ĠSak ura", + "C omb", + "ĠB our", + "Ġinadequ ate", + "ĠK ash", + "Ġus ername", + "ĠImpro ve", + "Ġbatt ling", + "ĠM AC", + "Ġcurric ulum", + "Ġs oda", + "ĠC annon", + "Ġsens ible", + "sp ons", + "De cember", + "Ġw icked", + "ĠP engu", + "Ġdict ators", + "ĠHe arts", + "og yn", + "Ġsimilar ities", + "ĠSt ats", + "Ġh ollow", + "it ations", + "\": [", + "Ġh over", + "ĠList en", + "s ch", + "S und", + "Ġc ad", + "ĠPar ks", + "Ġl ur", + "Ġhy pe", + "ĠL em", + "N AME", + "is ure", + "Fr iday", + "Ġshoot s", + "Ġclos es", + "Ġd b", + "ĠR idge", + "ĠDiff erent", + "Ġrepl ies", + "ĠBroad way", + "op ers", + "Ġint oler", + "ĠZe us", + "akes pe", + "Ġpropri etary", + "Ġrequest ing", + "Ġcontro llers", + "ĠM IN", + "im edia", + "be cca", + "Ġexp ans", + "Ġoil s", + "B ot", + "ĠCh and", + "Ġpr inter", + "Ġto pped", + "ĠP OL", + "ĠEar lier", + "S ocial", + "av in", + "Ġdecre ases", + "ĠSe b", + "Ġspecific ations", + "ĠBl ast", + "ĠK urt", + "Ġfre el", + "B rown", + "Ġdil ig", + "ro e", + "ĠPro blem", + "ĠQu ad", + "Ġdecent ral", + "ĠV ector", + "an ut", + "Ġplug ins", + "ĠGreg ory", + "Ġfuck ed", + "el ines", + "ĠAmb assador", + "t ake", + "Ġcle ans", + "ong yang", + "An onymous", + "st ro", + "\" }", + "al ine", + "ĠO dd", + "ĠE ug", + "2 16", + "Ġbo il", + "ĠP owers", + "Ġnurs es", + "Ob viously", + "ĠTechn ical", + "Ġexceed ed", + "OR S", + "Ġextrem ists", + "Ġtr aces", + "ex pl", + "Ġcom r", + "ĠS ach", + ") /", + "Ġm asks", + "Ġsc i", + "B on", + "Ġreg ression", + "we gian", + "Ġadvis or", + "it ures", + "ĠV o", + "ex ample", + "ĠInst ruct", + "Ġs iege", + "Ġredu ctions", + "pt r", + "Ġstat utory", + "Ġrem oves", + "Ġp uck", + "red its", + "Ġbe e", + "Ġsal ad", + "Ġpromot ions", + "ĠJosh ua", + "with standing", + "ET H", + "ĠCh a", + "im us", + "Ġexpend iture", + "aun ting", + "Ġdelight ed", + "Ġ15 5", + "be h", + "Ġcar pet", + "ĠSp art", + "Ġj ungle", + "l ists", + "Ġbull ying", + "ĠNob el", + "ĠGl en", + "Ġreferen ced", + "Ġintrodu ces", + "se in", + "Ġcho pped", + "gl ass", + "ĠW rest", + "Ġneutral ity", + "Ġâ Ļ", + "Ġinvestig ator", + "Ġshel ves", + "Ġun constitutional", + "Ġreprodu ction", + "Ġmer chant", + "m ia", + "Ġmet rics", + "Ġexplos ives", + "ĠSon ia", + "Ġbod ily", + "Ġthick ness", + "Ġpredomin antly", + "ĠAb ility", + "Ġmon itored", + "IC H", + "Ġ] .", + "ĠMart inez", + "Ġvis ibility", + "Ġqu eries", + "Ġgen ocide", + "ĠWar fare", + "Qu ery", + "Ġstud ios", + "Ġemb ry", + "Ġcorrid or", + "Ġclean ed", + "com plete", + "ĠM H", + "Ġenroll ment", + "ING S", + "Ġimpact ed", + "Ġdis astrous", + "ĠY un", + "ĠCl aire", + "ĠBas ically", + "y t", + "uster ity", + "Ġindirect ly", + "w ik", + "Ġd od", + "ĠCar r", + "Ġam p", + "Ġprohib it", + "ĠIn itial", + "ĠR d", + "ij i", + "Ġeduc ate", + "c orn", + "i ott", + "ĠBeaut y", + "Ġdetect ive", + "ĠCon n", + "s ince", + "Ġst agger", + "Ġob ese", + "Ġb ree", + "olog ic", + "is se", + "walk er", + "Ġbl ades", + "Ġlaw ful", + "fun c", + "ĠBeh ind", + "Ġappet ite", + "Ġ( *", + "Ġt ennis", + "Ġoff spring", + "Ġj ets", + "Ġstruct ured", + "Ġafore mentioned", + "N ov", + "Ġsc aling", + "f ill", + "Ġst ew", + "Ġcur b", + "ĠStep han", + "ed In", + "S F", + "ob ic", + "é ŃĶ", + "ou g", + "ĠM M", + "Ġgen etically", + "ope z", + "13 6", + "Ġu mb", + "anc ers", + "Ġcoh ort", + "Ġmerch andise", + "Ġimp osing", + "ĠLegisl ature", + "ĠArch ive", + "iv ia", + "ĠN aval", + "Ġoff ences", + "Ġmir acle", + "Ġsn apped", + "Ġf oes", + "Ġextensive ly", + "ĠR af", + "Ġc ater", + "ed ience", + "K it", + "ĠB in", + "Ġrecomm ends", + "ĠC ities", + "Ġrig id", + "ĠRE AD", + "ĠNob le", + "ĠT ian", + "Ġcertific ates", + "ant is", + "o iler", + "ĠBudd hist", + "d id", + "Ġsurvey ed", + "Ġdown ward", + "Ġprint s", + "ĠMot ion", + "ron ics", + "ĠS ans", + "oss ibly", + "u ctions", + "Ġcolon ies", + "ĠDan ish", + "un it", + "Ġsp oil", + "Ġadvis ory", + "ber ries", + "Pl an", + "Ġspecific ation", + "op hers", + "ĠRes ource", + "Ġsh irts", + "prising ly", + "commun ications", + "Ġtriv ial", + "Ġmention ing", + "ise xual", + "Ġsupp lements", + "Ġsuper vision", + "B P", + "v or", + "Ġw it", + "Ġco oldown", + "Ġplaint iff", + "ĠReview s", + "ĠS ri", + "ĠM int", + "ĠSug ar", + "Ġafter ward", + "ĠPri est", + "ĠInvest ment", + "og ene", + "ĠT aking", + "Ġstretch ing", + "Ġinflamm ation", + "ĠTe hran", + "Ġl ining", + "Ġfree zing", + "ĠEnt ity", + "Ġins piring", + "spe cial", + "pr ice", + "Ġsu e", + "ĠP orter", + "oun ge", + "ET A", + "ĠD erek", + "ĠLu is", + "u o", + "ym ph", + "Ġex terior", + "ih il", + "ĠAsh ley", + "in ator", + "Ġnut rients", + "ĠTh rones", + "Ġfin ances", + "ĠIn spect", + "Ġspe cially", + "ĠRequ ired", + "ĠP TS", + "ĠViol ence", + "oint ed", + "sh ots", + "Ġex cerpt", + "co on", + "IN S", + "ĠG ri", + "Ġrecogn ised", + "We ek", + "You ng", + "Ġv om", + "is le", + "ĠCur ry", + "ĠBudd h", + "Ġnot ebook", + "Ġd urable", + "/ ?", + "ĠG ad", + "ĠP upp", + "Ġforg ive", + "p ark", + "Ġpersonal ities", + "an alysis", + "cl amation", + "Ġelev ator", + "Ġware house", + "ĠR ole", + "un n", + "Ġillust ration", + "ĠSc an", + "Ġatmosp heric", + "Im port", + "AN C", + "rict ed", + "f u", + "01 0", + "Ġar che", + "Ġreward ed", + "akespe are", + "Ġintern ally", + "ĠR BI", + "alk er", + "Ġeleph ant", + "ow itz", + "ĠP izza", + "Ġbip artisan", + "é s", + "Ġslow ed", + "ĠSt ark", + "Ġover ride", + "OU S", + "Ġ3 20", + "undred s", + "ĠDe ck", + "ĠC ensus", + "be e", + "14 6", + "ot or", + "Ġ ip", + "Ġu b", + "oc ations", + "ĠBut ton", + "r ice", + "Ġc ripp", + "ff f", + "Ġorig inated", + "Ġoverwhel med", + "app a", + "Ġfore most", + "âĢ ij", + "ĠL EG", + "re lease", + "eat ured", + "at ches", + "Ġre ps", + "Ġl ending", + "ĠRe ference", + "ĠCl ient", + "16 5", + "vent h", + "Com plete", + "ĠPat rol", + "Ġsw orn", + "c am", + "Ġshut tle", + "ĠR alph", + "Ġh ometown", + "- ,", + "on al", + "ĠB P", + "å ı", + "Ġpersu ade", + "ĠAlex and", + "Ġcomb ines", + "Ġv ivid", + "ĠL ag", + "Ġenc oding", + "Ġsal vation", + "w en", + "ĠRec overy", + "i ya", + "Un iversity", + "ĠB iden", + "Ġbud gets", + "ĠTex ans", + "f its", + "Ġhon ored", + "Ġp ython", + "T D", + "## #", + "cl one", + "Ġbl ink", + "ĠL iquid", + "Ġunemploy ed", + "Ġcl ashes", + "ĠCoun sel", + "Ġdirect ing", + "Ġpun ct", + "ĠFal cons", + "Ġsh ark", + "ĠDam ascus", + "Ġje ans", + "Ġemb ark", + "Ġse ize", + "Ġup wards", + "2 80", + "ĠE z", + "ĠAny thing", + "Ġex otic", + "l ower", + "ĠCreat or", + "ĠU m", + "Ġsubur bs", + "ber ger", + "ĠW end", + "Ġm int", + "ĠX X", + "ĠD ro", + "Ġsuff ers", + "Ġher b", + "t ree", + "Ġfrag ile", + "Ġflood ed", + "ĠAl cohol", + "ole an", + "ny der", + "ĠK O", + "F ram", + "Ġ13 6", + "Ġow ed", + "ĠMe lee", + "ĠH ash", + "Ġwh isk", + "Ġsu do", + "r r", + "Qu ick", + "app ro", + "Ġi i", + "ĠEx amples", + "he e", + "Ġpromot es", + "per ature", + "k ar", + "ĠHon or", + "Ġs odium", + "ĠL if", + "ros so", + "intend ent", + "Ġcorrespond ent", + "F ound", + "sec ret", + "Ġident ifies", + "ag ne", + "Ġl ou", + "ĠP P", + "Ġcoinc idence", + "m ove", + "Ġmilit ia", + "Ġinf iltr", + "ĠPrim ary", + "Ġpitch ing", + "ĠI b", + "ĠGO OD", + "ãĤ ¸", + "ĠW izards", + "ir al", + "ĠVen us", + "R R", + "ĠâĢ ķ", + "ĠCase y", + "Ġsad ly", + "Ġadm ire", + "Ġembarrass ed", + "c b", + "M el", + "Ġtub es", + "Ġbeaut ifully", + "ĠQueens land", + "Bel ow", + "re z", + "qu et", + "ple asant", + "Ġ «", + "C amp", + "Ġdec isive", + "19 98", + "ĠL amb", + "ut ton", + "h n", + "ĠJ agu", + "au nder", + "ĠC ord", + "Ġcl erk", + "Ġca ffe", + "Ġwip ed", + "Ġre im", + "ĠMount ains", + "Ġimprison ed", + "Ġdevelop s", + "ĠP ra", + "Ġmodel ing", + "Any one", + "ance l", + "ĠS it", + "Ġshield s", + "Ġl awn", + "Ġcard iovascular", + "Ġdemonstr ating", + "Ġpar se", + "ĠIsrael is", + "Ġeuro s", + "14 3", + "Ġgl orious", + "ins ki", + "ec d", + "Ġcondition ing", + "Ġhel pless", + "Ġmicro sc", + "ĠHar bor", + "Ġst akes", + "Ġ2 60", + "Ġun equ", + "ĠFl oyd", + "Ġd amp", + "Ġappar atus", + "ĠLaw s", + "Ġcoun ters", + "Ġindu ce", + "at able", + "ĠAh med", + "Ġsl am", + "N ovember", + "Ġpers ist", + "Ġim minent", + "á n", + "Ġsh red", + "Ġph ases", + "ĠEd monton", + "ĠArm strong", + "ĠMe et", + "ĠK itty", + "Ñ Ģ", + "c irc", + "ĠAd ult", + "Ġa rose", + "ĠX en", + "D an", + "g ow", + "Ġsuper f", + "ĠAd mir", + "Ġend ure", + "Ġkey word", + "yr us", + "Ġy arn", + "Ġpath way", + "ĠHop kins", + "mid t", + "Ġcens orship", + "d ependent", + "Ġinstruct or", + "S ources", + "Ġto e", + "Ġball oon", + "N ob", + "Ġsw ear", + "ĠCast ro", + "Ġgl oss", + "ĠK avanaugh", + "Ġremark ably", + "Ph otos", + "ĠN om", + "ĠS outheast", + "y ers", + "Ġvalid ation", + "Ġcann on", + "ĠVict ory", + "ĠPier re", + "Ġcaut ious", + "Aud io", + "Ġf etch", + "ĠG ift", + "ĠH yp", + "Ġrem edy", + "Z E", + "Ġsc ent", + "Ġbe ard", + "ĠR ut", + "- \"", + "Ġpat ents", + "H y", + "Ġun just", + "Ġpot ato", + "Ġforth coming", + "Ġche f", + "ĠR ift", + "aff e", + "ĠR OM", + "ĠL aunch", + "Ġp ads", + "ĠNe o", + "Ġon set", + "Ġsquee ze", + "s afe", + "Ġpref ix", + "ĠT M", + "ĠN early", + "ĠClin ical", + "ĠM ental", + "ot iation", + "ĠUn ic", + "ant ry", + "ĠC ir", + "Ġep it", + "à ¦", + "Ġextract ed", + "verse ly", + "ri ad", + "Ġstr ains", + "Ġto ps", + "Ġpo em", + "ĠRand y", + "ĠMap le", + "TH ER", + "up iter", + "ĠSS D", + "ļ é", + "Ġun con", + "per ing", + "Ġsle pt", + "in ers", + "Ġunder water", + "ĠEv idence", + "g one", + "20 5", + "Ġhistor ians", + "Ġsynt hesis", + "Ġf rog", + "b asketball", + "Ġvibr ant", + "Ġsub ord", + "Ġ3 65", + "ĠD ial", + "Ġcooper ate", + "HA HA", + "Ġgreet ed", + "15 8", + "Ġj azz", + "Ġinto x", + "ĠWalk ing", + "Ġsuper visor", + "ĠF usion", + "ĠMer cedes", + "s end", + "H am", + "s d", + "n l", + "Ġtour s", + "ĠF IFA", + "Ġcul p", + "g d", + "30 4", + "Ġple as", + "Ġillust rates", + "ĠColomb ia", + "Ġhighlight ing", + "ĠSum mary", + "Ġexp osing", + "ĠD ru", + "Ġir ony", + "r itional", + "ĠCar roll", + "ĠEll is", + "P ict", + "ĠR apt", + "Ġad apter", + "Ġun m", + "Ġcor pse", + "Ġceleb rities", + "D en", + "at um", + "ĠAp ocalypse", + "ĠW ag", + "lin ing", + "Ġhorm ones", + "R ub", + "ĠX i", + "ĠV aults", + "20 8", + "alky rie", + "inos aur", + "Ġfeed s", + "v ity", + "Ġdefe ating", + "W ait", + "Ġemphas ize", + "ĠSteel ers", + "yr inth", + "le ys", + "ĠWhe never", + "Current ly", + "ĠCl ock", + "Ġcollect ively", + "any on", + "ĠJ P", + "Ġment ality", + "Ġdownload s", + "Ġsurround ings", + "ĠBarn es", + "Ġflags hip", + "Ġindic ators", + "Ġgra pp", + "Jan uary", + "ĠElement al", + "ĠAthen a", + "ib al", + "Ġs ights", + "Ġcap ita", + "ĠTreat y", + "Ġvo iced", + "ĠG az", + "let te", + "Ġy a", + "Ġexp ired", + "Leg end", + "H ot", + "n ature", + "Ġunst able", + "Ġ2 80", + "à º", + "Com ment", + "AL E", + "Ġquest s", + "Ġhand ler", + "n is", + "Ġvers atile", + "Ġconce al", + "enge ance", + "ĠInter active", + "Ġobs essed", + "ĠDog s", + "Ġcr acked", + "S ound", + "s v", + "ĠD ylan", + "ro ads", + "f x", + "ĠCath olics", + "ĠH ag", + "Ġsl ammed", + "Ġgl owing", + "s ale", + "Ġtiss ues", + "ĠCh i", + "ne e", + "Ġc her", + "s ic", + "ur rection", + "Ġb acon", + "ul atory", + ") .\"", + "Ġir regular", + "FOR M", + "ass ed", + "Ġintention al", + "Ġcompens ate", + "ĠSpe aking", + "ĠS ets", + "15 3", + "Ġconvent ions", + "b ands", + "em ade", + "Ġe cc", + "ĠWin ston", + "ĠAssass in", + "ĠBelg ian", + "Ġdepend ence", + "Ġnic he", + "Ġb ark", + "ĠJ azz", + "Ġdisadvant age", + "Ġgas oline", + "Ġ16 5", + "çļ Ħ", + "ess a", + "mod ule", + "ang ular", + "O Y", + "ĠTreat ment", + "it as", + "ol ation", + "ĠArn old", + "Ġfe ud", + "ĠN est", + "Ġthe atre", + "ew ater", + "Ġmin ors", + "olic y", + "ĠH aven", + "div ision", + "Ġtr unk", + "F ar", + "ĠP ull", + "Ġcapt uring", + "Ġ18 00", + "ĠTe en", + "Ġex empl", + "Ġclin ics", + "ĠB urg", + "Ġsubst it", + "Ġpay load", + "ĠL av", + "ĠT roy", + "ĠW itness", + "Ġfrag ments", + "Ġpass words", + "Ġg ospel", + "ĠG in", + "Ġten ants", + "ol ith", + "S ix", + "Pre vious", + "ĠAg es", + "ĠDar win", + "Ġbl at", + "Ġem pathy", + "sm ith", + "b ag", + "ĠE cho", + "ĠC amb", + "ĠM add", + "ĠB oo", + "Ġred e", + "ĠBurn ing", + "Ġsmooth ly", + "ĠAd rian", + "ĠV ampire", + "ĠMon sters", + "ste am", + "Sty le", + "M a", + "re a", + "ĠD war", + "aly st", + "urs or", + "Ġelim ination", + "Ġcrypt o", + "ch t", + "ĠE ternal", + "âĢ¦ ]", + "ĠS orce", + "I ll", + "N ER", + "Ġu h", + "Con clusion", + "w age", + "Ġresp ir", + "Ġrem inis", + "het ical", + "Ġg y", + "Ġutil ized", + "ic idal", + "Ġ19 00", + "Ġhun ters", + "ĠSw an", + "ĠRe act", + "Ġvis itor", + "ĠThanks giving", + "30 8", + "Post s", + "Ġh ips", + "19 97", + "om ers", + "Ġkn ocking", + "ĠVeh icle", + "Ġt il", + "Ġ13 8", + "Ġm i", + "ĠInvest igation", + "ĠKen ya", + "Ġcas ino", + "Ġmot ives", + "Ġreg ain", + "re x", + "Ġweek ends", + "Ġstab bed", + "bor o", + "Ġexplo ited", + "ĠHA VE", + "ĠTe levision", + "c ock", + "Ġprepar ations", + "Ġende av", + "ĠRem ote", + "ĠM aker", + "ĠPro du", + "ĠEv an", + "Ġinform ational", + "ĠLouis ville", + "15 4", + "ĠDream s", + "Ġpl ots", + "ĠRun ner", + "Ġhur ting", + "Ġacad emy", + "ĠMont gomery", + "n m", + "ĠL anc", + "ĠAl z", + "2 10", + "el ong", + "Ġretail er", + "Ġar ising", + "Ġrebell ion", + "Ġbl onde", + "play ed", + "Ġinstrument al", + "C ross", + "Ġret ention", + "Ġtherape utic", + "Ġse as", + "Ġinfant ry", + "ĠCl int", + "Ġprompt ing", + "Ġbit ch", + "Ġst ems", + "ĠK ra", + "Ġthe sis", + "ĠB og", + "ru ed", + "Ġk ings", + "Ġcl ay", + "ific ent", + "ĠY ES", + "ĠTh ing", + "ĠCub s", + "vey ard", + "els h", + "in arily", + "ĠE y", + "ĠRoll ing", + "Ġev olving", + "Ind ia", + "Ġrecogn izes", + "Ġgrad uation", + "is ers", + "Ġfert ility", + "ĠMil an", + "Comm and", + "Ġbox ing", + "Ġ19 43", + "Ġgl uten", + "ĠEm ir", + "Ġid ol", + "Ġcon ceived", + "ĠCre ation", + "Mer it", + "udd y", + "uss ions", + "ĠLie utenant", + "iet al", + "Ġunch anged", + "ĠSc ale", + "ĠCrime a", + "ball s", + "ator ial", + "Ġdepth s", + "Ġempir ical", + "Ġtrans m", + "Ġuns afe", + "miss ible", + "com fort", + "15 6", + "Ġmechan ic", + "00 2", + "l ins", + "Ġsm oked", + "P os", + "Ġslow ing", + "Ġl av", + "Tex as", + "Ġche ating", + "ĠMet ropolitan", + "eth yl", + "Ġdiscover ing", + "as se", + "Ġpen cil", + "ĠPy ongyang", + "Ġclos et", + "ĠShe et", + "ĠEnt ry", + "ou stic", + "Ġmy st", + "er ate", + "ari at", + "Ġminer als", + "Ġmusic ian", + "ĠP ul", + "ĠM az", + "24 9", + "Ġper missions", + "Ġ iv", + "en ary", + "ick ers", + "ĠB ing", + "he a", + "en able", + "Ġgri ev", + "Ġassert ed", + "ĠColon el", + "Ġaff idav", + "w o", + "Ġse ated", + "ĠR ide", + "Ġpaint ings", + "ĠP ix", + "Ġ13 7", + "ish i", + "umb ai", + "g otten", + "ĠEar l", + "Ġin ning", + "Ġc ensus", + "Ġtrave lled", + "ĠCons ult", + "18 5", + "b ind", + "Ġsimpl icity", + "Ġoverlook ed", + "ĠHelp ful", + "Ġmon key", + "Ġoverwhelming ly", + "Bl ood", + "ĠFl int", + "ĠJ ama", + "ĠPres ent", + "ĠR age", + "ĠT A", + "pt ive", + "Ġturn out", + "w ald", + "ĠD olphins", + "ĠV PN", + "Ġon ion", + "Ġcraft ing", + "m ma", + "ĠMerc ury", + "Ġarr ange", + "Ġalert s", + "ĠO T", + "zb ollah", + "Ġg ases", + "ĠRichards on", + "s al", + "l ar", + "Ġfro st", + "Ġlower ing", + "Ġacc laim", + "Ġstart ups", + "ĠG ain", + "ess ment", + "Ġguard ian", + "äº º", + "ĠP ie", + "ĠL inks", + "Ġmer its", + "Ġaw ake", + "Ġparent al", + "Ġexceed s", + "Ġid le", + "ĠPil ot", + "Ġe Bay", + "ĠAc cept", + "ipe g", + "C am", + "ĠK ot", + "Ġtrad ers", + "olit ics", + "unk er", + "ĠP ale", + "os i", + "an mar", + "Ġ19 47", + "ĠF ell", + "est ial", + "it ating", + "G F", + "ĠS r", + "if ted", + "Ġconnect or", + "ĠB one", + "ill es", + "2 60", + "h ma", + "Ġoverl ap", + "ĠGit Hub", + "Ġclean er", + "ĠBapt ist", + "ĠW AS", + "Ġlung s", + "Ñ ģ", + "ĠB UT", + "Ġc ite", + "Ġpit ched", + "reat ment", + "Ġtro phies", + "ĠN u", + "38 6", + "ĠPr ide", + "Ġattend ees", + "[ ]", + "17 9", + "Ġspat ial", + "Ġpri zes", + "ĠRel igion", + "Ġshow case", + "ĠC ategory", + "vid ia", + "T arget", + "Pro perty", + "? ,", + "Ġf usion", + "p ie", + "ĠU CLA", + "Ġsound track", + "Ġprin cess", + "ĠC aval", + "sh ould", + "Ġlim bs", + "Back ground", + "Ġlone ly", + "Ġc ores", + "ĠT ail", + "she et", + "Ġ13 2", + "R a", + "ãĤ «", + "ĠB olt", + "Ġbook ed", + "Ġadmin ister", + "Ġequ als", + "w y", + "Ġobserv ing", + "ĠBar on", + "ĠAd obe", + "Ġv irgin", + "ĠSocial ist", + "M ove", + "gh azi", + "ĠLind a", + "2 12", + "Ġbre wing", + "Ġmerch ants", + "bur se", + "Ġdiv or", + "Ġmet als", + "ĠN er", + "Ġsum s", + "ĠEn emy", + "Ġen vision", + "Ġgrant ing", + "ĠH oney", + "ĠSk yrim", + "Ġsoc io", + "gr aded", + "Ġselect ive", + "W ASHINGTON", + "Ġ19 48", + "ĠSir ius", + "ĠG ross", + "act ivity", + "ĠI van", + "Ġfur ious", + "BS D", + "ĠPre vious", + "Ġrespons ive", + "Ġchar itable", + "Ġle aning", + "ĠP ew", + "Ġviol ates", + "\\\\\\\\ \\\\\\\\", + "ĠCom ing", + "w ire", + "Ġpo et", + "Ġres olutions", + "comm and", + "ĠPortug uese", + "Ġnick name", + "Ġde af", + "Feb ruary", + "Ġrecogn ise", + "Ġentire ty", + "Ġseason al", + "pl aced", + "ĠTe legraph", + "Ġmicro phone", + "our ing", + "Ġgr ains", + "Ġgovern ed", + "Ġpost p", + "ĠW aters", + "in ement", + "Ġund ocumented", + "ĠCom cast", + "Ġf ox", + "Ġassault s", + "re on", + "man y", + "ĠJen kins", + "ĠAny way", + "Ġassess ments", + "Ġdown s", + "ĠM ouse", + "Ġsuper b", + "k t", + "ĠD ow", + "Ġtax ation", + "4 01", + "Ġsm iles", + "Ġundert aken", + "Ġex h", + "Ġenthusi astic", + "Ġtw ent", + "Ġgovernment al", + "Ġautonom y", + "ĠTechn ologies", + "ĠCh ain", + "Ġpreval ent", + "f b", + "Ġnic otine", + "og ram", + "j ob", + "Ġawa iting", + "ĠMen u", + "Ġdep uties", + "k ov", + "ish ops", + "But ton", + "ĠShan ghai", + "Ġdies el", + "ĠD uck", + "R yan", + "ĠPC s", + "N F", + "j ury", + "ent e", + "Ġinacc urate", + "edd y", + "Wh atever", + "Ġshow c", + "ĠN ad", + "od us", + "et r", + "Ġplaint iffs", + "ĠW OR", + "ĠAss ange", + "Ġpriv at", + "Ġpremium s", + "Ġt am", + "UR L", + "Ġel ites", + "ĠR anger", + "otten ham", + "ĠH off", + "ĠAt hens", + "Ġdefin ite", + "Ġs ighed", + "Ġeven ly", + "2 11", + "ĠAm ber", + "ak ia", + "Ġmail ing", + "Ġcr ashing", + "ĠConfeder ate", + "ru gged", + "W al", + "ĠDep ths", + "Ġjuven ile", + "Ġreact or", + "Introdu ction", + "ĠDel uxe", + "19 95", + "ĠS anchez", + "ĠM ead", + "iv able", + ": -", + "ĠPlan ning", + "ĠT rap", + "qu in", + "ĠProt ect", + "ve red", + "In formation", + "Ġkid ney", + "inn amon", + "l as", + "Ġpolic ing", + "Ġtoler ate", + "ĠQ i", + "Ġbi ased", + "F ort", + "ĠK i", + "s ave", + "Ġprivile ged", + "Ġbe asts", + "ĠGl as", + "ĠC inem", + "Ġcome back", + "Sund ay", + "Ġext inction", + "h ops", + "Ġtrans mit", + "Ġdoub les", + "ĠFl at", + "16 7", + "Ġdis puted", + "Ġinjust ice", + "f oo", + "V ict", + "role um", + "ĠJul ie", + "Con text", + "ĠR arity", + "iss ue", + "Comp onent", + "Ġcounsel ing", + "an ne", + "d ark", + "Ġobject ions", + "u ilt", + "Ġg ast", + "Ġpl ac", + "Ġun used", + "ãĥ ĩ", + "ĠT rial", + "ĠJ as", + "hed ral", + "ob b", + "Ġtempor al", + "ĠPR O", + "ĠN W", + "ĠAnn iversary", + "L arge", + "Ġther m", + "Ġd avid", + "Ġsystem ic", + "ĠSh ir", + "m ut", + "ĠNe pt", + "add ress", + "Ġscan ning", + "Ġunderstand able", + "Ġcan vas", + "C at", + "ĠZ oo", + "Ġang els", + "L O", + "ĠStat ement", + "ĠS ig", + "ov able", + "ĠA way", + "sh aring", + "ocr ats", + "st ated", + "Ġweigh ing", + "N or", + "w ild", + "B ey", + "Ġaston ishing", + "ĠReyn olds", + "Ġop ener", + "Ġtrain er", + "Ġsurg ical", + "p n", + "Ġadjust ing", + "whe el", + "Ġf rown", + "erv ative", + "Ġsusp end", + "With in", + "te in", + "Ġobst acle", + "Ġliber ties", + "ym es", + "Ġur anium", + "ans om", + "an ol", + "ub a", + "ĠL oss", + "Ġa rous", + "ĠHend erson", + "W ow", + "s pl", + "c ur", + "Ġ Ń", + "Ġtheir s", + "Dam age", + "Ġdownload ing", + "Ġdisc ern", + "ĠSt o", + "ĠFl a", + "Ġh ath", + "ĠA j", + "Ġun pleasant", + "Europe an", + "exp ensive", + "Ġscreens hot", + "ĠU V", + "Ġall ied", + "ĠPers ian", + "Ġmonop oly", + "Ġat om", + "ĠReds kins", + "\"> <", + "Ġcan cell", + "Ġcinem a", + "13 1", + "f air", + "ĠAlf red", + "Ġd uck", + "arg s", + "22 3", + "ĠIS I", + "Ġsign aling", + "in ar", + "Ġlaugh s", + "Ġfor wards", + "Ġreck less", + "Ġlisten ers", + "at ivity", + "Ġvast ly", + "n ant", + "L ess", + "ĠHun ting", + "ĠScient ific", + "IT ED", + "Ġkn ight", + "ĠH TC", + "us a", + "t mp", + "Ġr ude", + "ĠLegend ary", + "Ġar ises", + "B ad", + "ĠCl aim", + "pe g", + "Ġreal ities", + "Th ink", + "Ġ °", + "Ġro de", + "Ġstri ve", + "Ġan ecd", + "Ġshort s", + "Ġhypot hes", + "Ġcoord inated", + "ĠGand hi", + "ĠF PS", + "R ED", + "Ġsuscept ible", + "Ġshr ink", + "ĠCh art", + "Hel p", + "Ġ ion", + "de ep", + "rib es", + "ĠK ai", + "ĠCustom er", + "Sum mary", + "Ġc ough", + "w ife", + "Ġl end", + "Ġposition ing", + "Ġlot tery", + "ĠC anyon", + "Ġf ade", + "Ġbron ze", + "ĠKenn y", + "Ġbo asts", + "ĠEnh anced", + "rec ord", + "Ġemer gence", + "Ġa kin", + "ĠB ert", + "it ous", + "âĸ ij", + "Ġst ip", + "Ġexch anged", + "om ore", + "als h", + "Ġreserv oir", + "Ġstand point", + "W M", + "Ġiniti ate", + "Ġdec ay", + "Ġbrew ery", + "Ġter ribly", + "Ġmort al", + "lev ard", + "Ġrev is", + "N I", + "el o", + "Ġconf ess", + "ĠMS NBC", + "Ġsub missions", + "Cont roller", + "Ġ20 2", + "ĠR uth", + "} );", + "ĠAz ure", + "Ġ .\"", + "20 6", + "ĠMarket ing", + "Ġl aund", + "ien cies", + "Ġrenown ed", + "ĠT rou", + "ĠN GO", + "ble ms", + "Ġterr ified", + "Ġwar ns", + "Ġper t", + "Ġuns ure", + "4 80", + "ale z", + "ult z", + "ĠOut side", + "Ġst yl", + "ĠUnder ground", + "Ġp anc", + "Ġd ictionary", + "Ġf oe", + "rim inal", + "ĠNor wegian", + "Ġj ailed", + "Ġm aternal", + "é e", + "ĠLu cy", + "c op", + "Ch o", + "Ġuns igned", + "ĠZe lda", + "ĠIns ider", + "ĠContin ued", + "Ġ13 3", + "ĠNar uto", + "ĠMajor ity", + "16 9", + "ĠW o", + "ãĤ ĵ", + "Ġpast or", + "Ġinform al", + "Ð ½", + "an throp", + "jo in", + "ãģ Ĺ", + "it ational", + "N P", + "ĠWrit ing", + "f n", + "ĠB ever", + "19 5", + "Ġy elling", + "Ġdr astically", + "Ġe ject", + "Ġne ut", + "Ġth rive", + "ĠFre qu", + "ou x", + "Ġpossess es", + "ĠSen ators", + "ĠD ES", + "ĠSh akespeare", + "ĠFran co", + "ĠL B", + "uch i", + "Ġinc arn", + "Ġfound ers", + "F unction", + "Ġbright ness", + "ĠB T", + "Ġwh ale", + "ĠThe ater", + "m ass", + "ĠD oll", + "S omething", + "Ġecho ed", + "ĠHe x", + "c rit", + "af ia", + "Ġgodd ess", + "Ġele ven", + "ĠPre view", + "ĠAur ora", + "Ġ4 01", + "uls ive", + "ĠLog an", + "in burgh", + "ĠCent ers", + "ĠON LY", + "ĠA id", + "Ġparad ox", + "Ġh urd", + "ĠL C", + "D ue", + "c ourt", + "Ġoff ended", + "Ġeval uating", + "ĠMatthew s", + "Ġto mb", + "Ġpay roll", + "Ġextra ction", + "ĠH ands", + "if i", + "Ġsuper natural", + "ĠCOM M", + "] =", + "dog s", + "Ġ5 12", + "ĠMe eting", + "Rich ard", + "ĠMax imum", + "Ġide als", + "Th ings", + "m and", + "ĠReg ardless", + "Ġhum ili", + "b uffer", + "L ittle", + "ĠD ani", + "ĠN ak", + "Ġliber ation", + "ĠA be", + "ĠO L", + "Ġstuff ed", + "ac a", + "ind a", + "raph ic", + "Ġmos qu", + "Ġcampaign ing", + "Ġoccup y", + "S qu", + "r ina", + "ĠW el", + "ĠV S", + "Ġphys ic", + "Ġp uls", + "r int", + "oad ed", + "ET F", + "ĠArch ives", + "Ġven ues", + "h ner", + "ĠTur bo", + "Ġl ust", + "Ġappeal ed", + "que z", + "il ib", + "ĠTim othy", + "Ġo mn", + "d ro", + "Ġobs ession", + "ĠSav age", + "19 96", + "Gl obal", + "J es", + "2 14", + "Ġsl iding", + "Ġdisapp ro", + "ĠMag ical", + "Ġvolunt arily", + "g b", + "ane y", + "Ġprop het", + "ĠRe in", + "ĠJul ia", + "ĠW orth", + "aur us", + "Ġb ounds", + "ie u", + ")) )", + "Ġcro re", + "ĠCitiz en", + "S ky", + "Ġcolumn ist", + "Ġseek ers", + "ond o", + "IS A", + "ĠL ength", + "Ġnost alg", + "Ġnew com", + "Ġdet rim", + "ent ric", + "3 75", + "ĠG E", + "Ġaut op", + "Ġacadem ics", + "App Data", + "ĠS hen", + "Ġid iot", + "ĠTrans it", + "Ġteasp oon", + "W il", + "K O", + "ĠCom edy", + "> ,", + "Ġpop ulated", + "W D", + "Ġp igs", + "ĠO culus", + "Ġsymp athetic", + "Ġmar athon", + "19 8", + "Ġseiz ure", + "s ided", + "Ġd op", + "irt ual", + "L and", + "ĠFl oor", + "osa urs", + "... ]", + "Ġl os", + "Ġsubsid iary", + "E Y", + "ĠPart s", + "ĠSt ef", + "ĠJud iciary", + "Ġ13 4", + "Ġmir rors", + "Ġk et", + "t imes", + "Ġneuro log", + "Ġc av", + "ĠGu est", + "Ġtum or", + "sc ill", + "ĠLl oyd", + "E st", + "Ġcle arer", + "Ġstere otypes", + "Ġd ur", + "not hing", + "Red dit", + "Ġnegoti ated", + "---------------- --------", + "23 5", + "Ġfl own", + "ĠSe oul", + "ĠRes ident", + "ĠS CH", + "Ġdisappear ance", + "ĠV ince", + "g rown", + "Ġgrab s", + "r il", + "ĠInf inite", + "ĠTw enty", + "Ġpedest rian", + "Ġjer sey", + "ĠF ur", + "ĠInf inity", + "ĠEll iott", + "Ġment or", + "Ġmor ally", + "Ġob ey", + "sec ure", + "iff e", + "Ġantib iotics", + "ang led", + "ĠFre eman", + "ĠIntrodu ction", + "J un", + "Ġm arsh", + "ic ans", + "ĠEV ENTS", + "och ond", + "W all", + "icult y", + "Ġmisdem eanor", + "Ġl y", + "Th omas", + "ĠRes olution", + "Ġanim ations", + "ĠD ry", + "Ġinter course", + "ĠNew castle", + "ĠH og", + "ĠEqu ipment", + "17 7", + "Ġterrit orial", + "Ġarch ives", + "20 3", + "Fil ter", + "ĠMun ich", + "Ġcommand ed", + "ĠW and", + "Ġpit ches", + "ĠCro at", + "Ġrat ios", + "ĠM its", + "Ġaccum ulated", + "ĠSpecific ally", + "Ġgentle man", + "acer b", + "Ġp enn", + "Ġa ka", + "ĠF uk", + "Ġinterven e", + "ĠRef uge", + "ĠAlz heimer", + "Ġsuccess ion", + "oh an", + "d oes", + "L ord", + "Ġsepar at", + "Ġcorrespond ence", + "Ġsh iny", + "P rior", + "Ġs ulf", + "Ġmiser able", + "Ġded ication", + "( ).", + "Ġspecial ists", + "Ġdefect s", + "ĠC ult", + "ĠX ia", + "Ġje opard", + "ĠO re", + "Ab ility", + "Ġle ar", + "Ġamb itions", + "ĠB MI", + "ĠArab s", + "Ġ19 42", + "Ġpres ervation", + "ific ate", + "Ġash amed", + "l oss", + "ĠRest aur", + "Ġrese mble", + "Ġen rich", + "ĠK N", + "ĠCl an", + "fl oat", + "Ġplay able", + "IT T", + "Ġharm ony", + "arr ison", + "ĠWe instein", + "w ere", + "Ġpoison ing", + "ĠCom put", + "ĠWord Press", + "m ajor", + "ĠVal ve", + "F an", + "ĠTh row", + "ĠRom ans", + "ĠDep ression", + "ad os", + "Ġtort ured", + "Ġbal ancing", + "bott om", + "Ġacqu iring", + "ĠMon te", + "ard i", + "Ġa ura", + "Ġ# #", + "ĠStand ing", + "ĠAtl as", + "C F", + "Ġintr ins", + "ĠBen ghazi", + "Ġcamp ing", + "Ġt apped", + "bl ade", + "st rous", + "ĠR abb", + "ĠW ritten", + "t ip", + "ĠNe igh", + "ster dam", + "ĠAll ow", + "ĠHe aling", + "ĠR hod", + "n um", + "Ġcaffe ine", + "ĠPer cent", + "Ġbo o", + "Ġapp les", + "30 5", + "Ġwel coming", + "Ġappl aud", + "Ġa usterity", + " ±", + "ĠRe ality", + "ef e", + "å ®", + "Ġsu cks", + "Ġtab s", + "ĠPay Pal", + "Ġback pack", + "Ġgif ted", + "abul ary", + "ĠSc out", + "ir teen", + "Ġch in", + "Ġo mitted", + "Ġnegative ly", + "Ġaccess ing", + "ĠE arn", + "Ġambul ance", + "Ġhead phones", + "Ġ20 5", + "ĠRef resh", + "p resident", + "ĠKit chen", + "ĠEnt ered", + "ĠS nyder", + "00 5", + "om ical", + "Ġborrow ed", + "ĠN em", + "Ġav iation", + "Ġst all", + "rim ination", + "Ġuniform s", + "it ime", + "ĠSim mons", + "ener gy", + "ab lished", + "y y", + "qual ified", + "Ġrall ies", + "ĠSt uart", + "fl ight", + "Ġgang s", + "r ag", + "Ġv ault", + "lu x", + "ĠCom par", + "Ġdesign ation", + "20 9", + "ĠJ os", + "d ollar", + "z ero", + "Ġwell s", + "30 3", + "Ġconstitu ents", + "Ġhe ck", + "Ġc ows", + "Ġcommand ers", + "Ġdifferent ial", + "ĠC atherine", + "29 9", + "Ġval ve", + "Ġbr ace", + "Ġperspect ives", + "c ert", + "f act", + "icular ly", + "ĠMc N", + "pl anes", + "Ġint ric", + "Ġpe as", + "ov an", + "Ġtoss ed", + "ret ch", + "ĠL opez", + "Ġunf amiliar", + "de ath", + "ĠA part", + "ĠCh ang", + "Ġrelie ved", + "rop he", + "Ġair ports", + "Ġfre ak", + "ut il", + "M ill", + "ĠCh in", + "ĠOw en", + "m ale", + "ĠBro ken", + "ĠWind s", + "ro b", + "r ising", + "Ġfire fighters", + "Ġauthor itarian", + "Ġ14 8", + "Bit coin", + "ex ternal", + "Ġbrow sers", + "iche ver", + "or ian", + "Ġun b", + "Ġpo ke", + "ĠZ ot", + "M id", + "ĠPop ular", + "Ġco vert", + "Ġcont ributes", + "Ġ6 50", + "Ġcont ention", + "G ate", + "Ġcons oles", + "Ġchrom os", + "ĠI X", + "Ġvis ually", + "ĠE isen", + "Ġjewel ry", + "Ġdeleg ation", + "Ġacceler ate", + "ĠR iley", + "Ġsl ope", + "Ġind oor", + "it ially", + "Ġhuge ly", + "Ġtun nels", + "Ġfin ed", + "Ġdirect ive", + "Ġfore head", + "ustom ed", + "Ġsk ate", + "Mus ic", + "g as", + "Ġrecogn izing", + "am bo", + "Ġover weight", + "ĠGr ade", + "Ù Ĭ", + "Ġsound ing", + "Ġlock ing", + "ĠR EM", + "St ore", + "Ġexc av", + "ĠLike wise", + "ĠL ights", + "Ġel bow", + "ĠSupp ly", + "w ic", + "Ġhands ome", + "19 94", + "C oll", + "Ġadequ ately", + "ĠAssoci ate", + "Ġstri ps", + "Ġcrack down", + "Ġmar vel", + "ĠK un", + "Ġpass ages", + "@@ @@", + "ĠT all", + "Ġthought ful", + "names e", + "Ġprost itution", + "bus iness", + "Ġball istic", + "person al", + "c ig", + "iz ational", + "R ound", + "ĠÂłĠÂł ĠÂłĠÂł", + "ĠCole man", + "Ġadm itting", + "ĠPl ug", + "Ġbit coins", + "ĠSu z", + "Ġfair ness", + "Ġsupp lier", + "Ġcatast rophic", + "ĠHel en", + "o qu", + "M arc", + "ĠArt icles", + "g ie", + "Ġend angered", + "Ġdest iny", + "ĠVol t", + "ol ia", + "ax is", + "Ġche at", + "Ġun ified", + "IC O", + "qu ote", + "30 2", + "ĠS ed", + "Ġsupp ression", + "Ġanaly zing", + "Ġsqu at", + "Ġfig uring", + "Ġcoordin ates", + "Ġch unks", + "Ġ19 46", + "Ġsub p", + "Ġw iki", + "ĠFor bes", + "ĠJ upiter", + "ĠE rik", + "im er", + "ĠCom mercial", + "\\ )", + "Ġlegitim acy", + "Ġd ental", + "ĠMe an", + "Ġdefic its", + "5 50", + "Orig inally", + "ĠHor ror", + "Ġcontam ination", + "ll ah", + "Ġconf isc", + "ĠCl are", + "T B", + "ĠF ailed", + "an ed", + "Ġrul er", + "ĠCont roller", + "Ġfemin ists", + "F ix", + "g ay", + "20 7", + "Ġr abbit", + "Th ird", + "ownt own", + "Ġgl ue", + "Ġvol atile", + "Ġsh ining", + "Ġf oll", + "Ġimp aired", + "Ġsup ers", + "æ Ī", + "Ġcl utch", + "ļé ĨĴ", + "Ġpro let", + "Ġ( !", + "Ġy elled", + "ĠK iev", + "ĠEr n", + "ĠSh ock", + "K B", + "Ġsit uated", + "qu ery", + "ĠN as", + "Ġan nex", + "char acter", + "ĠHol iday", + "Ġautom ation", + "ĠJ ill", + "ĠRem astered", + "Ġl inem", + "Ġwild erness", + "ĠHor izon", + "ĠGu inea", + "A Z", + "Ġmain land", + "Ġsec recy", + "LE ASE", + "Ġp unk", + "ĠProv ince", + "( ),", + "Spe ed", + "Ġhand ing", + "ĠSeb ast", + "S ir", + "r ase", + "Ġj ournals", + "Ġcon gest", + "ĠT ut", + "ir rel", + "Ġschizophren ia", + "Ġmis ogyn", + "health y", + "I ron", + "Ġreact ed", + "- $", + "25 2", + "Ġpl ural", + "Ġpl um", + "Ġbarg ain", + "Ġground ed", + "f inder", + "Ġdis se", + "ĠL az", + "O OD", + "Ġat roc", + "F actory", + "Ġmin ions", + "Ġo ri", + "ĠB rave", + "ĠP RE", + "ĠMy anmar", + "ĠH od", + "Ġexped ition", + "Ġexpl ode", + "ĠCo ord", + "Ġext r", + "ĠB rief", + "ĠAD HD", + "Ġhard core", + "feed ing", + "Ġd ile", + "ĠF ruit", + "Ġvacc ination", + "ĠM ao", + "osp here", + "Ġcont ests", + "- |", + "Ġf ren", + "isp here", + "R om", + "ĠSh arp", + "ĠTre nd", + "Ġdis connect", + "âĢ¢ âĢ¢", + "Ġper secution", + "Ear th", + "Ġhealth ier", + "38 4", + "Ġc ob", + "ĠTr inity", + "OW S", + "AN N", + "Ġspecial ty", + "Ġg ru", + "Ġcooper ative", + "wh y", + "Start ing", + "ĠIss ues", + "st re", + "ens or", + "Ġ18 5", + "Ad v", + "! ?", + "ĠRe vel", + "em ia", + "ĠH ulk", + "Ġcelebr ations", + "ĠS ou", + "ra ud", + "ĠKle in", + "Ġun real", + "con text", + "Ġpartners hips", + "Ġadop ting", + "t ical", + "Ġspl ash", + "ĠHe zbollah", + "c ategory", + "cycl op", + "xt on", + "ĠD ot", + "urd y", + "t z", + "Ġenvelop e", + "ĠN L", + "â ķ", + "Ġwhere in", + "Spe c", + "18 4", + "Ġte lev", + "al iation", + "Ġmyth s", + "å °", + "Ġrig orous", + "Ġcommun icating", + "Ġobser ver", + "Ġre he", + "ĠW ash", + "Ġapolog ized", + "ĠT in", + "Ġexpend itures", + "work ers", + "d ocument", + "Ġhes itate", + "ĠLen in", + "Ġunpredict able", + "Ġrenew al", + "cl er", + "ok ia", + "ĠCON T", + "Ġpost season", + "Tok ens", + "Ġex acerb", + "Ġbet ting", + "Ġ14 7", + "Ġelev ation", + "W ood", + "ĠSol omon", + "19 4", + "00 4", + "out put", + "Ġredu nd", + "ĠM umbai", + "Ġp H", + "Ġreprodu ce", + "ĠD uration", + "MA X", + "Ġb og", + "C BS", + "ĠBal ance", + "ĠS gt", + "ĠRec ent", + "Ġc d", + "Ġpo pped", + "Ġincomp et", + "pro p", + "ay an", + "g uy", + "Pac ific", + "Ġty r", + "Ġ{ {", + "ĠMy stic", + "ĠD ana", + "Ġmast urb", + "Ġge ometry", + "à ¢", + "ĠCor rect", + "Ġtraject ory", + "Ġdistract ed", + "Ġf oo", + "ĠW elsh", + "L uc", + "m ith", + "Ġrug by", + "Ġrespir atory", + "Ġtri angle", + "Ġ2 15", + "Ġunder graduate", + "ĠSuper ior", + "ch anging", + "_ -", + "Ġright ly", + "Ġrefere e", + "Ġluc rative", + "Ġun authorized", + "Ġresemb les", + "ĠGN U", + "ĠDer by", + "Ġpath ways", + "ĠL ed", + "Ġend urance", + "Ġst int", + "Ġcollect or", + "F ast", + "Ġd ots", + "Ġnational s", + "ĠSec urities", + "Ġwh ip", + "Par am", + "Ġlearn s", + "M agic", + "Ġdetail ing", + "m oon", + "Ġbroadcast ing", + "Ġb aked", + "26 5", + "hol m", + "ĠS ah", + "ĠHus sein", + "ĠCourt esy", + "17 4", + "Ġ14 6", + "Ġge ographic", + "pe ace", + "Ġjud ging", + "ĠS tern", + "B ur", + "Ġstory line", + "G un", + "ĠSt ick", + "24 5", + "30 7", + "ãĤ´ ãĥ³", + "ĠAdminist rator", + "Ġbur nt", + "Ġp ave", + "ch oes", + "Ex ec", + "Ġcamp uses", + "Res ult", + "Ġmut ations", + "ĠCh arter", + "Ġcapt ures", + "Ġcomp ares", + "Ġbad ge", + "S cient", + "Ġer ad", + "ier y", + "o i", + "ett es", + "ĠE state", + "Ġst rap", + "Ġproud ly", + "Ġf ried", + "Ġwithd rawn", + "ĠV oy", + "ph ony", + "It ems", + "ĠP ierce", + "b ard", + "Ġann otation", + "ant on", + "ill on", + "Im pro", + "... )", + "Ġhapp ier", + "---- --", + "ad just", + "Ġstaff ers", + "Ġactiv ism", + "Ġper f", + "Ġal right", + "N eed", + "Ġcomm ence", + "Ġopio id", + "ĠAm anda", + "E s", + "ĠP ars", + "ĠK aw", + "W orks", + "24 8", + "Ġind o", + "t c", + "end ant", + "ĠM oto", + "Ġlegal ization", + "OT E", + "Ġtask ed", + "Ġt sp", + "ĠACT IONS", + "16 6", + "Ġrefres hing", + "ĠN R", + "ĠPere z", + "Ġinfring ement", + "S Y", + "List en", + "in ning", + "k u", + "Ġrot ate", + "pro gram", + "ar ah", + "Des ign", + "Ġ( £", + "Ġst oring", + "Ġwar rants", + "Ġjud gement", + "ĠB rist", + "us ually", + "ph oto", + "ĠR an", + "ĠP ine", + "Ġoutrage ous", + "ĠValent ine", + "lu ence", + "ĠEvery body", + "Al tern", + "Ġrele vance", + "Ġtermin ated", + "Ġd essert", + "Ġfulf illed", + "Ġprosecut ed", + "ĠW ords", + "Ġm igrant", + "Ġcultiv ation", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "idel ity", + "ĠV ern", + "ĠLog in", + "Ġmetaph or", + "ĠT ip", + "Ġrecru its", + "ĠP ig", + "rib ing", + "Ġenthusi asts", + "ex per", + "Ġfright ening", + "ĠH air", + "ans on", + "str ate", + "Ġh i", + "He ight", + "Ġown ing", + "n one", + "Ġdis like", + "Ġkn ives", + "pher d", + "Ġloud ly", + "ĠAP Is", + "Dis play", + "ĠL ac", + "ĠUS S", + "ab l", + "ver ages", + "J ew", + "Ġ17 2", + "ĠHist orical", + "at oon", + "ĠPhys ics", + "in tern", + "Ġwarm th", + "Ġto pp", + "D M", + "Ġgun man", + "Ġem peror", + "od i", + "ãĥ £", + "in atory", + "ĠR ib", + "Ġ13 1", + "ĠSat urn", + "ĠSh ining", + "Ġw aking", + "Qu otes", + "Ġcomed ian", + "en berg", + " ½", + "Ġbelie vers", + "Ġpaper work", + "c ustom", + "Ġle v", + "Ġl ament", + "Ġpour ing", + "22 2", + "p olitical", + "ĠSupp lement", + "m aid", + "Ġcruel ty", + "Ġt read", + "ys ics", + "A w", + "rit es", + "Ġmod ifier", + "ĠP osition", + "Ad am", + "l b", + "ub s", + "Ġimper fect", + "Ġcl usters", + "ĠEngine er", + "ĠC herry", + "Ġinaug uration", + "ĠS au", + "Ġembod iment", + "ĠUn cle", + "Ġover r", + "Ġexplos ions", + "c ule", + "ĠPrinc eton", + "ĠAndre a", + "Ġincorrect ly", + "Ġearn est", + "Ġpil gr", + "ĠS print", + "Ġslee ve", + "Ġhe ars", + "ĠAm azing", + "Ġbrow sing", + "ag in", + "Ġhom eland", + "Ġha w", + "Ġd iving", + "ist ered", + "17 8", + "Ġbarg aining", + "ĠArc ade", + "Ġdeleg ate", + "ters on", + "................................ ................................", + "ĠJackson ville", + "27 5", + "Ġst agn", + "Ġad am", + "ĠSher man", + "C B", + "Ġsub urb", + "ĠFood s", + "Ġconver ting", + "ĠAr ist", + "Ġch ambers", + "l ove", + "Ġam ino", + "ĠG an", + "Ġmad ness", + "m c", + "ĠUS E", + "def ined", + "Ġul tr", + "ind ust", + "Ġw olves", + "l ance", + "Add itionally", + "Ġcr acks", + "as ia", + "ĠRe ason", + "ĠP ump", + "Ġaccident al", + "ĠL aser", + "ĠR id", + "Ġinitial ized", + "ell i", + "Ġun named", + "Ġn oun", + "ĠPass ed", + "Ġhost age", + "ĠEth iop", + "sh irts", + "Ġun rel", + "ĠEmb assy", + "Ġ19 41", + "Ġat oms", + "Ġpur ported", + "16 4", + "ĠF i", + "Ġgall ons", + "ĠMon ica", + "Ġp g", + "en ment", + "Ġsort ed", + "ĠG ospel", + "Ġhe ights", + "Ġtr aced", + "Ġunder going", + "She ll", + "Ġs acks", + "Ġproport ions", + "Ġhall uc", + "F ont", + "ac et", + "Ġwar mer", + "ĠIN TER", + "Ġgrab bing", + "Pl ug", + "Ġreal ization", + "ĠBur ke", + "Ġen chant", + "AT ER", + "ĠSe ed", + "Ġabund ant", + "F M", + "Ġc ivic", + "V s", + "is i", + "Ġv ow", + "Ġre per", + "ĠPartners hip", + "Ġpenet ration", + "Ġax e", + "Ġsh attered", + "ĠZ ombies", + "Ġv inyl", + "ĠAl ert", + "e on", + "Ġoblig ed", + "ĠIll ust", + "ĠPl aza", + "ĠFront ier", + "Ġdavid jl", + "ĠSer ial", + "ĠH av", + "ĠNut rition", + "B i", + "Ġâĸ Ī", + "ĠJ ays", + "lin ux", + "Ġhur ry", + "Ġv oy", + "Ġhop eless", + "ĠSte alth", + "Ġ ãģ", + "ess ors", + "tt le", + "b org", + "ĠSaf ari", + "f ell", + "Ġw ary", + "d ue", + "ĠAb ove", + "H a", + "E LL", + "Ġnot or", + "ĠW on", + "T oo", + "Ġoccup ations", + "Ġposs essions", + "Ġinv iting", + "Ġpred ators", + "Ġacceler ated", + "Ġ15 7", + "uter te", + "ĠC ube", + "e ast", + "acc ount", + "G ive", + "Ġtrans plant", + "red ients", + "id able", + "Ġscreens hots", + "ĠG und", + "ĠF S", + "Ġtravel ers", + "Ġsens ory", + "ĠF iat", + "ĠRock ets", + "İ ĭ", + "_ {", + "F riend", + "Ġchar ming", + "AL S", + "Ġenjoy ment", + "m ph", + "Ġ5 000", + "ĠRE G", + "Ù Ĩ", + "b ia", + "Ġcomp ilation", + "ro st", + "ĠV P", + "ĠSch ne", + "201 9", + "Ġcop ying", + "M ORE", + "ĠFl ore", + "f alls", + "2 15", + "t otal", + "Ġdis ciples", + "d ouble", + "Ġexceed ing", + "Ġsm ashed", + "Ġconcept ual", + "ĠRom ania", + "ĠB rent", + "ĠI CE", + "ĠT ou", + "Ġg rap", + "Ġn ails", + "18 9", + "ãĥ ĺ", + "Ġproc ure", + "e ur", + "Ġconfir ming", + "ĠC ec", + "aw i", + "ĠEd en", + "Ġn g", + "Ġengine ered", + "at ics", + "Ġhook ed", + "Ġdisgust ing", + "ĠMur der", + "ãĤ ¿", + "L ibrary", + "Ġ16 8", + "Al most", + "hem atic", + "Men u", + "ĠNot re", + "ĠJ ur", + "Ġkidn apped", + "Ġhack er", + "ĠJ ade", + "Ġcreep y", + "Ġdraw ings", + "ĠSpons or", + "Ġcycl ists", + "ĠGob lin", + "Ġoptim ized", + "Ġst aged", + "ĠMc D", + "bet ween", + "A ge", + "en o", + "S ex", + "ĠW ide", + "n ings", + "av is", + "Ġincap able", + "ĠK ob", + "Ġreward ing", + "ĠL one", + "oles cent", + "Ġcontract ed", + "Ġstick y", + "J ose", + "B all", + "f est", + "ĠIn put", + "ĠRec ently", + "Ġto mat", + "squ are", + "App lication", + "Ġnit rogen", + "Ġdupl icate", + "ĠRec on", + "ĠD ear", + "L ondon", + "Ġint ra", + "Ġd ock", + "Ġout reach", + "ĠM illion", + "Ġmamm als", + "am pton", + "V AL", + "Ġsn aps", + "Ġd os", + "ĠWh ole", + "ĠRead y", + "T ry", + "ĠWinn ipeg", + "ear ance", + "Ġinc urred", + "ren ched", + "ĠNS W", + "il ot", + "rain e", + "Ġc ube", + "g ot", + "Ġrun way", + "etermin ed", + "ĠHaw ks", + "Ġsurviv or", + "ĠW ish", + "ĠD in", + "ĠDE F", + "ĠV ault", + "18 7", + "Ġmush rooms", + "Ġcris p", + "be y", + "ĠDisco very", + "Ġdevelopment al", + "Ġparad igm", + "Ġcha otic", + "ĠT su", + "Ġ3 33", + "b ons", + "Ġbacter ial", + "Ġcomm its", + "Ġcos mic", + "Ġme ga", + "oc ative", + "ĠP aint", + "ophob ic", + "Ġv ain", + "Ġcar ved", + "ĠTh ief", + "ĠG ul", + "ows hip", + "Ġc ites", + "ĠEd inburgh", + "Ġdimin ished", + "Ġacknowled ges", + "ĠK ills", + "Ġmic row", + "ĠHer a", + "Ġsen iors", + "Ġwhere by", + "H op", + "at ron", + "Ġun available", + "ĠN ate", + "Ġ4 80", + "Ġsl ated", + "ĠRe becca", + "ĠB attery", + "Ġgram mar", + "Ġhead set", + "Ġcurs or", + "Ġex cluding", + "any e", + "aunder ing", + "eb in", + "Ġfeas ible", + "ĠPub lishing", + "ĠLab s", + "ĠCl iff", + "ĠFerr ari", + "Ġp ac", + "vis ible", + "mark ed", + "pe ll", + "Ġpol ite", + "Ġstagger ing", + "ĠGal actic", + "Ġsuper st", + "Ġpar an", + "ĠOffic ers", + "ãĢ ģ", + "Ġspecific s", + "ul us", + "23 9", + "ĠP aste", + "AM P", + "ĠPan ama", + "ĠDe lete", + "angu ard", + "rest rial", + "Ġhero ic", + "ĠD y", + "ا ÙĦ", + "Ġincumb ent", + "Ġcr unch", + "t ro", + "Ġsc oop", + "Ġblog ger", + "Ġsell ers", + "ure n", + "Ġmedic ines", + "ĠC aps", + "ĠAnim ation", + "ox y", + "Ġout ward", + "Ġinqu iries", + "22 9", + "Ġpsych ologist", + "ĠS ask", + "ev il", + "Ġcontam inated", + "ãĤ ¨", + "he rence", + "Ġbrand ed", + "ĠAbd ul", + "z h", + "Ġparagraph s", + "Ġmin s", + "Ġcor related", + "er b", + "Ġimp art", + "Ġmil estone", + "ĠSol utions", + "ot le", + "Ġunder cover", + "Ġmar ched", + "ĠCharg ers", + "f ax", + "ĠSec rets", + "Ġr uth", + "we ather", + "Ġfemin ine", + "Ġsh am", + "Ġprest igious", + "igg ins", + "Ġs ung", + "hist ory", + "ett le", + "gg ie", + "Ġout dated", + "ol and", + "Ġper ceptions", + "ĠS ession", + "ĠDod gers", + "u j", + "ĠE ND", + "D oc", + "Ġdefic iency", + "Gr and", + "ĠJ oker", + "Ġretro spect", + "Ġdiagn ostic", + "Ġharm less", + "Ġro gue", + "ĠA val", + "E qu", + "Ġtrans c", + "ĠRoberts on", + "ĠDep ending", + "ĠBurn s", + "iv o", + "Ġhost ility", + "F eatures", + "ĵ ĺ", + "Ġdis comfort", + "ĠL CD", + "spec ified", + "ĠEx pect", + "3 40", + "Ġimper ative", + "ĠReg ular", + "Ch inese", + "Ġstate wide", + "Ġsy mm", + "Ġlo ops", + "Ġaut umn", + "N ick", + "Ġsh aping", + "Ġqu ot", + "Ġc herry", + "ĠCross ref", + "è¦ ļéĨĴ", + "Stand ard", + "he ed", + "ĠD ell", + "ĠViet namese", + "Ġo st", + "ĠV alkyrie", + "O A", + "Ass ad", + "Ġreb ound", + "ĠTra ffic", + "pl aces", + "æ ĺ", + "ĠB uc", + "17 2", + "Ġshel ters", + "Ġins isting", + "ĠCertain ly", + "ĠKenn eth", + "ĠT CP", + "Ġpen al", + "ĠRe play", + "he ard", + "Ġdial ect", + "iz a", + "ĠF Y", + "it cher", + "ĠD L", + "Ġspir al", + "Ġquarterback s", + "Ġh ull", + "Ġgo ogle", + "Ġto dd", + "ĠSter ling", + "ĠPl ate", + "Ġsp ying", + "mb ol", + "ĠReal m", + "ĠPro ced", + "ĠCr ash", + "Ġtermin ate", + "Ġprotest ing", + "C enter", + "gu ided", + "Ġun cover", + "Ġboy cott", + "Ġreal izes", + "s ound", + "Ġpret ending", + "ĠV as", + "19 80", + "Ġfram ed", + "Ġ13 9", + "Ġdesc ended", + "Ġrehab ilitation", + "Ġborrow ing", + "ĠB uch", + "Ġbl ur", + "R on", + "ĠFro zen", + "en za", + "Ch ief", + "ĠP oor", + "Ġtransl ates", + "M IN", + "Ġ2 12", + "J ECT", + "Ġerupt ed", + "Ġsuccess es", + "S EC", + "Ġpl ague", + "Ġg ems", + "d oms", + "Ġstret ches", + "ĠSp y", + "Ġstory telling", + "C redit", + "ĠP ush", + "Ġtra ction", + "Ġin effective", + "ĠL una", + "Ġt apes", + "Ġanaly tics", + "erc ise", + "Ġprogram mes", + "ĠCar bon", + "Ġbeh old", + "he avy", + "ĠConserv ation", + "ĠF IR", + "Ġs ack", + "ter min", + "ric ks", + "Ġhous ed", + "Ġunus ually", + "I ce", + "Ġexecut ing", + "ĠMor oc", + "ed ay", + "Ġed itions", + "Ġsm arter", + "ĠB A", + "Ġout law", + "Ġvan ished", + "ib a", + "AL SE", + "ĠSil va", + "23 8", + "C ould", + "Ġphilos opher", + "Ġevac uated", + "Sec ret", + "14 2", + "Ġvis as", + "ãĤ ¬", + "ĠM alt", + "ĠClear ly", + "ĠN iger", + "ĠC airo", + "ĠF ist", + "3 80", + "ĠX ML", + "aut o", + "it ant", + "Ġrein forced", + "Rec ord", + "ĠSurviv or", + "G Hz", + "Ġscrew s", + "parent s", + "Ġo ceans", + "ma res", + "Ġbra kes", + "vas ive", + "Ġhell o", + "ĠS IM", + "rim p", + "Ġo re", + "ĠArm our", + "24 7", + "Ġterr ific", + "Ġt ones", + "14 1", + "ĠMin utes", + "Ep isode", + "Ġcur ves", + "Ġinflamm atory", + "Ġbat ting", + "ĠBeaut iful", + "L ay", + "Ġunp op", + "v able", + "Ġr iots", + "ĠTact ics", + "b augh", + "ĠC ock", + "Ġorg asm", + "ĠS as", + "Ġconstruct or", + "et z", + "G ov", + "Ġant agon", + "Ġthe at", + "Ġde eds", + "ha o", + "c uts", + "ĠMc Cl", + "Ġu m", + "ĠScient ists", + "Ġgrass roots", + "ys sey", + "\"] =>", + "Ġsurf aced", + "Ġsh ades", + "Ġneighb ours", + "Ġad vertis", + "oy a", + "Ġmer ged", + "Up on", + "Ġg ad", + "Ġanticip ate", + "Any way", + "Ġsl ogan", + "Ġdis respect", + "I ran", + "ĠT B", + "act ed", + "Ġsubp oen", + "medi ately", + "OO OO", + "Ġwa iver", + "Ġvulner abilities", + "ott esville", + "ĠHuff ington", + "J osh", + "ĠD H", + "M onday", + "ĠEll en", + "K now", + "x on", + "it ems", + "22 8", + "Ġf ills", + "ĠN ike", + "Ġcum ulative", + "and als", + "I r", + "Ġ ì", + "Ġfr iction", + "ig ator", + "Ġsc ans", + "ĠVi enna", + "ld om", + "Ġperform ers", + "P rim", + "Ġb idding", + "M ur", + "Ġlean ed", + "ĠPri x", + "al ks", + "Ġ[ âĢ¦]", + "ĠTw itch", + "ĠDevelop er", + "ĠG ir", + "Ġcall back", + "Ab stract", + "Ġacc ustomed", + "Ġfreed oms", + "ĠP G", + "ur acy", + "Ġl ump", + "is man", + ",, ,,", + "19 92", + "ĠR ED", + "Ġwor m", + "M atch", + "ĠPl atinum", + "I J", + "ĠOwn er", + "Tri via", + "com pl", + "Ġnew born", + "Ġfant as", + "O wn", + "Ġ19 59", + "Ġsymp ath", + "Ġub iqu", + "Ġoutput s", + "Ġal lev", + "Ġpr ag", + "K evin", + "Ġfav ors", + "Ġbur ial", + "Ġn urt", + "so lete", + "c ache", + "Ġ15 6", + "Ġunl ocks", + "te chn", + "M aking", + "Ġcon quer", + "ad ic", + "æ ĸ", + "Ġel f", + "Ġelect orate", + "ĠKurd s", + "ĠSt ack", + "ĠSam urai", + "Ġâ ĺħ", + "Ġ{ }", + "ĠS aid", + "ĠFall out", + "Ġkind ness", + "ĠCustom s", + "ĠBou levard", + "Ġhelicop ters", + "ot ics", + "ĠVe get", + "com ment", + "Ġcritic ised", + "Ġpol ished", + "ĠRem ix", + "ĠC ultural", + "Ġrec ons", + "Ġdo i", + "at em", + "Sc reen", + "Ġbar red", + "Com ments", + "ĠGener ally", + "Ġsl ap", + "7 20", + "V ari", + "p ine", + "Ġem pt", + "Ġh ats", + "ĠPlay ing", + "l ab", + "a verage", + "form s", + "ĠC otton", + "Ġcan s", + "ĠD ON", + "ĠSom alia", + "C rypt", + "ĠIncre ases", + "E ver", + "mod ern", + "Ġsur geon", + "3 000", + "Ġrandom ized", + "================================ ================================", + "B ern", + "im pl", + "ĠC OR", + "Ġpro claim", + "th ouse", + "Ġto es", + "Ġam ple", + "Ġpres erving", + "Ġdis bel", + "gr and", + "B esides", + "Ġsil k", + "ĠPat tern", + "h m", + "Ġenter prises", + "Ġaffidav it", + "ĠAdvis ory", + "Ġadvert ised", + "ĠRel igious", + "se ctions", + "psy ch", + "ĠField s", + "aw ays", + "Ġhasht ag", + "ĠNight mare", + "Ġv ampire", + "Ġfore nsic", + "rosso ver", + "n ar", + "Ġn avy", + "Ġvac ant", + "ĠD uel", + "Ġhall way", + "Ġface book", + "ident ally", + "ĠN RA", + "Ġm att", + "Ġhur ricane", + "ĠKir by", + "ĠP uzzle", + "Ġsk irt", + "ou st", + "du llah", + "Ġanal ogy", + "in ion", + "Ġtomat oes", + "ĠN V", + "ĠPe ak", + "ĠMe yer", + "Ġappoint ments", + "Ġm asc", + "Ġal ley", + "re hend", + "Ġchar ities", + "Ġund o", + "Ġdest inations", + "ĠTest ing", + "\"> \"", + "c ats", + "* .", + "Ġgest ures", + "gener al", + "Le ague", + "Ġpack ets", + "ĠInspect or", + "ĠBer g", + "Ġfraud ulent", + "Ġcritic ize", + "F un", + "Ġbl aming", + "nd ra", + "Ġsl ash", + "ĠE ston", + "Ġpropos ing", + "Ġwh ales", + "Ġtherap ist", + "Ġsub set", + "Ġle isure", + "EL D", + "ĠC VE", + "ĠAct ivity", + "Ġcul min", + "sh op", + "ĠD AY", + "is cher", + "ĠAdmir al", + "ĠAtt acks", + "Ġ19 58", + "Ġmem oir", + "Ġfold ed", + "Ġsex ist", + "Ġ15 3", + "ĠL I", + "Ġread ings", + "Ġembarrass ment", + "ĠEmploy ment", + "w art", + "ch in", + "Ġcontin uation", + "l ia", + "Rec ently", + "Ġd uel", + "Ġevac uation", + "ĠKash mir", + "Ġdis position", + "ĠR ig", + "Ġbol ts", + "Ġins urers", + "4 67", + "M ex", + "Ġret aliation", + "Ġmis ery", + "Ġunre asonable", + "r aining", + "I mm", + "ĠP U", + "em er", + "Ġgen ital", + "ãĤ ³", + "ĠC andy", + "Ġon ions", + "ĠP att", + "lin er", + "Ġconced ed", + "Ġf a", + "Ġfor c", + "ĠH ernandez", + "ĠGe off", + "deb ian", + "ĠTe ams", + "Ġc ries", + "Ġhome owners", + "23 7", + "A BC", + "Ġst itch", + "Ġstat istic", + "Ġhead ers", + "ĠBi ology", + "Ġmot ors", + "ĠG EN", + "ĠL ip", + "Ġh ates", + "Ġhe el", + "S elf", + "i pl", + "ED IT", + "ort ing", + "Ġann ot", + "ĠSpe ech", + "old emort", + "ĠJ avascript", + "ĠLe Bron", + "Ġfoot print", + "Ġf n", + "Ġseiz ures", + "n as", + "h ide", + "Ġ19 54", + "ĠBe e", + "ĠDecl aration", + "ĠKat ie", + "Ġreserv ations", + "N R", + "f emale", + "Ġsatur ated", + "Ġb iblical", + "Ġtroll s", + "Dev ice", + "ph otos", + "Ġdr ums", + "ãĥīãĥ© ãĤ´ãĥ³", + "N ight", + "f ighter", + "ĠH ak", + "ri ber", + "Ġc ush", + "Ġdiscipl inary", + "ba um", + "ĠG H", + "ĠSch midt", + "ilib rium", + "Ġs ixty", + "ĠKush ner", + "ro ts", + "Ġp und", + "ĠR ac", + "Ġspr ings", + "Ġcon ve", + "Bus iness", + "F all", + "Ġqual ifications", + "Ġvers es", + "Ġnarc iss", + "ĠK oh", + "ĠW ow", + "ĠCharl ottesville", + "ed o", + "Ġinterrog ation", + "ĠW ool", + "36 5", + "B rian", + "Ġâľ ĵ", + "Ġalleg es", + "ond s", + "id ation", + "ĠJack ie", + "y u", + "Ġl akes", + "Ġworth while", + "Ġcryst als", + "ĠJud a", + "Ġcomp rehend", + "Ġfl ush", + "Ġabsor ption", + "ĠO C", + "Ġfright ened", + "ĠCh ocolate", + "Mart in", + "Ġbu ys", + "Ġbu cks", + "Ġapp ell", + "ĠChampions hips", + "Ġlist ener", + "ĠDef ensive", + "Ġc z", + "ud s", + "ĠM ate", + "Ġre play", + "Ġdecor ated", + "Ġs unk", + "ĠV IP", + "ĠAn k", + "Ġ19 5", + "aa aa", + "Nob ody", + "ĠMil k", + "ĠG ur", + "ĠM k", + "ĠS ara", + "Ġse ating", + "ĠW id", + "Tr ack", + "Ġemploy s", + "Ġgig antic", + "AP P", + "ãĤ §", + "in ventory", + "Ġtow el", + "at che", + "l asting", + "ĠT L", + "Ġlat ency", + "Ġkn e", + "B er", + "me aning", + "Ġup held", + "Ġplay ground", + "Ġm ant", + "S ide", + "Ġstere o", + "Ġnorth west", + "Ġexception ally", + "Ġr ays", + "Ġrec urring", + "D rive", + "Ġup right", + "Ġab duct", + "ĠMar athon", + "Ġgood bye", + "Ġal phabet", + "h p", + "Ġcourt room", + "ring ton", + "ot hing", + "T ag", + "Ġdiplom ats", + "Ġbar bar", + "ĠAqu a", + "18 3", + "33 33", + "Ġmat urity", + "Ġinst ability", + "ĠAp ache", + "Ġ= ==", + "Ġfast ing", + "ĠGr id", + "Mod Loader", + "Ġ15 2", + "A bs", + "ĠOper ating", + "ett i", + "Ġacqu aint", + "Don nell", + "ĠK em", + "ĠFor ge", + "Ġarm ored", + "M il", + "Ġphilos ophers", + "in vest", + "Pl ayers", + "â Ī", + "Ġmy riad", + "Ġcomr ades", + "R ot", + "Ġremember ing", + "Ġcorrespond s", + "Ġprogram mers", + "ĠLyn n", + "Ġo lig", + "Ġco herent", + "yn chron", + "ĠChem ical", + "Ġj ugg", + "p air", + "post s", + "E ye", + "ĠIn ner", + "Ġsem ester", + "ott est", + "ĠEmir ates", + "ric anes", + "or ously", + "m its", + "ĠW is", + "Ġd odge", + "l ocation", + "Ġf aded", + "Am azon", + "ĠPro ceed", + "ĠIN FO", + "j ournal", + "ĠTru ck", + "T en", + "Ġ2 17", + "Ġstat utes", + "m obile", + "ĠT ypes", + "Rec omm", + "b uster", + "pe x", + "Ġleg ends", + "Ġhead ache", + "f aced", + "ĠWi Fi", + "if ty", + "ĠH ER", + "Ġcirc uits", + "ER ROR", + "22 6", + "ol in", + "Ġcyl inder", + "osp ace", + "ik ers", + "P rem", + "Qu ant", + "Ġconflic ting", + "Ġslight est", + "Ġfor ged", + "ion age", + "Step hen", + "ĠK ub", + "ĠOpp ortun", + "ĠHe al", + "Ġbl o", + "Ġrul ers", + "Ġh uh", + "Ġsubmar ine", + "f y", + "ass er", + "Ġallow ance", + "ĠKas ich", + "ĠT as", + "ĠAustral ians", + "Forge ModLoader", + "ĠâĨ ij", + "ĠMat rix", + "am ins", + "Ġ12 00", + "ĠAc qu", + "23 6", + "D ocument", + "ĠBre aking", + "19 3", + "ĠSub st", + "ĠRoll er", + "ĠPro perties", + "ĠN I", + "t ier", + "Ġcr ushing", + "Ġadvoc ating", + "Further more", + "keep ers", + "Ġsex ism", + "x d", + "Ġcall er", + "ĠS ense", + "chie ve", + "ĠT F", + "Ġfuel ed", + "Ġreminis cent", + "Ġobs ess", + "ur st", + "Ġup hold", + "ĠF ans", + "het ics", + "Ġâ Ĺ", + "ĠB ath", + "Ġbe verage", + "Ġo scill", + "25 4", + "Ġpol es", + "Ġgrad ual", + "Ġex ting", + "ĠS uff", + "ĠS uddenly", + "Ġlik ing", + "Ġ19 49", + "un ciation", + "am ination", + "ĠO mar", + "ĠL V", + "ĠCon sequently", + "Ġsynt hes", + "ĠG IF", + "Ġp ains", + "Ġinteract ing", + "u ously", + "inc re", + "Ġrum or", + "ĠScient ology", + "19 7", + "ĠZ ig", + "Ġspe lling", + "ĠA SS", + "Ġexting u", + "ms on", + "Ġg h", + "Ġremark ed", + "ĠStrateg ic", + "ĠM ON", + "å ¥", + "g ae", + "ĠWH AT", + "E ric", + "ĠCamp us", + "Ġmeth ane", + "Ġimag in", + "J UST", + "ĠAl m", + "X T", + "i q", + "ĠR SS", + "Ġwrong doing", + "att a", + "Ġbig ot", + "Ġdemonstr ators", + "ĠCal vin", + "ĠV illa", + "Ġmembr ane", + "ĠAw esome", + "Ġbenef ic", + "26 8", + "Ġmagn ificent", + "ĠL ots", + "G reg", + "ĠBor is", + "Ġdetain ees", + "ĠH erman", + "Ġwhis pered", + "Ġa we", + "Prof essor", + "fund ing", + "Ġphys iological", + "ĠDest ruction", + "Ġlim b", + "Ġmanip ulated", + "Ġbub bles", + "Ġpse ud", + "Ġhyd ra", + "ĠBrist ol", + "Ġst ellar", + "ĠExp ansion", + "ĠK ell", + "ĠInterest ingly", + "Ġm ans", + "Ġdrag ging", + "Ġec ological", + "ĠF it", + "Ġg ent", + "Ġbenef ited", + "ĠHait i", + "Ġpoly g", + "ãĥ İ", + "Ġ20 30", + "Ġpro w", + "Ġrecon struction", + "Ġwas t", + "Ġpsych ic", + "ĠGree ks", + "Hand ler", + "16 2", + "ĠP ulse", + "Ġsol icit", + "Ġsy s", + "Ġinflu x", + "ĠG entle", + "per cent", + "Ġprolifer ation", + "Ġtax able", + "Ġdisreg ard", + "Ġesc aping", + "Ġg inger", + "Ġwith stand", + "Ġdevast ated", + "ĠD ew", + "ser ies", + "Ġinject ed", + "ela ide", + "Ġturn over", + "he at", + "Ļ Ĥ", + "H appy", + "ĠSil ent", + "ãĤ Ń", + "iv ism", + "Ġir rational", + "AM A", + "Ġre ef", + "r ub", + "Ġ16 2", + "Ġbank ers", + "ĠEth ics", + "v v", + "Ġcritic isms", + "K n", + "18 6", + "M ovie", + "ĠT ories", + "Ġno od", + "Ġdist ortion", + "F alse", + "od ore", + "Ġt asty", + "Res earch", + "ĠU ID", + "- )", + "Ġdivor ced", + "ĠM U", + "ĠHay es", + "ĠIs n", + "ian i", + "ĠH Q", + "Ġ\" #", + "ign ant", + "Ġtra umatic", + "ĠL ing", + "H un", + "Ġsab ot", + "on line", + "r andom", + "Ġren amed", + "ra red", + "K A", + "d ead", + "é t", + "ĠAss istance", + "Ġse af", + "++++ ++++", + "Ġse ldom", + "ĠWeb b", + "Ġbo olean", + "u let", + "Ġref rain", + "ĠDI Y", + "ru le", + "Ġshut ting", + "Ġutil izing", + "load ing", + "ĠPar am", + "co al", + "oot er", + "Ġattract ing", + "ĠD ol", + "Ġher s", + "ag netic", + "ĠRe ach", + "im o", + "Ġdisc arded", + "ĠP ip", + "01 5", + "ü r", + "Ġm ug", + "Im agine", + "C OL", + "Ġcurs ed", + "ĠSh ows", + "ĠCurt is", + "ĠSach s", + "spe aking", + "ĠV ista", + "ĠFram ework", + "ong o", + "Ġsub reddit", + "Ġcr us", + "ĠO val", + "R ow", + "g rowing", + "Ġinstall ment", + "Ġgl ac", + "ĠAdv ance", + "EC K", + "ĠLGBT Q", + "LE Y", + "Ġac et", + "Ġsuccess ive", + "ĠNic ole", + "Ġ19 57", + "Qu ote", + "Ġcircumst ance", + "ack ets", + "Ġ14 2", + "ort ium", + "Ġguess ed", + "ĠFr ame", + "Ġperpet rators", + "ĠAv iation", + "ĠBen ch", + "Ġhand c", + "A p", + "Ġ19 56", + "25 9", + "r and", + "Net Message", + "d in", + "urt les", + "h ig", + "ĠV III", + "ff iti", + "ĠSw ords", + "b ial", + "Ġkidn apping", + "dev ice", + "Ġb arn", + "ĠEl i", + "auc as", + "S end", + "Con structed", + "Ġ ½", + "Ġneed les", + "Ġad vertisements", + "Ġv ou", + "Ġexhib ited", + "ĠFort ress", + "As k", + "B erry", + "TY PE", + "Ġcan cers", + "ump ing", + "ĠTerrit ory", + "Ġpr ud", + "Ġn as", + "Ġathe ist", + "Ġbal ances", + "ãģ Ł", + "ĠSh awn", + "& &", + "Ġland sc", + "ĠR GB", + "Ġpet ty", + "Ġex cellence", + "Ġtransl ations", + "Ġpar cel", + "ĠChe v", + "E ast", + "ĠOut put", + "im i", + "Ġamb ient", + "ĠTh reat", + "Ġvill ains", + "Ġ5 50", + "IC A", + "Ġtall er", + "Ġle aking", + "c up", + "Ġpol ish", + "Ġinfect ious", + "ĠK C", + "Ġ@ @", + "back ground", + "Ġbureaucr acy", + "ĠS ai", + "un less", + "it ious", + "ĠSky pe", + "At l", + "ID ENT", + "00 8", + "Ġhyp ocr", + "Ġpit chers", + "Ġguess ing", + "ĠF INAL", + "Bet ween", + "Ġvill agers", + "Ġ25 2", + "f ashion", + "ĠTun is", + "Be h", + "ĠEx c", + "ĠM ID", + "28 8", + "ĠHas kell", + "19 6", + "ĠN OR", + "Ġspec s", + "Ġinv ari", + "Ġgl ut", + "ĠC ars", + "Ġimp ulse", + "Ġhon ors", + "g el", + "Ġjurisd ictions", + "ĠBund le", + "ul as", + "Calif ornia", + "ĠIncre ase", + "Ġp ear", + "Ġsing les", + "Ġc ues", + "Ġunder went", + "ĠW S", + "Ġexagger ated", + "Ġdub ious", + "Ġfl ashing", + "L OG", + ") ].", + "J ournal", + "t g", + "V an", + "ĠI stanbul", + "ĠIn sp", + "ĠFrank en", + "D raw", + "Ġsad ness", + "Ġiron ic", + "ĠF ry", + "x c", + "Ġ16 4", + "is ch", + "W ay", + "ĠProtest ant", + "h orn", + "Ġun aff", + "ĠV iv", + "ill as", + "ĠProduct ions", + "ĠH ogan", + "Ġper imeter", + "ĠS isters", + "Ġspont aneous", + "Ġdown side", + "Ġdescend ants", + "Ġor n", + "w orm", + "Japan ese", + "Ġ19 55", + "Ġ15 1", + "ĠDo ing", + "els en", + "umb les", + "Ġrad ically", + "ĠDr um", + "ĠB ach", + "Ġli abilities", + "ĠO B", + "ĠElement ary", + "Ġmem e", + "yn es", + "Ġfinger print", + "ĠGr ab", + "Ġundert ake", + "Mem bers", + "ĠRead er", + "ĠSim s", + "g od", + "Ġhypot hetical", + "s cient", + "ĠA J", + "Ġchar ism", + "Ġad missions", + "ĠMiss ile", + "tr ade", + "Ġexerc ising", + "ĠBack ground", + "W ritten", + "Ġvoc als", + "whe ther", + "Ġv i", + "ĠW inner", + "Ġl itter", + "ĠSh ooting", + "ST EM", + "ãĤ ¡", + "ĠA FL", + "Ġvari ability", + "Ġe ats", + "ĠD PS", + "b row", + "Ġeleph ants", + "Ġstr at", + "Ġ Å", + "Ġsett lers", + "Matt hew", + "Ġin advert", + "H I", + "ĠIM F", + "ĠGo al", + "Ġnerv es", + "John son", + "ey e", + "ablish ment", + "Th ursday", + "BIL ITY", + "H ad", + "am oto", + "het amine", + "ep s", + "Ġmit ochond", + "Ġcomp ressed", + "ĠTre vor", + "ĠAnim als", + "T ool", + "L ock", + "Ġtwe ak", + "Ġpin ch", + "Ġcancell ation", + "P ot", + "Ġfoc al", + "ĠAst ron", + "17 3", + "ĠA SC", + "ĠO THER", + "umn i", + "Ġdem ise", + "d l", + "Ù ħ", + "Sem itism", + "Ġcr acking", + "Ġcollabor ative", + "Ġexpl ores", + "s ql", + "Ġher bs", + "Ġconfig urations", + "m is", + "ĠRes ult", + "ace y", + "ĠSm oke", + "Ġsan ct", + "el ia", + "Ġdeg ener", + "Ġdeep est", + "Ġscream ed", + "Ġn ap", + "Soft ware", + "ĠST AR", + "E F", + "ĠX in", + "spons ored", + "mans hip", + "23 3", + "Ġprim aries", + "Ġfilter ing", + "Ġas semble", + "m il", + "ĠMy ers", + "b ows", + "Ġpun ched", + "M ic", + "Ġinnov ations", + "Ġfun c", + "and o", + "Ġfr acking", + "ĠV ul", + "о Ð", + "osh op", + "ĠIm mun", + "Ġsett ling", + "Ġadolesc ents", + "Ġreb uilding", + "Ġtransform ing", + "Ġpar ole", + "Ġhar bor", + "Ġbook ing", + "ot ional", + "onge vity", + "ĠY o", + "b ug", + "Ġemer ges", + "ĠMethod s", + "ĠCh u", + "P res", + "ĠDun geons", + "Ġtra iling", + "ĠR um", + "ĠH ugh", + "å¤ ©", + "ĠE ra", + "ĠBatt les", + "Res ults", + "ĠTr ading", + "Ġvers a", + "c ss", + "ax ies", + "he et", + "Ġgre ed", + "19 89", + "Ġgard ens", + "Ġconting ent", + "P ark", + "ĠLeaf s", + "h ook", + "ro be", + "Ġdiplom acy", + "ĠF uel", + "ĠInv asion", + "Ġupgr ading", + "M ale", + "Ġe lic", + "Ġrelent less", + "ĠCo venant", + "ap esh", + "ĠT rop", + "T y", + "pro duction", + "art y", + "Ġpun ches", + "ak o", + "cyclop edia", + "ĠR abbit", + "ĠHD MI", + "Ġ14 1", + "Ġf oil", + "Item Image", + "ĠF G", + "Ġimplement ations", + "ĠP om", + "ixt ures", + "Ġaw ait", + "Ġ3 30", + "am us", + "Ġumb rella", + "Ġfore see", + "se par", + "Ġcircum cision", + "Ġperipher al", + "S ay", + "ĠExper t", + "In c", + "Ġwithd rew", + "ĠAnd ers", + "f ried", + "Ġradio active", + "ĠOp ening", + "Ġboard ing", + "ĠN D", + "Ġover throw", + "Act iv", + "W P", + "ĠAct s", + "× Ļ", + "Ġmot ions", + "v ic", + "ĠM ighty", + "ĠDef ender", + "a er", + "Ġthank ful", + "ĠK illing", + "ĠBr is", + "mo il", + "Ġpredict ing", + "26 6", + "ch oice", + "Ġkill ers", + "Ġinc ub", + "ĠChe st", + "ather ing", + "Ġpro claimed", + "fl ower", + "oss om", + "umbled ore", + "ĠCy cling", + "ĠOccup y", + "AG ES", + "P en", + "ĠY ug", + "Ġpack aged", + "Ġheight ened", + "c ot", + "st ack", + "C ond", + "Ġst amps", + "m age", + "Ġpersu aded", + "Ġens l", + "ĠCard inal", + "Ġsol itary", + "Ġpossess ing", + "ĠC ork", + "Ġev id", + "ĠT ay", + "Ġbl ues", + "Ġextrem ism", + "Ġlun ar", + "Ġcl own", + "Te chn", + "Ġfest ivals", + "ĠPv P", + "ĠL ar", + "Ġconsequ ently", + "p resent", + "Ġsom eday", + "ç İĭ", + "ĠMet eor", + "Ġtour ing", + "c ulture", + "Ġbe aches", + "S hip", + "c ause", + "ĠFl ood", + "ãĥ ¯", + "Ġpur ity", + "th ose", + "Ġem ission", + "b olt", + "Ġch ord", + "ĠScript ure", + "L u", + "Ġ$ {", + "cre ated", + "Other s", + "25 8", + "Ġelement al", + "Ġannoy ed", + "ĠA E", + "d an", + "ĠS ag", + "Res earchers", + "Ġfair y", + "âĢĵ âĢĵ", + "======== ====", + "Sm art", + "GG GG", + "Ġskelet ons", + "Ġpup ils", + "link ed", + "Ġur gency", + "en abled", + "ĠF uck", + "Ġcoun cill", + "r ab", + "U AL", + "T I", + "Ġlif es", + "Ġconf essed", + "B ug", + "Ġharm on", + "ĠCON FIG", + "ĠNe utral", + "D ouble", + "Ġst aple", + "ĠSH A", + "Brit ish", + "ĠSN P", + "AT OR", + "oc o", + "Ġswing ing", + "ge x", + "ole on", + "pl ain", + "ĠMiss ing", + "ĠTro phy", + "v ari", + "ran ch", + "Ġ3 01", + "4 40", + "00000000 00000000", + "Ġrest oring", + "Ġha ul", + "uc ing", + "ner g", + "Ġfut ures", + "Ġstrateg ist", + "quest ion", + "Ġlater al", + "ĠB ard", + "Ġs or", + "ĠRhod es", + "ĠD owntown", + "????? -", + "ĠL it", + "ĠB ened", + "Ġco il", + "st reet", + "ĠPort al", + "FI LE", + "ĠG ru", + "* ,", + "23 1", + "ne um", + "Ġsuck ed", + "Ġr apper", + "Ġtend encies", + "ĠLaure n", + "cell aneous", + "26 7", + "Ġbrow se", + "Ġover c", + "head er", + "o ise", + "Ġbe et", + "ĠG le", + "St ay", + "Ġm um", + "Ġtyp ed", + "Ġdiscount s", + "T alk", + "ĠO g", + "ex isting", + "ĠS ell", + "u ph", + "C I", + "ĠAust rian", + "ĠW arm", + "Ġdismiss al", + "Ġaver ages", + "c amera", + "Ġalleg iance", + "L AN", + "=\" #", + "Ġcomment ators", + "ĠSet ting", + "ĠMid west", + "Ġpharm ac", + "ĠEX P", + "Ġstain less", + "Ch icago", + "Ġt an", + "24 4", + "Ġcountry side", + "ĠV ac", + "29 5", + "Ġpin ned", + "Ġcr ises", + "Ġstandard ized", + "T ask", + "ĠJ ail", + "ĠD ocker", + "col ored", + "f orth", + "\" },", + "Ġpat rons", + "Ġsp ice", + "Ġm ourn", + "ĠM ood", + "Ġlaund ry", + "Ġequ ip", + "ĠM ole", + "y ll", + "ĠTH C", + "n ation", + "ĠSher lock", + "Ġiss u", + "ĠK re", + "ĠAmeric as", + "ĠA AA", + "Ġsystem atically", + "Ġcont ra", + "ĠS ally", + "Ġrational e", + "Ġcar riage", + "Ġpe aks", + "Ġcontrad iction", + "ens ation", + "ĠFail ure", + "Ġpro ps", + "Ġnames pace", + "Ġc ove", + "field s", + "ãĤ ĭ", + "Ġw ool", + "ĠC atch", + "Ġpresum ed", + "ĠD iana", + "r agon", + "ig i", + "Ġh amm", + "Ġst unt", + "ĠG UI", + "ĠObserv atory", + "ĠSh ore", + "Ġsmell s", + "ann ah", + "Ġcock pit", + "ĠD uterte", + "8 50", + "Ġopp ressed", + "bre aker", + "ĠCont ribut", + "ĠPer u", + "ĠMons anto", + "ĠAtt empt", + "Ġcommand ing", + "Ġfr idge", + "ĠR in", + "ĠChe ss", + "ual ity", + "Ġo l", + "Republic an", + "ĠGl ory", + "ĠW IN", + ".... ...", + "ag ent", + "read ing", + "Ġin h", + "J ones", + "Ġcl icks", + "al an", + "Ġ[ ];", + "ĠMaj esty", + "ĠC ed", + "op us", + "ate l", + "à ª", + "AR C", + "ĠEc uador", + "ãĥ ł", + "ĠK uro", + "Ġritual s", + "Ġcapt ive", + "Ġoun ce", + "Ġdisag reement", + "Ġsl og", + "f uel", + "P et", + "M ail", + "Ġexerc ised", + "Ġsol ic", + "Ġrain fall", + "Ġdev otion", + "ĠAss essment", + "Ġrob otic", + "opt ions", + "ĠR P", + "ĠFam ilies", + "ĠFl ames", + "Ġassign ments", + "00 7", + "aked own", + "Ġvoc abulary", + "Re illy", + "Ġc aval", + "g ars", + "Ġsupp ressed", + "ĠS ET", + "ĠJohn s", + "Ġwar p", + "bro ken", + "Ġstat ues", + "Ġadvoc ated", + "Ġ2 75", + "Ġper il", + "om orph", + "ĠF emin", + "per fect", + "Ġh atch", + "L ib", + "5 12", + "Ġlif elong", + "3 13", + "Ġche eks", + "Ġnum bered", + "ĠM ug", + "B ody", + "ra vel", + "We ight", + "ĠJ ak", + "ĠHe ath", + "Ġkiss ing", + "ĠJ UST", + "Ġw aving", + "u pload", + "Ġins ider", + "ĠPro gressive", + "ĠFil ter", + "tt a", + "ĠBe am", + "Ġviol ently", + "ip ation", + "Ġskept icism", + "Ġ19 18", + "ĠAnn ie", + "ĠS I", + "Ġgen etics", + "Ġon board", + "at l", + "ĠFried man", + "ĠB ri", + "cept ive", + "Ġpir ate", + "ĠRep orter", + "27 8", + "Ġmyth ology", + "Ġe clipse", + "Ġsk ins", + "Ġgly ph", + "ing ham", + "F iles", + "C our", + "w omen", + "Ġreg imes", + "Ġphotograp hed", + "K at", + "ĠMA X", + "Offic ials", + "Ġunexpected ly", + "Ġimpress ions", + "F ront", + ";;;; ;;;;", + "Ġsuprem acy", + "Ġs ang", + "Ġaggrav ated", + "Ġabrupt ly", + "ĠS ector", + "Ġexc uses", + "Ġcost ing", + "ide press", + "St ack", + "ĠR NA", + "ob il", + "Ġghost s", + "ld on", + "at ibility", + "Top ics", + "Ġreim burse", + "ĠH M", + "ĠDe g", + "Ġth ief", + "y et", + "ogen esis", + "le aning", + "ĠK ol", + "ĠB asketball", + "Ġf i", + "ĠSee ing", + "Ġrecy cling", + "Ġ[ -", + "Cong ress", + "Ġlect ures", + "P sy", + "Ġne p", + "Ġm aid", + "Ġori ented", + "A X", + "Ġrespect ful", + "re ne", + "fl ush", + "ĠUn loaded", + "re quest", + "gr id", + "ĠAltern atively", + "ĠHug o", + "Ġdec ree", + "ĠBuddh ism", + "and um", + "And roid", + "ĠCong o", + "ĠJoy ce", + "Ġacknowled ging", + "hes ive", + "ĠTom orrow", + "ĠH iro", + "th ren", + "ĠM aced", + "Ġho ax", + "ĠIncre ased", + "ĠPr adesh", + "W ild", + "____ __", + "16 1", + "Ġa unt", + "Ġdistribut ing", + "ĠT ucker", + "ĠSS L", + "ĠW olves", + "B uilding", + "ou lt", + "ĠLu o", + "ĠY as", + "ĠSp ir", + "ĠSh ape", + "ĠCamb od", + "ĠIP v", + "Ġm l", + "Ġext rad", + "39 0", + "ĠPenn y", + "d ream", + "Ġstation ed", + "opt ional", + "ew orthy", + ". ", + "ĠWorks hop", + "ĠRet ail", + "ĠAv atar", + "6 25", + "N a", + "ĠV C", + "ĠSec ure", + "M Y", + "19 88", + "oss ip", + "Ġpro state", + "Ġund en", + "Ġg amer", + "ĠCont ents", + "ĠWar hammer", + "ĠSent inel", + "3 10", + "Ġse gregation", + "ĠF lex", + "ĠM AY", + "Ġdr ills", + "ĠDrug s", + "Islam ic", + "Ġsp ur", + "Ġca fe", + "Ġimag inary", + "Ġgu iding", + "Ġsw ings", + "ĠThe me", + "ob y", + "Ġn ud", + "Ġbe gging", + "Ġstr ongh", + "Ġreject ing", + "Ġpedest rians", + "ĠPro spect", + "R are", + "s le", + "Ġconcess ions", + "ĠConst itutional", + "Ġbe ams", + "Ġfib ers", + "p oon", + "Ġinstinct s", + "pro perty", + "ĠB IG", + "Sand ers", + "im ates", + "Ġco ating", + "Ġcorps es", + "ĠTR UE", + "check ed", + "Ġ16 6", + "A sh", + "ĠJ S", + "ĠF iction", + "Ġcommun al", + "Ġener getic", + "oooo oooo", + "Ġnow adays", + "IL D", + "ib o", + "ĠSU V", + "R en", + "Ġdwell ing", + "Sil ver", + "Ġt ally", + "ĠM oving", + "Ġcow ard", + "Ġgener als", + "Ġhorn s", + "Ġcirc ulated", + "Ġrob bed", + "ĠUn limited", + "Ġharass ed", + "Ġinhib it", + "Ġcomp oser", + "ĠSpot ify", + "Ġspread s", + "3 64", + "Ġsu icidal", + "Ġno ises", + "ĠSt ur", + "Ġs aga", + "ĠK ag", + "is o", + "Ġtheoret ically", + "M oney", + "Ġsimilar ity", + "Ġslic ed", + "ut ils", + "ing es", + "\" -", + "Ġan th", + "Ġimp ed", + "Mod ule", + "Through out", + "Ġmen us", + "comm ittee", + "and i", + "ob j", + "in av", + "f ired", + "ĠAb dullah", + "Ġund ead", + "Ġfont s", + "H old", + "EN G", + "Ġsustain ability", + "Ġfl ick", + "Ġr azor", + "ĠF est", + "ĠChar acters", + "Ġword ing", + "Ġpopul ist", + "Ġcritic izing", + "Ġm use", + "v ine", + "Ġcard board", + "Ġkind ly", + "Ġfr inge", + "ĠThe ft", + "icult ural", + "Ġgovern ors", + "Ġ ����", + "Ġ16 3", + "Ġtime out", + "ĠA uth", + "Child ren", + "A U", + "Ġred emption", + "ĠAl ger", + "Ġ19 14", + "Ġw aved", + "Ġastron auts", + "og rams", + "Ġsw amp", + "ĠFinn ish", + "Ġcand le", + "Ġton nes", + "ut m", + "Ġr ay", + "Ġsp un", + "Ġfear ful", + "art icles", + "Ġca us", + "or ically", + "ĠRequ ires", + "ĠG ol", + "Ġpop e", + "Ġinaug ural", + "Ġg le", + "AD A", + "ĠIS IL", + "ĠOff ensive", + "Ġwatch dog", + "Ġbal con", + "ent ity", + "ĠH oo", + "Ġgall on", + "AC C", + "Ġdoub ling", + "Ġimpl ication", + "ĠS ight", + "Ġdoct r", + "---- ---", + "Ġ\\ \\", + "Ġm alt", + "R oll", + "Ġâī ¥", + "Ġrec ap", + "add ing", + "u ces", + "ĠB end", + "fig ure", + "Ġtur key", + "Ġsoc ietal", + "ĠT ickets", + "Ġcommer cially", + "Ġsp icy", + "Ġ2 16", + "ĠR amp", + "Ġsuperior ity", + "à ¯", + "ĠTr acker", + "C arl", + "ĠC oy", + "ĠPatri ot", + "Ġconsult ed", + "Ġlist ings", + "Ġsle w", + "reens hot", + "ĠG one", + "Ġ[ ...]", + "30 9", + "Ġh ottest", + "Ø ±", + "Ġrock y", + "ĠD iaz", + "Ġmass age", + "Ġpar aly", + "Ġp ony", + "A z", + "Ġcart ridge", + "ĠN Z", + "Ġsn ack", + "ĠLam ar", + "ple ment", + "ĠLes lie", + "Ġm ater", + "Ġsn ipp", + "24 6", + "Ġjoint ly", + "ĠBris bane", + "ĠiP od", + "Ġpump ing", + "Ġgo at", + "ĠSh aron", + "eal ing", + "Ġcor on", + "Ġan omal", + "rah im", + "ĠConnect ion", + "Ġsculpt ure", + "Ġsched uling", + "ĠD addy", + "at hing", + "Ġeyeb rows", + "Ġcur ved", + "Ġsent iments", + "Ġdraft ing", + "D rop", + "( [", + "Ġnom inal", + "ĠLeaders hip", + "ĠG row", + "Ġ17 6", + "Ġconstruct ive", + "iv ation", + "Ġcorrupt ed", + "ger ald", + "ĠC ros", + "ĠChe ster", + "ĠL ap", + "ãģ ª", + "OT H", + "D ATA", + "Ġal mond", + "pro bably", + "I mp", + "Ġfe ast", + "ĠWar craft", + "F lor", + "Ġcheck point", + "Ġtrans cription", + "Ġ20 4", + "Ġtwe aks", + "Ġrel ieve", + "S cience", + "Ġperform er", + "Z one", + "Ġtur moil", + "ig ated", + "hib it", + "ĠC afe", + "the med", + "Ġflu or", + "ben ch", + "Ġde com", + "ĠU nt", + "ĠBar rett", + "ĠF acts", + "Ġt asting", + "ĠPTS D", + "ĠSe al", + "ĠJuda ism", + "ĠDynam ic", + "ĠC ors", + "V e", + "ĠM ing", + "ĠTrans form", + "v on", + "ĠDef enders", + "ĠTact ical", + "ĠV on", + "ĠUn ivers", + "Ġdist orted", + "ĠB reath", + "?' \"", + "Ġag on", + "ĠDead ly", + "Ġl an", + "ĠCy cle", + "orn ed", + "Ġrel iably", + "Ġgl or", + "ĠMon key", + "ãĥ ¡", + "Ġad ren", + "Ġmicrow ave", + "ĠAl ban", + "irc raft", + "dig it", + "sm art", + "ĠD read", + "¯¯¯¯¯¯¯¯ ¯¯¯¯¯¯¯¯", + "{ {", + "ĠRoc hester", + "Ġsimpl ified", + "Ġinf licted", + "Ġtake over", + "Ġyour selves", + "ad itional", + "Ġmus cular", + "K S", + "Ġing en", + "T ax", + "ĠFe ature", + "27 7", + "Ġcru c", + "Ġcr ate", + "Ġun identified", + "Ġacclaim ed", + "ĠM anga", + "ĠFr ances", + "ĠNep al", + "ĠG erald", + "ĠKu wait", + "Ġsl ain", + "ĠHe b", + "ĠG oku", + "ãģ® æ", + "28 6", + "M rs", + "ĠC ody", + "ĠSan ctuary", + "01 6", + "Ġdism ant", + "Ġdatas et", + "ĠH ond", + "b uck", + "ĠPat terson", + "Ġpal ette", + "ĠG D", + "ic ol", + "ĠL odge", + "Ġplanet ary", + "ak in", + "ĠRegist ered", + "ab we", + "ĠPeters burg", + "Ġha iled", + "ĠP iece", + "S che", + "ĠDO J", + "Ġen umer", + "18 1", + "ĠObs erver", + "ĠB old", + "f ounded", + "com merce", + "Ġexplo its", + "ĠF inding", + "UR N", + "ĠS ne", + "ĠAc id", + "ay ette", + "ĠVal ues", + "Ġdr astic", + "Ġarchitect ural", + "Ġ\" .", + "× ķ", + "ump ed", + "Ġwra pping", + "Ġwid ow", + "ĠSl ayer", + "l ace", + "on ce", + "German y", + "av oid", + "Ġtem ples", + "P AR", + "à ´", + "ĠLuc ifer", + "ĠFl ickr", + "l ov", + "for ces", + "Ġsc outing", + "Ġlou der", + "tes y", + "Ġbefore hand", + "Ä ĵ", + "ĠNe on", + "ĠW ol", + "ĠTyp ically", + "ĠPolit ico", + "-+ -+", + "Ġbuild er", + "Ġder ive", + "K ill", + "Ġp oker", + "Ġambig uous", + "Ġlif ts", + "Ġcy t", + "Ġrib s", + "ood le", + "ĠS ounds", + "h air", + "ĠSynd rome", + "t f", + "Ġproport ional", + "u id", + "Ġper taining", + "ĠKind le", + "ĠNeg ro", + "Ġreiter ated", + "ĠTon ight", + "oth s", + "ĠCorn ell", + "Ġo wing", + "Ġ20 8", + "elf are", + "oc ating", + "ĠB irds", + "Sub scribe", + "Ġess ays", + "Ġburd ens", + "Ġillust rations", + "ar ious", + "ER AL", + "ĠCal cul", + "Ġx en", + "ĠLink edIn", + "ĠJ ung", + "Ġredes ign", + "Con nor", + "29 6", + "Ġrevers al", + "ĠAd elaide", + "ĠL L", + "Ġs inking", + "Ġg um", + "US H", + "c apt", + "ĠGr imm", + "Ġfoot steps", + "ĠCB D", + "isp ers", + "Ġpro se", + "Wed nesday", + "ĠM ovies", + "ed in", + "Ġoverturn ed", + "Ġcontent ious", + "US B", + "~~~~~~~~ ~~~~~~~~", + "ĠCo pper", + "Ġpoint less", + "N V", + "val ues", + "olph in", + "d ain", + "Ġdepos ited", + "ĠG W", + "Ġpreced ed", + "ĠCl a", + "ĠGo lem", + "ĠN im", + "ĠÎ ²", + "ĠEngine ers", + "m iddle", + "Ġfl att", + "oper ative", + "Ġcouncil s", + "imb abwe", + "el in", + "Ġstress ful", + "ĠL D", + "Ġres h", + "l ake", + "Ġwheel chair", + "ĠAltern ative", + "Ġoptim ize", + "oper ation", + "Ġpe ek", + "Ġones elf", + "ig il", + "Ġtrans itions", + "op athy", + "bl ank", + "Ġ16 9", + "17 1", + "________________________________ ________________________________", + "Ġl aundering", + "En c", + "ĠD EC", + "Ġwork outs", + "Ġsp ikes", + "Ġdin osaurs", + "Ġdiscrim inatory", + "P ool", + "R ather", + "38 5", + "R NA", + "tes ters", + "et o", + "ĠIdent ity", + "Ġve in", + "ĠBur ton", + "Ġarc ade", + "4 20", + "Ult imately", + "ĠSad ly", + "à °", + "p ill", + "Ġcub ic", + "ĠSpect rum", + "the se", + "st ates", + "Ġun official", + "h awks", + "ĠEVER Y", + "Ġrain bow", + "Ġincarcer ation", + "and ing", + "Ġsy ll", + "ĠEver ton", + "Ġ17 9", + "ĠSer bia", + "Ġ18 9", + "m eter", + "ĠMic key", + "Ġant iqu", + "Ġfact ual", + "ne ck", + "ĠN are", + "n orm", + "m ust", + "Ġhigh ways", + "Ġgl am", + "Ġdivid ing", + "ĠSquad ron", + "ĠMar tha", + "Ġbirth s", + "C over", + "//////// ////////", + "ĠW ong", + "Ph ot", + "ĠA LS", + "ri o", + "ĠNon etheless", + "ĠL emon", + "Ġ20 6", + "ĠE E", + "Ġderiv ative", + "ĠWW II", + "v ote", + "Ġthere in", + "Ġsepar ating", + "44 6", + "sy nc", + "ĠStre ets", + "Ġr att", + "Ġmunicip ality", + "ĠShort ly", + "Ġmon k", + ") ,\"", + "Ġscr ub", + "Ġoper atives", + "Ne ither", + "Pl ace", + "ĠLim it", + "F emale", + "ĠAct or", + "Char acter", + "Ġconstit uted", + "35 7", + "Ġprotest ed", + "ĠSt raw", + "ĠHe ight", + "ild a", + "ĠTy ph", + "Ġflood s", + "Ġcos metic", + "W AY", + "pert ure", + "up on", + "t ons", + "ess ing", + "ĠP ocket", + "Ġro oft", + "ĠC aucas", + "Ġant idepress", + "Ġincomp atible", + "EC D", + "Ġoper a", + "ĠCont est", + "Ġgener ators", + "l ime", + "Def ense", + "19 87", + "for um", + "Ġsav age", + "ĠHung arian", + "n z", + "Ġmet allic", + "Ġex pelled", + "Ġres idency", + "Ġdress es", + "66 6", + "ĠC lement", + "f ires", + "C ategory", + "Ġge ek", + "al is", + "Ġc emetery", + "educ ated", + "Ġc rawl", + "ĠUn able", + "ĠT yson", + "ak is", + "Ġp ardon", + "ĠW ra", + "Ġstrengthen ed", + "ĠF ors", + "33 5", + "ĠH C", + "ĠM ond", + "Ġvisual s", + "ĠBeat les", + "ett lement", + "Ġ ï", + "g ro", + "Ġb ash", + "Ġpo orest", + "Ġex cel", + "Ġaspir ations", + "ĠM unicip", + "ens ible", + "Ġceremon ies", + "Ġintimid ation", + "ĠCON TR", + "be ck", + "ĠK ap", + "as u", + "Ġtradem arks", + "ĠS ew", + "ĠComp etition", + "net work", + "ĠAr ri", + "ĠT et", + "Ro aming", + "W C", + "D at", + "Ġso b", + "Ġpair ing", + "Ġoverd ose", + "SA Y", + "ab er", + "Ġrev olt", + "ĠF ah", + "act ing", + "e q", + "est ation", + "F ight", + "ĠMar ks", + "27 3", + "Ġ17 8", + "R aw", + "ãģ ĭ", + "34 9", + "bl ocks", + "Ġver ge", + "est ine", + "ĠPod esta", + "Ġinv asive", + "Ġprofound ly", + "ĠA o", + "e ach", + "Ġl est", + "inter pret", + "Ġshr inking", + "Ġerr one", + "Ġche es", + "ly s", + "ĠI vy", + "ĠDirect ory", + "Ġhint ed", + "V ICE", + "Ġcontact ing", + "ĠG ent", + "he i", + "Ġlabel ing", + "Ġmerc ury", + "ĠL ite", + "Ġexp ires", + "Ġdest abil", + "rit is", + "c u", + "Ġfeather s", + "Ġste er", + "Ġprogram med", + "ĠV ader", + "Go ing", + "ĠE lim", + "Ġy o", + "ĠMic he", + "Ġ20 3", + "Ġslee ves", + "Ġb ully", + "ĠHum ans", + "36 8", + "Ġcomp ress", + "ĠBan ner", + "AR S", + "Ġa while", + "Ġcal ib", + "Ġspons orship", + "ĠDiff iculty", + "ĠP apers", + "Ġident ifier", + "} .", + "Ġy og", + "ĠSh ia", + "Ġclean up", + "Ġvib e", + "int rodu", + "im ming", + "Austral ia", + "Ġout lines", + "ĠY outube", + "tr ain", + "ĠM akes", + "Ġde ported", + "Ġcent r", + "ĠD ug", + "ĠB oulder", + "ĠBuff y", + "Ġinj unction", + "ĠHar ley", + "ĠG roups", + "ĠD umbledore", + "ĠCl ara", + "Ġ\" -", + "Ġsacrific ed", + "ep h", + "Sh adow", + "ib ling", + "Ġfreel ance", + "Ġevident ly", + "ph al", + "Ġret ains", + "M ir", + "Ġfin ite", + "d ar", + "ĠC ous", + "Ġrep aired", + "Ġperiod ic", + "Ġchampions hips", + "Ġaster oid", + "bl ind", + "Ġexpress ly", + "ĠAst ros", + "Ġsc aled", + "Ġge ographical", + "ĠRap ids", + "En joy", + "Ġel astic", + "ĠMoh amed", + "Mark et", + "be gin", + "Ġdisco vers", + "Ġtele communications", + "Ġscan ner", + "Ġen large", + "Ġsh arks", + "Ġpsy chedel", + "ĠRou ge", + "Ġsnap shot", + "is ine", + "X P", + "Ġpestic ides", + "ĠL SD", + "ĠDist ribution", + "re ally", + "Ġde gradation", + "Ġdisgu ise", + "Ġbi om", + "ĠEX T", + "Ġequ ations", + "Ġhaz ards", + "ĠComp ared", + ") *", + "Ġvirt ues", + "Ġeld ers", + "Ġenh ancing", + "ĠAc ross", + "er os", + "ang ling", + "Ġcomb ust", + "ucc i", + "Ġconc ussion", + "Ġcontrace ption", + "ĠK ang", + "Ġexpress es", + "Ġa ux", + "ĠP ione", + "Ġexhib its", + "Deb ug", + "OT AL", + "ĠAl ready", + "ĠWheel er", + "Ġexp ands", + "? :", + "Ġreconc iliation", + "Ġpir ates", + "Ġpur se", + "Ġdiscour age", + "Ġspect acle", + "R ank", + "Ġwra ps", + "ĠTh ought", + "Ġimp ending", + "O pp", + "ĠAng lo", + "ĠE UR", + "Ġscrew ed", + "ret ched", + "Ġencour agement", + "mod els", + "Ġconf use", + "mm m", + "ĠVit amin", + "âĸij âĸij", + "C ru", + "Ġkn ights", + "Ġdisc ard", + "Ġb ishops", + "ĠW ear", + "ĠGar rett", + "k an", + "ãĥ Ł", + "Ġmascul ine", + "cap ital", + "ĠA us", + "Ġfat ally", + "th anks", + "ĠA U", + "ĠG ut", + "12 00", + "Ġ 00000000", + "Ġsur rog", + "ĠBI OS", + "ra its", + "ĠWat ts", + "Ġresur rection", + "ĠElect oral", + "ĠT ips", + "4 000", + "Ġnut rient", + "Ġdepict ing", + "Ġspr ink", + "Ġm uff", + "ĠL IM", + "ĠS ample", + "ps c", + "ib i", + "gener ated", + "Ġspec imens", + "Ġdiss atisf", + "Ġtail ored", + "Ġhold ings", + "ĠMonth ly", + "ĠE at", + "po ons", + "Ġne c", + "ĠC age", + "ĠLot us", + "ĠLan tern", + "Ġfront ier", + "Ġp ensions", + "Ġj oked", + "ĠHard y", + "=-=- =-=-", + "r ade", + "U ID", + "Ġr ails", + "Ġem it", + "Ġsl ate", + "Ġsm ug", + "Ġsp it", + "ĠCall s", + "ĠJac obs", + "f eat", + "ĠU E", + "Ġrest ruct", + "Ġregener ation", + "Ġenerg ies", + "ĠCon nor", + "OH N", + "ĠChe ese", + "Ġg er", + "Ġresur rect", + "man agement", + "N W", + "Ġpres ently", + "ĠBru ins", + "M ember", + "ĠM ang", + "id an", + "Ġboost ing", + "w yn", + "+ .", + "requ isite", + "ĠNY PD", + "ĠMe gan", + "ĠCond itions", + "Ġp ics", + "nes ium", + "ĠR ash", + "Ġ17 4", + "ĠD ucks", + "Ġemb ro", + "z u", + "on ian", + "rel igious", + "Ġc raz", + "ĠAC A", + "ĠZ ucker", + "EM A", + "ĠPro s", + "We apon", + "ĠKn ox", + "ĠAr duino", + "Ġst ove", + "Ġheaven s", + "ĠP urchase", + "Ġher d", + "Ġfundra iser", + "Dig ital", + "5 000", + "Ġprop onents", + "/ âĢĭ", + "Ġj elly", + "ĠVis a", + "Ġmon ks", + "Ġadvance ment", + "ĠW er", + "Ġ18 7", + "e us", + "ert ility", + "Ġfet al", + "Ġ19 36", + "L o", + "Ġout fits", + "Ġstair case", + "b omb", + "Ġcustom ized", + "cl air", + "T ree", + "Ġm apped", + "ĠConsider ing", + "ĠTor res", + "Ġmeth yl", + "Ġapprox imate", + "Ġdo om", + "ĠHans en", + "Ġc rossover", + "Ġstand alone", + "ä ¼", + "Ġinv ites", + "Ġgra veyard", + "Ġh p", + "Donald Trump", + "Ġesc ort", + "G ar", + "Ġpredec essors", + "Ġh ay", + "Ġen zyme", + "ĠStra ight", + "vis ors", + "I ng", + "ane ously", + "ĠApp lied", + "Ġf ec", + "ĠDur ant", + "Ġout spoken", + "or b", + "Ġz eal", + "Ġdisgr ace", + "' ).", + "ĠChe ng", + "28 9", + "ĠRen a", + "ĠSu icide", + "29 4", + "Ġout raged", + "ĠNew man", + "ĠN vidia", + "ĠA ber", + "ĠB ers", + "Ġrecre ation", + "Wind ow", + "ĠD P", + "x e", + "Ġped oph", + "Ġfall out", + "ambo o", + "Ġpresent ations", + "ĠApp s", + "Ġh tml", + "3 45", + "ĠX XX", + "Ġrub bing", + "ĠLe ather", + "Ġhum idity", + "se ys", + "est ablished", + "ĠUn its", + "64 6", + "Ġrespect able", + "A uto", + "Ġthri ving", + "ĠInn ovation", + "ang s", + "Ext ra", + "reg ulation", + "29 8", + "p ick", + "Ex amples", + "ĠC J", + "Att ack", + "Ġdr acon", + "L T", + "Ġstick er", + "re rs", + "Ġsun ny", + "I ss", + "reg ulated", + "d im", + "ĠAb stract", + "Ġhus bands", + "Off ice", + "om ination", + "it ars", + "AN GE", + "asc al", + "ĠK ris", + "ĠInf antry", + "Ġm alf", + "ĠA the", + "ĠR ally", + "bal anced", + "................ ........", + "OU P", + "Ġmole cule", + "met ics", + "ĠSpl it", + "ĠInstruct ions", + "ĠN ights", + "c ards", + "Ġt ug", + "Ġcon e", + "å Ń", + "Ġt x", + "ĠDisc ussion", + "Ġcatast rophe", + "pp e", + "g io", + "Ġcommun ism", + "Ġhal ted", + "ĠGu ant", + "cle an", + "ĠSc hed", + "ĠK anye", + "Ġw ander", + "ĠSer iously", + "Ġ18 8", + "enn ial", + "f ollow", + "product ive", + "ĠFl ow", + "ĠS ail", + "Ġc raw", + "Ġsim ulations", + "or u", + "ang les", + "ĠN olan", + "Ġmen stru", + "4 70", + "Ġ20 7", + "aj a", + "Ġcas ually", + "board ing", + "Ġ2 22", + "ov y", + "ĠN umbers", + "um at", + "O E", + "28 7", + "ĠCle mson", + "Ġcert s", + "Ġsl id", + "ĠT ribe", + "Ġto ast", + "Ġfort unes", + "Ġf als", + "ĠComm ittees", + "Ġg p", + "Ġf iery", + "ĠN ets", + "ĠAn ime", + "Pack age", + "ĠComp are", + "l aughter", + "in fect", + "Ġatroc ities", + "Ġjust ices", + "Ġins ults", + "ĠVern on", + "Ġsh aken", + "Ġperson a", + "est amp", + "36 7", + "br ain", + "Ġexperiment ing", + "K en", + "ĠElect ronics", + "Ġ16 1", + "dom ain", + "Ġgraph ical", + "b ishop", + "Ġwho pping", + "ĠEv angel", + "Ġadvertis ers", + "ĠSpe ar", + "Ġb ids", + "Ġdestro ys", + "ut z", + "Ġunders c", + "ĠAD D", + "Ġan ts", + "ĠC um", + "ipp les", + "ĠF ill", + "Ġgl anced", + "Ġind icted", + "ĠE ff", + "Ġmis con", + "ĠDes ktop", + "Ġab ide", + "ãĥ Ģ", + "ĠI o", + "ĠC oul", + "Ġcaps ule", + "ĠCh rys", + "M ON", + "Ġund es", + "ĠI RA", + "Ġc itation", + "Ġdict ate", + "ĠNet works", + "ĠConf lict", + "ĠSt uff", + "x a", + "is ec", + "ĠChem istry", + "Ġquarter ly", + "William s", + "an an", + "O pt", + "ĠAlexand ria", + "out heastern", + "ĠSpring field", + "ĠBlack s", + "Ġge ography", + "24 2", + "Ġut most", + "ĠEx xon", + "ab outs", + "E VA", + "ĠEn able", + "ĠBar r", + "Ġdisag reed", + "ĠCy prus", + "Ġdement ia", + "Ġlab s", + "Ġubiqu itous", + "ĠLO VE", + "Ġconsolid ated", + "s r", + "Ġcream y", + "ĠTim ber", + "Reg ardless", + "ĠCert ificate", + "Ġ\" ...", + "ogen ous", + "Capt ain", + "Ġinsult ing", + "ĠSor os", + "ĠInst r", + "ĠBulgar ia", + "bet ter", + "Ġsuck ing", + "ĠDavid son", + "at z", + "Ġcoll ateral", + "g if", + "Ġplag ued", + "ĠC ancel", + "ĠGard ner", + "R B", + "Ġsix teen", + "Rem ove", + "ur istic", + "c ook", + "R od", + "Ġcompr ising", + "f le", + ") âĢĶ", + "ĠVik ing", + "g rowth", + "agon al", + "Ġsr f", + "af ety", + "m ot", + "N early", + "st own", + "ĠF actor", + "Ġautom obile", + "Ġproced ural", + "m ask", + "amp ires", + "Ġdisapp ears", + "j ab", + "3 15", + "Ġ19 51", + "ne eded", + "Ġd aring", + "le ader", + "Ġp odium", + "Ġun healthy", + "Ġm und", + "Ġpy ramid", + "oc re", + "Ġkiss ed", + "Ġdream ed", + "ĠFant astic", + "ĠG ly", + "å Ĭ", + "Ġgreat ness", + "Ġsp ices", + "Ġmet ropolitan", + "Ġcomp uls", + "i ets", + "101 6", + "ĠSh am", + "ĠP yr", + "fl ies", + "ĠMid night", + "Ġswall owed", + "Ġgen res", + "ĠL ucky", + "ĠRew ards", + "Ġdisp atch", + "ĠI PA", + "ĠApp ly", + "Ġa ven", + "al ities", + "3 12", + "th ings", + "Ġ( ).", + "Ġm ates", + "ĠS z", + "ĠC OP", + "ol ate", + "O FF", + "Ġre charge", + "c aps", + "ĠYork er", + "ic one", + "Ġgal axies", + "ile aks", + "D ave", + "ĠP uzz", + "ĠCelt ic", + "ĠA FC", + "27 6", + "ĠS ons", + "Ġaffirm ative", + "H or", + "Ġtutorial s", + "ĠC ITY", + "ĠR osa", + "ĠExt ension", + "Ser ies", + "Ġf ats", + "Ġr ab", + "l is", + "Ġun ic", + "Ġe ve", + "ĠSp in", + "Ġadul thood", + "ty p", + "Ġsect arian", + "Ġcheck out", + "ĠCy cl", + "S ingle", + "Ġmart yr", + "Ġch illing", + "88 8", + "ou fl", + "Ġ] ;", + "Ġcongest ion", + "m k", + "ĠWhere as", + "Ġ19 38", + "ur rencies", + "er ion", + "Ġbo ast", + "ĠPat ients", + "Ġch ap", + "ĠB D", + "real DonaldTrump", + "Ġexam ines", + "h ov", + "Ġstart ling", + "ĠBab ylon", + "w id", + "om ew", + "br ance", + "ĠOd yssey", + "w ig", + "Ġtor ch", + "ĠV ox", + "ĠMo z", + "ĠT roll", + "ĠAn s", + "Similar ly", + "ĠF ul", + "00 6", + "Un less", + "ĠAl one", + "st ead", + "ĠPub lisher", + "r ights", + "t u", + "ĠDoes n", + "Ġprofession ally", + "Ġcl o", + "ic z", + "Ġste als", + "Ġ á", + "19 86", + "Ġst urdy", + "ĠJoh ann", + "Ġmed als", + "Ġfil ings", + "ĠFr aser", + "d one", + "Ġmult inational", + "Ġf eder", + "Ġworth less", + "Ġp est", + "Yes terday", + "ank ind", + "Ġg ays", + "Ġb orne", + "ĠP OS", + "Pict ure", + "Ġpercent ages", + "25 1", + "r ame", + "Ġpot ions", + "AM D", + "ĠLeban ese", + "Ġr ang", + "ĠL SU", + "ong s", + "Ġpen insula", + "ĠCl ause", + "AL K", + "oh a", + "ĠMac Book", + "Ġunanim ous", + "Ġl enders", + "Ġhang s", + "Ġfranch ises", + "ore rs", + "ĠUp dates", + "Ġisol ate", + "and ro", + "S oon", + "Ġdisrupt ive", + "ĠSur ve", + "Ġst itches", + "ĠSc orp", + "ĠDomin ion", + "Ġsupp lying", + "Ar g", + "Ġtur ret", + "ĠL uk", + "Ġbr ackets", + "* )", + "ĠRevolution ary", + "ĠHon est", + "Ġnot icing", + "ĠSh annon", + "Ġafford ed", + "Ġth a", + "ĠJan et", + "! --", + "ĠNare ndra", + "ĠPl ot", + "H ol", + "se ver", + "e enth", + "Ġobst ruction", + "Ġ10 24", + "st aff", + "j as", + "or get", + "sc enes", + "l aughs", + "ĠF argo", + "cr ime", + "Ġorche str", + "Ġde let", + "ili ary", + "rie ved", + "Ġmilit ar", + "ĠGreen e", + "âĹ ı", + "ãģ ¦", + "ĠGu ards", + "Ġunle ashed", + "ĠWe ber", + "Ġadjust able", + "Ġcal iber", + "Ġmotiv ations", + "Ġà ł", + "m Ah", + "ĠL anka", + "hand le", + "Ġp ent", + "ĠR av", + "ĠAng ular", + "ĠK au", + "umb ing", + "Ġphil anthrop", + "Ġde hyd", + "Ġtox icity", + "e er", + "ĠY ORK", + "w itz", + "å ¼", + "ĠI E", + "commun ity", + "ĠA H", + "Ġret ali", + "Ġmass ively", + "ĠDani els", + "ĠD EL", + "Ġcar cin", + "Ur l", + "Ġrout ing", + "ĠNPC s", + "ĠR AF", + "ry ce", + "Ġwa ived", + "ĠGu atem", + "Every body", + "Ġco venant", + "Ġ17 3", + "Ġrelax ing", + "Ġqu art", + "al most", + "Ġguard ed", + "ĠSold iers", + "ĠPL AY", + "Ġout going", + "L AND", + "Ġre write", + "ĠM OV", + "ĠIm per", + "ĠS olution", + "Ġphenomen al", + "Ġl ongevity", + "Ġimp at", + "ĠN issan", + "ir ie", + "Ġod or", + "ĠZ ar", + "ok s", + "Ġmilit ias", + "ĠSP EC", + "Ġtoler ated", + "ars er", + "ĠBrad ford", + "+ ,", + "Ġsur real", + "s f", + "Can adian", + "Ġresemb lance", + "Ġcarbohyd rate", + "VI EW", + "Ġaccess ory", + "me al", + "larg est", + "ieg el", + "Some one", + "Ġtoug hest", + "os o", + "Ġfun nel", + "Ġcondemn ation", + "lu ent", + "Ġw ired", + "ĠSun set", + "Jes us", + "ĠP ST", + "ĠP ages", + "ĠTy coon", + "ĠP F", + "Ġselect ions", + "Ġ à¤", + "part isan", + "Ġhigh s", + "ĠR une", + "Ġcraft s", + "le ad", + "ĠParent s", + "Ġre claim", + "ek er", + "ĠAll ied", + "ae per", + "Ġlo oming", + "Ġbenefic iaries", + "ĠH ull", + "Stud ents", + "Jew ish", + "d j", + "Ġp act", + "tem plate", + "ĠOffic ials", + "ĠBay lor", + "Ġhe mp", + "Ġyouth s", + "ĠLevel s", + "ĠX iao", + "ĠC hes", + "Ġende avor", + "ĠRem oved", + "Ġhipp ocamp", + "H ell", + "ãĤ Ĭ", + "80 5", + "Ġd inosaur", + "ĠWr ath", + "ĠIndones ian", + "Ġcalcul ator", + "ĠD ictionary", + "Ġ4 20", + "ĠM AG", + "( _", + "! ,", + "t arians", + "Ġrestrict ing", + "rac use", + "Ġweek day", + "OU NT", + "Ġsh rugged", + "leg round", + "Ġb ald", + "ĠDo ctors", + "Ġt outed", + "ĠMax well", + "Ġ2 14", + "Ġdiplom at", + "Ġrep ression", + "Ġconstitu ency", + "v ice", + "r anked", + "ĠNap oleon", + "g ang", + "ĠFore ver", + "t un", + "Ġbul b", + "ĠPD T", + "ĠC isco", + "V EN", + "Ġres umed", + "Ste ven", + "ĠManit oba", + "Ġfab ulous", + "ĠAg ents", + "19 84", + "Ġam using", + "ĠMyster ies", + "Ġor thodox", + "fl oor", + "Ġquestion naire", + "Ġpenet rate", + "Ġfilm makers", + "ĠUn c", + "Ġst amped", + "Ġth irteen", + "Ġout field", + "Ġforward ed", + "Ġapp ra", + "Ġa ided", + "t ry", + "Ġunf ocused", + "ĠL iz", + "ĠWend y", + "ĠSc ene", + "Ch arg", + "Ġreject s", + "Ġleft ist", + "ĠProv idence", + "ĠBr id", + "reg n", + "Ġprophe cy", + "ĠL IVE", + "4 99", + "Ġfor ge", + "ĠF ML", + "Ġintrins ic", + "ĠF rog", + "Ġw ont", + "ĠH olt", + "Ġfam ed", + "CL US", + "aeper nick", + "ĠH ate", + "ĠC ay", + "Ġregister ing", + "ort ality", + "rop y", + "ocaly ptic", + "a an", + "n av", + "Ġfasc ist", + "IF IED", + "Ġimpl icated", + "ĠRes ort", + "ĠChand ler", + "ĠBr ick", + "P in", + "ys c", + "Us age", + "ĠHel m", + "us ra", + "âĺħ âĺħ", + "ĠAb bas", + "Ġunanim ously", + "Ġke eper", + "Ġadd icted", + "?? ?", + "Ġhelm ets", + "Ġant ioxid", + "aps ed", + "80 8", + "gi ene", + "Ġwa its", + "Ġmin ion", + "ra ved", + "ĠP orsche", + "Ġdream ing", + "Ġ17 1", + "ĠC ain", + "Ġun for", + "ass o", + "ĠConfig uration", + "k un", + "hard t", + "Ġn ested", + "ĠL DS", + "L ES", + "Ġt ying", + "en os", + "Ġc ue", + "ĠMar qu", + "sk irts", + "Ġclick ed", + "Ġexp iration", + "ĠAccording ly", + "ĠW C", + "Ġbless ings", + "Ġaddict ive", + "ĠN arr", + "y x", + "ĠJagu ars", + "Ġrent s", + "ĠS iber", + "Ġt ipped", + "ous se", + "ĠFitz gerald", + "Ġhier arch", + "out ine", + "Ġwa velength", + "> .", + "ch id", + "ĠProcess ing", + "/ +", + "r anking", + "E asy", + "ĠConst ruct", + "Ġt et", + "ins ured", + "H UD", + "Ġqu oting", + "Ġcommun icated", + "in x", + "Ġin mate", + "Ġerect ed", + "ĠAbs olutely", + "ĠSure ly", + "Ġun im", + "ĠThr one", + "he id", + "Ġcl aws", + "Ġsuper star", + "ĠL enn", + "ĠWh is", + "U k", + "ab ol", + "Ġsk et", + "ĠN iet", + "Ġper ks", + "Ġaff inity", + "Ġopen ings", + "phas is", + "Ġdiscrim inate", + "T ip", + "v c", + "Ġgr inding", + "ĠJenn y", + "Ġast hma", + "hol es", + "ĠHom er", + "Ġreg isters", + "ĠGl ad", + "Ġcre ations", + "Ġlith ium", + "Ġappl ause", + "unt il", + "Just ice", + "ĠTur ks", + "Ġsc andals", + "Ġb ake", + "t ank", + "M ech", + "ĠMe ans", + "ĠM aid", + "Republic ans", + "is al", + "wind ows", + "ĠSant os", + "Ġveget ation", + "33 8", + "t ri", + "Ġfl ux", + "ins ert", + "Ġclar ified", + "Ġmort g", + "ĠCh im", + "ĠT ort", + "Ġdiscl aim", + "met al", + "ĠAs ide", + "Ġindu ction", + "Ġinf l", + "Ġathe ists", + "amp h", + "Ġe ther", + "ĠV ital", + "ĠBu ilt", + "M ind", + "Ġweapon ry", + "S ET", + "Ġ18 6", + "ad min", + "g am", + "cont ract", + "af a", + "Ġderiv atives", + "Ġsn acks", + "Ġch urn", + "E conom", + "Ġca pped", + "ĠUnder standing", + "ĠH ers", + "ĠI z", + "Ġd uct", + "I ENT", + "augh ty", + "Ġâľ Ķ", + "ĠN P", + "Ġsa iling", + "In itialized", + "Ġt ed", + "Ġreact ors", + "ĠL omb", + "Ġcho ke", + "ĠW orm", + "Ġadm iration", + "Ġsw ung", + "ens ibly", + "Ġr ash", + "ĠGo als", + "ĠImport ant", + "Sh ot", + "ĠR as", + "Ġtrain ers", + "ĠB un", + "Work ing", + "Ġhar med", + "ĠPand ora", + "ĠL TE", + "Ġmush room", + "ĠCH AR", + "ĠF ee", + "ĠM oy", + "B orn", + "ol iberal", + "ĠMart ial", + "Ġgentle men", + "Ġling ering", + "Offic ial", + "Ġgra ffiti", + "ĠN ames", + "D er", + "Ġqu int", + "ist rate", + "aze era", + "ĠNOT ICE", + "ĠFlore nce", + "Ġpay able", + "Ġdep icts", + "ĠSpe cies", + "He art", + "âĶĢâĶĢâĶĢâĶĢ âĶĢâĶĢâĶĢâĶĢ", + "Ġencl osed", + "Incre ases", + "D aily", + "ĠL is", + "Ġenact ment", + "ĠB acon", + "ĠSt eele", + "dem and", + "Ġ18 3", + "Ġmouth s", + "Ġstr anded", + "Ġenhance ment", + "01 1", + "ĠWh ats", + "Ġhe aled", + "en y", + "ĠR ab", + "Ġ3 40", + "ĠLab yrinth", + "ro ach", + "ĠY osh", + "ĠCl ippers", + "Ġconcert s", + "Intern et", + "35 5", + "Ġstick ers", + "Ġter med", + "ĠAx e", + "Ġgrand parents", + "Fr ance", + "ĠCl im", + "ĠU h", + "ul ic", + "Ġthr ill", + "cent ric", + "ĠOver view", + "ĠCond uct", + "Ġsubstant ive", + "Ġ18 2", + "m ur", + "Ġstr ay", + "ĠCo ff", + "Ġrep etitive", + "ĠFor gotten", + "Ġqual ification", + "ew itness", + "ĠZ imbabwe", + "Ġsim ulated", + "ĠJ D", + "25 3", + "ĠW are", + "Ġun sc", + "T imes", + "Ġsum mons", + "Ġdis connected", + "Ġ18 4", + "ci us", + "ĠGu jar", + "od ka", + "Ġer ase", + "ĠTob acco", + "elect ed", + "Ġun cont", + "ĠShe pard", + "ĠL amp", + "Ġalert ed", + "Ġoper ative", + "arn a", + "u int", + "Ġneglig ence", + "ac ements", + "Ġsup ra", + "Ġprev ail", + "ĠSh ark", + "Ġbel ts", + "ãģ «", + "Ġt ighter", + "Engine ers", + "Ġin active", + "Ġexp onent", + "ĠWill ie", + "a ples", + "Ġhe ir", + "ĠH its", + "ian n", + "ĠS ays", + "Ġcurrent s", + "ĠBeng al", + "Ġar ist", + "B uffer", + "Ġbree ze", + "ĠWes ley", + "Col a", + "Ġpron oun", + "Ġde ed", + "ĠK ling", + "Ġof t", + "Ġinf lict", + "Ġpun ishing", + "Ġn m", + "ik u", + "OD UCT", + "01 4", + "Ġsubsid y", + "ĠDE A", + "ĠHer bert", + "ĠJ al", + "B ank", + "Ġdef erred", + "Ġship ment", + "B ott", + "Ġal le", + "b earing", + "HT ML", + "Off line", + "Ġ2 13", + "Ġscroll ing", + "Ġsc anned", + "ĠLib yan", + "ĠT OP", + "ch rom", + "d t", + "col umn", + "Psy NetMessage", + "Z ero", + "Ġtor so", + "0 50", + "âķ IJ", + "Ġimp erson", + "ĠSchw artz", + "ud ic", + "Ġpiss ed", + "ĠS app", + "25 7", + "ĠIS Ps", + "og l", + "Ġsuper vised", + "Ġad olescent", + "Ġatt ained", + "ĠDel ivery", + "ĠB unny", + "Ġ19 37", + "Ġmini ature", + "Ġo s", + "Ġ3 70", + "60 8", + "ĠMour inho", + "Ġinn ate", + "Ġtem po", + "ĠN M", + "ĠFall en", + "00 9", + "Ġprov ocative", + "Stream er", + "ĠBened ict", + "ĠBol she", + "Ġt urtle", + "ĠPC B", + "ĠEqu al", + "Direct or", + "ĠR end", + "Ġflu ids", + "Author ities", + "Ġcous ins", + "requ ency", + "ĠNeigh bor", + "s ets", + "sh ared", + "Char les", + "pass word", + "Ġg ears", + "Ġ2 11", + "ĠHard ware", + "ri ka", + "Ġup stream", + "H om", + "Ġdisproportion ately", + "iv ities", + "Ġund efined", + "Ġelect rons", + "Ġcommem or", + "Event ually", + "Ġ> <", + "Ġir responsible", + "2 18", + "ĠRe leased", + "ĠO VER", + "ĠI GN", + "ĠB read", + "st ellar", + "ĠS age", + "tt ed", + "dam age", + "ed ition", + "ĠPre c", + "Ġl ime", + "Ġconf inement", + "Ġcal orie", + "we apon", + "Ġdiff ering", + "ĠS ina", + "m ys", + "am d", + "Ġintric ate", + "k k", + "ĠP AT", + "ã o", + "st ones", + "lin ks", + "Ġr anch", + "Sem itic", + "Ġdifferent iate", + "ĠS inger", + "occup ied", + "Ġfort ress", + "c md", + "Ġinter ception", + "ĠAnk ara", + "Ġre pt", + "ĠSol itaire", + "Ġrem ake", + "p red", + "Ġd ared", + "aut ions", + "ĠB ACK", + "Run ning", + "Ġdebug ging", + "Ġgraph s", + "3 99", + "ĠNig el", + "Ġb un", + "Ġpill ow", + "Ġprog ressed", + "fashion ed", + "Ġob edience", + "ER N", + "Ġrehe ars", + "C ell", + "t l", + "S her", + "Ġher ald", + "ĠPay ment", + "ĠC ory", + "ĠDe pt", + "Ġrep ent", + "ĠWe ak", + "uck land", + "Ġple asing", + "Ġshort ages", + "Ġjur ors", + "ĠK ab", + "q qa", + "Ant i", + "Ġw ow", + "ĠRC MP", + "Ġt sun", + "ĠS ic", + "Ġcomp rises", + "Ġsp ies", + "Ġprec inct", + "n u", + "Ġur ges", + "Ġtim ed", + "Ġstrip es", + "ĠB oots", + "Ġy en", + "Adv anced", + "Ġdisc rete", + "ĠArch angel", + "employ ment", + "D iff", + "Ġmon uments", + "Ġ20 9", + "work er", + "Ġ19 6", + "ĠI g", + "utter stock", + "T PS", + "J ac", + "Ġhomeless ness", + "Ġcomment ator", + "Ġrac ially", + "f ing", + "se ed", + "E le", + "ell ation", + "Ġeth anol", + "Ġpar ish", + "ĠD ong", + "ĠAw akening", + "Ġdev iation", + "ĠB earing", + "ĠTsu k", + "Ġrec ess", + "Ġl ymph", + "ĠCann abis", + "å ľ", + "ĠNEW S", + "Ġd ra", + "ĠStef an", + "ĠWr ong", + "ĠS AM", + "Ġloose ly", + "Ġinterpre ter", + "ĠPl ain", + "Go vernment", + "Ġbigot ry", + "Ġgren ades", + "ave z", + "pict ured", + "Ġmand ated", + "ĠMon k", + "ĠPed ro", + "Ġl ava", + "27 4", + "Ġcyn ical", + "ĠScroll s", + "l ocks", + "M p", + "Ġcon gregation", + "orn ings", + "ph il", + "ĠI bid", + "Ġf erv", + "Ġdisapp earing", + "Ġarrog ant", + "sy n", + "ĠMa ver", + "ĠSu it", + "24 1", + "Ġab bre", + "ack ers", + "P a", + "ĠY el", + "Whe never", + "Ġ23 5", + "ĠV ine", + "ĠAn at", + "Ġext inct", + "LE T", + "Ġexecut able", + "V ERS", + "ox ide", + "D NA", + "ĠP rel", + "Ġresent ment", + "Ġcompr ise", + "ĠAv iv", + "Ġinter ceptions", + "Ġprol ific", + "IN A", + "ĠEr in", + "though t", + "2 19", + "ĠPsychiat ry", + "un ky", + "chem ist", + "H o", + "ĠMcC oy", + "Ġbr icks", + "L os", + "ri ly", + "ĠUS SR", + "Ġr ud", + "Ġl aud", + "ĠW ise", + "ĠEmer ald", + "Ġrev ived", + "Ġdam ned", + "ĠRep air", + "id em", + "ct ica", + "Ġpatri arch", + "ĠN urs", + "me g", + "Ġcheap est", + "re ements", + "empt y", + "ĠCele br", + "Ġdepri vation", + "ch anted", + "ĠTh umbnails", + "E nergy", + "ĠEth an", + "ĠQ ing", + "Ġopp oses", + "W IND", + "v ik", + "ĠM au", + "ĠS UB", + "66 7", + "G RE", + "ĠVol unte", + "nt on", + "C ook", + "å IJ", + "es que", + "Ġplum met", + "Ġsu ing", + "Ġpron ounce", + "Ġresist ing", + "ĠF ishing", + "ĠTri als", + "Ġy ell", + "Ġ3 10", + "Ġin duct", + "Ġpersonal ized", + "oft en", + "R eb", + "EM BER", + "Ġview point", + "Ġexist ential", + "() )", + "rem ove", + "MENT S", + "l asses", + "Ġev apor", + "Ġa isle", + "met a", + "Ġreflect ive", + "Ġentit lement", + "Ġdev ised", + "mus ic", + "asc ade", + "Ġwind ing", + "off set", + "Ġaccess ibility", + "ke red", + "Bet ter", + "ĠJohn ston", + "th inking", + "S now", + "ĠCroat ia", + "ĠAt omic", + "27 1", + "34 8", + "Ġtext book", + "ĠSix th", + "Ġ اÙĦ", + "Ġsl ider", + "ĠBur ger", + "b ol", + "S ync", + "Ġgrand children", + "Ġc erv", + "+ )", + "Ġe ternity", + "Ġtweet ing", + "Ġspec ulative", + "Ġpiv otal", + "ĠW P", + "ĠT ER", + "ynam ic", + "Ġu pl", + "ĠC ats", + "per haps", + "Ġclass mates", + "Ġblat ant", + "' -", + "Ġl akh", + "ant ine", + "ĠB org", + "i om", + "/ (", + "ĠAthlet ic", + "Ġs ar", + "OT A", + "ĠHoff man", + "Never theless", + "Ġad orable", + "Ġspawn ed", + "Ass ociated", + "ĠDom estic", + "Ġimpl ant", + "ĠLux em", + "ĠK ens", + "Ġp umps", + "ĠS AT", + "Att ributes", + "50 9", + "av our", + "Ġcentral ized", + "ĠT N", + "Ġfresh ly", + "ĠA chieve", + "Ġouts iders", + "her ty", + "ĠRe e", + "ĠT owers", + "ĠD art", + "ak able", + "Ġm p", + "ĠHeaven ly", + "Ġr ipe", + "ĠCarol ine", + "ry an", + "Ġclass ics", + "Ġret iring", + "Ġ2 28", + "Ġa h", + "Ġdeal ings", + "Ġpunch ing", + "ĠChap man", + "O ptions", + "max well", + "vol ume", + "Ġst al", + "Ġex ported", + "ĠQu ite", + "Ġnumer ical", + "B urn", + "F act", + "ĠKey stone", + "Ġtrend ing", + "Ġalter ing", + "ĠAfric ans", + "47 8", + "ĠM N", + "ĠKn ock", + "Ġtempt ation", + "Ġprest ige", + "Over view", + "ĠTrad itional", + "ĠBah rain", + "Priv ate", + "ĠH OU", + "Ġbar r", + "ĠT at", + "C ube", + "US D", + "ĠGrand e", + "ĠG at", + "ĠFl o", + "Ġres ides", + "Ġind ec", + "vol ent", + "Ġperpet ual", + "ub es", + "Ġworld view", + "ĠQuant um", + "Ġfil tered", + "Ġen su", + "orget own", + "ERS ON", + "ĠM ild", + "37 9", + "OT T", + "à ¥", + "Ġvit amins", + "Ġrib bon", + "Ġsincere ly", + "ĠH in", + "Ġeight een", + "Ġcontradict ory", + "Ġgl aring", + "Ġexpect ancy", + "Ġcons pir", + "Ġmon strous", + "Ġ3 80", + "re ci", + "Ġhand ic", + "Ġpump ed", + "Ġindic ative", + "Ġr app", + "Ġav ail", + "ĠLEG O", + "ĠMar ijuana", + "19 85", + "ert on", + "Ġtwent ieth", + "################ ################", + "ĠSw amp", + "Ġval uation", + "Ġaffili ates", + "adjust ed", + "ĠFac ility", + "26 2", + "Ġenz ymes", + "itud inal", + "Ġimp rint", + "S ite", + "Ġinstall er", + "ĠT RA", + "m ology", + "lin ear", + "ĠCollect ive", + "ig ating", + "ĠT oken", + "Ġspec ulated", + "K N", + "ĠC ly", + "or ity", + "Ġdef er", + "Ġinspect ors", + "appro ved", + "R M", + "ĠSun s", + "Ġinform ing", + "ĠSy racuse", + "ib li", + "7 65", + "Ġgl ove", + "Ġauthor ize", + "âĢ¦âĢ¦âĢ¦âĢ¦ âĢ¦âĢ¦âĢ¦âĢ¦", + "ĠCru ise", + "Ġcontract ing", + "she ll", + "IF E", + "ĠJew el", + "p ract", + "ĠPhot oshop", + "ĠKnow ing", + "h arm", + "Ġattract ions", + "ad an", + "et us", + "01 8", + "w agen", + "Al t", + "Ġmultip ly", + "Ġequ ilibrium", + ": {", + "ĠF ighters", + "ĠEd gar", + "Ġfour teen", + "Go vern", + "Ġmis use", + "Ġab using", + "Ġancest ry", + "ram er", + "64 4", + "Ġwor ms", + "Ġthick er", + "ĠComb ine", + "Ġpeas ants", + "Ġv ind", + "Ġcon quest", + "Ġm ocked", + "Ġc innamon", + "ĠC ald", + "ĠGall up", + "Ġavoid ance", + "Ġincarn ation", + "ĠStr at", + "Ġt asted", + "ent a", + "ĠN eal", + "p ared", + "Ġtermin ology", + "ject ion", + "Scient ists", + "ĠIN S", + "ĠDe e", + "Ġdirect ories", + "R oad", + "ĠSh ap", + "br ight", + "ĠDirect ors", + "ĠCol umn", + "Ġb ob", + "Ġprefer ably", + "Ġgl itch", + "f urt", + "Ġe g", + "id is", + "C BC", + "Ġsur rendered", + "Ġtest ament", + "33 6", + "ug gest", + "ĠN il", + "an other", + "Ġpat hetic", + "ĠDon na", + "Ġ2 18", + "ĠA very", + "Ġwhis key", + "Ġf ixture", + "ĠCon quest", + "Ġbet s", + "O cc", + "ĠLe icester", + "] .\"", + "Ġ) );", + "Ġfl ashes", + "45 6", + "Ġmask ed", + "ge bra", + "Ġcomput ed", + "che l", + "aud er", + "Ġdefe ats", + "ĠLiber ation", + "ĠOs ama", + "ĠV ive", + "Ch anges", + "Ch annel", + "Ġtar iffs", + "Ġm age", + "ĠS ax", + "Ġinadvert ently", + "ĠC RE", + "ĠRe aper", + "ink y", + "gr ading", + "Ġstere otyp", + "Ġcur l", + "ĠF ANT", + "Ġfram eworks", + "M om", + "ĠAn ch", + "Ġflav our", + "car bon", + "Ġperm itting", + "let cher", + "ĠMo zilla", + "ĠPark ing", + "ĠCh amp", + "Sc roll", + "Ġmurd erer", + "Ġrest ed", + "Ġow es", + "ĠP oss", + "AD D", + "IF F", + "res olution", + "ĠMin ing", + "Ġcompar ative", + "D im", + "Ġneighbour ing", + "ĠA ST", + "ĠT oxic", + "Ġbi ases", + "Ġgun fire", + "ur ous", + "ĠMom ent", + "19 83", + "Ġper vasive", + "tt p", + "ĠNorm ally", + "r ir", + "S arah", + "ĠAlb any", + "Ġun sett", + "ĠS MS", + "ip ers", + "l ayer", + "ĠWh ites", + "up le", + "Ġtur bo", + "ĠLe eds", + "Ġthat s", + "ĠMin er", + "M ER", + "ĠRe ign", + "Ġper me", + "ĠBl itz", + "Ġ19 34", + "Ġintimid ating", + "t ube", + "Ġecc entric", + "ab olic", + "box es", + "ĠAssoci ates", + "v otes", + "Ġsim ulate", + "um bo", + "aster y", + "Ġship ments", + "FF FF", + "an th", + "Ġseason ed", + "Ġexperiment ation", + "âĸ ł", + "law s", + "Me et", + "idd les", + "ant ics", + "R ating", + "IS IS", + "h ift", + "Ġfront s", + "b uf", + "01 7", + "Ġun att", + "ĠD il", + "le ases", + "ĠGard ens", + "77 7", + "t ouch", + "ve ll", + "45 8", + "Ġ= ====", + "s aving", + "Ġer osion", + "ĠQu in", + "Ġearn s", + "Ġaccomplish ment", + "ĠWe i", + "Ġ< [", + "____ _", + "Ġir rig", + "ĠT eddy", + "Ġconqu ered", + "ĠArm ored", + "Ġassert s", + "Ġmanip ulating", + "r é", + "Ġtranscript s", + "G allery", + "Ġplot ting", + "Ne il", + "Ġbetray al", + "load er", + "ĠS ul", + "Ġdispl acement", + "Ġroy alty", + "ĠW I", + "he it", + "ĠDev ices", + "alle l", + "Ġmunicipal ities", + "Ġcan al", + "St ars", + "ĠU AE", + "Ġ\" âĢ¦", + "ĠC U", + "ab ove", + "Ġreson ance", + "ĠguiActive Un", + "add ed", + "ĠBra ves", + "ĠI bn", + "Ġhere by", + "ĠB RE", + "Ġshare holder", + "ĠH ir", + "ĠJ i", + "Ġstrange ly", + "Ġadm ired", + "Ġpl ight", + "Ġb achelor", + "ĠP ole", + "cipl inary", + "T ony", + "ĠArmen ian", + "Ġun man", + "ĠZion ist", + "St age", + "isco ver", + "Ġautom otive", + "Ġs idelines", + "Ġsl ick", + "ĠRena issance", + "ĠF UN", + "Im ages", + "ĠH aj", + "Ġp ing", + "Ġshort cut", + "ĠBl vd", + "ĠLook s", + "Ġbur sts", + "Ġcl amp", + "Ġm ish", + "Ġsort ing", + "Ġpatri ot", + "Ġcorrect ness", + "ĠScand inav", + "ĠCaval iers", + "p ython", + "az ar", + "Ġ3 75", + "ĠJa une", + "40 9", + "Ġdetrim ental", + "Ġstab bing", + "Ġpoison ed", + "Ġf ountain", + "oc ent", + "or st", + "ĠMar i", + "Ġr ains", + "ĠO vers", + "ĠInst itution", + "ud get", + "AM Y", + "t ale", + "ĠK R", + "ĠPr ices", + "Ġhead aches", + "Ġlands l", + "ĠA ura", + "Bon us", + "ĠZ hao", + "ĠH ip", + "Ġhop s", + "ĠKurd istan", + "Ġexplo iting", + "ry n", + "Ġhypocr isy", + "op ening", + "Ġgun shot", + "Ġw ed", + "inter stitial", + "Inter stitial", + "Ġam en", + "Bre aking", + "Ġmarket ed", + "W ire", + "ĠC rowd", + "Contin ue", + "ĠK nown", + "ĠEffect ive", + "ore an", + "iz ons", + "Jose ph", + "Ġescal ation", + "us ername", + "Ġcur tain", + "AT ES", + "ĠP AR", + "ĠM iy", + "Ġcounter fe", + "l ene", + "Ġcont enders", + "d aily", + "ĠAs c", + "ĠPhill ip", + "most ly", + "Ġfil ename", + "he ne", + "Ġresemb ling", + "Ġst aging", + "ĠCh loe", + "Ġw iring", + "H on", + "ĠRen ew", + "ott age", + "ĠHy brid", + "m uch", + "Ġstro kes", + "Ġpolicy makers", + "AP TER", + "ĠArk ham", + "pl ot", + "Ġassist ants", + "Ġde port", + "ĠSe ga", + "Ġinflu enza", + "ĠC ursed", + "ĠK obe", + "Ġskin ny", + "Prov ider", + "ĠR ip", + "Ġincrement al", + "product s", + "B F", + "Ġd ome", + "ĠC redits", + "Ġlos ers", + "int s", + "ĠBet ty", + "ĠTal ent", + "ĠD AM", + "L v", + "E ss", + "Ġd ens", + "tem p", + "J udge", + "od ic", + "Ġ' (", + "UR ES", + "ets k", + "V O", + "Ġretrie ved", + "Ġarchitect s", + "Ù ĩ", + "Ġeth ic", + "ĠSecond ary", + "st ocks", + "ad ia", + "Ġ3 25", + "ĠOp inion", + "Ġsimultane ous", + "Ġd izz", + "ul p", + "Ġsmugg ling", + "ipp ery", + "R andom", + "f acing", + "ĠD as", + "Ġstock p", + "Ġdiscl osures", + "po inter", + "Ġcor al", + "ĠSe lection", + "ĠP ike", + "ival ent", + "Ġruth less", + "ĠR im", + "Ġensu ing", + "ĠExper iment", + "Ġcongress man", + "Ġbelie ver", + "Ġun specified", + "ĠM ord", + "Ġknowledge able", + "ĠV ERY", + "T X", + "Ġstra ps", + "Ġtur f", + "apesh ifter", + "Ġmar ital", + "Ġfl ock", + "ãģ Ĩ", + "26 3", + "AM ES", + "ĠOpp osition", + "Ġtre asures", + "ĠG OD", + "Ġmodel ed", + "ĠWOR LD", + "Ġ( [", + "ĠUs age", + "H F", + "Ġ$ (", + "uss ed", + "Ġpione er", + "E ight", + "par se", + "b read", + "rit z", + "ĠMir anda", + "ĠK ant", + "++ )", + "ore n", + "Ġprov oked", + "Ġbre eds", + "ĠIn cludes", + "ĠPast ebin", + "ĠFl ip", + "J ava", + "Ġbr ink", + "Ġrum ored", + "Ġun seen", + "Ġgar nered", + "ĠDef in", + "al ted", + "Ġtatt oos", + "Ġhes itation", + "is itions", + "ĠWe aver", + "ĠReport ing", + "Ġtherap ies", + "Ġconsult ants", + "Ġresid ual", + "ĠMal i", + "ĠRom a", + "i ago", + "ĠRes idents", + "ub i", + "Ġremed ies", + "Ġadapt ive", + "ĠAl ive", + "ĠBar cl", + "Ġwal lets", + "c rypt", + "etermin ation", + "ĠPel osi", + "Ġsl ipping", + "oton in", + "Ġall iances", + "pat rick", + "ir is", + "Ġor th", + "ĠPer kins", + "ĠDe V", + "ĠG ets", + "Ġdry ing", + "ge e", + "fore st", + "ĠFor get", + "ore m", + "33 9", + "Ġvague ly", + "ĠD ion", + "ĠP orn", + "ĠH OW", + "Ġp neum", + "Ġrub ble", + "ĠT aste", + "enc ia", + "ĠG el", + "Ġd st", + "Ġ24 5", + "ĠMoroc co", + "inf lamm", + "ĠTw ins", + "Ġb ots", + "d aughter", + "ĠB alk", + "Ġbre thren", + "Ġlog os", + "Ġgo bl", + "f ps", + "Ġsub division", + "Ġp awn", + "Ġsquee zed", + "Ġmor ale", + "ĠD W", + "' \"", + "Ġkn ot", + "ook y", + "Ġdiv isive", + "Ġboost ed", + "ch y", + "ãĥ IJ", + "if act", + "Ġnewcom ers", + "ĠWrest ling", + "Ġsc outs", + "w olves", + "R at", + "Ġnin eteenth", + "ĠOs borne", + "St ats", + "Ġem powered", + "Ġpsych opath", + "ĠO EM", + "ugg age", + "ĠP K", + "ĠMoh ammad", + "P ak", + "Ġanarch ists", + "ĠExt ract", + "est hes", + "ĠStock holm", + "l oo", + "ĠG raph", + "Ġdeploy ing", + "ĠStr anger", + "ĠM old", + "Ġstaff er", + "Ġdiscount ed", + "uck le", + "ple ase", + "ĠLand ing", + "ÃŃ a", + "Ġ19 3", + "Ġan te", + "Ġrep etition", + "Ġ+ /-", + "Ġpar ody", + "Ġlive ly", + "AA A", + "ĠHor us", + "Ġp its", + "ind ers", + "L OC", + "ĠVen ice", + "40 6", + "ĠDis cover", + "â Ĩ", + "ellect ual", + "Ġp ens", + "Ġey el", + "ig uous", + "Im pl", + "Ġj oking", + "Ġinv al", + "ĠBel fast", + "Ġcredit ors", + "ĠSky walker", + "ov sky", + "Ġcease fire", + "Ġse als", + "is oft", + ") ).", + "ĠFel ix", + "IT S", + "Ġt resp", + "ĠBlock chain", + "ew are", + "ĠSch war", + "en ne", + "mount ed", + "ĠBe acon", + "les h", + "Ġimmense ly", + "Ġche ering", + "Em ploy", + "sc ene", + "ish ly", + "atche wan", + "ĠNic olas", + "Ġdr ained", + "ĠEx it", + "ĠAz erb", + "j un", + "Ġflo ated", + "u ania", + "De ep", + "Ġsuper v", + "Ġmyst ical", + "ĠD ollar", + "ĠApost le", + "ĠR EL", + "ĠProv ided", + "ĠB ucks", + "ãĥ ´", + "cut ting", + "Ġenhance ments", + "ĠPengu ins", + "ĠIsa iah", + "Ġj erk", + "ĠW yn", + "Ġst alled", + "Ġcryptoc urrencies", + "ĠR oland", + "sing le", + "Ġl umin", + "ĠF ellow", + "ĠCap acity", + "ĠKaz akh", + "W N", + "Ġfin anced", + "38 9", + "Ġt id", + "Ġcoll usion", + "ĠMy r", + "î Ģ", + "Sen ator", + "Ġped iatric", + "Ġneat ly", + "Ġsandwic hes", + "ĠArchitect ure", + "Ġt ucked", + "Ġbalcon y", + "Ġearthqu akes", + "qu ire", + "F uture", + "Ġhe fty", + "é Ĺ", + "Ġspecial izes", + "Ġstress es", + "Ġs ender", + "Ġmisunder standing", + "Ġep ile", + "Ġprov oke", + "ĠCol ors", + "Ġdis may", + "uk o", + "[ _", + "58 6", + "ne utral", + "Ġdon ating", + "ĠRand all", + "Mult i", + "Ġconvenient ly", + "ĠS ung", + "ĠC oca", + "Ġt ents", + "ĠAc celer", + "Ġpart nered", + "27 2", + "ir ming", + "ĠB AS", + "s ometimes", + "Ġobject ed", + "ub ric", + "p osed", + "LC S", + "gr ass", + "Ġattribut able", + "V IS", + "Israel i", + "Ġrepe ats", + "ĠR M", + "v ag", + "ut a", + "in ous", + "Ġin ert", + "ĠMig uel", + "æ Ń", + "ĠHawai ian", + "B oard", + "Ġart ific", + "ĠAzerb ai", + "as io", + "ĠR ent", + "A IN", + "Ġappl iances", + "Ġnational ity", + "Ġass hole", + "ĠN eb", + "Ġnot ch", + "h ani", + "ĠBr ide", + "Av ailability", + "Ġintercept ed", + "Ġcontin ental", + "Ġsw elling", + "ĠPers pect", + "b ies", + ". <", + "ith metic", + "ĠL ara", + "Ġtempt ing", + "add r", + "Ġoversee ing", + "cl ad", + "ĠD V", + "ĠGing rich", + "Ġm un", + "ĠApp ropri", + "Ġalter ations", + "ĠPat reon", + "Ġha voc", + "Ġdiscipl ines", + "Ġnotor iously", + "aku ya", + "ier i", + "? ).", + "ĠW ent", + "Ġsil icon", + "Ġtre mb", + "Cont ainer", + "K nown", + "Ġmort ar", + "est e", + "ick a", + "Ar thur", + "ĠPre viously", + "ĠMart y", + "Ġsp arse", + "g ins", + "Ġin ward", + "ĠParticip ant", + "C opy", + "ĠM isc", + "Ġantib iotic", + "ĠRet ro", + "Ġel usive", + "Ġass ail", + "ĠBatt alion", + "ĠB ought", + "Ġdimin ish", + "ĠEuro pa", + "s ession", + "ĠDanger ous", + "ies el", + "Ġdisbel ief", + "Ġbl asts", + "ext reme", + "ĠBoy d", + "ĠProject s", + "ĠGu ys", + "Ġunder gone", + "Ġgr ill", + "ĠDw ight", + "Ġ19 7", + "US ER", + "Ġfiles ystem", + "Ġcl ocks", + "T aylor", + "Ġwra pper", + "Ġfold ing", + "ous and", + "ĠPhilipp ine", + "ATION AL", + "ĠPer th", + "Ġas hes", + "Ġaccum ulate", + "ĠGate way", + "Sh op", + "orks hire", + "H an", + "ĠBar rel", + "ĠLe h", + "ĠX V", + "Ġwh im", + "Ġrep o", + "ĠC G", + "ĠM am", + "Ġincorpor ating", + "Ġbail out", + "Ġlingu istic", + "Ġdis integ", + "C LE", + "Ġcinem atic", + "ĠF iber", + "S yn", + "il ion", + "ĠCom pos", + "c hens", + "Ġne oc", + "Ġbo iled", + "F INE", + "on o", + "un cle", + "ik en", + "ĠB M", + "Î ¹", + "Ġreceipt s", + "Ġdisp osed", + "ĠTh irty", + "ĠR ough", + "ĠA BS", + "Ġnot withstanding", + "oll en", + "# $", + "Ġunrel iable", + "Ġbl oom", + "Ġmedi ocre", + "Ġtr am", + "ĠTas man", + "Ġsh akes", + "Ġmanifest o", + "ĠM W", + "Ġsatisf actory", + "Ġsh ores", + "Ġcomput ation", + "Ġassert ions", + "orm ons", + "ar ag", + "ab it", + "Dem ocrats", + "ĠL oot", + "ĠVol ks", + "ha ired", + "Ġgrav itational", + "S ing", + "ĠM iz", + "Ġthro ttle", + "Ġtyr anny", + "ĠView s", + "Ġrob ber", + "ĠMinor ity", + "Ġsh rine", + "sc ope", + "pur pose", + "Ġnucle us", + "our cing", + "ĠUS DA", + "ĠD HS", + "w ra", + "ĠBow ie", + "Sc ale", + "ĠB EL", + "x i", + "I ter", + "Ġ( ),", + "w right", + "Ġsail ors", + "ous ed", + "NAS A", + "ĠPro of", + "ĠMin eral", + "t oken", + "ĠF D", + "R ew", + "Ġe ll", + "6 30", + "Ġchance llor", + "ĠG os", + "Ġamount ed", + "ĠRec re", + "ome z", + "ĠOpt im", + "ĠOl ive", + "Ġtrack er", + "ow ler", + "ĠUn ique", + "R oot", + "Ġmar itime", + "ĠQur an", + "ĠAd apt", + "Ġecosystem s", + "ĠRe peat", + "ĠS oy", + "ĠI MP", + "Ġgrad uating", + "and em", + "P ur", + "ĠRes et", + "ĠTr ick", + "ĠPh illy", + "ĠT ue", + "ĠMalays ian", + "Ġclim ax", + "Ġb ury", + "Ġcons pic", + "ĠSouth ampton", + "ĠFl owers", + "Ġesc orted", + "ĠEduc ational", + "ĠI RC", + "Ġbrut ally", + "e ating", + "Ġpill ar", + "ĠS ang", + "ĠJ ude", + "ar ling", + "ĠAm nesty", + "Ġrem inding", + "ĠAdminist rative", + "hes da", + "Ġfl ashed", + "ĠP BS", + "per ate", + "fe ature", + "Ġsw ipe", + "Ġgra ves", + "oult ry", + "26 1", + "bre aks", + "ĠGu er", + "Ġsh rimp", + "ĠV oting", + "qu ist", + "Ġanaly tical", + "Ġtables poons", + "ĠS OU", + "Ġresear ched", + "Ġdisrupt ed", + "Ġj our", + "Ġrepl ica", + "Ġcart oons", + "b ians", + "} )", + "c opy", + "G ot", + "ou ched", + "P UT", + "Ġsw arm", + "not ations", + "s aid", + "Ġreb uilt", + "Ġcollabor ate", + "Ġr aging", + "Ġn ar", + "Ġdem ographics", + "ĠD DR", + "Ġdist rust", + "oss ier", + "ĠK ro", + "Ġpump kin", + "Ġreg rets", + "Ġfatal ities", + "ĠL ens", + "ĠO le", + "p d", + "Ġpupp et", + "ĠOut look", + "ĠSt am", + "O l", + "F air", + "U U", + "Ġre written", + "Ä ±", + "Ġfasc inated", + "Ġve ctors", + "Ġtrib unal", + "u ay", + "ĠM ats", + "ĠCo ins", + "[ [", + "Ġ18 1", + "Ġrend ers", + "ĠK aepernick", + "Ġesp ionage", + "Ġsum m", + "Ġd itch", + "Acc ount", + "Ġspread sheet", + "Ġmut ant", + "p ast", + "40 7", + "Ġd ye", + "Ġinit iation", + "Ġ4 000", + "Ġpunish able", + "Ġth inner", + "ĠKh al", + "Ġinter medi", + "D un", + "ĠGoth am", + "Ġeager ly", + "Ġvag inal", + "p owers", + "V W", + "ĠWATCH ED", + "Ġpred ator", + "ams ung", + "Ġdispar ity", + "Ġ[ *", + "Ġam ph", + "Ġout skirts", + "ĠSpir its", + "Ġskelet al", + "Ð »", + "ĠR ear", + "Ġissu ance", + "ĠLog ic", + "re leased", + "Z Z", + "ĠB ound", + "Ent ry", + "Ġex its", + "is ol", + "ĠFound er", + "Ġw re", + "ĠGreen land", + "ĠM MO", + "t aker", + "IN C", + "ãģ ¾", + "Ġhour ly", + "hen ko", + "Ġfantas ies", + "Ġdis ob", + "Ġdemol ition", + "ãĥ ĭ", + "Ġen listed", + "rat ulations", + "Ġmis guided", + "Ġens ured", + "Ġdiscour aged", + "m ort", + "Ġfl ank", + "Ġc ess", + "Ġreact s", + "ĠS ere", + "s ensitive", + "ĠSer pent", + "ass ad", + "Ġ24 7", + "Ġcalm ly", + "b usters", + "Ġble ed", + "ĠSt ro", + "Ġamuse ment", + "ĠAntar ctica", + "Ġs cept", + "ĠG aw", + "a q", + "ason ic", + "Ġsp rawling", + "n ative", + "atur ated", + "ĠBattle field", + "IV ERS", + "E B", + "ĠG ems", + "ĠNorth western", + "ĠFil ms", + "ĠAut omatic", + "Ġappre hend", + "ãģ ¨", + "Ġgui Name", + "Ġback end", + "Ġevid enced", + "ge ant", + "01 2", + "ĠS iege", + "Ġexternal To", + "Ġunfocused Range", + "ĠguiActiveUn focused", + "Ġgui Icon", + "ĠexternalTo EVA", + "ĠexternalToEVA Only", + "F ri", + "ch ard", + "en aries", + "Ġchief s", + "Ġc f", + "ĠH UD", + "Ġcorro bor", + "Ġd B", + "ĠT aken", + "ĠPat ricia", + "ra il", + "ĠCh arm", + "ĠLiber tarian", + "rie ve", + "Person al", + "ĠO UR", + "ger ies", + "Ġdump ing", + "Ġneurolog ical", + "it imate", + "ĠClint ons", + "raft ed", + "ĠM olly", + "Ġtermin als", + "reg ister", + "Ġfl are", + "Ġenc oded", + "Ġautop sy", + "p el", + "m achine", + "Ġexempt ions", + "ĠRoy als", + "d istance", + "Ġdraft s", + "Ġl ame", + "ĠC unning", + "Ġsp ouses", + "ĠMark ets", + "ĠCar rier", + "Ġimp lying", + "ĠY ak", + "s id", + "Ġl oser", + "Ġvigil ant", + "Ġimpe achment", + "Ġaug mented", + "ĠEmploy ees", + "Ġunint ended", + "tern ally", + "ĠW att", + "Ġrecogn izable", + "ess im", + "æ Ŀ", + "Ġco ated", + "r ha", + "Ġlie utenant", + "ĠLegisl ation", + "pub lished", + "44 4", + "01 3", + "Ġide ally", + "ĠPass word", + "Ġsimpl ify", + "ĠMet a", + "ĠM RI", + "Ġple ading", + "organ ized", + "hand ler", + "Ġun ravel", + "cor rect", + "Ġ icy", + "Ġparan oid", + "Ġpass er", + "Ġinspect ions", + "of er", + "ĠHealth care", + "28 3", + "ĠBr ut", + "iol a", + "for ge", + "ĠMed ieval", + "MS N", + "ie vers", + "ĠProgram ming", + "å ī", + "Ġ2 23", + "m u", + "ĠC LE", + "ug a", + "Ġsho ppers", + "Ġinform ative", + "ĠPl ans", + "Ġsupplement ation", + "ĠT ests", + "ty ard", + "ocy tes", + "ĠVeg a", + "ĠGujar at", + "erman ent", + "Ex cept", + "ĠL OT", + "all a", + "ĠC umm", + "ĠO sw", + "Ġven om", + "ĠDeb t", + "ĠD OWN", + "Ġreun ion", + "Ġm uc", + "ĠRel ief", + "Ġge op", + "ĠðŁ ĺ", + "al ogue", + "An th", + "ech o", + "Ġcor ros", + "Ġrepl ication", + "ĠBl azing", + "ĠD aughter", + "Ġinf lic", + "ĠLind sey", + "Ù Ī", + "28 4", + "Ex it", + "Ġgl oom", + "TA IN", + "Ġundermin ing", + "Ġadv ising", + "h idden", + "Ġover flow", + "Ġg or", + "urd ue", + "Ġe choes", + "enh agen", + "Ġimp uls", + "d rug", + "c ash", + "Ġas ync", + "Ġmir ac", + "at ts", + "p unk", + "Ġpiv ot", + "ĠLegisl ative", + "Ġblog gers", + "ĠCl aw", + "s burg", + "d yl", + "ĠRecomm end", + "Ġver te", + "Ġprohib iting", + "ĠPant her", + "Jon athan", + "Ġo min", + "Ġhate ful", + "28 1", + "ĠOr che", + "ĠMurd och", + "down s", + "Ġas ymm", + "G ER", + "Al ways", + "Ġinform s", + "ĠW M", + "ĠP ony", + "ĠApp endix", + "ĠAr lington", + "J am", + "Ġmedic inal", + "ĠS lam", + "IT IES", + "Ġre aff", + "ĠR i", + "F G", + "S pring", + "b ool", + "Ġthigh s", + "Ġmark ings", + "ĠRa qqa", + "ĠL ak", + "p oll", + "ts ky", + "ĠMort y", + "ĠDef inition", + "Ġdeb unk", + "end ered", + "ĠLe one", + "a vers", + "Ġmortg ages", + "App arently", + "N ic", + "ha us", + "ĠTh ousands", + "au ld", + "Ġm ash", + "sh oot", + "Ġdi arr", + "Ġconscious ly", + "H ero", + "e as", + "ĠN aturally", + "ĠDestroy er", + "Ġdash board", + "serv ices", + "R og", + "Ġmillenn ials", + "Ġinv ade", + "- (", + "Ġcomm issions", + "ĠA uckland", + "Ġbroadcast s", + "Ġfront al", + "Ġcr ank", + "ĠHist oric", + "Ġrum ours", + "CT V", + "Ġster il", + "Ġboost er", + "rock et", + "ãĤ ¼", + "ut sche", + "ĠP I", + "Ġ2 33", + "ĠProdu cer", + "ĠAnaly tics", + "Ġinval uable", + "Ġunint ention", + "ĠC Y", + "Ġscrut in", + "Ġg igg", + "Ġeng ulf", + "Ġprolet ariat", + "Ġh acks", + "ĠH ew", + "ar ak", + "ĠSl ime", + "ield ing", + "ag her", + "ĠEll iot", + "Ġtele com", + "Ġ2 19", + "ult an", + "ĠAr bor", + "ĠSc outs", + "B an", + "Ġlifes pan", + "Ġbl asp", + "38 8", + "Ġjud iciary", + "ĠContin ental", + "ask ing", + "Mc C", + "L ED", + "Ġbag gage", + "ĠSorce rer", + "Ġrem nants", + "ĠGriff ith", + "ets u", + "ĠSub aru", + "ĠPerson ality", + "des igned", + "ush ima", + "agn ar", + "Ġrec oil", + "Ġpass ions", + "\\ \":", + "Ġte e", + "Ġabol ition", + "ĠCreat ing", + "j ac", + "Ġ19 4", + "01 9", + "Ġpill ars", + "ric hed", + "/ \"", + "t k", + "Ġlive lihood", + "Ġro asted", + "ah on", + "ĠH utch", + "ass ert", + "Ġdivid end", + "Ġkn it", + "Ġd aunting", + "Ġdisturb ance", + "Ġsh ale", + "Ġcultiv ated", + "Ġrefriger ator", + "L B", + "ĠN ET", + "Ġcommercial s", + "Ġthink ers", + "45 5", + "Ġch op", + "B road", + "Ġsuspic ions", + "Ġtag ged", + "l ifting", + "Ġsty lish", + "ĠShield s", + "Short ly", + "Ġt ails", + "A uth", + "ST E", + "ĠG AME", + "Ġse ism", + "ĠK is", + "olog ne", + "Ġcow ork", + "Ġforc ibly", + "Ġthy roid", + "ĠP B", + "AN E", + "mar ried", + "h orse", + "Ġpoly mer", + "ĠCh al", + "od or", + "DE BUG", + "ĠCon text", + "Ġbl iss", + "Ġpin point", + "ĠMat hemat", + "leg ram", + "ĠWeek end", + "Ġlab elled", + "Ġb art", + "it les", + "Ġest rogen", + "âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ", + "\" '", + "Ġvis ibly", + "Ġouts ider", + "aid a", + "Are a", + "Ġdisse min", + "Ġdish onest", + "ĠCl osed", + "ĠBullet in", + "ĠRam sey", + "sw ord", + "ĠX I", + "our ced", + "S ame", + "34 6", + "ĠRe pe", + "ĠK ou", + "c ake", + "em is", + "C ache", + "ĠMe aning", + "ĠEn light", + "onom y", + "Ġmanifest ation", + "sw orth", + "J ay", + "Ġch ore", + "ö r", + "D ream", + "Ġsanction ed", + "Ġcult urally", + "ĠA ra", + "N av", + "Ġthe ological", + "Ġstr ut", + "ĠV O", + "ĠHand book", + "Ġconstruct ing", + "Ġ ¶", + "ĠBenef its", + "ĠPsych ological", + "s ac", + "å ¸", + "p olicy", + "ĠMat ters", + "ĠReport ed", + "ĠBy te", + "Ġvit ro", + "ĠM aiden", + "Ġl am", + "ĠJenn ings", + "Ġgar ment", + "ĠRut gers", + "ĠStaff ord", + "ĠWell ington", + "Ġinter mitt", + "Ġn pm", + "Ġord eal", + "Ġplug ged", + "o oming", + "in ished", + "fram ework", + "Ġtim ber", + "Ġc ass", + "Ġ8 50", + "il ess", + "ĠRed ux", + "7 68", + "St re", + "Ġsurpass ed", + "w hel", + "Ġparalle ls", + "Ġve il", + "ĠG I", + "ĠR EST", + "Ġread iness", + "s ort", + "Ġmod ifying", + "ĠSl ate", + "ru ff", + "Ġmar ble", + "Ġinf rared", + "Ġaud itor", + "ĠFANT ASY", + "ĠP overty", + "ĠS PD", + "Ġ\" (", + "K y", + "RA Y", + "Ġexecut ions", + "ĠBever ly", + "ĠMarx ism", + "ĠBur st", + "ĠK ali", + "est ones", + "Clear ly", + "E ll", + "ãģ §", + "ĠProceed ings", + "T oken", + "IF IC", + "ñ a", + "Cent ral", + "ĠH aley", + "ĠD rama", + "Ġform ations", + "OR N", + "Book s", + "Ġdom inating", + "ĠFly ers", + "ĠCompan ion", + "Ġdiscipl ined", + "ĠYug oslav", + "ĠSpell s", + "Ġv engeance", + "Ġland lords", + "L en", + "ĠO gre", + "ano ia", + "Ġpier cing", + "Ġcon greg", + "Ġscore r", + "ob ia", + "Ġnic kel", + "ĠLear ns", + "Ġre jo", + "Ġmaster piece", + "Fl ash", + "Ġinhab ited", + "ĠOpen GL", + "ĠD ud", + "ĠI CO", + "Ġar ter", + "Ġpl ur", + "Ġmaster y", + "Ġlong standing", + "st ed", + "Ġw ines", + "Ġtelev ised", + "ĠSh rine", + "ĠBay ern", + "Ġâ ĵĺ", + "Ġencl osure", + "j ohn", + "Ġprophe ts", + "ĠRes urrection", + "ĠOrd ers", + "Ġun even", + "r als", + "Ġd wind", + "ĠL ah", + "ĠSl oven", + "37 8", + "Ġins istence", + "aff le", + "ĠCl one", + "Ġhard ship", + "ĠCongress man", + "Ġple ad", + "Ġreview ers", + "Ġc ured", + "Ġ19 35", + "as ley", + "f ake", + "ĠTh inking", + "yd ia", + "P ART", + "ĠD ota", + "o it", + "Ġwh ipped", + "Ġb ouncing", + "ĠHispan ics", + "com ings", + "Ġcann abin", + "ĠCh ambers", + "ĠZ ack", + "Option al", + "Ġco ats", + "Ġprow ess", + "ĠNort on", + "Ġplain ly", + "Ġfre ight", + "Ġinhib ition", + "Ġcl am", + "Ġ30 3", + "ke f", + "ale igh", + "L uke", + "Ġpsych o", + "ator ium", + "M ED", + "Ġtreat ies", + "Ġind isc", + "Ġd c", + "OP S", + "Ġresil ient", + "ĠInter state", + "Ġsl ack", + "Ġmund ane", + "Ġestab lishes", + "35 9", + "Ġstr ained", + "Ġn ond", + "S us", + "Ġcast e", + "ar ate", + "ie ving", + "Ġunfair ly", + "Ġpars er", + "on ial", + "urs ive", + "V ia", + "ĠOtt o", + "ĠAuthor ities", + "stro ke", + "K R", + "ĠMer cy", + "Ġfurn ished", + "Ġout set", + "Ġmet ic", + "19 82", + "olith ic", + "ĠT ent", + "og ical", + "ĠA ircraft", + "Ġh ides", + "ĠBec ame", + "Ġeduc ators", + "re aching", + "Ġvol atility", + "Ġtodd ler", + "ĠNAS CAR", + "ĠTw elve", + "ĠHigh lights", + "Ġgra pe", + "Ġspl its", + "Ġpe asant", + "Ġre neg", + "ĠMS I", + "Tem p", + "st ars", + "Ġtre k", + "ĠHy de", + "b inding", + "Ġreal ism", + "Ġox ide", + "ĠH os", + "Ġmount s", + "Ġbit ing", + "Ġcollaps ing", + "Ġpost al", + "Ġmuse ums", + "Ġdet ached", + "Ġrespect ing", + "Ġmonop ol", + "Ġwork flow", + "ĠC ake", + "Tem plate", + "ĠOrgan isation", + "Ġpers istence", + "36 9", + "C oming", + "B rad", + "Ġredund ant", + "ĠG TA", + "Ġb ending", + "Ġrev oked", + "Ġoff ending", + "Ġfram ing", + "Ġprint f", + "Comm un", + "mem bers", + "Out side", + "Ġconst rued", + "Ġc oded", + "F ORE", + "Ġch ast", + "Ch at", + "Ind ian", + "ĠY ard", + "? !\"", + "ĠP orts", + "ĠX avier", + "ĠR ET", + "' .\"", + "ĠBo at", + "iv ated", + "ich t", + "umer able", + "D s", + "ĠDun n", + "Ġcoff in", + "Ġsecure ly", + "ĠRapt ors", + "ĠB es", + "Install ation", + "Ġin ception", + "ĠHealth y", + "end ants", + "Ġpsych ologists", + "ĠShe ikh", + "c ultural", + "ĠBlack Berry", + "sh ift", + "F red", + "oc he", + "Ġc akes", + "ĠS EO", + "ĠG ian", + "ĠAs ians", + "og ging", + "e lement", + "Ġpund its", + "ĠV augh", + "ĠG avin", + "Ġh itter", + "Ġdrown ed", + "Ġch alk", + "ĠZ ika", + "Ġmeas les", + "80 2", + "âĢ¦ ..", + "ĠAW S", + "] \"", + "Ġdist ort", + "ĠM ast", + "Ġantib odies", + "ĠM ash", + "Mem ory", + "ĠUg anda", + "ĠPro b", + "Ġvom iting", + "ĠTurn s", + "Ġoccup ying", + "Ġev asion", + "ĠTher apy", + "Ġprom o", + "Ġelect r", + "Ġblue print", + "ĠD re", + "pr iced", + "ĠDep ot", + "Ġallev iate", + "ĠSom ali", + "m arg", + "n ine", + "Ġnostalg ia", + "ĠShe pherd", + "Ġcaval ry", + "Ġtor ped", + "ĠBlood y", + "x b", + "Ġs ank", + "Ġgo alt", + "report print", + "embed reportprint", + "clone embedreportprint", + "ĠIn itially", + "ĠF ischer", + "Ġnot eworthy", + "c ern", + "Ġin efficient", + "raw download", + "rawdownload cloneembedreportprint", + "c ation", + "ĠD ynasty", + "l ag", + "D ES", + "Ġdistinct ly", + "ĠEston ia", + "Ġopen ness", + "Ġg ossip", + "ru ck", + "W idth", + "ĠIb rahim", + "Ġpet roleum", + "Ġav atar", + "ĠH ed", + "ath a", + "ĠHog warts", + "Ġc aves", + "67 8", + "Ġsafegu ard", + "ĠM og", + "iss on", + "ĠDur ham", + "sl aught", + "ĠGrad uate", + "Ġsub conscious", + "ĠEx cellent", + "ĠD um", + "---- -", + "Ġp iles", + "ĠW ORK", + "ĠG arn", + "ĠF ol", + "ĠAT M", + "Ġavoid s", + "ĠT ul", + "Ġble ak", + "EL Y", + "iv ist", + "light ly", + "P ers", + "ĠD ob", + "ĠL S", + "Ġins anity", + "Î µ", + "atal ie", + "En large", + "Ġtw ists", + "Ġfault y", + "Ġpir acy", + "Ġimp over", + "Ġrug ged", + "ĠF ashion", + "Ġs ands", + "' ?", + "sw ick", + "Ġn atives", + "Ġhe n", + "ĠNo ise", + "ãĥ Ĺ", + "Ġg reens", + "Ġfree zer", + "Ġd ynasty", + "ĠFather s", + "ĠNew ark", + "Ġarchae ological", + "Ġo t", + "ob ar", + "Ġblock ade", + "Ġall erg", + "L V", + "Ġdeb it", + "ĠR FC", + "ĠMil ton", + "ĠPress ure", + "Ġwill ingly", + "Ġdisproportion ate", + "Ġopp ressive", + "Ġdiamond s", + "Ġbelong ings", + "19 70", + "Ġbell s", + "Ġimperial ism", + "Ġ2 27", + "Ġexpl oding", + "ĠE clipse", + "Ġ19 19", + "Ġr ant", + "Ġnom inations", + "34 7", + "Ġpeace fully", + "ric a", + "ĠF UCK", + "Ġvib ration", + "mal ink", + "Ġro pes", + "ĠIv anka", + "ĠBrew ery", + "ĠBook er", + "ĠOw ens", + "go ers", + "Serv ices", + "ĠSn ape", + "Ġ19 1", + "39 5", + "Ġ2 99", + "just ice", + "Ġb ri", + "Ġdisc s", + "Ġprom inently", + "Ġvul gar", + "Ġsk ipping", + "l ves", + "Ġtsun ami", + "37 4", + "ĠU rug", + "ĠE id", + "rec ated", + "p hen", + "Ġfault s", + "ĠStart ed", + "9 50", + "Ġp i", + "Ġdetect or", + "Ġbast ard", + "Ġvalid ated", + "Space Engineers", + "OUR CE", + "Ġ( ~", + "Ġuns ur", + "Ġaff irmed", + "Ġfasc ism", + "Ġres olving", + "ĠCh avez", + "ĠC yn", + "Ġdet ract", + "L ost", + "Ġrig ged", + "Ġhom age", + "ĠBrun o", + "55 5", + "ec a", + "Ġpress es", + "Ġhum our", + "Ġsp acing", + "Ġ' /", + "olk ien", + "C oun", + "OP ER", + "T re", + "S on", + "ĠCambod ia", + "ier re", + "m ong", + "o zy", + "Ġliquid ity", + "ĠSov iets", + "ĠFernand o", + "Ġ2 29", + "Ġsl ug", + "ĠCatal an", + "elect ric", + "Ġsc enery", + "ĠH earth", + "Ġconst rained", + "Ġgoal ie", + "ĠGu idelines", + "ĠAm mo", + "ĠPear son", + "Ġtax ed", + "Ġfet us", + "Resp onse", + "ĠAlex is", + "th ia", + "G uy", + "Ġrecon struct", + "Ġextrem es", + "Ġconclud ing", + "ĠP eg", + "ook s", + "Ġded uctions", + "R ose", + "Ġground breaking", + "ĠT arg", + "ãĥ ģ", + "ĠRe ve", + "res ource", + "Ġmo ons", + "Ġelectrom agnetic", + "Ġamid st", + "ĠVik tor", + "N ESS", + "B ACK", + "Ġcomm ute", + "ĠAna heim", + "Ġfluct uations", + "6 40", + "Ġnood les", + "ĠCop enhagen", + "ĠT ide", + "ĠGri zz", + "ĠS EE", + "Ġpip elines", + "Ġsc ars", + "end o", + "ag us", + "ĠE TF", + "/ #", + "ĠBec ome", + "44 8", + "Ġvis c", + "ĠRecomm ended", + "Ġj umper", + "Ġcogn ition", + "Ġassass in", + "Ġwitness ing", + "ĠSet up", + "Ġl ac", + "v im", + "IS M", + "p ages", + "SS L", + "35 8", + "Ġad ject", + "indust rial", + "l ore", + "cher y", + "Ġgl itter", + "Ġc alf", + "Flor ida", + "Ġspoil ers", + "Ġsucceed s", + "Ġch anting", + "Ġslog ans", + "ĠTr acy", + "Vis it", + "rol ogy", + "Ġm ornings", + "Ġline age", + "Ġs ip", + "Ġintense ly", + "Ġflour ish", + "ĠSle eping", + "ĠF em", + "or por", + "ĠK lan", + "ĠDar th", + "h ack", + "ĠNi elsen", + "Ġtum ors", + "Ġprocure ment", + "ĠY orkshire", + "Ġra ided", + "K Y", + "An na", + "Ġ// [", + "ĠDis order", + "ĠMust ang", + "ĠW en", + "ĠTry ing", + "s q", + "Ġdeliver ies", + "Ġshut ter", + "Ġcere bral", + "Ġbip olar", + "ĠC N", + "l ass", + "j et", + "Ġdeb ating", + "> :", + "Ġe agle", + "gr ades", + "ĠD ixon", + "UG C", + "M AS", + "ĠDr aco", + "ĠMach ines", + "aff er", + "Ġem an", + " ²", + "pr on", + "ĠG ym", + "Ġcompar atively", + "ĠTrib unal", + "PR O", + "Ġle x", + "Ġfert ile", + "Ġdep ressing", + "Ġsuperf icial", + "ess ential", + "ĠHun ters", + "g p", + "Ġprom inence", + "L iber", + "ĠAn cest", + "ote chnology", + "Ġm ocking", + "ĠTra ff", + "ĸ ļ", + "Med ium", + "I raq", + "Ġpsychiat rist", + "Quant ity", + "ĠL ect", + "Ġno isy", + "5 20", + "G Y", + "Ġsl apped", + "ĠM TV", + "Ġpar a", + "p ull", + "Mult iple", + "as her", + "Ġn our", + "ĠSe g", + "Spe ll", + "v ous", + "ord ial", + "Sen ior", + "ĠGold berg", + "ĠPl asma", + "ne ed", + "Ġmess enger", + "ere t", + "Ġteam ed", + "Ġliter acy", + "ĠLe ah", + "ĠD oyle", + "Ġem itted", + "U X", + "Ġev ade", + "Ġm aze", + "Ġwrong ly", + "ĠL ars", + "Ġstere otype", + "Ġpled ges", + "Ġarom a", + "ĠM ET", + "Ġac re", + "ĠO D", + "Ġf f", + "Ġbrew eries", + "ĠH ilton", + "und le", + "ĠK ak", + "ĠThank fully", + "ĠCan ucks", + "in ctions", + "ĠApp ears", + "Ġco er", + "Ġundermin ed", + "ro vers", + "And re", + "Ġbl aze", + "um ers", + "Ġfam ine", + "amp hetamine", + "ulk an", + "Am ount", + "Ġdesper ation", + "wik ipedia", + "develop ment", + "ĠCor inth", + "uss ia", + "Jack son", + "L I", + "N ative", + "R s", + "Oh io", + "ĠKath leen", + "F ortunately", + "Ġattend ant", + "ĠPre ferred", + "ĠDid n", + "ĠV s", + "M is", + "Ġrespond ent", + "Ġb oun", + "st able", + "Ġp aved", + "Ġunex pl", + "ĠChe ney", + "L M", + "ĠC ull", + "bl own", + "Ġconfront ing", + "oc ese", + "serv ing", + "W i", + "ĠLith uania", + "ann i", + "Ġst alk", + "h d", + "Ġv ener", + "AP H", + "ynchron ous", + "UR R", + "um ably", + "hist oric", + "H alf", + "H ay", + "Ġresil ience", + "spe ction", + "Ġabandon ing", + "O bs", + "ĠDeb bie", + "Ġgrad ient", + "ĠPl aint", + "ĠCan al", + "AR CH", + "Ġexpans ive", + "Ġfun g", + "Ġb ounced", + "U nd", + "Ġprec autions", + "Ġclar ification", + "Ġd agger", + "Ġgri ps", + "Ġ µ", + "ĠRiver a", + "ĠUnd ead", + "is ites", + "ĠFIR ST", + "ñ o", + "aud i", + "Ġhost ages", + "Ġcompl iant", + "Ġal umni", + "Se ven", + "Ġcyber security", + "e ither", + "Col lect", + "Ġinvari ably", + "ĠS oci", + "Ġlaw maker", + "Ġa le", + "ĠPerson ally", + "N azi", + "Ġcustom ization", + "ĠPro c", + "ĠSask atchewan", + "eat uring", + "Ġsp ared", + "Ġdiscontin ued", + "Ġcomput ational", + "ĠMotor ola", + "Ġsuprem acist", + "government al", + "Ġparad ise", + "ĠDown ing", + "ĠNik on", + "Ġcat alyst", + "ber ra", + "Tor onto", + "8 75", + "bet a", + "ĠMac ron", + "Ġunreal istic", + "ve ctor", + "ĠVeh icles", + "it iveness", + "ĠR V", + "ĠCol bert", + "s in", + "o ji", + "ent in", + "ĠKr ish", + "hell o", + "ff ield", + "ok y", + "ĠT ate", + "Ġmap le", + "Ġa ids", + "chem ical", + "33 4", + "n uts", + "ĠWar p", + "Ġx x", + "ĠRob b", + "umer ous", + "_- _", + "ft ime", + "ĠV W", + "Ġw inger", + "ĠD ome", + "t ools", + "ĠP V", + "ĠGe orgetown", + "Ġg eared", + "Ġjihad ists", + "Ġc p", + "Ġster oids", + "M other", + "cler osis", + "ĠDR M", + "nes ia", + "Ġl inger", + "Ġimm ersive", + "ĠC OUN", + "Ġoutwe igh", + "ens ual", + "B and", + "Ġtransform s", + "mat ched", + "ps ons", + "ĠJud icial", + "f actor", + "Ġrefer ral", + "Ġodd ly", + "ĠW enger", + "B ring", + "ĠB ows", + "60 2", + "IC LE", + "Ġl ions", + "ĠAcad emic", + "ĠTh orn", + "ĠRa ider", + "kef eller", + "St orage", + "L ower", + "ĠOr t", + "ĠEqu ality", + "AL T", + "ĠS OC", + "T ypes", + "Ġl yn", + "ĠAss et", + "co at", + "TP P", + "C VE", + "ĠPione er", + "app lication", + "Mod ern", + "ĠH K", + "En vironment", + "Al right", + "R ain", + "IP P", + "ĠShi ite", + "Ġm ound", + "ĠAb ilities", + "cond ition", + "St aff", + "Ġcompet ence", + "ĠM oor", + "ĠDi ablo", + "Ġwith held", + "Ġost ensibly", + "ĠB rom", + "Ġms g", + "Ġden omin", + "ĠRef erences", + "ĠF P", + "Ġplun ged", + "Ġp amph", + "m oving", + "cent ral", + "Ġdown right", + "Ġf ading", + "T al", + "T yp", + "ĠTh y", + "uk es", + "it he", + "Ġo ve", + "Ġbatt led", + "Ġseaf ood", + "Ġfig ur", + "ĠR D", + "c rop", + "Ġsqu ads", + "{ \\", + "à ¹", + "ĠE h", + "Ġinterview ing", + "ĠQ in", + "Ġas piring", + "PL IC", + "Ġcla uses", + "ĠG ast", + "ĠN ir", + "Ġl uggage", + "Ġh ose", + "Ġsystem d", + "Ġdesc ending", + "ĠRev ised", + "ĠR ails", + "al ign", + "70 9", + "33 7", + "Ġf ug", + "charg ing", + "t ags", + "Ġut er", + "k ish", + "WAR NING", + "49 0", + "prof its", + "Ġvoy age", + "Ġa ce", + "ĠV anguard", + "ĠT anks", + "ĠM uk", + "Ġ2 26", + "S afe", + "Ar mor", + "Ġvolcan ic", + "Ġwom b", + "ĠM IL", + "Ġbegin ner", + "ĠRec ogn", + "ĠA AP", + "PL AY", + ") !", + "Ġdetect ing", + "c n", + "Ġbre aches", + "Bas ically", + "ĠP ag", + "ĠMunicip al", + "ĠInd ie", + "ĠL af", + "ĠDis able", + "ĠOl son", + "Ġrest rained", + "Ġrul ings", + "Ġhum ane", + "ev ents", + "ĠCinem a", + "display Text", + "ĠH atch", + "action Date", + "onna issance", + "Ġassault ing", + "ĠL ug", + "CH AT", + "Ġvig orous", + "ĠPer se", + "Ġintoler ance", + "ĠSnap chat", + "ĠSh arks", + "Ġd ummy", + "ĠDi agn", + "ĠGu itar", + "im eters", + "40 3", + "RE G", + "A x", + "Ġsepar ates", + "ĠMah m", + "Ġt v", + "j ah", + "O OL", + "C irc", + "ĠWinds or", + "uss ian", + "Ġintu ition", + "Ġdis dain", + "ĠDon ovan", + "Ġ2 21", + "E mb", + "Ġcondem ning", + "Ġgener osity", + "zz y", + "Ġpant ies", + "ĠPre vent", + "Action Code", + "AN A", + "34 2", + "external ActionCode", + "Ġspec ifying", + "Ġcryst all", + "J ere", + "Ġru pt", + "ĠApp rentice", + "Ġprof iling", + "Ð º", + "St rike", + "Ġsid eline", + "Ġoblig ated", + "Ġocc ult", + "Ġbureaucr atic", + "ant ically", + "rupt ed", + "neg ative", + "ĠEthiop ia", + "ĠC ivic", + "Ġins iders", + "el igible", + "ĠTV s", + "ĠB AR", + "ĠT I", + "i ologist", + "ĠA IR", + "Ġsubstit uted", + "Ar ab", + "ĠS aul", + "ĠY og", + "p rem", + "Ġbuild ers", + "Ġstation ary", + "Ġdoubt ful", + "Ġvig orously", + "Ġthr illing", + "Ph ysical", + "ĠCare y", + "ĠHyd ra", + "geon ing", + "ĠS ly", + "y ton", + "Ġborrow ers", + "ĠPark inson", + "Ġ ë", + "ĠJama ica", + "Ġsat ir", + "Ġinsurg ents", + "ĠF irm", + "Ġis ot", + "ĠK arn", + "our ning", + "ak ens", + "doc s", + "l ittle", + "ĠMon aco", + "CL ASS", + "Tur key", + "L y", + "ĠCon an", + "ass ic", + "Ġstar red", + "ĠPac ers", + "et ies", + "Ġt ipping", + "M oon", + "ĠR w", + "s ame", + "Ġcav ity", + "Ġgo of", + "ĠZ o", + "Sh ock", + "um mer", + "Ġemphas izes", + "Ġreg rett", + "Ġnovel ty", + "Ġen vy", + "ĠPass ive", + "r w", + "50 5", + "Ġind ifferent", + "ĠR ica", + "ĠHim self", + "ĠFred die", + "Ġad ip", + "ä¸ Ģ", + "Ġbreak out", + "Ġhur ried", + "ĠHu ang", + "ĠD isk", + "Ġro aming", + "?????- ?????-", + "U V", + "ĠRick y", + "ĠS igma", + "Ġmarginal ized", + "Ġed its", + "Ġ30 4", + "mem ory", + "Ġspec imen", + "29 3", + "ãģ ¯", + "Ġvert ically", + "Ġaud ition", + "ĠHe ck", + "Ġc aster", + "ĠHold ings", + "ad al", + "ĠC ron", + "ĠL iam", + "Ġdef lect", + "P ick", + "ĠDeb ug", + "RE F", + "Ġvers atility", + "ot hes", + "class ified", + "ĠMah ar", + "ĠH ort", + "C ounter", + "st asy", + "not iced", + "33 1", + "ĠSh im", + "f uck", + "ĠB ie", + "Ġair ing", + "ĠPro tein", + "ĠHold ing", + "Ġspect ators", + "ili ated", + "ĠThat cher", + "n osis", + "ãĥ¼ ãĥ³", + "Te le", + "B oston", + "ĠTem pl", + "st ay", + "Ġdecl arations", + "47 9", + "Vol ume", + "ĠDesign er", + "ĠOver watch", + "id ae", + "Ġon wards", + "Ġn ets", + "ĠMan ila", + "part icularly", + "Ġpolit ic", + "o other", + "Ġport raits", + "Ġpave ment", + "c ffff", + "Ġs aints", + "Ġbegin ners", + "ES PN", + "Ġshort comings", + "âķIJ âķIJ", + "Ġcom et", + "ĠOrgan ic", + "qu el", + "Ġhospital ized", + "Bre ak", + "Ġpe el", + "dyl ib", + "asp x", + "ur ances", + "ĠT IM", + "P g", + "Ġread able", + "ĠMal ik", + "Ġm uzzle", + "Ġbench marks", + "d al", + "ĠV acc", + "ĠH icks", + "60 9", + "ĠB iblical", + "he ng", + "Ġover load", + "ĠCivil ization", + "Ġimm oral", + "Ġf ries", + "ãĤ Ĵ", + "Ġreprodu ced", + "Ġform ulation", + "j ug", + "ire z", + "g ear", + "Ġco ached", + "Mp Server", + "ĠS J", + "ĠK w", + "In it", + "d eal", + "ĠO ro", + "ĠL oki", + "ĠSong s", + "Ġ23 2", + "ĠLou ise", + "asion ally", + "Ġunc ond", + "olly wood", + "Ġprogress ives", + "ĠEn ough", + "ĠDo e", + "Ġwreck age", + "Ġbr ushed", + "ĠBase Type", + "Ġz oning", + "ish able", + "het ically", + "ĠC aucus", + "ĠH ue", + "Ġk arma", + "ĠSport ing", + "Ġtrad er", + "Ġseem ing", + "ĠCapt ure", + "4 30", + "b ish", + "Ġt unes", + "Ġindo ors", + "ĠSp here", + "ĠD ancing", + "TER N", + "Ġno b", + "ĠG ST", + "m aps", + "Ġpe ppers", + "F it", + "Ġoverse es", + "ĠRabb i", + "ĠR uler", + "vert ising", + "off ice", + "xx x", + "Ġra ft", + "Ch anged", + "Ġtext books", + "L inks", + "ĠO mn", + "ãĢ ij", + "Ġinconven ience", + "ĠDon etsk", + "= ~", + "Ġimplicit ly", + "Ġboost s", + "ĠB ones", + "ĠBo om", + "Cour tesy", + "Ġsens ational", + "AN Y", + "Ġgre edy", + "ed en", + "Ġinex per", + "ĠL er", + "ĠV ale", + "Ġtight en", + "ĠE AR", + "ĠN um", + "Ġancest or", + "S ent", + "ĠH orde", + "urg ical", + "all ah", + "Ġsa p", + "amb a", + "ĠSp read", + "tw itch", + "Ġgrand son", + "Ġfract ure", + "Ġmoder ator", + "ĠSe venth", + "ĠRe verse", + "Ġestim ation", + "Cho ose", + "Ġpar ach", + "Ġbar ric", + "ãĢ IJ", + "Ġcomp ass", + "Ġall ergic", + "âĢ ķ", + "OT HER", + "err illa", + "Ġw agon", + "Ġz inc", + "Ġrub bed", + "ĠFull er", + "ĠLuxem bourg", + "ĠHoo ver", + "Ġli ar", + "ĠEven ing", + "ĠCob b", + "est eem", + "Ġselect or", + "ĠB rawl", + "is ance", + "ĠE k", + "Ġtro op", + "Ġg uts", + "ĠApp eal", + "ĠTibet an", + "Ġrout ines", + "ĠM ent", + "Ġsummar ized", + "steam apps", + "Ġtr anqu", + "Ġ19 29", + "or an", + "ĠAut hent", + "Ġg maxwell", + "Ġappre hens", + "Ġpo ems", + "Ġsa usage", + "ĠWeb ster", + "ur us", + "Ġthem ed", + "Ġl ounge", + "Ġcharg er", + "Sp oiler", + "Ġsp illed", + "h og", + "ĠSu nder", + "ĠA in", + "ĠAng ry", + "Ġdis qual", + "ĠFrequ ency", + "ĠEther net", + "Ġhel per", + "Per cent", + "Ġhorr ifying", + "Ġa il", + "ĠAll an", + "EE E", + "ĠCross ing", + "44 9", + "Ġh olog", + "ĠPuzz les", + "ĠGo es", + "eren n", + "60 4", + "ãģ ı", + "ĠRaf ael", + "Ġatt en", + "ĠE manuel", + "Ġup ro", + "ĠSus p", + "P sych", + "ĠTr ainer", + "ĠN ES", + "ĠHun ts", + "bec ue", + "Ġcounsel or", + "R ule", + "Ġtox ins", + "Ġb anners", + "r ifice", + "Ġgreet ing", + "Ġfren zy", + "Ġall ocate", + "Ġ* )", + "ex pr", + "50 3", + "ĠCh ick", + "ĠT orn", + "Ġconsolid ation", + "ĠF letcher", + "sw itch", + "fr ac", + "cl ips", + "ĠMcK in", + "ĠLun ar", + "Mon th", + "IT CH", + "Ġscholar ly", + "rap ed", + "39 8", + "Ġ19 10", + "Ġe greg", + "Ġin secure", + "Ġvict orious", + "cffff cc", + "Ġsing led", + "Ġel ves", + "ĠW ond", + "bur st", + "Ġcam oufl", + "ĠBL ACK", + "Ġcondition ed", + "ç ī", + "ans wered", + "Ġcompuls ory", + "asc ist", + "Ġpodcast s", + "ĠFrank furt", + "bn b", + "Ġne oliberal", + "ĠKey board", + "ĠBel le", + "w arm", + "Ġtrust s", + "Ġins ured", + "ĠBu cc", + "us able", + "60 7", + "ĠPl ains", + "Ġ18 90", + "Ġsabot age", + "Ġlod ged", + "f elt", + "Ġg a", + "ĠN arc", + "ĠSal em", + "Ġsevent y", + "ĠBl ank", + "p ocket", + "Ġwhis per", + "Ġm ating", + "om ics", + "ĠSal man", + "ĠK ad", + "Ġan gered", + "Ġcoll isions", + "Ġextraord inarily", + "Ġcoerc ion", + "G host", + "b irds", + "è Ģ", + "k ok", + "Ġper missible", + "avor able", + "Ġpo inters", + "Ġdiss ip", + "ac i", + "Ġtheat rical", + "ĠCos mic", + "Ġforget ting", + "Ġfinal ized", + "å¤ §", + "y out", + "l ibrary", + "Ġbo oming", + "ĠBel ieve", + "ĠTe acher", + "ĠL iv", + "ĠGOOD MAN", + "ĠDomin ican", + "OR ED", + "ĠPart ies", + "Ġprecip itation", + "ĠSl ot", + "R oy", + "ĠComb ined", + "Ġinteg rating", + "Ġch rome", + "Ġintest inal", + "ĠRe bell", + "Ġmatch ups", + "Ġblock buster", + "ĠLore n", + "ĠLe vy", + "Ġpre aching", + "ĠS ending", + "ĠPur pose", + "ra x", + "f if", + "Ġauthor itative", + "ĠP ET", + "ast ical", + "Ġdish on", + "Ġchat ting", + "Ġ\"$ :/", + "Connect ion", + "Ġrecre ate", + "Ġdel inqu", + "Ġbro th", + "ĠD irty", + "ĠAd min", + "z man", + "Ġscholars hips", + "Ġ25 3", + "cont act", + "als a", + "7 67", + "c reen", + "abb age", + "Ġ19 15", + "Ġbl ended", + "Ġal armed", + "L anguage", + "35 6", + "Ġbl ends", + "ĠCh anged", + "W olf", + "Ġhe pat", + "Creat ing", + "Ġper secut", + "Ġsweet ness", + "art e", + "Ġforfe iture", + "ĠRober to", + "im pro", + "N FL", + "ĠMag net", + "Det ailed", + "Ġinsign ificant", + "ĠPOL IT", + "ĠBB Q", + "ĠC PS", + "Ġse aw", + "amin er", + "m L", + "end if", + "f inals", + "Ġ26 5", + "u ish", + "Ġ} )", + "ĠPro blems", + "Ġem blem", + "Ġserious ness", + "Ġpars ing", + "Ġsubst itution", + "Ġpress ured", + "Ġrecy cled", + "ale b", + "Rub y", + "Ġprof iciency", + "Dri ver", + "ĠW ester", + ": '", + "AF TA", + "Ġm antle", + "ĠClay ton", + "fl ag", + "Ġpractition er", + "c overed", + "ĠSt ruct", + "add afi", + "4 25", + "ĠTown ship", + "ĠHyd ro", + "Lou is", + "34 3", + "Ġcond o", + "ĠT ao", + "Ġutil ization", + "Ġnause a", + "ĠDem s", + "rid ges", + "p ause", + "Ġform ulas", + "Ġchall enger", + "37 6", + "Ġdefect ive", + "ĠRail way", + "ĠPub Med", + "Ġyog urt", + "l bs", + "ĠNor folk", + "OP E", + "ĠMood y", + "Ġdistribut or", + "Ġscroll s", + "Ġextract s", + "St an", + "Ġv iability", + "Ġexp oses", + "Ġstar vation", + "ĠStep s", + "ĠD odd", + "f ew", + "ST D", + "33 2", + "Ġclos ures", + "Ġcomplement ary", + "ĠS asha", + "ump y", + "Ġmon et", + "Ġartic ulate", + "ĠDo ct", + "k iller", + "Ġsc rim", + "Ġ2 64", + "Ġprost itutes", + "Ġse vered", + "Ġattach ments", + "Ġcool ed", + "L ev", + "ĠF alk", + "f ail", + "Ġpolic eman", + "ĠD ag", + "Ġpray ed", + "ĠK ernel", + "Ġcl ut", + "Ġc ath", + "Ġan omaly", + "St orm", + "em aker", + "ĠBreak fast", + "ul i", + "o ire", + "J J", + "h z", + "Oper ation", + "ĠS ick", + "35 4", + "ĠGuatem ala", + "R ate", + "Ġexp osures", + "f aces", + "ĠArch ae", + "ra f", + "ĠM ia", + "Ġ20 25", + "Ġop aque", + "Ġdisgu ised", + "ĠHead quarters", + "S ah", + "Ġp ots", + "9 78", + "ĠM alf", + "Ġfrown ed", + "Ġpoison ous", + "ĠCon vers", + "ee ks", + "Ġcr ab", + ".\" \"", + "Ġtre ason", + "Ġr anc", + "Ġescal ating", + "Ġwar r", + "Ġmob s", + "Ġl amps", + "ĠSun shine", + "ĠBrun swick", + "Ph ones", + "Ġspe lled", + "ĠSk ip", + "Ġ20 50", + "Ġ19 11", + "ĠPl uto", + "ĠAm end", + "Ġme ats", + "38 7", + "Ġst omp", + "ĠZh ou", + "ĠLevi athan", + "ĠHaz ard", + "ad v", + "ĠOr well", + "Ġal oud", + "Ġb umper", + "ĠAn arch", + "ub untu", + "ĠSer ious", + "f itting", + "ĠOption al", + "ĠCec il", + "RE AM", + "Ġser otonin", + "Ġcultiv ate", + "ag ogue", + "} \\", + "Ġmos ques", + "ĠSun ny", + "Ġre active", + "rev olution", + "ĠL up", + "ĠFed ora", + "Ġdefense man", + "ĠV ID", + "ist ine", + "Ġdrown ing", + "ĠBroad casting", + "Ġthr iller", + "ĠS cy", + "Ġacceler ating", + "Ġdirect s", + "od ied", + "b ike", + "d uration", + "Ġpain fully", + "R edd", + "Ġproduct ions", + "Ġg ag", + "Ġwh ist", + "Ġs ock", + "Ġinf initely", + "ĠConc ern", + "ĠCit adel", + "Ġlie u", + "Ġcand les", + "ogene ous", + "arg er", + "Ġheaven ly", + "inflamm atory", + "Per formance", + "C s", + "ruct ose", + "az aki", + "Ġp essim", + "Ġinf erence", + "Ġpow d", + "ĠZ oe", + "Ġpain ts", + "Ġd azz", + "pt a", + "-------- ---", + "Ġins pir", + "ĠExper imental", + "ĠKn ife", + "reg or", + "b ors", + "Ġshow ers", + "rom eda", + "Ġs aint", + "Ġben ign", + "ĠJ iang", + "Ġenvision ed", + "Ġsh roud", + "IF T", + "H O", + "Ġsh uff", + "ĠI CC", + "Ġse greg", + "Ġrevis it", + "ighth ouse", + "L i", + "Ġsub strate", + "ĠSe as", + "ĠRew ard", + "ĠH ep", + "ĠBr ass", + "s bm", + "Ġelim inates", + "Ġst amina", + "ĠV AT", + "ĠLo an", + "Ġconst raint", + "Ġappropri ated", + "Ġp es", + "ĠA LE", + "r anging", + "Ġ40 4", + "39 2", + "Ġintellectual s", + "ach u", + "Ġrestruct uring", + "ĠLe vin", + "Ġrun es", + "Ġdelight ful", + "Ġcarbohyd rates", + "ĠMod els", + "ĠExp o", + "Ġtransport ing", + "all oc", + "Ġring ing", + "S amsung", + "Ġscarce ly", + "ĠURL s", + "ĠM AS", + "Ġprot otypes", + "Ġnarr ator", + "ĠCPU s", + "cd n", + "ĠBart on", + "Ġdecided ly", + "ĠSh u", + "ix ir", + "oc ious", + "ĠMy st", + "N intendo", + "Ġre use", + "Ġforg iven", + "F ew", + "in ical", + "n at", + "Ġseam less", + "ĠEv a", + "ĠE VE", + "ĠJ O", + "land ers", + "Ġso fter", + "neg ie", + "Ġtrans ient", + "Ġorb ital", + "Ġfulf il", + "ĠK om", + "Hop efully", + "Ġdynam ically", + "ĠHun ger", + "å Ľ", + "ĠArmen ia", + "el man", + "ber to", + "Ġp ige", + "ĠID s", + "lim it", + "Ġve ins", + "Ġso aring", + "p acks", + "Gold en", + "ĠCr ab", + "ist or", + "ĠR PM", + "Ġ$ $", + "g ression", + "Ġjihad ist", + "Ġgam ble", + "Ġcare g", + "Ġinf lated", + "F ace", + "ĠFire arms", + "ĠEm manuel", + "â Ŀ", + "Ġsh ocks", + "gr ab", + "Ġspl end", + "ĠHP V", + "ab ortion", + "Ab ove", + "Ent ity", + "play ers", + "Ġcomm enced", + "ul ence", + "Ġfulfill ment", + "Ġembod iments", + "ĠW elfare", + "Ġha il", + "Ġ< @", + "tt en", + "Ġcat cher", + "ĠJ azeera", + "Ġvolcan o", + "Ġstabil ize", + "ĠHand ler", + "Ġintens ified", + "ĠAb rams", + "Ġhum iliation", + "p aced", + "60 5", + "ĠCent OS", + "Spe cific", + "Ġhe ed", + "ĠC AM", + "ĠGal ile", + "D ie", + "Ġabol ished", + "ĠThom son", + "ĠTe achers", + "ĠW ass", + "j ong", + "ĠIS BN", + "ĠAll ies", + "sh ake", + "å ·", + "v ict", + "How ard", + "Ġde em", + "Ġexceed ingly", + "ĠSmart stocks", + "ib e", + "Ġdoor way", + "Ġcompet ed", + "ig mat", + "Ġnational ists", + "Ġg room", + "ĠKe en", + "Ġdispos able", + "de cl", + "ĠT olkien", + "ĠSche me", + "Ġb iod", + "Ġav id", + "ĠEl on", + "ag ar", + "ĠT SA", + "R oman", + "Ġartific ially", + "Ġadvis ors", + "X L", + "ĠInf erno", + "36 6", + "Ġted ious", + "ĠPhot ography", + "ĠCar rie", + "Ġtro pe", + "ĠSand ra", + "Ġdec imal", + "Que en", + "ĠGund am", + "ĠO M", + "ote ch", + "N BA", + "Ġ19 32", + "Ġent renched", + "ĠMar ion", + "Ġfr aternity", + "Lab our", + "Hen ry", + "Ġlat itude", + "E ither", + "Ġenh ances", + "ĠPot ential", + "Ġsh ines", + "id ad", + "Ġbread th", + "Ġcapac ities", + "ĠðŁ ĻĤ", + "ĠBron x", + "Ġsex es", + "Ġdifferent iation", + "Ġheavy weight", + "ĠT aj", + "d ra", + "Ġmigr ate", + "Ġexhaust ion", + "ĠR UN", + "els ius", + "ĠCu omo", + "Ġgu itars", + "Ġcl ones", + "ĠSom ew", + "ĠP ry", + "------------ -", + "Ġwarr anted", + "cy cles", + "Ġsalv age", + "Ġdis ks", + "R ANT", + "ĠNGO s", + "ĠMart ian", + "\":[ {\"", + "Ġadd icts", + "oj ure", + "il let", + "Ġamazing ly", + "art ments", + "p ixel", + "ĠGPU s", + "Lay out", + "è £", + "ĠTam il", + "ĠBas il", + "Ġimpart ial", + "ĠSt ructure", + "f ork", + "b ryce", + "Ġr idge", + "ĠHamb urg", + "ri ous", + "Ġbl itz", + "cig arettes", + "Ġcan ned", + "40 2", + "Ġiron ically", + "Ġcompassion ate", + "ĠHaw kins", + ". #", + "ĠCat hedral", + "Ġrall ied", + "in ternal", + "Ġqu ota", + "st akes", + "T EXT", + "m om", + "Ġcomple tes", + "Ġ23 8", + "Ġsh rug", + "ãĥ ij", + "ĠN inth", + "Ġrev ise", + "ĠProv ider", + "Ġtre acher", + "Ġqu asi", + "ĠPR ES", + "Ġdep osition", + "Ġconfidential ity", + "iss ors", + "Ġim balance", + "Ġspan ning", + "Ġang ular", + "ĠC ul", + "commun ication", + "ĠNor a", + "ĠGen ius", + "op ter", + "Ġs acked", + "Sp ot", + "Ġfine ly", + "ĠCH R", + "28 2", + "w aves", + "Pal est", + "ĠRo hing", + "N L", + "è ¿", + "Ġsh itty", + "ĠSc alia", + "4 75", + "Pro gress", + "Ġreferen cing", + "Ġclass rooms", + "ab ee", + "Ġs od", + "hes ion", + "70 8", + "ĠZucker berg", + "ĠFin ish", + "ĠScot ia", + "ĠSav ior", + "ĠInstall ation", + "an tha", + "( -", + "Ġ30 2", + "ĠP unk", + "Ġcr ater", + "yout u", + "Ġro ast", + "Ġinflu encing", + "Ġd up", + "ĠJ R", + "ĠG rav", + "Ġstat ure", + "Ġbath rooms", + "A side", + "W iki", + "me an", + "ĠZ ak", + "ĠOn es", + "ĠN ath", + "Ġhyper t", + "Ġcommence ment", + "C ivil", + "Ġmoder ately", + "Ġdistribut ors", + "Ġbreast feeding", + "Ġ9 80", + "ĠS ik", + "ĠC ig", + "ĠAM ER", + "R IP", + "ĠCare er", + "ust ing", + "Ġmess ed", + "Ġe h", + "ĠJ ensen", + "/ $", + "Ġblack mail", + "Ġconvers ions", + "Ġscientific ally", + "Ġmant ra", + "p aying", + "Ġiv ory", + "ĠCour ts", + "OU GH", + "aunt let", + "Ser ial", + "B row", + "ĠH undreds", + "3 23", + "Ġpe e", + "Ġlin ux", + "Ġsub mer", + "ĠPrinc ipal", + "48 5", + "ĠD SL", + "ĠCous ins", + "Ġdoctr ines", + "ĠAthlet ics", + "Ġ3 15", + "ĠK arma", + "Ġatt ent", + "ur ger", + "Ġpresc ribe", + "Ġenc aps", + "ĠC ame", + "Ġsecret ive", + "ĠCr imes", + "d n", + "C lean", + "ĠEgypt ians", + "ĠCar penter", + "Ġ ll", + "H um", + "ĠMil o", + "Ġcapital ists", + "Ġbrief ed", + "T we", + "ĠBas in", + "elve t", + "M os", + "Ġplun ge", + "ĠKa iser", + "ĠFu j", + "ill in", + "Ġsafegu ards", + "Ġo ste", + "ĠOpportun ity", + "ĠM afia", + "ĠCall ing", + "ap a", + "ur ban", + "br ush", + "ill ard", + "c é", + "int elligence", + "ĠL ob", + "ĠDru id", + "Ġsm oother", + "Ġfoot ing", + "Ġmotor ists", + "arc ity", + "Ġmascul inity", + "Ġm ism", + "Ġabdom inal", + "ĠTa vern", + "ĠR oh", + "Ġesc apes", + "s igned", + "Anth ony", + "Ġsacrific ing", + "Ġintim acy", + "Ġan terior", + "ĠK od", + "Ġmot if", + "Ġg raz", + "Ġvisual ization", + "Ġguitar ist", + "ĠTro tsky", + "m agic", + "D ar", + "ĠMor i", + "Ġw ards", + "Ġtoile ts", + "l est", + "Ġtele port", + "ĠSund ays", + "ĠPl at", + "ET S", + "Ġe Sports", + "Pat rick", + "ĠK atherine", + "en ko", + "Ġhas sle", + "ĠM ick", + "gg les", + "Ġh ob", + "aint ain", + "Ġair borne", + "Ġsp ans", + "Ġch ili", + "Ġa perture", + "Ġvolunte ered", + "ĠInc ident", + "ĠF res", + "ĠVeter an", + "augh tered", + "ing o", + "Ġun insured", + "CL OSE", + "Ġf use", + "Ġer otic", + "Ġadvert ise", + "ra ising", + "Text ure", + "Ġatt ends", + "ĠRE AL", + "udd led", + "Ġsm oot", + "Ġ30 5", + "ĠWill is", + "Ġbl ond", + "An alysis", + "ĠV T", + "on ica", + "Ġstrongh old", + "R F", + "N M", + ". >>", + "Ġprosper ous", + "Ġbo asted", + "29 2", + "ĠManufact uring", + "PR ESS", + "g ren", + "Ġpharm acy", + "ĠRoc kefeller", + "k ai", + "Ġth umbs", + "ĠH ut", + "Ġmother board", + "Ġguard ians", + "ĠAl ter", + "ll ular", + "Ġsh ack", + "Ġwise ly", + "Ġback bone", + "erv a", + "Ġsu icides", + "ĠMcG regor", + "ij ah", + "E mer", + "ĠB rav", + "Ġdesign ate", + "P OST", + "produ ced", + "Ġcleans ing", + "irl wind", + "ex istent", + "ĠHum ph", + "ĠPay ne", + "Ġv ested", + "Å ¡", + "Ġstring ent", + "ion a", + "Ġuns ub", + "Ġsum med", + "ĠHer cules", + "sub ject", + "ĠR agnar", + "ĠN os", + "Ġcharacter ization", + "Ġsav vy", + "ĠDaw son", + "ĠCas ino", + "Ġf ri", + "ĠBar rier", + "Ġmis information", + "Ġins ulation", + "Ġcorrid ors", + "Ġair planes", + "ĠNo ct", + "ah i", + "Ġ19 16", + "k b", + "arm ac", + "Ġsh un", + "Ġsche ma", + "Ġhorr ified", + "Ġ23 9", + "aund ers", + "N B", + "i ates", + "er ity", + "ĠSh ard", + "Ġr arity", + "Ġgroup ed", + "ĠGh ana", + "again st", + "ĠBi ological", + "ĠA ware", + "ow ell", + "Ï Ħ", + "ĠBe au", + "sh aw", + "H ack", + "ĠJul ius", + "US S", + "ol son", + "aun a", + "c ru", + "ĠMaur ice", + "ĠI k", + "Ġsequ encing", + "Ġradical s", + "Ġ( ?,", + "v irtual", + "Ġany ways", + "Ġreper c", + "Ġhand lers", + "Ġhes itant", + "é ĥ", + "ĠM F", + "ple mentation", + "ass ociated", + "Ġcampaign ed", + "ĠY ue", + "ut ations", + "ĠY oga", + "Ġsim mer", + "Ġro ds", + "Ġmel ody", + "Ġconv oy", + "v ideos", + "Ġscreen ed", + "N eg", + "ochem ical", + "Ġ( ))", + "Ġultr as", + "Ġant ip", + "ĠIsland ers", + "70 4", + "Ġfet ish", + "Ġridic ulously", + "ĠK art", + "Ġmitochond rial", + "Ġinterf ering", + "Build er", + "Ġover fl", + "Ġac ne", + "ĠM ud", + "ĠK err", + "f lex", + "ĠPost al", + "ĠBalt ic", + "47 7", + "ĠPers ons", + "our age", + "H B", + "ĠM use", + "ĠImm ortal", + "ĠDri ving", + "Ġpet itions", + "Ġsubsc ript", + "Ġs orce", + "ĠProcess or", + "ut on", + "S ony", + "Ġph on", + "Ġr aced", + "ĠAnth rop", + "Ġday time", + "ĠEx ercise", + "Add ing", + "Ġeng ages", + "ĠQual comm", + "Ġmir acles", + "Ġmem es", + "ĠDr ink", + "ĠOri oles", + "Ġhair s", + "ĠPol ar", + "ath om", + "Ġsl ippery", + "ĠR emy", + "Ġcar amel", + "ĠY EAR", + "Ġal k", + "I gn", + "a ution", + "ĠMer lin", + "ĠC ran", + "Ġap ologies", + "Ġ4 10", + "Ġout ing", + "ĠMem ories", + "app ointed", + "Ġcount ered", + "u ld", + "pos ing", + "Ġfire wall", + "ĠW ast", + "ĠW et", + "work ed", + "se ller", + "Ġrepe aled", + "ere o", + "ass uming", + "BL IC", + "m ite", + "ĠCEO s", + "ĠChap el", + "ellig ent", + "________________ ________", + "D og", + "Ġw art", + "Ġsubsc riber", + "s ports", + "Ġbe gged", + "ĠM V", + "Ġsem if", + "eth ical", + "Ġpre ach", + "Ġrev ital", + "Ġpun itive", + "Ġshort cuts", + "Ġinstit uted", + "ĠWars aw", + "Ġabdom en", + "ĠK ING", + "Ġsuper intendent", + "Ġf ry", + "ĠGe o", + "T OR", + "Ġcontrad ictions", + "apt ic", + "Ġlandsc apes", + "b ugs", + "Ġcl ust", + "Ġvol ley", + "c ribed", + "Ġt andem", + "Ġrob es", + "WH AT", + "Ġpromot er", + "Ġel oqu", + "review ed", + "ĠD K", + "ĠPl ato", + "Ġf ps", + "T ank", + "ĠDer rick", + "Ġpriorit ize", + "as per", + "ĠHond uras", + "ĠCom pleted", + "ne c", + "Ġm og", + "n ir", + "ĠMay o", + "DE F", + "st all", + "in ness", + "ĠVolks wagen", + "Ġprec aution", + "ĠM ell", + "i ak", + "ist ries", + "Ġ24 8", + "Ġoverl apping", + "Sen ate", + "ĠEnh ance", + "res y", + "rac ial", + "OR TS", + "ĠM ormons", + "Str ong", + "ĠCo ch", + "Mex ico", + "ĠMad uro", + "Ġj ars", + "Ġcan e", + "W ik", + "oll a", + "iff erence", + "Ġphysic ist", + "ĠMag gie", + "Ġ28 5", + "Ġdep iction", + "ĠMcL aren", + "J u", + "Ġsl ows", + "Ġcommission ers", + "ĠWill ow", + "ĠExpl os", + "hov ah", + "Ġtechn ician", + "Ġhom icides", + "ĠFl av", + "ĠTr uman", + "Ġ100 00", + "u ctor", + "Ġsh ader", + "News letter", + "45 7", + "Ġre ver", + "Ġhard ened", + "Ġwhere abouts", + "Ġrede velop", + "Ġcar bs", + "Ġtra vers", + "Ġsqu irrel", + "Ġfoll ower", + "Ġs ings", + "50 8", + "Ġrabb its", + "emon ium", + "Ġdocument ing", + "Ġmisunder stood", + ") '", + "R ick", + "gg ies", + "Ġprem ie", + "Ġsk ating", + "Ġpass ports", + "Ġf ists", + "aged don", + "H aw", + "AC P", + "0 80", + "ĠThough ts", + "ĠCarl son", + "Ġpriest hood", + "h ua", + "Ġdun geons", + "ĠLo ans", + "Ġant is", + "Ġfamiliar ity", + "ĠS abb", + "op al", + "ĠIn k", + "st rike", + "Ġc ram", + "Ġlegal ized", + "Ġcu isine", + "Ġfib re", + "Tra vel", + "ĠMon ument", + "OD Y", + "eth y", + "Ġinter state", + "ĠP UR", + "em porary", + "ĠArab ian", + "develop ed", + "Ġsadd le", + "Ġg ithub", + "ĠOff er", + "ĠIS P", + "ro let", + "ĠSUP ER", + "ĠDen is", + "Ġmultipl ier", + "Ġstir red", + "Interest ingly", + "Ġcustom ary", + "Ġbill ed", + "he x", + "Ġmultipl ied", + "Ġfl ipping", + "ĠCros by", + "Ġfundament als", + "ia e", + "ĠPlay ed", + "ĠAt om", + "am azon", + "ĠFl am", + "ee z", + "activ ated", + "Ġtables poon", + "Ġliberal ism", + "ĠPal in", + "ĠP atel", + "N um", + "ĠT AM", + "Ġs urn", + "ĠRel oaded", + "Ġco ined", + "\" ],", + "ĠCl ash", + "ĠAg u", + "Ġprag matic", + "ĠActiv ate", + "Ġ8 02", + "Ġtrail ers", + "Ġsil hou", + "Ġprob es", + "Ġcirc us", + "ĠB ain", + "ĠLind say", + "ĠAb bey", + "Del ivery", + "Ġconcess ion", + "Ġgast ro", + "ĠSpr ite", + "Ä Ł", + "and el", + "Ġg imm", + "Ġaut obi", + "ĠT urtle", + "Ġwonder fully", + "ĠHar am", + "ĠWorld wide", + "ĠHand le", + "Ġtheor ists", + "Ġsle ek", + "ĠZh u", + "ograph ically", + "EG A", + "ĠOwn ers", + "ath s", + "ĠAntar ctic", + "n atal", + "=\" \"", + "fl ags", + "`` ``", + "Ġs ul", + "K h", + "Ġpot assium", + "Ġlinem an", + "Ġcere al", + "ĠSe asons", + "Ġ20 22", + "Ġmat hematic", + "Ġastron omers", + "prof essional", + "Ġf ares", + "cknow led", + "Ġch i", + "Ġyoung sters", + "Ġmistaken ly", + "Ġhem isphere", + "ĠDiv inity", + "r one", + "Ġ\" ,", + "r ings", + "Ġattract s", + "v ana", + "å ¹", + "C AP", + "Ġplay list", + "Ġpor ch", + "ãģ £", + "Ġincorpor ates", + "Ġso ak", + "Ġassert ing", + "ĠTerror ism", + "ĠP ablo", + "J a", + "ces ter", + "Ġfear ing", + "ĠPr ayer", + "Ġescal ated", + "G W", + "Ġro be", + "ĠBright on", + "ac ists", + "ĠSym phony", + "ĠDwar f", + "ĠPar ade", + "ĠLe go", + "Ġinex pl", + "Ġl ords", + "le af", + "RA G", + "l iber", + "Ġcig ars", + "ĠJe hovah", + "60 6", + "WIND OWS", + "ĠLiber ia", + "eb us", + "He avy", + "Ġl ubric", + "ĠR W", + "angu ages", + "Ġnarrow ed", + "com puter", + "ĠE mber", + "Ġmurder ing", + "Ġdown stream", + "ĠT uls", + "ĠT ables", + "Top ic", + "ĠAcc uracy", + "= /", + "l ost", + "ĠRe i", + "Ġprogress es", + "b ear", + "Ġestablish ments", + "Just in", + "ĠPe ach", + "ĠG omez", + "å ¿", + "ĠTri angle", + "Id ent", + "ĠH ive", + "Res ources", + "Ġmix es", + "ĠAss uming", + "M u", + "Ġhyp oc", + "Ġs ane", + "ĠW an", + "id ious", + "Su ccess", + "Ġ io", + "Ang el", + "Ġdanger ously", + "ĠCreat ure", + "W ORK", + ": [", + "ĠKat rina", + "List ener", + "M iller", + "ĠId lib", + "h ang", + "Ġcircum vent", + "h ref", + "Ġcel estial", + "ĠWe eks", + "ĠP ug", + "ĠDal ton", + "Ġsubpoen a", + "uk u", + "Ġpers isted", + "pe i", + "old ing", + "ĠDoc uments", + "ĠH ast", + "ĠC ENT", + "Ġprim er", + "Ġsyn onymous", + "Ġn ib", + "om bs", + "Ġnot ation", + "ĠD ish", + "ĠAt mosp", + "Ġforb id", + "ĠAN G", + "pat tern", + "l os", + "Ġproject iles", + "b rown", + ".\" ,", + "ĠVen om", + "Ġfierce ly", + "ub lished", + "ĠU ran", + "ĠNic arag", + "4 10", + "ĠC AL", + "OT OS", + "ĠMir acle", + "ĠEn chant", + "Ġguard ing", + "app end", + "Att ach", + "Ġlevel ed", + "Ġcond oms", + "ih ilation", + "64 9", + "Ġnight mares", + "ĠTHE Y", + "ĠST ART", + "ĠK inn", + "Ġroomm ate", + "Ġhy giene", + "o pping", + "J ob", + "Ġl vl", + "ĠV ER", + "ĠKe eping", + "ab etic", + "Ġformat ting", + "eral a", + "Ġrev isions", + "Ġres urg", + "T el", + "ĠGood man", + "35 3", + "p od", + "Ġind isp", + "ĠTrans lation", + "Ġg own", + "ĠM und", + "Ġc is", + "Ġby stand", + "col lect", + "ĠPun jab", + "act ively", + "ĠG amb", + "te ll", + "Ġimport ing", + "g encies", + "Ġloc om", + "ĠBr ill", + "H oly", + "ĠBer ger", + "Ġshow down", + "Ġrespond ers", + "IL Y", + "Ġt akedown", + "le ted", + "Ġmat tered", + "Ġpredict ive", + "Ġover lay", + "G PU", + "ĠV ick", + "Ġconvey ed", + "T ab", + "pe er", + "Sc an", + "Ġdefensive ly", + "v ae", + "Ġappro ving", + "Ġt iers", + "ĠV ia", + "quer ade", + "ĠSaud is", + "Ġdemol ished", + "ĠProp he", + "Ġmon o", + "Ġhospital ity", + "H AM", + "ĠAri el", + "M OD", + "ĠTor ah", + "Ġbl ah", + "ĠBel arus", + "erent ial", + "ĠT uc", + "Ġbank er", + "39 7", + "Ġmosqu it", + "ĠScient ist", + "ĠMus ical", + "Ġh ust", + "Sh ift", + "Ġtor ment", + "Ġstand off", + "E duc", + "ĠF og", + "Ġampl ifier", + "Sh ape", + "Inst ance", + "ĠCrit ics", + "Ġda emon", + "H ouston", + "Ġmatt ress", + "ĠID F", + "Ġobsc ene", + "ĠA mer", + "hett i", + "Ġcomp iling", + "35 2", + "vere tt", + "ĠRed uction", + "ist ration", + "ĠBl essed", + "ĠB achelor", + "3 16", + "Ġpr ank", + "ĠVul can", + "dd ing", + "Ġm ourning", + "ĠQu int", + "ĠBl aster", + "test ing", + "Ġsed iment", + ">> >", + "ĠE ternity", + "ĠWH ERE", + "ĠM aze", + "Ġreact ing", + "ĠAl v", + "oms day", + "ĠC RA", + "Ġtransl ator", + "Ġbog us", + "at u", + "We bsite", + "oll s", + "Ġbapt ism", + "Ġs ibling", + "ĠAut umn", + "ve z", + "ãģ® é", + "gu ards", + "Ge org", + "assad ors", + "ĠFre ud", + "Ġcontin ents", + "ĠReg istry", + "Bern ie", + "ĸļ 士", + "Ġtoler ant", + "ĠU W", + "Ġhor ribly", + "99 5", + "ĠMID I", + "Ġimpat ient", + "oc ado", + "er i", + "ĠWor st", + "ĠNor ris", + "ĠTalk ing", + "Ġdef ends", + "ens able", + "Ġ20 21", + "Ġanat omy", + "L ew", + "Ġdraw er", + "ĠCan berra", + "Ġpatri otic", + "é¾įå ĸļ士", + "ĠAv g", + "AR M", + "Ġundis closed", + "Ġfare well", + "45 9", + "b able", + "ĠAll ison", + "OL OG", + "Ġcon co", + "t ight", + "ĠAC PI", + "ĠM ines", + "l ich", + "ĠâĶ ľ", + "represent ed", + "200 000", + "Ġenthusi ast", + "OT S", + "b il", + "ĠIng redients", + "Ġinvent or", + "ĠMy SQL", + "³³ Âł", + "ĠAB OUT", + "with in", + "Ġm k", + "B ul", + "ĠF ake", + "Ġdracon ian", + "W a", + "hel m", + "ĠTer ran", + "erv ille", + "Ġcommon place", + "SI ZE", + "Ġ\" <", + "re place", + "ograph s", + "ĠSE LECT", + "inc ible", + "ĠMost ly", + "ĠShe ffield", + "ĠID E", + "ugg le", + "Ġcit ations", + "h urst", + "ĠUn ix", + "Ġunle ash", + "ĠP iper", + "ĠN ano", + "Ġsucc umb", + "Ġreluct ance", + "Ġ25 00", + "ĠMer chant", + "Ġwire t", + "Ġcomb os", + "ĠBirth day", + "Ġchar coal", + "ĠU PS", + "ĠFair fax", + "Ġdrive way", + "ĠT ek", + "ĠP itch", + "ove re", + "Ġtechn icians", + "ĠAct ual", + "fl ation", + "ĠF iscal", + "ĠEm pty", + "an amo", + "Ġmag nesium", + "Ġsl ut", + "Ġgrow ers", + "Invest igators", + "( ):", + "ĠS atellite", + "ĠKe ynes", + "miss ive", + "l ane", + "Ġb orough", + "3 44", + "ĠTE AM", + "ĠBet hesda", + "C V", + "h ower", + "ĠR AD", + "Ġch ant", + "ĠR iy", + "Ġcompos itions", + "Ġmild ly", + "Ġmedd ling", + "Ġag ility", + "ane ers", + "5 01", + "Ġsyn th", + "ling er", + "29 1", + "Ġex claimed", + "Part y", + "Ġcont amin", + "ĠMan or", + "ĠResp ond", + "Ġpra ising", + "Ġman ners", + "fle et", + "Sum mer", + "ĠLy nd", + "ĠDef initely", + "gr im", + "Ġbow ling", + "st ri", + "ç Ľ", + "y nt", + "Ġmand ates", + "D IV", + "Ġreconc ile", + "view s", + "ĠDam on", + "vet te", + "F lo", + "ĠGreat est", + "il on", + "ic ia", + "Ġportray al", + "Ġcush ion", + "50 4", + "19 79", + "oss al", + "App lic", + "sc ription", + "Ġmit igation", + "AT S", + "p ac", + "Ġer ased", + "Ġdefic iencies", + "ĠHolland e", + "ĠX u", + "Ġb red", + "Ġpregn ancies", + "f emin", + "Ġem ph", + "Ġpl anners", + "Ġout per", + "utter ing", + "Ġperpet rator", + "Ġm otto", + "ĠEll ison", + "ĠNE VER", + "Ġadmitted ly", + "AR I", + "ĠAzerbai jan", + "Ġmill isec", + "Ġcombust ion", + "ĠBott le", + "ĠL und", + "ĠP s", + "ĠD ress", + "Ġfabric ated", + "Ġbat tered", + "Ġs idel", + "ĠNot ting", + "Fore ign", + "ĠJer ome", + "0 20", + "ĠAr bit", + "Ġkn ots", + "ĠR IGHT", + "M oving", + "ãģ Ļ", + "Ġsur geries", + "Ġcour thouse", + "Ġm astered", + "Ġhover ing", + "ĠBr an", + "ĠAl ison", + "Ġsaf est", + "m ilitary", + "Ġbull ied", + "Ġbar rage", + "Read er", + "ES E", + "ĠGe ographic", + "T ools", + "3 14", + "ĠGe ek", + "ro th", + "gl ers", + "ĠF IN", + "Ï ģ", + "ĠA ston", + "al tern", + "48 8", + "Ġveter in", + "G amer", + "Ġint el", + "ren ches", + "Sh ield", + "Ġam nesty", + "ĠB har", + "Ġp iled", + "Ġhonor able", + "ĠInst itutes", + "Ġso aked", + "Ġcom a", + "ĠE FF", + "34 1", + "by tes", + "ĠG mail", + "le in", + "ĠCanad iens", + "m aterial", + "I l", + "Ġinstruct ors", + "ĠK Y", + "Ġconce ive", + "ub b", + "ĠP ossible", + "Ġeas ing", + "ĠChrist ina", + "Ġcar ic", + "ĠHD R", + "R OM", + "Ġsho vel", + "de lete", + "Ġp uff", + "ĠCh anging", + "Ġseam lessly", + "Att ribute", + "Ġacqu isitions", + "ak ery", + "ĠE F", + "Ġaut istic", + "ĠT akes", + "ĠPow der", + "ĠSt ir", + "5 10", + "ĠBub ble", + "sett ings", + "ĠF owler", + "Ġmust ard", + "Ġmore over", + "Ġcopyright ed", + "ĠLED s", + "15 00", + "æ ī", + "ĠH IS", + "en f", + "Ġcust od", + "ĠH uck", + "G i", + "Ġim g", + "An swer", + "C t", + "j ay", + "ĠInf rastructure", + "Ġfeder ally", + "L oc", + "Ġmicro bes", + "Ġover run", + "dd s", + "ot ent", + "adi ator", + ">>>> >>>>", + "Ġtorn ado", + "Ġadj ud", + "Ġintrig ued", + "Ġs i", + "ĠRevel ation", + "pro gress", + "Ġburgl ary", + "ĠSai yan", + "ĠK athy", + "Ġser pent", + "ĠAndre as", + "Ġcomp el", + "ess ler", + "ĠPl astic", + "ĠAd vent", + "ĠPos itive", + "ĠQ t", + "ĠHind us", + "reg istered", + "ular ity", + "Ġrighteous ness", + "Ġdemon ic", + "u itive", + "ĠB DS", + "ĠGre gg", + "c ia", + "ĠCrus ade", + "ĠSina i", + "W ARE", + "+ (", + "Ġme ll", + "Ġder ail", + "y ards", + "A st", + "Ġnotice ably", + "ĠO ber", + "R am", + "Ġun noticed", + "Ġse q", + "av age", + "T s", + "Ġ6 40", + "Ġconced e", + "Ġ] )", + "F ill", + "Ġcapt ivity", + "ĠImprove ment", + "ĠCrus ader", + "ara oh", + "M AP", + "æ Ĺ", + "Ġstr ide", + "al ways", + "F ly", + "N it", + "Ġal gae", + "ĠCook ing", + "ĠDo ors", + "Mal ley", + "Ġpolic emen", + "ãģ į", + "Ġastron aut", + "access ible", + "49 5", + "ĠR AW", + "cl iffe", + "udic rous", + "Ġdep ended", + "al ach", + "Ġvent ures", + "ra ke", + "Ġt its", + "ĠH ou", + "Ġcond om", + "ormon al", + "Ġind ent", + "Ġupload ing", + "Foot note", + "Import ant", + "Ġ27 1", + "Ġmind ful", + "Ġcont ends", + "C ra", + "Ġcal ibr", + "ĠO ECD", + "plug in", + "F at", + "ĠIS S", + "ĠDynam ics", + "ans en", + "68 6", + "' ),", + "Ġsp rite", + "Ġhand held", + "ĠH ipp", + "=~ =~", + "Tr ust", + "Ġsem antics", + "ĠBund es", + "ĠRen o", + "ĠLiter ature", + "s ense", + "G ary", + "ĠA eg", + "ĠTr in", + "EE K", + "Ġcler ic", + "ĠSS H", + "Ġch rist", + "Ġinv ading", + "ib u", + "Ġen um", + "aur a", + "Ġal lege", + "ĠInc redible", + "B BC", + "Ġth ru", + "Ġsa iled", + "Ġem ulate", + "Ġin security", + "Ġc rou", + "Ġaccommod ations", + "Ġincompet ent", + "Ġsl ips", + "ĠEarth qu", + "s ama", + "IL LE", + "Ġi Phones", + "as aki", + "Ġby e", + "Ġar d", + "Ġext ras", + "Ġsl aughtered", + "Ġcrowd funding", + "res so", + "Ġfil ib", + "ĠER ROR", + "ĠT LS", + "e gg", + "ĠIt al", + "Ġen list", + "ĠCatal onia", + "ĠSc ots", + "Ġser geant", + "Ġdiss olve", + "N H", + "Ġstand ings", + "ri que", + "I Q", + "Ġbenef iciary", + "Ġaqu arium", + "You Tube", + "ĠPower Shell", + "Ġbright est", + "ĠWar rant", + "S old", + "Writ ing", + "Ġbegin nings", + "ĠRes erved", + "ĠLatin os", + "head ing", + "Ġ4 40", + "Ġrooft op", + "AT ING", + "Ġ3 90", + "VP N", + "G s", + "k ernel", + "turn ed", + "Ġprefer able", + "Ġturn overs", + "ĠH els", + "S a", + "ĠShin ji", + "ve h", + "ĠMOD ULE", + "V iol", + "Ġex iting", + "Ġj ab", + "ĠVan illa", + "Ġac ron", + "ĠG ap", + "ber n", + "A k", + "ĠMc Gu", + "Ġend lessly", + "ĠFar age", + "ĠNo el", + "V a", + "M K", + "Ġbr ute", + "ĠK ru", + "ĠES V", + "ĠOl ivia", + "âĢ ł", + "ĠK af", + "Ġtrust ing", + "Ġh ots", + "3 24", + "Ġmal aria", + "Ġj son", + "Ġp ounding", + "ort ment", + "Count ry", + "Ġpostp oned", + "Ġunequ iv", + "? ),", + "ĠRo oney", + "udd ing", + "ĠLe ap", + "ur rence", + "sh apeshifter", + "ĠH AS", + "os ate", + "Ġca vern", + "Ġconserv atism", + "ĠB AD", + "Ġmile age", + "Ġarrest ing", + "V aults", + "Ġmix er", + "Dem ocratic", + "ĠB enson", + "Ġauth ored", + "8 000", + "Ġpro active", + "ĠSpirit ual", + "t re", + "Ġincarcer ated", + "ĠS ort", + "Ġpe aked", + "Ġwield ing", + "re ciation", + "×Ļ ×", + "P atch", + "ĠEm my", + "Ġex qu", + "tt o", + "ĠRat io", + "ĠP icks", + "ĠG ry", + "ph ant", + "Ġf ret", + "Ġeth n", + "Ġarch ived", + "% -", + "c ases", + "ĠBl aze", + "Ġim b", + "c v", + "y ss", + "im ony", + "Ġcount down", + "Ġaw akening", + "ĠTunis ia", + "ĠRe fer", + "ĠM J", + "Ġun natural", + "ĠCar negie", + "iz en", + "ĠN uggets", + "he ss", + "Ġev ils", + "64 7", + "Ġintrodu ctory", + "l oving", + "ĠMcM ahon", + "Ġambig uity", + "L abel", + "ĠAlm ighty", + "Ġcolor ing", + "ĠCl aus", + "set ting", + "N ULL", + "ĠF avorite", + "ĠS IG", + "> (", + "ĠSh iva", + "ĠMay er", + "Ġstorm ed", + "ĠCo verage", + "we apons", + "igh am", + "Ġun answered", + "Ġle ve", + "Ġc oy", + "c as", + "b ags", + "as ured", + "Se attle", + "ĠSant orum", + "ser ious", + "Ġcourage ous", + "ĠS oup", + "Ġconfisc ated", + "Ġ// /", + "Ġuncon ventional", + "Ġmom s", + "ĠRohing ya", + "ĠOrche stra", + "ĠPot ion", + "Ġdisc redit", + "ĠF IL", + "f ixed", + "ĠDe er", + "do i", + "ĠDim ension", + "Ġbureaucr ats", + "et een", + "Ġaction Group", + "oh m", + "Ġb umps", + "ĠUt ility", + "Ġsubmar ines", + "ren heit", + "re search", + "ĠShap iro", + "Ġsket ches", + "Ġde ceptive", + "ĠV il", + "es ame", + "ĠEss entially", + "Ġramp age", + "isk y", + "Ġmut tered", + "th ritis", + "Ġ23 6", + "f et", + "b ars", + "Ġpup il", + "ĠTh ou", + "o S", + "s ong", + "Ġfract ured", + "Ġre vert", + "pict ure", + "Ġcrit erion", + "us her", + "Ġreperc ussions", + "ĠV intage", + "ĠSuper intendent", + "Offic ers", + "Ġflag ged", + "Ġbl ames", + "Ġin verse", + "ograp hers", + "Ġmakes hift", + "Ġdev oid", + "Ġfoss ils", + "ĠArist otle", + "ĠFund s", + "Ġde pleted", + "ĠFl u", + "ĠY uan", + "Ġw oes", + "Ġlip id", + "Ġsit u", + "requ isites", + "Ġfurn ish", + "ĠSam ar", + "Ġshame ful", + "Ġadverse ly", + "Ġad ept", + "Ġrem orse", + "Ġmurder ous", + "uck les", + "ĠE SL", + "Ġ3 14", + "s ent", + "Ġred ef", + "ĠC ache", + "ĠP urs", + "ig ans", + "Ġ4 60", + "Ġpres criptions", + "Ġf res", + "F uck", + "ocr ates", + "Tw enty", + "ĠWe ird", + "ĠT oggle", + "ĠC alled", + "itiz ens", + "Ġp oultry", + "Ġharvest ing", + "ãĤ¦ ãĤ¹", + "Bott om", + "Ġcaution ed", + "t n", + "39 6", + "ĠNik ki", + "Ġeval uations", + "Ġharass ing", + "Ġbind ings", + "ĠMon etary", + "Ġhit ters", + "Ġadvers ary", + "un ts", + "Ġset back", + "Ġenc rypt", + "ĠC ait", + "Ġl ows", + "eng es", + "ĠN orn", + "Ġbul bs", + "Ġbott led", + "ĠVoy ager", + "3 17", + "Ġsp heres", + "p olitics", + "Ġsubt ract", + "Ġsens ations", + "Ġapp alling", + "Ġ3 16", + "Ġenvironment ally", + "ĠST EM", + "Ġpub lishes", + "5 60", + "Ġdilig ence", + "48 4", + "Ġadv ises", + "Ġpet rol", + "Ġimag ining", + "Ġpatrol s", + "ĠInt eger", + "ĠAs hes", + "act us", + "ĠRad iant", + "ĠL T", + "it ability", + "ht aking", + "Set ting", + "Ġnu anced", + "ĠRe ef", + "ĠDevelop ers", + "N i", + "pie ces", + "99 0", + "Lic ense", + "Ġlow ers", + "ĠOtt oman", + "3 27", + "oo o", + "Ġqu itting", + "mark ets", + "Beh ind", + "Ġbas in", + "Ġdoc s", + "an ie", + "fl ash", + "ct l", + "Ġcivil ized", + "ĠFuk ushima", + "\"] ,\"", + "ĠK S", + "ĠHonest ly", + "ar at", + "Ġconstruct s", + "ĠL ans", + "ĠD ire", + "ĠLI KE", + "ĠTrou ble", + "Ġwith holding", + "ĠOb livion", + "Ġsan ity", + "any a", + "Con st", + "Ġgro cer", + "ĠC elsius", + "Ġrecount ed", + "ĠW ife", + "B order", + "ate red", + "h appy", + "Ġspo iler", + "Ġlog ically", + "H all", + "Ġsucceed ing", + "Ġpoly morph", + "Ġax es", + "ĠShot gun", + "ĠS lim", + "ĠPrin ciples", + "ĠL eth", + "art a", + "Ġsc or", + "Sc reenshot", + "Ġrelax ation", + "#$ #$", + "Ġdeter rent", + "idd y", + "Ġpower less", + "Ġles bians", + "Ġch ords", + "ĠEd ited", + "se lected", + "Ġseparat ists", + "000 2", + "Ġair space", + "Ġturn around", + "Ġc unning", + "P ATH", + "P oly", + "Ġbomb ed", + "Ġt ion", + "x s", + "Ġwith hold", + "Ġw aged", + "ĠLiber ties", + "Fl ag", + "Ġcomfort ing", + "45 4", + "ĠI ris", + "are rs", + "Ġr ag", + "Ġrel ocated", + "ĠGu arant", + "Ġstrateg ically", + "Ġgam ma", + "uber ty", + "ĠLock heed", + "g res", + "Ġgr illed", + "ĠLow e", + "st ats", + "ĠR ocks", + "Ġsens ing", + "Ġrent ing", + "ĠGe ological", + "ا Ø", + "ot rop", + "Ġse w", + "Ġimproper ly", + "48 6", + "Ġâĸ ł", + "Ġstar ving", + "ĠB j", + "Disc ussion", + "3 28", + "ĠCom bo", + "ĠFix es", + "N AT", + "Ġstri ving", + "th ora", + "Ġharvest ed", + "ĠP ing", + "Ġplay ful", + "Ġaven ues", + "Ġoccup ational", + "Ġw akes", + "ĠCou rier", + "Ġdrum mer", + "ĠBrow ser", + "ĠH outh", + "it u", + "Ġapp arel", + "p aste", + "Ġhun ted", + "ĠSecond ly", + "l ain", + "X Y", + "ĠP IN", + "ic ons", + "Ġcock tails", + "Ġs izable", + "Ġhurd les", + "est inal", + "ĠRecre ation", + "Ġe co", + "64 8", + "ĠD ied", + "m int", + "Ġfinger prints", + "Ġdis pose", + "ĠBos nia", + "ts y", + "22 00", + "Ġins pected", + "ĠF ou", + "Ġf uss", + "Ġamb ush", + "ĠR ak", + "Ġmanif ested", + "Pro secut", + "Ġsuff ice", + "ren ces", + "Ġcompens ated", + "ĠC yrus", + "Ġgen us", + "ĠWolver ine", + "ĠTrend s", + "Ġh ikes", + "ĠSe en", + "Ġen rol", + "C old", + "Ġpol itely", + "ĠSl av", + "ĠRu pert", + "Ġey ewitness", + "ĠAl to", + "Ġun comp", + "Ġposter ior", + "M ust", + "ĠHer z", + "Ġprogress ively", + "Ġ23 4", + "Ġind ifference", + "ĠCunning ham", + "Ġacadem ia", + "Ġse wer", + "Ġast ounding", + "ĠA ES", + "r ather", + "Ġeld est", + "Ġclim bs", + "ĠAdd s", + "Ġout cry", + "Ġcont ag", + "ĠH ouses", + "Ġpe pt", + "ĠMel ania", + "interest ed", + "ĠU CH", + "ĠR oots", + "ĠHub bard", + "ĠT BD", + "ĠRoman ian", + "fil ename", + "St one", + "ĠIm pl", + "Ġchromos ome", + "C le", + "d x", + "Ġscram bled", + "ĠP t", + "Ġ24 2", + "OP LE", + "Ġtremend ously", + "St reet", + "Ġcra ving", + "Ġbund led", + "ĠR G", + "p ipe", + "Ġinj uring", + "Ġarc ane", + "Part icip", + "ĠHero ic", + "st y", + "Ġto pping", + "ĠTemp est", + "rent ices", + "b h", + "Ġpar anoia", + "ĠUnic ode", + "Ġegreg ious", + "Ġ\\ '", + "ĠOsw ald", + "Ġgra vel", + "ĠSim psons", + "Ġbl and", + "ĠGuant anamo", + "Writ er", + "lin ers", + "ĠD ice", + "J C", + "Ġpar ity", + "Ġs ided", + "Ġ23 7", + "ĠPyr rha", + "at ters", + "d k", + "F ine", + "comp an", + "Ġform ulated", + "ĠId ol", + "il ers", + "hem oth", + "ĠF av", + "Ġintr usion", + "Ġcar rots", + "ĠL ayer", + "ĠH acker", + "Ġ ----------------", + "Ġmoder ation", + "é ģ", + "oc oc", + "Ġcharacter ize", + "ĠTe resa", + "Ġsocio economic", + "Ġper k", + "ĠParticip ation", + "tr aining", + "ĠPaul o", + "ph ys", + "Ġtrust worthy", + "Ġembod ied", + "ĠMer ch", + "c urrency", + "ĠPrior ity", + "Ġte asing", + "Ġabsor bing", + "Ġunf inished", + "ĠCompar ison", + "Ġdis ple", + "writ ers", + "Ġprofess ions", + "ĠPengu in", + "Ġang rily", + "ĠL INK", + "68 8", + "ĠCor respond", + "Ġprev ailed", + "Ġcart el", + "l p", + "as ms", + "ĠRed emption", + "ĠIslam ists", + "effect s", + "d ose", + "ĠL atter", + "ĠHal ifax", + "Ġv as", + "ĠTop ics", + "ĠN amed", + "advert ising", + "zz a", + "IC ES", + "Ġret arded", + "ach able", + "ĠPupp et", + "ĠItem Level", + "Ġret ract", + "Ġident ifiable", + "A aron", + "ĠB uster", + "s ol", + "hel le", + "as semb", + "H ope", + "r anged", + "B a", + "ĠP urch", + "é Ģ", + "ĠSir i", + "Ġarri vals", + "Ġ19 12", + "Ġshort ened", + "Ġ3 12", + "Ġdiscrep ancy", + "ĠTem perature", + "ĠWal ton", + "Ġkind erg", + "p olit", + "Ġrem ix", + "Ġconnect ors", + "ãĥĺ ãĥ©", + "ĠKazakh stan", + "dom inated", + "Ġsu gars", + "im ble", + "ĠPan ic", + "ĠDem and", + "ĠCol ony", + "on en", + "ĠM ER", + "7 75", + "ur ia", + "aza ar", + "ĠDeg ree", + "P ri", + "Ġsun shine", + "Ġ25 1", + "Ġpsychedel ic", + "Ġdigit ally", + "ĠBra un", + "Ġsh immer", + "Ġsh ave", + "ĠTel esc", + "ĠAst ral", + "ĠVenezuel an", + "ĠO G", + "Ġc rawling", + "Int eg", + "ĠFe ather", + "Ġunfold ing", + "Ġappropri ation", + "Ġè£ı è", + "ĠMob ility", + "ĠN ey", + "- .", + "b ilt", + "L IN", + "ĠT ube", + "ĠCon versely", + "Ġkey boards", + "ĠC ao", + "Ġover th", + "Ġla ure", + ">> \\", + "ĠV iper", + "ach a", + "Off set", + "ĠR aleigh", + "ĠJ ae", + "J ordan", + "j p", + "Ġtotal itarian", + "Connect or", + "Ġobserv es", + "ĠSpart an", + "ĠIm mediately", + "ĠSc al", + "C ool", + "Ġt aps", + "Ġro ar", + "P ast", + "Ġch ars", + "ĠB ender", + "ĠShe ldon", + "Ġpain ter", + "Ġbe acon", + "ĠCreat ures", + "Ġdownt urn", + "Ġh inder", + "ĠAnd romeda", + "à Ľ", + "cc oli", + "ĠF itness", + "et rical", + "Ġutil izes", + "Ġsen ate", + "Ġen semble", + "Ġche ers", + "T W", + "Ġaff luent", + "k il", + "ry lic", + "ord ering", + "Com puter", + "Ġgru esome", + "ost ics", + "ĠUb isoft", + "ĠKel ley", + "Ġw rench", + "Ġbourgeois ie", + "IB LE", + "ĠPrest on", + "w orn", + "ar ist", + "reat ing", + "Ġst ained", + "ar ine", + "Ġsl ime", + "EN N", + "Ġche sts", + "Ġground water", + "ann ot", + "ĠTr ay", + "ĠLoc ke", + "ĠC TR", + "Ġd udes", + "ĠEx ternal", + "ĠDec oder", + "Ġpar amed", + "ĠMed line", + "80 9", + "ĠD inner", + "rup al", + "g z", + "ĠG um", + "ĠDem o", + "j ee", + "Ġd h", + "ber man", + "arch s", + "Ġen qu", + "ĠEp stein", + "Ġdevast ation", + "Ġfriends hips", + "ĠAr d", + "Ġ23 1", + "ĠRub in", + "ĠDist ance", + "Ġsp urred", + "Ġd ossier", + "Ġover looking", + "\\\\\\\\\\\\\\\\ \\\\\\\\\\\\\\\\", + "Fore st", + "ĠCom es", + "\\ \",", + "ĠIran ians", + "Ġf ixtures", + "L aughs", + "Ġcur ry", + "ĠKing ston", + "Ġsqu ash", + "Ġcat alogue", + "Ġabnormal ities", + "Ġdigest ive", + ".... .....", + "Ġsubord inate", + "og ly", + "Ġ24 9", + "M iddle", + "Ġmass ac", + "Ġburg ers", + "Ġdown stairs", + "Ġ19 31", + "39 4", + "ĠV G", + "Ġl asers", + "ĠS ikh", + "ĠAlex a", + "der ived", + "Ġcycl ist", + "ãģ® éŃĶ", + "onel iness", + "!!!! !!!!", + "Ġbuff s", + "leg ate", + "Ġrap ing", + "Ġrecomm ending", + "ro red", + "Ġmult icultural", + "un ique", + "Ġbusiness men", + "Ġune asy", + "ĠM AP", + "Ġdisp ersed", + "cipl ine", + "J ess", + "ĠK erala", + "å §", + "Ġabst raction", + "Sur v", + "U h", + "Ġprin ters", + "ij a", + "ow der", + "Ġanalog ous", + "ĠA SP", + "af er", + "Ġunfold ed", + "Ġlevel ing", + "Ġbre ached", + "ĠH earing", + "Ġn at", + "Ġtransl ating", + "crit ical", + "Ġant agonist", + "ĠYes terday", + "Ġfuzz y", + "w ash", + "m ere", + "Ġbe wild", + "ĠM ae", + "V irgin", + "ph rase", + "Ġsign aled", + "ĠH IGH", + "Ġprot ester", + "Ġgar ner", + "unk nown", + "Ġk ay", + "Ġabduct ed", + "Ġst alking", + "am n", + "Ġdes erving", + "ĠR iv", + "ĠJ orge", + "Ġscratch ing", + "ĠS aving", + "ip ing", + "Ġte ase", + "Ġmission ary", + "ĠMor row", + "T IME", + "P resent", + "Ġchem otherapy", + "tern ess", + "ĠH omes", + "ĠP urdue", + "Ġst aunch", + "ĠWhit ney", + "ĠTH ERE", + "Î ¼", + "iat us", + "ĠErn est", + "ĠDe ploy", + "Ġcove ted", + "F ML", + "ĠDial ogue", + "Ġex ited", + "f ruit", + "Ġner d", + "\":\" \",\"", + "Ġv ivo", + "ru ly", + "4 60", + "ĠAm en", + "rehens ible", + "Ġâ ĺ", + "D IR", + "Ġad herence", + "Ġche w", + "ĠCo ke", + "ĠSerge i", + "dig ital", + "ĠNe ck", + "g ently", + "enth al", + "/ )", + "Ġwe ary", + "Ġgu ise", + "ĠConc ord", + "ĠOn ion", + "at cher", + "Ġb inge", + "ĠDirect ive", + "Ġman ned", + "ans k", + "Ġill usions", + "Ġbillion aires", + "38 3", + "oly n", + "odynam ic", + "ĠWhe at", + "ĠA lic", + "Ġcol oured", + "ĠN AFTA", + "ab o", + "Ġmac ros", + "ind ependent", + "s weet", + "Ġsp ac", + "ĠK abul", + "Ġ Ä", + "em e", + "Ġdict ated", + "Ġsh outs", + "= {", + "Ġr ipping", + "ĠSh ay", + "ĠCr icket", + "direct ed", + "Ġanalys ed", + "ĠWAR RANT", + "ag ons", + "ĠBlaz ers", + "Ġche ered", + "Ġar ithmetic", + "ĠTan z", + "37 3", + "ĠFl ags", + "Ġ29 5", + "Ġw itches", + "ĠIn cluded", + "ĠG ained", + "ĠBl ades", + "G am", + "ĠSam antha", + "ĠAtl antis", + "ĠPr att", + "Ġspo iled", + "ĠI B", + "ĠRam irez", + "Pro bably", + "re ro", + "ĠN g", + "ĠWar lock", + "t p", + "Ġover he", + "Ġadministr ations", + "Ġt int", + "Ġreg iment", + "Ġpist ols", + "Ġblank ets", + "Ġep ist", + "Ġbowl s", + "Ġhydra ulic", + "Ġde an", + "Ġj ung", + "Ġasc end", + "70 5", + "ĠSant iago", + "à ®", + "Ġun avoid", + "ĠSh aman", + "re b", + "Ġstem ming", + "99 8", + "ĠM G", + "st icks", + "esthes ia", + "ER O", + "Ġmor bid", + "ĠGr ill", + "ĠP oe", + "any l", + "Ġdele ting", + "ĠSurve illance", + "Ġdirect ives", + "Ġiter ations", + "ĠR ox", + "ĠMil ky", + "F ather", + "Ġpat ented", + "44 7", + "Ġprec ursor", + "Ġm aiden", + "ĠP hen", + "ĠVe gan", + "ĠPat ent", + "K elly", + "Redd itor", + "Ġn ods", + "Ġvent ilation", + "ĠSchwar z", + "Ġw izards", + "Ġomin ous", + "ĠHe ads", + "ĠB G", + "Ġl umber", + "ĠSp iel", + "Ġis Enabled", + "Ġancest ral", + "ĠSh ips", + "Ġwrest ler", + "ph i", + "Ġy uan", + "ĠRebell ion", + "Ġice berg", + "Ġmag ically", + "Ġdivers ion", + "ar ro", + "yth m", + "ĠR iders", + "ĠRob bie", + "ĠK ara", + "ĠMain tenance", + "ĠHer b", + "Ġhar ms", + "p acked", + "ĠFe instein", + "Ġmarry ing", + "Ġbl ending", + "ĠR ates", + "Ġ18 80", + "Ġwr ink", + "ĠUn ch", + "ĠTor ch", + "desc ribed", + "Ġhuman oid", + "ilit ating", + "ĠCon v", + "ĠFe ld", + "IGH TS", + "Ġwhistlebl ower", + "ort mund", + "ets y", + "arre tt", + "ĠMon o", + "ĠI ke", + "ĠC NBC", + "ĠW AY", + "ĠMD MA", + "ĠIndividual s", + "Ġsupplement al", + "Ġpower house", + "ĠSt ru", + "F ocus", + "aph ael", + "ĠCol leg", + "att i", + "Z A", + "Ġp erenn", + "ĠSign ature", + "ĠRod ney", + "Ġcub es", + "idd led", + "ĠD ante", + "ĠIN V", + "iling ual", + "ĠC th", + "Ġso fa", + "Ġintimid ate", + "ĠR oe", + "ĠDi plom", + "ĠCount ries", + "ays on", + "Ġextrad ition", + "Ġdis abling", + "ĠCard iff", + "Ġmemor andum", + "ĠTr ace", + "Ġ?? ?", + "se ctor", + "ĠRou hani", + "ĠY ates", + "ĠFree ze", + "Ġbl adder", + "M otor", + "ĠProm ise", + "ant asy", + "Ġforesee able", + "ĠC ologne", + "cont ainer", + "ĠTre es", + "ĠG ors", + "ĠSin clair", + "Ġbar ring", + "key e", + "Ġsl ashed", + "ĠStat istical", + "é ĩ", + "Ġâĸ º", + "All ows", + "Ġhum ility", + "Ġdr illed", + "ĠF urn", + "44 3", + "Ġse wage", + "Ġhome page", + "Ġcour tyard", + "Ġv ile", + "Ġsubsid iaries", + "aj o", + "direct ory", + "Ġam mon", + "V ers", + "charg es", + "Ġ} }", + "ĠCh ains", + "Ġ24 6", + "n ob", + "Ġper cept", + "Ġg rit", + "Ġfisher men", + "ĠIraq is", + "ĠDIS TR", + "ĠF ULL", + "ĠEval uation", + "g raph", + "at ial", + "Ġcooper ating", + "Ġmel an", + "Ġenlight ened", + "Ġal i", + "t ailed", + "Ġsal ute", + "Ġweak est", + "ĠBull dogs", + "U A", + "ĠAll oy", + "Ġsem en", + "oc ene", + "ĠWilliam son", + "s pr", + ", âĢĶ", + "ĠG F", + "itt ens", + "Be at", + "ĠJ unk", + "iph ate", + "ĠFarm ers", + "ĠBit coins", + "ig ers", + "d h", + "ĠL oyal", + "p ayer", + "Ġentert ained", + "Ġpenn ed", + "Ġcoup on", + "Que ue", + "Ġweaken ing", + "c arry", + "Ġunderest imate", + "Ġshoot out", + "Ġcharism atic", + "ĠProced ure", + "Ġprud ent", + "in ances", + "Ġric hes", + "Ġcort ical", + "Ġstr ides", + "Ġd rib", + "ĠOil ers", + "5 40", + "ĠPer form", + "ĠBang kok", + "Ġe uth", + "S ER", + "Ġsimpl istic", + "t ops", + "camp aign", + "Q uality", + "Ġimpover ished", + "ĠEisen hower", + "Ġaug ment", + "ĠH arden", + "Ġinterven ed", + "Ġlist ens", + "ĠK ok", + "Ġs age", + "Ġrub bish", + "ĠD ed", + "Ġm ull", + "pe lling", + "Ġvide ot", + "Produ ction", + "D J", + "m iah", + "Ġadapt ations", + "Ġmed ically", + "Ġboard ed", + "Ġarrog ance", + "Ġscra pped", + "Ġopp ress", + "FORM ATION", + "Ġj unction", + "4 15", + "EE EE", + "S kill", + "Ġsub du", + "ĠSug gest", + "ĠP ett", + "Ġle tt", + "ĠMan ip", + "ĠC af", + "ĠCooper ation", + "T her", + "Ġreg ained", + "¶ æ", + "ref lect", + "Ġth ugs", + "ĠShel by", + "Ġdict ates", + "ĠWe iner", + "ĠH ale", + "Ġbatt leground", + "s child", + "Ġcond ol", + "h unt", + "osit ories", + "Ġacc uses", + "Fil ename", + "Ġsh ri", + "Ġmotiv ate", + "Ġreflect ions", + "N ull", + "ĠL obby", + "¥ µ", + "ĠS ATA", + "ĠBack up", + "Ñ ĥ", + "n in", + "ĠCor rection", + "Ġju icy", + "ut ra", + "ĠP ric", + "Ġrest raining", + "ĠAir bnb", + "ĠAr rest", + "Ġappropri ations", + "Ġsl opes", + "Ġmans laughter", + "Ġwork ings", + "ĠH uss", + "ĠF rey", + "Le ave", + "ĠHarm ony", + "ĠF eder", + "Ġ4 30", + "Ġt rench", + "Ġglad ly", + "Ġbull pen", + "ĠG au", + "b ones", + "Ġgro ove", + "Ġpre text", + "ã ħĭ", + "Ġtransm itter", + "ĠComp onent", + "Ġunder age", + "ĠEm pires", + "T ile", + "Ġo y", + "ĠMar vin", + "ĠC AS", + "Ġbl oss", + "Ġrepl icated", + "ĠMar iners", + "Marc us", + "ĠBl ocks", + "Ġliber ated", + "Ġbutter fly", + "Fe el", + "Ġfer mentation", + "Ġyou tube", + "Ġoff end", + "ĠTer m", + "res ist", + "Ġcess ation", + "Ġinsurg ency", + "Ġb ir", + "ĠRa ise", + "59 5", + "Ġhypothes es", + "50 2", + "Ġpl aque", + "ocr at", + "Ġjack ets", + "ĠHuff Post", + "am ong", + "Ġconf er", + "48 7", + "ĠL illy", + "Ġadapt ing", + "ĠF ay", + "Ġsh oved", + "ve c", + "Ġref ine", + "Ġg on", + "Ġgun men", + "z ai", + "ĠShut tle", + "ĠI zan", + "Ġ19 13", + "Ġple thora", + "· ·", + "Ġ5 10", + "Ġp uberty", + "Ġ24 1", + "ĠWe alth", + "ĠAl ma", + "ĠM EM", + "ĠAd ults", + "C as", + "pr ison", + "R ace", + "Ġwater proof", + "Ġathlet icism", + "Ġcapital ize", + "ĠJu ice", + "Ġillum inated", + "ĠP ascal", + "Ġirrit ation", + "ĠWitness es", + "ad le", + "ĠAst ro", + "Ġf ax", + "ĠEl vis", + "Prim ary", + "ĠL ich", + "ĠEl ves", + "Ġres iding", + "Ġst umble", + "3 19", + "ĠP KK", + "Ġadvers aries", + "D OS", + "ĠR itual", + "Ġsm ear", + "Ġar son", + "ident al", + "Ġsc ant", + "Ġmon archy", + "Ġhal ftime", + "Ġresid ue", + "Ġind ign", + "ĠSh aun", + "ĠEl m", + "aur i", + "A ff", + "W ATCH", + "ĠLy on", + "hel ps", + "36 1", + "Ġlobby ist", + "Ġdimin ishing", + "Ġout breaks", + "Ġgo ats", + "f avorite", + "ĠN ah", + "son ian", + "ĠBo oster", + "Ġsand box", + "ĠF are", + "ĠMalt a", + "Ġatt Rot", + "ĠM OR", + "ld e", + "Ġnavig ating", + "T ouch", + "Ġunt rue", + "ĠDis aster", + "Ġl udicrous", + "Pass word", + "ĠJ FK", + "blog spot", + "4 16", + "ĠUN DER", + "ern al", + "Ġdelay ing", + "T OP", + "Ġimpl ants", + "ĠAV G", + "ĠH uge", + "att r", + "Ġjournal istic", + "ĠPe yton", + "ĠI A", + "R ap", + "go al", + "ĠProgram me", + "Ġsm ashing", + "w ives", + "print ln", + "ĠPl ague", + "in us", + "EE P", + "Ġcru iser", + "ĠPar ish", + "umin ium", + "Ġoccup ants", + "ĠJ ihad", + "m op", + "Ġp int", + "Ġhe ct", + "ĠMe cca", + "direct or", + "ĠFund ing", + "ĠM ixed", + "Ġst ag", + "T ier", + "Ġg ust", + "Ġbright ly", + "ors i", + "Ġup hill", + "R D", + "Ġles ions", + "ĠBund y", + "liv ious", + "Ġbi ologist", + "ĠFac ulty", + "ĠAuthor ization", + "Ġ24 4", + "All ow", + "ï ¸", + "ĠGi ul", + "Ġpert inent", + "ot aur", + "es se", + "ĠRo of", + "Ġunman ned", + "35 1", + "ĠSh ak", + "ĠO rient", + "Ġend anger", + "D ir", + "Ġrepl en", + "ed ient", + "Ġtail or", + "Ġgad gets", + "Ġaud ible", + "âĺ Ĩ", + "N ice", + "Ġbomb ard", + "ĠR ape", + "Ġdef iance", + "ĠTW O", + "ĠFilip ino", + "Ġunaff ected", + "erv atives", + "Ġso ared", + "ĠBol ton", + "Ġcomprom ising", + "ĠBrew ers", + "R AL", + "ĠA HL", + "icy cle", + "Ġv ampires", + "Ġdi pped", + "oy er", + "ĠX III", + "Ġsidew ays", + "ĠW aste", + "ĠD iss", + "ĠâĶľ âĶĢâĶĢ", + "$ .", + "Ġhabit ats", + "ĠBe ef", + "tr uth", + "tr ained", + "spl it", + "R us", + "And y", + "ĠB ram", + "RE P", + "p id", + "è£ ħ", + "ĠMut ant", + "An im", + "ĠMar ina", + "Ġfut ile", + "hig hest", + "f requency", + "Ġepile psy", + "Ġcop ing", + "Ġconc ise", + "Ġtr acing", + "ĠS UN", + "pan el", + "ĠSoph ie", + "ĠCrow ley", + "ĠAd olf", + "ĠShoot er", + "Ġsh aky", + "ĠI G", + "ĠL ies", + "ĠBar ber", + "p kg", + "Ġupt ake", + "Ġpred atory", + "UL TS", + "/ **", + "Ġintox icated", + "ĠWest brook", + "od der", + "he ment", + "Ġbas eman", + "AP D", + "st orage", + "ĠFif ty", + "ed itor", + "G EN", + "UT ION", + "ir ting", + "Ġse wing", + "r ift", + "Ġag ony", + "ĠS ands", + "Ġ25 4", + "C ash", + "Ġl odge", + "Ġp unt", + "N atural", + "ĠIde as", + "Ġerrone ous", + "ĠSens or", + "ĠHann ity", + "Ġ19 21", + "Ġm ould", + "ĠG on", + "kay a", + "Ġanonym ously", + "ĠK EY", + "Ġsim ulator", + "W inter", + "Ġstream ed", + "50 7", + "? \",", + "Ġte ased", + "Ġco efficient", + "Ġwart ime", + "ĠTH R", + "' '.", + "ĠBank ing", + "mp ire", + "Ġf andom", + "Ġl ia", + "G a", + "Ġdown hill", + "Ġinterpre ting", + "Ind ividual", + "N orm", + "Ġjealous y", + "bit coin", + "Ġple asures", + "ĠToy s", + "ĠChev rolet", + "ĠAd visor", + "IZ E", + "Ġrecept ions", + "70 6", + "C ro", + "Ġ26 2", + "Ġcit rus", + "ir u", + "Review er", + "ject ed", + "U ES", + "an z", + "19 81", + "ĠWork er", + "Ġcompl ied", + "ores cent", + "contin ental", + "T on", + "ĠPr ism", + "ĠShe ep", + "Ġ28 8", + "n ox", + "ĠV og", + "O rd", + "Ġreal ms", + "te k", + "Ġirrig ation", + "Ġbicy cles", + "Ġelectron ically", + "p oly", + "t all", + "() );", + "Ġaest hetics", + "ĠInteg rated", + "Expl ore", + "Ġd unk", + "47 6", + "p ain", + "ĠJac ques", + "ĠD mit", + "Fram es", + "Ġreun ited", + "Ġhum id", + "D ro", + "P olitical", + "Ġyouth ful", + "Ġent ails", + "Ġmosqu ito", + "36 3", + "spe cies", + "Ġcoord inating", + "ĠMay hem", + "ĠMagn us", + "M ount", + "Impro ved", + "ĠST ATE", + "ATT LE", + "Ġflow ed", + "Ġtack led", + "Ġfashion ed", + "Ġre organ", + "iv ari", + "f inger", + "Ġreluct antly", + "et ting", + "ĠV and", + "you ng", + "ĠGar land", + "Ġpresum ption", + "Ġamen ities", + "ĠPle asant", + "on ential", + "ĠO xy", + "Ġmor als", + "ĠY ah", + "Read y", + "Sim on", + "En h", + "D emon", + "Ġcl ich", + "Mon itor", + "ĠD U", + "Ġwel comes", + "Ġstand out", + "Ġdread ful", + "Ġban anas", + "Ġball oons", + "h ooting", + "bas ic", + "Ġsuff ix", + "Ġd uly", + "can o", + "Ch ain", + "at os", + "Ġgeop olitical", + "Ġ( &", + "ĠGem ini", + "ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ ÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤÃĥÃĤ", + "Ġacqu itted", + "L uck", + "prot ect", + "10 24", + "Ġsc arcity", + "Ġmind fulness", + "ec ided", + "D N", + "pr ime", + "ĠPres idents", + "ĠVID EO", + "Ġ( âĪĴ", + "add ock", + "N OR", + "ĠP ru", + "p un", + "ĠL OL", + ")) ))", + "ĠL iqu", + "ĠS AS", + "Ġsty ling", + "Ġpunish ments", + "Ġnum b", + "Ġasc ertain", + "ĠRock ies", + "f lu", + "Th umbnail", + "Ġperpet rated", + "ĠSem i", + "Ġdis arm", + "ĠOld er", + "ĠEx ception", + "Ġexponent ially", + "ĠCommun ities", + "Ġabol ish", + "ĠPart ner", + "pt oms", + "Ġ7 77", + "ĠFo ley", + "ĠC ases", + "Ġgre ase", + "ĠReb irth", + "G round", + "Ġ; )", + "ĠDoct rine", + "ik ini", + "Y e", + "ĠBl ossom", + "Ġpers ists", + "b ill", + "Ġinf usion", + "Ġbud dies", + "9 11", + "ĠPat ient", + "Ġdem os", + "Ġacquaint ance", + "ĠP aw", + "at ari", + "Ġx ml", + "Ġfasc ination", + "ĠSer ve", + "Ï Ĥ", + "br anded", + "Ġa z", + "Return s", + "Ġover shadow", + "Ġro am", + "Ġspeed y", + "n umbered", + "hel ial", + "Ġdisc iple", + "Ġass urances", + "g iven", + "pect ing", + "ĠN atalie", + "çĶ °", + "Ġmosquit oes", + "rote in", + "Ġnumer ic", + "Ġindepend ents", + "Ġtrans itional", + "Ġreaction ary", + "ĠMech dragon", + "do ctor", + "Ġshort est", + "Ġsequ ential", + "ĠB ac", + "ĠAccount s", + "ãģ Į", + "ach y", + "ract ive", + "ĠReg iment", + "Ġbreat htaking", + "ffic iency", + "ĠB ates", + "Ġ3 11", + "Ġward robe", + "ft s", + "ĠBer k", + "Sim ply", + "ĠRivers ide", + "iver ing", + "ident ial", + "lu cent", + "Ġen riched", + "ĠCon ver", + "ĠG iving", + "ãĥ Ļ", + "Ġlegal ize", + "ĠF TC", + "Ġfre aking", + "M ix", + "Ġter restrial", + "es ian", + "ci ents", + "W ing", + "LO AD", + "Ġled ge", + "ĠViol ent", + "ĠMet all", + "Ġ30 8", + "Ġs outheastern", + "hett o", + "M eat", + "Ġslow down", + "Ġret reated", + "Jere my", + "end as", + "**** *", + "er ic", + "Ġre ins", + "opp able", + "ĠHuman ity", + "ear ances", + "rig an", + "C amera", + "Ġwa ivers", + "s oc", + "Ġalter ation", + "trans form", + "ĠC emetery", + "50 6", + "Ġindef inite", + "Ġstim ulating", + "y g", + "60 3", + "ĠS op", + "Ġdescript ive", + "Ph ase", + "ĠEd mund", + "Ġpneum onia", + "vent us", + "A mb", + "Ġlabor atories", + "ĠEx clusive", + "ug ar", + "W ere", + "Ġmalf unction", + "Ġhomosexual s", + "Ġ---- ---", + "un i", + "Ġturb ines", + "ĠEqu ity", + "D u", + "Ġmind ed", + "ĠR H", + "ĠBlack hawks", + "Ġfe ats", + "Ġ17 00", + "re pl", + "36 2", + "lad en", + "Ġindisp ensable", + "ly ss", + "tt i", + "Ġre el", + "Ġdiver ted", + "Ġlik eness", + "Ġsubscript ions", + "Ġfing ert", + "Ġfil thy", + "dest ruct", + "d raft", + "ĠBernard ino", + "l aunch", + "Ġper plex", + "ĠS UM", + "car b", + "Ġswe ater", + "ĠVent ure", + "ĠJ ag", + "ĠCele b", + "ĠV oters", + "Ġstead fast", + "Ġathlet ics", + "ĠHans on", + "ĠDr ac", + "Tr acker", + "Ġcomm end", + "ĠPres idency", + "ĠD ID", + "in formed", + "Ġweb page", + "P retty", + "Ġforce fully", + "ãĥĥ ãĤ¯", + "Ġrel ocation", + "Ġsat ire", + "â ī", + "ĠSunder land", + "æ Ħ", + "V oice", + "???? ????", + "Ġinform ant", + "Ġbow el", + "ĠUn iform", + "Ġ ...\"", + "Ġpur ge", + "Ġpic nic", + "ĠU mb", + "ĠU PDATE", + "ĠSapp hire", + "ĠSt all", + "le arn", + "Ġobject ively", + "Ġob liter", + "Ġlooph ole", + "Ġjour neys", + "Ġo mission", + "Pro s", + "ĠSid ney", + "pl oma", + "Ġspray ed", + "Ġg uru", + "Ġtra itor", + "Ġtim et", + "Ġsn apping", + "ĠSe vent", + "urn al", + "ĠUk ip", + "Ġb owed", + "por al", + "l iberal", + "R os", + "Quest ions", + "i OS", + "Ġsummar ize", + "ST AT", + "Ġ18 50", + "ap est", + "Ġl ender", + "ĠVari able", + "br inging", + "ĠL ORD", + ", )", + "Ġcollaps es", + "x iety", + "ĠN ed", + "Y D", + "ĠSch a", + "Ġantib ody", + "Ġdis band", + "y re", + "ill usion", + "Ġro ver", + "s hed", + "ĠHiro sh", + "cc i", + "Ġcal am", + "ĠMort on", + "P interest", + "Ġ19 28", + "ĠE uras", + "ord es", + "Ġf ences", + "ĠIn ventory", + "ĠVal encia", + "ĠU d", + "ĠT iff", + "Ġsqu e", + "Ġqu otation", + "Ġtroubles ome", + "er ker", + "QU EST", + "ĠKing doms", + "s outh", + "Ġle vy", + "Pr ince", + "ĠSt ing", + "Ġnick named", + "Ġapp e", + "Ġphot ographic", + "Ġcorp us", + "re ference", + "ĠT rog", + "U nt", + ") =(", + "ĠLat via", + "Ġactiv ating", + "Ġlicense e", + "Ġdispar ities", + "ĠNews letter", + "ãĥĥ ãĥĪ", + "Ġfree ing", + "ĠJe ep", + "ĠPer ception", + "ins k", + "Ġsil icone", + "ĠHay den", + "Le an", + "ĠSuz uki", + "ibr arian", + "66 8", + "Ġsp or", + "Ġcorrel ations", + "ag hetti", + "Ġtu ber", + "ĠIP CC", + "il us", + "ĠV u", + "Ġwealth iest", + "ĠCarb uncle", + "an za", + "Ġfool ed", + "ĠZ ur", + "Ġd addy", + "ran o", + "il ian", + "Ġknock out", + "f man", + "requ ired", + "ĠWik ileaks", + "ĠD uffy", + "ON T", + "Ġins ol", + "ĠObject s", + "Ġb ou", + "ĠNord ic", + "ĠIns ert", + "sc an", + "Ġd ancers", + "Ġid iots", + "major ity", + "ĠNev ille", + "ĠFree BSD", + "Ġt art", + "pan ic", + "69 0", + "Ġcoc oa", + "Ġsam pled", + "Ġlook up", + "Ind ust", + "Ġinject ions", + "gen re", + "Ġa u", + "Ġroad way", + "Ġgen itals", + "K ind", + "ĠEx aminer", + "ĠY az", + "F resh", + "Ġpar alysis", + "ĠAl uminum", + "Ġre ap", + "ok é", + "Ġsl oppy", + "ĠTun nel", + "pos ium", + "ner y", + "en ic", + "Ġher bal", + "ĠOut er", + "ĠBuild er", + "Ġinc ur", + "Ġide ologies", + "Ġback ups", + "cons uming", + "ĠDet ect", + "de ck", + "ĠKN OW", + "ĠG ret", + "ĠM IC", + "Ġtough ness", + "ĠEx hibit", + "Ġh ive", + "L es", + "ĠSCH OOL", + "ĠAt ari", + "ald e", + "ĠN ull", + "and estine", + "m ouse", + "Ġbrig ade", + "48 9", + "Ġrev ol", + "ĠLaw son", + "ĠW ah", + "op oly", + "eb ted", + "ĠS aunders", + "Ġ3 13", + "ĠW inc", + "Ġtab oo", + "ĠHel met", + "Ġw edge", + "ch ip", + "ĠT ina", + "b g", + "Ġinf uri", + "r n", + "Ġanomal ies", + "ĠSy nc", + "ĠEx am", + "ĠComm it", + "ĠDi ary", + "ĠALS O", + "ĠDe bor", + "omed ical", + "Ġcomprehens ion", + "6 55", + "Ġempower ing", + "Ġ ire", + "Ġju ices", + "ĠE TH", + "ĠBox ing", + "=\" /", + "Ġfacilit ated", + "p oke", + "ĠPars ons", + "ĠMod er", + "tra vel", + "Ġcivil izations", + "Ġliber tarians", + "Ġrun e", + "ĠCl arks", + "at hed", + "Ġcampaign ers", + "ĠDis patch", + "ĠFah renheit", + "ĠCap com", + "-------- --", + "Ġl ace", + "Ġdr aining", + "Ġl iner", + "ĠArt ificial", + "é n", + "t ask", + "] ).", + "ĠGM O", + "ĠOper ator", + "ord inary", + "ĠInf luence", + "ĠU ps", + "Ġpot ency", + "uss en", + "osp ons", + "ĠSw im", + "ĠDead line", + "Un ity", + "Ġcul inary", + "Ġenlight enment", + "Ġwe arer", + "Ġmin ed", + "Ġp ly", + "Ġinc est", + "ĠDVD s", + "W alk", + "B TC", + "Tr ade", + "Ġdev al", + "ib and", + "ĠOvers ight", + "Palest inian", + "Ġd art", + "Ġm ul", + "L R", + "Ġrem ovable", + "ĠReal ms", + "ì Ŀ", + "Ġmisc ar", + "ĠV ulkan", + "68 5", + "è re", + "ĠS ap", + "Ġmer ging", + "ĠCar ly", + "che ster", + "Ġbr isk", + "Ġlux urious", + "ĠGener ator", + "Ġbit terness", + "Ġed ible", + "Ġ24 3", + "T G", + "Ġrect angle", + "With No", + "bel ow", + "J enn", + "Ġdark est", + "Ġh itch", + "Ġdos age", + "Ġsc aven", + "ĠK eller", + "ĠIllust rated", + "Certain ly", + "ĠMaver icks", + "Marg inal", + "Ġdiarr hea", + "Ġenorm ously", + "Ġ9 99", + "sh r", + "qu art", + "Ġadam ant", + "ĠM ew", + "Ġren ovation", + "Ġcerv ical", + "ĠPercent age", + "en ers", + "ĠKim ber", + "Ġflo ats", + "Ġde x", + "ĠW itcher", + "ĠSwan sea", + "d m", + "Ġsal ty", + "y ellow", + "Ġca pe", + "ĠDr ain", + "ĠPaul a", + "ĠTol edo", + "les i", + "Mag azine", + "ĠW ick", + "ĠM n", + "ĠA ck", + "ĠR iding", + "AS ON", + "Ġhom ophobic", + "AR P", + "Ġwand ered", + "C PU", + "ood oo", + "ĠP ipe", + "Ġtight ening", + "ĠBut t", + "3 18", + "Ġdesert ed", + "S ession", + "Ġfacilit ating", + "J ump", + "Ġemer gencies", + "OW ER", + "Ġexhaust ive", + "ĠAF TER", + "Ġheart beat", + "ĠLab el", + "ack y", + "ĠCert ified", + "ilt ration", + "Z e", + "ĠU tt", + "Ġ13 00", + "Ġpres ume", + "ĠDis p", + "Ġsur ged", + "Ġdoll s", + "Col umb", + "Ġchim pan", + "ĠR azor", + "Ġt icks", + "Ġcouncill or", + "Ġpilgr image", + "ĠReb els", + "ĠQ C", + "ĠA uction", + "x ia", + "ik k", + "b red", + "Ġinsert ion", + "Ġco arse", + "d B", + "SE E", + "ĠZ ap", + "ĠF oo", + "Ġcontem por", + "ĠQuarter ly", + "ot ions", + "ĠAl chemist", + "ĠT rey", + "ĠDu o", + "S weet", + "80 4", + "ĠGi ov", + "Ġfun n", + "N in", + "h off", + "Ġram ifications", + "Ġ19 22", + "ĠExper ts", + "az es", + "Ġgar ments", + "ar ial", + "ĠN ab", + "Ġ25 7", + "ĠV ed", + "Ġhum orous", + "ĠPom pe", + "Ġn ylon", + "Ġlur king", + "ĠSerge y", + "ĠMatt is", + "Ġmisogyn y", + "ĠComp onents", + "ĠWatch ing", + "ĠF olk", + "ract ical", + "B ush", + "Ġt aped", + "Ġgroup ing", + "Ġbe ads", + "Ġ20 48", + "Ġcon du", + "quer que", + "Read ing", + "Ġgriev ances", + "Ult ra", + "Ġend point", + "H ig", + "ĠSt atic", + "ĠScar borough", + "L ua", + "ĠMess i", + "a qu", + "ĠPsy Net", + "ĠR udd", + "Ġa venue", + "v p", + "J er", + "Ġsh ady", + "ĠRes ist", + "ĠArt emis", + "Ġcare less", + "Ġbro kers", + "Ġtemper ament", + "Ġ5 20", + "T ags", + "ĠTurn ing", + "Ġut tered", + "Ġp edd", + "Ġimpro vised", + "Ġ: (", + "Ġtab l", + "Ġpl ains", + "16 00", + "press ure", + "ĠEss ence", + "marg in", + "friend s", + "ĠRest oration", + "Ġpoll ut", + "ĠPok er", + "ĠAugust ine", + "ĠC IS", + "ĠSE AL", + "or ama", + "Ġth wart", + "se ek", + "Ġp agan", + " º", + "cp u", + "Ġg arn", + "Ġass ortment", + "ĠI LCS", + "t ower", + "Recomm ended", + "Ġun born", + "ĠRandom Redditor", + "ĠRandomRedditor WithNo", + "Ġparaly zed", + "Ġeru ption", + "Ġinter sect", + "ĠSt oke", + "ĠS co", + "B ind", + "å ¾", + "ĠP NG", + "ĠNeg ative", + "ĠNO AA", + "Le on", + "Ġall oy", + "ĠL ama", + "ĠD iversity", + "5 75", + "Ġunderest imated", + "ĠSc or", + "Ġm ural", + "Ġb usted", + "so on", + "l if", + "Ġnone x", + "Ġall ergy", + "ĠUnder world", + "ĠR ays", + "ĠBl asio", + "Ġh rs", + "ĠD ir", + "Ġ3 27", + "by ter", + "Ġrepl acements", + "Ġactiv ates", + "ri ved", + "M H", + "Ġp ans", + "ĠH I", + "Ġlong itudinal", + "Ġnu isance", + "al er", + "Ġsw ell", + "ĠS igned", + "s ci", + "ĠIs les", + "ĠA GA", + "Ġdef iant", + "Ġson ic", + "oc on", + "K C", + "ĠA im", + "t ie", + "ah ah", + "Ġm L", + "D X", + "Ġb isc", + "ĠBill board", + "ĠSY STEM", + "NE Y", + "ga ard", + "Ġdist ressed", + "former ly", + "Al an", + "Ġche fs", + "Ġopt ics", + "ĠC omet", + "ĠAM C", + "Ġredes igned", + "irm ation", + "Ġsight ings", + "38 2", + "3 11", + "ĠW B", + "Ġcont raction", + "ĠT OTAL", + "D ual", + "Ġstart led", + "Ġunderstand ably", + "Ġsung lasses", + "ETH OD", + "Ġd ocker", + "Ġsurf ing", + "ĠH EL", + "ĠSl ack", + "ton es", + "Ġsh alt", + "Vis ual", + "49 8", + "Dep artment", + "c ussion", + "Ġunrest ricted", + "Ġt ad", + "Ġre name", + "employ ed", + "Ġeduc ating", + "Ġgrin ned", + "bed room", + "ĠActiv ities", + "ĠV elvet", + "ĠSW AT", + "Ġsh uffle", + "ig or", + "Ġsatur ation", + "F inding", + "c ream", + "ic ter", + "Ġv odka", + "tr acking", + "te c", + "Ġfore ground", + "iest a", + "Ġve hement", + "ĠEC B", + "ĠT ie", + "E y", + "Ġt urtles", + "ĠRail road", + "ĠKat z", + "ĠFram es", + "Ġmen ace", + "ĠFell owship", + "ĠEss ential", + "ugg ish", + "Ġdri p", + "ch witz", + "ĠKy oto", + "s b", + "ĠN ina", + "Param eter", + "Ġal arms", + "ĠCl aud", + "Ġpione ering", + "Ġchief ly", + "ĠSc ream", + "Col lection", + "Ġthank fully", + "ĠRonald o", + "åŃ IJ", + "st rip", + "ĠDisney land", + "com mercial", + "See ing", + "S oul", + "Ġevac uate", + "Ġc iv", + "ĠAs he", + "Ġdiv ides", + "ĠD agger", + "rehens ive", + "Ġber ries", + "ĠD F", + "Ġs ushi", + "Ġplur ality", + "W I", + "Ġdisadvant aged", + "Ġbatt alion", + "ob iles", + "45 1", + "Ġcl ing", + "Ġunden iable", + "ĠL ounge", + "Ġha unt", + "p he", + "Ġquant ify", + "Ġdiff ered", + "Ġ[* ]", + "ĠV iz", + "c um", + "sl ave", + "Ġvide og", + "Ġqu ar", + "Ġbund les", + "ĠAl onso", + "t ackle", + "Ġneur onal", + "Ġlandsl ide", + "conf irmed", + "ĠDep th", + "Ġrenew ables", + "B ear", + "ĠMaced onia", + "Ġjer seys", + "Ġb unk", + "ĠSp awn", + "ĠControl s", + "ĠBuch anan", + "Ġrobot ics", + "Ġemphas izing", + "ĠTut orial", + "h yp", + "ist on", + "Ġmonument al", + "æ °", + "ĠCar ry", + "Ġt bsp", + "en ance", + "H ill", + "art hed", + "Ġro tten", + "De an", + "Ġtw isting", + "Ġgood will", + "Ġimm ersion", + "L iving", + "Ġbr ushes", + "ĠC GI", + "ĠAt k", + "tr aditional", + "Ġph antom", + "ĠSt amina", + "Ġexpans ions", + "ĠMar in", + "Ġembark ed", + "ĠE g", + "int estinal", + "ĠPE OPLE", + "ĠBo oth", + "ĠApp alach", + "Ġreleg ated", + "V T", + "M IT", + "Ġmust er", + "Ġwithdraw ing", + "Ġmicrosc ope", + "ĠG athering", + "ĠC rescent", + "ĠArgent ine", + "ĠDec re", + "ĠDomin ic", + "Ġbud s", + "ant age", + "ĠI on", + "Ġwid ened", + "ONS ORED", + "ĠGl oves", + "iann opoulos", + "raz en", + "fe el", + "Ġrepay ment", + "Ġhind sight", + "ĠRE ALLY", + "ĠPist ol", + "ĠBra h", + "Ġwat ts", + "Ġsurv ives", + "Ġfl urry", + "iss y", + "Al ert", + "ĠUrug uay", + "Ph oenix", + "S low", + "ĠG rave", + "ĠF ir", + "Ġmanage able", + "Ġtar iff", + "ĠU DP", + "ĠPist ons", + "ĠNiger ian", + "Ġstrike outs", + "Ġcos metics", + "whel ming", + "f ab", + "c ape", + "pro xy", + "Ġre think", + "Ġover coming", + "sim ple", + "Ġw oo", + "Ġdistract ing", + "ĠSt anton", + "ĠTuls a", + "ĠD ock", + "65 9", + "Ġdisc ord", + "ĠEm acs", + "ĠV es", + "ĠR OB", + "Ġreass uring", + "Ġcons ortium", + "Muslim s", + "3 21", + "Ġprompt s", + "se i", + "ĠH itch", + "imp osed", + "ĠF ool", + "Ġindisc rim", + "wr ong", + "bu querque", + "D avis", + "! ]", + "Ġtim eless", + "ĠNE ED", + "Ġpestic ide", + "Ġrally ing", + "ĠCal der", + "Ġå ¤", + "Ġx p", + "ĠUn le", + "ĠEx port", + "lu aj", + "B uff", + ") [", + "Ġsq or", + "S audi", + "Ġis tg", + "Ġindul ge", + "pro c", + "Ġdisg usted", + "Ġcomp ounded", + "Ġn em", + "Ġschool ing", + "ĠC ure", + "process ing", + "S ol", + "Ġpro verb", + "it ized", + "ĠAlv arez", + "Ġscar f", + "Ġrect angular", + "re ve", + "Ġh ormonal", + "ĠSt ress", + "itiz en", + "Ġ4 25", + "girl s", + "ĠNo ir", + "ĠR app", + "Ġmar ches", + "ch urch", + "ĠUs es", + "Ġ40 5", + "ĠBer m", + "Ġord inances", + "ĠJud gment", + "Charg es", + "ĠZ in", + "Ġdust y", + "Ġstraw berries", + "Ġper ce", + "ĠTh ur", + "ĠDebor ah", + "net flix", + "ĠLam bert", + "Ġam used", + "ĠGu ang", + "Y OU", + "R GB", + "ĠC CTV", + "Ġf iat", + "r ang", + "Ġf ederation", + "ĠM ant", + "ĠB ust", + "ĠM are", + "respect ive", + "ĠM igration", + "ĠB IT", + "59 0", + "Ġpatriot ism", + "Ġout lining", + "reg ion", + "ĠJos é", + "Ġbl asting", + "ĠEz ra", + "B s", + "Ġundermin es", + "ĠSm ooth", + "Ġcl ashed", + "rad io", + "Ġtransition ing", + "ĠBucc aneers", + "ĠOw l", + "Ġplug s", + "Ġh iatus", + "ĠPin ball", + "Ġm ig", + "ĠNut r", + "ĠWolf e", + "Ġinteg ers", + "Ġor bits", + "ĠEd win", + "ĠDirect X", + "b ite", + "Ġbl azing", + "v r", + "Ed ge", + "ĠP ID", + "ex it", + "ĠCom ed", + "ĠPath finder", + "ĠGu id", + "ĠSign s", + "ĠZ er", + "ĠAg enda", + "Ġreimburse ment", + "M esh", + "i Phone", + "ĠMar cos", + "ĠS ites", + "h ate", + "en burg", + "Ġs ockets", + "p end", + "Bat man", + "v ir", + "ĠSH OW", + "Ġprovision al", + "con n", + "ĠDeath s", + "AT IVE", + "Pro file", + "sy m", + "J A", + "Ġnin ja", + "inst alled", + "id ates", + "eb ra", + "ĠOm aha", + "Ġse izing", + "ĠBe asts", + "Ġsal ts", + "M ission", + "Gener ally", + "ĠTr ilogy", + "he on", + "leg ates", + "Ġd ime", + "Ġf aire", + "par able", + "G raph", + "Ġtotal ing", + "Ġdiagram s", + "ĠYan uk", + "ple t", + "ĠMe h", + "Ġmyth ical", + "ĠStep hens", + "aut ical", + "ochem istry", + "Ġkil ograms", + "Ġel bows", + "anc ock", + "ĠB CE", + "ĠPr ague", + "Ġimpro v", + "ĠDev in", + "Ġ\" \\", + "par alle", + "Ġsuprem acists", + "ĠB illion", + "Ġreg imen", + "inn acle", + "Ġrequ isite", + "ang an", + "ĠBur lington", + "ain ment", + "ĠObject ive", + "oms ky", + "G V", + "Ġun ilateral", + "Ġt c", + "Ġh ires", + "ment al", + "Ġinvol untary", + "Ġtrans pl", + "ĠASC II", + " ¨", + "Ev ents", + "Ġdoub ted", + "ĠKa plan", + "ĠCour age", + "ig on", + "ĠMan aging", + "ĠT art", + "Ġfalse hood", + "ĠV iolet", + "Ġair s", + "Ġfertil izer", + "Brit ain", + "Ġaqu atic", + "ou f", + "W ords", + "ĠHart ford", + "Ġeven ings", + "ĠV engeance", + "qu ite", + "G all", + "ĠP ret", + "Ġp df", + "ĠL M", + "ĠSo chi", + "ĠInter cept", + "9 20", + "Ġprofit ability", + "ĠId le", + "ĠMac Donald", + "ĠEst ablishment", + "um sy", + "Ġgather ings", + "ĠN aj", + "Charl ie", + "Ġas cent", + "ĠProt ector", + "Ġal gebra", + "Ġbi os", + "for ums", + "EL S", + "Introdu ced", + "Ġ3 35", + "Ġastron omy", + "Cont ribut", + "ĠPol ic", + "Pl atform", + "Ġcontain ment", + "w rap", + "Ġcoron ary", + "ĠJ elly", + "man ager", + "Ġheart breaking", + "c air", + "ĠChe ro", + "c gi", + "Med ical", + "ĠAccount ability", + "! !\"", + "oph ile", + "Ġpsych otic", + "ĠRest rict", + "Ġequ itable", + "iss ues", + "Ġ19 05", + "ĠN ek", + "c ised", + "ĠTr acking", + "Ġo zone", + "Ġcook er", + "ros is", + "Ġre open", + "Ġinf inity", + "ĠPharm aceutical", + "ens ional", + "Att empt", + "ĠR ory", + "Mar co", + "Ġawa its", + "H OW", + "t reated", + "Ġbol st", + "Ġreve red", + "Ġp ods", + "opp ers", + "00 10", + "Ġampl itude", + "ric an", + "SP ONSORED", + "Ġtrou sers", + "Ġhal ves", + "ĠK aine", + "ĠCut ler", + "ĠA UTH", + "Ġsplend id", + "Ġprevent ive", + "ĠDud ley", + "if acts", + "umin ati", + "ĠY in", + "Ġad mon", + "ĠV ag", + "Ġin verted", + "Ġhast ily", + "ĠH ague", + "L yn", + "Ġled ger", + "Ġastron omical", + "get ting", + "Ġcirc a", + "ĠC ic", + "ĠTenn is", + "Lim ited", + "Ġd ru", + "ĠBY U", + "Ġtrave llers", + "Ġp ane", + "ĠInt ro", + "Ġpatient ly", + "Ġa iding", + "Ġlo os", + "ĠT ough", + "Ġ29 3", + "Ġconsum es", + "Source File", + "Ġ\"\" \"", + "Ġbond ing", + "Ġtil ted", + "Ġmenstru al", + "ĠCel estial", + "UL AR", + "Plug in", + "Ġrisk ing", + "N az", + "ĠRiy adh", + "Ġacc redited", + "Ġsk irm", + "é Ľ", + "Ġexam iner", + "Ġmess ing", + "Ġnear ing", + "ĠC hern", + "ĠBeck ham", + "Ġsw apped", + "Ġgo ose", + "K ay", + "Ġlo fty", + "ĠWal let", + "Ġ[ '", + "Ġap ocalypse", + "Ġb amboo", + "ĠSP ACE", + "ĠEl ena", + "Ġ30 6", + "ac ons", + "Ġtight ened", + "Ġadolesc ence", + "Ġrain y", + "Ġvandal ism", + "ĠNew town", + "Ġcon ject", + "c akes", + "Ġche ated", + "Ġmoder ators", + "par ams", + "E FF", + "Ġdece it", + "ĠST L", + "ĠTanz ania", + "ĠR I", + "Ġ19 23", + "ĠEx ile", + "the l", + "Ġthe olog", + "Ġquir ky", + "ĠIr vine", + "Ġneed y", + "or is", + "U m", + "K a", + "Ġmail box", + "3 22", + "Ġb os", + "ĠPet ra", + "K ING", + "Ġenlarg ed", + "O ften", + "Ġbad ass", + "Ġ3 43", + "ĠPl aces", + "ĠC AD", + "Ġpr istine", + "Ġinterven ing", + "d irection", + "Ġl az", + "ĠD SM", + "Ġproject ing", + "ĠF unk", + "ag og", + "pay ment", + "n ov", + "Ġch atter", + "AR B", + "Ġexam inations", + "ĠHouse hold", + "ĠG us", + "F ord", + "4 14", + "B oss", + "Ġmy stic", + "Ġle aps", + "ĠB av", + "ul z", + "b udget", + "Foot ball", + "Ġsubsid ized", + "Ġfirst hand", + "Ġcoinc ide", + "oc ular", + "Con n", + "ĠColl abor", + "Ġfool s", + "am ura", + "ah ar", + "r ists", + "Ġsw ollen", + "Ġexp ended", + "ĠP au", + "s up", + "Ġsp ar", + "Ġkey note", + "s uff", + "Ġunequ al", + "Ġprogress ing", + "str ings", + "ĠGamer gate", + "Dis ney", + "ĠEle ven", + "om nia", + "Ġscript ed", + "Ġear ners", + "bro ther", + "ĠEn abled", + "æ ³", + "Ġlar vae", + "ĠL OC", + "m ess", + "Wil son", + "ĠTem plate", + "success fully", + "Ġparam ount", + "Ġcamoufl age", + "Ġbind s", + "ĠQu iet", + "ĠSh utterstock", + "r ush", + "Ġmasc ot", + "fort une", + "ĠCol t", + "ĠBe yon", + "hab i", + "Ġha irc", + "Ġ26 7", + "ĠDe us", + "Ġtw itch", + "Ġconcent rating", + "Ġn ipples", + "c ible", + "Ġg ir", + "N Z", + "M ath", + "n ih", + "Requ ired", + "Ġp onder", + "ĠS AN", + "Ġwedd ings", + "Ġl oneliness", + "N ES", + "ĠMah jong", + "69 5", + "add le", + "ĠGar ner", + "ĠC OUR", + "Br idge", + "Ġsp ree", + "ĠCald well", + "Ġbri bery", + "Ġ���� ����", + "plug ins", + "Ġr acket", + "Ġchamp agne", + "vers ible", + "V ote", + "Ġmod ifiers", + "May or", + "6 80", + "Ġassemb lies", + "ĠS ultan", + "ĠN ing", + "ĠLad ies", + "Ġsulf ur", + "Ġor bs", + "Ġ---- -", + "____ ___", + "ĠJournal ism", + "Ġes ports", + "Ġl ush", + "Ġh ue", + "Ġspect ral", + "H onest", + "ãĥ ı", + "Ġbus hes", + "Ġrein forcement", + "Ġre opened", + "ĠWhe els", + "ĠM org", + "rie ving", + "Ġaux iliary", + "Ġj Query", + "ĠB AT", + "tes que", + "Ġver tex", + "p ure", + "f rey", + "ãĤ º", + "d os", + "Ġty ph", + "Ġc ull", + "Ġe q", + "Ġdec on", + "Ġtoss ing", + "Ġdispar ate", + "ĠBr igham", + "print f", + "led ged", + "Ġsu nd", + "Ġco zy", + "Ġhepat itis", + "per forming", + "Ġav al", + "ĠG G", + "f uture", + "Ġpet ertodd", + "ĠKos ovo", + "Ġmagn ets", + "Al ready", + "ĠEd ison", + "ĠCe res", + "ĠRA ID", + "Ġbrill iance", + "57 6", + "Ġder ives", + "Ġhypert ension", + "ĠÎ Ķ", + "Ġlamb da", + "Ġfl air", + "Ġmission aries", + "Ġrap es", + "ĠSt arter", + "ĠMon ths", + "Ġdef y", + "Ġseism ic", + "ĠR aphael", + "Ġeuro zone", + "65 6", + "z sche", + "Ġscr atched", + "Ġb ows", + "ĠLenn on", + "ĠGa ia", + "Ġdri pping", + "f acts", + "A le", + "Ġfrog s", + "ĠBre ast", + "ogene ity", + "ĠProsecut or", + "Ġampl ified", + "ĠHod g", + "ĠF n", + "Th ousands", + "ĠNI H", + "ĠMonitor ing", + "FT WARE", + "ĠPri ebus", + "ĠG rowing", + "hun ter", + "Ġdiagn ose", + "ĠM ald", + "ĠL R", + "Ġcrown ed", + "Ġburst ing", + "Ġdiss olution", + "j avascript", + "Ġuseful ness", + "ĠExec ution", + ": (", + "ĠIv ory", + "a ah", + "Ġpersecut ed", + "viol ence", + "ist as", + "ĠCr ate", + "Ġimpuls es", + "ĠSp ani", + "ed es", + "Hand le", + "ĠZ erg", + "think able", + "Last ly", + "Ġspont aneously", + "Ġinconven ient", + "Ġdismiss ing", + "Ġpl otted", + "Ġeight y", + "Ġ7 37", + "r ish", + "ĠThor nton", + "ath am", + "Ġsit com", + "V en", + "Rec ipe", + "t el", + "l und", + "Ġcle ars", + "ĠSas uke", + "Ġ25 8", + "Ġopt ing", + "Ġen raged", + "est hetic", + "ĠA e", + "uch s", + "Pre p", + "Fl ow", + "Ġrun off", + "ĠE ating", + "ĠG iles", + "ĠAct ing", + "res ources", + "ib aba", + "Ġr pm", + "Ġske wed", + "ĠBl anc", + "ĠS akuya", + "Ġhot ter", + "Ġ19 24", + "op ian", + "ck o", + "Ġcr umbling", + "Ġcapt ains", + "ĠAppropri ations", + "le aders", + "dro pping", + "an uts", + "Ġrevers ing", + "ĠP ose", + "ĠS ek", + "Sc ot", + "ĠIde a", + "c ise", + "ĠSloven ia", + "Ġ3 17", + "Do ctor", + "Ġcro cod", + "ald i", + "Se a", + "ĠFar rell", + "Ġmerc enaries", + "ĠR NC", + "ĠGu ess", + "Ġp acing", + "M achine", + "Streamer Bot", + "ĠChar ity", + "Ġ29 8", + "Ġcann ons", + "ĠTob y", + "TPP StreamerBot", + "ĠPass ion", + "cf g", + "Th om", + "Ġbad ges", + "ĠBern stein", + ". âĢĵ", + "ĠP OP", + "ĠCon j", + "Ġinitial ization", + "Ġbiod iversity", + "D ub", + "Ġfeud al", + "Ġdisclaim er", + "Ġc row", + "Ġign ition", + "ar f", + "S HA", + "Ġk Hz", + "h azard", + "ĠArt ists", + "oe uv", + "67 9", + "ĠRud y", + "N ine", + "ĠRam adan", + "å ½", + "itt o", + "Ġadren aline", + "C ert", + "Ġsmell ed", + "Ġimp unity", + "Ġag endas", + "ĠRe born", + "ĠCon cent", + "ĠSe ems", + "Ġo mega", + "ĠDust in", + "Ġback er", + "ĠSau ce", + "ĠBoy le", + "W IN", + "Ġsp ins", + "Ġpa uses", + "u pt", + "Ġshred ded", + "Ġstra pped", + "ĠCor ruption", + "Ġscr atches", + "Ġn i", + "Ġatt ire", + "ĠS AF", + "Factory Reloaded", + "ĠI PS", + "Ġ( %", + "Ġsem inar", + "f ocus", + "c ivil", + "Ġ18 60", + "int osh", + "Ġcontin ual", + "Ġabbre vi", + "ĠS ok", + "oc obo", + "X M", + "Ġfr antic", + "Ġunavoid able", + "Ġar tery", + "Ġannot ations", + "b ath", + "Cl imate", + "Ġd ors", + "ĠSl ide", + "co ord", + "ĠRel oad", + "ĠL DL", + "ĠLove craft", + "Ġunim agin", + "Ġresemb led", + "Ġbarr acks", + "n p", + "Ġsurrog ate", + "Ġcategor ized", + "ãĤ ©", + "Ġvacc inated", + "Ġdrain age", + "Ġind ist", + "ĠWhats App", + "Ġ18 70", + "oler ance", + "inv oke", + "am orph", + "Ġrecon nect", + "Ġem anc", + "Ġblind ness", + "Ġ12 80", + "intern et", + "c ollar", + "Ġalt ru", + "Ġab yss", + "ĠT RI", + "65 7", + "Ġinf used", + "HE AD", + "Ġforest ry", + "ĠWood y", + "ĠC i", + "w i", + "s am", + "78 4", + "hol iday", + "Ġmog ul", + "ĠF ees", + "ĠD EN", + "In ternal", + "ur bed", + "f usc", + "at om", + "ĠIll usion", + "Ġpoll ed", + "Ġfl ap", + "Ġco ax", + "L GBT", + "An aly", + "ĠSect ions", + "ĠCalif orn", + "em n", + "Ġh ither", + "ĠN IGHT", + "Ġn ailed", + "ĠPip eline", + "39 1", + "o of", + "ĠPr imal", + "vere nd", + "Ġsl ashing", + "Ġret ri", + "avi our", + "Ġdepart ing", + "g il", + "IS C", + "Ġmid way", + "Ġultras ound", + "Ġbeh aving", + "ĠT ara", + "class es", + "V irtual", + "ĠColon ial", + "Ġstri pping", + "Ġorchestr ated", + "ĠGra ves", + "45 2", + "ĠIron ically", + "ĠWrit ers", + "Ġl ends", + "ĠMan z", + "Ġra ven", + "Ġoxid ative", + "Ġ26 6", + "EL F", + "act ually", + "asc ar", + "D raft", + "Ġfavour able", + "Ġhumili ating", + "Ġf idelity", + "ĠH of", + "ĠX uan", + "49 6", + "Ġlay ered", + "at is", + "79 0", + "Ġpay check", + "it on", + "K ar", + "ĠVM ware", + "ĠFar mer", + "Ġserv ic", + "gl omer", + "Ġsl ump", + "ĠFab ric", + "ĠD OC", + "est ing", + "Ġreass ure", + "Ġph yl", + "v olt", + "it ory", + "R ules", + "Ġoxid ation", + "Ġpri zed", + "Ġmist ress", + "ĠDj ango", + "WAR N", + "å ij", + "Ġenc ode", + "ĠFeed back", + "Ġstupid ity", + "I an", + "ĠYugoslav ia", + "× ¨", + "ac l", + "UT E", + "19 77", + "Ġqual ifies", + "Ġpuls es", + "pret ty", + "Ġfro ze", + "Ġs s", + "Iter ator", + "Ġur gently", + "Ġm ailed", + "ĠCh am", + "Ġsust aining", + "Ġbas il", + "Ġpupp ies", + "il ant", + "ĠP LEASE", + "l ap", + "ace ous", + "F ear", + "ĠMaster y", + "aut omatic", + "ĠT AG", + "Ġant im", + "ag les", + "47 3", + "fram es", + "Ġwh ispers", + "ĠWho ever", + "Ġbra very", + "ĠUK IP", + "ract ions", + "\"\" \"", + "Ġt ame", + "Ġpart ed", + "every thing", + "CON T", + "Ġind ebted", + "Ġadd r", + "re k", + "IR ED", + "Ġem inent", + "cl inton", + "Ġo usted", + "Ġreview er", + "Ġmelt down", + "Ġre arr", + "ĠY ao", + "the real", + "aby te", + "Ġst umbling", + "Ġbat ches", + "Ġ25 9", + "Ġcontrace ptive", + "Ġprost itute", + "ens is", + "De cl", + "ĠSt rikes", + "M ilitary", + "ĠO ath", + "v acc", + "pp ings", + "05 2", + "Ġpart Name", + "amp ing", + "Rep orts", + "K I", + "CH R", + "Ġsubt ly", + "sw ers", + "Bl ake", + "us ual", + "Ġcontest ants", + "Ġcart ridges", + "ĠGRE AT", + "Ġbl ush", + "ĠâĢ º", + "47 2", + "Ġreason ed", + "ãĥ ¤", + "paralle led", + "Ġd yn", + "ag ate", + "Ġnight ly", + "å Ĩ", + "55 6", + "Ġsem antic", + "ĠAdv oc", + "Ġ !!", + "Ġdisag rees", + "ĠB W", + "V eh", + "Ġharm ing", + "Ġembr aces", + "Ġstri ves", + "Ġin land", + "ĠK ard", + "Ġhe ats", + "ĠGin ny", + "ut an", + "ern aut", + "yl ene", + "ĠE lev", + "J D", + "Ġh ars", + "ĠStar r", + "Ġsk ysc", + "Ġcollabor ators", + "Us ually", + "Ġrev olutions", + "ĠSTAT S", + "Ġdism antle", + "Ġconfident ly", + "Ġkin etic", + "Al i", + "Ġpercent ile", + "Ġextract ing", + "ill ian", + "est ead", + "Ġphysic ists", + "ĠMarsh al", + "Ġfell owship", + "Ġd ashed", + "ĠU R", + "ĠSi oux", + "ĠComp act", + "am ide", + "P ython", + "ĠLe igh", + "ĠPharm ac", + "ist rates", + "her ical", + "Ġf ue", + "ĠE min", + "Ġ( {", + "ĠNeighbor hood", + "Ġdisrupt ing", + "ĠD up", + "Ġg land", + "ĠSe v", + "ĠMar ian", + "arg on", + "ĠD und", + "Ġ< !--", + "Ġstr and", + "Ġstadium s", + "z os", + "Ġpsych osis", + "ĠR ack", + "Ġbrilliant ly", + "ï¸ ı", + "Ġsubmer ged", + "ĠInst it", + "ĠCh ow", + "Ġc ages", + "ĠH ats", + "ĠU rs", + "Ġdil uted", + "us at", + "ien ne", + "ĠMembers hip", + "ĠBur k", + "Ġ ie", + "Ġarche type", + "D rug", + "ult on", + "ĠSp ock", + "ĠMcK ay", + "ĠDep end", + "F eatured", + "S oc", + "19 78", + "ĠB ere", + "Ġrelent lessly", + "Ġcripp ling", + "Ġar thritis", + "çĶ Ł", + "ĠTrop ical", + "ĠBul g", + "ĠCher yl", + "Ġadm irable", + "Ġsub title", + "Over ride", + "Ġorig inating", + "ĠC CP", + "Ġsw ore", + "ĠSo le", + "ĠDis orders", + "3 29", + "Ġprocess ion", + "Ġref urb", + "Ġimm ersed", + "requ ently", + "Ġskept ics", + "Ġcer amic", + "m itter", + "en stein", + "b elt", + "ĠT IT", + "b idden", + "Ġf ir", + "m ist", + "> ]", + "Ġwe ave", + "ĠParad ox", + "Ġentr usted", + "ĠBarcl ays", + "Ġnovel ist", + "og ie", + "80 6", + "Ġnin ety", + "Ġdisag reements", + "@@@@ @@@@", + "ĠAus chwitz", + "c ars", + "ĠL ET", + "t ub", + "arant ine", + "P OS", + "Ġback story", + "Ġcheer ful", + "ĠR ag", + "ek a", + "bi ased", + "Ġinexper ienced", + "ak ra", + "ĠW itt", + "t an", + "Ġrap ist", + "Ġplate au", + "ch al", + "ĠInqu is", + "exp ression", + "Ġc ipher", + "Ġsh aving", + "add en", + "re ly", + "( \\", + "ism a", + "ĠReg ulatory", + "CH AR", + "ily n", + "N VIDIA", + "G U", + "Ġmur m", + "la us", + "Christ opher", + "Ġcontract ual", + "ĠPro xy", + "ĠJa ime", + "ĠMethod ist", + "Ġstew ards", + "st a", + "per ia", + "Ġphys iology", + "Ġbump ed", + "Ġf ructose", + "Austral ian", + "ĠMet allic", + "ĠMas querade", + "ar b", + "Ġprom ul", + "Ġdown fall", + "Ġbut cher", + "Ġb our", + "ĠIN FORMATION", + "ĠB is", + "pect s", + "ad ena", + "Ġcontempl ating", + "ar oo", + "cent ered", + "ĠPe aks", + "Us ed", + "Ġmod em", + "Ġg enders", + "Ġ8 000", + "37 1", + "Ġm aternity", + "ĠR az", + "Ġrock ing", + "Ġhandgun s", + "ĠD ACA", + "Aut om", + "ĠN ile", + "Ġtum ult", + "ĠBenef it", + "ĠAppro ach", + "works hop", + "ĠLe aving", + "G er", + "inst ead", + "Ġvibr ations", + "Ġrep ositories", + "49 7", + "ĠA unt", + "ĠJ ub", + "ĠExp edition", + "Al pha", + "Ġs ans", + "Ġoverd ue", + "Ġoverc rowd", + "Ġlegisl atures", + "Ġp aternal", + "ĠLeon ardo", + "Ġexp ressive", + "Ġdistract ions", + "Ġsil enced", + "tr ust", + "Ġb iking", + "Ġ5 60", + "Ġpropri et", + "Ġimp osition", + "Ġcon glomer", + "Ġ= ================================================================", + "ĠTe aching", + "ĠY ose", + "int ensive", + "T own", + "Ġtroll ing", + "ĠGr ac", + "ĠAS US", + "Y o", + "Ġspecial s", + "ĠNep h", + "ĠGod zilla", + "Dat abase", + "ĠHe gel", + "Ġ27 2", + "19 76", + "ĠGl oria", + "Ġdis emb", + "ĠInvestig ations", + "ĠB ane", + "ag ements", + "St range", + "Ġtre asury", + "ĠPl ays", + "Ġundes irable", + "Ġwid ening", + "Ġverb ally", + "Ġinf ancy", + "Ġcut ter", + "f ml", + "Ġ21 00", + "prot otype", + "f ine", + "Ġdec riminal", + "Ġdysfunction al", + "Ġbes ie", + "ĠErn st", + "z eb", + "Ġnort heastern", + "Ġa ust", + "por ate", + "ĠMar lins", + "Ġsegreg ated", + "ew orld", + "ĠMa her", + "Ġtra verse", + "Ġmon astery", + "ur gy", + "G ear", + "s and", + "Com pl", + "ĠE MP", + "Ġpl ent", + "ĠMer cer", + "Ġ27 6", + "TA BLE", + "Config uration", + "H undreds", + "Ġpr ic", + "Ġcollabor ating", + "ĠPar amount", + "ĠCumm ings", + "Ġ( <", + "Ġrecord er", + "Ġfl ats", + "Ġ4 16", + "wh ose", + "Font Size", + "ĠOr bit", + "Y R", + "Ġwr ists", + "Ġb akery", + ") }", + "ĠB ounty", + "ĠLanc aster", + "Ġend ings", + "acc ording", + "ĠSal am", + "e asy", + "75 5", + "ĠBur r", + "ĠBarn ett", + "onom ous", + "Un ion", + "Ġpreced ence", + "ĠScholars hip", + "ĠU X", + "Ġroll out", + "Ġbo on", + "al m", + "ĠCan ter", + "æ µ", + "Ġround ing", + "Ġcl ad", + "Ġv ap", + "ĠF eatured", + "is ations", + "Ġ5 40", + "pol ice", + "Ġunsett ling", + "Ġdr ifting", + "ĠLum ia", + "ĠObama Care", + "ĠF avor", + "Hy per", + "ĠRoth schild", + "ĠMil iband", + "an aly", + "ĠJul iet", + "H u", + "Ġrec alling", + "a head", + "69 6", + "Ġunf avorable", + "Ġd ances", + "O x", + "Ġleg ality", + "Ġ40 3", + "rom ancer", + "Ġinqu ire", + "ĠM oves", + "\\ \">", + "ĠVari ant", + "ĠMess iah", + "ĠL CS", + "ĠBah á", + "75 6", + "Ġeyeb row", + "Ġ ¥", + "ĠMc F", + "ĠFort y", + "M as", + "Ġpan icked", + "Ġtransform ations", + "q q", + "Ġrev olves", + "ring e", + "ĠA i", + "ax e", + "Ġon ward", + "ĠC FR", + "ĠB are", + "log in", + "Ġliqu ids", + "Ġde comp", + "second ary", + "il an", + "ĠCon vert", + "ami ya", + "Ġprosecut ing", + "Ġâī ¡", + "ĠYork ers", + "ĠByr ne", + "sl ow", + "aw ei", + "J ean", + "Ġ26 9", + "ĠSky dragon", + "Ġ é", + "ĠNicarag ua", + "ĠHuck abee", + "ĠHigh ly", + "Ġamph ib", + "ĠPast or", + "ĠL ets", + "Ġbl urred", + "Ġvisc eral", + "ĠC BO", + "Ġcollabor ated", + "z ig", + "Leg al", + "Ġapart heid", + "Ġbr id", + "Ġpres et", + "ĠD ET", + "ĠAM A", + "× Ķ", + "arch ing", + "auc uses", + "build er", + "Ġpo etic", + "Ġem ulator", + "ĠMole cular", + "Ġhon oring", + "ise um", + "Ġtract or", + "ĠCl uster", + "ĠCal m", + "ared evil", + "Ġsidew alks", + "Ġviol in", + "Ġgeneral ized", + "ĠAle c", + "Ġemb argo", + "Ġfast ball", + "ĠHT TPS", + "ĠL ack", + "ĠCh ill", + "ri ver", + "C hel", + "ĠSw arm", + "ĠLev ine", + "ro ying", + "L aunch", + "Ġkick er", + "Ġadd itive", + "ĠDe als", + "W idget", + "cont aining", + "Ġescal ate", + "ĠOP EN", + "Ġtwe aked", + "Ġst ash", + "Ġsp arks", + "ĠEs sex", + "ĠE cc", + "Ġconv ict", + "Ġblog ging", + "I ER", + "ĠH L", + "Ġmurd erers", + "75 9", + "ĠH ib", + "Ġde pl", + "ĠJ ord", + "S ac", + "Ġdis sect", + "ĠHow e", + "os her", + "Ġcustom izable", + "ĠFran z", + "Ġat ro", + "Ä ĩ", + "Ġ000 4", + "Ġout post", + "R oss", + "Ġglyph osate", + "ĠHast ings", + "ĠBE FORE", + "Ġsh ove", + "o pped", + "ĠSc ala", + "Ġam ulet", + "an ian", + "Ġexacerb ated", + "Ġe ater", + "47 1", + "UM E", + "Ġpul p", + "izont al", + "ĠZ am", + "ĠAT I", + "imm une", + "aby tes", + "Ġunnecess arily", + "ĠC AT", + "ĠAx is", + "Ġvisual ize", + "à ī", + "ĠRad ical", + "f m", + "Doc uments", + "ĠFor rest", + "Ġcontext ual", + "ĠSy mbol", + "Ġtent ative", + "ĠDO ES", + "ĠGood s", + "Ġintermitt ent", + "} :", + "medi ated", + "Ġridic ule", + "Ġathe ism", + "Ġpath ogens", + "ĠM um", + "Ġre introdu", + "Ġ30 7", + "i HUD", + "Ġflash light", + "Ġsw earing", + "Ġp engu", + "B u", + "Ġrot ated", + "ĠCr ane", + "Ġ() );", + "Ġfashion able", + "Ġendors ing", + "46 3", + ") [", + "Ġingest ion", + "Ġcook s", + "Ġ9 50", + "ot omy", + "ĠIm am", + "Ġk a", + "Ġte aser", + "ĠGhost s", + "ĠãĤ µ", + "19 69", + "Ï ĥ", + "ub by", + "Ġconver ter", + "zan ne", + "end e", + "ĠPre par", + "ĠNic kel", + "ĠChim era", + "h im", + "ĠTyr ann", + "ĠSabb ath", + "ĠNich ols", + "Ġra pt", + "ih ar", + "Ġshe lling", + "Ġillum inate", + "Ġdent ist", + "ut or", + "ĠInteg ration", + "Ġwh ims", + "ĠLiter ary", + "Be aut", + "Ġp archment", + "ag ara", + "Br and", + "Ġder og", + "âĢ¦ )", + "ĠNor se", + "Ġunw itting", + "Ġc uc", + "Ġborder line", + "Ġupset ting", + "Ġrec ourse", + "Ġd raped", + "ĠRad ar", + "Ġcold er", + "ĠPep si", + "im inary", + "], [", + "65 8", + "V i", + "ĠF rem", + "ĠP es", + "Ġveter inary", + "ĠT ED", + "ĠEp idem", + "n ova", + "k id", + "Ġdev out", + "o ct", + "j ad", + "M oh", + "ĠP AY", + "Ġge ometric", + "Ġ3 23", + "Ġcircum ference", + "ich ick", + "19 75", + "ĠY uri", + "ĠSh all", + "ĠH over", + "un in", + "S pr", + "Ġg raft", + "ĠHapp iness", + "Ġdisadvant ages", + "att acks", + "Ġhub s", + "ĠStar Craft", + "é ĸ", + "Ġgall eries", + "ĠKor ra", + "Ġgrocer ies", + "ĠGors uch", + "Ġrap ists", + "Ġfun gi", + "ĠTyph oon", + "V ector", + "ĠEm press", + "b attle", + "4 68", + "Ġparas ite", + "ĠBom ber", + "S G", + "ex ist", + "ĠP f", + "Ġun se", + "Ġsurge ons", + "B irth", + "ĠUn sure", + "ĠPrint ed", + "ĠBehavior al", + "ĠA ster", + "Pak istan", + "Ġun ethical", + "Ġs v", + "ĠIo T", + "Ġlay outs", + "P ain", + "Ġconst ants", + "ĠL W", + "ĠB ake", + "Ġtow els", + "Ġdeterior ation", + "ĠBol ivia", + "Ġblind ed", + "ĠW arden", + "ĠMist ress", + "Ġon stage", + "Ġcl ans", + "ĠB EST", + "19 60", + "Ġant ique", + "Ġrhet orical", + "ĠPer cy", + "ĠRw anda", + ", .", + "B ruce", + "Ġtra umat", + "ĠParliament ary", + "Ġfoot note", + "id ia", + "ĠLear ned", + "se eking", + "gen ic", + "Ġdim ensional", + "H ide", + "èĢ ħ", + "Ġintrig ue", + "in se", + "Ġle ases", + "Ġapp rentices", + "w ashing", + "Ġ19 26", + "V ILLE", + "Ġsw oop", + "s cl", + "Ġbed rooms", + "on ics", + "ĠCr unch", + "comp atible", + "Ġincap ac", + "ĠYemen i", + "ash tra", + "z hou", + "d anger", + "Ġmanifest ations", + "ĠDem ons", + "AA F", + "Secret ary", + "ACT ED", + "L OD", + "Ġam y", + "ra per", + "eth nic", + "4 17", + "Ġpos itives", + "Ġ27 3", + "ĠRefuge es", + "Ġus b", + "ĠV ald", + "odd y", + "ĠMahm oud", + "As ia", + "Ġskull s", + "ĠEx odus", + "ĠComp et", + "ĠL IC", + "ĠM ansion", + "ĠA me", + "Ġconsolid ate", + "storm s", + "ont ent", + "99 6", + "Ġcl en", + "Ġm ummy", + "fl at", + "75 8", + "ĠV OL", + "oter ic", + "n en", + "ĠMin ute", + "S ov", + "Ġfin er", + "R h", + "ly cer", + "Ġreinforce ments", + "ĠJohann es", + "ĠGall agher", + "Ġgym n", + "S uddenly", + "Ġext ortion", + "k r", + "i ator", + "T a", + "Ġhippocamp us", + "N PR", + "ĠComput ing", + "Ġsquare ly", + "Ġmod elling", + "ĠFor ums", + "ĠL isp", + "ĠKrish na", + "Ġ3 24", + "Ġr ushes", + "Ġens ued", + "Ġcre eping", + "on te", + "n ai", + "il ater", + "ĠHorn ets", + "Ġob livious", + "IN ST", + "55 9", + "Ġjeopard y", + "Ġdistingu ishing", + "j ured", + "Ġbeg s", + "sim ilar", + "ph ot", + "5 30", + "ĠPark way", + "Ġs inks", + "ĠHearth stone", + "ib ur", + "ĠBat on", + "Av oid", + "Ġd ancer", + "Ġmag istrate", + "ary n", + "Ġdisturb ances", + "ĠRom ero", + "Ġpar aph", + "Ġmis chief", + "âĸ ĵ", + "ĠSh aria", + "Ġur inary", + "r oute", + "iv as", + "f itted", + "Ġeject ed", + "ĠAl buquerque", + "Ġ4 70", + "Ġirrit ated", + "ĠZ ip", + "ĠB iol", + "à į", + "Ġden ounce", + "Ġbin aries", + "ĠVer se", + "Ġopp os", + "ĠKend rick", + "ĠG PL", + "Ġsp ew", + "ĠEl ijah", + "ĠE as", + "Ġdr ifted", + "so far", + "Ġannoy ance", + "ĠB ET", + "47 4", + "ĠSt rongh", + "it ates", + "ĠCogn itive", + "oph one", + "ĠIdent ification", + "ocr ine", + "connect ion", + "Ġbox er", + "ĠAS D", + "ĠAre as", + "Y ang", + "t ch", + "ull ah", + "Ġdece ive", + "Comb at", + "ep isode", + "cre te", + "W itness", + "Ġcondol ences", + "ht ar", + "Ġhe als", + "Ġbuck ets", + "ĠLA W", + "B lu", + "Ġsl ab", + "ĠOR DER", + "oc l", + "att on", + "ĠSteven son", + "ĠG inger", + "ĠFriend ly", + "ĠVander bilt", + "sp irit", + "ig l", + "ĠReg arding", + "ĠPR OG", + "Ġse aling", + "start ing", + "Ġcard inal", + "ĠV ec", + "ĠBe ir", + "Ġmillisec onds", + "we ak", + "per se", + "Ġster ile", + "ĠCont emporary", + "ĠPh ant", + "ĠCl o", + "Ġout p", + "Ġex iled", + "Ġ27 7", + "Ġself ie", + "Ġman ic", + "Ġn ano", + "ter ms", + "Alex ander", + "Ġres olves", + "Ġmillenn ia", + "Ġexpl odes", + "Ġconst ellation", + "Ġadul tery", + "m otion", + "D OC", + "Ġbroad casters", + "Ġkinderg arten", + "ĠMay weather", + "ĠE co", + "ich o", + "Ġ28 7", + "l aun", + "Ġm ute", + "Ġdisc reet", + "Ġpres chool", + "Ġpre empt", + "De lete", + "ĠFre ed", + "P i", + "H K", + "Ġblock er", + "ĠC umber", + "Ġw rought", + "d ating", + "Ġins urer", + "Ġquot as", + "Ġpre ached", + "Ġev iction", + "ĠReg ina", + "ĠP ens", + "Ġsevent een", + "ĠN ass", + "D ick", + "Ġfold s", + "Ġd otted", + "ĠA ad", + "Un iversal", + "Ġp izz", + "ĠG uru", + "Ġso ils", + "Ġno vice", + "ĠNe ander", + "Ġst ool", + "Ġdeton ated", + "ĠPik achu", + "ĠMass ive", + "IV ER", + "ĠAb del", + "Ġsubdu ed", + "Ġtall est", + "Ġprec arious", + "Ġa y", + "r ification", + "ĠOb j", + "c ale", + "Ġun question", + "cul osis", + "ad as", + "igr ated", + "D ays", + "Ġque ens", + "ĠGaz ette", + "ĠCol our", + "ĠBow man", + "ĠJ J", + "ï ve", + "Ġdomin ates", + "Stud ent", + "Ġm u", + "Ġback log", + "ĠElect ro", + "Tr uth", + "48 3", + "Ġcond ensed", + "r ules", + "ĠCons piracy", + "Ġacron ym", + "hand led", + "ĠMat te", + "j ri", + "ĠImp ossible", + "l ude", + "cre ation", + "Ġwar med", + "ĠSl ave", + "Ġmis led", + "Ġfer ment", + "ĠK ah", + "ink i", + "ke leton", + "cy l", + "ĠKar in", + "Hun ter", + "Reg ister", + "ĠSur rey", + "Ġst ares", + "ĠW idth", + "ĠN ay", + "ĠSk i", + "Ġblack list", + "uck et", + "Ġexp ulsion", + "im et", + "Ġret weet", + "vant age", + "Fe ature", + "Ġtro opers", + "Ġhom ers", + "9 69", + "Ġconting ency", + "ĠW TC", + "ĠBrew er", + "fore ign", + "W are", + "S olar", + "Ġund ue", + "RE C", + "ulner able", + "path ic", + "ĠBo ise", + "Ġ3 22", + "Ġarous ed", + "ĠY ing", + "ä¸ į", + "uel ess", + "Ġp as", + "Ġmor p", + "Ġfl oral", + "Ex press", + "ud ging", + "k B", + "ĠGr anted", + "Ø ¯", + "ĠMich a", + "ĠGoth ic", + "ĠSPEC IAL", + "ĠRic ardo", + "F ran", + "Ġadminister ing", + "6 20", + "por a", + "Ġ ®", + "Ġcomprom ises", + "Ġb itten", + "Ac cept", + "Th irty", + "Ð ²", + "Ġmater ially", + "ĠTer r", + "ig matic", + "ch ains", + "Ġdo ve", + "stad t", + "Mar vel", + "FA ULT", + "Ġwind shield", + "Ġ3 36", + "ad ier", + "Ġsw apping", + "Ġflaw less", + "ĠPred ator", + "ĠMiche le", + "Ġprop ulsion", + "ĠPsych ic", + "Ġassign ing", + "Ġfabric ation", + "Ġbar ley", + "l ust", + "Ġtow ering", + "Ġalter cation", + "ĠBent ley", + "Sp here", + "Ġtun a", + "ĠClass es", + "Fre edom", + "un er", + "L ady", + "v oice", + "Ġcool est", + "or r", + "Ġpal p", + "$ {", + "Ġhyster ia", + "ĠMet atron", + "p ants", + "Ġspawn ing", + "Exper ts", + "ĠInvest ors", + "ĠAn archy", + "Ġshr unk", + "ĠVict im", + "Ġ28 9", + "Ġec stasy", + "ĠB inding", + "58 5", + "ĠMel ody", + "57 8", + "ot ally", + "ĠE tsy", + "lig a", + "Ġapplaud ed", + "Ġswe ating", + "Ġredist ributed", + "Ġpop corn", + "Ġsem inal", + "f ur", + "ĠNeuro science", + "R and", + "ĠO st", + "ĠMadd en", + "ĠIncre asing", + "ĠDaw kins", + "ĠSub way", + "Ġar sen", + "cons erv", + "B UR", + "Ġsp iked", + "ĠLy ft", + "ĠImper ium", + "ĠDrop box", + "Ġfav oured", + "Ġencomp asses", + "gh ost", + "Ġins pires", + "Ġbur geoning", + "ĠY oshi", + "ĠVert ical", + "ĠAud itor", + "Ġint ending", + "Ġfilib uster", + "Bl oom", + "f ac", + "ĠCav s", + "ign ing", + "Ġcowork ers", + "ĠBarb arian", + "rem ember", + "FL AG", + "Ġaudit ory", + "ason ry", + "Col lege", + "Ġmut ed", + "gem ony", + "ob in", + "ĠPsych o", + "9 68", + "Ġlav ish", + "Ġhierarch ical", + "ĠDr one", + "ou k", + "Ġcripp led", + "ĠMax im", + "Sl ot", + "Ġqu iz", + "ĠV id", + "if ling", + "Ġarchae ologists", + "Ġabandon ment", + "d ial", + "le on", + "ĠF as", + "T ed", + "Ġr aspberry", + "Ġmaneu vers", + "Ġbehavi ours", + "Ġins ure", + "Ġrem od", + "Sw itch", + "h oe", + "Ġsp aced", + "Ġafford ability", + "ĠF ern", + "not ation", + "ĠBal anced", + "Ġoccup ies", + "en vironment", + "Ġneck lace", + "Ġsed an", + "F U", + "ĠBrav o", + "Ġab users", + "ĠAn ita", + "met adata", + "ĠG ithub", + "ait o", + "ĠF aster", + "ĠWass erman", + "ĠF lesh", + "Ġth orn", + "r arily", + "ĠMer ry", + "w ine", + "Ġpopul ace", + "ĠL ann", + "Ġrepair ing", + "Ġpsy che", + "Ġmod ulation", + "aw aru", + "âĢĭ âĢĭ", + "ari j", + "Ġdecor ations", + "Ġapolog ise", + "ĠG arg", + "app ly", + "Ġgive away", + "ĠFl an", + "ĠWy att", + "U ber", + "Ġauthor ised", + "ĠMor al", + "HAHA HAHA", + "activ ate", + "Ġtorped o", + "ĠF AR", + "Ġam assed", + "ĠA ram", + "ark in", + "ĠVict ims", + "st ab", + "Ġo m", + "ĠE CO", + "Ġopio ids", + "Ġpurpose ly", + "ĠV est", + "Ġer g", + "at an", + "ĠSur gery", + "Ġcorrect ing", + "ĠOrt iz", + "ĠBe et", + "Ġrev oke", + "Ġfre eway", + "ĠH iggins", + "F ail", + "ĠFar ms", + "ĠAT P", + "h ound", + "Ġp oking", + "ĠCommun ists", + "mon ster", + "iment ary", + "Ġunlock ing", + "Ġunf it", + "we ed", + "en ario", + "at ical", + "ĠEnlight enment", + "ĠN G", + "ĠComp ensation", + "de en", + "ĠWid ow", + "ĠCind y", + "ĠAfter wards", + "Ġ6 000", + "ikh ail", + "ag ically", + "Ġrat ified", + "Ġcasual ty", + "H OME", + "p sey", + "f ee", + "Ġspark ling", + "Ġd é", + "Ġconcert ed", + "C atal", + "Ġcomp lying", + "ĠA res", + "ĠD ent", + "Sh ut", + "Ġsk im", + "ad minist", + "Ġhost ilities", + "ĠG ins", + "Ġ6 08", + "Ġm uddy", + "ĠMc Int", + "ĠDec ay", + "5 25", + "Ġconspic uous", + "ĠEx posure", + "Ġresc ind", + "Ġwear able", + "Ġ3 28", + "our met", + "ah s", + "ĠRob ots", + "Ġe clips", + "inst ance", + "ĠRE PORT", + "ĠApp l", + "0 30", + "ĠSk ies", + "01 00", + "Ġfall acy", + "S ocket", + "ĠRece iver", + "Ġsol ves", + "ĠButter fly", + "ĠSho pping", + "ĠFI RE", + "65 4", + "Med ic", + "Ġsing ers", + "ĠNeed less", + "'' ''", + "isher s", + "ĠD ive", + "58 8", + "Ġselect ively", + "Ġcl umsy", + "88 9", + "Ġpurch aser", + "ear ned", + "ard y", + "Ġbenef iting", + "eng lish", + "Ġyield ing", + "ĠP our", + "Ġspin ach", + "Ġdel ve", + "ĠC rom", + "6 10", + "Ġexport ing", + "ĠMA KE", + "Ġ26 3", + "Ġg rop", + "Ġenv oy", + "ĠInqu iry", + "ĠLu igi", + "d ry", + "ĠT uring", + "Thumbnail Image", + "ĠVar iety", + "Ġfac et", + "Ġfl uffy", + "Ġexcerpt s", + "Ġsh orth", + "ĠOl sen", + "CL UD", + "Ġrel iant", + "ĠUN C", + "T our", + "Ġbat hing", + "Comp any", + "Ġglobal ization", + "P red", + "ĠMalf oy", + "Ġh oc", + "j am", + "craft ed", + "ĠBond s", + "ĠKiss inger", + "Eng land", + "Ġorder ly", + "cat entry", + "Ġ26 1", + "Ġexch anging", + "ĠInt ent", + "ĠAmend ments", + "D OM", + "Ġst out", + "³³³³³³³³ ³³³³³³³³", + "ĠAir bus", + "Ġ27 8", + "hy de", + "P oll", + "Item ThumbnailImage", + "Ġlooph oles", + "ĠPill ar", + "Ġexpl or", + "St retch", + "A part", + "Ġun married", + "Lim it", + "ĠTransform ers", + "Ġintellect ually", + "unct ure", + "18 00", + "Ġd arn", + "B razil", + "Ġleft over", + "ber us", + "f red", + "Mine craft", + "3 26", + "ĠForm s", + "Ġproof s", + "ĠDes igned", + "Ġindex es", + "ĠSupp ose", + "EM S", + "ĠL oving", + "ĠBon nie", + "im ating", + "OT US", + "Ġconduct or", + "Ġbehav ed", + "ĠF ren", + "Ġsy nerg", + "Ġmillenn ium", + "Ġcater ing", + "ĠL auder", + "W r", + "ĠY iannopoulos", + "ĠAT F", + "Ġensl aved", + "Ġawaken ed", + "D VD", + "ĠED ITION", + "ĠConc ert", + "ĠChall enger", + "ĠH aku", + "umer ic", + "Ġdep recated", + "ĠSH AR", + "4 12", + "Ġdy stop", + "Ġtremb ling", + "Ġdread ed", + "ĠSp ac", + "p adding", + "Re pl", + "ĠG arrison", + "M ini", + "Ġun paralleled", + "am ar", + "URR ENT", + "w reck", + "c ertain", + "t al", + "ĠC LS", + "app ings", + "Ġsens ed", + "Ġf encing", + "ĠPas o", + "ĠDes k", + "Ġsc off", + "Ġcontem plate", + "ĠL iga", + "l iquid", + "75 7", + "Ġapp rentice", + "ĠUCH IJ", + "5 70", + "ĠTh ousand", + "ĠIll um", + "Ġchampion ed", + "ãĤ Į", + "Ġelect ors", + "Ġ3 98", + "ĠH ancock", + "round ed", + "ĠJ OHN", + "Ġuns atisf", + "Ġqual ifier", + "ĠGad get", + "EN E", + "Ġdead liest", + "ĠPl ants", + "Ġ ions", + "Ġacc ents", + "Ġtwe aking", + "Ġsh aved", + "F REE", + "ĠCh aser", + "Again st", + "9 60", + "Ġmeth amphetamine", + "Ġnormal ized", + "Ġ$ \\", + "ĠPre cision", + "ĠGu am", + "Ġch oked", + "ĠX II", + "ĠCast ing", + "Tor rent", + "Ġscal p", + "ĠJagu ar", + "w it", + "Ġsem ic", + "ix ie", + "ĠG ould", + "Ġconf ines", + "N usra", + "ĠL on", + "ĠJ ugg", + "y cle", + "ĠCod ec", + "E gypt", + "Ġrest rain", + "ĠAl iens", + "Ġch oking", + "ĠD unk", + "ĠBell a", + "ab c", + "Ġsl ang", + "Ġneuro trans", + "s av", + "Ġempower ment", + "â ĨĴ", + "Ġclim bers", + "ĠM im", + "ĠF ra", + "ros se", + "Cap ital", + "ĠCth ulhu", + "Inter face", + "Ġprof icient", + "ĠIN TO", + "Ġ3 18", + "ront al", + "5 80", + "ĠDes pair", + "K enn", + "Ġscrim mage", + "ĠCo at", + "as ions", + "Ġwall paper", + "ĠJ ol", + "Ġresurg ence", + "Ġant iv", + "ĠB alls", + "² ¾", + "Ġbuff ers", + "Ġsub system", + "ĠSt ellar", + "ĠL ung", + "A IDS", + "Ġerad icate", + "Ġblat antly", + "Ġbehav es", + "ĠN un", + "Ġant ics", + "ex port", + "DE V", + "w b", + "Ġph p", + "ĠInteg rity", + "Ġexplore r", + "Ġrev olving", + "auth ored", + "g ans", + "Ġbas k", + "Ġas ynchronous", + "å į", + "TH ING", + "69 8", + "G ene", + "ĠR acer", + "ĠN ico", + "iss ued", + "Ġser mon", + "p ossibly", + "Ġsize of", + "Ġentrepreneur ial", + "ox in", + "ĠMin erva", + "Ġpl atoon", + "n os", + "ri ks", + "A UT", + "ĠAval anche", + "ĠDes c", + "ij 士", + "ĠP oc", + "Ġconf erred", + "Î »", + "Ġpat ched", + "F BI", + "66 2", + "Ġfract ures", + "Ġdetect s", + "Ġded icate", + "Ġconstitu ent", + "Ġcos mos", + "W T", + "Ġswe ats", + "Ġspr ung", + "b ara", + "s olid", + "Ġuns us", + "Ġbul ky", + "ĠPhilipp e", + "ĠFen rir", + "Ġtherap ists", + "ore al", + "^^ ^^", + "Ġtotal ed", + "Ġboo ze", + "ĠR PC", + "Prosecut ors", + "Ġdis eng", + "ĠSh ared", + "Ġmotor cycles", + "Ġinvent ions", + "Ġlett uce", + "ĠMer ge", + "ĠJ C", + "Ġspiritual ity", + "ĠWAR NING", + "Ġunl ucky", + "ĠT ess", + "Ġtong ues", + "ĠD UI", + "T umblr", + "Ġle ans", + "Ġinv aders", + "Ġcan opy", + "ĠHur ricanes", + "ĠB ret", + "ĠAP PLIC", + "id ine", + "ick le", + "Reg arding", + "Ġve ggies", + "Ġe jac", + "ju ven", + "F ish", + "D EM", + "ĠD ino", + "Th row", + "ĠCheck ing", + "be ard", + "( &", + "Ġj ails", + "Ġh r", + "trans fer", + "iv ating", + "Ġfle ets", + "ĠIm ag", + "ĠMc Donnell", + "Ġsnipp et", + "Is a", + "ĠCh att", + "ĠSt ain", + "ĠSet FontSize", + "ĠO y", + "ĠMathemat ics", + "49 4", + "Ġelectro ly", + "ĠG ott", + "ĠBr as", + "B OOK", + "ĠF inger", + "d ump", + "Ġmut ants", + "Ġrent als", + "Ġinter tw", + "Ġc reek", + "ail a", + "Bro ther", + "ĠDisc ord", + "pe e", + "raw ler", + "Ġcar p", + "Ġ27 9", + "ãĤ· ãĥ£", + "rel ations", + "Ġcontr asts", + "Col umn", + "Ġrec onnaissance", + "Ġun know", + "Ġl ooting", + "Ġregul ates", + "Ġopt imum", + "ĠChero kee", + "ĠA ry", + "Lat est", + "Ġroad side", + "Ġd anced", + "ĠUnic orn", + "A cknowled", + "Ġuncont roll", + "ĠM US", + "at io", + "ch ance", + "ha ven", + "VAL UE", + "Ġfavour ites", + "Ġceremon ial", + "b inary", + "pe ed", + "wood s", + "EM P", + "Ġv ascular", + "Ġcontempl ated", + "Ġbar ren", + "ĠL IST", + "Y ellow", + "ospons ors", + "Ġwhisk y", + "ĠM amm", + "ĠDeV os", + "min imum", + "H ung", + "44 2", + "P ic", + "ĠSnap dragon", + "77 6", + "Ġcar ving", + "Ġund ecided", + "Ġadvantage ous", + "Ġpal ms", + "ĠA Q", + "Ġst arch", + "L oop", + "Ġpadd le", + "Ġfl aming", + "ĠHor izons", + "An imation", + "bo ost", + "Ġprob abilities", + "ĠM ish", + "Ġex odus", + "ĠEditor ial", + "Ġfung us", + "Ġdissent ing", + "ĠDel icious", + "rog ram", + "ĠD yn", + "d isk", + "t om", + "Ġfab rics", + "ĠC ove", + "ĠB ans", + "Ġsoft en", + "ĠCON S", + "Ġin eligible", + "Ġestim ating", + "ĠLex ington", + "pract ice", + "of i", + "Ġshe dding", + "ĠN ope", + "Ġbreat hed", + "ĠCorinth ians", + "y ne", + "ek i", + "B ull", + "Ġatt aching", + "reens hots", + "Ġanaly se", + "ĠK appa", + "Ġuns ustainable", + "Ġinter pol", + "ank y", + "he mer", + "Ġprot agonists", + "Ġform atted", + "ĠBry ce", + "ĠAch illes", + "ĠAb edin", + "sh ock", + "Ġb um", + "b os", + "qu a", + "ĠW arn", + "q t", + "ĠDi abetes", + "8 64", + "ĠIn visible", + "Ġvan ish", + "Ġtrans mitting", + "Ġmur ky", + "ĠFe i", + "Ġawa ited", + "ĠJur assic", + "umm ies", + "Ġmen acing", + "g all", + "C ath", + "B uilt", + "ild o", + "ĠV otes", + "Ġon t", + "Ġmun itions", + "ĠFre em", + "ÃŃ n", + "Ġdec ency", + "lo pp", + "ie ved", + "ĠG ord", + "Ġun thinkable", + "ĠNews week", + "Ġ3 21", + "He at", + "Ġpresent er", + "ji ang", + "Ġpl ank", + "ĠAval on", + "Ġben z", + "ĠR out", + "Ġslam ming", + "ĠD ai", + "ou ter", + "ĠCook ie", + "ĠAlic ia", + "ge y", + "Ġvan ity", + "Ġow l", + "á µ", + "t ested", + "ĠAw akens", + "Ġcan v", + "Ġblind ly", + "ĠRid ley", + "ĠEm ails", + "Requ ires", + "ĠSer bian", + "ograp hed", + "if rame", + "eter ia", + "Ġaltern ating", + "qu iet", + "Ġsoc iology", + "ĠUn lock", + "ĠCommun ism", + "Ġo ps", + "Ġatt ribution", + "Ġab duction", + "ĠAb ram", + "Ġsidel ined", + "ĠB OOK", + "Ġref ining", + "ĠFe eling", + "ĠOs lo", + "ĠPru itt", + "r ack", + "ang ible", + "Ġcaut iously", + "ĠM ARK", + "eed s", + "M ouse", + "ĠStep h", + "ĠP air", + "S ab", + "99 7", + "ĠBa al", + "B ec", + "Ġcomm a", + "ĠP all", + "ĠG ael", + "Ġmisunder stand", + "ĠP esh", + "Order able", + "Ġdis mal", + "ĠSh iny", + "% \"", + "Ġreal istically", + "Ġpat io", + "ĠG w", + "ĠVirt ue", + "Ġexhaust ing", + "wh atever", + "oph ys", + "y ip", + "4 18", + "Ad just", + "ĠWa iting", + "ess on", + "ĠMaz da", + "ĠDo zens", + "Ġstream lined", + "Ġincompet ence", + "ĠM eth", + "Ġeth os", + "ON ES", + "Ġincent iv", + "Ġgr itty", + "ĠBut cher", + "Head er", + "Ġexp onential", + "à Ł", + "Ġcorrel ate", + "Ġcons ensual", + "s ounding", + "R ing", + "Orig in", + "Ġcon clusive", + "fe et", + "ac ly", + "ĠF ernandez", + "Buy able", + "Ġd ucks", + "aunt lets", + "Ġel ong", + "Ġ28 6", + "Ġsim ul", + "G as", + "ĠK irst", + "Ġprot r", + "ĠRob o", + "ĠAo E", + "op ol", + "Ġpsych ologically", + "sp in", + "ilater ally", + "ĠCon rad", + "W ave", + "44 1", + "ĠAd vertisement", + "ĠHarm on", + "ĠOri ental", + "is Special", + "Ġpresum ptive", + "Ġw il", + "ĠK ier", + "ne a", + "Ġp pm", + "Ġhar bour", + "ĠW ired", + "comp any", + "Ġcor oner", + "atur days", + "ĠP roud", + "ĠN EXT", + "ĠFl ake", + "val ued", + "ce iver", + "Ġfra ught", + "Ġc asing", + "Ġrun away", + "Ġg in", + "ĠLaure nt", + "ĠHar lem", + "ĠCur iosity", + "qu ished", + "Ġneuro science", + "ĠH ulu", + "Ġborrow er", + "Ġpetition er", + "ĠCo oldown", + "W ARD", + "Ġinv oking", + "conf idence", + "For ward", + "Ġst s", + "pop ulation", + "Delivery Date", + "Fil m", + "ĠC ov", + "quick Ship", + "quickShip Available", + "prim ary", + "isSpecial Orderable", + "inventory Quantity", + "channel Availability", + "BO X", + "ĠMulti player", + "ĠJen ner", + "77 8", + "ĠM d", + "Ġ~ /.", + "M N", + "Ġchild ish", + "Ġantioxid ant", + "ĠChrom ebook", + "Ġ27 4", + "Ġscreen play", + "Ġadvent urous", + "ĠRelations hip", + "respons ive", + "ming ton", + "Ġcorner stone", + "ĠF ey", + "F IR", + "Ġrook ies", + "ĠF eaturing", + "Ġorig inate", + "Ġelectro des", + "ant es", + "Ġscript ures", + "Ġgl ued", + "Ġdiscont ent", + "Ġaff licted", + "lay out", + "B rave", + "Ġm osa", + "ĠQuant ity", + "ĠH ik", + "w inner", + "H ours", + "Ġent ail", + "ĠCell s", + "olog ue", + "Ġv il", + "Ġpre acher", + "Ġdecor ative", + "d ifferent", + "Ġprejud ices", + "ĠSm oking", + "ĠNotting ham", + "so Type", + "Ġrhyth ms", + "ĠAl ph", + "bl ast", + "Ste el", + "ĠDaniel le", + "Ġstr ife", + "Ġrem atch", + "so DeliveryDate", + "ĠF ork", + "t rip", + "ol ulu", + "hes es", + "C G", + "ĠPOLIT ICO", + "ost a", + "ĠDr ift", + "é¾įå ¥", + "é¾įå¥ ij士", + "Ġvet ting", + "ĠJin ping", + "ĠRec ession", + "Min or", + "ĠF raud", + "enf ranch", + "Ġconven ed", + "ĠNA ACP", + "ĠMill ions", + "ĠFarm ing", + "ĠW oo", + "ĠFl are", + "rit o", + "imm igrant", + "Ġvac ancy", + "ĠHE AD", + "ĠV aj", + "eg al", + "ĠV igil", + "Stud y", + "Ġru ining", + "Ġr acks", + "Ġhe ater", + "ĠRand olph", + "ĠBr ush", + "ĠT ir", + "Ø ¨", + "Ġc ov", + "% ]", + "Ġrecount s", + "ĠO PT", + "ĠM elt", + "Ġtr uce", + "Ġcas inos", + "Ġcrus ade", + "Ġcarn age", + "Ġstri pe", + "ĠK yl", + "Text ures", + "Ġ6 98", + "Ġpro clamation", + "Ġgood ies", + "Ġ........ ..", + "pro claimed", + "P olit", + "Ġtop ical", + "Ġspecial ize", + "ĠA min", + "g m", + "Ġanch ored", + "Ġbear ings", + "s ample", + "ĠHigh land", + "ĠAut ism", + "Ġmerc enary", + "Ġinterview er", + "L ER", + "ĠSom ers", + "Ġembry o", + "ĠAss y", + "Ġ28 1", + "ĠEd iting", + "ĠCh osen", + "6 60", + "Ġp ci", + "ĠThunder bolt", + "BI LL", + "Ġchuck led", + "jri wal", + "h of", + "Ġearth ly", + "() {", + "ind ependence", + "Ġdisp ers", + "ĠV endor", + "ĠG areth", + "Ġp als", + "P enn", + "ĠSub mit", + "ic um", + "Th u", + "Ġcl andestine", + "Ġcann ibal", + "ĠCl erk", + "E Stream", + "gal itarian", + "âĻ ¥", + "g ew", + "Ġhor rend", + "ĠL ov", + "ĠRe action", + "ocr in", + "Class ic", + "Ġecho ing", + "Ġdiscl osing", + "ĠIns ight", + "og un", + "ĠInc arn", + "upload s", + "pp erc", + "guy en", + "Ġ19 01", + "ĠB ars", + "68 7", + "Ġb ribes", + "ĠFres no", + "ur at", + "ĠRe ese", + "Ġintr usive", + "Ġgri pping", + "ĠBlue print", + "ĠR asm", + "un ia", + "man aged", + "ĠHeb do", + "Ġ3 45", + "Ġdec oding", + "Ġpo ets", + "Ġj aws", + "ĠF IGHT", + "am eless", + "ĠMead ows", + "ĠHar baugh", + "Inter view", + "ĠH osp", + "ĠB RA", + "Ġdelet ion", + "m ob", + "W alker", + "ĠMoon light", + "ĠJ ed", + "ĠSoph ia", + "Ġus ur", + "Ġfortun ately", + "ĠPut ting", + "ĠF old", + "Ġsan itation", + "Ġpart isans", + "IS ON", + "B ow", + "ĠCON C", + "ĠRed uced", + "ĠS utton", + "Ġtouch screen", + "Ġembry os", + "âĢ¢âĢ¢ âĢ¢âĢ¢", + "ĠK rug", + "com bat", + "ĠPet roleum", + "Ġam d", + "ĠCos mos", + "Ġpresc ribing", + "Ġconform ity", + "ours es", + "Ġplent iful", + "Ġdis illusion", + "ĠEc ology", + "itt al", + "Ġf anc", + "Ġassass inated", + "regn ancy", + "Ġperenn ial", + "ĠBul lets", + "Ġst ale", + "Ġc ached", + "ĠJud ith", + "ĠDise ases", + "All en", + "Ġl as", + "Ġsh ards", + "ĠSu arez", + "ĠFriend ship", + "inter face", + "ĠSupp orters", + "add ons", + "46 2", + "ĠIm ran", + "ĠW im", + "Ġnew found", + "ĠM b", + "An imal", + "Ġd arling", + "and e", + "Ġrh y", + "ĠTw isted", + "pos al", + "yn ski", + "Var ious", + "× ľ", + "ĠK iw", + "uy omi", + "Ġwell being", + "ĠL au", + "an os", + "Ġunm ist", + "Ġmac OS", + "Ġrest room", + "ĠOl iv", + "ĠAir ways", + "Ġtimet able", + "9 80", + "Ġrad ios", + "v oy", + "ias co", + "Ġcloud y", + "ĠDraw ing", + "Any thing", + "Sy ria", + "ĠH ert", + "st aking", + "Ġun checked", + "Ġb razen", + "ĠN RS", + "69 7", + "onom ic", + "est ablish", + "Ġl eng", + "Ġdi agonal", + "ĠF ior", + "L air", + "ĠSt ard", + "Ġdef icient", + "jo ining", + "be am", + "Ġomn ip", + "Ġbl ender", + "Ġsun rise", + "Mo ore", + "ĠF ault", + "ĠCost ume", + "ĠM ub", + "Fl ags", + "an se", + "Ġpay out", + "ĠGovern ors", + "ĠD illon", + "ĠBan ana", + "N ar", + "Ġtra iled", + "Ġimperial ist", + "um ann", + "ats uki", + "4 35", + "ĠRoad s", + "Ġsl ur", + "ĠIde ally", + "Ġt renches", + "C trl", + "Ġmir rored", + "ĠZ el", + "ĠC rest", + "Comp at", + "ĠRoll s", + "sc rib", + "ĠTra ils", + "omet ers", + "w inter", + "Ġimm ortality", + "il ated", + "Ġcontrad icts", + "un iversal", + "ill ions", + "ĠM ama", + "opt im", + "AT URE", + "Ġge o", + "et ter", + "ĠCar lo", + "4 24", + "Ġcanon ical", + "ĠStrongh old", + "n ear", + "Ġperf ume", + "Ġorche stra", + "od iac", + "Ġup he", + "Ġreign ing", + "vers ive", + "Ġc aucuses", + "ĠD EM", + "Ġinsult ed", + "Ġ---- --", + "ĠCr ush", + "Ġroot ing", + "ĠWra ith", + "Ġwh ore", + "Ġto fu", + "C md", + "ĠB ree", + "Ġ$ _", + "Ġr ive", + "ĠAd vertising", + "Ġw att", + "ĠH O", + "Ġpersu asive", + "ĠParam eters", + "Ġobserv ational", + "ĠN CT", + "ĠMo j", + "ĠSal on", + "Ġtr unc", + "Ġexqu isite", + "ĠMar a", + "Ġpo op", + "ĠAN N", + "Ex c", + "ĠWonder ful", + "ĠT aco", + "Ġhome owner", + "ĠSmith sonian", + "orpor ated", + "mm mm", + "Ġlo af", + "ĠYam ato", + "ĠInd o", + "Ġcl inging", + "á s", + "Ġimm utable", + "h ub", + "Or ange", + "Ġfingert ips", + "ĠWood en", + "ĠK idd", + "ĠJ PM", + "ĠDam n", + "C ow", + "c odes", + "48 2", + "Ġiniti ating", + "ĠEl k", + "ĠCut ting", + "Ġabsent ee", + "ĠV ance", + "ĠLil ith", + "G UI", + "Ġobsc ured", + "Ġdwar ves", + "ĠCh op", + "ĠB oko", + "Val ues", + "Ġmult imedia", + "Ġbrew ed", + "Reg ular", + "CRIP TION", + "ĠMort al", + "Ġa pex", + "Ġtravel er", + "Ġbo ils", + "Ġspray ing", + "Rep resent", + "ĠStars hip", + "4 28", + "Ġdisappro val", + "Ġshadow y", + "Ġlament ed", + "ĠRe place", + "ĠFran ç", + "67 7", + "d or", + "Ġunst oppable", + "Ġcoh orts", + "gy n", + "ĠClass ics", + "ĠAm ph", + "Ġsl uggish", + "ĠAdd iction", + "ĠPad res", + "Ġins cription", + "Ġin human", + "min us", + "ĠJere miah", + "at ars", + "Ter ror", + "ĠT os", + "ĠSh arma", + "ast a", + "c atch", + "Ġpl umbing", + "ĠTim bers", + "Sh ar", + "H al", + "ĠO sc", + "Ġcou pling", + "hum ans", + "Ġsp onge", + "Ġid ols", + "ĠSp a", + "ĠAdv ocate", + "ĠBe ats", + "lu a", + "Ġtick ing", + "Ġload er", + "ĠG ron", + "8 10", + "Ġstim ulated", + "Ġside bar", + "ĠManufact urer", + "ore And", + "19 73", + "Ġpra ises", + "ĠFl ores", + "dis able", + "ĠElect rical", + "ra ise", + "E th", + "Ġmigr ated", + "Ġlect urer", + "K ids", + "ĠCa vern", + "Ġk ettle", + "Ġgly c", + "ĠMand ela", + "ĠF ully", + "å§ «", + "FIN EST", + "Ġsquee zing", + "ĠRy der", + "amp oo", + "oreAnd Online", + "Inst oreAndOnline", + "Buyable InstoreAndOnline", + "Ġcommem orate", + "ĠRamp age", + "Aust in", + "ĠSh roud", + "ĠRu ins", + "9 15", + "ĠK H", + "Ġwater front", + "ĠE SC", + "b aby", + "ĠC out", + "ĠEm blem", + "Ġequival ents", + "49 2", + "Un ique", + "ĠNiet zsche", + "brow ser", + "Ġim itation", + "ĠWere wolf", + "ĠKir in", + "ac as", + "' ,\"", + "Ġà ¾", + "Review ed", + "Ġc unt", + "Ġvo ic", + "ĠLen ovo", + "Ġbond ed", + "48 1", + "Ġinhib itors", + "Ġendeav ors", + "ĠHav ana", + "ĠSt out", + "ĠJ olly", + "A ctor", + "*/ (", + "Ġoccur rences", + "ĠT ens", + "Incre ased", + "ĠACT ION", + "Ġ ãĢĮ", + "ĠRank ings", + "ĠB reat", + "Ġ30 9", + "D ou", + "Ġimpact ing", + "ĠDuc hess", + "pre fix", + "Q B", + "Ġsummon ing", + "Ġbest owed", + "ĠKe pler", + "ĠPOW ER", + "c ube", + "ĠK its", + "ĠG rip", + "Ġop ium", + "Ġrep utable", + "t oc", + "ich ael", + "ĠR ipple", + "Ġcaf é", + "ĠZ oom", + "ĠBur ma", + "Ġwa ive", + "Ġst alls", + "Ġdem eanor", + "inc erity", + "Ġfluor ide", + "ĠSH OULD", + "Par is", + "Ġlong ing", + "Ġpl at", + "Ġgross ly", + "Ġbull s", + "Ġshowc asing", + "ex pected", + "ĠG addafi", + "engine ering", + "Re peat", + "ĠK ut", + "Ġconce ivable", + "Ġtrim med", + "osc ope", + "ĠCand idate", + "ĠT ears", + "rol og", + "Lew is", + "S UP", + "Ġroad map", + "Ġsal iva", + "Ġtrump et", + "Jim my", + "Ġmirac ulous", + "Ġcolon ization", + "Ġam put", + "ĠGN OME", + "ate ch", + "D ifferent", + "ĠE LE", + "ĠGovern ments", + "ĠA head", + "ãħĭ ãħĭ", + "word press", + "L IB", + "ĠIn clude", + "ĠDor othy", + "0 45", + "ĠColomb ian", + "Ġle ased", + "88 4", + "Ġde grading", + "ĠDa isy", + "i ations", + "Ġbapt ized", + "Ġsurn ame", + "co x", + "Ġblink ed", + "ãĥ ¢", + "Ġpoll en", + "Ġder mat", + "Ġre gex", + "ĠNich olson", + "ĠE ater", + "ç ľ", + "rad or", + "Ġnarrow er", + "Ġhur ricanes", + "Ġhalluc inations", + "r idden", + "ISS ION", + "ĠFire fly", + "Ġattain ment", + "Ġnom inate", + "Ġav ocado", + "ĠM eredith", + "Ġt s", + "Ġreve rence", + "Ġe uph", + "Ġcr ates", + "ĠT EXT", + "Ġ4 43", + "Ġ3 19", + "J SON", + "iqu ette", + "Ġshort stop", + "ic key", + "Ġpro pelled", + "Ġap i", + "ĠTh ieves", + "77 9", + "Ġovers aw", + "Ġcol i", + "ĠNic ola", + "Ġover cl", + "ik awa", + "ĠC yr", + "Ġ38 4", + "78 9", + "ĠAll ows", + "10 27", + "Det roit", + "TR Y", + "set up", + "ĠSocial ism", + "Sov iet", + "s usp", + "ĠAP R", + "ĠShut down", + "Ġal uminium", + "zb ek", + "ĠL over", + "GGGG GGGG", + "Ġdemocr acies", + "Ġ19 08", + "ĠMer rill", + "ĠFranco is", + "gd ala", + "Ġtraff ickers", + "ĠT il", + "ĠGo at", + "Ġsp ed", + "ĠRes erv", + "Ġpro d", + "55 2", + "Ġc ac", + "ĠUn iv", + "ĠSch we", + "Ġsw irling", + "ĠWild erness", + "ĠEgg s", + "Ġsadd ened", + "Ġarch aic", + "H yd", + "Ġexcess ively", + "B RE", + "Ġaer ospace", + "ĠVo ices", + "Cra ig", + "Ġign ited", + "In itially", + "ĠMc A", + "Ġhand set", + "Ġreform ing", + "Ġfrust rations", + "ĠDead pool", + "ĠBel ichick", + "ract or", + "ĠRagnar ok", + "ĠD rupal", + "ĠApp roximately", + "19 20", + "ĠHub ble", + "arm or", + "ĠSar as", + "ĠJon as", + "Ġnostalg ic", + "Ġfeas ibility", + "Sah aran", + "Ġorb iting", + "Ġ9 70", + "R u", + "Ġsh in", + "ĠInvestig ators", + "Ġinconsist encies", + "ĠP AN", + "B G", + "Ġgraz ing", + "Ġdetect ors", + "ĠStart up", + "ĠFun ny", + "ĠNa omi", + "Consider ing", + "Ġh og", + "ut f", + "ce mic", + "Ġfort ified", + "ĠFun ctions", + "Ġcod ec", + "nut rition", + "H at", + "\" !", + "micro soft", + "55 8", + "ĠTh in", + "ĠA CE", + "Al ias", + "ĠO PS", + "p apers", + "P K", + "ãĢ İ", + "Ġimpro bable", + "N orthern", + "equ al", + "Ġlook out", + "Ġty res", + "ĠMod ified", + "ĠK op", + "Abs olutely", + "Ġbuild up", + "sil ver", + "Ġaud i", + "Ġgro tesque", + "ĠSab er", + "ĠPres byter", + "ON Y", + "Ġglac iers", + "ĠSho als", + "ĠK ass", + "ĠH RC", + "ĠNic ol", + "ĠL unch", + "ĠF oss", + "âĸ Ĵ", + "AD RA", + "ĠOne Plus", + "o ing", + "ground s", + "Ġincident al", + "Ġdatas ets", + "68 9", + "ĠClarks on", + "Ġassemb ling", + "ĠCorrect ions", + "Ġdrink ers", + "Ġqual ifiers", + "Ġle ash", + "Ġunf ounded", + "ĠH undred", + "Ġkick off", + "T i", + "Ġrecon cil", + "ĠGr ants", + "ĠCompl iance", + "ĠDexter ity", + "Ġ19 06", + "w arn", + "D allas", + "Max imum", + "n ard", + "av ia", + "be aut", + "ens itivity", + "tr ace", + "Ġpione ers", + "ĠF ract", + "ãĢ ı", + "Ġpre cept", + "Ġgloss y", + "ĠI EEE", + "Ac ross", + "Ġ6 80", + "S leep", + "che on", + "Ġsatir ical", + "ĠMin otaur", + "ĠCla ude", + "Ġr é", + "ape go", + "Ġcar rot", + "ĠSem in", + "ino a", + "Ġz o", + "Ind ependent", + "Ġdiagn oses", + "ĠC ue", + "M AR", + "Ġrend ition", + "ĠK ik", + "Ġpath ology", + "Ġselect s", + "Link edIn", + "Ġass ay", + "ĠD res", + "Ġtext ual", + "post ed", + "IT AL", + "ĠM aul", + "N eal", + "Ġinter connected", + "Ġerr atic", + "ĠVir us", + "Ġ5 30", + "Ġenvironmental ists", + "ĠP helps", + "Ġeng agements", + "ĠIN ST", + "Ġeconom ical", + "nox ious", + "Ġg earing", + "izz y", + "Ġfavor ably", + "ĠMcG ill", + "T erm", + "Ġh anged", + "Ġball park", + "ĠRe yes", + "Ġbe ware", + "ĠP sal", + "ĠMass acre", + "q i", + "Ġin accessible", + "acly sm", + "Ġfr ay", + "ill ac", + "Ġbitter ly", + "ĠCert ification", + "Mich igan", + "Ġir respective", + "al ore", + "Em pty", + "Ġendorse ments", + "Ġund et", + "f g", + "equ ipped", + "Ġmerc iless", + "ĠC ust", + "Ġimm ature", + "Ġvou cher", + "ĠBlack well", + "Ñ ı", + "h awk", + "dis ciplinary", + "ile e", + "ĠMak oto", + "ĠD ude", + "ãĥĩ ãĤ£", + "Y ears", + "Ġin ver", + "Ġsh aman", + "ĠY ong", + "ip el", + "ell en", + "ĠCath y", + "br ids", + "Ġs arc", + "65 1", + "N ear", + "Ġground work", + "Ġam az", + "Ġ4 15", + "ĠHunting ton", + "hew s", + "ĠB ung", + "Ġarbit rarily", + "ĠW it", + "ĠAl berto", + "Ġdis qualified", + "best os", + "46 1", + "Ġp c", + "Ġ28 4", + "ro bat", + "Rob in", + "Ġh ugs", + "ĠTrans ition", + "ĠOcc asionally", + "Ġ3 26", + "ĠWh ilst", + "ĠLe y", + "Ġspaces hip", + "cs v", + "Ġun successfully", + "ĠA u", + "le ck", + "ĠWing ed", + "ĠGrizz lies", + ". �", + "Ġne arer", + "ĠSorce ress", + "ĠInd igo", + "El se", + "8 40", + "let es", + "Co ach", + "Ġup bringing", + "ĠK es", + "Ġseparat ist", + "Ġrac ists", + "Ġch ained", + "Ġabst inence", + "lear ning", + "Ġrein stated", + "Ġsymm etry", + "Ġremind ers", + "ĠChe vy", + "Ġm ont", + "Ġexempl ary", + "ĠT OR", + "Z X", + "Ġqual itative", + "ĠSt amp", + "ĠSav annah", + "ĠRoss i", + "Ġp aed", + "Ġdispens aries", + "ĠWall s", + "ĠCh ronic", + "Ġcompliment ary", + "ĠBeir ut", + "Ġ+ ---", + "igs list", + "Ġcrypt ographic", + "mas ters", + "ĠCap itals", + "Ġmax imal", + "Ġent ropy", + "Point s", + "Ġcombat ants", + "l ip", + "ĠGl ob", + "ĠB MC", + "ph ase", + "th ank", + "HT TP", + "Ġcomm uter", + "Ġ\\( \\", + ".. /", + "ĠReg ener", + "ĠDO I", + "ĠActiv ision", + "Ġsl it", + "os al", + "RE M", + "Ġch ants", + "Y u", + "Ke ys", + "Bre xit", + "ĠFor ced", + "Ari zona", + "Ġsquad ron", + "IS O", + "ĠMal one", + "Ġ3 38", + "Ġcontrast ing", + "Ġt idal", + "Ġlib el", + "Ġimpl anted", + "Ġupro ar", + "ĠC ater", + "Ġpropos itions", + "M anchester", + "ĠEuro s", + "it amin", + "G il", + "ĠEl ven", + "ĠSe ek", + "ĠB ai", + "Ġredevelop ment", + "ĠTown s", + "ĠL ub", + "! \",", + "al on", + "K rist", + "Ġmeas urable", + "Ġimagin able", + "Ġapost les", + "Y N", + "7 60", + "Ġster oid", + "Ġspecific ity", + "ĠL ocated", + "ĠBeck er", + "ĠE du", + "ĠDiet ary", + "uts ch", + "ĠMar ilyn", + "Ġbl ister", + "ĠM EP", + "ĠK oz", + "ĠC MS", + "y ahoo", + "ĠCar ney", + "Ġbo asting", + "ĠC aleb", + "By te", + "read s", + "ad en", + "Pro blem", + "ĠWood ward", + "S we", + "S up", + "ĠK GB", + "Set up", + "Ġtac it", + "Ġret ribution", + "Ġd ues", + "ĠM ü", + ". ?", + "ä¸ Ń", + "p ots", + "Ġcame o", + "ĠP AL", + "educ ation", + "A my", + "like ly", + "g ling", + "Ġconstitution ally", + "ĠHam m", + "ĠSpe ak", + "Ġwid gets", + "br ate", + "Ġcra ppy", + "ĠI ter", + "Ġanticip ating", + "ĠB out", + "P ixel", + "ĠY ep", + "ĠLaur ie", + "Ġh ut", + "Ġbullet in", + "ĠSal vation", + "Ġch ats", + "ear able", + "Honest ly", + "AL TH", + "onse qu", + "c ult", + "isco very", + "ovy ch", + "Ġse lves", + "ĠSat oshi", + "S ounds", + "Ġconver gence", + "ĠRosen berg", + "19 74", + "Ġnas al", + "Ġfull est", + "Ġfer ocious", + "x us", + "ist e", + "AM S", + "Ġlobb ied", + "Ġso othing", + "ĠGun n", + "t oday", + "0 24", + "Ġinspir ational", + "ĠN BN", + "p b", + "g ewater", + "or ah", + "all owed", + "ĠCol iseum", + "Ġspecial izing", + "Ġinsane ly", + "ĠT ape", + "del ay", + "Ġt arn", + "ĠP ound", + "Ġmel anch", + "Ġdeploy ments", + "il and", + "Ġless en", + "Ġfur ry", + "ĠUE FA", + "Ġblood shed", + "ĠMe ier", + "ither ing", + "Ġhe irs", + "ĠJ aw", + "ax ter", + "ĠPublic ations", + "Ġal ters", + "int ention", + "ĠWinc hester", + "d etermination", + "ĠLif etime", + "th in", + "Mon ster", + "7 80", + "Ġapprox imation", + "Ġsuper markets", + "ĠSecond s", + "or os", + "h uge", + "Ġb ribe", + "ĠLIM ITED", + "un ed", + "Ġmis interpret", + "ĠIn jury", + "Ġ3 67", + "Ġthreshold s", + "ĠCarn ival", + "Ġgastro intestinal", + "Ġguid eline", + "Ġde ceived", + "f eatures", + "Ġpurported ly", + "ĠRon nie", + "ĠNew t", + "Ġsp acious", + "as us", + "Ġsuperhero es", + "ĠCyn thia", + "le gged", + "k amp", + "ch io", + "Ġth umbnail", + "ĠShir ley", + "ill ation", + "Ġshe ds", + "ĠZ y", + "E PA", + "Ġdam s", + "Ġy awn", + "n ah", + "ĠPe ggy", + "ĠE rie", + "ĠJu ventus", + "ĠF ountain", + "r x", + "don ald", + "al bum", + "ĠComp rehensive", + "Ġc aching", + "ĠU z", + "ulner ability", + "ĠPrinc iple", + "ĠJ ian", + "ing ers", + "cast s", + "ĠOs iris", + "ch art", + "t ile", + "ĠTiff any", + "ĠPatt on", + "ĠWh ip", + "Ġovers ized", + "J e", + "ĠCind erella", + "ĠB orders", + "ĠDa esh", + "M ah", + "Ġdog ma", + "Ġcommun ists", + "v u", + "Coun cil", + "Ġfresh water", + "Ġw ounding", + "Ġdeb acle", + "Ġyoung ster", + "Ġthread ed", + "ĠB ots", + "ĠSav ings", + "ãģ Ĥ", + "ol ing", + "oh o", + "Ġillum ination", + "M RI", + "Ġlo osen", + "tr ump", + "ag ency", + "ur ion", + "Ġmoment arily", + "ĠCh un", + "ĠBud apest", + "ĠAl ley", + "D isk", + "Ġaston ished", + "ĠCon quer", + "ĠAccount ing", + "h aving", + "ĠWe in", + "ĠAl right", + "Ġrev olver", + "Ġdel usion", + "Ġrelic s", + "Ġad herent", + "qu ant", + "Ġhand made", + "or io", + "Ġcomb ating", + "c oded", + "Ġquad ru", + "re th", + "N ik", + "ĠTrib al", + "ĠMyster ious", + "Ġin hal", + "ĠWin ning", + "ĠClass ification", + "ch anged", + "Ġun ab", + "Ġsc orn", + "icip ated", + "w l", + "ond uctor", + "Ġrein forcing", + "ĠChild hood", + "an ova", + "Ġadventure r", + "Ġdoctor al", + "ĠStrateg ies", + "Ġengulf ed", + "ĠEnc ounter", + "Ġl ashes", + "Crit ical", + "ric ular", + "ĠU TF", + "oci ation", + "check ing", + "ĠConsult ing", + "Run time", + "per iod", + "ĠAs gard", + "Ġdist illed", + "ĠPas adena", + "ĠD ying", + "ĠCOUN TY", + "Ġgran ite", + "Ġsm ack", + "Ġparach ute", + "ĠS UR", + "Virgin ia", + "ĠF urious", + "78 7", + "ĠO kin", + "Ġcam el", + "ĠM bps", + "19 72", + "ĠCh ao", + "ĠC yan", + "j oice", + "ef er", + "ĠW rap", + "ĠDeb ate", + "S eg", + "Ġfore arm", + "ĠIgn ore", + "Ġtim estamp", + "Ġprob ing", + "ĠNo on", + "ĠGra il", + "f en", + "Ġdorm ant", + "ĠFirst ly", + "ĠE ighth", + "ĠH UN", + "ĠDes ire", + "or as", + "Girl s", + "ĠDes mond", + "z ar", + "am ines", + "O AD", + "exec ute", + "Ġbo obs", + "ĠAT L", + "_ (", + "Chel sea", + "Ġmasturb ation", + "ĠCo C", + "Ġdestroy er", + "ĠCh omsky", + "Ġsc atter", + "ĠAss ets", + "79 6", + "ĠC argo", + "Ġrecept ive", + "ĠSc ope", + "Ġmarket ers", + "Ġlaun chers", + "Ġax le", + "ĠSE A", + "se q", + "ĠM off", + "f inding", + "ĠGib bs", + "Georg ia", + "extreme ly", + "N J", + "Ġlab orers", + "st als", + "Ġmed iation", + "ĠH edge", + "at own", + "Ġi od", + "des pite", + "v ill", + "J ane", + "ex istence", + "Ġcoinc ided", + "ĠUt ilities", + "ĠChe ap", + "Ġlog istical", + "Ġcul mination", + "ĠNic otine", + "p ak", + "F older", + "Ġrod ents", + "st uff", + "Ġlaw fully", + "Ġreper to", + "io ch", + "j j", + "Dial ogue", + "HH HH", + "lic tion", + "Look s", + "Ġ29 7", + "Ġtur rets", + "ĠAb andon", + "Ġinc ess", + "ĠTraff ord", + "Ġcur led", + "Ġprefer ring", + "Ġprivat ization", + "Ġir resist", + "ĠP anda", + "ĠSh ake", + "ĠMc Gr", + "ãĥ Ħ", + "und ers", + "Ġdiscrim inated", + "Ġbart ender", + "I LE", + "Atl antic", + "Ġprop ensity", + "ĠW iz", + "ĠG im", + "con ference", + "Ġrein forces", + "G h", + "w agon", + "Ġe erie", + "F al", + "Ġhug ged", + "rac ist", + "R IC", + "F u", + "Ġf iller", + "ĠSt ub", + "Ġeng raved", + "ĠWrest le", + "Ġimagin ative", + "ĠPe er", + "ĠFact ors", + "an us", + "ĠDrac ula", + "mon itor", + "Ġrou ters", + "ib ia", + "ĠBoo lean", + "end ale", + "ĠSl aughter", + "ĠSh ack", + "R FC", + "ĠSpiel berg", + "S ax", + "ĠPH OTO", + "ĠCl over", + "ĠR ae", + "Dep ending", + "ĠMem or", + "ar am", + "Ġpier ced", + "Ġcur tains", + "v ale", + "ĠInqu isition", + "ĠP oke", + "Ġforecast ing", + "Ġcompl ains", + "S ense", + "ĠHer mes", + "isc overed", + "Ġb ible", + "ĠMor ph", + "Ġg erm", + "78 5", + "D ON", + "Ġcon gen", + "Ġcr ane", + "ĠD PR", + "Ġrespect fully", + "R oom", + "ĠN aw", + "ĠDal ai", + "re ason", + "ĠAng us", + "Educ ation", + "ĠTitan ic", + "Ë ľ", + "Ġo val", + "un ited", + "Ġthird s", + "Ġmoist ur", + "ĠC PC", + "M iami", + "Ġtent acles", + "ĠPol aris", + "ex c", + "ex clusive", + "ĠPra irie", + "Ġcol ossal", + "ĠBl end", + "sur prisingly", + "ÃŃ s", + "Ġindo ctr", + "Ġbas al", + "ĠMP EG", + "und o", + "Spl it", + "Develop ment", + "Ġlan tern", + "19 71", + "Ġprov ocation", + "Ġang uish", + "ĠB ind", + "ĠLe ia", + "duc ers", + "ipp y", + "conserv ancy", + "Ġinitial ize", + "ĠTw ice", + "ĠSu k", + "Ġpred ic", + "Ġdi ploma", + "Ġsoc iop", + "Ing redients", + "Ġhamm ered", + "ĠIr ma", + "Q aida", + "Ġglim ps", + "ĠB ian", + "Ġst acking", + "Ġf end", + "gov track", + "Ġun n", + "dem ocratic", + "ig ree", + "Ġ5 80", + "Ġ29 4", + "Ġstraw berry", + "ID ER", + "Ġcher ished", + "ĠH ots", + "Ġinfer red", + "Ġ8 08", + "ĠS ocrates", + "O regon", + "ĠR oses", + "ĠFO IA", + "Ġins ensitive", + "Ġ40 8", + "Recomm end", + "ĠSh ine", + "Ġpain staking", + "UG E", + "ĠHell er", + "ĠEnter prises", + "I OR", + "ad j", + "N RS", + "L G", + "Ġalien ated", + "Ġacknowled gement", + "ĠA UD", + "ĠRen eg", + "Ġvou chers", + "Ġ9 60", + "Ġm oot", + "ĠDim ensions", + "Ġc abbage", + "B right", + "g at", + "ĠK lu", + "Ġlat ent", + "Ġz e", + "ĠM eng", + "Ġdis perse", + "Ġpand emonium", + "H Q", + "Ġvirt uous", + "ĠLoc ations", + "ee per", + "prov ided", + "Ġse ams", + "ĠW T", + "iz o", + "PR OV", + "Ġtit anium", + "Ġrecol lection", + "Ġcr an", + "Ġ7 80", + "ĠN F", + "49 1", + "64 2", + "p acking", + "59 8", + "text ure", + "Sp ider", + "fre edom", + "cipl ed", + "ĠTAM ADRA", + "âĻ ¦", + "aut hent", + "ĠW ANT", + "r ified", + "Ġr ites", + "Ġuter us", + "k iss", + "Ġâī ¤", + "Ġsk illet", + "Ġdis enfranch", + "ĠGa al", + "Comp an", + "Ġage ing", + "gu ide", + "B alt", + "Ġiter ator", + "Ġdiscretion ary", + "t ips", + "Ġprim ates", + "ĠTechn ique", + "ĠPay ments", + "az el", + "ĠR OCK", + "stant ial", + "0 60", + "Ġd mg", + "ĠJack ets", + "ĠPlay off", + "Ġnurs ery", + "ĠSy mb", + "art on", + "Ġannex ation", + "Color ado", + "Ġco ils", + "ĠSh oes", + "âĦ¢ :", + "ĠRo z", + "COM PLE", + "ĠEve rest", + "ĠTri umph", + "J oy", + "G rid", + "à ¼", + "process or", + "ĠPros per", + "ĠSever us", + "ĠSelect ed", + "r g", + "ĠTay yip", + "St ra", + "Ġski ing", + "Ġ? )", + "Ġpe g", + "Tes la", + "Ġtime frame", + "Ġmaster mind", + "ĠN B", + "scient ific", + "ĠSh it", + "gener ic", + "IN TER", + "N UM", + "Ġst roll", + "ĠEn ix", + "ĠM MR", + "ĠE MS", + "m ovie", + "Ĥ ª", + "Ġminim izing", + "idd ling", + "Ġilleg itimate", + "Ġprot otyp", + "Ġpremature ly", + "Ġmanual s", + "obb ies", + "ĠCass idy", + "D EC", + "des ktop", + "Ġaer os", + "Ġscreen ings", + "Ġdeb ilitating", + "ĠGr ind", + "nature conservancy", + "Ġf ades", + "ter mination", + "assets adobe", + "F actor", + "Ġdefinitive ly", + "P oké", + "ap ult", + "ĠLaf ayette", + "C orn", + "ĠCor al", + "Ġstagn ant", + "T ue", + "Ġdissatisf action", + "G ender", + "Ġkid neys", + "ĠG ow", + "ĠDef eat", + "ĠAsh ton", + "Ġcart els", + "Ġfore closure", + "ĠExpl ore", + "stre ngth", + "ot in", + "Ġveterin arian", + "Ġf umble", + "Ġpar ap", + "ĠSt rait", + "r ils", + "Ġpr ick", + "ĠBerm uda", + "ĠAm munition", + "skin ned", + "Ġab ound", + "ĠB raz", + "Ġshar per", + "ĠAsc ension", + "Ġ9 78", + "Ġpreview s", + "Ġcommun ion", + "ĠX Y", + "Ġph ony", + "Ġnewcom er", + "Ġ3 32", + ".\" ,\"", + "Ġredist ribution", + "Prot ect", + "ĠSo f", + "K al", + "Ġlip stick", + "w orst", + "Ġtang led", + "Ġretrospect ive", + "int eger", + "Ġvolunte ering", + "Ġ19 07", + "Ġ --------------------", + "ic hen", + "Ġunve iling", + "Ġsen seless", + "Ġfisher ies", + "\\ -", + "Ġh inges", + "Ġcalcul us", + "My th", + "Ġund efeated", + "Ġoptim izations", + "Ġdep ress", + "Ġbill board", + "ĠY ad", + "ĠPy ramid", + "Is n", + "I de", + "Ġleg ion", + "ĠK ramer", + "ent anyl", + "Ġpenet rating", + "ĠHaw th", + "ĠPR ODUCT", + "ĠGer ard", + "ĠP act", + "ĠIn cluding", + "ĠEl ias", + "ĠEl aine", + "vis ual", + "Ġhum ming", + "Ġcond esc", + "ĠF asc", + "ä¸ Ĭ", + "Ġe galitarian", + "Ġdev s", + "ĠD ahl", + "O ps", + "D H", + "ĠB ounce", + "id ated", + "ald o", + "Ġrepublic an", + "Ġh amb", + "ĠS ett", + "ograph ies", + "CH APTER", + "Ġtrans sexual", + "Ġsky rocket", + "ans wer", + "Ġmark up", + "Ø ª", + "Ġhero ine", + "Comp are", + "ĠT av", + "Be ast", + "Ġsuccess ors", + "Ġna ïve", + "ĠBuck ley", + "st ress", + "me at", + "Ġdownload able", + "Ġindex ed", + "Ġsc aff", + "ĠL ump", + "ĠHom o", + "Stud io", + "In sp", + "Ġr acked", + "far ious", + "ĠPet ty", + "Ex ternal", + "Ġ19 09", + "W ars", + "com mit", + "put ers", + "Ġun ob", + "ĠEr r", + "ĠE G", + "ĠAl am", + "ĠSiber ia", + "ĠAtmosp heric", + "IS TER", + "ĠSatan ic", + "trans lation", + "ĠL oud", + "tra umatic", + "l ique", + "Ġreson ate", + "ĠWel ch", + "Ġspark ing", + "ĠT OM", + "t one", + "Ġout l", + "Ġhandc uffed", + "ĠSer ie", + "8 01", + "Ġland marks", + "ĠRee ves", + "Ġsoft ened", + "Ġdazz ling", + "ĠW anted", + "month s", + "Mag ikarp", + "Ġunt reated", + "ĠBed ford", + "M i", + "ĠDynam o", + "O re", + "79 5", + "Ġwrong ful", + "Ġl ured", + "Ġcort isol", + "Ġve x", + "d rawn", + "ile t", + "Download ha", + "ĠF action", + "Ġlab yrinth", + "Ġhij acked", + "w aters", + "er ick", + "Ġsuper iors", + "ĠRow ling", + "ĠGu inness", + "Ġt d", + "99 2", + "Ġune arthed", + "Ġcentr if", + "Ġsham eless", + "P od", + "ĠF ib", + "Ġ icing", + "Ġpredict or", + "Ġ29 2", + "fore station", + "con struct", + "C and", + "@ #", + "Ġag itated", + "Ġre pr", + "OV A", + "Ġkn itting", + "ĠLim a", + "Ġf odder", + "68 4", + "ĠPerson a", + "k l", + "7 01", + "Ġbreak up", + "á ¸", + "Ġapp alled", + "Ġantidepress ants", + "ĠSus sex", + "Har ris", + "ĠTher mal", + "ee ee", + "U pload", + "Ġg ulf", + "Ġdoor step", + "ĠSh ank", + "L U", + "ĠM EN", + "ĠP ond", + "s orry", + "Ġmis fortune", + "n ance", + "Ġb ona", + "M ut", + "Ġde graded", + "ĠL OG", + "ĠN ess", + "an imal", + "Ġa version", + "und own", + "Ġsupplement ed", + "ĠC ups", + "Ġ50 4", + "Ġdep rive", + "ĠSpark le", + "Å Ĥ", + "ĠMed itation", + "auth ors", + "ĠSab an", + "ĠN aked", + "air d", + "ĠMand arin", + "ĠScript ures", + "ĠPerson nel", + "ĠMahar ashtra", + "Ġ19 03", + "ĠP ai", + "ĠMir age", + "omb at", + "Access ory", + "Ġfrag mented", + "T ogether", + "Ġbelie vable", + "ĠGl adiator", + "al igned", + "ĠSl ug", + "M AT", + "Ġconvert ible", + "ĠBour bon", + "amer on", + "ĠRe hab", + "nt ax", + "Ġpowd ered", + "pill ar", + "Ġsm oker", + "ĠMans on", + "ĠB F", + "5 11", + "ĠGood ell", + "ĠD AR", + "m ud", + "g art", + "Ġob edient", + "ĠTrans mission", + "ĠDon ation", + "8 80", + "Ġbother ing", + "Material s", + "ãĤ ±", + "dest roy", + "Ġfore going", + "Ġanarch ism", + "ĠK ry", + "ice ps", + "Ġl ittered", + "ĠSch iff", + "Ġanecd otal", + "un its", + "Ġf ian", + "ĠSt im", + "ĠS OME", + "ĠInv aders", + "Ġbehaviour al", + "ĠVent ures", + "Ġsub lime", + "Ġfru ition", + "ĠPen alty", + "Ġcorros ion", + "¶ ħ", + "Ġlik ened", + "Ġbesie ged", + "ween ey", + "ĠCre ep", + "Ġlinem en", + "mult i", + "ic ably", + "ud der", + "Ġvital ity", + "Ġshort fall", + "ĠP ants", + "ap ist", + "H idden", + "ĠDro ps", + "med ical", + "Ġpron unciation", + "ĠN RL", + "Ġinsight ful", + "J V", + "ĠBe ard", + "ĠCh ou", + "Ġchar ms", + "Ġb ins", + "Ġamb assadors", + "ĠS aturdays", + "Ġinhib itor", + "ĠFr anch", + "6 01", + "', '", + "ĠCon or", + "art ney", + "ĠX peria", + "g rave", + "be es", + "ĠProtest ants", + "Ġso aking", + "ĠM andal", + "Ġph ased", + "Ġ6 60", + "Ġsc ams", + "Ġbuzz ing", + "ĠItal ians", + "ĠLoren zo", + "ĠJ A", + "Ġhes itated", + "Ġcl iffs", + "ĠG OT", + "ingu ishable", + "Ġk o", + "Ġinter ruption", + "Z ip", + "Lear ning", + "Ġundersc ores", + "ĠBl ink", + "K u", + "57 9", + "ĠAut ob", + "I RE", + "Ġwater ing", + "Ġpast ry", + "8 20", + "Ġvision ary", + "ĠTempl ar", + "awa ited", + "Ġpist on", + "Ġant id", + "current ly", + "Ġp ard", + "Ġw aging", + "Ġnob ility", + "ĠY us", + "Ġinject ing", + "f aith", + "ĠP ASS", + "å º", + "Ġret ake", + "ĠPR OC", + "Ġcat hedral", + "b ash", + "Ġwrest lers", + "Ġpartner ing", + "Ġn oses", + "Ġ3 58", + "Trans form", + "am en", + "Ġb outs", + "ĠId eal", + "ĠConstant in", + "Ġse p", + "ĠMon arch", + "att en", + "ĠPe oples", + "mod ified", + "Ġmor atorium", + "Ġpen chant", + "Ġoffensive ly", + "Ġprox ies", + "ok ane", + "ĠTaiwan ese", + "ĠP oo", + "ĠH OME", + "us ional", + "Ġver bs", + "ĠO man", + "vis ory", + "Ġpersu asion", + "Ġmult it", + "Ġsc issors", + "G ay", + "ow ay", + "oph ysical", + "l us", + "gn u", + "Ġap ocalyptic", + "Ġabsurd ity", + "Ġplay book", + "Ġautobi ography", + "I UM", + "Ġsne aking", + "ĠSim ulation", + "pp s", + "ell ery", + "Plan et", + "Ġright fully", + "Ġn iece", + "ĠN EC", + "ĠIP O", + "ĠDis closure", + "lean or", + "ous y", + "ST ER", + "Ġ28 2", + "Cru z", + "Ch all", + "64 3", + "ĠSurv ive", + "ĠF atal", + "ĠAm id", + "ap o", + "We apons", + "D EN", + "7 70", + "ĠGreen wald", + "Ġlin en", + "al os", + "Ġpollut ants", + "ĠPCI e", + "k at", + "Ġp aw", + "ĠK raft", + "C hem", + "ĠTermin ator", + "Ġre incarn", + "Ġ] [", + "ĠSe eds", + "Ġsilhou ette", + "ĠSt ores", + "Ġgro oming", + "ĠD irection", + "ĠIs abel", + "ĠBr idges", + "ðŁ ij", + "E ED", + "ĠM orsi", + "Ġval ves", + "ĠRank ed", + "ĠPh arma", + "ĠOrgan izations", + "Ġpenet rated", + "ĠRod ham", + "ĠProt oss", + "Ġove rest", + "Ġex asper", + "ĠT J", + "Ġ 000000", + "Ġtrick le", + "Ġbour bon", + "WH O", + "Ġw retched", + "Ġmicrosc opic", + "Ġcheck list", + "Ġad orned", + "R oyal", + "Ad minist", + "ĠRet irement", + "ĠHig hest", + "We ather", + "ile ge", + "Ġincre ments", + "ĠC osponsors", + "Ġmas se", + "ĠS inn", + "r f", + "Ġh ordes", + "as sembly", + "75 4", + "ĠNat asha", + "ĠTY PE", + "ĠGEN ERAL", + "Ġarr anging", + "Ġ40 7", + "l ator", + "Ġg lean", + "Ġdisc redited", + "Ġclin icians", + "UN E", + "Ġachie ves", + "ĠEm erson", + "com plex", + "= [", + "Ġprincip ally", + "Ġfra il", + "p icked", + "Ġthan king", + "Ġre cl", + "ĠL AST", + "Ġsupp ressing", + "il ic", + "Ġantidepress ant", + "ĠLis bon", + "Ġth or", + "Ġsp a", + "Ġking doms", + "ĠPear ce", + "em o", + "Ġpl ung", + "Ġdiv est", + "Ġ ********************************", + "b is", + "osp els", + "ad r", + "Sp irit", + "hall a", + "P ink", + "end ez", + "Ġresurrect ed", + "esc ape", + "ĠRosen stein", + "Ġge ological", + "Ġnecess ities", + "Ġcarn iv", + "ĠE lys", + "ĠBar ney", + "Ġ29 6", + "dig y", + "ST ON", + "D OWN", + "Ġmil estones", + "Ġk er", + "Ġdismant ling", + "Ġre prim", + "Ġcross ings", + "19 45", + "Ġpatri archy", + "Ġblasp hemy", + "Ġ3 59", + "met ry", + "ĠOb esity", + "ĠDiff erences", + "bl ocking", + "ãĥķ ãĤ¡", + "ich ita", + "ĠSab ha", + "ph alt", + "ĠCol o", + "ual a", + "effic ients", + "ĠMed ina", + "con sole", + "55 7", + "ĠHann ibal", + "ĠHab it", + "ĠF ever", + "Ġthen ce", + "Ġsyn agogue", + "Ġessential s", + "Ġw ink", + "ĠTr ader", + "ID A", + "ĠSp oiler", + "ĠIceland ic", + "ĠHay ward", + "Ġpe ac", + "Ġmal ice", + "Ġflash back", + "Ġth w", + "Ġlay offs", + "L iquid", + "Ġtro oper", + "Ġh inge", + "ĠRead ers", + "Ph ill", + "ĠB auer", + "Cre ated", + "Ġaud its", + "ac compan", + "Ġunsus pecting", + "ier a", + "6666 6666", + "Ġbro ch", + "Ġapprehend ed", + "ĠM alk", + "cer ning", + "ĠCod ex", + "O VER", + "M arsh", + "ĠD eng", + "ĠExp ression", + "Ġdisrespect ful", + "Ġasc ending", + "t ests", + "ĠPlaint iff", + "ster y", + "ĠAl ibaba", + "din and", + "ĠDem psey", + "Applic ations", + "mor al", + "Ġthrough put", + "Ġquar rel", + "Ġm ills", + "Ġhe mor", + "ĠC ASE", + "terror ist", + "st im", + "ifest yle", + "ro zen", + "CE PT", + "Ar k", + "u ci", + "lect ic", + "Ġirrit ating", + "she ets", + "A y", + "Ġrede emed", + "Ġhorn y", + "ĠTe ach", + "ĠS ear", + "dem ocracy", + "4 65", + "ĠRest ore", + "Ġstand by", + "ĠP is", + "iff in", + "Ġsleep y", + "Ġextr ater", + "Ġcompl iments", + "Fram eworks", + "Ġinstall s", + "Ġb anging", + "sur face", + "found land", + "Ġmetaph ysical", + "Ġ28 3", + "oul s", + "dev ices", + "Ar gs", + "ĠSac rifice", + "ĠMcC orm", + "es on", + "Cons ervative", + "ĠM ikhail", + "see ing", + "is ively", + "ĠRo oms", + "ĠGener ic", + "Ġenthusi astically", + "Ġgri pped", + "Ġcomed ic", + "ĠElectric ity", + "Ġgu errilla", + "Ġdec oration", + "ĠPerspect ive", + "Ġconsult ations", + "Ġun amb", + "Ġplag iar", + "Ġmagic ian", + "Ġe rection", + "ĠTour ism", + "or ied", + "ro xy", + "11 00", + "T am", + "Ī è", + "Î ³", + "× ª", + "ĠPred ators", + "Nit rome", + "Ġtelesc opes", + "project s", + "Ġun protected", + "Ġst ocked", + "ĠEnt reprene", + "nex pected", + "Ġwast ewater", + "V ill", + "Ġint imately", + "Ġi Cloud", + "ĠConst able", + "Ġspo of", + "Ġne farious", + "Ġfin s", + "Ġcens or", + "ĠMod es", + "ĠEs per", + "ar bon", + "Ġinter sections", + "Ġlaud ed", + "Ġphys i", + "Ġgener ously", + "ĠThe Nitrome", + "ĠTheNitrome Fan", + "Ġar isen", + "ĠÙ Ī", + "Ġg lands", + "ĠPav ilion", + "ĠGu pta", + "Ġuniform ly", + "Ġr amps", + "ri et", + "ĠWH EN", + "ĠVan essa", + "Ġrout ed", + "Ġlim p", + "ĠC PI", + "p ter", + "int uitive", + "Ġv aping", + "Ġexperiment ed", + "ĠOlymp us", + "ĠAm on", + "Ġsight ing", + "Ġinfiltr ate", + "ĠGentle man", + "Ġsign ings", + "ĠMe ow", + "ĠNav igation", + "che cks", + "4 33", + "Ġel apsed", + "ĠBulg arian", + "esp ie", + "ĠS OM", + "d uring", + "Ġsp ills", + "anc a", + "ĠPly mouth", + "M AL", + "Ġdomest ically", + "ĠWater gate", + "ĠF AM", + "k illed", + "ed ited", + "ĠYour self", + "Ġsynchron ization", + "ĠPract ices", + "ST EP", + "Ġgen omes", + "ĠQ R", + "not ice", + "Ġloc ating", + "z in", + "Ġ3 29", + "al cohol", + "Ġk itten", + "V o", + "Ġr inse", + "Ġgrapp le", + "ĠSc rew", + "ĠD ul", + "A IR", + "Ġle asing", + "ĠCaf é", + "Ġro ses", + "ĠRes pect", + "Ġmis lead", + "Ġperfect ed", + "Ġnud ity", + "Ġnon partisan", + "ĠCons umption", + "Report ing", + "Ġnu ances", + "Ġdeduct ible", + "ĠSh ots", + "Ġ3 77", + "Ġæ ľ", + "ano oga", + "Ben ef", + "ĠB am", + "ĠS amp", + "if ix", + "Ġgal van", + "ĠMed als", + "rad ius", + "Ġno bles", + "Ġe aves", + "igr ate", + "K T", + "ĠHar bour", + "u ers", + "Ġrisk ed", + "re q", + "Ġneuro t", + "get table", + "ain a", + "Rom ney", + "Ġunder pin", + "Ġlo ft", + "ĠSub committee", + "ĠMong ol", + "b iz", + "Ġmanif ests", + "ass isted", + "ĠG aga", + "Ġsy nergy", + "Ġreligious ly", + "ĠPre f", + "ĠG erry", + "T AG", + "ĠCho i", + "4 66", + "beh ind", + "ĠO u", + "Gold Magikarp", + "Ġhemor rh", + "R iver", + "Ġtend on", + "Ġinj ure", + "ĠF iona", + "Ġp ag", + "Ġag itation", + "|| ||", + "ur an", + "ĠE SA", + "Ġest eem", + "Ġdod ging", + "Ġ4 12", + "r ss", + "Ġce ases", + "ex cluding", + "Ġint akes", + "Ġinsert s", + "Ġemb old", + "ĠO ral", + "up uncture", + "4 11", + "ĠUn ified", + "ĠDe le", + "Ġfurn ace", + "ĠCoy otes", + "ĠBr ach", + "L abor", + "Ġhand shake", + "Ġbru ises", + "Gr ade", + "éĹ ĺ", + "ĠGram my", + "ile en", + "St ates", + "ĠScandinav ian", + "ĠKard ash", + "8 66", + "Ġeffort lessly", + "ĠDI RECT", + "ĠTH EN", + "ĠMe i", + "ert ation", + "19 68", + "Ġgro in", + "w itch", + "Requ irements", + "98 5", + "Ġroof s", + "Ġest ates", + "ĠH F", + "Ġha ha", + "Ġdense ly", + "ĠO CT", + "Ġpl astics", + "Ġincident ally", + "ĠTr acks", + "ĠTax es", + "Ġch anted", + "Ġforce ful", + "ĠBie ber", + "ĠK ahn", + "K ent", + "ĠC ot", + "lic ts", + "F ed", + "Ġhide ous", + "ĠVer d", + "ĠSynd icate", + "ĠIl legal", + "J et", + "ĠD AV", + "re asonable", + "c rew", + "Ġfundamental ist", + "Ġtruth ful", + "ĠJ ing", + "Ġl il", + "Ġdown ed", + "Ġen chanted", + "ĠPolic ies", + "ĠMcM aster", + "ĠH are", + "ides how", + "Ġpar ams", + "en cers", + "gorith m", + "Ġallow ances", + "Ġturb ulent", + "Ġcomplex ities", + "ĠK T", + "Ġ3 37", + "ĠGen etic", + "F UN", + "D oug", + "t ick", + "Ġg igs", + "ument hal", + "Ġpatriarch al", + "Ġcal c", + ", ...", + "Ġc out", + "ĠGu an", + "Ġpath ological", + "ĠR ivals", + "Ġunder rated", + "Ġflu orescent", + "ĠJ iu", + "arna ev", + "ĠQu an", + "Ġ4 29", + "Ġ à¨", + "M ario", + "Con struct", + "ĠC itation", + "ĠR acial", + "ĠR SA", + "ĠF idel", + "Ġ3 95", + "Person ally", + "C ause", + "à »", + "rad ical", + "in en", + "Ġvehement ly", + "ĠPap a", + "Ġintern ship", + "Ġfl akes", + "ĠRe ck", + "Luck ily", + "B ra", + "20 20", + "rav ings", + "R N", + "W onder", + "Ser iously", + "Ġre usable", + "Ġpoll uted", + "ĠP eng", + "le igh", + "ind le", + "Ġcircuit ry", + "ĠMad onna", + "ĠB ART", + "Res idents", + "att ribute", + "Phil adelphia", + "Cl ub", + "Ġplan ner", + "Ġfr antically", + "Ġfaith fully", + "ĠTerrit ories", + "ĠL AT", + "ĠAnders en", + "an u", + "ĠP ARK", + "ĠS ora", + "i age", + "ĠPlay offs", + "ĠG CC", + "4 27", + "Ġab norm", + "ĠL ever", + "Ġdisob edience", + "As ync", + "ĠShe a", + "V ert", + "Ġsk irts", + "ĠSaw yer", + "x p", + "Ġwors ening", + "Ġsc apego", + "ĠAng le", + "oth al", + "Ġtro ve", + "ĠSt y", + "ĠN guyen", + "mar ine", + "ide on", + "Dep ths", + "Bl og", + "ĠIll uminati", + "Ġtract s", + "Ġorgan ise", + "Ġo str", + "F s", + "Ġlever aging", + "ĠD aredevil", + "as ar", + "Ġl ang", + "Ġex termin", + "urs ions", + "ĠRom o", + "ãĤ¤ ãĥĪ", + "Ġcont ended", + "Ġencounter ing", + "ĠTable t", + "ĠAltern ate", + "sk ill", + "Ġswe ets", + "Ġco hesive", + "cap acity", + "Ġrep ud", + "Ġl izard", + "ro o", + "Ġpilgr ims", + "ĠR uff", + "ĠInstr ument", + "ĠLog o", + "uit ous", + "E H", + "Ġsales man", + "Ġank les", + "L ed", + "ĠPat ty", + "ud os", + "Own er", + "Ġdiscrep ancies", + "k j", + "M U", + "Ġuncond itional", + "Dragon Magazine", + "i ard", + "O ak", + "ĠConvers ation", + "be er", + "ĠOs aka", + "D elta", + "us ky", + "Ġsecret ion", + "Ġpl aza", + "Ġm ing", + "Ġde pletion", + "ĠM ous", + "ĠI TS", + "ĠH imal", + "ĠFle ming", + "Ġcyt ok", + "ĠH ick", + "Ġbat ters", + "ĠInt ellectual", + "6 75", + "é r", + "IS ION", + "ĠQu entin", + "ĠCh apters", + "ih adi", + "Ġco aster", + "WAY S", + "ĠL izard", + "ĠY or", + "and ering", + "S kin", + "ha ust", + "ab by", + "Ġportray ing", + "Ġwield ed", + "d ash", + "Ġprop onent", + "Ġr ipple", + "Ġgrap hene", + "Ġfly er", + "Ġrec urrent", + "Ġdev ils", + "Ġwater fall", + "æĺ ¯", + "go o", + "Text Color", + "Ġtam pering", + "IV ES", + "TR UMP", + "ĠAb el", + "ĠS AL", + "ĠHend ricks", + "ĠLu cius", + "b ots", + "Ġ40 96", + "IST ORY", + "Gu est", + "ĠN X", + "in ant", + "Ben z", + "ĠLoad ed", + "ĠCle ver", + "t reatment", + "Ġta vern", + "Ġ3 39", + "ĠT NT", + "ific antly", + "Tem perature", + "F el", + "Ġunder world", + "ĠJud ges", + "Ġ< +", + "Ġst ump", + "Ġoccup ancy", + "Ġab er", + "ĠF inder", + ") \",", + "ĠN unes", + "res et", + "in et", + "ect omy", + "Ġwell ness", + "ĠP eb", + "quart ered", + "and an", + "Ġneg atives", + "ĠTh iel", + "ĠCl ip", + "ĠL TD", + "Ġbl ight", + "Ġreperto ire", + "K yle", + "Ġqu er", + "ĠC es", + "Ġha pl", + "98 9", + "ĠTh ames", + "isc opal", + "Des k", + "ivari ate", + "ĠEx cellence", + "found ation", + "Ġâ ĩ", + "X i", + "Ġmyster iously", + "esty les", + "Ġper ish", + "ĠEng els", + "ĠDE AD", + "09 0", + "}} }", + "ĠUn real", + "Ġrest less", + "ID ES", + "orth odox", + "ĠInter mediate", + "Ġdin ners", + "ĠTr out", + "ĠSe ym", + "ĠHall s", + "og ged", + "Ġtraged ies", + "Ġdid nt", + "67 6", + "Ġail ments", + "Ġobserv able", + "ĠV ide", + "ad apt", + "ĠD usk", + "Ġprofessional ism", + "ĠPres cott", + "ĠInd ies", + "p ox", + "ĠMe hran", + "W ide", + "Ġend emic", + "ĠPar an", + "B ird", + "Ġped als", + "ĠI U", + "ĠAdam ant", + "ĠH urt", + "Ġcorrel ates", + "urd en", + "Ġspons oring", + "cl imate", + "ĠUnivers ities", + "ĠK not", + "enn es", + "ĠDam ian", + "ĠAx el", + "S port", + "Ġbar b", + "ĠS no", + "sh own", + "ste en", + "ud ence", + "Ġnon violent", + "Ġhom ophobia", + "Ġbiom ass", + "ĠDet ail", + "Ġsrf N", + "ĠT une", + "accompan ied", + "I ENCE", + "Al bert", + "ĠMong o", + "z x", + "ĠCer berus", + "or bit", + "c ens", + "Ġsl ay", + "SH ARE", + "H Y", + "Ġb rawl", + "ĠPro be", + "Ġnonex istent", + "ĠClare nce", + "ĠBlack burn", + "Ġport als", + "ĠR ita", + "ĠRem ain", + "ĠLe vant", + "Ġtrick ed", + "ĠF erry", + "aver ing", + "ĠStraw berry", + "ĠAn swers", + "Ġhorrend ous", + "ĠA man", + "Supp lement", + "ĠT oad", + "Ġpe eled", + "Ġman oeuv", + "ĠU zbek", + "mond s", + "ĠH ector", + "Ġ40 2", + "pe es", + "fix es", + "Ġd j", + "Ġres umes", + "Ġaccount ant", + "Ġadvers ity", + "Ġham pered", + "ĠL arson", + "Ġd oping", + "part s", + "H ur", + "Ġbe arded", + "Ġy r", + "ĠPlug in", + "å¥ ³", + "Ġ/ **", + "rol ley", + "Ġwaters hed", + "ĠSub mission", + "if lower", + "AS C", + "Ġcho ir", + "Ġsculpt ures", + "m A", + "incre asing", + "ai i", + "Ġsne akers", + "Ġconfront s", + "ĠEle phant", + "ĠEl ixir", + "Ġrec al", + "ĠT TL", + "w idget", + "ĠW ax", + "ĠGr ayson", + "Ġha irst", + "Ġhumili ated", + "ĠWAR N", + "app iness", + "ĠT TC", + "F uel", + "Ġpol io", + "Ġcomplex es", + "Ġbab e", + "ĠX IV", + "P F", + "). [", + "P arts", + "Ġ4 35", + "M eg", + "ĠY ards", + "ĠAL P", + "Ġy ells", + "Ġprin ces", + "Ġbull ies", + "ĠCapital ism", + "ex empt", + "FA Q", + "ĠSp onge", + "ĠAl a", + "Ġpleas antly", + "Ġbu f", + "Ġden ote", + "Ġunp ublished", + "Ġkne eling", + "asc a", + "Ġl apse", + "al ien", + "99 4", + "Ġrefere es", + "ĠLaw yers", + "S anta", + "Ġpuzz ling", + "ĠProm etheus", + "ĠPh araoh", + "ĠDel ay", + "Ġfacilit ates", + "ĠC ES", + "Ġjew els", + "Ġbook let", + "ond ing", + "Ġpolar ization", + "ĠMor an", + "ĠSal ad", + "ĠS OS", + "ĠAdv ice", + "PH OTOS", + "IC AN", + "iat ures", + "ex press", + "ĠWonder land", + "ĠC ODE", + "ĠCL ASS", + "9 75", + "Ġg rep", + "ĠD iesel", + "ĠGl ac", + "! ?\"", + "Ġr m", + "o ine", + "disc rimination", + "ĠN urse", + "m allow", + "Ġv ortex", + "ĠCons ortium", + "Ġlarge Download", + "stra ight", + "augh lin", + "G rad", + "Ġpublic ized", + "ĠW aves", + "ĠRed d", + "Ġfest ivities", + "ĠM ane", + "ar ov", + "Ġfleet ing", + "ĠDr unk", + "ug en", + "C ele", + "Ġchromos omes", + "ĠD OT", + "-+-+ -+-+", + "Ġbus iest", + "ĠBe aver", + "Sy rian", + "ĠK yr", + "k as", + "ĠCross Ref", + "19 50", + "76 01", + "Ġrepe aling", + "ĠWin ners", + "ĠMac ro", + "ĠD OD", + "bl ance", + "S ort", + "64 1", + "Ġmet re", + "ĠD irk", + "Ġgo ggles", + "Ġdraw backs", + "Ġcomplain ant", + "Ġauthor izing", + "Ġantit rust", + "oper ated", + "Ġm ah", + "Ġexagger ation", + "Am azing", + "ĠSer aph", + "Ġha ze", + "w ow", + "Ġextingu ished", + "Ġcan yon", + "ĠB osh", + "Ġv ents", + "Ġsc rape", + "Cor rect", + "4 26", + "Ġav g", + "Dem and", + "ĠâĪ ¼", + "Ġmicrobi ota", + "\"} ],\"", + "ĠSt ev", + "B io", + "ĠPlan es", + "Ġsuggest ive", + "Ġdec ipher", + "ĠRefuge e", + "ĠKe jriwal", + "ĠGreen peace", + "Ġdecl ass", + "ĠSound ers", + "Ġth o", + "Ġdec rypt", + "Ġbr ushing", + "ĠJane iro", + "ip op", + "S i", + "8 77", + "ĠGeoff rey", + "Ġc pu", + "ĠHaz el", + "Ġview points", + "Ġcris py", + "ĠNot ification", + "Ġsold er", + "ĠMod est", + "ĠHem isphere", + "Ġcass ette", + "in cludes", + "Ġident ifiers", + "ĠC ALL", + "in cent", + "T odd", + "ĠSwe ep", + "Ġ3 34", + "b oss", + "Ġsm ir", + "gin x", + "Ġtown ship", + "Ġg rieving", + "ĠMos que", + "Net flix", + "AS ED", + "ĠMillenn ials", + "oc om", + "19 67", + "Ġbold ly", + "s leep", + "Ġes che", + "arij uana", + "Ġsw irl", + "ĠPen al", + "Ġneglig ent", + "ĠStephen son", + "K ER", + "ĠZ oro", + "ris is", + "Ġlocal ization", + "ĠSeym our", + "ĠAng lic", + "red itation", + "prot ection", + "ĠPa ige", + "Ġo mit", + "ĠR ousse", + "ĠT ub", + "Ġinv itations", + "t ty", + "Ġm oss", + "ph ysical", + "C redits", + "Ġan archy", + "Ġchild care", + "Ġl ull", + "ĠM ek", + "ĠL anguages", + "lat est", + "ĠSan ford", + "Ġus ability", + "Ġdiff use", + "ĠD ATA", + "Ġsp rites", + "ĠVeget a", + "ĠProm otion", + "ãĥ¼ ãĤ¯", + "rict ing", + "z ee", + "Tur kish", + "ĠTD s", + "pro ven", + "57 1", + "Ġsmug glers", + "707 10", + "Ġreform ed", + "ĠLo is", + "Ġun fl", + "ĠWITH OUT", + "ĠReturn ing", + "ann ie", + "ĠTom as", + "Fr anc", + "ĠProf it", + "ĠSER V", + "ĠR umble", + "ik uman", + "es an", + "Ġt esters", + "Ġgad get", + "Ġbrace let", + "ĠF SA", + "comp onent", + "Ġparamed ics", + "Ġj an", + "ĠRem em", + "ĠSk inner", + "Ġl ov", + "ĠQu ake", + "rom a", + "Ġfl ask", + "Pr inc", + "Ġover power", + "Ġlod ging", + "ĠK KK", + "ret te", + "Ġabsor bs", + "w rote", + "Ġ ,\"", + "K ings", + "ĠH ail", + "ĠFall ing", + "xt ap", + "ĠHel ena", + "ire ns", + "L arry", + "Ġpamph let", + "ĠC PR", + "G ro", + "ĠHirosh ima", + "Ġhol istic", + "\". [", + "Ġdet achment", + "Ġas pire", + "Ġcompl icit", + "ĠGreen wood", + "Ġresp awn", + "ĠSt upid", + "ĠFin ished", + "f al", + "b ass", + "Ġab hor", + "Ġmock ery", + "ĠFe ast", + "VID EO", + "Ġcon sec", + "ĠHung ry", + "P ull", + "ĠH ust", + "it ance", + "? ãĢį", + ") --", + "ĠPar allel", + "con v", + "4 69", + "ha ar", + "w ant", + "P aper", + "m ins", + "ĠTor o", + "ĠTR UMP", + "ĠR ai", + "D W", + "ĠW icked", + "ĠL ep", + "Ġfun ky", + "Ġdetrim ent", + "ios is", + "ache v", + "Ġde grade", + "im ilation", + "Ġret ard", + "Ġfrag mentation", + "Ġcow boy", + "ĠY PG", + "ĠH AL", + "Parent s", + "ĠS ieg", + "ĠStra uss", + "ĠRub ber", + "× IJ", + "Fr ag", + "Ġp t", + "Ġoption ally", + "ĠZ IP", + "ĠTrans cript", + "ĠD well", + "88 2", + "M erc", + "ĠM OT", + "ãĥ¯ ãĥ³", + "Ġhun ts", + "Ġexec utes", + "In cludes", + "Ġacid ic", + "ĠRespons ibility", + "ĠD umb", + "we i", + "And erson", + "ĠJas per", + "ight on", + "abs olutely", + "Ad ult", + "Ġpl under", + "Mor ning", + "ĠT ours", + "ĠD ane", + "Î º", + "ĠT EST", + "ĠG ina", + "Ġcan ine", + "aw an", + "Ġsocial ists", + "ĠS oda", + "Ġimp etus", + "ĠSupplement ary", + "oli ath", + "ĠKinn ikuman", + "mitted ly", + "second s", + "Ġorganis ers", + "Ġdocument aries", + "Vari able", + "GRE EN", + "Ġres orts", + "Ġbr agging", + "Ġ3 68", + "Art ist", + "w k", + "bl ers", + "Un common", + "ĠRet rieved", + "Ġhect ares", + "Ġtox in", + "r ank", + "Ġfaith s", + "ĠG raphic", + "Ġve c", + "ĠL IA", + "Af rican", + "Ġard ent", + "end iary", + "L ake", + "ĠD OS", + "cient ious", + "ĠOk awaru", + "ĠAll y", + "ĠTim eline", + "D ash", + "ĠI c", + "contin ue", + "Ġt idy", + "Ġinstinct ively", + "ĠP ossibly", + "ĠOut door", + "ĠWould n", + "Ġl ich", + "ĠBr ay", + "ĠA X", + "Ġà ī", + "Ġ+ #", + "\\ '", + "Direct ory", + "ab iding", + "Ġf eral", + "ic ative", + "but t", + "Ġper verse", + "S alt", + "Ġwar ped", + "Ġnin eteen", + "Ġcabin ets", + "Ġsrf Attach", + "ĠSl oan", + "Ġpower ing", + "reg ation", + "F light", + "se vere", + "Ġst ren", + "Ġc og", + "ap ache", + "Ġâ Ŀ", + "Ġcaf eteria", + "p aces", + "ĠGrim oire", + "uton ium", + "Ġr aining", + "Ġcir cling", + "Ġlineback ers", + "c redit", + "Ġrep atri", + "ĠCam den", + "lic ense", + "Ġly ric", + "Ġdescript or", + "Ġval leys", + "Ġre q", + "Ġback stage", + "ĠPro hibition", + "ĠK et", + "Op ening", + "S ym", + "æĸ ¹", + "Ġserv ings", + "Ġoverse en", + "Ġaster oids", + "ĠMod s", + "ĠSpr inger", + "ĠCont ainer", + "è »", + "ĠM ens", + "Ġmult im", + "Ġfire fighter", + "pe c", + "Ġchlor ine", + "Ð ¼", + "end i", + "Ġsp aring", + "Ġpolyg amy", + "ĠR N", + "ĠP ell", + "Ġt igers", + "Ġflash y", + "ĠMad ame", + "S word", + "Ġpref rontal", + "Ġpre requisite", + "uc a", + "Ġw ifi", + "Ġmiscon ception", + "Ġharsh ly", + "ĠStream ing", + "ot om", + "ĠGiul iani", + "foot ed", + "Ġtub ing", + "ind ividual", + "z ek", + "n uclear", + "m ol", + "Ġright ful", + "49 3", + "Ġspecial ization", + "Ġpassion ately", + "ĠVel ocity", + "ĠAv ailability", + "T enn", + "Ġl atch", + "ĠSome body", + "Ġhel ium", + "cl aw", + "Ġdi pping", + "XX X", + "Ġinter personal", + "7 10", + "Ġsub ter", + "Ġbi ologists", + "ĠLight ing", + "Ġopt ic", + "Ġden im", + "end on", + "ĠC orm", + "Ġ3 41", + "ĠC oup", + "Ġfear less", + "Ġal ot", + "ĠCliff ord", + "ĠRun time", + "ĠProv ision", + "up dated", + "lene ck", + "Ġneur on", + "Ġgrad ing", + "ĠC t", + "sequ ence", + "in ia", + "con cept", + "Ġro aring", + "ri val", + "ĠCaucas ian", + "Ġmon og", + "key es", + "Ġappell ate", + "Ġlia ison", + "EStream Frame", + "ĠPl um", + "! .", + "Ġsp herical", + "Ġper ished", + "Ġbl ot", + "Ġben ches", + "Ġ4 11", + "Ġpione ered", + "Ġhur led", + "Jenn ifer", + "ĠYose mite", + "Ch air", + "Ġreef s", + "Ġelect or", + "ĠAnt hem", + "65 2", + "Ġun install", + "Ġimp ede", + "Ġbl inking", + "Ġgot o", + "Dec re", + "A ren", + "Ġstabil ization", + "ĠDis abled", + "ĠYanuk ovych", + "Ġoutlaw ed", + "ĠVent ura", + "ten ess", + "Ġplant ation", + "Ġy acht", + "ĠHu awei", + "Ġsol vent", + "Ġgr acious", + "Ġcur iously", + "Ġcapac itor", + "Ġc x", + "ĠRef lex", + "Ph ys", + "ĠC f", + "pt in", + "cons ervative", + "Ġinv ocation", + "c our", + "F N", + "ĠNew ly", + "H our", + "As ian", + "ĠLe ading", + "ĠAer ospace", + "An ne", + "Ġpre natal", + "Ġdeterior ating", + "H CR", + "ĠNorm andy", + "ol ini", + "ĠAm bro", + "9 10", + "Ġset backs", + "ĠT RE", + "Ġs ig", + "ĠSc ourge", + "59 7", + "79 8", + "Game play", + "Ġm sec", + "M X", + "Ġprice y", + "ĠL LP", + "aker u", + "Ġover arching", + "ĠB ale", + "Ġworld ly", + "Cl ark", + "Ġscen ic", + "Ġdisl iked", + "ĠCont rolled", + "T ickets", + "ĠE W", + "ab ies", + "ĠPl enty", + "Non etheless", + "Ġart isan", + "Trans fer", + "ĠF amous", + "Ġinf ield", + "ble y", + "Ġunres olved", + "ĠML A", + "ãĤ Ĥ", + "Cor rection", + "Ġdemocr at", + "ĠMore no", + "ro cal", + "il ings", + "Ġsail or", + "Ġr ife", + "h ung", + "Ġtrop es", + "Ġsn atched", + "ĠL IN", + "ĠB ib", + "ES A", + "ĠPre v", + "ĠCam el", + "run time", + "Ġob noxious", + "4 37", + "Ġsum mers", + "Ġunexpl ained", + "ĠWal ters", + "cal iber", + "Ġg ull", + "ĠEnd urance", + "ä½ ľ", + "Ġ3 47", + "Ir ish", + "Ġaer obic", + "Ġcr amped", + "ĠHon olulu", + "à ©", + "us erc", + "ec ast", + "AC Y", + "ĠQu ery", + "ãĤ¹ ãĥĪ", + "Bet a", + "Ġsuscept ibility", + "ĠSh iv", + "ĠLim baugh", + "Ġà ĸ", + "ĠN XT", + "ĠM uss", + "ĠBrit ons", + "ES CO", + "EG IN", + "Ġ% %", + "Ġsec ession", + "ĠPat ron", + "ĠLu a", + "n aires", + "ĠJPM organ", + "us b", + "ocy te", + "Ġcouncill ors", + "ĠLi ang", + "f arm", + "Ġnerv ously", + "Ġattract iveness", + "ĠK ov", + "j ump", + "Pl ot", + "Ġst ains", + "ĠStat ue", + "ĠApost les", + "he ter", + "ĠSUP PORT", + "Ġoverwhel m", + "Y ES", + "Ġ29 1", + "d ensity", + "Ġtra pping", + "M it", + "Ġf ide", + "ĠPam ela", + "atl antic", + "Dam n", + "Ġp ts", + "OP A", + "Ġserv icing", + "Ġoverfl owing", + "ul o", + "ĠE rit", + "t icket", + "light ing", + "ĠH mm", + "ãĥ¼ ãĥ«", + "im oto", + "Ġchuck le", + "4 23", + "ãģ ķ", + "sh ape", + "Ġque ues", + "Ġanch ors", + "ãĤ¼ ãĤ¦ãĤ¹", + "F er", + "Ġaw oke", + "Ġ6 66", + "h ands", + "Ġdiver gence", + "Ġ50 5", + "T ips", + "Ġdep ot", + "Ġske w", + "ĠDel iver", + "op ot", + "Ġdiv ul", + "ĠE B", + "uns igned", + "ĠUn i", + "X box", + "Ġfor ks", + "Ġ7 02", + "å ¯", + "Ġpromot ers", + "ĠV apor", + "Ġlev ied", + "sl ot", + "Ġpig ment", + "Ġcyl inders", + "C RE", + "Ġsn atch", + "Ġperpet ually", + "Ġl icking", + "ĠFe et", + "ĠKra ken", + "ĠHold en", + "ĠCLS ID", + "m r", + "Ġproject or", + "Ġden otes", + "Ġchap el", + "ĠTor rent", + "b ler", + "R oute", + "ĠDef endant", + "ĠPublisher s", + "ĠM ales", + "ĠInn ov", + "ĠAg ility", + "rit er", + "ty mology", + "st ores", + "L ind", + "Ġf olly", + "ĠZur ich", + "B le", + "Ġnurt ure", + "Ġcoast line", + "uch in", + "D omin", + "Ġfri vol", + "ĠCons olid", + "res ults", + "M J", + "Ġphyl ogen", + "Ġha uled", + "ĠW iley", + "ĠJess ie", + "ĠPrep are", + "ĠE ps", + "Ġtreasure r", + "I AS", + "Ġcolon ists", + "Ġin und", + "ĠWW F", + "ĠCon verted", + "6 000", + "out side", + "ĠApp earance", + "ĠRel ic", + "ĠM ister", + "s aw", + "Ġresult ant", + "Ġadject ive", + "ĠLaure l", + "ĠHind i", + "b da", + "Pe ace", + "Ġreb irth", + "Ġmembr anes", + "Ġforward ing", + "Ġcoll ided", + "ĠCar olyn", + "K ansas", + "5 99", + "ĠSolid GoldMagikarp", + "Be ck", + "Ġstress ing", + "ĠGo o", + "ĠCooper ative", + "Ġf s", + "ĠAr chie", + "L iter", + "ĠK lopp", + "J erry", + "Ġfoot wear", + "War ren", + "Ġsc ree", + "h are", + "Under standing", + "P ed", + "Ġanth ology", + "ĠAnn ounce", + "M ega", + "Ġflu ent", + "Ġbond age", + "ĠDisc ount", + "il ial", + "C art", + "ĠNight mares", + "Sh am", + "ĠB oll", + "uss ie", + "H ttp", + "Atl anta", + "Ġun recogn", + "ĠB id", + "Ġunder grad", + "Ġforg iving", + "ĠGl over", + "AAAA AAAA", + "4 45", + "V G", + "pa io", + "kill ers", + "Ġrespons ibly", + "Ġmobil ize", + "Ġeffect ed", + "ĠL umin", + "Ġk ale", + "Ġinfring ing", + "ann ounced", + "Ġf itt", + "b atch", + "ĠT ackle", + "ĠL ime", + "ĠAP P", + "uke mia", + "Ġrub y", + "Ġex oner", + "ĠCas ual", + "0 70", + "Ġpel vic", + "Ġautom ate", + "ĠK ear", + "ĠCoast al", + "Ġcre ed", + "Ġbored om", + "ĠSt un", + "ri ott", + "Ĥ İ", + "Ġregener ate", + "Ġcomed ians", + "ĠOP ER", + "Sp ons", + "id ium", + "on is", + "L ocated", + "05 7", + "Ġsusp ense", + "ĠD ating", + "C ass", + "Ġneoc ons", + "ĠShin zo", + "Ġaw oken", + "ch rist", + "ĠMess ages", + "att led", + "ĠSpr ay", + "ĠSp ice", + "C W", + "Ġshield ing", + "ĠG aul", + "Am id", + "Ġparam ilitary", + "Ġmult if", + "ĠTan ner", + "il k", + "Ġgodd amn", + "g ements", + "Ġbe friend", + "m obi", + "Ġ3 88", + "fold er", + "acc a", + "Ġins in", + "g ap", + "N ev", + "fif th", + "Ġpsychiat ry", + "b anks", + "TH IS", + "Ġhar b", + "ac qu", + "Ġfac ade", + "ĠPower Point", + "80 3", + "Ġbl uff", + "Sh ares", + "Ġfavor ing", + "El izabeth", + "Ãį Ãį", + "Ġr anger", + "77 2", + "ĠAr che", + "h ak", + "ĠGen etics", + "ĠF EMA", + "Ġev olves", + "Ġest e", + "ĠP ets", + "ĠM é", + "ĠInterest ing", + "ĠCanter bury", + "ch apter", + "ĠStar fleet", + "Sp anish", + "Ġdraw back", + "ĠNor wich", + "9 70", + "n orth", + "ag anda", + "Ġtransform ative", + "ram ids", + "bi ology", + "ad ay", + "Ġpropag ation", + "ĠGam ma", + "ĠDen ise", + "ĠCalcul ator", + "ent imes", + "ĠB ett", + "Ġapp endix", + "ĠHD D", + "AK ING", + "Ġst igmat", + "Ġhol ster", + "Ġord inarily", + "Ch ance", + "ĠCont rary", + "Ġad hesive", + "Ġgather s", + "6 12", + "re au", + "ony ms", + "ew ays", + "Ġindu ces", + "Ġinterchange able", + "se m", + "Wh it", + "Ġtr ance", + "Ġincorpor ation", + "ĠExt ras", + "Fin ancial", + "Ġawkward ly", + "ĠStur geon", + "ĠH Y", + "Norm ally", + "ĠEnd ing", + "ĠAss ist", + "enc rypted", + "Ġsub jug", + "Ġn os", + "Ġfan atic", + "C ub", + "C U", + "?\" .", + "Ġirre versible", + "å Ĥ", + "03 1", + "ĠH AR", + "sp read", + "ul ia", + "= $", + "Sc ope", + "L ots", + "Ġlif estyles", + "ol on", + "Ġf eds", + "Ġcongrat ulate", + "web kit", + "Ġindist inguishable", + "ĠSw ing", + "Ġcommand ments", + "qu ila", + "ab ella", + "m ethyl", + "ann abin", + "Ġo vere", + "Ġlob ster", + "ĠQU EST", + "ĠCONT IN", + "bern atorial", + ":::: ::::", + "ĠTra ve", + "ĠSam oa", + "AN I", + "75 2", + "Ð ´", + "userc ontent", + "ĠMod erate", + "y eah", + "ĠK itt", + "Ġwe e", + "Ġstuff ing", + "ĠInter vention", + "ĠD ign", + "Ġware houses", + "ĠF iji", + "Ġpel lets", + "Ġtake away", + "ĠT ABLE", + "ĠClass ical", + "col lection", + "Ġland fall", + "ĠMus cle", + "Ġsett les", + "ĠAD V", + "Ġ3 44", + "L aura", + "Ġf ared", + "ĠPart ial", + "4 36", + "oss ibility", + "ĠD aly", + "ĠT arant", + "ĠFu ji", + "am l", + "c ence", + "55 1", + "ĠProced ures", + "ĠO CD", + "ĠU D", + "t in", + "Q UI", + "ach o", + "4 38", + "Ġgl itches", + "Ġenchant ment", + "Ġcalcul ates", + "IR O", + "ĠH ua", + "alys es", + "ĠL ift", + "um o", + "Ġle apt", + "Ġhypothes ized", + "ĠGust av", + "it ans", + "VERS ION", + "æ ł", + "Rog er", + "Ġr and", + "ĠAd apter", + "Ġ3 31", + "ĠPet ition", + "k ies", + "M ars", + "Ġunder cut", + "ze es", + "ĠLy ons", + "ĠDH CP", + "Miss ing", + "Ġretire es", + "Ġins idious", + "el i", + "> )", + ". ãĢį", + "Ġfinal ists", + "ĠA ure", + "Ġacc user", + "Ġwas tes", + "ĠY s", + "ĠL ori", + "Ġconstitu encies", + "Ġsupp er", + "Ġmay hem", + "or ange", + "Ġmis placed", + "Ġmanager ial", + "Ġex ce", + "ĠCL I", + "Ġprim al", + "ĠL ent", + "Cry stal", + "h over", + "ĠN TS", + "end um", + "Ġd w", + "ĠAl c", + "n ostic", + "Ġpres erves", + "ĠTs arnaev", + "Ġtri pled", + "rel ative", + "Arc ade", + "k illing", + "ĠW EEK", + "ĠH anna", + "D ust", + "Com pleted", + "ģ «", + "Ġappro ves", + "ĠSur f", + "ĠLuther an", + "ven ants", + "Ġrobber ies", + "we ights", + "soft ware", + "at ana", + "ug al", + "Ġgrav y", + "ĠC ance", + "OLOG Y", + "ly ak", + "Ton ight", + "Ġunve il", + "Ġ19 04", + "ĠMin ion", + "ent ious", + "st ice", + "pack ages", + "ĠG EAR", + "Ġg ol", + "ĠHutch inson", + "ĠProf ession", + "ĠG UN", + "ĠDiff erence", + "ĠTsuk uyomi", + "ĠLes bian", + "6 70", + "Ġfug itive", + "ĠPlan etary", + "-------------------------------- ------------------------", + "Ġacc rued", + "Ġch icks", + "Ġsto pp", + "Ġblock ers", + "C od", + "Ġcomment ers", + "ĠSomew here", + "ĠPhot ographer", + "the me", + "Ġmay oral", + "w u", + "Ġanten nas", + "Ġrev amped", + "ĠSubject s", + "it é", + "im ura", + "Ġentr ances", + "liter ally", + "Ġten ets", + "ĠO MG", + "ĠMP H", + "ĠDon key", + "ĠOff ense", + "Ġ\" +", + "Sn ap", + "ĠAF B", + "Ġan imate", + "ĠS od", + "His panic", + "Ġinconsist ency", + "D b", + "F Y", + "Ex port", + "Ġa pe", + "Ġpear l", + "ib el", + "ĠPAC s", + "Ġ{ \\", + "Ġact u", + "ĠHS BC", + "camp us", + "Ġpay off", + "Ġde ities", + "ĠN ato", + "ou ple", + "Ġcens ored", + "ĠCl ojure", + "Ġconf ounding", + "en i", + "Ġreck on", + "op he", + "Ġspot ting", + "Ġsign ifies", + "Ġprop el", + "Ġfest ive", + "S uggest", + "Ġpled ging", + "ĠB erman", + "Ġrebell ious", + "Ġovershadow ed", + "Ġinfiltr ated", + "j obs", + "67 2", + "Ġscal able", + "Ġdomin ion", + "ĠNew foundland", + "ĠMead ow", + "Ġpart itions", + "AM I", + "Ġsupplement ary", + "str ument", + "Ġhair y", + "Ġperpet uate", + "Ġnuts hell", + "ĠPot ato", + "ĠHob bit", + "Ġcur ses", + "Flo at", + "Ġquiet er", + "Ġfuel ing", + "Ġcaps ules", + "ĠL ust", + "ĠH aunted", + "Exec utive", + "Ġchild birth", + "G re", + "Ġrad iant", + "å İ", + "Ġm alls", + "Ġin ept", + "ĠWarrant y", + "Ġspect ator", + "E h", + "t hens", + "Ġculmin ating", + "æ ©", + "ary a", + "ãĤ ®", + "ilit arian", + "ĠOR IG", + "ĠSp ending", + "pt ives", + "ĠS iren", + "ĠRec ording", + "ay ne", + "Ġv im", + "Ġspr ang", + "T ang", + "ĠM FT", + "mor ning", + "ĠWe ed", + "m peg", + "cess ion", + "ĠCh ung", + "7 30", + "w arning", + "56 2", + "handed ly", + "P oor", + "P olitics", + ": #", + "Ġp ian", + "Ġfec es", + "ĠDocument ation", + "Ġban ished", + "Ġ3 99", + "ĠAR C", + "Ġhe inous", + "J ake", + "ĠAm ir", + "way ne", + "v re", + "os henko", + "Ġnotebook s", + "Ġfound ational", + "Ġmarvel ous", + "ixt ape", + "Ġwithdraw als", + "Ġh orde", + "ĠD habi", + "is able", + "ĠK D", + "Ġcontag ious", + "ĠD ip", + "ĠAr rows", + "Ġpronoun s", + "Ġmorph ine", + "ĠB US", + "68 2", + "Ġk osher", + "fin ished", + "ĠInstr uments", + "Ġf used", + "yd en", + "ĠSal mon", + "F ab", + "aff ected", + "K EN", + "C ENT", + "Dom ain", + "Ġpoke mon", + "ĠDr inking", + "G rowing", + "ĠInvestig ative", + "ĠA ether", + "em i", + "Ġtabl oid", + "Ġrep ro", + "ĠNot withstanding", + "ĠBers erker", + "Ġdram as", + "Ġclich é", + "Ġb ung", + "ĠU RI", + "ĠD os", + "0 44", + "Ġpast ors", + "Ġl s", + "Ġac rylic", + "aun ts", + "Ed ward", + "Ġmajor ities", + "B ang", + "Ġfield ing", + "ĠRepl acement", + "ĠAl chemy", + "pp ard", + "ĠRome o", + "ĠSan ct", + "ĠLav rov", + "ib ble", + "Inst ruct", + "Ġimp ractical", + "ĠPlay boy", + "ce phal", + "Ġsw aps", + "Ġk an", + "ĠThe o", + "Ġillust rating", + "Ġdismant led", + "ĠTrans gender", + "ĠG uth", + "UG H", + "Ġtriumph ant", + "Ġencomp ass", + "Ġbook mark", + "udd in", + "j er", + "Ġpred icate", + "ES H", + "Ġwhen ce", + "ĠAB E", + "Ġnon profits", + "Se qu", + "Ġdi abetic", + "Ġp end", + "Ġheart felt", + "sh i", + "Ġinter acts", + "ĠTele com", + "Ġbombard ment", + "dep ending", + "ĠLow ry", + "ĠAd mission", + "ĠBl ooming", + "ust ration", + "ene gger", + "B rew", + "Ġmol ten", + "ĠNer d", + "P IN", + "âĸ Ģ", + "ave ment", + "Ġtou red", + "Ġco efficients", + "ĠTray von", + "ans son", + "Ġsand y", + "t old", + "fl ows", + "Ġpop ulous", + "ĠT inder", + "ĠBl iss", + "R achel", + "Min imum", + "Ġcontest ant", + "ĠRed uce", + "ĠMor se", + "ĠGrass ley", + "ĠClick er", + "Ġexp r", + "Ġs incerity", + "Ġmar qu", + "Ġelic it", + "ĠPro position", + "ĠDemon ic", + "Ġtac os", + "G reek", + "Ġpost war", + "Ġin sofar", + "ĠP ork", + "Ġ35 2", + "doctor al", + "walk ing", + "Ġmid term", + "ĠSam my", + "sight ed", + "ĠTR ANS", + "ic i", + "AL D", + "ĠUS L", + "ĠF ISA", + "ĠAm pl", + "ĠAlex andra", + "ine lli", + "Tr ain", + "Ġsign ify", + "ĠVers us", + "Ġob fusc", + "Ġk h", + "Ġagg ro", + "ĠRen ault", + "Ġ3 48", + "5 18", + "ox icity", + "0 22", + "ĠTw ist", + "Ġgoof y", + "D ynamic", + "Ġbrief ings", + "m ight", + "8 99", + "Ġderog atory", + "T ro", + "Ġfor ging", + "ĠKor an", + "ĠMar ried", + "ĠBuc s", + "Ġpal ate", + "ĠCon version", + "m able", + "4 13", + "Ġ( _", + "Ġs iph", + "ĠN EO", + "col lege", + "Ġmarg inally", + "Ġfl irt", + "ĠTra ps", + "ĠP ace", + "é »Ĵ", + "Ġgoalt ender", + "Ġforb ids", + "Ġcler ks", + "ĠT ant", + "ĠRobb ins", + "ĠPrint ing", + "Ġpremie red", + "Ġmagn ification", + "ĠT G", + "ĠR ouse", + "ĠM ock", + "odynam ics", + "Ġpre clude", + "ism o", + "ĠPul itzer", + "Ġaval anche", + "ĠK odi", + "rib une", + "ĠL ena", + "Elect ric", + "Ġref inery", + "Ġend owed", + "Ġcounsel ors", + "Ġd olphin", + "ĠM ith", + "Ġarm oured", + "hib ited", + "Beg in", + "ĠP W", + "O il", + "ĠV or", + "ĠShar if", + "ĠFraz ier", + "est ate", + "Ġj ams", + "Pro xy", + "Ġband its", + "ĠPresbyter ian", + "ĠPrem iere", + "t iny", + "ĠCru el", + "Test ing", + "Ġhom er", + "ĠV ERS", + "ĠPro l", + "ĠDep osit", + "ĠCoff in", + "Ġsemin ars", + "Ġs ql", + "ĠDef endants", + "Altern atively", + "ĠR ats", + "ç «", + "ethy st", + "' >", + "Ġiss uer", + "58 9", + "Ġch aired", + "ĠAccess ories", + "man ent", + "Ġmar row", + "ĠPrim ordial", + "C N", + "Ġlimit less", + "ĠCarn age", + "Ġund rafted", + "q v", + "IN ESS", + "on ew", + "Ġco hesion", + "98 7", + "Ġne cks", + "Ġfootball er", + "ĠG ER", + "Ġdetect able", + "ĠSupport ing", + "ĠCS V", + "oc ally", + "k Hz", + "Ġund e", + "Ġsh one", + "Ġbud ding", + "tra k", + "Stand ing", + "ĠStar craft", + "ĠKem p", + "Ben ch", + "Ġthw arted", + "ĠGround s", + "ath i", + "L isa", + "Dial og", + "ĠS X", + "V ision", + "Ġingen ious", + "Ù IJ", + "Ġfost ering", + "ĠZ a", + "ĠIn gram", + "Ġ\" @", + "N aturally", + "6 16", + "0 35", + "ĠF AC", + "H mm", + "55 4", + "Ġacceler ator", + "ĠV end", + "Ġsun screen", + "Ġtuber culosis", + "rav iolet", + "ĠFunction al", + "ĠEr rors", + "ed ar", + "19 66", + "ĠSpect re", + "ĠRec ipes", + "88 5", + "ĠM ankind", + "L iverpool", + "Ġ| --", + "Ġsubst itutes", + "ĠX T", + "w ired", + "Ġinc o", + "ĠAf gh", + "E va", + "ic c", + "S ong", + "K night", + "Ġdilig ently", + "ĠBroad cast", + "A id", + "Ġaf ar", + "ĠH MS", + "aton in", + "ĠGr ateful", + "Ġfire place", + "ĠOm ni", + "e uro", + "ĠF RE", + "ĠSh ib", + "ĠDig est", + "t oggle", + "Ġheads ets", + "Ġdiff usion", + "ĠSqu irrel", + "ĠF N", + "Ġdark ened", + "out her", + "Ġsleep s", + "ĠX er", + "gun s", + "Ġset ups", + "Ġpars ed", + "Ġmamm oth", + "ĠCur ious", + "g ob", + "ĠFitz patrick", + "ĠEm il", + "im ov", + "........ .....", + "ĠB enny", + "Second ly", + "Ġheart y", + "Ġcons on", + "st ained", + "Ġgal actic", + "cl ave", + "Ġplummet ed", + "Ġp ests", + "Ġsw at", + "Ġrefer rals", + "ĠLion el", + "h oly", + "Ġunder dog", + "ĠSl ater", + "ĠProv ide", + "ĠAm ar", + "ress or", + "å Į", + "ong a", + "Ġtim id", + "Ġp iety", + "ĠD ek", + "Ġsur ging", + "az o", + "Ġ6 10", + "Ġdes ks", + "ĠSp okane", + "ĠAn field", + "Ġwars hips", + "ĠCob ra", + "Ġar ming", + "clus ively", + "ĠBad ge", + "ag ascar", + "ĠPR ESS", + "ĠMcK enzie", + "ĠFer dinand", + "burn ing", + "Af ee", + "Ġtyr ann", + "ĠI w", + "ĠBo one", + "100 7", + "ĠRe pt", + "Ċ Âł", + "Ġcar avan", + "ĠD ill", + "ĠBundes liga", + "Ch uck", + "Ġheal er", + "ãĥ¼ãĥ Ĩ", + "ĠH obby", + "Ġneg ate", + "Ġcrit iques", + "section al", + "mop olitan", + "Ġd x", + "Ġouts ourcing", + "ĠC ipher", + "t ap", + "Sh arp", + "Ġup beat", + "Ġhang ar", + "Ġcru ising", + "ĠNi agara", + "Ġ3 42", + "ill us", + "ĠS v", + "Ġsubt itles", + "Ġsqu ared", + "Ġbook store", + "Ġrevolution aries", + "ĠCarl ton", + "ab al", + "Ut ah", + "Ġdesp ise", + "ĠU M", + "cons ider", + "aid o", + "Ġc arts", + "ĠT urtles", + "Tr aining", + "Ġhonor ary", + " ¢", + "Ġtri angles", + "4 22", + "Ġreprint ed", + "Ġgrace ful", + "ĠMong olia", + "Ġdisrupt ions", + "ĠB oh", + "Ġ3 49", + "Ġdr ains", + "Ġcons ulate", + "Ġb ends", + "Ġm afia", + "ur on", + "ĠF ulton", + "m isc", + "Ġren al", + "Ġin action", + "ck ing", + "Ġphot ons", + "Ġbru ised", + "ĠC odes", + "og i", + "Ġn ests", + "ĠLove ly", + "ĠLib re", + "ĠD aryl", + "Ġ# ##", + "S ys", + ". ,\"", + "Ġfree zes", + "est ablishment", + "and owski", + "Ġcum bers", + "ĠSt arg", + "ĠBom bs", + "Ġleg ions", + "Ġhand writing", + "Ġgr un", + "ĠC ah", + "sequ ent", + "Ġm oth", + "ĠMS M", + "Ins ert", + "F if", + "Ġmot el", + "Ġdex ter", + "ĠB ild", + "hearted ly", + "Ġpro pe", + "ĠText ure", + "ĠJ unction", + "ynt hesis", + "oc ard", + "ĠVer a", + "ĠBar th", + "Ġμ g", + "Ġl ashed", + "Ġ35 1", + "ĠZ amb", + "ĠSt aples", + "ĠCort ex", + "ĠCork er", + "Ġcontinu um", + "ĠWR ITE", + "unt a", + "rid or", + "Ġde ems", + "0 33", + "ĠG OLD", + "p as", + "Ġrep ressive", + "ãĥĨ ãĤ£", + "Ġbaff led", + "Sc ar", + "Ġc rave", + "Ġ ______", + "Ġentrepreneurs hip", + "ĠDirector ate", + "Ġ' [", + "Ġv ines", + "Ġasc ended", + "ĠGR OUP", + "ĠGood bye", + "Ġdo gged", + "ãĥ´ ãĤ¡", + "Man ufact", + "Ġunimagin able", + "ri ots", + "ier rez", + "Ġrel ativity", + "ĠCraft ing", + "ra ught", + "ud en", + "c ookie", + "Ġassass ins", + "Ġdissatisf ied", + "ac ci", + "Ġcondu it", + "Sp read", + "ĠR ican", + "n ice", + "izz le", + "Ġsc ares", + "ĠWH Y", + "ph ans", + "5 35", + "Ġprot racted", + "ĠKrist en", + "5 36", + "ĠSc rib", + "ĠNe h", + "Ġtwent ies", + "Ġpredic ament", + "Ġhandc uffs", + "Ġfruit ful", + "ĠU L", + "ĠLud wig", + "Ġatt est", + "ĠBre aker", + "Ġbi ologically", + "ĠDeal er", + "Ġrenov ations", + "f w", + "ess en", + "Al ice", + "ĠHen ri", + "Ġun ilaterally", + "ĠS idd", + "h ai", + "ĠSt retch", + "S ales", + "Ġcumbers ome", + "ĠJ avier", + "Ġtrend y", + "Ġrot ting", + "ĠChall enges", + "Ġscra ps", + "Ġfac ets", + "ĠVer onica", + "ĠVer ge", + "ĠS ana", + "Al ien", + "ĠR ih", + "Ġrad ial", + "ect ar", + "Ġ6 30", + "cl i", + "Mar ie", + "Ġwild fire", + "ĠCat o", + "h ander", + "Ġwait ress", + "Ġch ops", + "ĠS ECTION", + "Ġblunt ly", + "ĠCat alog", + "n ian", + "stud y", + "Ġpat rolling", + "ĠT enth", + "nex us", + "ĠN ON", + "op sy", + "Ġsc athing", + "s ie", + "Ġdeterior ated", + "V B", + "Naz is", + "Ġdep ictions", + "Ġauthent icated", + "ĠCon ce", + "k rit", + "Ġpromul g", + "ĠL ONG", + "U FC", + "ĠVis itors", + "ĠRec all", + "Ġrehab ilit", + "ĠSL I", + "Ġglac ier", + "ĠB ite", + "Ġ50 3", + "Ġvom it", + "Ġfer mented", + "ĠKh alid", + "Ġgrad ed", + "ĠMag icka", + "ĠIch igo", + "power ful", + "ic ators", + "75 3", + "Ġsh rew", + "Ġ35 6", + "Ġlegal izing", + "Ġall otted", + "ĠArch demon", + "ith ing", + "igg urat", + "V OL", + "Le od", + "Ġo ily", + "Ġindu cing", + "Ġamy gdala", + "Ġadm ins", + "ĠAcqu isition", + "C AN", + "Ġsche matic", + "Ġmo an", + "ĠCamer oon", + "Ġt ink", + "Ġmer ry", + "Ġbutter flies", + "ĠGo ff", + "Ġworks pace", + "ĠCor ona", + "Ġj avascript", + "ĠD olphin", + "ĠCant or", + "4 64", + "to e", + "AP S", + "ĠAg ing", + "Ġpadd ed", + "ĠZ heng", + "ĠHe ld", + "Ġest ranged", + "Ġ7 70", + ". }", + "ĠDun ham", + "Ġsm okes", + "Ġcap itals", + "und ai", + "Sh in", + "ĠFound ing", + "Ġent itle", + "Ġcenter piece", + "D iscover", + "Ġthere to", + "al ert", + "ĠN ou", + "ĠAnaly st", + "l c", + "F H", + "FI ELD", + "ĠP OV", + "gr ay", + "Ġar cs", + "ĠH OT", + "Ġr s", + "Ġoblig atory", + "ĠArchitect s", + "ĠS ven", + "ĠF EC", + "0 200", + "Christ mas", + "ĠAlban ia", + "rat om", + "58 7", + "Ġhard ships", + "Ġaut os", + "ĠCharg es", + "Ġap es", + "Ġ3 76", + "wal let", + "Ġintox ication", + "Ġgobl in", + "Ġ5 70", + "++++++++ ++++++++", + "ĠYel p", + "ĠMag netic", + "ĠBr iggs", + "R ail", + "Ġspawn s", + "ĠW iggins", + "Ġshowc ased", + "Ġres orted", + "ub en", + "Ġwh ipping", + "Ġim itate", + "Ġdigest ion", + "ĠUS PS", + "ĠG est", + "Ġye a", + "ĠT ight", + "ind al", + "ic as", + "` .", + "C AST", + "'' ;", + "ĠF et", + "opath ic", + "In valid", + "Ġregrett ed", + "Ġbro ccoli", + "ĠSc ores", + "e ve", + "Ġpost ings", + "Ġaccum ulating", + "Ġneed less", + "elf th", + "Ġmay ors", + "Ġsc rib", + "Ġanecd otes", + "Ġbot ched", + "ĠRib bon", + "ĠConstant ine", + "i uses", + "ess es", + "Ġdev ise", + "Comp ared", + "Ġp udding", + "Ġg arg", + "Ġev oke", + "79 7", + "Ġdet ox", + "9 09", + "ĠPie ces", + "ĠMcC artney", + "Ġmet ast", + "ĠK rypt", + "P OR", + "Ġt ending", + "ĠMerch ants", + "Pro of", + "ĠV arg", + "ĠPort able", + "ãĥ¼ãĥĨ ãĤ£", + "B rain", + "25 00", + "Ġfol iage", + "Ø ¹", + "Ġment ors", + "ĠA ires", + "Ġminimal ist", + "Ġing ested", + "ĠTro jan", + "ĠQ ian", + "inv olved", + "0 27", + "Ġer oded", + "RA FT", + "Ġbl urry", + "M ob", + "Ġbuff et", + "ĠFn atic", + "ae a", + "KN OWN", + "ĠIn it", + "s afety", + "en um", + "ACT ION", + "ĠCrus her", + "ĠD ates", + "Ġ ................", + "c alling", + "ak ov", + "Ġvent ured", + "Ġ5 55", + "au ga", + "H art", + "ĠA ero", + "M AC", + "Ġthin ly", + "Ġar ra", + "ST ATE", + "ild e", + "ĠJac qu", + "ĠFem ales", + "Ġthe orem", + "Ġ3 46", + "Ġsmart est", + "ĠPU BLIC", + "ĠK ron", + "ĠB its", + "ĠV essel", + "ĠTele phone", + "Ġdec ap", + "Ġadj unct", + "ĠS EN", + "mer ga", + "Ġred acted", + "Ġpre historic", + "Ġexplan atory", + "ĠRun s", + "ĠUtt ar", + "ĠM anny", + "ĠAUTH OR", + "ĠUnle ashed", + "ĠBow ling", + "be ans", + "79 3", + "Ġunivers es", + "Ġsens it", + "ĠK ung", + "re peat", + "ctr l", + "Ġp aced", + "Ġfull er", + "Cl ock", + "Ġrec omb", + "ĠF aul", + "ĠB unker", + "Ġpool ed", + "Ġan a", + "ĠM outh", + "LL OW", + "hum ane", + "Ġbull do", + "ĠMicha els", + "f am", + "Ġwreck ed", + "Ġport rays", + "ĠWh ale", + "ĠH es", + "Ġguess es", + "ĠBrow se", + "ĠL APD", + "Ġconsequ ential", + "ĠInn ocent", + "ĠD RAG", + "Ġtrans gress", + "ĠO aks", + "Ġtri via", + "ĠRes on", + "ĠA DS", + "-- +", + "ĠT oll", + "Ġgrasp ing", + "ĠTHE M", + "ĠT ags", + "ĠCon clusion", + "Ġpract icable", + "Ġho op", + "Ġunintention ally", + "Ġign ite", + "ĠM ov", + "ur ized", + "le hem", + "Ter min", + "Ġcolour ful", + "ĠLin ear", + "ĠEll ie", + "G y", + "Ġman power", + "Ġj s", + "Ġem oji", + "ĠSHAR ES", + "_ .", + "0000 7", + "Ġsophistic ation", + "Ġunders core", + "Ġpract ise", + "Ġbl ob", + "op ens", + "Uk raine", + "Ke eping", + "Y C", + "J R", + "ult imate", + "Cl aim", + "Ġautom obiles", + "99 3", + "ste el", + "Ġpart ing", + "ĠL ank", + "... ?", + "Ġ38 5", + "Ġremem brance", + "Ġe ased", + "Ġcov ari", + "ĠS ind", + "Effect ive", + "Ġdisse mination", + "ĠMo ose", + "ĠCl apper", + "br ates", + "App ly", + "Ġinv is", + "Ġwors ened", + "âĢĶ -", + "Ġlegisl ator", + "ĠL ol", + "ĠRow e", + "Ġdealers hip", + "um ar", + "id ences", + "Ġinvestig ates", + "Ġc ascade", + "Ġbid der", + "ĠB EN", + "Iron ically", + "Ġpres iding", + "Ġd ing", + "Ġcontrad icted", + "Ġshut s", + "ĠF IX", + "Ġ3 66", + "Dist rict", + "Ġsin ful", + "ĠChar isma", + "o ops", + "Ġtot ality", + "Ġrest itution", + "ĠOpt imus", + "ĠD ah", + "Ġcl ueless", + "urn ed", + "Ġnut rit", + "Ġland owners", + "Ġfl ushed", + "Ġbroad en", + "m ie", + "Ġprint ln", + "Ġn ig", + "ĠCorp us", + "J en", + "Ġprot o", + "ĠWik imedia", + "ĠPal o", + "C OR", + "Ġstory lines", + "Ġevangel icals", + "ĠDar rell", + "Ġrot or", + "ĠH W", + "sk illed", + "ery l", + "Ġbe gg", + "ĠBl umenthal", + "Ġwe aving", + "Ġdown wards", + "ĠJack et", + "ĠANG EL", + "Te chnology", + "Ġes oteric", + "alde hyde", + "Ġfur iously", + "Ġforeign er", + "We ak", + "CH O", + "ĠH ound", + "Exper ience", + "ĠPlay station", + "ĠM IA", + "ĠU ng", + "cl oth", + "ag all", + "Ġcal ming", + "iz ens", + "St ruct", + "ĠW itches", + "ĠCeleb ration", + "Ġ........ ......", + "pt roller", + "ĠTC U", + "Ġb unny", + "ãĥ į", + "ut orial", + "Ġup scale", + "ĠSt a", + "ĠCol ossus", + "Ġchlor ide", + "ĠZ ac", + "ĠRe asons", + "ĠBrook ings", + "ĠWH ITE", + "][ /", + "ĠL ose", + "9 05", + "Ġunders ide", + "ern els", + "Ġv ape", + "do zen", + "upp et", + "ĠST OP", + "mat ical", + "ĠStat ements", + "hed dar", + "P AC", + "Custom er", + "Ġmem os", + "ĠP J", + "end ars", + "ĠLim its", + "l augh", + "Ġstabil ized", + "ĠALE C", + "Y A", + "Up grade", + "al am", + "Ġtechn o", + "Ġan ew", + "fore seen", + "Ġcolleg iate", + "ĠPy ro", + "ĠD ism", + "Ġfront line", + "Ġammon ia", + "I U", + "Qu ite", + "John ny", + "ass in", + "G OP", + "ĠSt yles", + "ĠSovere ign", + "acter ial", + "5 49", + "ĠR IP", + "ĠL ists", + "Ġ3 64", + "ĠRece p", + "s ocket", + "ĠByr d", + "ĠCand le", + "An cient", + "Ġappell ant", + "en forcement", + "ace a", + "ans ki", + "Ġold s", + "88 6", + "Ġsl urs", + "Ġem pires", + "Ġbuck le", + "Ġalien ation", + "ĠAber deen", + "Ġunic orn", + "Ġoverr iding", + "ĠL X", + "pp a", + "Ġdesp ised", + "ĠB ugs", + "ĠB ST", + "S outhern", + "5 33", + "Ġhall mark", + "ĠPost er", + "Ġstem med", + "Ġprincip als", + "ĠT ECH", + "ĠSand wich", + "It aly", + "Ġche esy", + "ĠSet TextColor", + "ĠProt ective", + "ĠC ohn", + "J O", + "apt op", + "Re ason", + "Lead er", + "ĠUnder stand", + "ĠFr idays", + "ĠContin uous", + "Ġcl ipping", + "ĠR ye", + "Ġber th", + "tim er", + "ann is", + "re act", + "Ġbuff alo", + "ĠPar as", + "Ġ6 55", + "Ġpres ided", + "ĠSun rise", + "Ġve ts", + "Ġcl oves", + "ĠMcC ull", + "Stre ngth", + "G AN", + "Ġill iter", + "ĠPric ing", + "l é", + "Ġresist or", + "Ġbr un", + "ĠSuff olk", + "Ñ ĭ", + "ĠL iver", + "Re leased", + "Ġwhat s", + "8 60", + "ĠMe asures", + "Ġden ouncing", + "ĠRy zen", + "Ġsou ven", + "Ġcareg ivers", + "ch ini", + "ĠScar lett", + "Ġt rough", + "Cong ratulations", + "Ġtax is", + "ĠTrad ition", + "j it", + "Ġtable top", + "Ġhither to", + "Ġdis information", + "off ensive", + "h ra", + "ĠDISTR ICT", + "Ġcompl icate", + "chen ko", + "ĠRecon struction", + "Ġpalp able", + "Ġa usp", + "Ġ4 28", + "Ġshowc ases", + "ĠPublic ation", + "know ledge", + "inn on", + "4 19", + "Ġretri eval", + "and ers", + "Ġref ute", + "Ġinqu ired", + "g ur", + "Ġneg ativity", + "Ġcons erve", + "Ġafter life", + "Ġpres upp", + "ĠGill espie", + "Ġm t", + "ĠD N", + "T ap", + "Ġper pend", + "ĠS my", + "does n", + "Ġsp illing", + "Ġhyp ers", + "K ate", + "® ,", + "ke pt", + "ĠP owered", + "Ġj a", + "ĠK lux", + "ard e", + "ab an", + "Ġ4 44", + "Ġflatt ened", + "ĠImprove ments", + "urg a", + "ĠK und", + "Ġins cribed", + "Ġfac ult", + "Ġunpre pared", + "ĠCons umers", + "Ġsatisf ies", + "Ġpul monary", + "Ġinf iltration", + "Ġex ternally", + "Ġcongrat ulations", + "ag han", + "Ġair liner", + "Ġfl ung", + "Ġfly ers", + "G D", + "Ġsnipp ets", + "Ġrec ursive", + "Ġmaster ing", + "L ex", + "Ġovert ly", + "v g", + "Ġluck ily", + "Ġenc ro", + "ĠLanc et", + "ĠAbyss al", + "function al", + "Ġs ow", + "Ġsqu id", + "Ġnar ration", + "Ġn aughty", + "ĠHon our", + "ĠSpart ans", + "Ġsh atter", + "ĠTac oma", + "ĠCal ories", + "ĠR aces", + "Sub mit", + "Ġpurpose fully", + "w av", + "ĠY ok", + "F est", + "ĠG err", + "Met ro", + "Ġit iner", + "f amous", + "Ġ\" {", + "in line", + "was her", + "Iss ue", + "ĠCL IENT", + "oz o", + "Vers ions", + "7 25", + "ĠGl ock", + "Ġshield ed", + "ĠPC R", + "ENC Y", + "ĠWe ld", + "ĠSim pl", + "Ġredirect ed", + "ĠK ham", + "Ġ( >", + "Ġlab ou", + "Ġdi apers", + "ss l", + "Ġcell ar", + "organ isms", + "ore sc", + "ĠBer ks", + "did n", + "Sh ipping", + "C hest", + "Ġund one", + "Ġmillion aire", + "Ġc ords", + "ĠYoung er", + "appropri ately", + "Ġsequ els", + "u ve", + "ant icipated", + "Ġle wd", + "ĠSh irt", + "ĠDmit ry", + "V eter", + "Ġsl aying", + "ĠY ar", + "Ġcompl ication", + "I owa", + "ĠEric a", + "ĠBL M", + "g irlfriend", + "b odied", + "6 26", + "19 63", + "Ġintermedi ary", + "Ġcons olation", + "M ask", + "ĠSi em", + "ow an", + "Beg inning", + "Ġfix me", + "Ġculmin ated", + "Ġcon duc", + "ĠVolunte er", + "Ġpos itional", + "Ġgre ets", + "ĠDefin itions", + "Ġthink er", + "Ġingen uity", + "Ġfresh men", + "ĠMom ents", + "Ġ35 7", + "ate urs", + "ĠFed Ex", + "s g", + "69 4", + "Ġdwind ling", + "ĠBO X", + "sel age", + "Ġt mp", + "Ġst en", + "ĠS ut", + "Ġneighbourhood s", + "Ġclass mate", + "f ledged", + "Ġleft ists", + "Ġclim ates", + "ATH ER", + "ĠScy the", + "ul iffe", + "Ġs ag", + "Ġho pped", + "ĠF t", + "ĠE ck", + "ĠC K", + "ĠDo omsday", + "k ids", + "Ġgas ped", + "Ġmon iker", + "ĠL od", + "ĠC FL", + "t ions", + "r ums", + "fol ios", + "Ġm d", + "Ġunc anny", + "Ġtrans ports", + "ĠLab rador", + "Ġrail ways", + "Ġappl iance", + "ĠCTR L", + "æ Ģ", + "Pop ulation", + "ĠConfeder acy", + "Ġunb earable", + "Ġdors al", + "ĠIn form", + "op ted", + "ĠK ILL", + "Mar x", + "Ġhypoc ritical", + "q us", + "ĠN umerous", + "ĠGeorg ian", + "ĠAmbro se", + "ĠL och", + "Ġgu bernatorial", + "ĠX eon", + "ĠSupp orts", + "ens er", + "ee ly", + "ĠAven ger", + "19 65", + "Ar my", + "Ġju xtap", + "Ġcho pping", + "ĠSpl ash", + "ĠS ustainable", + "ĠFin ch", + "Ġ18 61", + "ict ive", + "at meal", + "ĠG ohan", + "Ġlights aber", + "ĠG PA", + "ug u", + "ĠRE PL", + "vari able", + "Ġher pes", + "Ġdesert s", + "ac iously", + "Ġsitu ational", + "week ly", + "ob l", + "Ġtext ile", + "ĠCorn wall", + "Ġcontrace ptives", + "ĠA ke", + "] -", + "ä¹ ĭ", + ": ,", + "ĠW em", + "ĠB ihar", + "Ġ' .", + "Ġbe re", + "Ġanal ogue", + "ĠCook ies", + "Ġtake off", + "Whe el", + "Ġmaj estic", + "Ġcomm uting", + "0 23", + "ĠCor pse", + "ass ment", + "min i", + "Ġgor illa", + "ĠAl as", + "ere e", + "Ġacquaint ances", + "ĠAd vantage", + "Ġspirit ually", + "Ġey ed", + "pm wiki", + "ĠE nder", + "Ġtrans lucent", + "Ġnight time", + "ĠIM AGES", + "5 45", + "ĠK amp", + "ĠFre ak", + "Ġ ig", + "Port land", + "4 32", + "ĠM ata", + "Ġmar ines", + "Ġh ors", + "ater asu", + "ĠAtt ribution", + "Ġ-------- -", + "Ġk ins", + "ĠBEL OW", + "++ +", + "Ġre eling", + "ol ed", + "Ġcl utter", + "ĠRel ative", + "Ġ4 27", + "B US", + "Ġa vert", + "ĠChe ong", + "ĠA ble", + "ĠPry or", + "Develop er", + "Ġen cyclopedia", + "ĠUSA F", + "ĠG arry", + "Sp ain", + "Bl ocks", + "Ġexp osition", + "ĠGamer Gate", + "W OR", + "Ġstockp ile", + "Ġclot hed", + "ĠT one", + "ĠR ue", + "t umblr", + "Ġtreacher ous", + "Ġf rying", + "Ñ Į", + "ĠS ph", + "Ġrest raints", + "Ġemb odies", + "ĠG es", + "S afety", + "Ġnegoti ators", + "min ing", + "ĠAppalach ian", + "L OS", + "ĠJenn a", + "Ġpass ers", + "ç ĭ", + "sn ap", + "Ġshort en", + "creat or", + "Ġinn umerable", + "uther land", + "67 4", + "ĠW OM", + "ĠAs cend", + "ĠArm ory", + "ĠTrans action", + "K ick", + "Ġsuit case", + "day Name", + "Ġwaste ful", + "mar riage", + "ĠMcC abe", + "ite ch", + "ĠO ss", + "Cl osure", + "ĠTreasure r", + "Ġindec ent", + "ĠD ull", + "Ġresid ences", + "19 59", + "ĠS ettlement", + "Ham ilton", + "Ġself ies", + "ĠRank ing", + "ĠBark ley", + "ĠB ore", + "ĠW CS", + "ĠMar itime", + "ĠH uh", + "ĠForest ry", + "Ġcultiv ating", + "ĠBall ard", + "Ġg arrison", + "ĠSD L", + "9 30", + "Ġnas cent", + "Ġirresist ible", + "Ġaw fully", + "\\/ \\/", + "Ġequ ate", + "Ġanthrop ology", + "ĠSylv ia", + "Ġintest ine", + "Ġinnoc uous", + "cess ive", + "ag ra", + "ĠMet roid", + "G rant", + "8 55", + "ģ ĸ", + "Ġ\" _", + "ãĥĥ ãĥī", + "Ġappra isal", + "ĠFred dy", + "04 6", + "Ġ40 6", + "Ġ18 30", + "Ġd ocking", + "St atic", + "Ġp ont", + "ĠVolt age", + "ĠSt ead", + "ĠMort gage", + "ĠJon ah", + "Y L", + "CLASS IFIED", + "Ġas bestos", + "nik ov", + "Ġcoll agen", + "ĠOrb ital", + "P ocket", + "7 99", + "Ġhy brids", + "inc hes", + "Ġinv oice", + "und y", + "Ġinequ alities", + "T rend", + "w ashed", + "B ALL", + "Ġluc id", + "ĠComment ary", + "Ġw itty", + "Br andon", + "Ġbru ising", + "Ġ6 20", + "es cent", + "box ing", + "P OL", + "Ġ3 78", + "R ect", + "Ġlic ences", + "ĠMcG ee", + "p ressed", + "D anny", + "Ġj ammed", + "ord inate", + "Ġle th", + "Ġdistingu ishes", + "ĠYam aha", + "IL S", + "ĠH ume", + "ĠC ategories", + "Rober ts", + "Ch art", + "Ġbeet le", + "ĠGra veyard", + "Ġ($ )", + "o ÄŁ", + "Ġtw ilight", + "are lla", + "á ½", + "Ġbooth s", + "ĠH HS", + "ĠFeld man", + "Ġexcav ation", + "Ġphilosoph ies", + "at ography", + "ĠGar age", + "te chnology", + "Ġunfor gettable", + "Ġver ifying", + "Ġsubord inates", + "E ls", + "Ġne b", + "G aming", + "EN A", + "ĠAchieve ment", + "it ters", + "ĠG abe", + "Ġd umps", + "for cer", + "Ġpo ignant", + "ĠM BA", + "ĠHe idi", + "ime i", + "Ġm ages", + "Ġliber ate", + "Ġcircum cised", + "ĠMer maid", + "ĠMat th", + "t ogether", + "ĠW ichita", + "Ġstore front", + "ĠAd in", + "V II", + "Four th", + "Ġexplore rs", + "W ER", + "Not able", + "Bro ok", + "m ens", + "F aith", + "-------- -", + "ĠJ ou", + "¬ ¼", + "Ġpine apple", + "Ġam alg", + "el n", + "ark able", + "ĠãĤµ ãĥ¼ãĥĨãĤ£", + "ĠãĤµãĥ¼ãĥĨãĤ£ ãĥ¯ãĥ³", + "Ġov arian", + "ĠE choes", + "Ġhairc ut", + "Ġp av", + "Ġch illed", + "anas ia", + "Ġsty led", + "Ġd ab", + "ni per", + "Ġminister ial", + "ĠD UP", + "T an", + "Ġsul ph", + "ĠD eter", + "ĠBo hem", + "od an", + "Ġeduc ator", + "â ĵĺ", + "sp ir", + "Ch icken", + "ĠE leanor", + "Ġqu i", + "Ġheav iest", + "Ġgrasp ed", + "U RA", + "Ġcro oked", + "Jess ica", + "pro blem", + "Ġpred etermined", + "Ġman iac", + "Ġbreath s", + "ĠLauder dale", + "Ġh obbies", + "y z", + "Cr ime", + "Ġcharism a", + "d L", + "Ġle aping", + "Ġk ittens", + "Ang elo", + "ĠJ ACK", + "ĠSu zanne", + "Ġhal ting", + "ENT ION", + "Ġswall owing", + "ĠEarthqu ake", + "Ġeight eenth", + "ĠN IC", + "ĠIN F", + "ĠCons cious", + "Ġparticular s", + "circ le", + "7 40", + "Ġbene volent", + "Ġ7 47", + "Ġ4 90", + "Ġr undown", + "ĠVal erie", + "ĠB UR", + "Ġcivil isation", + "ĠS chn", + "W B", + "ot ide", + "intern ational", + "Ġj ohn", + "Ġ19 02", + "Ġpe anuts", + "Ġflav ored", + "k us", + "Ġro ared", + "Ġcut off", + "é £", + "Ġorn ament", + "Ġarchitect ures", + "Ġ3 69", + "ol or", + "ĠWild e", + "ĠC RC", + "ĠAdjust ed", + "Ġprov oking", + "land ish", + "Ġrational ity", + "Ġjust ifies", + "Ġdisp el", + "Ġa meric", + "ĠPol es", + "Ø ©", + "Ġen vis", + "ĠD oodle", + "ä½ ¿", + "igs aw", + "auld ron", + "Techn ical", + "T een", + "up hem", + "ĠX iang", + "Ġdetract ors", + "ĠZ i", + "ĠJournal ists", + "Ġconduc ive", + "ĠVolunte ers", + "Ġs d", + "Know ing", + "Ġtrans missions", + "ĠPL AN", + "ĠL IB", + "Ġall uded", + "Ġob e", + "Ġd ope", + "ĠGold stein", + "Ġwavelength s", + "ĠDest ination", + "nd a", + "ug i", + "Ġattent ive", + "ĠLe an", + "ral tar", + "Ġman g", + "mb uds", + "ak ings", + "b ender", + "Ġacc ol", + "Ġcraw led", + "N OW", + "Min nesota", + "Ġflour ished", + "ĠZ up", + "ĠSuper visor", + "ĠOliv ier", + "Ex cellent", + "Ġwid en", + "D one", + "Ġw ig", + "Ġmiscon ceptions", + "Cor p", + "W an", + "Ġvener able", + "ĠNot ably", + "ĠKling on", + "an imate", + "Bo ost", + "ĠS AY", + "miss ing", + "ibli ography", + "mel on", + "Ġpay day", + "Ø ³", + "bo le", + "Ġve iled", + "ĠAl phabet", + "It alian", + "Ġever lasting", + "ĠR IS", + "ĠC ree", + "rom pt", + "Ġh ating", + "Ġgrin ning", + "Ġge ographically", + "OS H", + "Ġwe eping", + "ĠÂłĠÂłĠÂłĠÂł ĠÂłĠÂłĠÂłĠÂł", + "Ġimpe cc", + "Let ter", + "Ġblo ated", + "PL A", + "ĠFe in", + "Ġper sever", + "Th under", + "Ġa ur", + "ĠR L", + "Ġpit falls", + "âĸ º", + "Ġpredomin ant", + "Ġ5 25", + "7 18", + "AP E", + "7 14", + "Ġfarm land", + "ĠQ iao", + "Ġv iolet", + "ĠBah amas", + "Ġinflic ting", + "ĠE fficiency", + "Ġhome brew", + "Ġundert ook", + "Ġcur ly", + "ĠHard ing", + "man ia", + "59 6", + "Ġtem pered", + "Ġhar rowing", + "ĠP ledge", + "ĠFranken stein", + "è ª", + "M otion", + "Ġpredict ably", + "ĠExpl osion", + "oc using", + "er d", + "col o", + "FF ER", + "Ġback field", + "ĠV IDE", + "ue bl", + "N arr", + "ĠArg ument", + "Ġgen omic", + "Ġbout ique", + "Ġbatt ed", + "ĠB inary", + "Ġg amb", + "ĠRh ythm", + "67 3", + "Ġa float", + "ĠOlymp ia", + "Y ING", + "Ġend if", + "is in", + "Ġwin ters", + "Ġsc attering", + "I v", + "D istance", + "Ġtr u", + "ĠCom fort", + "Ġne xus", + "Ġair flow", + "ĠByz antine", + "p ayers", + "con i", + "ĠB etsy", + "D eal", + "ĠN ug", + "ĠContin ent", + "red ibly", + "Ġoptim izing", + "al beit", + "Ġec static", + "ĠPro to", + "ç ·", + "iv ot", + "âĸ Ħ", + "em p", + "rou nder", + "Ġcl out", + "ĠI ST", + "66 3", + "ĠDoll ars", + "ĠD AC", + "Ġsubsc ribed", + "Ġrehears al", + "Ġam ps", + "ĠSh ang", + "es m", + "Ġspr inkle", + "Ġassail ant", + "ĠO o", + "ĠCoin base", + "T act", + "Ġret ina", + "Ġn uns", + "R ON", + "att o", + "Ġj ug", + "ĠSV G", + "Ġb ikini", + "ĠFI LE", + "ĠFound ers", + "ep ort", + "ĠK P", + "Ġrest ores", + "ĠTh ick", + "Ġash ore", + "Ġappro vals", + "R ender", + "M AG", + "G raham", + "ĠCort ana", + "ãĥ³ ãĤ¸", + "ss h", + "or ians", + "ars ity", + "ĠInsp ired", + "u pper", + "Ġsign alling", + "Ġreb uke", + "Ġfl ares", + "Ġdownt ime", + "Stud ies", + "Ġstagn ation", + "ĠSequ ence", + "Ġgr unt", + "Ġass ures", + "ĠPL A", + "59 2", + "Ġintra ven", + "d epend", + "Sus an", + "ĠManz iel", + "Man ia", + "Cont ract", + "Ġsl ams", + "Ġcult ured", + "Ġcred itor", + "L IST", + "ĠH UM", + "ĠChatt anooga", + "serv ed", + "Ġclo aked", + "ĠF TP", + "p owder", + "ĠSt ella", + "uct ive", + "Ġcheap ly", + "ĠMU CH", + "ĠGalile o", + "Ġsu ites", + "spe ech", + "Ġdeliber ations", + "ĠCh ips", + "« ĺ", + "Bal ance", + "ĠWyn ne", + "ĠAk ron", + "Ass et", + "Ġhon oured", + "Ġed ged", + "Like wise", + "anim ous", + "ĠW age", + "ĠEz ek", + "ad vertisement", + "ĠRT X", + "ĠM AD", + "Ġmigr ating", + "ĠS QU", + "Ġ4 75", + "Ed ited", + "Ġshorth and", + "ĠBas ics", + "Ġcro tch", + "ĠEV EN", + "Ġv m", + "effic iency", + "Ġcal ves", + "ĠF rie", + "ĠBrill iant", + "Ġstri kers", + "Ġrepent ance", + "Ġarter ies", + "r l", + "B ed", + "h ap", + "Ġcrypt ography", + "ĠSab res", + "Ġ4 14", + "vi ks", + "ih ara", + "aps es", + "T alking", + "Ġintertw ined", + "Ġdoc ks", + "Ġalle le", + "ĠArt ifact", + "ĠH IM", + "t orn", + "ç ķ", + "Ġop acity", + "ĠE ly", + "os uke", + "Ġn ipple", + "Ġhand written", + "ĠV K", + "ĠChamber lain", + "ĠLa os", + "ig raph", + "g row", + "Ġtr illions", + "Ġdescend ant", + "ĠSail or", + "as uring", + "Ġce ilings", + "ĠWare house", + "f lying", + "ĠGl ow", + "Ġn ont", + "Ġmiscar riage", + "Ġrig s", + "Ġmin istries", + "Ġelabor ated", + "Ġdel usional", + "ĠHum ane", + "Ġ3 79", + "n ets", + "Ġblack out", + "add ers", + "Ġn p", + "ĠT ire", + "ro sc", + "Ġsub div", + "Ġlink age", + "Ġchron ological", + "ĠHER O", + "Ġres ettlement", + "ĠVin yl", + "Ġpast oral", + "ĠMob il", + "ĠBar bar", + "Co oldown", + "ĠF ritz", + "c riminal", + "re pe", + "Ġbell ig", + "ĠBre ed", + "Ġ4 18", + "Ġsem blance", + "ij k", + "Ġcur tail", + "Ġclin ch", + "cont ained", + "ĠProm pt", + "ast on", + "Ġw i", + "Ġpursu its", + "5 15", + "ĠGl oss", + "Ġfl ips", + "Ġcoup ons", + "Ġcl oning", + "ĠLike ly", + "Rem oved", + "ĠQu artz", + "r ices", + "ĠSpe ars", + "Ġp ious", + "Ġdep reciation", + "ĠD are", + "oun ces", + "am az", + "O nt", + "Ġp innacle", + "d ocker", + "0 26", + "ĠW yr", + "ĠPro per", + "Ë Ī", + "n il", + "By tes", + "Ġseek er", + "t rial", + "Ġunf olds", + "ĠMar se", + "Ġextravag ant", + "ĠSurviv ors", + "RED ACTED", + "ĠSpeed way", + "ĠCra igslist", + "sub mit", + "ĠGener ations", + "Ġup holding", + "Ġblood stream", + "ĠMiss ions", + "ĠL awn", + "Ġlim bo", + "ene i", + "H uh", + "ĠWild cats", + "pre p", + "ĠMark us", + "ĠFor bidden", + "rit ic", + "IN O", + "Ġexhib iting", + "requ ent", + "ch uk", + "Ġhabit ual", + "ĠComp atibility", + "Dr ag", + "RIP T", + "uj ah", + "GR OUND", + "Ġdelinqu ent", + "Ġburn er", + "Ġcontempor aries", + "Ġgimm ick", + "load s", + "Ġno zzle", + "p odcast", + "ĠW ak", + "ĠStat en", + "ĠK uh", + "ãģ ĵ", + "inter rupted", + "Ġinv incible", + "ĠBurn ett", + "cig arette", + "ĠPeb ble", + "ĠTem porary", + "ĠMar ino", + "58 2", + "Ġwast eland", + "ident ly", + "T x", + "Ġr ite", + "ĠPan asonic", + "ĠM iddles", + "ĠHort on", + "ae us", + "Ġc uring", + "Ġm ats", + "Ġadj ourn", + "Ġfears ome", + "pe z", + "bo ats", + "Ġpro pell", + "Ġconflic ted", + "ĠAng er", + "Ġinsurg ent", + "K arl", + "Ġco ales", + "Ġsouth western", + "Ġdis su", + "ĠO vert", + "******** ****", + "Ġbox ed", + "ĠBr une", + "aa a", + "Ġgard ening", + "ĠEng el", + "tr acks", + "Ġpur ified", + "Ġplace holder", + "ĠL ikes", + "Ġd an", + "G ab", + "Ġe ct", + "ĠF aw", + "ĠEl iot", + "Ġ' ,", + "otrop ic", + "ĠRu in", + "hed on", + "Ġca ul", + "Ġa ft", + "ĠCad illac", + "gh a", + "ass ian", + "ud eb", + "ĠT ick", + "Ġadjust s", + "AR GET", + "5 37", + "isc he", + "ant y", + "ĠFried rich", + "ĠBl izz", + "ĠA OL", + "Camp aign", + "Ġmamm al", + "ĠVe il", + "ĠK ev", + "ĠMaur it", + "ĠDam ien", + "N ation", + "E astern", + "Ġ{ :", + "Ġ= ================================", + "Ġstereotyp ical", + "Ġatt ic", + "ĠCy borg", + "requ ire", + "Ġaward ing", + "ĠPap ua", + "bt n", + "b ent", + "B oo", + "Ġ( =", + "ĠX ander", + "ĠSomers et", + "Ġcatch y", + "Ġcert ify", + "STR UCT", + "Ġit al", + "Ġt ides", + "ĠBr ands", + "G ray", + "comp etitive", + "Ġcur ator", + "ĠD G", + "omin ium", + "ĠGM Os", + "ci ating", + "ĠCarm en", + "ow ard", + "Balt imore", + "Ġr gb", + "C u", + "Ġwip es", + "spe ll", + "IT NESS", + "Ġsummar izes", + "ĠRe vis", + "Ġwhistlebl owers", + "ĠBre ach", + "Ġcro chet", + "k os", + "ews ki", + "Ġrep et", + "Ġcrim son", + "ĠKar achi", + "read able", + "dim ension", + "ĠI gor", + "ild ed", + "ĠZ ed", + "ĠKe ane", + "ĠCos metic", + "DE P", + "Ġretreat ing", + "ĠU A", + "ens ical", + "Ġd usk", + "ĠDick ens", + "Ġaren as", + "ĠPass age", + "level s", + "Ġcur v", + "P ope", + "Ġch ores", + "ĠEl ise", + "ĠComp ass", + "b ub", + "Ġmamm alian", + "ĠSans krit", + "ĠAN C", + "ĠCr ack", + "Q ual", + "L aun", + "amp unk", + "Ġlearn ers", + "Ġglam orous", + "Ġfur the", + "erm ott", + "c and", + "Gener ic", + "Ġnarr ated", + "Ġdisorder ly", + "ĠTrans actions", + "ĠDet ention", + "ĠR oku", + "Ä į", + "Ġunder statement", + "ĠS aur", + "ĠRodrig o", + "ĠAS AP", + "S in", + "Ġre joice", + "Method s", + "Ġelectro de", + "Ġworsh ipped", + "Ġid i", + "ĠPhys icians", + "Ġpop up", + "Ġde ft", + "ĠRem oval", + "ĠBu enos", + "ver bs", + "Ġfun k", + "ush a", + "rict ion", + "ore a", + "ĠBang alore", + "ĠKen obi", + "zz i", + "Ġnorm ative", + "Ġgobl ins", + "Ġcaf es", + "ĠUN CLASSIFIED", + "ĠF ired", + "S IGN", + "Ġs clerosis", + "ĠV oter", + "ĠSon ny", + "ĠExt end", + "ĠEV s", + "Ar senal", + "Ġp si", + "Ġwid est", + "ĠT us", + "Ġlo oms", + "Ġjust ifying", + "ĠGr anger", + "è ¯", + "Ref er", + "58 3", + "Ġflour ishing", + "ab re", + "Ġr ave", + "ĠCont ra", + "Ġ18 98", + "Add s", + "Ġf ul", + "ĠCo oke", + "some one", + "= #", + "67 1", + "Ġy ak", + "Ġar te", + "ĠMis cellaneous", + "ĠDet ection", + "ĠCl ancy", + "â ģ", + "ass ies", + "Ġval iant", + "ĠFemin ist", + "cor ruption", + "V el", + "P ear", + "Ġsucc inct", + "Ġquick est", + "k w", + "Ġsp itting", + "ĠL ibraries", + "åħ ī", + "ant z", + "D ad", + "ĠSpec ifications", + "rup ulous", + "and r", + "RES ULTS", + "Ġsnow ball", + "Ġpred is", + "ĠB axter", + "ĠNurs ing", + "ĠCh aff", + "s we", + "Ġout age", + "Ġnest ing", + "Ġnotor iety", + "tr igger", + "on ite", + "j on", + "Ġf ou", + "ook ed", + "ĠCelebr ity", + "re ality", + "Ġfat ig", + "Ġhug ging", + "Ġbother s", + "ĠPan zer", + "ĠCh andra", + "fig ured", + "Ġvol ts", + "ĠCloud s", + "Ġfee ble", + "ĠCur ve", + "ĠAs us", + "78 6", + "abs or", + "ĠV ICE", + "ĠH ess", + "Ġmanufact ures", + "Ġgri zz", + "ĠPower ful", + "ac id", + "Ġsub sections", + "ĠKrug man", + "ĠAl ps", + "is u", + "Ġsequ est", + "ĠUlt ron", + "ĠT inker", + "ĠGo ose", + "Ġmism atch", + "Att orney", + "Ġmorph ology", + "ĠSix ers", + "ut tered", + "ĠE LECT", + "gr an", + "Rus sell", + "ĠG SL", + "Ġfort night", + "Ġ. )", + "Ġapost le", + "pr one", + "el ist", + "Unt itled", + "ĠIm plementation", + "ist ors", + "Ġtank er", + "Ġpl ush", + "Ġattend ants", + "ĠT ik", + "ĠGreen wich", + "ĠY on", + "ĠSP L", + "cell s", + "unt led", + "S olution", + "ĠQu é", + "Ġvac ated", + "Ġupt ick", + "ĠMer idian", + "æ ĥ", + "ĠDr ill", + "9 25", + "58 4", + "Ġrenov ated", + "ĠKub rick", + "zy k", + "Ġl ousy", + "pp el", + "ohyd rate", + "ĠI zzy", + "lesi astical", + "CC C", + "ĠAj ax", + "Ġad apters", + "ĠPetra eus", + "Ġaffirm ation", + "ĠST OR", + "le ms", + "ad oes", + "ĠConstantin ople", + "Ġp onies", + "Ġl ighthouse", + "Ġadherent s", + "ĠBre es", + "omorph ic", + "Fight ing", + "Ġpl aster", + "ĠP VC", + "ĠOb st", + "Ġdear ly", + "ĠTo oth", + "icks on", + "Ġsh aming", + "P lex", + "A gg", + "ĠâĢ¦ \"", + "Ġsub reddits", + "Ġpige on", + "ĠResident ial", + "ĠPass ing", + "Ġl um", + "ĠP ension", + "Ġpessim istic", + "Ġ4 32", + "z inski", + "c ade", + "0 75", + "Ġapolog ised", + "iy ah", + "Put ting", + "Ġgloom y", + "ĠLy me", + "=-=-=-=- =-=-=-=-", + "ĠT ome", + "ĠPsych iatric", + "ĠH IT", + "c ms", + "ap olog", + "Ġbreak er", + "Ġdeep en", + "Ġtheor ist", + "ĠHigh lands", + "Ġb aker", + "Ġst aples", + "Ġinterf ered", + "ĠAb ortion", + "jo ined", + "ch u", + "Ġform ulate", + "Ġvacc inations", + "Ġban ter", + "phe us", + "Ġoutfield er", + "ĠM eter", + "Ġ# ####", + "Ġ18 95", + "Ġnarrow ing", + "ĠST ORY", + "f p", + "ĠC ST", + "ign ore", + "Ġproclaim ing", + "ĠR U", + "ĠB ALL", + "yn a", + "65 3", + "Ġpos it", + "P RE", + "59 4", + "ĠRegist rar", + "ĠPil grim", + "ic io", + "Ġpre tt", + "Ġlif eless", + "Ġ__ _", + "Ne igh", + "ĠCh urches", + "orn o", + "Ġor cs", + "Ġkind red", + "ĠAud it", + "Ġmillenn ial", + "ĠPers ia", + "g ravity", + "ĠDis ability", + "ĠD ARK", + "W s", + "od on", + "Ġgrand daughter", + "ĠBro oke", + "ĠA DA", + "ER A", + "Ġpick ups", + "ĠWil kinson", + "ĠSh ards", + "ĠN K", + "Ġexp el", + "ĠKis lyak", + "Ġj argon", + "Ġpolar ized", + "ian e", + "Pub lisher", + "Ġreb utt", + "Ġapprehens ion", + "ĠK essler", + "Ġpr ism", + "F UL", + "19 64", + "ĠL oll", + "ä ¿", + "le thal", + "Å Ł", + "Ġg hetto", + "Ġb oulder", + "ĠSlow ly", + "ĠOsc ars", + "ĠInst ruction", + "ĠUl tr", + "ĠM oe", + "N ich", + "ĠP ATH", + "( *", + "ĠRE LEASE", + "un ing", + "rou se", + "en eg", + "Ġre imb", + "ĠDet ected", + "Do S", + "Ġster ling", + "Ġaggreg ation", + "ĠLone ly", + "ĠAtt end", + "hig her", + "Ġairst rike", + "ks on", + "SE LECT", + "Ġdef lation", + "ĠHer rera", + "C ole", + "rit ch", + "Ġadvis able", + "F ax", + "Ġwork around", + "Ġp id", + "mort em", + "ers en", + "Ġtyp o", + "Ġal um", + "78 2", + "ĠJam al", + "script s", + "Ġcapt ives", + "ĠPres ence", + "ĠLie berman", + "angel o", + "Ġalcohol ism", + "ass i", + "Ġrec ite", + "Ġgap ing", + "Ġbask ets", + "ĠG ou", + "Brow ser", + "ne au", + "Ġcorrect ive", + "und a", + "sc oring", + "ĠX D", + "Ġfil ament", + "Ġdeep ening", + "ĠStain less", + "Int eger", + "Ġbu ggy", + "Ġten ancy", + "ĠMub arak", + "Ġt uple", + "ĠD roid", + "ĠS itting", + "Ġforfe it", + "ĠRasm ussen", + "ixt ies", + "es i", + "ĠKim mel", + "Ġmetic ulously", + "Ġap opt", + "ĠS eller", + "08 8", + "ec ake", + "hem atically", + "T N", + "Ġmind less", + "Ġdig s", + "ĠAcc ord", + "ons ense", + "em ing", + "br ace", + "Ġe Book", + "ĠDist ribut", + "ĠInvest ments", + "w t", + "] ),", + "beh avior", + "56 3", + "Ġbl inding", + "ĠPro testers", + "top ia", + "Ġreb orn", + "ĠKel vin", + "ĠDo ver", + "ĠD airy", + "ĠOut s", + "Ġ[ /", + "Ï Ģ", + "b p", + "ĠVan ity", + "ĠRec ap", + "ĠHOU SE", + "ĠF ACE", + "Ġ4 22", + "69 2", + "ĠAnt ioch", + "cook ed", + "Ġcoll ide", + "Ġa pr", + "Ġsle eper", + "ĠJar vis", + "Ġalternative ly", + "ĠLe aves", + "ĠM aw", + "Ġantiqu ity", + "ĠAdin ida", + "Ġab user", + "Poké mon", + "Ġass orted", + "ĠRev ision", + "ĠP iano", + "ĠG ideon", + "O cean", + "Ġsal on", + "Ġbust ling", + "ogn itive", + "ĠRah man", + "Ġwa iter", + "Ġpres ets", + "ĠO sh", + "ĠG HC", + "oper ator", + "Ġrept iles", + "Ġ4 13", + "ĠG arr", + "ĠCh ak", + "Ġhas hes", + "Ġfail ings", + "Ġfolk lore", + "Ġab l", + "ĠC ena", + "ĠMac Arthur", + "ĠCOUR T", + "Ġperipher y", + "app ers", + "Ġreck oned", + "ĠInf lu", + "ĠC ET", + "Ġ3 72", + "ĠDefin itive", + "ass ault", + "4 21", + "Ġreservoir s", + "Ġd ives", + "ĠCo il", + "DA Q", + "Ġvivid ly", + "ĠR J", + "ĠBel lev", + "Ġec lectic", + "ĠShow down", + "ĠK M", + "ip ed", + "reet ings", + "ĠAs uka", + "L iberal", + "ĠÏ Ħ", + "Ġbystand ers", + "ĠGood win", + "uk ong", + "S it", + "ĠT rem", + "Ġcrim inally", + "ĠCirc us", + "ch rome", + "88 7", + "Ġnan op", + "ĠOb i", + "ĠL OW", + "o gh", + "ĠAuth ors", + "ob yl", + "Ur ban", + "Ġt i", + "ĠWe ir", + "t rap", + "ag y", + "Ġparent heses", + "Ġout numbered", + "Ġcounter productive", + "ĠTob ias", + "ub is", + "P arser", + "ST AR", + "Ġsyn aptic", + "ĠG ears", + "Ġh iber", + "Ġdebunk ed", + "Ġex alted", + "aw atts", + "H OU", + "Ch urch", + "ĠPix ie", + "ĠU ri", + "ĠForm ation", + "ĠPred iction", + "C EO", + "Ġthro tt", + "ĠBrit ann", + "ĠMad agascar", + "ë ĭ", + "Ġbill boards", + "ĠRPG s", + "ĠBe es", + "complete ly", + "F IL", + "Ġdoes nt", + "ĠGreen berg", + "re ys", + "Ġsl ing", + "Ġempt ied", + "ĠPix ar", + "ĠDh arma", + "l uck", + "ingu ished", + "Ġend ot", + "Ġbab ys", + "05 9", + "che st", + "r ats", + "Ġr idden", + "Ġbeet les", + "Ġillum inating", + "Ġfict itious", + "ĠProv incial", + "Ġ7 68", + "Ġshe pherd", + "ĠR ender", + "Ġ18 96", + "C rew", + "Ġmold ed", + "ĠXia omi", + "ĠSp iral", + "Ġdel im", + "Ġorgan ising", + "Ġho ops", + "ĠBe i", + "z hen", + "Ġfuck in", + "Ġdec ad", + "Ġun biased", + "am my", + "sw ing", + "Ġsmugg led", + "Ġk ios", + "ĠP ERSON", + "ĠInquis itor", + "Ġsnow y", + "Ġscrap ing", + "ĠBurg ess", + "P tr", + "ag ame", + "R W", + "Ġdro id", + "ĠL ys", + "ĠCass andra", + "Jac ob", + "Ġ35 4", + "Ġpast ure", + "Ġfr anc", + "ĠScot ch", + "ĠEnd s", + "ĠI GF", + "def inition", + "Ġhyster ical", + "ĠBrown e", + "77 1", + "Ġmobil ization", + "æ ķ", + "iqu eness", + "Th or", + "Ġspear headed", + "Ġembro iled", + "Ġconject ure", + "jud icial", + "Ch oice", + "Ġpaper back", + "P ir", + "Ġrec overs", + "ĠSur ge", + "ĠSh ogun", + "ĠPed iatrics", + "ãģ ł", + "Ġsweep s", + "ĠLabor atories", + "ĠP acks", + "al us", + "add in", + "Ġhead lights", + "g ra", + "Ev idence", + "COL OR", + "Ad min", + "Ĭ ±", + "Ġconco ct", + "s ufficient", + "Ġun marked", + "Ġrich ness", + "Ġdiss ertation", + "Ġseason ing", + "Ġg ib", + "ĠM ages", + "un ctions", + "ĠN id", + "che at", + "ĠTM Z", + "c itizens", + "ĠCatholic ism", + "n b", + "Ġdisemb ark", + "ĠPROG RAM", + "a ques", + "Ty ler", + "Or g", + "ĠSl ay", + "ĠN ero", + "ĠTown send", + "IN TON", + "te le", + "Ġmes mer", + "9 01", + "Ġfire ball", + "ev idence", + "aff iliated", + "ĠFrench man", + "ĠAugust a", + "0 21", + "Ġs led", + "Ġre used", + "ĠImmun ity", + "Ġwrest le", + "assemb led", + "Mar ia", + "Ġgun shots", + "ĠBarb ie", + "Ġcannabin oids", + "ĠTo ast", + "ĠK inder", + "IR D", + "Ġre juven", + "Ġg ore", + "Ġrupt ure", + "Ġbre aching", + "ĠCart oon", + "Ġ4 55", + "ĠPale o", + "6 14", + "Ġspe ars", + "ĠAm es", + "ab us", + "Mad ison", + "GR OUP", + "Ġab orted", + "y ah", + "Ġfel on", + "Ġcaus ation", + "Ġprep aid", + "Ġp itted", + "op lan", + "ĠShel ley", + "ĠRus so", + "ĠP agan", + "Ġwill fully", + "ĠCan aver", + "und rum", + "ĠSal ary", + "ĠAr paio", + "read er", + "ĠR ational", + "ĠOver se", + "ĠCa uses", + "Ġ* .", + "Ġw ob", + "Ke ith", + "ĠCons ent", + "man ac", + "77 3", + "6 23", + "Ġfate ful", + "et imes", + "Ġspir ited", + "ĠD ys", + "Ġhe gemony", + "Ġboy cot", + "ĠEn rique", + "em outh", + "Ġtim elines", + "ĠSah ara", + "ĠRel ax", + "ĠQuin cy", + "ĠLess ons", + "ĠE QU", + "SE A", + "N K", + "ĠCost co", + "Incre ase", + "Ġmotiv ating", + "ĠCh ong", + "am aru", + "ĠDiv ide", + "Ġped igree", + "ĠTasman ia", + "ĠPrel ude", + "L as", + "9 40", + "57 4", + "Ġch au", + "ĠSp iegel", + "un ic", + "-- >", + "ĠPhil ips", + "ĠKaf ka", + "Ġuphe aval", + "Ġsent imental", + "Ġsa x", + "ĠAk ira", + "ser ial", + "Mat rix", + "Ġelect ing", + "Ġcomment er", + "ĠNeb ula", + "ple ts", + "ĠNad u", + "ĠAd ren", + "Ġen shr", + "ĠR AND", + "fin ancial", + "ĠCly de", + "uther ford", + "Ġsign age", + "Ġde line", + "Ġphosph ate", + "rovers ial", + "f ascist", + "ĠV all", + "ĠBeth lehem", + "Ġfor s", + "Ġeng lish", + "S olid", + "N ature", + "Ġv a", + "ĠGu ests", + "Ġtant al", + "Ġauto immune", + ";;;;;;;; ;;;;", + "ĠTot ally", + "ĠO v", + "Ġdef ences", + "ĠCoc onut", + "Ġtranqu il", + "Ġpl oy", + "Ġflav ours", + "ĠFl ask", + "ãĤ¨ ãĥ«", + "ĠWest on", + "ĠVol vo", + "8 70", + "Ġmicro phones", + "ver bal", + "R PG", + "Ġi ii", + "; }", + "0 28", + "Ġhead lined", + "Ġprim ed", + "Ġho ard", + "ĠSh ad", + "ĠEN TER", + "Ġtri angular", + "Ġcap it", + "l ik", + "ĠAn cients", + "Ġl ash", + "Ġconv ol", + "Ġcolon el", + "en emy", + "G ra", + "Ġpub s", + "ut ters", + "Ġassign s", + "ĠPen et", + "ĠMon strous", + "ĠBow en", + "il ver", + "H aunted", + "ĠD ing", + "start ed", + "pl in", + "Ġcontamin ants", + "ĠDO E", + "ff en", + "ĠTechn ician", + "R y", + "Ġrob bers", + "Ġhot line", + "ĠGuard iola", + "ĠKau fman", + "row er", + "ĠDres den", + "ĠAl pine", + "E lf", + "Ġf mt", + "ĠS ard", + "urs es", + "g pu", + "Un ix", + "Ġunequiv ocally", + "ĠCitizens hip", + "qu ad", + "m ire", + "ĠS weeney", + "B attery", + "6 15", + "Ġpanc akes", + "Ġo ats", + "M aps", + "ĠCont rast", + "mbuds man", + "ĠE PS", + "Ġsub committee", + "Ġsour cing", + "Ġs izing", + "ĠBuff er", + "ĠMand atory", + "Ġmoder ates", + "ĠPattern s", + "ĠCh ocobo", + "ĠZ an", + "ĠSTAT ES", + "ĠJud ging", + "ĠIn her", + "* :", + "Ġb il", + "ĠY en", + "Ġexh ilar", + "oll ower", + "z ers", + "Ġsn ug", + "max imum", + "Ġdesp icable", + "ĠP ACK", + "ĠAn nex", + "Ġsarcast ic", + "Ġlate x", + "Ġt amp", + "ĠS ao", + "b ah", + "ĠRe verend", + "ĠChin atown", + "ĠA UT", + "d ocumented", + "ĠGA BA", + "ĠCan aan", + "ĠÙ ħ", + "Ġgovern s", + "pre v", + "E sc", + "ĠEst imates", + "OS P", + "Ġendeav our", + "ĠCl osing", + "omet ime", + "every one", + "Ġwor sen", + "Ġsc anners", + "Ġdev iations", + "ĠRobot ics", + "ĠCom pton", + "Ġsorce rer", + "Ġend ogenous", + "Ġem ulation", + "ĠPier cing", + "ĠA ph", + "ĠS ocket", + "Ġb ould", + "ĠO U", + "ĠBorder lands", + "Ġ18 63", + "G ordon", + "ĠW TO", + "Ġrestrict s", + "Ġmosa ic", + "Ġmel odies", + "ç Ħ", + "T ar", + "Ġdis son", + "ĠProv ides", + "Ġ ......", + "b ek", + "F IX", + "Ġbro om", + "ans hip", + "Do ctors", + "Ġner ds", + "ĠReg ions", + "na issance", + "Ġmet e", + "Ġcre pt", + "pl ings", + "Ġgirlfriend s", + "kn it", + "ig ent", + "ow e", + "Ġus hered", + "ĠB az", + "M obil", + "4 34", + "ĠPres ents", + "orig in", + "Ġins omnia", + "ĠA ux", + "4 39", + "ĠCh ili", + "irs ch", + "G AME", + "Ġgest ation", + "alg ia", + "rom ising", + "$ ,", + "c row", + "ĠIn spection", + "at omic", + "Rel ations", + "J OHN", + "rom an", + "ĠClock work", + "ĠBak r", + "m one", + "M ET", + "Ġthirst y", + "Ġb c", + "Ġfacult ies", + "R um", + "Ġnu ance", + "ĠD arius", + "ple ting", + "fter s", + "etch up", + "Reg istration", + "ĠK E", + "R ah", + "Ġpref erential", + "ĠL ash", + "ĠH H", + "Val id", + "ĠN AV", + "Ġstar ve", + "ĠG ong", + "z ynski", + "ĠAct ress", + "Ġw ik", + "Ġun accompanied", + "lv l", + "Br ide", + "AD S", + "ĠCommand o", + "ĠVaugh n", + "Wal let", + "Ġho pping", + "ĠV ie", + "Ġcave ats", + "Ġal as", + "if led", + "ab use", + "66 1", + "Ġib n", + "Ġg ul", + "Ġrob bing", + "t il", + "IL A", + "Ġmit igating", + "Ġapt ly", + "Ġty rant", + "Ġmid day", + "ĠGil more", + "ĠDe cker", + "Ġ§ §", + "part ial", + "Ex actly", + "Ġphen otype", + "Ġ[+ ]", + "ĠP lex", + "ĠI ps", + "vers ions", + "Ġe book", + "Ġch ic", + "g ross", + "\":\" \"},{\"", + "ĠSur prisingly", + "M organ", + "Ġresid ues", + "ĠConf ederation", + "in feld", + "Ġl yr", + "mod erate", + "Ġperpend icular", + "V K", + "Ġsynchron ized", + "Ġrefres hed", + "Ġad ore", + "ĠTor ment", + "ol ina", + "Ġ26 00", + "Item Tracker", + "Ġp ies", + "ĠF AT", + "ĠR HP", + "0 48", + "ĠRES P", + "ĠB J", + "all ows", + "P and", + "Ġunw elcome", + "ĠV oc", + "ĠBast ard", + "ĠO W", + "ĠL AR", + "ĠHeal er", + "Environment al", + "ĠKen yan", + "ĠTr ance", + "ĠP ats", + "Ġali ases", + "ĠGar field", + "Ġcampaign er", + "Ġadvance ments", + "ĠOkin awa", + "ĠC oh", + "ows ky", + "Ġstar ved", + "Ġsize able", + "Ġ: -)", + "Ġm RNA", + "Ġsusp ensions", + "ist ar", + "Scot land", + "Pr in", + "-------------------------------- ----------------", + "Ġ50 2", + "Ġteasp oons", + "Ġ10 50", + "Ġcoerc ive", + "ĠMason ic", + "edd ed", + "ĠPass enger", + "Ġl att", + "Ġbr aces", + "ĠSt eal", + "ĠNY T", + "ĠK ats", + "ĠCel est", + "ae z", + "T u", + "ĠCoul ter", + "ðŁ ĺ", + "Fl ickr", + "ĠWil mington", + "ith s", + "++ ;", + "Ġv ending", + "Ġneg ro", + "ĠPh i", + "ĠYellow stone", + "Call back", + "Ġsh ampoo", + "ĠSh ades", + "w at", + "Ġsuper human", + "Ġridic uled", + "Ġhol iest", + "om bo", + "Ġintern s", + "Ġh one", + "ĠPar agu", + "UR I", + "Ġd angling", + "ãĤ »", + "so v", + "ict ional", + "av ailability", + "Ġrev ocation", + "Ġd ow", + "in ic", + "ĠTHE IR", + "Ġis o", + "Ġout ings", + "ĠLeth al", + "Ġ) ))", + "Ġinacc ur", + "Ġout landish", + "Ġan us", + "let ico", + "id on", + "l ol", + "Ġun regulated", + "Ġsuccumb ed", + "Ġc uff", + "ĠWast eland", + "let al", + "Ġsub str", + "Ġcoff ers", + "Ġautom akers", + "ov i", + "ĠX ue", + "ĠDayton a", + "Ġjar ring", + "Ġf umes", + "Ġdisband ed", + "z ik", + "itt on", + "Ġstriking ly", + "Ġsp ores", + "Ad apter", + ".) :", + "ĠLynd on", + "ival ry", + "Ġor ally", + "Ġtumult uous", + "Ġdisple asure", + "Ġcon es", + "or rect", + "Ġappe ase", + "Ġder by", + "ĠTrip oli", + "ĠAl ess", + "Ġp oked", + "ĠGu ilty", + "v P", + "En ough", + "Ġorig inals", + "6 99", + "Ġrabb i", + "Ġproverb ial", + "Ġpostp one", + "el ope", + "ĠMist y", + "Ġstaff ed", + "ĠUn employment", + "redit ary", + "Ġdilig ent", + "re comm", + "me asures", + "as in", + "8 25", + "Ġpond s", + "Ġmm ol", + "ĠS AR", + "ĠC ARE", + "Ġ3 71", + "Ġclen ched", + "ĠCors air", + "Ġcaric ature", + "z n", + "att ach", + "ĠSch ro", + "spe ak", + "p ainted", + "ĠS uc", + "ĠE NT", + "Ġcell ul", + "ĠP aid", + "di agn", + "WH ERE", + "Ġtext ed", + "B arn", + "Ġret racted", + "ĠRe ferred", + "S av", + "Ġup keep", + "Ġwork places", + "ĠTok ens", + "Ġampl ify", + "cl inical", + "Ġmult ic", + "mber g", + "Ġconvol uted", + "Reg ion", + "5 65", + "ĠTop ic", + "Ġsn ail", + "Ġsal ine", + "Ġins urrection", + "ĠPet r", + "f orts", + "B AT", + "ĠNav ajo", + "Ġrud imentary", + "ĠLak sh", + "OND ON", + "Me asure", + "Ġtransform er", + "ĠGodd ard", + "Ġcoinc ides", + "ir in", + "R ex", + "ĠB ok", + "qu it", + "Ġshotgun s", + "Ġprolet arian", + "Ġsc orp", + "ĠAd a", + "5 14", + "Ġsl ander", + "record ed", + "Ġemb ell", + "ris ome", + "Ġapolog izing", + "ĠMul cair", + "ĠGib raltar", + "Cl a", + "Ġall ot", + "ĠAtt ention", + "Ġ4 33", + "le ave", + "Ġwh ine", + "ĠIss a", + "ĠFa ust", + "ĠBar ron", + "hen y", + "Ġvictim ized", + "J ews", + "Ġnurt uring", + "ett el", + "W inged", + "ĠSub tle", + "Ġflavor ful", + "ĠRep s", + "eng ed", + "call back", + "Ġdirection al", + "Ġcl asp", + "ĠDirect ions", + "plan et", + "icult ure", + "Hel per", + "ic ion", + "ac ia", + "Ġç ¥ŀ", + "Ġsur ges", + "Ġcan oe", + "ĠPrem iership", + "be en", + "Ġdef ied", + "ĠTro oper", + "Ġtrip od", + "Ġgas p", + "ĠE uph", + "ĠAd s", + "vern ight", + "high ly", + "R ole", + "Ġent angled", + "ĠZe it", + "6 18", + "ĠRust y", + "Ġhaven s", + "ĠVaugh an", + "HA EL", + "ĠSER VICE", + "/ ,", + "Ġstr icken", + "Ġdel usions", + "Ġb is", + "ĠH af", + "Ġgrat ification", + "Ġent icing", + "UN CH", + "Ad ams", + "ĠOL ED", + "ĠBeet le", + "Ġ18 99", + "ĠSO FTWARE", + "ateg or", + "V L", + "ĠTot em", + "ĠG ators", + "AT URES", + "Ġimped ance", + "Reg istered", + "ĠC ary", + "ĠAer ial", + "on ne", + "en ium", + "Ġd red", + "ĠBe g", + "Ġconcurrent ly", + "Ġsuper power", + "ĠX an", + "j ew", + "imes ter", + "ĠDick inson", + "âĶ ģ", + "F la", + "Ġp ree", + "ĠRoll ins", + "© ¶æ", + "Ġden omination", + "ĠL ana", + "5 16", + "Ġinc iting", + "sc ribed", + "j uries", + "ĠWond ers", + "app roximately", + "Ġsusp ending", + "Ġmountain ous", + "ĠL augh", + "oid al", + "N s", + "Det ect", + ") =", + "ĠL uthor", + "ĠSchwarz enegger", + "ĠMull er", + "ĠDev i", + "ec ycle", + "J ar", + "6 13", + "ĠL ongh", + "B ah", + "ĠSP ORTS", + "n w", + "Ġref inement", + "Ġwater ways", + "Ġd iner", + "Bl ade", + "68 3", + "F ac", + "Ġinitial s", + "Ġro g", + "Ġparan ormal", + "B UT", + "Ġ[ (", + "ĠSw anson", + "ĠM esh", + "âĸ ¬", + "Impro ve", + "ĠRad iation", + "ĠEst her", + "ĠE sk", + "ĠA ly", + "ik y", + "Ġir rad", + "ĠBuck ingham", + "Ġref ill", + "Ġ. _", + "Re pe", + "CON CLUS", + "Ġdifferent iated", + "Ġchi rop", + "ĠAt kins", + "Pat tern", + "Ġexc ise", + "Ġcab al", + "N SA", + "ĠST A", + "ĠS IL", + "ĠPar aly", + "Ġr ye", + "ĠHow ell", + "ĠCount down", + "ness es", + "alys ed", + "Ġres ize", + "ãĤ ½", + "Ġbudget ary", + "ĠStr as", + "w ang", + "Ġap iece", + "Ġprecinct s", + "Ġpe ach", + "Ġsky line", + "Ġ35 3", + "pop ular", + "App earances", + "ĠMechan ics", + "ĠDev Online", + "S ullivan", + "Z en", + "Ġp u", + "op olis", + "5 44", + "Ġde form", + "Ġcounter act", + "ĠL ange", + "Ġ4 17", + "Con sole", + "77 4", + "Ġnodd ing", + "Ġpopul ism", + "Ġhe p", + "Ġcoun selling", + "compl iance", + "U FF", + "Ġunden iably", + "Ġrail ing", + "ĠHor owitz", + "ĠSim one", + "ĠBung ie", + "Ġa k", + "ĠTal ks", + "x ff", + "fl ake", + "Cr ash", + "Ġsweat y", + "Ġban quet", + "ĠOFF IC", + "Ġinvent ive", + "Ġastron omer", + "ĠStam ford", + "ĠSc are", + "ĠGRE EN", + "olic ited", + "Ġr usher", + "Ġcent rist", + "ight ing", + "Ġsub class", + "Ġdis av", + "Ġdef und", + "ĠN anto", + "oci ate", + "m ast", + "Ġpac if", + "Ġm end", + "e ers", + "imm igration", + "ESS ION", + "Ġnumber ing", + "Ġlaugh able", + "ĠEnd ed", + "v iation", + "em ark", + "P itt", + "Ġmetic ulous", + "ĠL F", + "Ġcongrat ulated", + "ĠBir ch", + "Ġsway ed", + "Ġsemif inals", + "Ġhum ankind", + "m atter", + "ĠEqu ip", + "opa usal", + "S aid", + "ĠLay out", + "Ġvo icing", + "Ġth ug", + "Ġporn ographic", + "I PS", + "Ġmo aning", + "Ġgriev ance", + "Ġconf essions", + "esc al", + "TEXT URE", + "Aut hent", + "os aurus", + "P urchase", + "Ġreleg ation", + "al ter", + "ĠÂł Âł", + "Ġr iddled", + "Ġo gre", + "ĠLow ell", + "Occ up", + "E at", + "ĠHy der", + "ĠAdvis er", + "Com merce", + "H unt", + "ĠOr th", + "ĠComp etitive", + "ĠCL A", + "CD C", + "Ġsal ads", + "F le", + "Ġindustrial ized", + "` ,", + "ĠO WN", + "Ġbec k", + "ĠPart icularly", + "oub t", + "Ġm M", + "ĠHuss ain", + "ĠChen nai", + "Ġ9 20", + "Ġappoint ing", + "ĠCull en", + ",,,, ,,,,", + "Ġp ores", + "ver ified", + "Ġbi ochemical", + "em ate", + "Ġcoward ly", + "ĠHels inki", + "ĠEthiop ian", + "S OURCE", + "ER C", + "est ro", + "Ġbi otech", + "ĠS our", + "Ġbrew er", + "Bloom berg", + "Ġintens ify", + "Gl ass", + "an co", + "ĠF DR", + "gre SQL", + "ĠF ires", + "©¶æ ¥µ", + "ec o", + "100 1", + "ĠHom eless", + "Ġinstant aneous", + "ĠH aste", + "ig el", + "D iamond", + "Ġp aving", + "Ġland fill", + "Ġd ads", + "h oun", + ": ]", + "Ġinc endiary", + "ĠLiving ston", + "ĠHil bert", + "ĠChe cks", + "st yles", + "in ators", + "ĠCl ive", + "ph rine", + "Ġchimpan zees", + "Ġp all", + "ĠJ M", + "ĠAad haar", + "ð Ŀ", + "Ġachie vable", + "dis abled", + "P ET", + "OOOO OOOO", + "M ot", + "Ġint angible", + "Ġbal let", + "ĠWe bs", + "ĠEst imated", + "Effect s", + "Ġb ailed", + "Josh ua", + "Ġturb ulence", + "Ġoccup ant", + "ĠDay light", + "Ġ36 1", + "me et", + "Ġstat ically", + "Ġon look", + "Ġk i", + "il legal", + "Ġvel vet", + "Ġdehyd ration", + "Ġacqu ies", + "ĠRe z", + "ak ura", + "ĠU pton", + "at ro", + "Ġincomp rehensible", + "Ġback door", + "ĠRh ino", + "7 27", + "Ġmath s", + ") +", + "Ġhe resy", + "Ġd f", + "ĠRoc he", + "ĠL ydia", + "Ġpanc reat", + "re ply", + "arre ll", + "Ġsolicit ation", + "Ġcirc adian", + "BI P", + "Ġfor ay", + "Ġcrypt ic", + "iz u", + "ime o", + "ĠTom ato", + "ĠH oms", + "ex amination", + "Ġqu arry", + "ĠVal iant", + "ĠJer icho", + "ĠIN CLUD", + "Ġ18 40", + "5 19", + "Ġres ists", + "Ġsnap shots", + "ĠSp ur", + "ĠAnt iqu", + "Log in", + "Ġbest selling", + "Ġant ic", + "ĠS utherland", + "ãĤ¢ ãĥ«", + "Ġ~ /", + "ĠP arm", + "è ĥ", + "P ages", + "int ensity", + "Ġimm obil", + "Ġ18 65", + "zz o", + "Ġn ifty", + "Ġf entanyl", + "ĠPres ervation", + "op hen", + "Ġd arts", + "ĠD inosaur", + "po inters", + "ĠR ite", + "s uggest", + "aware ness", + "ĠSher idan", + "Ġst ances", + "Ġsor cery", + "Ġper jury", + "ĠNik ola", + "ie ver", + "Ġf iance", + "ĠJordan ian", + "ĠBall oon", + "Ġn ab", + "Ġk b", + "Ġhuman ities", + "ĠTan aka", + "hill ary", + "Ġconsult ancy", + "ĠZ ub", + "Ġrem ission", + "Ġconf id", + "CH Q", + "ĠF ug", + "Ġimpro vis", + "Y ep", + "/ _", + "Ġunwilling ness", + "Ġport folios", + "05 5", + "ĠInstruct or", + "aim an", + "Ġclaim ants", + "M bps", + "ĠBy e", + "re ceived", + "T weet", + "Ġind emn", + "ri z", + "am ara", + "N at", + "Ġeval uates", + "ĠL ur", + "ep ad", + "FO X", + "ĠTh ro", + "Ġrust y", + "Ġbed rock", + "ĠOp rah", + "J B", + "Ġmanip ulative", + "Ġwill ful", + "Ġrel apse", + "Ġext ant", + "The me", + "S ensor", + "ĠSt ability", + "go vern", + "Ġpo ppy", + "Ġkn ack", + "Ġins ulated", + "ĠT ile", + "ĠExt rem", + "Ġunt old", + "Ġconver ge", + "Ġref uel", + "ig roup", + "Ġdistort ions", + "Ġrav aged", + "Ġmechan ically", + "ĠRe illy", + "ĠN ose", + "ĠIncarn ation", + "ĠBeck y", + "abb ling", + "Ġt aco", + "Ġr ake", + "Ġmelanch oly", + "Ġillust rious", + "ĠDart mouth", + "Gu ide", + "ĠR azer", + "ĠBen z", + "Ult imate", + "ĠSur prise", + "Ġpage ant", + "off er", + "Who ever", + "Ġw iser", + "Ġchem ist", + "ĠHE LL", + "ĠBul k", + "Ġpl utonium", + "ĠCO VER", + "Ö ¼", + "f ailed", + "Ġtire lessly", + "Ġinf ertility", + "ĠTr ident", + "ĠShow time", + "ĠC iv", + "V ice", + "requ ires", + "itt ance", + "Ġun controlled", + "interest ing", + "56 1", + "Ġinnov ate", + "ateg ic", + "L ie", + "ĠS elling", + "U l", + "Ġsav ior", + "ĠT osh", + "Ġsw ast", + "P ASS", + "Ġr ink", + "Ġcard io", + "ĠI ro", + "ud i", + "Ġv antage", + "Ġv ans", + "ĠNi ño", + "+ =", + "Ġpropag ate", + "< ?", + "Ġmethod ological", + "204 39", + "Ġtrig lycer", + "Ġing rained", + "ĠAn notations", + "arr anted", + "6 17", + "ĠS odium", + "ĠA AC", + "techn ical", + "mult ipl", + "Ġ3 73", + "å ĭ", + "Ġdec isively", + "Ġboost ers", + "Ġdessert s", + "ĠGren ade", + "Ġtest ifying", + "ĠSc ully", + "ID s", + "Ġlock down", + "ĠSc her", + "ĠR é", + "ĠWhit man", + "ĠRams ay", + "rem ote", + "Ġh ikers", + "ĠHy undai", + "Ġcons cientious", + "Ġcler ics", + "ĠSiber ian", + "ut i", + "is bury", + "Ġrel ayed", + "Ġqu artz", + "ĠC BI", + "seek ers", + "ull a", + "Ġweld ing", + "ĠSh al", + "ble acher", + "T ai", + "ĠSam son", + "Ġt umble", + "ĠInvest or", + "Ġsub contract", + "ĠShin ra", + "ow icz", + "j andro", + "d ad", + "Ġtermin ating", + "ĠNe ural", + "ä» £", + "Ġleak age", + "ĠMid lands", + "ĠCaucas us", + "í ķ", + "c it", + "ll an", + "iv ably", + "ĠAlb ion", + "Ġ4 57", + "Ġregist rations", + "Ġcomr ade", + "Ġclip board", + "0 47", + "Ġdiscour aging", + "ĠO ops", + "Ad apt", + "Ġem path", + "n v", + "ĠPR OT", + "ĠDon n", + "ĠP ax", + "ĠB ayer", + "t is", + "Squ are", + "Ġfoot prints", + "part icip", + "ĠChile an", + "B rend", + "ind ucing", + "M agn", + "Ġclub house", + "ĠMagn um", + "Ġenc amp", + "ĠEth nic", + "uch a", + "ere y", + "Ġw atered", + "ĠCal ais", + "Ġcomplex ion", + "Ġsect s", + "Ġren ters", + "Ġbr as", + "oÄŁ an", + "Time out", + "Man agement", + "Ġinf ographic", + "P okemon", + "Cl ar", + "Ġloc ality", + "Ġfl ora", + "as el", + "P ont", + "Ġpop ulate", + "ĠO ng", + "Ġsubs istence", + "Ġa uctions", + "ĠMcA uliffe", + "ĠL OOK", + "br inger", + "Ġtit an", + "Ġmanif old", + "ĠâĹ ı", + "Ġcalibr ated", + "Ġcal iphate", + "ĠSH E", + "ĠCommission ers", + "ce ivable", + "j c", + "W inner", + "5 24", + "Ġcond one", + "Other wise", + "Ġp iling", + "Ġem body", + "ĠCrime an", + "ut ics", + "ĠEx hibition", + "Ġ4 26", + "e ering", + "Ġv ying", + "ĠH UGE", + "* =-", + "Ġprin cipled", + "à ¦", + "Ġquir ks", + "ĠEdit ors", + "put ing", + "G ES", + "ĠF TA", + "ठ¾", + "add on", + "ĠH AM", + "ĠFrie za", + "W oman", + ". $", + "Ġc rib", + "ĠHer od", + "Ġtim ers", + "ĠSp aces", + "ĠMac intosh", + "at aka", + "Ġgl ide", + "Ġsmell ing", + "ĠB AL", + "Ġun su", + "Ġcond os", + "Ġbicy cl", + "ĠRev ival", + "55 3", + "Ġjugg ling", + "H ug", + "ĠKardash ian", + "ĠBalk ans", + "mult iple", + "Ġnutrit ious", + "oc ry", + "19 00", + "Ġinteg rates", + "Ġad joining", + "ĠF older", + "roll ment", + "ven ient", + "Ġu ber", + "y i", + "Ġwh iff", + "ĠJu ven", + "ĠB orough", + "net te", + "Ġb ilingual", + "ĠSp arks", + "ph thal", + "man ufact", + "Ġt outing", + "ĠPH I", + "Ke efe", + "Rew ard", + "Ġinf all", + "ĠTem per", + "typ ically", + "ĠNik ol", + "Ġregular s", + "Ġpseud onym", + "Ġexhib itions", + "Ġbl aster", + "Ġ40 9", + "w arming", + "Ġrever ber", + "Ġrecip rocal", + "Ġ6 70", + "ip ient", + "b ett", + "ĠBe gins", + "Ġit ching", + "ĠPh ar", + "Ass uming", + "Ġem itting", + "ĠML G", + "Ġbirth place", + "Ġt aunt", + "ĠL uffy", + "ĠAm it", + "Ġcir cled", + "ĠN ost", + "enn ett", + "Ġde forestation", + "ĠHist orically", + "ĠEvery day", + "Ġovert ake", + "79 2", + "Ġn un", + "ĠLuc ia", + "Ġaccompan ies", + "ĠSe eking", + "ĠTr ash", + "an ism", + "R ogue", + "Ġnorth western", + "ĠSupplement al", + "ĠNY U", + "ĠF RI", + "ĠSat isf", + "x es", + "5 17", + "Ġreass ured", + "Ġspor adic", + "Ġ7 01", + "Ġmed ial", + "Ġcannabin oid", + "Ġbarbar ic", + "Ġep is", + "ĠExplos ive", + "ĠD ough", + "Ġuns olved", + "Support ed", + "Ġacknowled gment", + "sp awn", + "Ġkit chens", + "Ġ- =", + "talk ing", + "ic ist", + "ĠPeg asus", + "ĠPS U", + "Ġphot on", + "ĠAuthent ication", + "R G", + "@# &", + "76 2", + "ĠCl air", + "Ġdi aper", + "Ġbr ist", + "ĠProsecut ors", + "ĠJ em", + "6 28", + "ĠEvery where", + "ĠJean ne", + "equ ality", + "ãĥ© ãĥ³", + "object s", + "ĠPel icans", + "Ġ39 2", + "Ġbl u", + "b ys", + "ĠA go", + "Ġinstruction al", + "Ġdiscrim inating", + "ĠTR AN", + "ĠCorn el", + "ag os", + "Ġty re", + "Ġas piration", + "ĠBrid gewater", + "\": -", + "! \".", + "ĠEn s", + "ĠCoc o", + "P ie", + "Ġdet ach", + "ĠC ouch", + "Ġphys ique", + "ĠOccup ations", + "osc opic", + "en ough", + "B uzz", + "App earance", + "Y P", + "Ġrac er", + "Ġcompl icity", + "r pm", + "T oy", + "Ġinterrupt s", + "ĠCat alyst", + "Ġut ilitarian", + "imp act", + "Ġsp aghetti", + "Ġp orous", + "Ġeste emed", + "Ġinc iner", + "ĠI OC", + "7 48", + "Ġesp resso", + "ĠSm ile", + "abil ia", + "6 35", + "Ġmathematic ian", + "Ġ4 24", + "ĠK L", + "ĠH IP", + "Ġover heard", + "ĠT ud", + "ĠT ec", + "Ġqu izz", + "Ġfl attering", + "Ġcon n", + "âĢ İ", + "Ġatt aches", + "ĠR OS", + "ĠAC S", + "Ġt cp", + "ĠSh ame", + "sk ip", + "res pected", + "ĠTrin idad", + "gr ain", + "Ġfooth old", + "ĠUnch arted", + "ĠJul io", + "z l", + "av ored", + "ĠAn xiety", + "er rors", + "ĠCent auri", + "its ch", + "D addy", + "Ġclutch ing", + "ĠIm plement", + "ĠGut ierrez", + "Ġ7 60", + "Ġtele portation", + "end ra", + "Ġrevers ible", + "st ros", + "Ad venture", + "08 3", + "Ġliber ating", + "Ġas phalt", + "ĠSp end", + "AR DS", + "im sy", + "PR ES", + "ĠEmer ging", + "Ġwild fires", + "Ġtechn ologically", + "Ġem its", + "ĠART ICLE", + "Ġirregular ities", + "Ġcher ish", + "çī Ī", + "Ġst ink", + "ĠR ost", + "Econom ic", + "Ġcough ing", + "ĠMcC ann", + "pro perties", + "ilant ro", + "Ġreneg oti", + "Trans lation", + "Ġin quest", + "ĠGra pe", + "oot ers", + "gu i", + "ĠSwords man", + "ace ae", + "h itting", + "Ġr c", + "Ġexert ed", + "ĠS AP", + "it ent", + "Ġperil ous", + "Ġobsc urity", + "Ġassass inate", + "Ġab original", + "Ġresc uing", + "ĠSh attered", + "lock ing", + "all ion", + "Ch anging", + "ĠHar rington", + "ĠB ord", + "ĠAfgh ans", + "Jam ie", + "aret z", + "ĠAugust us", + "Ġ38 6", + "8 30", + "Ġj og", + "ok ingly", + "Tr igger", + "ĠH OR", + "Stat istics", + "Ġviewers hip", + "Ġadd itives", + "h ur", + "Ġmaxim izing", + "ĠR ove", + "ĠLou ie", + "ĠBuck et", + "ĠCHR IST", + "ou sel", + "Ġstre aks", + "ir ted", + "Ġt ert", + "Ġcolonial ism", + "Ġbur ying", + "y k", + "Cond ition", + "ĠDPR K", + "By Id", + "75 1", + "âĹ ¼", + "Ġwor risome", + "Ġvoc ational", + "sl ice", + "Ġsa ils", + "ĠCorrection al", + "95 4", + "Ġt ul", + "K id", + "l uster", + "Ġfam ilial", + "ĠSp it", + "ĠEp iscopal", + "Specific ally", + "ĠVol cano", + "run s", + "q s", + "Ġve tted", + "Ġcram med", + "t rop", + "here r", + "Thank fully", + "Ġper cussion", + "Ġor anges", + "Ġround up", + "Ġ4 99", + "x ious", + "Char acters", + "ĠZion ism", + "ĠR ao", + "ÃĽ ÃĽ", + "W F", + "Ġunintention al", + "ONE Y", + "Gr ab", + "Com mercial", + "Ġglut amate", + "ĠMcK enna", + "ru ciating", + "ning ton", + "ih u", + "Ch an", + "ĠSw ap", + "Ġleaf lets", + "Ġfunction ally", + "er ous", + "F arm", + "Ġcal oric", + "ĠLiter ally", + "con cert", + "Ġshe nan", + "Ġrep aid", + "ey es", + "Ġbas hing", + "ĠG orge", + "Ġcollabor ations", + "Ġun account", + "itch ie", + "Ġteam work", + "pp elin", + "Ġpip ing", + "Ġmin ced", + "Ġd iam", + "ri eg", + "Ġmasc ara", + "Ġsuck er", + "ĠMo ons", + "App s", + "ĠPe ck", + "Ġper v", + "ĠFl oat", + "o ley", + "ĠN ish", + "im ize", + "Ġarom atic", + "u in", + "end ish", + "! /", + "ĠB icycle", + "ĠAS IC", + "ile ged", + "ĠQuad ro", + "ios yn", + "Ġlock out", + "ĠW ink", + "SP EC", + "Attempt s", + "Ġseed ed", + "red o", + "ias is", + "Ġsn ag", + "ãĥķ ãĤ©", + "ãĤ ¶", + "Ġground ing", + "Ġrelie ver", + "Ġfrivol ous", + "ĠG ifts", + "ĠF aces", + "Es pecially", + "Ġmicrobi ome", + "im ag", + "ĠSch l", + "ĠP les", + "ĠBle ach", + "ĠIr win", + "ĠE aton", + "ĠDisc iple", + "Ġmultipl ication", + "Ġcoer ced", + "Ġ4 19", + "st h", + "E vil", + "B omb", + "Ġex orc", + "Ġstag gered", + "L ESS", + "Ġinert ia", + "ĠED IT", + "Ġgo b", + "Tr aditional", + "Ġclass y", + "Lear y", + "ĠP AGE", + "yr s", + "Ġtrans porter", + "Ġmat ured", + "Ġhij ab", + "Ġbi ome", + "Where as", + "Ġex termination", + "ĠT ues", + "ĠT akeru", + "ĠAud rey", + "er ial", + "ĠAd en", + "aff les", + "Ġnarciss istic", + "ĠB aird", + "UT F", + "I re", + "ĠCon nie", + "Ch amp", + "Ġwhis pering", + "ĠH att", + "D K", + "Ġdis infect", + "Ġdeduct ed", + "Ġpart ake", + "Ġdown grade", + "ĠEs ports", + "ĠContin uing", + "Ġdemocr atically", + "icro bial", + "itt a", + "Ġlim estone", + "Ġexempt ed", + "ĠFren zy", + "H erm", + "7 28", + "Ġfled gling", + "Met a", + "765 61", + "69 3", + "% :", + "w ake", + "5 26", + "ĠDis cipline", + "Ġvirgin ity", + "ĠLeg ions", + "ĠFrank ie", + "int ent", + "Ġrest rooms", + "ĠRou ter", + "da q", + "Ġobjection able", + "âĨ ij", + "w ark", + "ĠRah ul", + "g ain", + "activ ation", + "abs olute", + "ĠAccess ed", + "Ġ24 00", + "ogg les", + "Ġsecond ly", + "ĠDEF ENSE", + "Ġpost age", + "wra pper", + "sh arp", + "7 29", + "Ġcommun icates", + "Ġadd on", + "ĠMil itia", + "H ong", + "Ġsl umped", + "ĠJP EG", + "ĠI car", + "ad ish", + "68 1", + "Ġmaj esty", + "ĠWolf gang", + "ĠEl astic", + "u per", + "Ġv iz", + "Ġunconscious ly", + "ĠST D", + "ĠS ass", + "Ġflower ing", + "ĠHel ic", + "ĠDra per", + "ĠAm ateur", + "Ġman ure", + "Ġdis ingen", + "ĠLe i", + "br ing", + "9 49", + "Ġinhib ited", + "Ġhead quartered", + "Ġen igmatic", + "�� �", + "Ġred ress", + "R H", + "Ġratt led", + "Ġd iction", + "l io", + "ĠT BA", + "ĠSN AP", + "C alling", + "Ġfasc ists", + "ĠD ove", + "iew icz", + "0 36", + "Ġco asts", + "ĠR ect", + "Ġ) ]", + "L ot", + "6 29", + "ĠS EM", + "ĠPeters en", + "ĠExpl ain", + "ĠBo ards", + "ĠBe zos", + "ĠJ ournals", + "Ġ20 24", + "p arser", + "Ġmist rust", + "Ġgr ate", + "ĠL ocked", + "bo a", + "S aint", + "g aming", + "Ġvow el", + "in ately", + "bl ow", + "All ah", + "Ġun matched", + "Ġb ordering", + "ĠExp end", + "n r", + "Or acle", + "rou ch", + "Ġcont iguous", + "ac us", + "Ġdist raught", + "58 1", + "Ġanat omical", + "O X", + "ap ixel", + "8 33", + "ĠPL US", + "Ġres usc", + "Ġab iding", + "57 3", + "Ġvac ancies", + "Em ily", + "Ġhyp othal", + "ĠWer ner", + "ĠWe e", + "ĠDJ s", + "5 13", + "Ġwitch craft", + "Ġac upuncture", + "ent ary", + "benef it", + "Product s", + "ĠP SP", + "ĠMP G", + "ĠJ inn", + "ĠJ arrett", + "Ġ4 45", + "ĠIm aging", + "ĠP yth", + "Fin ish", + "Ġte x", + "Ġjuven iles", + "Ġhero ism", + "Ġdoubt less", + "ĠA ki", + "ĠT end", + "ĠPatri arch", + "Ġbit ters", + "ĠTele communications", + "it atively", + "ag na", + "Ġr g", + "ĠS OLD", + "Ġcomp ulsion", + "ĠN asa", + "ĠKath ryn", + "Ġmillion aires", + "Ġintrins ically", + "Ġbolst ered", + "time out", + "fl o", + "Ġtut or", + "p our", + "Stat ement", + "Ġ{ *", + "ĠRud olph", + "ĠKimber ly", + "rog ens", + "adi q", + "] +", + "Ġindign ation", + "Ġfract uring", + "ĠRe leases", + "ĠGr ain", + "pro tein", + "L ago", + "Ġvac ations", + "Ġboot ed", + "ĠTH REE", + "ĠH G", + "oresc ence", + "Ġt f", + "Ġso ar", + "iosyn cr", + "Ġgl ances", + "ĠSp oon", + "ĠJ ury", + "ĠCow boy", + "Ġcreat ively", + "Hig her", + "Ġsolic itor", + "Ġhaw k", + "ac io", + "89 6", + "Ġsuperf lu", + "Ġbombs hell", + "ct ure", + "Ġbroker age", + "Ġraid ing", + "Ġf rench", + "Ġang led", + "Trans action", + "ĠGen ocide", + "u pe", + "ĠHait ian", + "57 2", + "! :", + "Ġunwitting ly", + "iter ator", + "sc roll", + "Ġtall ied", + "Ġbi omedical", + "ĠC ARD", + "Ġe uphem", + "Ġbrain storm", + "a quin", + "K o", + "Mic helle", + "ĠR unes", + "ĠBall istic", + "ud ers", + "Ġmod esty", + "ĠiP ads", + "ĠEzek iel", + "Y E", + "Ġstars hip", + "Ġpower fully", + "Ġper l", + "ĠSh ade", + "ĠQu art", + "ĠE EG", + "Ġfisher man", + "OS ED", + "ĠTyp ical", + "df x", + "Ġmes hes", + "Ġet ched", + "worth iness", + "Ġtopp led", + "Ġ3 96", + "or ius", + "We iss", + "Ġmy sql", + "ĠVal halla", + "Ù Ĵ", + "le asing", + "Ġrec omp", + "rap nel", + "S el", + "04 3", + "Ġder ailed", + "ĠGu ides", + "IR T", + "Ġde human", + "ĠBritt any", + "\" ))", + "Ġex claim", + "Ġb alk", + "Ġ8 40", + "CLA IM", + "int el", + "L AB", + "Ġpe gged", + "Ġast roph", + "sm oking", + "Ġrig ging", + "Ġfix ation", + "Ġcat apult", + "ins ide", + "ĠC ascade", + "ĠBolshe vik", + "G aza", + "Dep th", + "Ġloud spe", + "Ġalmond s", + "me yer", + "l eness", + "j en", + "f resh", + "Ġunbeat en", + "ĠSqu id", + "ĠPres umably", + "Tim er", + "B W", + "Ġro sters", + "Ġell ipt", + "ĠHar riet", + "dat abase", + "ĠMut ual", + "ĠComm odore", + "uk ed", + "kn ife", + "ĠCOMM UN", + "h ya", + "Ġmel ts", + "arch ives", + "Ġrat ification", + "Ġmultip lying", + "Ġinter oper", + "Ġasc ert", + "w ings", + "ver ting", + "ĠScorp ion", + "ay e", + "ĠPorts mouth", + "ĠM TA", + "n it", + "iaz ep", + "Ġqu arantine", + "Ġslides how", + "Ġcent imeters", + "Ġsyn opsis", + "Ġsp ate", + "th irst", + "Ġnom inating", + "ĠMel vin", + "Pre view", + "Ġthro b", + "Ġgener ational", + "ĠRad ius", + "rest ling", + "put able", + "aw ar", + "N ECT", + "Ġunlaw fully", + "ĠRevel ations", + "Wik ipedia", + "sur v", + "Ġeye ing", + "ij n", + "ĠF W", + "Ġbr unt", + "Ġinter stellar", + "Ġcl itor", + "ĠCroat ian", + "ĠCh ic", + "ev a", + "ĠDis app", + "ĠA kin", + "iner ies", + "d ust", + "Interest ed", + "Ġgen esis", + "ĠE ucl", + "ö n", + "p icking", + "Ġmut ated", + "Ġdisappro ve", + "ĠHD L", + "Ġ6 25", + "Ì ¶", + "c ancer", + "Ġsqu ats", + "Ġle vers", + "Disc uss", + "= ]", + "D ex", + "ĠVIDE OS", + "A UD", + "Ġtrans act", + "ĠKin ect", + "ĠK uala", + "ĠC yp", + "7 47", + "Ġsh attering", + "Ġarsen ic", + "ĠInt ake", + "ĠAngel o", + "ĠQu it", + "ĠK he", + "Ġ18 93", + "M aker", + "0 29", + "ĠPain ting", + "Dis able", + "9 16", + "Ġanal ges", + "Ġtact ile", + "Ġprop hes", + "Ġd iced", + "ĠTravel s", + "ĠHe ader", + "ĠClub s", + "Ass istant", + "Ġinc rim", + "Ġd ips", + "Ġcruc ifix", + "ĠShan ahan", + "ĠInter pret", + "Ġ40 90", + "al ogy", + "abb a", + "Ġsimul ac", + "hus band", + "S IM", + "Ġrecy cle", + "uc er", + "ed ged", + "Ġre naissance", + "ĠBomb ay", + "Cath olic", + "ĠL INE", + "ĠCl othing", + "re ports", + "Ġpl aus", + "Ġd ag", + "ĠM ace", + "Z I", + "Ġintr uder", + "ĠVeter inary", + "g ru", + "Ġsne aky", + "ĠS ie", + "ĠC innamon", + "P OSE", + "Ġcou rier", + "ĠC NS", + "Ġemanc ipation", + "s it", + "Ġplay through", + "ĠFac ilities", + "v irt", + "ĠG auntlet", + "Thom pson", + "Ġunbeliev ably", + "Param eters", + "Ġst itching", + "ign e", + "ĠTH ESE", + "Priv acy", + "Ġshenan igans", + "Ġvit ri", + "ĠVal id", + "59 1", + "Ń ·", + "ĠProt otype", + "ink a", + "SC P", + "ĠT id", + "è Ī", + "old ed", + "Ġindividual ity", + "Ġbark ing", + "Ġm ars", + "ĠW D", + "Ġ8 20", + "Ġt ir", + "Ġsl apping", + "Ġdisgr untled", + "ĠAng ola", + "ri us", + "ĠTorn ado", + "ĠTh urs", + "Ġcapt cha", + "Ġang st", + "ĠP og", + "ĠAssass ins", + "ĠAd idas", + "Ġjoy ful", + "Ġwh ining", + "Emer gency", + "Ġphosph orus", + "Ġatt rition", + "oph on", + "ĠTimber wolves", + "ĠJ ah", + "ĠBr inging", + "ĠW ad", + "ĠEn sure", + "oh l", + "ĠX ie", + "omm el", + "c mp", + "Ġz ipper", + "Ġrel at", + "ĠCor ridor", + "m ilo", + "T ING", + "Av g", + "Ġcro pped", + "] }", + "Ġr aged", + "ĠLump ur", + "ĠGuer rero", + "our ke", + "N ut", + "Ġoff sets", + "og lu", + "dr m", + "Ġmort als", + "lat able", + "Ġdismiss ive", + "ä¸ ī", + "Ġthro ats", + "Ġchips et", + "ĠSpot light", + "Catal og", + "art ist", + "G b", + "Ġch illy", + "Ġst oked", + "Ġ3 74", + "W ard", + "L atin", + "Ġf iasco", + "Ġble ach", + "Ġb rav", + "Enh anced", + "Ġin oc", + "ĠFior ina", + "_ >", + "Ġle ukemia", + "Ġel uc", + "Ġannoun cer", + "ĠLith uan", + "ĠArm ageddon", + "å ĩ", + "Len in", + "ĠR uk", + "Ġpe pp", + "ĠRom antic", + "ĠP IT", + "ĠInter stellar", + "ĠAt kinson", + "R aid", + "J s", + "Go al", + "C ourse", + "Ġvan ishing", + "es ley", + "ĠR ounds", + "Els a", + "59 3", + "Ġredund ancy", + "ĠST AND", + "Ġprop hetic", + "Ġhabit able", + "ry u", + "Ġfaint ly", + "M ODE", + "Ġfl anked", + "IR C", + "Aw esome", + "Ġsp urious", + "ĠZ ah", + "ĠMS G", + "Ġsh ading", + "Ġmotiv ational", + "ĠSant ana", + "ĠS PR", + "Ġexc ruciating", + "om ial", + "ĠM iko", + "ĠLe opard", + "A byss", + "Ġ[ |", + "d irty", + "Ġbath s", + "Ġdem oral", + "and re", + "P B", + "Ġun ification", + "Ġsac rament", + "Ġ[ &", + "Ġpric eless", + "Ġgel atin", + "Ġeman ating", + "ĠAll aah", + "98 6", + "Ġout burst", + "Ġer as", + "ĠX VI", + "ĠSP I", + "O tt", + "ĠLaz arus", + "PL IED", + "F lying", + "blog s", + "W isconsin", + "R aven", + "Ġreb ate", + "Ġcreep s", + "ĠSp an", + "ĠPain ter", + "ĠKir a", + "ĠAm os", + "ĠCor vette", + "Cons umer", + "ĠRec over", + "ck i", + "Ġpes ky", + "ĠIn vention", + "Compan ies", + "Ġchalleng ers", + "ad emic", + "ĠUkrain ians", + "ĠNeuro log", + "ĠFors aken", + "Ġent rants", + "Ġemb attled", + "Ġdef unct", + "ĠGlac ier", + "Ġpo isons", + "ĠH orses", + "m akes", + "ĠD irt", + "Ġ4 23", + "hh h", + "ĠTrans formation", + "QUI RE", + "................ ..", + "Ġtrave ller", + "ĠSe xy", + "ĠK ern", + "ip olar", + "Ġransom ware", + "oooooooo oooooooo", + "E c", + "rub y", + "Prof essional", + "ĠOut break", + "arg ument", + "G rey", + "ĠFif a", + "ĠCH O", + "ĠFOR M", + "ĠAm trak", + "- [", + "Ġcr adle", + "Ġantioxid ants", + "ãģ®å ®", + "7 36", + "ĠNAS L", + "ĠContribut ions", + "Ind iana", + "ĠST EP", + "C SS", + "Ġsal ient", + "Ġall ocations", + "yr ights", + "Ġm ashed", + "ĠCut ter", + "Sex ual", + "Ġp ounded", + "Ġfan base", + "Ġc asc", + "ĠTrans parency", + "Ġanaly tic", + "ĠSummon er", + "× ŀ", + "ĠAD C", + "det ail", + "Ġvan quished", + "Ġcr abs", + "ar ie", + "Dest roy", + "ĠS ack", + "Ġtrans istor", + "Al abama", + "ĠK oen", + "ĠFisher ies", + "c one", + "Ġannex ed", + "ĠM GM", + "es a", + "Ġf aked", + "ĠCong ratulations", + "Ġhind ered", + "Ġcorrection al", + "ĠI TV", + "lee ve", + "Ġin appropriately", + "lic ks", + "Ġtresp ass", + "Ġp aws", + "Ġnegoti ator", + "ĠChrist ensen", + "lim its", + "ĠDian ne", + "Ġeleg ance", + "ĠContract s", + "an ke", + "Ob j", + "Ġvigil ance", + "Ġcast les", + "ĠN AD", + "ĠHol o", + "Ġemph atically", + "ĠTit us", + "ĠServ ing", + "ĠRich ie", + "ĠP igs", + "5 68", + "Ġanim osity", + "ĠAtt ributes", + "ĠU riel", + "M Q", + "my ra", + "ĠApplic ant", + "Ġpsychiat rists", + "ĠV ij", + "ĠAb by", + "ag ree", + "P ush", + "Ġk Wh", + "hib a", + "Ġinc ite", + "ĠWe asley", + "ĠTax i", + "minist ic", + "hy per", + "ĠF arn", + "Ġ6 01", + "ĠNation wide", + "F ake", + "95 2", + "Ġma ize", + "Ġinteract ed", + "Ġtransition ed", + "Ġparas itic", + "Ġharm onic", + "Ġdec aying", + "Ġbas eless", + "ns ics", + "Ġtrans pired", + "Ġabund antly", + "ĠFore nsic", + "Ġtread mill", + "ĠJ av", + "ab and", + "Ġssh d", + "Ġfront man", + "ĠJak arta", + "oll er", + "dro ps", + "ĠSERV ICES", + "rompt u", + "oph ical", + "h ospital", + "bled on", + "6 45", + "Ġmid range", + "ĠEV ENT", + "cul ated", + "raw led", + "Ġper ched", + "Ġover board", + "ĠPe el", + "ĠP wr", + "ĠCar th", + "ĠCOM PLE", + "co e", + "sh all", + "Ġdeter rence", + "M ETHOD", + "ĠAbs ent", + "M EN", + "Ġs ill", + "ĠLE VEL", + "Y ork", + "Ġsin ners", + "ĠOP EC", + "ĠN ur", + "ĠDesign s", + "se lection", + "Ġunw orthy", + "CH A", + "Ġstreng thens", + "88 3", + "ed ly", + "Ġslic ing", + "Ġmal nutrition", + "Ġfilm making", + "ĠPol k", + "ur ated", + "Ġ4 21", + "bre akers", + "!' \"", + "Ġwet lands", + "ĠDisc rimination", + "Ġallow able", + "Ġste ered", + "ĠSic ily", + "S AM", + "Ġmust ache", + "Ġm ids", + "Ġcl ipped", + "Ġcirc ulate", + "Ġbr ittle", + "ĠBuild ings", + "ra ised", + "ĠRound up", + "Ġwealth ier", + "Ġoverw rite", + "Ġover powered", + "ĠGerr ard", + "s ites", + "PD ATED", + "Ġacute ly", + "ĠGam ble", + "Ġp im", + "ĠK us", + "Typ ically", + "De ploy", + "ĠMoroc can", + "p otion", + "com be", + "Ġvigil ante", + "Ġ36 3", + "St ew", + "ĠB agg", + "Ġres ided", + "ĠSp o", + "Ġrem nant", + "Ġempt iness", + "br ainer", + "Ġout patient", + "pri ority", + "Ġle ptin", + "ĠPay ton", + "ĠGle aming", + "ĠS hed", + "ĠPol o", + "ĠMormon ism", + "rest ricted", + "arl ane", + "w x", + "Ġcreat ine", + "ĠAn on", + "ĠST UD", + "ĠJ UL", + "ĠT ee", + "5 28", + "08 9", + "Ġhat ched", + "Dis patch", + "ĠCompos ite", + "Ġ45 1", + "p uff", + "ĠX COM", + "ĠOr n", + "ĠTH ANK", + "END ED", + "ĠAshe ville", + "Ġà ľ", + "Ġman go", + "ĠS lightly", + "world ly", + "ĠW ander", + "ĠExp and", + "ĠCh r", + "M ist", + "Ġorthodox y", + "ĠUN ESCO", + "reg ate", + "Else where", + "k ie", + "ir led", + "Ġtopp le", + "Ġadopt ive", + "ĠLeg s", + "d ress", + "ĠS agan", + "b are", + "ĠGl ou", + "Cr unch", + "Ġhelp ers", + "Ġchron ically", + "ĠH uma", + "1 0000", + "Ġaccommod ating", + "äº Ķ", + "Ġwrink les", + "Ġdod ged", + "four th", + "Ġpre con", + "Ġcompress or", + "ĠK are", + "Ġev ict", + "ĠWar wick", + "im ar", + "Ġmodern ization", + "Ġband wagon", + "Ġref uted", + "Ġnet ted", + "ĠNa ples", + "ĠGen ie", + "per ors", + "Ġfield ed", + "Ġde re", + "ĠPar ables", + "le es", + "Ġtr out", + "asp ers", + "Ġn ihil", + "Ġhapp iest", + "Ġflo ppy", + "ĠLo ft", + "ĠHe ard", + "Ġun ison", + "Ġl ug", + "ĠRed mond", + "class ic", + "Supp orters", + "SH IP", + "G MT", + "Ġfue lled", + "ç IJ", + "Ġd d", + "ĠEmin em", + "Ġ18 97", + "NY SE", + "Ġsecret aries", + "ĠF IA", + "ĠCanaver al", + "F avorite", + "Ġp omp", + "Ġdetain ee", + "ers hip", + "aim on", + "i our", + "ĠA pex", + "Ġplant ations", + "am ia", + "ac ion", + "R ust", + "Ġtow ed", + "ĠTru ly", + "5 77", + "Ġshel tered", + "r ider", + "W o", + "Ġl air", + "ĠInt elligent", + "impro ve", + "m atically", + "Ġet iquette", + "ad ra", + "all o", + "ĠJun o", + "any thing", + "ĠStru ggle", + "ĠPred ict", + "ĠGr imes", + "ĠAMER ICA", + "ct x", + "ĠSit uation", + "W OOD", + "Ġsol uble", + "me ier", + "Ġintoler able", + "ang ering", + "Ġun interrupted", + "Ġtool tip", + "Ġinterrog ated", + "Ġgun ned", + "ĠSne ak", + "æŃ ¦", + "Ġt ether", + "Ġcr umble", + "L ens", + "Ġclust ered", + "ĠSy l", + "ĠHas an", + "Ġdystop ian", + "w ana", + "Ġjoy stick", + "ĠTh ib", + "amm u", + "Tom orrow", + "5 46", + "Ġoverc ame", + "Ġminim ized", + "cept or", + "Run ner", + "ENG TH", + "ĠBrend a", + "ĠAchieve ments", + "Ġtor ches", + "Ġrapp ort", + "ĠInvestig ator", + "ĠHand ling", + "rel ation", + "g rey", + "8 15", + "Ġk cal", + "ĠComm ands", + "d q", + "Ġcur ls", + "Ġbe arer", + "Ġcyn icism", + "it ri", + "ĠUse ful", + "B ee", + "D CS", + "Ġab ras", + "P ract", + "BIL ITIES", + "7 12", + "Ġdebug ger", + "Ġdebt or", + "ĠL ia", + "ĠK ers", + "Ġexacerb ate", + "ĠSt acy", + "ĠB land", + "ĠSc enes", + "Ġbranch ing", + "âĸĪâĸĪâĸĪâĸĪ âĸĪâĸĪâĸĪâĸĪ", + "ape ake", + "Ġs alsa", + "Ġmish and", + "ĠKon ami", + "ĠN ib", + "Ġanecd ote", + "Ġagree able", + "Ï ī", + "ĠNath aniel", + "ĠHe isman", + "ĠB eware", + "Ġ18 86", + "spect ive", + "69 1", + "5 22", + "Ġinhib its", + "Ġhas hing", + "Ġ18 89", + "å° Ĩ", + "v ich", + "P ure", + "Ġsolid ly", + "Ġaspir in", + "im aru", + "Ġstreet car", + "ĠU CS", + "ĠJ udd", + "Ġflash backs", + "p ins", + "Ġ14 40", + "ĠUN HCR", + "ĠSym ptoms", + "T IT", + "5 38", + "F ra", + "% );", + "Ġo oz", + "Ġcur few", + "Ġcal med", + "Ġparticip ates", + "Te X", + "Ġnons ensical", + "Ġfull back", + "ĠDe L", + "mon key", + "h ari", + "Ġmetabol ites", + "Ġloot ed", + "ĠAL WAYS", + "ĠB CC", + "L t", + "oc het", + "B one", + "Ġveto ed", + "Ġg cc", + "ĠCL ICK", + "Ġ18 88", + "s af", + "Ġstiff ness", + "Ġlow ly", + "ĠGe h", + "vers on", + "ors et", + "Ġun foreseen", + "Ġan esthesia", + "ĠOpt ical", + "Ġrecon structed", + "ĠT up", + "sh ows", + "NEW S", + "ĠNewsp aper", + "ĠA SA", + "ter a", + "N umbers", + "Ġinexpl icable", + "× ij", + "Ġhard ness", + "unt arily", + "ĠA cer", + "grad ient", + "ARD IS", + "Ġwood land", + "Ġmetaph ors", + "ĠWem bley", + "ĠPa vel", + "phil is", + "Ġre writing", + "Ġpercept ual", + "Ġ10 70", + "worm s", + "ĠDown s", + "Ġunsur prisingly", + "Ġtag ging", + "fl ame", + "Ġlit res", + "Ġboun ces", + "ĠB abe", + "sh ut", + "Ġoverd oses", + "ĠShe ila", + "ĠCh au", + "ĠBl ess", + "Capt ure", + "ĠSign ificant", + "ĠSc ion", + "Ġ38 9", + "ĠMc H", + "ĠTitan ium", + "ĠMe al", + "amed a", + "ag ents", + "agg ressive", + "B illy", + "76 3", + "ĠS aying", + "DER R", + "it one", + "Coll ins", + "B ound", + "Ġbol ted", + "ĠDM CA", + "95 3", + "Ġun iqueness", + "Ġep igen", + "un ci", + "ant am", + "Ġreck oning", + "ch airs", + "OG R", + "ĠSen egal", + "Ġ18 62", + "re levant", + "Ġ ¯", + "Ġpharm acies", + "ĠG eral", + "v ier", + "Y an", + "OR PG", + "Ġrab id", + "b ending", + "ĠUN ITED", + "Ġ4 65", + "As sembly", + "Ġwe ep", + "Ġbe hest", + "ĠMother s", + "ĠJ ace", + "h id", + "Ġwh irlwind", + "ĠUN IVERS", + "Ġut opian", + "Ġkidn ap", + "Ph ilipp", + "K in", + "89 3", + "Ġlivest ream", + "ĠM ISS", + "Ġsub versive", + "ĠTechn iques", + "ĠJUST ICE", + "ĠB ASE", + "Ġ38 7", + "Ġassail ants", + "ĠHard core", + "Ġsprink led", + "ĠP se", + "é ļ", + "print ed", + "ĠH au", + "OR GE", + "ĠT OUR", + "Ġl aced", + "Ġit ch", + "G iving", + "Ġport ed", + "78 1", + "//////////////// ////////////////", + "bre eding", + "Ġlog ger", + "ĠH OL", + "inn ie", + "First ly", + "Ġembry onic", + "Ġdeleg ated", + "p ai", + "O IL", + "Ġcentr ally", + "ĠR x", + "ĠSc outing", + "D utch", + "Ġhe reditary", + "ĠCru iser", + "s at", + "5 29", + "ĠMar riott", + "other mal", + "Ġprohib itions", + "E arn", + "ĠSt ab", + "ĠColleg es", + "ĠBel ief", + "st retched", + "ĠL H", + "ĠEntity Item", + "C IA", + "Ġun rem", + "Ġlaure ate", + "Ġdenomin ations", + "sum mary", + "h ler", + "S pect", + "ĠK laus", + "ĠBe ans", + "Ġins ur", + "ĠPA X", + "Ġfield er", + "ĠV et", + "ĠSp arrow", + "z ie", + "ĠS Q", + "ĠMond ays", + "ĠOff line", + "ĠLer ner", + "ĠExt ensions", + "Ire land", + "Ġpatron age", + "Ġcontrast ed", + "ĠMan ia", + "h irt", + "Mos cow", + "Ġcondem ns", + "ĠAn ge", + "Ġcomp osing", + "ĠPe pe", + "ĠP addock", + "Ġheter ogeneity", + "Ġide ologically", + "Ġf ishes", + "Ġcur sing", + "ĠR utherford", + "ĠFlo ating", + "ĠAm elia", + "Te a", + "Syn opsis", + "Ġstun ts", + "Ġbe ad", + "Ġstock ing", + "ĠM ILL", + "ob ook", + "mass ive", + "\\ <", + "Ġh ump", + "ĠPref erences", + "Engine Debug", + "ge ist", + "ĠNiet o", + "ome ver", + "ish y", + "eval uate", + "col onial", + "Altern ative", + "ĠGo Pro", + "ĠV ortex", + "ĠNET WORK", + "ans ky", + "Sec ure", + "ĠTh rust", + "Sn ake", + "Ġparcel s", + "Ġsam urai", + "Ġactress es", + "N ap", + "M F", + "ifer ation", + "Be er", + "5 23", + "ĠI ly", + "oint ment", + "P ing", + "Ġstri ped", + "ĠMell on", + "oss ession", + "Ġneut ron", + "end ium", + "Ġa ph", + "ĠFlav oring", + "Ġ38 3", + "Ġrespons iveness", + "ĠJ indal", + "ĠHitch cock", + "Den ver", + "ĠDRAG ON", + "sm anship", + "ĠDu pl", + "Ġs ly", + "Ġweb cam", + "ĠTw ain", + "ĠDar ling", + "ili ate", + "cons umer", + "D IT", + "Ġnames ake", + "Ġun orthodox", + "Ġfun er", + "ĠPL oS", + "ĠCONTR OL", + "ozy g", + "ogl obin", + "F ACE", + "ER G", + "ĠD ia", + "ĠF iesta", + "ce le", + "0 34", + "Ġencl ave", + "âĸ¬ âĸ¬", + "on ement", + "al ist", + "M and", + "Ġhome grown", + "ĠF ancy", + "Ġconcept ions", + "ĠCont ains", + "ure en", + "Ġreiter ate", + "Ġme ager", + "Ġinstall ments", + "Sp awn", + "6 27", + "Ġphot oc", + "ĠCab rera", + "ĠRos enthal", + "ĠLans ing", + "is ner", + "Ġinvest s", + "ĠUFO s", + "EX P", + "Hard ware", + "Ġtr agically", + "Ġconced es", + "ie ft", + "ch am", + "bor gh", + "ĠSch r", + "ĠMel anie", + "ĠH oy", + "Ġvisit ation", + "Ġid iosyncr", + "Ġfract ions", + "Ġfore skin", + "ob os", + "Ġpo aching", + "ĠVI EW", + "Ġstimul ates", + "ĠG ork", + "can on", + "M IC", + "ĠNem esis", + "ĠInd ra", + "ĠDM V", + "Ġ5 29", + "Ġinspect ing", + "Ġgrand ma", + "ĠW hedon", + "ĠSh ant", + "ĠP urg", + "ik an", + "ĠT eg", + "ĠCL R", + "z ac", + "Vict oria", + "ĠVer ify", + "ion ics", + "Ġpart ying", + "ĠM ou", + "col our", + "Ġtestim onies", + "l ations", + "Ġpress uring", + "hi ro", + "ac ers", + "Ġf id", + "ang ler", + "ĠCS I", + "Ġhere after", + "Ġdiss idents", + "report ing", + "iph any", + "che v", + "Ġsol itude", + "Ġl obe", + "Ġind is", + "Ġcred ential", + "re cent", + "ad ult", + "ĠNir vana", + "ĠFranch ise", + "L ayer", + "H yp", + "ĠBerks hire", + "Ġwill s", + "t if", + "Ġtot em", + "ĠJud ah", + "rep air", + "Inst ant", + "5 48", + "Ġemb assies", + "Ġbott leneck", + "Ġb ount", + "Ġtyp ew", + "ĠAl vin", + "j ing", + "im ilar", + "R ush", + "Ġbr im", + "ĠHEL P", + "A im", + "] '", + "Ġpass ively", + "Ġbound ed", + "ĠR ated", + "Ġcriminal ity", + "Ġbiom ark", + "Ġdisp atcher", + "ĠTow ards", + "Ġ+ ++", + "right eous", + "f rog", + "ĠP anc", + "C arter", + "0 32", + "æ© Ł", + "Ġult raviolet", + "ĠLic ensed", + "ĠT ata", + "ĠBl essing", + "ĠG AM", + "Ġchem ically", + "ĠSe af", + "ĠRE LE", + "ĠMerc enary", + "capital ist", + "Ġform ulations", + "Ġann ihilation", + "ĠVer b", + "ĠAr gon", + "Ġun loaded", + "Ġmorp hed", + "Ġconqu ering", + "back er", + "I ELD", + "Ġtheft s", + "Ġfront runner", + "ĠRoy ale", + "ĠFund amental", + "el ight", + "C hip", + "necess ary", + "ay n", + "ĠSl ip", + "Ġ4 48", + "cern ed", + "P ause", + "Ġshock ingly", + "ĠAB V", + "Ġcomp osure", + "7 33", + "ĠMotors port", + "ah ime", + "Mur ray", + "M ach", + "Ġgr ids", + "Ġdeb ian", + "Ġfurther more", + "Ġdexter ity", + "ĠCollect ions", + "os lov", + "il age", + "b j", + "ĠMont eneg", + "Ġstrut Connector", + "Ġmassac res", + "Ġbrief s", + "fet ched", + "uv ian", + "ol ition", + "Fail ure", + "emon ic", + "Ġfl ared", + "Ġclaim ant", + "Ġc ures", + "Ġgive aways", + "ĠSubst ance", + "al ions", + "Ġcr inge", + "ĠK ul", + "Ġarist ocracy", + "ĠUl ster", + "ol ated", + "h ousing", + "ĠM IS", + "Ġgl ared", + "ĠWil helm", + "ne eds", + "lam bda", + "build ers", + "ĠV IS", + "Ġradi ator", + "ĠGhost busters", + "Ġ4 36", + "act ual", + "Ġher ds", + "ç a", + "watch ing", + "Ġcounter ing", + "Ch arge", + "Ġchar red", + "Ġwar heads", + "Ġiod ine", + "ĠM acy", + "04 1", + "Ġdepart ures", + "ĠS ins", + "Ġdy ed", + "ĠConcept s", + "g ado", + "7 13", + "Ġquot ations", + "Ġg ist", + "ĠChrist y", + "Ġant igen", + "ĠHem p", + "ĠD rawn", + "ĠB arg", + "ez vous", + "Ġp aternity", + "Ġar du", + "ĠAnch orage", + "ĠR ik", + "Ġover loaded", + "ĠUs ername", + "ĠTam my", + "ĠN au", + "ĠCell ular", + "Ġw aning", + "Ġrod ent", + "ĠWor cester", + "il ts", + "ĠT ad", + "Ġdwell ings", + "Ġbull ish", + "4 31", + "Ġretali ate", + "Ġmig raine", + "ĠChev ron", + "CH ECK", + "Ġdon key", + "c rim", + "SP A", + "ĠAn alog", + "Ġmarqu ee", + "ĠHa as", + "B ir", + "ĠGD DR", + "ĠDownload s", + "Ġwill power", + "ĠFor th", + "ĠRecord ed", + "Ġimp ossibility", + "ĠLog ged", + "ĠFr anks", + "ĠR att", + "in itions", + "Ġclean ers", + "Ġsore ly", + "Ġflick ering", + "ĠEx amination", + "c atching", + "allow een", + "Ms g", + "Ġdun no", + "F a", + "Ġdys ph", + "c razy", + ".' '.", + "Ġmain line", + "Ġc s", + "Ġp tr", + "ĠW ally", + "ig un", + "95 1", + "ĠBig foot", + "f ights", + "Ġretrie ving", + "J r", + "Ġdupl ication", + "ĠExpl an", + "Ġrel ational", + "Ġqu aint", + "Ġbisc uits", + "Ġad o", + "Ġsh udder", + "Ġantid ote", + "blood ed", + "ks h", + "Ġsa uces", + "Ġrein vest", + "Ġdispens ary", + "ĠD iver", + "Ġ9 000", + "stud ent", + "Ġin separ", + "esc ap", + "Ġtodd lers", + "ĠGP IO", + "ĠAss ignment", + "head ers", + "Ġlack luster", + "Ġab ack", + "95 6", + "Ġtool bar", + "7 45", + "Ġo ust", + "Ġcontempl ation", + "ĠPRES IDENT", + "Ġ4 58", + "==== ==", + "Ġguarantee ing", + "ĠHe ist", + "ĠCann es", + "Ļ ½", + "Ġcollabor ator", + "ĠAm p", + "Ġg ou", + "ĠSH ALL", + "st ories", + "78 3", + "Ġmobil ized", + "Ġbro od", + "ĠL U", + "ĠðŁ ij", + "Ġref in", + "ĠAnthrop ology", + "v ind", + "ill i", + "Ġwarrant ies", + "ĠB abel", + "Ġsw ath", + "Ġc aches", + "Ġantagon ists", + "art ifacts", + "Ġhot ly", + "ĠSt arts", + "ĠG ö", + "z ag", + "!! !!!", + "Ġsc ourge", 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\ No newline at end of file diff --git a/models/prompt_expansion/put_prompt_expansion_here b/models/prompt_expansion/put_prompt_expansion_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/style_models/put_t2i_style_model_here b/models/style_models/put_t2i_style_model_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/unet/put_unet_files_here b/models/unet/put_unet_files_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/upscale_models/put_esrgan_and_other_upscale_models_here b/models/upscale_models/put_esrgan_and_other_upscale_models_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae/put_vae_here b/models/vae/put_vae_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here b/models/vae_approx/put_taesd_encoder_pth_and_taesd_decoder_pth_here new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/modules/anisotropic.py b/modules/anisotropic.py new file mode 100644 index 0000000000000000000000000000000000000000..576822240762b7dfcfb27e49364314ee1cb436d9 --- /dev/null +++ b/modules/anisotropic.py @@ -0,0 +1,200 @@ +import torch + + +Tensor = torch.Tensor +Device = torch.DeviceObjType +Dtype = torch.Type +pad = torch.nn.functional.pad + + +def _compute_zero_padding(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + ky, kx = _unpack_2d_ks(kernel_size) + return (ky - 1) // 2, (kx - 1) // 2 + + +def _unpack_2d_ks(kernel_size: tuple[int, int] | int) -> tuple[int, int]: + if isinstance(kernel_size, int): + ky = kx = kernel_size + else: + assert len(kernel_size) == 2, '2D Kernel size should have a length of 2.' + ky, kx = kernel_size + + ky = int(ky) + kx = int(kx) + return ky, kx + + +def gaussian( + window_size: int, sigma: Tensor | float, *, device: Device | None = None, dtype: Dtype | None = None +) -> Tensor: + + batch_size = sigma.shape[0] + + x = (torch.arange(window_size, device=sigma.device, dtype=sigma.dtype) - window_size // 2).expand(batch_size, -1) + + if window_size % 2 == 0: + x = x + 0.5 + + gauss = torch.exp(-x.pow(2.0) / (2 * sigma.pow(2.0))) + + return gauss / gauss.sum(-1, keepdim=True) + + +def get_gaussian_kernel1d( + kernel_size: int, + sigma: float | Tensor, + force_even: bool = False, + *, + device: Device | None = None, + dtype: Dtype | None = None, +) -> Tensor: + + return gaussian(kernel_size, sigma, device=device, dtype=dtype) + + +def get_gaussian_kernel2d( + kernel_size: tuple[int, int] | int, + sigma: tuple[float, float] | Tensor, + force_even: bool = False, + *, + device: Device | None = None, + dtype: Dtype | None = None, +) -> Tensor: + + sigma = torch.Tensor([[sigma, sigma]]).to(device=device, dtype=dtype) + + ksize_y, ksize_x = _unpack_2d_ks(kernel_size) + sigma_y, sigma_x = sigma[:, 0, None], sigma[:, 1, None] + + kernel_y = get_gaussian_kernel1d(ksize_y, sigma_y, force_even, device=device, dtype=dtype)[..., None] + kernel_x = get_gaussian_kernel1d(ksize_x, sigma_x, force_even, device=device, dtype=dtype)[..., None] + + return kernel_y * kernel_x.view(-1, 1, ksize_x) + + +def _bilateral_blur( + input: Tensor, + guidance: Tensor | None, + kernel_size: tuple[int, int] | int, + sigma_color: float | Tensor, + sigma_space: tuple[float, float] | Tensor, + border_type: str = 'reflect', + color_distance_type: str = 'l1', +) -> Tensor: + + if isinstance(sigma_color, Tensor): + sigma_color = sigma_color.to(device=input.device, dtype=input.dtype).view(-1, 1, 1, 1, 1) + + ky, kx = _unpack_2d_ks(kernel_size) + pad_y, pad_x = _compute_zero_padding(kernel_size) + + padded_input = pad(input, (pad_x, pad_x, pad_y, pad_y), mode=border_type) + unfolded_input = padded_input.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx) + + if guidance is None: + guidance = input + unfolded_guidance = unfolded_input + else: + padded_guidance = pad(guidance, (pad_x, pad_x, pad_y, pad_y), mode=border_type) + unfolded_guidance = padded_guidance.unfold(2, ky, 1).unfold(3, kx, 1).flatten(-2) # (B, C, H, W, Ky x Kx) + + diff = unfolded_guidance - guidance.unsqueeze(-1) + if color_distance_type == "l1": + color_distance_sq = diff.abs().sum(1, keepdim=True).square() + elif color_distance_type == "l2": + color_distance_sq = diff.square().sum(1, keepdim=True) + else: + raise ValueError("color_distance_type only acceps l1 or l2") + color_kernel = (-0.5 / sigma_color**2 * color_distance_sq).exp() # (B, 1, H, W, Ky x Kx) + + space_kernel = get_gaussian_kernel2d(kernel_size, sigma_space, device=input.device, dtype=input.dtype) + space_kernel = space_kernel.view(-1, 1, 1, 1, kx * ky) + + kernel = space_kernel * color_kernel + out = (unfolded_input * kernel).sum(-1) / kernel.sum(-1) + return out + + +def bilateral_blur( + input: Tensor, + kernel_size: tuple[int, int] | int = (13, 13), + sigma_color: float | Tensor = 3.0, + sigma_space: tuple[float, float] | Tensor = 3.0, + border_type: str = 'reflect', + color_distance_type: str = 'l1', +) -> Tensor: + return _bilateral_blur(input, None, kernel_size, sigma_color, sigma_space, border_type, color_distance_type) + + +def adaptive_anisotropic_filter(x, g=None): + if g is None: + g = x + s, m = torch.std_mean(g, dim=(1, 2, 3), keepdim=True) + s = s + 1e-5 + guidance = (g - m) / s + y = _bilateral_blur(x, guidance, + kernel_size=(13, 13), + sigma_color=3.0, + sigma_space=3.0, + border_type='reflect', + color_distance_type='l1') + return y + + +def joint_bilateral_blur( + input: Tensor, + guidance: Tensor, + kernel_size: tuple[int, int] | int, + sigma_color: float | Tensor, + sigma_space: tuple[float, float] | Tensor, + border_type: str = 'reflect', + color_distance_type: str = 'l1', +) -> Tensor: + return _bilateral_blur(input, guidance, kernel_size, sigma_color, sigma_space, border_type, color_distance_type) + + +class _BilateralBlur(torch.nn.Module): + def __init__( + self, + kernel_size: tuple[int, int] | int, + sigma_color: float | Tensor, + sigma_space: tuple[float, float] | Tensor, + border_type: str = 'reflect', + color_distance_type: str = "l1", + ) -> None: + super().__init__() + self.kernel_size = kernel_size + self.sigma_color = sigma_color + self.sigma_space = sigma_space + self.border_type = border_type + self.color_distance_type = color_distance_type + + def __repr__(self) -> str: + return ( + f"{self.__class__.__name__}" + f"(kernel_size={self.kernel_size}, " + f"sigma_color={self.sigma_color}, " + f"sigma_space={self.sigma_space}, " + f"border_type={self.border_type}, " + f"color_distance_type={self.color_distance_type})" + ) + + +class BilateralBlur(_BilateralBlur): + def forward(self, input: Tensor) -> Tensor: + return bilateral_blur( + input, self.kernel_size, self.sigma_color, self.sigma_space, self.border_type, self.color_distance_type + ) + + +class JointBilateralBlur(_BilateralBlur): + def forward(self, input: Tensor, guidance: Tensor) -> Tensor: + return joint_bilateral_blur( + input, + guidance, + self.kernel_size, + self.sigma_color, + self.sigma_space, + self.border_type, + self.color_distance_type, + ) diff --git a/modules/async_worker.py b/modules/async_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..a8661f4ddf1b1ce491f1008395fb09a527b5deb7 --- /dev/null +++ b/modules/async_worker.py @@ -0,0 +1,914 @@ +import threading +from modules.patch import PatchSettings, patch_settings, patch_all + +patch_all() + +class AsyncTask: + def __init__(self, args): + self.args = args + self.yields = [] + self.results = [] + self.last_stop = False + self.processing = False + + +async_tasks = [] + + +def worker(): + global async_tasks + + import os + import traceback + import math + import numpy as np + import cv2 + import torch + import time + import shared + import random + import copy + import modules.default_pipeline as pipeline + import modules.core as core + import modules.flags as flags + import modules.config + import modules.patch + import ldm_patched.modules.model_management + import extras.preprocessors as preprocessors + import modules.inpaint_worker as inpaint_worker + import modules.constants as constants + import extras.ip_adapter as ip_adapter + import extras.face_crop + import fooocus_version + import args_manager + + from modules.sdxl_styles import apply_style, apply_wildcards, fooocus_expansion, apply_arrays + from modules.private_logger import log + from extras.expansion import safe_str + from modules.util import remove_empty_str, HWC3, resize_image, \ + get_image_shape_ceil, set_image_shape_ceil, get_shape_ceil, resample_image, erode_or_dilate, ordinal_suffix + from modules.upscaler import perform_upscale + from modules.flags import Performance + from modules.meta_parser import get_metadata_parser, MetadataScheme + + pid = os.getpid() + print(f'Started worker with PID {pid}') + + try: + async_gradio_app = shared.gradio_root + flag = f'''App started successful. Use the app with {str(async_gradio_app.local_url)} or {str(async_gradio_app.server_name)}:{str(async_gradio_app.server_port)}''' + if async_gradio_app.share: + flag += f''' or {async_gradio_app.share_url}''' + print(flag) + except Exception as e: + print(e) + + def progressbar(async_task, number, text): + print(f'[Fooocus] {text}') + async_task.yields.append(['preview', (number, text, None)]) + + def yield_result(async_task, imgs, do_not_show_finished_images=False): + if not isinstance(imgs, list): + imgs = [imgs] + + async_task.results = async_task.results + imgs + + if do_not_show_finished_images: + return + + async_task.yields.append(['results', async_task.results]) + return + + def build_image_wall(async_task): + results = [] + + if len(async_task.results) < 2: + return + + for img in async_task.results: + if isinstance(img, str) and os.path.exists(img): + img = cv2.imread(img) + img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) + if not isinstance(img, np.ndarray): + return + if img.ndim != 3: + return + results.append(img) + + H, W, C = results[0].shape + + for img in results: + Hn, Wn, Cn = img.shape + if H != Hn: + return + if W != Wn: + return + if C != Cn: + return + + cols = float(len(results)) ** 0.5 + cols = int(math.ceil(cols)) + rows = float(len(results)) / float(cols) + rows = int(math.ceil(rows)) + + wall = np.zeros(shape=(H * rows, W * cols, C), dtype=np.uint8) + + for y in range(rows): + for x in range(cols): + if y * cols + x < len(results): + img = results[y * cols + x] + wall[y * H:y * H + H, x * W:x * W + W, :] = img + + # must use deep copy otherwise gradio is super laggy. Do not use list.append() . + async_task.results = async_task.results + [wall] + return + + def apply_enabled_loras(loras): + enabled_loras = [] + for lora_enabled, lora_model, lora_weight in loras: + if lora_enabled: + enabled_loras.append([lora_model, lora_weight]) + + return enabled_loras + + @torch.no_grad() + @torch.inference_mode() + def handler(async_task): + execution_start_time = time.perf_counter() + async_task.processing = True + + args = async_task.args + args.reverse() + + prompt = args.pop() + negative_prompt = args.pop() + style_selections = args.pop() + performance_selection = Performance(args.pop()) + aspect_ratios_selection = args.pop() + image_number = args.pop() + output_format = args.pop() + image_seed = args.pop() + sharpness = args.pop() + guidance_scale = args.pop() + base_model_name = args.pop() + refiner_model_name = args.pop() + refiner_switch = args.pop() + loras = apply_enabled_loras([[bool(args.pop()), str(args.pop()), float(args.pop()), ] for _ in range(modules.config.default_max_lora_number)]) + input_image_checkbox = args.pop() + current_tab = args.pop() + uov_method = args.pop() + uov_input_image = args.pop() + outpaint_selections = args.pop() + inpaint_input_image = args.pop() + inpaint_additional_prompt = args.pop() + inpaint_mask_image_upload = args.pop() + + disable_preview = args.pop() + disable_intermediate_results = args.pop() + disable_seed_increment = args.pop() + adm_scaler_positive = args.pop() + adm_scaler_negative = args.pop() + adm_scaler_end = args.pop() + adaptive_cfg = args.pop() + sampler_name = args.pop() + scheduler_name = args.pop() + overwrite_step = args.pop() + overwrite_switch = args.pop() + overwrite_width = args.pop() + overwrite_height = args.pop() + overwrite_vary_strength = args.pop() + overwrite_upscale_strength = args.pop() + mixing_image_prompt_and_vary_upscale = args.pop() + mixing_image_prompt_and_inpaint = args.pop() + debugging_cn_preprocessor = args.pop() + skipping_cn_preprocessor = args.pop() + canny_low_threshold = args.pop() + canny_high_threshold = args.pop() + refiner_swap_method = args.pop() + controlnet_softness = args.pop() + freeu_enabled = args.pop() + freeu_b1 = args.pop() + freeu_b2 = args.pop() + freeu_s1 = args.pop() + freeu_s2 = args.pop() + debugging_inpaint_preprocessor = args.pop() + inpaint_disable_initial_latent = args.pop() + inpaint_engine = args.pop() + inpaint_strength = args.pop() + inpaint_respective_field = args.pop() + inpaint_mask_upload_checkbox = args.pop() + invert_mask_checkbox = args.pop() + inpaint_erode_or_dilate = args.pop() + + save_metadata_to_images = args.pop() if not args_manager.args.disable_metadata else False + metadata_scheme = MetadataScheme(args.pop()) if not args_manager.args.disable_metadata else MetadataScheme.FOOOCUS + + cn_tasks = {x: [] for x in flags.ip_list} + for _ in range(flags.controlnet_image_count): + cn_img = args.pop() + cn_stop = args.pop() + cn_weight = args.pop() + cn_type = args.pop() + if cn_img is not None: + cn_tasks[cn_type].append([cn_img, cn_stop, cn_weight]) + + outpaint_selections = [o.lower() for o in outpaint_selections] + base_model_additional_loras = [] + raw_style_selections = copy.deepcopy(style_selections) + uov_method = uov_method.lower() + + if fooocus_expansion in style_selections: + use_expansion = True + style_selections.remove(fooocus_expansion) + else: + use_expansion = False + + use_style = len(style_selections) > 0 + + if base_model_name == refiner_model_name: + print(f'Refiner disabled because base model and refiner are same.') + refiner_model_name = 'None' + + steps = performance_selection.steps() + + if performance_selection == Performance.EXTREME_SPEED: + print('Enter LCM mode.') + progressbar(async_task, 1, 'Downloading LCM components ...') + loras += [(modules.config.downloading_sdxl_lcm_lora(), 1.0)] + + if refiner_model_name != 'None': + print(f'Refiner disabled in LCM mode.') + + refiner_model_name = 'None' + sampler_name = 'lcm' + scheduler_name = 'lcm' + sharpness = 0.0 + guidance_scale = 1.0 + adaptive_cfg = 1.0 + refiner_switch = 1.0 + adm_scaler_positive = 1.0 + adm_scaler_negative = 1.0 + adm_scaler_end = 0.0 + + print(f'[Parameters] Adaptive CFG = {adaptive_cfg}') + print(f'[Parameters] Sharpness = {sharpness}') + print(f'[Parameters] ControlNet Softness = {controlnet_softness}') + print(f'[Parameters] ADM Scale = ' + f'{adm_scaler_positive} : ' + f'{adm_scaler_negative} : ' + f'{adm_scaler_end}') + + patch_settings[pid] = PatchSettings( + sharpness, + adm_scaler_end, + adm_scaler_positive, + adm_scaler_negative, + controlnet_softness, + adaptive_cfg + ) + + cfg_scale = float(guidance_scale) + print(f'[Parameters] CFG = {cfg_scale}') + + initial_latent = None + denoising_strength = 1.0 + tiled = False + + width, height = aspect_ratios_selection.replace('×', ' ').split(' ')[:2] + width, height = int(width), int(height) + + skip_prompt_processing = False + + inpaint_worker.current_task = None + inpaint_parameterized = inpaint_engine != 'None' + inpaint_image = None + inpaint_mask = None + inpaint_head_model_path = None + + use_synthetic_refiner = False + + controlnet_canny_path = None + controlnet_cpds_path = None + clip_vision_path, ip_negative_path, ip_adapter_path, ip_adapter_face_path = None, None, None, None + + seed = int(image_seed) + print(f'[Parameters] Seed = {seed}') + + goals = [] + tasks = [] + + if input_image_checkbox: + if (current_tab == 'uov' or ( + current_tab == 'ip' and mixing_image_prompt_and_vary_upscale)) \ + and uov_method != flags.disabled and uov_input_image is not None: + uov_input_image = HWC3(uov_input_image) + if 'vary' in uov_method: + goals.append('vary') + elif 'upscale' in uov_method: + goals.append('upscale') + if 'fast' in uov_method: + skip_prompt_processing = True + else: + steps = performance_selection.steps_uov() + + progressbar(async_task, 1, 'Downloading upscale models ...') + modules.config.downloading_upscale_model() + if (current_tab == 'inpaint' or ( + current_tab == 'ip' and mixing_image_prompt_and_inpaint)) \ + and isinstance(inpaint_input_image, dict): + inpaint_image = inpaint_input_image['image'] + inpaint_mask = inpaint_input_image['mask'][:, :, 0] + + if inpaint_mask_upload_checkbox: + if isinstance(inpaint_mask_image_upload, np.ndarray): + if inpaint_mask_image_upload.ndim == 3: + H, W, C = inpaint_image.shape + inpaint_mask_image_upload = resample_image(inpaint_mask_image_upload, width=W, height=H) + inpaint_mask_image_upload = np.mean(inpaint_mask_image_upload, axis=2) + inpaint_mask_image_upload = (inpaint_mask_image_upload > 127).astype(np.uint8) * 255 + inpaint_mask = np.maximum(inpaint_mask, inpaint_mask_image_upload) + + if int(inpaint_erode_or_dilate) != 0: + inpaint_mask = erode_or_dilate(inpaint_mask, inpaint_erode_or_dilate) + + if invert_mask_checkbox: + inpaint_mask = 255 - inpaint_mask + + inpaint_image = HWC3(inpaint_image) + if isinstance(inpaint_image, np.ndarray) and isinstance(inpaint_mask, np.ndarray) \ + and (np.any(inpaint_mask > 127) or len(outpaint_selections) > 0): + progressbar(async_task, 1, 'Downloading upscale models ...') + modules.config.downloading_upscale_model() + if inpaint_parameterized: + progressbar(async_task, 1, 'Downloading inpainter ...') + inpaint_head_model_path, inpaint_patch_model_path = modules.config.downloading_inpaint_models( + inpaint_engine) + base_model_additional_loras += [(inpaint_patch_model_path, 1.0)] + print(f'[Inpaint] Current inpaint model is {inpaint_patch_model_path}') + if refiner_model_name == 'None': + use_synthetic_refiner = True + refiner_switch = 0.5 + else: + inpaint_head_model_path, inpaint_patch_model_path = None, None + print(f'[Inpaint] Parameterized inpaint is disabled.') + if inpaint_additional_prompt != '': + if prompt == '': + prompt = inpaint_additional_prompt + else: + prompt = inpaint_additional_prompt + '\n' + prompt + goals.append('inpaint') + if current_tab == 'ip' or \ + mixing_image_prompt_and_vary_upscale or \ + mixing_image_prompt_and_inpaint: + goals.append('cn') + progressbar(async_task, 1, 'Downloading control models ...') + if len(cn_tasks[flags.cn_canny]) > 0: + controlnet_canny_path = modules.config.downloading_controlnet_canny() + if len(cn_tasks[flags.cn_cpds]) > 0: + controlnet_cpds_path = modules.config.downloading_controlnet_cpds() + if len(cn_tasks[flags.cn_ip]) > 0: + clip_vision_path, ip_negative_path, ip_adapter_path = modules.config.downloading_ip_adapters('ip') + if len(cn_tasks[flags.cn_ip_face]) > 0: + clip_vision_path, ip_negative_path, ip_adapter_face_path = modules.config.downloading_ip_adapters( + 'face') + progressbar(async_task, 1, 'Loading control models ...') + + # Load or unload CNs + pipeline.refresh_controlnets([controlnet_canny_path, controlnet_cpds_path]) + ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_path) + ip_adapter.load_ip_adapter(clip_vision_path, ip_negative_path, ip_adapter_face_path) + + if overwrite_step > 0: + steps = overwrite_step + + switch = int(round(steps * refiner_switch)) + + if overwrite_switch > 0: + switch = overwrite_switch + + if overwrite_width > 0: + width = overwrite_width + + if overwrite_height > 0: + height = overwrite_height + + print(f'[Parameters] Sampler = {sampler_name} - {scheduler_name}') + print(f'[Parameters] Steps = {steps} - {switch}') + + progressbar(async_task, 1, 'Initializing ...') + + if not skip_prompt_processing: + + prompts = remove_empty_str([safe_str(p) for p in prompt.splitlines()], default='') + negative_prompts = remove_empty_str([safe_str(p) for p in negative_prompt.splitlines()], default='') + + prompt = prompts[0] + negative_prompt = negative_prompts[0] + + if prompt == '': + # disable expansion when empty since it is not meaningful and influences image prompt + use_expansion = False + + extra_positive_prompts = prompts[1:] if len(prompts) > 1 else [] + extra_negative_prompts = negative_prompts[1:] if len(negative_prompts) > 1 else [] + + progressbar(async_task, 3, 'Loading models ...') + pipeline.refresh_everything(refiner_model_name=refiner_model_name, base_model_name=base_model_name, + loras=loras, base_model_additional_loras=base_model_additional_loras, + use_synthetic_refiner=use_synthetic_refiner) + + progressbar(async_task, 3, 'Processing prompts ...') + tasks = [] + + for i in range(image_number): + if disable_seed_increment: + task_seed = seed + else: + task_seed = (seed + i) % (constants.MAX_SEED + 1) # randint is inclusive, % is not + + task_rng = random.Random(task_seed) # may bind to inpaint noise in the future + task_prompt = apply_wildcards(prompt, task_rng) + task_prompt = apply_arrays(task_prompt, i) + task_negative_prompt = apply_wildcards(negative_prompt, task_rng) + task_extra_positive_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_positive_prompts] + task_extra_negative_prompts = [apply_wildcards(pmt, task_rng) for pmt in extra_negative_prompts] + + positive_basic_workloads = [] + negative_basic_workloads = [] + + if use_style: + for s in style_selections: + p, n = apply_style(s, positive=task_prompt) + positive_basic_workloads = positive_basic_workloads + p + negative_basic_workloads = negative_basic_workloads + n + else: + positive_basic_workloads.append(task_prompt) + + negative_basic_workloads.append(task_negative_prompt) # Always use independent workload for negative. + + positive_basic_workloads = positive_basic_workloads + task_extra_positive_prompts + negative_basic_workloads = negative_basic_workloads + task_extra_negative_prompts + + positive_basic_workloads = remove_empty_str(positive_basic_workloads, default=task_prompt) + negative_basic_workloads = remove_empty_str(negative_basic_workloads, default=task_negative_prompt) + + tasks.append(dict( + task_seed=task_seed, + task_prompt=task_prompt, + task_negative_prompt=task_negative_prompt, + positive=positive_basic_workloads, + negative=negative_basic_workloads, + expansion='', + c=None, + uc=None, + positive_top_k=len(positive_basic_workloads), + negative_top_k=len(negative_basic_workloads), + log_positive_prompt='\n'.join([task_prompt] + task_extra_positive_prompts), + log_negative_prompt='\n'.join([task_negative_prompt] + task_extra_negative_prompts), + )) + + if use_expansion: + for i, t in enumerate(tasks): + progressbar(async_task, 5, f'Preparing Fooocus text #{i + 1} ...') + expansion = pipeline.final_expansion(t['task_prompt'], t['task_seed']) + print(f'[Prompt Expansion] {expansion}') + t['expansion'] = expansion + t['positive'] = copy.deepcopy(t['positive']) + [expansion] # Deep copy. + + for i, t in enumerate(tasks): + progressbar(async_task, 7, f'Encoding positive #{i + 1} ...') + t['c'] = pipeline.clip_encode(texts=t['positive'], pool_top_k=t['positive_top_k']) + + for i, t in enumerate(tasks): + if abs(float(cfg_scale) - 1.0) < 1e-4: + t['uc'] = pipeline.clone_cond(t['c']) + else: + progressbar(async_task, 10, f'Encoding negative #{i + 1} ...') + t['uc'] = pipeline.clip_encode(texts=t['negative'], pool_top_k=t['negative_top_k']) + + if len(goals) > 0: + progressbar(async_task, 13, 'Image processing ...') + + if 'vary' in goals: + if 'subtle' in uov_method: + denoising_strength = 0.5 + if 'strong' in uov_method: + denoising_strength = 0.85 + if overwrite_vary_strength > 0: + denoising_strength = overwrite_vary_strength + + shape_ceil = get_image_shape_ceil(uov_input_image) + if shape_ceil < 1024: + print(f'[Vary] Image is resized because it is too small.') + shape_ceil = 1024 + elif shape_ceil > 2048: + print(f'[Vary] Image is resized because it is too big.') + shape_ceil = 2048 + + uov_input_image = set_image_shape_ceil(uov_input_image, shape_ceil) + + initial_pixels = core.numpy_to_pytorch(uov_input_image) + progressbar(async_task, 13, 'VAE encoding ...') + + candidate_vae, _ = pipeline.get_candidate_vae( + steps=steps, + switch=switch, + denoise=denoising_strength, + refiner_swap_method=refiner_swap_method + ) + + initial_latent = core.encode_vae(vae=candidate_vae, pixels=initial_pixels) + B, C, H, W = initial_latent['samples'].shape + width = W * 8 + height = H * 8 + print(f'Final resolution is {str((height, width))}.') + + if 'upscale' in goals: + H, W, C = uov_input_image.shape + progressbar(async_task, 13, f'Upscaling image from {str((H, W))} ...') + uov_input_image = perform_upscale(uov_input_image) + print(f'Image upscaled.') + + if '1.5x' in uov_method: + f = 1.5 + elif '2x' in uov_method: + f = 2.0 + else: + f = 1.0 + + shape_ceil = get_shape_ceil(H * f, W * f) + + if shape_ceil < 1024: + print(f'[Upscale] Image is resized because it is too small.') + uov_input_image = set_image_shape_ceil(uov_input_image, 1024) + shape_ceil = 1024 + else: + uov_input_image = resample_image(uov_input_image, width=W * f, height=H * f) + + image_is_super_large = shape_ceil > 2800 + + if 'fast' in uov_method: + direct_return = True + elif image_is_super_large: + print('Image is too large. Directly returned the SR image. ' + 'Usually directly return SR image at 4K resolution ' + 'yields better results than SDXL diffusion.') + direct_return = True + else: + direct_return = False + + if direct_return: + d = [('Upscale (Fast)', 'upscale_fast', '2x')] + uov_input_image_path = log(uov_input_image, d, output_format=output_format) + yield_result(async_task, uov_input_image_path, do_not_show_finished_images=True) + return + + tiled = True + denoising_strength = 0.382 + + if overwrite_upscale_strength > 0: + denoising_strength = overwrite_upscale_strength + + initial_pixels = core.numpy_to_pytorch(uov_input_image) + progressbar(async_task, 13, 'VAE encoding ...') + + candidate_vae, _ = pipeline.get_candidate_vae( + steps=steps, + switch=switch, + denoise=denoising_strength, + refiner_swap_method=refiner_swap_method + ) + + initial_latent = core.encode_vae( + vae=candidate_vae, + pixels=initial_pixels, tiled=True) + B, C, H, W = initial_latent['samples'].shape + width = W * 8 + height = H * 8 + print(f'Final resolution is {str((height, width))}.') + + if 'inpaint' in goals: + if len(outpaint_selections) > 0: + H, W, C = inpaint_image.shape + if 'top' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[int(H * 0.3), 0], [0, 0], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[int(H * 0.3), 0], [0, 0]], mode='constant', + constant_values=255) + if 'bottom' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[0, int(H * 0.3)], [0, 0], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[0, int(H * 0.3)], [0, 0]], mode='constant', + constant_values=255) + + H, W, C = inpaint_image.shape + if 'left' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[0, 0], [int(H * 0.3), 0], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[0, 0], [int(H * 0.3), 0]], mode='constant', + constant_values=255) + if 'right' in outpaint_selections: + inpaint_image = np.pad(inpaint_image, [[0, 0], [0, int(H * 0.3)], [0, 0]], mode='edge') + inpaint_mask = np.pad(inpaint_mask, [[0, 0], [0, int(H * 0.3)]], mode='constant', + constant_values=255) + + inpaint_image = np.ascontiguousarray(inpaint_image.copy()) + inpaint_mask = np.ascontiguousarray(inpaint_mask.copy()) + inpaint_strength = 1.0 + inpaint_respective_field = 1.0 + + denoising_strength = inpaint_strength + + inpaint_worker.current_task = inpaint_worker.InpaintWorker( + image=inpaint_image, + mask=inpaint_mask, + use_fill=denoising_strength > 0.99, + k=inpaint_respective_field + ) + + if debugging_inpaint_preprocessor: + yield_result(async_task, inpaint_worker.current_task.visualize_mask_processing(), + do_not_show_finished_images=True) + return + + progressbar(async_task, 13, 'VAE Inpaint encoding ...') + + inpaint_pixel_fill = core.numpy_to_pytorch(inpaint_worker.current_task.interested_fill) + inpaint_pixel_image = core.numpy_to_pytorch(inpaint_worker.current_task.interested_image) + inpaint_pixel_mask = core.numpy_to_pytorch(inpaint_worker.current_task.interested_mask) + + candidate_vae, candidate_vae_swap = pipeline.get_candidate_vae( + steps=steps, + switch=switch, + denoise=denoising_strength, + refiner_swap_method=refiner_swap_method + ) + + latent_inpaint, latent_mask = core.encode_vae_inpaint( + mask=inpaint_pixel_mask, + vae=candidate_vae, + pixels=inpaint_pixel_image) + + latent_swap = None + if candidate_vae_swap is not None: + progressbar(async_task, 13, 'VAE SD15 encoding ...') + latent_swap = core.encode_vae( + vae=candidate_vae_swap, + pixels=inpaint_pixel_fill)['samples'] + + progressbar(async_task, 13, 'VAE encoding ...') + latent_fill = core.encode_vae( + vae=candidate_vae, + pixels=inpaint_pixel_fill)['samples'] + + inpaint_worker.current_task.load_latent( + latent_fill=latent_fill, latent_mask=latent_mask, latent_swap=latent_swap) + + if inpaint_parameterized: + pipeline.final_unet = inpaint_worker.current_task.patch( + inpaint_head_model_path=inpaint_head_model_path, + inpaint_latent=latent_inpaint, + inpaint_latent_mask=latent_mask, + model=pipeline.final_unet + ) + + if not inpaint_disable_initial_latent: + initial_latent = {'samples': latent_fill} + + B, C, H, W = latent_fill.shape + height, width = H * 8, W * 8 + final_height, final_width = inpaint_worker.current_task.image.shape[:2] + print(f'Final resolution is {str((final_height, final_width))}, latent is {str((height, width))}.') + + if 'cn' in goals: + for task in cn_tasks[flags.cn_canny]: + cn_img, cn_stop, cn_weight = task + cn_img = resize_image(HWC3(cn_img), width=width, height=height) + + if not skipping_cn_preprocessor: + cn_img = preprocessors.canny_pyramid(cn_img, canny_low_threshold, canny_high_threshold) + + cn_img = HWC3(cn_img) + task[0] = core.numpy_to_pytorch(cn_img) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + for task in cn_tasks[flags.cn_cpds]: + cn_img, cn_stop, cn_weight = task + cn_img = resize_image(HWC3(cn_img), width=width, height=height) + + if not skipping_cn_preprocessor: + cn_img = preprocessors.cpds(cn_img) + + cn_img = HWC3(cn_img) + task[0] = core.numpy_to_pytorch(cn_img) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + for task in cn_tasks[flags.cn_ip]: + cn_img, cn_stop, cn_weight = task + cn_img = HWC3(cn_img) + + # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 + cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) + + task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_path) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + for task in cn_tasks[flags.cn_ip_face]: + cn_img, cn_stop, cn_weight = task + cn_img = HWC3(cn_img) + + if not skipping_cn_preprocessor: + cn_img = extras.face_crop.crop_image(cn_img) + + # https://github.com/tencent-ailab/IP-Adapter/blob/d580c50a291566bbf9fc7ac0f760506607297e6d/README.md?plain=1#L75 + cn_img = resize_image(cn_img, width=224, height=224, resize_mode=0) + + task[0] = ip_adapter.preprocess(cn_img, ip_adapter_path=ip_adapter_face_path) + if debugging_cn_preprocessor: + yield_result(async_task, cn_img, do_not_show_finished_images=True) + return + + all_ip_tasks = cn_tasks[flags.cn_ip] + cn_tasks[flags.cn_ip_face] + + if len(all_ip_tasks) > 0: + pipeline.final_unet = ip_adapter.patch_model(pipeline.final_unet, all_ip_tasks) + + if freeu_enabled: + print(f'FreeU is enabled!') + pipeline.final_unet = core.apply_freeu( + pipeline.final_unet, + freeu_b1, + freeu_b2, + freeu_s1, + freeu_s2 + ) + + all_steps = steps * image_number + + print(f'[Parameters] Denoising Strength = {denoising_strength}') + + if isinstance(initial_latent, dict) and 'samples' in initial_latent: + log_shape = initial_latent['samples'].shape + else: + log_shape = f'Image Space {(height, width)}' + + print(f'[Parameters] Initial Latent shape: {log_shape}') + + preparation_time = time.perf_counter() - execution_start_time + print(f'Preparation time: {preparation_time:.2f} seconds') + + final_sampler_name = sampler_name + final_scheduler_name = scheduler_name + + if scheduler_name == 'lcm': + final_scheduler_name = 'sgm_uniform' + if pipeline.final_unet is not None: + pipeline.final_unet = core.opModelSamplingDiscrete.patch( + pipeline.final_unet, + sampling='lcm', + zsnr=False)[0] + if pipeline.final_refiner_unet is not None: + pipeline.final_refiner_unet = core.opModelSamplingDiscrete.patch( + pipeline.final_refiner_unet, + sampling='lcm', + zsnr=False)[0] + print('Using lcm scheduler.') + + async_task.yields.append(['preview', (13, 'Moving model to GPU ...', None)]) + + def callback(step, x0, x, total_steps, y): + done_steps = current_task_id * steps + step + async_task.yields.append(['preview', ( + int(15.0 + 85.0 * float(done_steps) / float(all_steps)), + f'Step {step}/{total_steps} in the {current_task_id + 1}{ordinal_suffix(current_task_id + 1)} Sampling', y)]) + + for current_task_id, task in enumerate(tasks): + execution_start_time = time.perf_counter() + + try: + if async_task.last_stop is not False: + ldm_patched.model_management.interrupt_current_processing() + positive_cond, negative_cond = task['c'], task['uc'] + + if 'cn' in goals: + for cn_flag, cn_path in [ + (flags.cn_canny, controlnet_canny_path), + (flags.cn_cpds, controlnet_cpds_path) + ]: + for cn_img, cn_stop, cn_weight in cn_tasks[cn_flag]: + positive_cond, negative_cond = core.apply_controlnet( + positive_cond, negative_cond, + pipeline.loaded_ControlNets[cn_path], cn_img, cn_weight, 0, cn_stop) + + imgs = pipeline.process_diffusion( + positive_cond=positive_cond, + negative_cond=negative_cond, + steps=steps, + switch=switch, + width=width, + height=height, + image_seed=task['task_seed'], + callback=callback, + sampler_name=final_sampler_name, + scheduler_name=final_scheduler_name, + latent=initial_latent, + denoise=denoising_strength, + tiled=tiled, + cfg_scale=cfg_scale, + refiner_swap_method=refiner_swap_method, + disable_preview=disable_preview + ) + + del task['c'], task['uc'], positive_cond, negative_cond # Save memory + + if inpaint_worker.current_task is not None: + imgs = [inpaint_worker.current_task.post_process(x) for x in imgs] + + img_paths = [] + for x in imgs: + d = [('Prompt', 'prompt', task['log_positive_prompt']), + ('Negative Prompt', 'negative_prompt', task['log_negative_prompt']), + ('Fooocus V2 Expansion', 'prompt_expansion', task['expansion']), + ('Styles', 'styles', str(raw_style_selections)), + ('Performance', 'performance', performance_selection.value)] + + if performance_selection.steps() != steps: + d.append(('Steps', 'steps', steps)) + + d += [('Resolution', 'resolution', str((width, height))), + ('Guidance Scale', 'guidance_scale', guidance_scale), + ('Sharpness', 'sharpness', sharpness), + ('ADM Guidance', 'adm_guidance', str(( + modules.patch.patch_settings[pid].positive_adm_scale, + modules.patch.patch_settings[pid].negative_adm_scale, + modules.patch.patch_settings[pid].adm_scaler_end))), + ('Base Model', 'base_model', base_model_name), + ('Refiner Model', 'refiner_model', refiner_model_name), + ('Refiner Switch', 'refiner_switch', refiner_switch)] + + if refiner_model_name != 'None': + if overwrite_switch > 0: + d.append(('Overwrite Switch', 'overwrite_switch', overwrite_switch)) + if refiner_swap_method != flags.refiner_swap_method: + d.append(('Refiner Swap Method', 'refiner_swap_method', refiner_swap_method)) + if modules.patch.patch_settings[pid].adaptive_cfg != modules.config.default_cfg_tsnr: + d.append(('CFG Mimicking from TSNR', 'adaptive_cfg', modules.patch.patch_settings[pid].adaptive_cfg)) + + d.append(('Sampler', 'sampler', sampler_name)) + d.append(('Scheduler', 'scheduler', scheduler_name)) + d.append(('Seed', 'seed', task['task_seed'])) + + if freeu_enabled: + d.append(('FreeU', 'freeu', str((freeu_b1, freeu_b2, freeu_s1, freeu_s2)))) + + for li, (n, w) in enumerate(loras): + if n != 'None': + d.append((f'LoRA {li + 1}', f'lora_combined_{li + 1}', f'{n} : {w}')) + + metadata_parser = None + if save_metadata_to_images: + metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) + metadata_parser.set_data(task['log_positive_prompt'], task['positive'], + task['log_negative_prompt'], task['negative'], + steps, base_model_name, refiner_model_name, loras) + d.append(('Metadata Scheme', 'metadata_scheme', metadata_scheme.value if save_metadata_to_images else save_metadata_to_images)) + d.append(('Version', 'version', 'Fooocus v' + fooocus_version.version)) + img_paths.append(log(x, d, metadata_parser, output_format)) + + yield_result(async_task, img_paths, do_not_show_finished_images=len(tasks) == 1 or disable_intermediate_results) + except ldm_patched.modules.model_management.InterruptProcessingException as e: + if async_task.last_stop == 'skip': + print('User skipped') + async_task.last_stop = False + continue + else: + print('User stopped') + break + + execution_time = time.perf_counter() - execution_start_time + print(f'Generating and saving time: {execution_time:.2f} seconds') + async_task.processing = False + return + + while True: + time.sleep(0.01) + if len(async_tasks) > 0: + task = async_tasks.pop(0) + generate_image_grid = task.args.pop(0) + + try: + handler(task) + if generate_image_grid: + build_image_wall(task) + task.yields.append(['finish', task.results]) + pipeline.prepare_text_encoder(async_call=True) + except: + traceback.print_exc() + task.yields.append(['finish', task.results]) + finally: + if pid in modules.patch.patch_settings: + del modules.patch.patch_settings[pid] + pass + + +threading.Thread(target=worker, daemon=True).start() diff --git a/modules/auth.py b/modules/auth.py new file mode 100644 index 0000000000000000000000000000000000000000..3ba111424523c19174f8b741b3bbac7b43b7bb6c --- /dev/null +++ b/modules/auth.py @@ -0,0 +1,41 @@ +import json +import hashlib +import modules.constants as constants + +from os.path import exists + + +def auth_list_to_dict(auth_list): + auth_dict = {} + for auth_data in auth_list: + if 'user' in auth_data: + if 'hash' in auth_data: + auth_dict |= {auth_data['user']: auth_data['hash']} + elif 'pass' in auth_data: + auth_dict |= {auth_data['user']: hashlib.sha256(bytes(auth_data['pass'], encoding='utf-8')).hexdigest()} + return auth_dict + + +def load_auth_data(filename=None): + auth_dict = None + if filename != None and exists(filename): + with open(filename, encoding='utf-8') as auth_file: + try: + auth_obj = json.load(auth_file) + if isinstance(auth_obj, list) and len(auth_obj) > 0: + auth_dict = auth_list_to_dict(auth_obj) + except Exception as e: + print('load_auth_data, e: ' + str(e)) + return auth_dict + + +auth_dict = load_auth_data(constants.AUTH_FILENAME) + +auth_enabled = auth_dict != None + + +def check_auth(user, password): + if user not in auth_dict: + return False + else: + return hashlib.sha256(bytes(password, encoding='utf-8')).hexdigest() == auth_dict[user] diff --git a/modules/config.py b/modules/config.py new file mode 100644 index 0000000000000000000000000000000000000000..a68bd2187f7f766708482edabd8bdb0647e3cacb --- /dev/null +++ b/modules/config.py @@ -0,0 +1,607 @@ +import os +import json +import math +import numbers +import args_manager +import modules.flags +import modules.sdxl_styles + +from modules.model_loader import load_file_from_url +from modules.util import get_files_from_folder, makedirs_with_log +from modules.flags import Performance, MetadataScheme + +def get_config_path(key, default_value): + env = os.getenv(key) + if env is not None and isinstance(env, str): + print(f"Environment: {key} = {env}") + return env + else: + return os.path.abspath(default_value) + +config_path = get_config_path('config_path', "./config.txt") +config_example_path = get_config_path('config_example_path', "config_modification_tutorial.txt") +config_dict = {} +always_save_keys = [] +visited_keys = [] + +try: + with open(os.path.abspath(f'./presets/default.json'), "r", encoding="utf-8") as json_file: + config_dict.update(json.load(json_file)) +except Exception as e: + print(f'Load default preset failed.') + print(e) + +try: + if os.path.exists(config_path): + with open(config_path, "r", encoding="utf-8") as json_file: + config_dict.update(json.load(json_file)) + always_save_keys = list(config_dict.keys()) +except Exception as e: + print(f'Failed to load config file "{config_path}" . The reason is: {str(e)}') + print('Please make sure that:') + print(f'1. The file "{config_path}" is a valid text file, and you have access to read it.') + print('2. Use "\\\\" instead of "\\" when describing paths.') + print('3. There is no "," before the last "}".') + print('4. All key/value formats are correct.') + + +def try_load_deprecated_user_path_config(): + global config_dict + + if not os.path.exists('user_path_config.txt'): + return + + try: + deprecated_config_dict = json.load(open('user_path_config.txt', "r", encoding="utf-8")) + + def replace_config(old_key, new_key): + if old_key in deprecated_config_dict: + config_dict[new_key] = deprecated_config_dict[old_key] + del deprecated_config_dict[old_key] + + replace_config('modelfile_path', 'path_checkpoints') + replace_config('lorafile_path', 'path_loras') + replace_config('embeddings_path', 'path_embeddings') + replace_config('vae_approx_path', 'path_vae_approx') + replace_config('upscale_models_path', 'path_upscale_models') + replace_config('inpaint_models_path', 'path_inpaint') + replace_config('controlnet_models_path', 'path_controlnet') + replace_config('clip_vision_models_path', 'path_clip_vision') + replace_config('fooocus_expansion_path', 'path_fooocus_expansion') + replace_config('temp_outputs_path', 'path_outputs') + + if deprecated_config_dict.get("default_model", None) == 'juggernautXL_version6Rundiffusion.safetensors': + os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') + print('Config updated successfully in silence. ' + 'A backup of previous config is written to "user_path_config-deprecated.txt".') + return + + if input("Newer models and configs are available. " + "Download and update files? [Y/n]:") in ['n', 'N', 'No', 'no', 'NO']: + config_dict.update(deprecated_config_dict) + print('Loading using deprecated old models and deprecated old configs.') + return + else: + os.replace('user_path_config.txt', 'user_path_config-deprecated.txt') + print('Config updated successfully by user. ' + 'A backup of previous config is written to "user_path_config-deprecated.txt".') + return + except Exception as e: + print('Processing deprecated config failed') + print(e) + return + + +try_load_deprecated_user_path_config() + +preset = args_manager.args.preset + +if isinstance(preset, str): + preset_path = os.path.abspath(f'./presets/{preset}.json') + try: + if os.path.exists(preset_path): + with open(preset_path, "r", encoding="utf-8") as json_file: + config_dict.update(json.load(json_file)) + print(f'Loaded preset: {preset_path}') + else: + raise FileNotFoundError + except Exception as e: + print(f'Load preset [{preset_path}] failed') + print(e) + + +def get_path_output() -> str: + """ + Checking output path argument and overriding default path. + """ + global config_dict + path_output = get_dir_or_set_default('path_outputs', '../outputs/', make_directory=True) + if args_manager.args.output_path: + print(f'[CONFIG] Overriding config value path_outputs with {args_manager.args.output_path}') + config_dict['path_outputs'] = path_output = args_manager.args.output_path + return path_output + + +def get_dir_or_set_default(key, default_value, as_array=False, make_directory=False): + global config_dict, visited_keys, always_save_keys + + if key not in visited_keys: + visited_keys.append(key) + + if key not in always_save_keys: + always_save_keys.append(key) + + v = os.getenv(key) + if v is not None: + print(f"Environment: {key} = {v}") + config_dict[key] = v + else: + v = config_dict.get(key, None) + + if isinstance(v, str): + if make_directory: + makedirs_with_log(v) + if os.path.exists(v) and os.path.isdir(v): + return v if not as_array else [v] + elif isinstance(v, list): + if make_directory: + for d in v: + makedirs_with_log(d) + if all([os.path.exists(d) and os.path.isdir(d) for d in v]): + return v + + if v is not None: + print(f'Failed to load config key: {json.dumps({key:v})} is invalid or does not exist; will use {json.dumps({key:default_value})} instead.') + if isinstance(default_value, list): + dp = [] + for path in default_value: + abs_path = os.path.abspath(os.path.join(os.path.dirname(__file__), path)) + dp.append(abs_path) + os.makedirs(abs_path, exist_ok=True) + else: + dp = os.path.abspath(os.path.join(os.path.dirname(__file__), default_value)) + os.makedirs(dp, exist_ok=True) + if as_array: + dp = [dp] + config_dict[key] = dp + return dp + + +paths_checkpoints = get_dir_or_set_default('path_checkpoints', ['../models/checkpoints/'], True) +paths_loras = get_dir_or_set_default('path_loras', ['../models/loras/'], True) +path_embeddings = get_dir_or_set_default('path_embeddings', '../models/embeddings/') +path_vae_approx = get_dir_or_set_default('path_vae_approx', '../models/vae_approx/') +path_upscale_models = get_dir_or_set_default('path_upscale_models', '../models/upscale_models/') +path_inpaint = get_dir_or_set_default('path_inpaint', '../models/inpaint/') +path_controlnet = get_dir_or_set_default('path_controlnet', '../models/controlnet/') +path_clip_vision = get_dir_or_set_default('path_clip_vision', '../models/clip_vision/') +path_fooocus_expansion = get_dir_or_set_default('path_fooocus_expansion', '../models/prompt_expansion/fooocus_expansion') +path_outputs = get_path_output() + +def get_config_item_or_set_default(key, default_value, validator, disable_empty_as_none=False): + global config_dict, visited_keys + + if key not in visited_keys: + visited_keys.append(key) + + v = os.getenv(key) + if v is not None: + print(f"Environment: {key} = {v}") + config_dict[key] = v + + if key not in config_dict: + config_dict[key] = default_value + return default_value + + v = config_dict.get(key, None) + if not disable_empty_as_none: + if v is None or v == '': + v = 'None' + if validator(v): + return v + else: + if v is not None: + print(f'Failed to load config key: {json.dumps({key:v})} is invalid; will use {json.dumps({key:default_value})} instead.') + config_dict[key] = default_value + return default_value + + +default_base_model_name = get_config_item_or_set_default( + key='default_model', + default_value='model.safetensors', + validator=lambda x: isinstance(x, str) +) +previous_default_models = get_config_item_or_set_default( + key='previous_default_models', + default_value=[], + validator=lambda x: isinstance(x, list) and all(isinstance(k, str) for k in x) +) +default_refiner_model_name = get_config_item_or_set_default( + key='default_refiner', + default_value='None', + validator=lambda x: isinstance(x, str) +) +default_refiner_switch = get_config_item_or_set_default( + key='default_refiner_switch', + default_value=0.8, + validator=lambda x: isinstance(x, numbers.Number) and 0 <= x <= 1 +) +default_loras_min_weight = get_config_item_or_set_default( + key='default_loras_min_weight', + default_value=-2, + validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10 +) +default_loras_max_weight = get_config_item_or_set_default( + key='default_loras_max_weight', + default_value=2, + validator=lambda x: isinstance(x, numbers.Number) and -10 <= x <= 10 +) +default_loras = get_config_item_or_set_default( + key='default_loras', + default_value=[ + [ + "None", + 1.0 + ], + [ + "None", + 1.0 + ], + [ + "None", + 1.0 + ], + [ + "None", + 1.0 + ], + [ + "None", + 1.0 + ] + ], + validator=lambda x: isinstance(x, list) and all(len(y) == 2 and isinstance(y[0], str) and isinstance(y[1], numbers.Number) for y in x) +) +default_max_lora_number = get_config_item_or_set_default( + key='default_max_lora_number', + default_value=len(default_loras) if isinstance(default_loras, list) and len(default_loras) > 0 else 5, + validator=lambda x: isinstance(x, int) and x >= 1 +) +default_cfg_scale = get_config_item_or_set_default( + key='default_cfg_scale', + default_value=7.0, + validator=lambda x: isinstance(x, numbers.Number) +) +default_sample_sharpness = get_config_item_or_set_default( + key='default_sample_sharpness', + default_value=2.0, + validator=lambda x: isinstance(x, numbers.Number) +) +default_sampler = get_config_item_or_set_default( + key='default_sampler', + default_value='dpmpp_2m_sde_gpu', + validator=lambda x: x in modules.flags.sampler_list +) +default_scheduler = get_config_item_or_set_default( + key='default_scheduler', + default_value='karras', + validator=lambda x: x in modules.flags.scheduler_list +) +default_styles = get_config_item_or_set_default( + key='default_styles', + default_value=[ + "Fooocus V2", + "Fooocus Enhance", + "Fooocus Sharp" + ], + validator=lambda x: isinstance(x, list) and all(y in modules.sdxl_styles.legal_style_names for y in x) +) +default_prompt_negative = get_config_item_or_set_default( + key='default_prompt_negative', + default_value='', + validator=lambda x: isinstance(x, str), + disable_empty_as_none=True +) +default_prompt = get_config_item_or_set_default( + key='default_prompt', + default_value='', + validator=lambda x: isinstance(x, str), + disable_empty_as_none=True +) +default_performance = get_config_item_or_set_default( + key='default_performance', + default_value=Performance.SPEED.value, + validator=lambda x: x in Performance.list() +) +default_advanced_checkbox = get_config_item_or_set_default( + key='default_advanced_checkbox', + default_value=False, + validator=lambda x: isinstance(x, bool) +) +default_max_image_number = get_config_item_or_set_default( + key='default_max_image_number', + default_value=32, + validator=lambda x: isinstance(x, int) and x >= 1 +) +default_output_format = get_config_item_or_set_default( + key='default_output_format', + default_value='png', + validator=lambda x: x in modules.flags.output_formats +) +default_image_number = get_config_item_or_set_default( + key='default_image_number', + default_value=2, + validator=lambda x: isinstance(x, int) and 1 <= x <= default_max_image_number +) +checkpoint_downloads = get_config_item_or_set_default( + key='checkpoint_downloads', + default_value={}, + validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) +) +lora_downloads = get_config_item_or_set_default( + key='lora_downloads', + default_value={}, + validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) +) +embeddings_downloads = get_config_item_or_set_default( + key='embeddings_downloads', + default_value={}, + validator=lambda x: isinstance(x, dict) and all(isinstance(k, str) and isinstance(v, str) for k, v in x.items()) +) +available_aspect_ratios = get_config_item_or_set_default( + key='available_aspect_ratios', + default_value=[ + '704*1408', '704*1344', '768*1344', '768*1280', '832*1216', '832*1152', + '896*1152', '896*1088', '960*1088', '960*1024', '1024*1024', '1024*960', + '1088*960', '1088*896', '1152*896', '1152*832', '1216*832', '1280*768', + '1344*768', '1344*704', '1408*704', '1472*704', '1536*640', '1600*640', + '1664*576', '1728*576' + ], + validator=lambda x: isinstance(x, list) and all('*' in v for v in x) and len(x) > 1 +) +default_aspect_ratio = get_config_item_or_set_default( + key='default_aspect_ratio', + default_value='1152*896' if '1152*896' in available_aspect_ratios else available_aspect_ratios[0], + validator=lambda x: x in available_aspect_ratios +) +default_inpaint_engine_version = get_config_item_or_set_default( + key='default_inpaint_engine_version', + default_value='v2.6', + validator=lambda x: x in modules.flags.inpaint_engine_versions +) +default_cfg_tsnr = get_config_item_or_set_default( + key='default_cfg_tsnr', + default_value=7.0, + validator=lambda x: isinstance(x, numbers.Number) +) +default_overwrite_step = get_config_item_or_set_default( + key='default_overwrite_step', + default_value=-1, + validator=lambda x: isinstance(x, int) +) +default_overwrite_switch = get_config_item_or_set_default( + key='default_overwrite_switch', + default_value=-1, + validator=lambda x: isinstance(x, int) +) +example_inpaint_prompts = get_config_item_or_set_default( + key='example_inpaint_prompts', + default_value=[ + 'highly detailed face', 'detailed girl face', 'detailed man face', 'detailed hand', 'beautiful eyes' + ], + validator=lambda x: isinstance(x, list) and all(isinstance(v, str) for v in x) +) +default_save_metadata_to_images = get_config_item_or_set_default( + key='default_save_metadata_to_images', + default_value=False, + validator=lambda x: isinstance(x, bool) +) +default_metadata_scheme = get_config_item_or_set_default( + key='default_metadata_scheme', + default_value=MetadataScheme.FOOOCUS.value, + validator=lambda x: x in [y[1] for y in modules.flags.metadata_scheme if y[1] == x] +) +metadata_created_by = get_config_item_or_set_default( + key='metadata_created_by', + default_value='', + validator=lambda x: isinstance(x, str) +) + +example_inpaint_prompts = [[x] for x in example_inpaint_prompts] + +config_dict["default_loras"] = default_loras = default_loras[:default_max_lora_number] + [['None', 1.0] for _ in range(default_max_lora_number - len(default_loras))] + +possible_preset_keys = [ + "default_model", + "default_refiner", + "default_refiner_switch", + "default_loras_min_weight", + "default_loras_max_weight", + "default_loras", + "default_max_lora_number", + "default_cfg_scale", + "default_sample_sharpness", + "default_sampler", + "default_scheduler", + "default_performance", + "default_prompt", + "default_prompt_negative", + "default_styles", + "default_aspect_ratio", + "default_save_metadata_to_images", + "checkpoint_downloads", + "embeddings_downloads", + "lora_downloads", +] + + +REWRITE_PRESET = False + +if REWRITE_PRESET and isinstance(args_manager.args.preset, str): + save_path = 'presets/' + args_manager.args.preset + '.json' + with open(save_path, "w", encoding="utf-8") as json_file: + json.dump({k: config_dict[k] for k in possible_preset_keys}, json_file, indent=4) + print(f'Preset saved to {save_path}. Exiting ...') + exit(0) + + +def add_ratio(x): + a, b = x.replace('*', ' ').split(' ')[:2] + a, b = int(a), int(b) + g = math.gcd(a, b) + return f'{a}×{b} \U00002223 {a // g}:{b // g}' + + +default_aspect_ratio = add_ratio(default_aspect_ratio) +available_aspect_ratios = [add_ratio(x) for x in available_aspect_ratios] + + +# Only write config in the first launch. +if not os.path.exists(config_path): + with open(config_path, "w", encoding="utf-8") as json_file: + json.dump({k: config_dict[k] for k in always_save_keys}, json_file, indent=4) + + +# Always write tutorials. +with open(config_example_path, "w", encoding="utf-8") as json_file: + cpa = config_path.replace("\\", "\\\\") + json_file.write(f'You can modify your "{cpa}" using the below keys, formats, and examples.\n' + f'Do not modify this file. Modifications in this file will not take effect.\n' + f'This file is a tutorial and example. Please edit "{cpa}" to really change any settings.\n' + + 'Remember to split the paths with "\\\\" rather than "\\", ' + 'and there is no "," before the last "}". \n\n\n') + json.dump({k: config_dict[k] for k in visited_keys}, json_file, indent=4) + +model_filenames = [] +lora_filenames = [] +sdxl_lcm_lora = 'sdxl_lcm_lora.safetensors' + + +def get_model_filenames(folder_paths, name_filter=None): + extensions = ['.pth', '.ckpt', '.bin', '.safetensors', '.fooocus.patch'] + files = [] + for folder in folder_paths: + files += get_files_from_folder(folder, extensions, name_filter) + return files + + +def update_all_model_names(): + global model_filenames, lora_filenames + model_filenames = get_model_filenames(paths_checkpoints) + lora_filenames = get_model_filenames(paths_loras) + return + + +def downloading_inpaint_models(v): + assert v in modules.flags.inpaint_engine_versions + + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/fooocus_inpaint_head.pth', + model_dir=path_inpaint, + file_name='fooocus_inpaint_head.pth' + ) + head_file = os.path.join(path_inpaint, 'fooocus_inpaint_head.pth') + patch_file = None + + if v == 'v1': + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint.fooocus.patch', + model_dir=path_inpaint, + file_name='inpaint.fooocus.patch' + ) + patch_file = os.path.join(path_inpaint, 'inpaint.fooocus.patch') + + if v == 'v2.5': + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v25.fooocus.patch', + model_dir=path_inpaint, + file_name='inpaint_v25.fooocus.patch' + ) + patch_file = os.path.join(path_inpaint, 'inpaint_v25.fooocus.patch') + + if v == 'v2.6': + load_file_from_url( + url='https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch', + model_dir=path_inpaint, + file_name='inpaint_v26.fooocus.patch' + ) + patch_file = os.path.join(path_inpaint, 'inpaint_v26.fooocus.patch') + + return head_file, patch_file + + +def downloading_sdxl_lcm_lora(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/sdxl_lcm_lora.safetensors', + model_dir=paths_loras[0], + file_name=sdxl_lcm_lora + ) + return sdxl_lcm_lora + + +def downloading_controlnet_canny(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/control-lora-canny-rank128.safetensors', + model_dir=path_controlnet, + file_name='control-lora-canny-rank128.safetensors' + ) + return os.path.join(path_controlnet, 'control-lora-canny-rank128.safetensors') + + +def downloading_controlnet_cpds(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_xl_cpds_128.safetensors', + model_dir=path_controlnet, + file_name='fooocus_xl_cpds_128.safetensors' + ) + return os.path.join(path_controlnet, 'fooocus_xl_cpds_128.safetensors') + + +def downloading_ip_adapters(v): + assert v in ['ip', 'face'] + + results = [] + + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/clip_vision_vit_h.safetensors', + model_dir=path_clip_vision, + file_name='clip_vision_vit_h.safetensors' + ) + results += [os.path.join(path_clip_vision, 'clip_vision_vit_h.safetensors')] + + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_ip_negative.safetensors', + model_dir=path_controlnet, + file_name='fooocus_ip_negative.safetensors' + ) + results += [os.path.join(path_controlnet, 'fooocus_ip_negative.safetensors')] + + if v == 'ip': + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus_sdxl_vit-h.bin', + model_dir=path_controlnet, + file_name='ip-adapter-plus_sdxl_vit-h.bin' + ) + results += [os.path.join(path_controlnet, 'ip-adapter-plus_sdxl_vit-h.bin')] + + if v == 'face': + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/ip-adapter-plus-face_sdxl_vit-h.bin', + model_dir=path_controlnet, + file_name='ip-adapter-plus-face_sdxl_vit-h.bin' + ) + results += [os.path.join(path_controlnet, 'ip-adapter-plus-face_sdxl_vit-h.bin')] + + return results + + +def downloading_upscale_model(): + load_file_from_url( + url='https://huggingface.co/lllyasviel/misc/resolve/main/fooocus_upscaler_s409985e5.bin', + model_dir=path_upscale_models, + file_name='fooocus_upscaler_s409985e5.bin' + ) + return os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin') + + +update_all_model_names() diff --git a/modules/constants.py b/modules/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..667fa8682306e192465f11733fc9814bacedfe89 --- /dev/null +++ b/modules/constants.py @@ -0,0 +1,5 @@ +# as in k-diffusion (sampling.py) +MIN_SEED = 0 +MAX_SEED = 2**63 - 1 + +AUTH_FILENAME = 'auth.json' diff --git a/modules/core.py b/modules/core.py new file mode 100644 index 0000000000000000000000000000000000000000..bfc449661d9c636e096b8e9555daa3bebb5f50e7 --- /dev/null +++ b/modules/core.py @@ -0,0 +1,339 @@ +import os +import einops +import torch +import numpy as np + +import ldm_patched.modules.model_management +import ldm_patched.modules.model_detection +import ldm_patched.modules.model_patcher +import ldm_patched.modules.utils +import ldm_patched.modules.controlnet +import modules.sample_hijack +import ldm_patched.modules.samplers +import ldm_patched.modules.latent_formats + +from ldm_patched.modules.sd import load_checkpoint_guess_config +from ldm_patched.contrib.external import VAEDecode, EmptyLatentImage, VAEEncode, VAEEncodeTiled, VAEDecodeTiled, \ + ControlNetApplyAdvanced +from ldm_patched.contrib.external_freelunch import FreeU_V2 +from ldm_patched.modules.sample import prepare_mask +from modules.lora import match_lora +from modules.util import get_file_from_folder_list +from ldm_patched.modules.lora import model_lora_keys_unet, model_lora_keys_clip +from modules.config import path_embeddings +from ldm_patched.contrib.external_model_advanced import ModelSamplingDiscrete + + +opEmptyLatentImage = EmptyLatentImage() +opVAEDecode = VAEDecode() +opVAEEncode = VAEEncode() +opVAEDecodeTiled = VAEDecodeTiled() +opVAEEncodeTiled = VAEEncodeTiled() +opControlNetApplyAdvanced = ControlNetApplyAdvanced() +opFreeU = FreeU_V2() +opModelSamplingDiscrete = ModelSamplingDiscrete() + + +class StableDiffusionModel: + def __init__(self, unet=None, vae=None, clip=None, clip_vision=None, filename=None): + self.unet = unet + self.vae = vae + self.clip = clip + self.clip_vision = clip_vision + self.filename = filename + self.unet_with_lora = unet + self.clip_with_lora = clip + self.visited_loras = '' + + self.lora_key_map_unet = {} + self.lora_key_map_clip = {} + + if self.unet is not None: + self.lora_key_map_unet = model_lora_keys_unet(self.unet.model, self.lora_key_map_unet) + self.lora_key_map_unet.update({x: x for x in self.unet.model.state_dict().keys()}) + + if self.clip is not None: + self.lora_key_map_clip = model_lora_keys_clip(self.clip.cond_stage_model, self.lora_key_map_clip) + self.lora_key_map_clip.update({x: x for x in self.clip.cond_stage_model.state_dict().keys()}) + + @torch.no_grad() + @torch.inference_mode() + def refresh_loras(self, loras): + assert isinstance(loras, list) + + if self.visited_loras == str(loras): + return + + self.visited_loras = str(loras) + + if self.unet is None: + return + + print(f'Request to load LoRAs {str(loras)} for model [{self.filename}].') + + loras_to_load = [] + + for name, weight in loras: + if name == 'None': + continue + + if os.path.exists(name): + lora_filename = name + else: + lora_filename = get_file_from_folder_list(name, modules.config.paths_loras) + + if not os.path.exists(lora_filename): + print(f'Lora file not found: {lora_filename}') + continue + + loras_to_load.append((lora_filename, weight)) + + self.unet_with_lora = self.unet.clone() if self.unet is not None else None + self.clip_with_lora = self.clip.clone() if self.clip is not None else None + + for lora_filename, weight in loras_to_load: + lora_unmatch = ldm_patched.modules.utils.load_torch_file(lora_filename, safe_load=False) + lora_unet, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_unet) + lora_clip, lora_unmatch = match_lora(lora_unmatch, self.lora_key_map_clip) + + if len(lora_unmatch) > 12: + # model mismatch + continue + + if len(lora_unmatch) > 0: + print(f'Loaded LoRA [{lora_filename}] for model [{self.filename}] ' + f'with unmatched keys {list(lora_unmatch.keys())}') + + if self.unet_with_lora is not None and len(lora_unet) > 0: + loaded_keys = self.unet_with_lora.add_patches(lora_unet, weight) + print(f'Loaded LoRA [{lora_filename}] for UNet [{self.filename}] ' + f'with {len(loaded_keys)} keys at weight {weight}.') + for item in lora_unet: + if item not in loaded_keys: + print("UNet LoRA key skipped: ", item) + + if self.clip_with_lora is not None and len(lora_clip) > 0: + loaded_keys = self.clip_with_lora.add_patches(lora_clip, weight) + print(f'Loaded LoRA [{lora_filename}] for CLIP [{self.filename}] ' + f'with {len(loaded_keys)} keys at weight {weight}.') + for item in lora_clip: + if item not in loaded_keys: + print("CLIP LoRA key skipped: ", item) + + +@torch.no_grad() +@torch.inference_mode() +def apply_freeu(model, b1, b2, s1, s2): + return opFreeU.patch(model=model, b1=b1, b2=b2, s1=s1, s2=s2)[0] + + +@torch.no_grad() +@torch.inference_mode() +def load_controlnet(ckpt_filename): + return ldm_patched.modules.controlnet.load_controlnet(ckpt_filename) + + +@torch.no_grad() +@torch.inference_mode() +def apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent): + return opControlNetApplyAdvanced.apply_controlnet(positive=positive, negative=negative, control_net=control_net, + image=image, strength=strength, start_percent=start_percent, end_percent=end_percent) + + +@torch.no_grad() +@torch.inference_mode() +def load_model(ckpt_filename): + unet, clip, vae, clip_vision = load_checkpoint_guess_config(ckpt_filename, embedding_directory=path_embeddings) + return StableDiffusionModel(unet=unet, clip=clip, vae=vae, clip_vision=clip_vision, filename=ckpt_filename) + + +@torch.no_grad() +@torch.inference_mode() +def generate_empty_latent(width=1024, height=1024, batch_size=1): + return opEmptyLatentImage.generate(width=width, height=height, batch_size=batch_size)[0] + + +@torch.no_grad() +@torch.inference_mode() +def decode_vae(vae, latent_image, tiled=False): + if tiled: + return opVAEDecodeTiled.decode(samples=latent_image, vae=vae, tile_size=512)[0] + else: + return opVAEDecode.decode(samples=latent_image, vae=vae)[0] + + +@torch.no_grad() +@torch.inference_mode() +def encode_vae(vae, pixels, tiled=False): + if tiled: + return opVAEEncodeTiled.encode(pixels=pixels, vae=vae, tile_size=512)[0] + else: + return opVAEEncode.encode(pixels=pixels, vae=vae)[0] + + +@torch.no_grad() +@torch.inference_mode() +def encode_vae_inpaint(vae, pixels, mask): + assert mask.ndim == 3 and pixels.ndim == 4 + assert mask.shape[-1] == pixels.shape[-2] + assert mask.shape[-2] == pixels.shape[-3] + + w = mask.round()[..., None] + pixels = pixels * (1 - w) + 0.5 * w + + latent = vae.encode(pixels) + B, C, H, W = latent.shape + + latent_mask = mask[:, None, :, :] + latent_mask = torch.nn.functional.interpolate(latent_mask, size=(H * 8, W * 8), mode="bilinear").round() + latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round().to(latent) + + return latent, latent_mask + + +class VAEApprox(torch.nn.Module): + def __init__(self): + super(VAEApprox, self).__init__() + self.conv1 = torch.nn.Conv2d(4, 8, (7, 7)) + self.conv2 = torch.nn.Conv2d(8, 16, (5, 5)) + self.conv3 = torch.nn.Conv2d(16, 32, (3, 3)) + self.conv4 = torch.nn.Conv2d(32, 64, (3, 3)) + self.conv5 = torch.nn.Conv2d(64, 32, (3, 3)) + self.conv6 = torch.nn.Conv2d(32, 16, (3, 3)) + self.conv7 = torch.nn.Conv2d(16, 8, (3, 3)) + self.conv8 = torch.nn.Conv2d(8, 3, (3, 3)) + self.current_type = None + + def forward(self, x): + extra = 11 + x = torch.nn.functional.interpolate(x, (x.shape[2] * 2, x.shape[3] * 2)) + x = torch.nn.functional.pad(x, (extra, extra, extra, extra)) + for layer in [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5, self.conv6, self.conv7, self.conv8]: + x = layer(x) + x = torch.nn.functional.leaky_relu(x, 0.1) + return x + + +VAE_approx_models = {} + + +@torch.no_grad() +@torch.inference_mode() +def get_previewer(model): + global VAE_approx_models + + from modules.config import path_vae_approx + is_sdxl = isinstance(model.model.latent_format, ldm_patched.modules.latent_formats.SDXL) + vae_approx_filename = os.path.join(path_vae_approx, 'xlvaeapp.pth' if is_sdxl else 'vaeapp_sd15.pth') + + if vae_approx_filename in VAE_approx_models: + VAE_approx_model = VAE_approx_models[vae_approx_filename] + else: + sd = torch.load(vae_approx_filename, map_location='cpu') + VAE_approx_model = VAEApprox() + VAE_approx_model.load_state_dict(sd) + del sd + VAE_approx_model.eval() + + if ldm_patched.modules.model_management.should_use_fp16(): + VAE_approx_model.half() + VAE_approx_model.current_type = torch.float16 + else: + VAE_approx_model.float() + VAE_approx_model.current_type = torch.float32 + + VAE_approx_model.to(ldm_patched.modules.model_management.get_torch_device()) + VAE_approx_models[vae_approx_filename] = VAE_approx_model + + @torch.no_grad() + @torch.inference_mode() + def preview_function(x0, step, total_steps): + with torch.no_grad(): + x_sample = x0.to(VAE_approx_model.current_type) + x_sample = VAE_approx_model(x_sample) * 127.5 + 127.5 + x_sample = einops.rearrange(x_sample, 'b c h w -> b h w c')[0] + x_sample = x_sample.cpu().numpy().clip(0, 255).astype(np.uint8) + return x_sample + + return preview_function + + +@torch.no_grad() +@torch.inference_mode() +def ksampler(model, positive, negative, latent, seed=None, steps=30, cfg=7.0, sampler_name='dpmpp_2m_sde_gpu', + scheduler='karras', denoise=1.0, disable_noise=False, start_step=None, last_step=None, + force_full_denoise=False, callback_function=None, refiner=None, refiner_switch=-1, + previewer_start=None, previewer_end=None, sigmas=None, noise_mean=None, disable_preview=False): + + if sigmas is not None: + sigmas = sigmas.clone().to(ldm_patched.modules.model_management.get_torch_device()) + + latent_image = latent["samples"] + + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = ldm_patched.modules.sample.prepare_noise(latent_image, seed, batch_inds) + + if isinstance(noise_mean, torch.Tensor): + noise = noise + noise_mean - torch.mean(noise, dim=1, keepdim=True) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + previewer = get_previewer(model) + + if previewer_start is None: + previewer_start = 0 + + if previewer_end is None: + previewer_end = steps + + def callback(step, x0, x, total_steps): + ldm_patched.modules.model_management.throw_exception_if_processing_interrupted() + y = None + if previewer is not None and not disable_preview: + y = previewer(x0, previewer_start + step, previewer_end) + if callback_function is not None: + callback_function(previewer_start + step, x0, x, previewer_end, y) + + disable_pbar = False + modules.sample_hijack.current_refiner = refiner + modules.sample_hijack.refiner_switch_step = refiner_switch + ldm_patched.modules.samplers.sample = modules.sample_hijack.sample_hacked + + try: + samples = ldm_patched.modules.sample.sample(model, + noise, steps, cfg, sampler_name, scheduler, + positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, + start_step=start_step, + last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, + callback=callback, + disable_pbar=disable_pbar, seed=seed, sigmas=sigmas) + + out = latent.copy() + out["samples"] = samples + finally: + modules.sample_hijack.current_refiner = None + + return out + + +@torch.no_grad() +@torch.inference_mode() +def pytorch_to_numpy(x): + return [np.clip(255. * y.cpu().numpy(), 0, 255).astype(np.uint8) for y in x] + + +@torch.no_grad() +@torch.inference_mode() +def numpy_to_pytorch(x): + y = x.astype(np.float32) / 255.0 + y = y[None] + y = np.ascontiguousarray(y.copy()) + y = torch.from_numpy(y).float() + return y diff --git a/modules/default_pipeline.py b/modules/default_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..f8edfae105fa45a103f9e1463e6abbee2c19444c --- /dev/null +++ b/modules/default_pipeline.py @@ -0,0 +1,498 @@ +import modules.core as core +import os +import torch +import modules.patch +import modules.config +import ldm_patched.modules.model_management +import ldm_patched.modules.latent_formats +import modules.inpaint_worker +import extras.vae_interpose as vae_interpose +from extras.expansion import FooocusExpansion + +from ldm_patched.modules.model_base import SDXL, SDXLRefiner +from modules.sample_hijack import clip_separate +from modules.util import get_file_from_folder_list + + +model_base = core.StableDiffusionModel() +model_refiner = core.StableDiffusionModel() + +final_expansion = None +final_unet = None +final_clip = None +final_vae = None +final_refiner_unet = None +final_refiner_vae = None + +loaded_ControlNets = {} + + +@torch.no_grad() +@torch.inference_mode() +def refresh_controlnets(model_paths): + global loaded_ControlNets + cache = {} + for p in model_paths: + if p is not None: + if p in loaded_ControlNets: + cache[p] = loaded_ControlNets[p] + else: + cache[p] = core.load_controlnet(p) + loaded_ControlNets = cache + return + + +@torch.no_grad() +@torch.inference_mode() +def assert_model_integrity(): + error_message = None + + if not isinstance(model_base.unet_with_lora.model, SDXL): + error_message = 'You have selected base model other than SDXL. This is not supported yet.' + + if error_message is not None: + raise NotImplementedError(error_message) + + return True + + +@torch.no_grad() +@torch.inference_mode() +def refresh_base_model(name): + global model_base + + filename = get_file_from_folder_list(name, modules.config.paths_checkpoints) + + if model_base.filename == filename: + return + + model_base = core.StableDiffusionModel() + model_base = core.load_model(filename) + print(f'Base model loaded: {model_base.filename}') + return + + +@torch.no_grad() +@torch.inference_mode() +def refresh_refiner_model(name): + global model_refiner + + filename = get_file_from_folder_list(name, modules.config.paths_checkpoints) + + if model_refiner.filename == filename: + return + + model_refiner = core.StableDiffusionModel() + + if name == 'None': + print(f'Refiner unloaded.') + return + + model_refiner = core.load_model(filename) + print(f'Refiner model loaded: {model_refiner.filename}') + + if isinstance(model_refiner.unet.model, SDXL): + model_refiner.clip = None + model_refiner.vae = None + elif isinstance(model_refiner.unet.model, SDXLRefiner): + model_refiner.clip = None + model_refiner.vae = None + else: + model_refiner.clip = None + + return + + +@torch.no_grad() +@torch.inference_mode() +def synthesize_refiner_model(): + global model_base, model_refiner + + print('Synthetic Refiner Activated') + model_refiner = core.StableDiffusionModel( + unet=model_base.unet, + vae=model_base.vae, + clip=model_base.clip, + clip_vision=model_base.clip_vision, + filename=model_base.filename + ) + model_refiner.vae = None + model_refiner.clip = None + model_refiner.clip_vision = None + + return + + +@torch.no_grad() +@torch.inference_mode() +def refresh_loras(loras, base_model_additional_loras=None): + global model_base, model_refiner + + if not isinstance(base_model_additional_loras, list): + base_model_additional_loras = [] + + model_base.refresh_loras(loras + base_model_additional_loras) + model_refiner.refresh_loras(loras) + + return + + +@torch.no_grad() +@torch.inference_mode() +def clip_encode_single(clip, text, verbose=False): + cached = clip.fcs_cond_cache.get(text, None) + if cached is not None: + if verbose: + print(f'[CLIP Cached] {text}') + return cached + tokens = clip.tokenize(text) + result = clip.encode_from_tokens(tokens, return_pooled=True) + clip.fcs_cond_cache[text] = result + if verbose: + print(f'[CLIP Encoded] {text}') + return result + + +@torch.no_grad() +@torch.inference_mode() +def clone_cond(conds): + results = [] + + for c, p in conds: + p = p["pooled_output"] + + if isinstance(c, torch.Tensor): + c = c.clone() + + if isinstance(p, torch.Tensor): + p = p.clone() + + results.append([c, {"pooled_output": p}]) + + return results + + +@torch.no_grad() +@torch.inference_mode() +def clip_encode(texts, pool_top_k=1): + global final_clip + + if final_clip is None: + return None + if not isinstance(texts, list): + return None + if len(texts) == 0: + return None + + cond_list = [] + pooled_acc = 0 + + for i, text in enumerate(texts): + cond, pooled = clip_encode_single(final_clip, text) + cond_list.append(cond) + if i < pool_top_k: + pooled_acc += pooled + + return [[torch.cat(cond_list, dim=1), {"pooled_output": pooled_acc}]] + + +@torch.no_grad() +@torch.inference_mode() +def clear_all_caches(): + final_clip.fcs_cond_cache = {} + + +@torch.no_grad() +@torch.inference_mode() +def prepare_text_encoder(async_call=True): + if async_call: + # TODO: make sure that this is always called in an async way so that users cannot feel it. + pass + assert_model_integrity() + ldm_patched.modules.model_management.load_models_gpu([final_clip.patcher, final_expansion.patcher]) + return + + +@torch.no_grad() +@torch.inference_mode() +def refresh_everything(refiner_model_name, base_model_name, loras, + base_model_additional_loras=None, use_synthetic_refiner=False): + global final_unet, final_clip, final_vae, final_refiner_unet, final_refiner_vae, final_expansion + + final_unet = None + final_clip = None + final_vae = None + final_refiner_unet = None + final_refiner_vae = None + + if use_synthetic_refiner and refiner_model_name == 'None': + print('Synthetic Refiner Activated') + refresh_base_model(base_model_name) + synthesize_refiner_model() + else: + refresh_refiner_model(refiner_model_name) + refresh_base_model(base_model_name) + + refresh_loras(loras, base_model_additional_loras=base_model_additional_loras) + assert_model_integrity() + + final_unet = model_base.unet_with_lora + final_clip = model_base.clip_with_lora + final_vae = model_base.vae + + final_refiner_unet = model_refiner.unet_with_lora + final_refiner_vae = model_refiner.vae + + if final_expansion is None: + final_expansion = FooocusExpansion() + + prepare_text_encoder(async_call=True) + clear_all_caches() + return + + +refresh_everything( + refiner_model_name=modules.config.default_refiner_model_name, + base_model_name=modules.config.default_base_model_name, + loras=modules.config.default_loras +) + + +@torch.no_grad() +@torch.inference_mode() +def vae_parse(latent): + if final_refiner_vae is None: + return latent + + result = vae_interpose.parse(latent["samples"]) + return {'samples': result} + + +@torch.no_grad() +@torch.inference_mode() +def calculate_sigmas_all(sampler, model, scheduler, steps): + from ldm_patched.modules.samplers import calculate_sigmas_scheduler + + discard_penultimate_sigma = False + if sampler in ['dpm_2', 'dpm_2_ancestral']: + steps += 1 + discard_penultimate_sigma = True + + sigmas = calculate_sigmas_scheduler(model, scheduler, steps) + + if discard_penultimate_sigma: + sigmas = torch.cat([sigmas[:-2], sigmas[-1:]]) + return sigmas + + +@torch.no_grad() +@torch.inference_mode() +def calculate_sigmas(sampler, model, scheduler, steps, denoise): + if denoise is None or denoise > 0.9999: + sigmas = calculate_sigmas_all(sampler, model, scheduler, steps) + else: + new_steps = int(steps / denoise) + sigmas = calculate_sigmas_all(sampler, model, scheduler, new_steps) + sigmas = sigmas[-(steps + 1):] + return sigmas + + +@torch.no_grad() +@torch.inference_mode() +def get_candidate_vae(steps, switch, denoise=1.0, refiner_swap_method='joint'): + assert refiner_swap_method in ['joint', 'separate', 'vae'] + + if final_refiner_vae is not None and final_refiner_unet is not None: + if denoise > 0.9: + return final_vae, final_refiner_vae + else: + if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834 + return final_vae, None + else: + return final_refiner_vae, None + + return final_vae, final_refiner_vae + + +@torch.no_grad() +@torch.inference_mode() +def process_diffusion(positive_cond, negative_cond, steps, switch, width, height, image_seed, callback, sampler_name, scheduler_name, latent=None, denoise=1.0, tiled=False, cfg_scale=7.0, refiner_swap_method='joint', disable_preview=False): + target_unet, target_vae, target_refiner_unet, target_refiner_vae, target_clip \ + = final_unet, final_vae, final_refiner_unet, final_refiner_vae, final_clip + + assert refiner_swap_method in ['joint', 'separate', 'vae'] + + if final_refiner_vae is not None and final_refiner_unet is not None: + # Refiner Use Different VAE (then it is SD15) + if denoise > 0.9: + refiner_swap_method = 'vae' + else: + refiner_swap_method = 'joint' + if denoise > (float(steps - switch) / float(steps)) ** 0.834: # karras 0.834 + target_unet, target_vae, target_refiner_unet, target_refiner_vae \ + = final_unet, final_vae, None, None + print(f'[Sampler] only use Base because of partial denoise.') + else: + positive_cond = clip_separate(positive_cond, target_model=final_refiner_unet.model, target_clip=final_clip) + negative_cond = clip_separate(negative_cond, target_model=final_refiner_unet.model, target_clip=final_clip) + target_unet, target_vae, target_refiner_unet, target_refiner_vae \ + = final_refiner_unet, final_refiner_vae, None, None + print(f'[Sampler] only use Refiner because of partial denoise.') + + print(f'[Sampler] refiner_swap_method = {refiner_swap_method}') + + if latent is None: + initial_latent = core.generate_empty_latent(width=width, height=height, batch_size=1) + else: + initial_latent = latent + + minmax_sigmas = calculate_sigmas(sampler=sampler_name, scheduler=scheduler_name, model=final_unet.model, steps=steps, denoise=denoise) + sigma_min, sigma_max = minmax_sigmas[minmax_sigmas > 0].min(), minmax_sigmas.max() + sigma_min = float(sigma_min.cpu().numpy()) + sigma_max = float(sigma_max.cpu().numpy()) + print(f'[Sampler] sigma_min = {sigma_min}, sigma_max = {sigma_max}') + + modules.patch.BrownianTreeNoiseSamplerPatched.global_init( + initial_latent['samples'].to(ldm_patched.modules.model_management.get_torch_device()), + sigma_min, sigma_max, seed=image_seed, cpu=False) + + decoded_latent = None + + if refiner_swap_method == 'joint': + sampled_latent = core.ksampler( + model=target_unet, + refiner=target_refiner_unet, + positive=positive_cond, + negative=negative_cond, + latent=initial_latent, + steps=steps, start_step=0, last_step=steps, disable_noise=False, force_full_denoise=True, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + refiner_switch=switch, + previewer_start=0, + previewer_end=steps, + disable_preview=disable_preview + ) + decoded_latent = core.decode_vae(vae=target_vae, latent_image=sampled_latent, tiled=tiled) + + if refiner_swap_method == 'separate': + sampled_latent = core.ksampler( + model=target_unet, + positive=positive_cond, + negative=negative_cond, + latent=initial_latent, + steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=False, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=0, + previewer_end=steps, + disable_preview=disable_preview + ) + print('Refiner swapped by changing ksampler. Noise preserved.') + + target_model = target_refiner_unet + if target_model is None: + target_model = target_unet + print('Use base model to refine itself - this may because of developer mode.') + + sampled_latent = core.ksampler( + model=target_model, + positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip), + negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip), + latent=sampled_latent, + steps=steps, start_step=switch, last_step=steps, disable_noise=True, force_full_denoise=True, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=switch, + previewer_end=steps, + disable_preview=disable_preview + ) + + target_model = target_refiner_vae + if target_model is None: + target_model = target_vae + decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled) + + if refiner_swap_method == 'vae': + modules.patch.patch_settings[os.getpid()].eps_record = 'vae' + + if modules.inpaint_worker.current_task is not None: + modules.inpaint_worker.current_task.unswap() + + sampled_latent = core.ksampler( + model=target_unet, + positive=positive_cond, + negative=negative_cond, + latent=initial_latent, + steps=steps, start_step=0, last_step=switch, disable_noise=False, force_full_denoise=True, + seed=image_seed, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=0, + previewer_end=steps, + disable_preview=disable_preview + ) + print('Fooocus VAE-based swap.') + + target_model = target_refiner_unet + if target_model is None: + target_model = target_unet + print('Use base model to refine itself - this may because of developer mode.') + + sampled_latent = vae_parse(sampled_latent) + + k_sigmas = 1.4 + sigmas = calculate_sigmas(sampler=sampler_name, + scheduler=scheduler_name, + model=target_model.model, + steps=steps, + denoise=denoise)[switch:] * k_sigmas + len_sigmas = len(sigmas) - 1 + + noise_mean = torch.mean(modules.patch.patch_settings[os.getpid()].eps_record, dim=1, keepdim=True) + + if modules.inpaint_worker.current_task is not None: + modules.inpaint_worker.current_task.swap() + + sampled_latent = core.ksampler( + model=target_model, + positive=clip_separate(positive_cond, target_model=target_model.model, target_clip=target_clip), + negative=clip_separate(negative_cond, target_model=target_model.model, target_clip=target_clip), + latent=sampled_latent, + steps=len_sigmas, start_step=0, last_step=len_sigmas, disable_noise=False, force_full_denoise=True, + seed=image_seed+1, + denoise=denoise, + callback_function=callback, + cfg=cfg_scale, + sampler_name=sampler_name, + scheduler=scheduler_name, + previewer_start=switch, + previewer_end=steps, + sigmas=sigmas, + noise_mean=noise_mean, + disable_preview=disable_preview + ) + + target_model = target_refiner_vae + if target_model is None: + target_model = target_vae + decoded_latent = core.decode_vae(vae=target_model, latent_image=sampled_latent, tiled=tiled) + + images = core.pytorch_to_numpy(decoded_latent) + modules.patch.patch_settings[os.getpid()].eps_record = None + return images diff --git a/modules/flags.py b/modules/flags.py new file mode 100644 index 0000000000000000000000000000000000000000..6f12bc8f3f27c4b9ae06f2ee7ac0a90e46122b16 --- /dev/null +++ b/modules/flags.py @@ -0,0 +1,125 @@ +from enum import IntEnum, Enum + +disabled = 'Disabled' +enabled = 'Enabled' +subtle_variation = 'Vary (Subtle)' +strong_variation = 'Vary (Strong)' +upscale_15 = 'Upscale (1.5x)' +upscale_2 = 'Upscale (2x)' +upscale_fast = 'Upscale (Fast 2x)' + +uov_list = [ + disabled, subtle_variation, strong_variation, upscale_15, upscale_2, upscale_fast +] + +CIVITAI_NO_KARRAS = ["euler", "euler_ancestral", "heun", "dpm_fast", "dpm_adaptive", "ddim", "uni_pc"] + +# fooocus: a1111 (Civitai) +KSAMPLER = { + "euler": "Euler", + "euler_ancestral": "Euler a", + "heun": "Heun", + "heunpp2": "", + "dpm_2": "DPM2", + "dpm_2_ancestral": "DPM2 a", + "lms": "LMS", + "dpm_fast": "DPM fast", + "dpm_adaptive": "DPM adaptive", + "dpmpp_2s_ancestral": "DPM++ 2S a", + "dpmpp_sde": "DPM++ SDE", + "dpmpp_sde_gpu": "DPM++ SDE", + "dpmpp_2m": "DPM++ 2M", + "dpmpp_2m_sde": "DPM++ 2M SDE", + "dpmpp_2m_sde_gpu": "DPM++ 2M SDE", + "dpmpp_3m_sde": "", + "dpmpp_3m_sde_gpu": "", + "ddpm": "", + "lcm": "LCM" +} + +SAMPLER_EXTRA = { + "ddim": "DDIM", + "uni_pc": "UniPC", + "uni_pc_bh2": "" +} + +SAMPLERS = KSAMPLER | SAMPLER_EXTRA + +KSAMPLER_NAMES = list(KSAMPLER.keys()) + +SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "lcm", "turbo"] +SAMPLER_NAMES = KSAMPLER_NAMES + list(SAMPLER_EXTRA.keys()) + +sampler_list = SAMPLER_NAMES +scheduler_list = SCHEDULER_NAMES + +refiner_swap_method = 'joint' + +cn_ip = "ImagePrompt" +cn_ip_face = "FaceSwap" +cn_canny = "PyraCanny" +cn_cpds = "CPDS" + +ip_list = [cn_ip, cn_canny, cn_cpds, cn_ip_face] +default_ip = cn_ip + +default_parameters = { + cn_ip: (0.5, 0.6), cn_ip_face: (0.9, 0.75), cn_canny: (0.5, 1.0), cn_cpds: (0.5, 1.0) +} # stop, weight + +output_formats = ['png', 'jpg', 'webp'] + +inpaint_engine_versions = ['None', 'v1', 'v2.5', 'v2.6'] +inpaint_option_default = 'Inpaint or Outpaint (default)' +inpaint_option_detail = 'Improve Detail (face, hand, eyes, etc.)' +inpaint_option_modify = 'Modify Content (add objects, change background, etc.)' +inpaint_options = [inpaint_option_default, inpaint_option_detail, inpaint_option_modify] + +desc_type_photo = 'Photograph' +desc_type_anime = 'Art/Anime' + + +class MetadataScheme(Enum): + FOOOCUS = 'fooocus' + A1111 = 'a1111' + + +metadata_scheme = [ + (f'{MetadataScheme.FOOOCUS.value} (json)', MetadataScheme.FOOOCUS.value), + (f'{MetadataScheme.A1111.value} (plain text)', MetadataScheme.A1111.value), +] + +lora_count = 5 + +controlnet_image_count = 4 + + +class Steps(IntEnum): + QUALITY = 60 + SPEED = 30 + EXTREME_SPEED = 8 + + +class StepsUOV(IntEnum): + QUALITY = 36 + SPEED = 18 + EXTREME_SPEED = 8 + + +class Performance(Enum): + QUALITY = 'Quality' + SPEED = 'Speed' + EXTREME_SPEED = 'Extreme Speed' + + @classmethod + def list(cls) -> list: + return list(map(lambda c: c.value, cls)) + + def steps(self) -> int | None: + return Steps[self.name].value if Steps[self.name] else None + + def steps_uov(self) -> int | None: + return StepsUOV[self.name].value if Steps[self.name] else None + + +performance_selections = Performance.list() diff --git a/modules/gradio_hijack.py b/modules/gradio_hijack.py new file mode 100644 index 0000000000000000000000000000000000000000..181429ec39a0336ffa43ebf23e4fa2b87dd97674 --- /dev/null +++ b/modules/gradio_hijack.py @@ -0,0 +1,480 @@ +"""gr.Image() component.""" + +from __future__ import annotations + +import warnings +from pathlib import Path +from typing import Any, Literal + +import numpy as np +import PIL +import PIL.ImageOps +import gradio.routes +import importlib + +from gradio_client import utils as client_utils +from gradio_client.documentation import document, set_documentation_group +from gradio_client.serializing import ImgSerializable +from PIL import Image as _Image # using _ to minimize namespace pollution + +from gradio import processing_utils, utils +from gradio.components.base import IOComponent, _Keywords, Block +from gradio.deprecation import warn_style_method_deprecation +from gradio.events import ( + Changeable, + Clearable, + Editable, + EventListenerMethod, + Selectable, + Streamable, + Uploadable, +) +from gradio.interpretation import TokenInterpretable + +set_documentation_group("component") +_Image.init() # fixes https://github.com/gradio-app/gradio/issues/2843 + + +@document() +class Image( + Editable, + Clearable, + Changeable, + Streamable, + Selectable, + Uploadable, + IOComponent, + ImgSerializable, + TokenInterpretable, +): + """ + Creates an image component that can be used to upload/draw images (as an input) or display images (as an output). + Preprocessing: passes the uploaded image as a {numpy.array}, {PIL.Image} or {str} filepath depending on `type` -- unless `tool` is `sketch` AND source is one of `upload` or `webcam`. In these cases, a {dict} with keys `image` and `mask` is passed, and the format of the corresponding values depends on `type`. + Postprocessing: expects a {numpy.array}, {PIL.Image} or {str} or {pathlib.Path} filepath to an image and displays the image. + Examples-format: a {str} filepath to a local file that contains the image. + Demos: image_mod, image_mod_default_image + Guides: image-classification-in-pytorch, image-classification-in-tensorflow, image-classification-with-vision-transformers, building-a-pictionary_app, create-your-own-friends-with-a-gan + """ + + def __init__( + self, + value: str | _Image.Image | np.ndarray | None = None, + *, + shape: tuple[int, int] | None = None, + height: int | None = None, + width: int | None = None, + image_mode: Literal[ + "1", "L", "P", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F" + ] = "RGB", + invert_colors: bool = False, + source: Literal["upload", "webcam", "canvas"] = "upload", + tool: Literal["editor", "select", "sketch", "color-sketch"] | None = None, + type: Literal["numpy", "pil", "filepath"] = "numpy", + label: str | None = None, + every: float | None = None, + show_label: bool | None = None, + show_download_button: bool = True, + container: bool = True, + scale: int | None = None, + min_width: int = 160, + interactive: bool | None = None, + visible: bool = True, + streaming: bool = False, + elem_id: str | None = None, + elem_classes: list[str] | str | None = None, + mirror_webcam: bool = True, + brush_radius: float | None = None, + brush_color: str = "#000000", + mask_opacity: float = 0.7, + show_share_button: bool | None = None, + **kwargs, + ): + """ + Parameters: + value: A PIL Image, numpy array, path or URL for the default value that Image component is going to take. If callable, the function will be called whenever the app loads to set the initial value of the component. + shape: (width, height) shape to crop and resize image when passed to function. If None, matches input image size. Pass None for either width or height to only crop and resize the other. + height: Height of the displayed image in pixels. + width: Width of the displayed image in pixels. + image_mode: "RGB" if color, or "L" if black and white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html for other supported image modes and their meaning. + invert_colors: whether to invert the image as a preprocessing step. + source: Source of image. "upload" creates a box where user can drop an image file, "webcam" allows user to take snapshot from their webcam, "canvas" defaults to a white image that can be edited and drawn upon with tools. + tool: Tools used for editing. "editor" allows a full screen editor (and is the default if source is "upload" or "webcam"), "select" provides a cropping and zoom tool, "sketch" allows you to create a binary sketch (and is the default if source="canvas"), and "color-sketch" allows you to created a sketch in different colors. "color-sketch" can be used with source="upload" or "webcam" to allow sketching on an image. "sketch" can also be used with "upload" or "webcam" to create a mask over an image and in that case both the image and mask are passed into the function as a dictionary with keys "image" and "mask" respectively. + type: The format the image is converted to before being passed into the prediction function. "numpy" converts the image to a numpy array with shape (height, width, 3) and values from 0 to 255, "pil" converts the image to a PIL image object, "filepath" passes a str path to a temporary file containing the image. + label: component name in interface. + every: If `value` is a callable, run the function 'every' number of seconds while the client connection is open. Has no effect otherwise. Queue must be enabled. The event can be accessed (e.g. to cancel it) via this component's .load_event attribute. + show_label: if True, will display label. + show_download_button: If True, will display button to download image. + container: If True, will place the component in a container - providing some extra padding around the border. + scale: relative width compared to adjacent Components in a Row. For example, if Component A has scale=2, and Component B has scale=1, A will be twice as wide as B. Should be an integer. + min_width: minimum pixel width, will wrap if not sufficient screen space to satisfy this value. If a certain scale value results in this Component being narrower than min_width, the min_width parameter will be respected first. + interactive: if True, will allow users to upload and edit an image; if False, can only be used to display images. If not provided, this is inferred based on whether the component is used as an input or output. + visible: If False, component will be hidden. + streaming: If True when used in a `live` interface, will automatically stream webcam feed. Only valid is source is 'webcam'. + elem_id: An optional string that is assigned as the id of this component in the HTML DOM. Can be used for targeting CSS styles. + elem_classes: An optional list of strings that are assigned as the classes of this component in the HTML DOM. Can be used for targeting CSS styles. + mirror_webcam: If True webcam will be mirrored. Default is True. + brush_radius: Size of the brush for Sketch. Default is None which chooses a sensible default + brush_color: Color of the brush for Sketch as hex string. Default is "#000000". + mask_opacity: Opacity of mask drawn on image, as a value between 0 and 1. + show_share_button: If True, will show a share icon in the corner of the component that allows user to share outputs to Hugging Face Spaces Discussions. If False, icon does not appear. If set to None (default behavior), then the icon appears if this Gradio app is launched on Spaces, but not otherwise. + """ + self.brush_radius = brush_radius + self.brush_color = brush_color + self.mask_opacity = mask_opacity + self.mirror_webcam = mirror_webcam + valid_types = ["numpy", "pil", "filepath"] + if type not in valid_types: + raise ValueError( + f"Invalid value for parameter `type`: {type}. Please choose from one of: {valid_types}" + ) + self.type = type + self.shape = shape + self.height = height + self.width = width + self.image_mode = image_mode + valid_sources = ["upload", "webcam", "canvas"] + if source not in valid_sources: + raise ValueError( + f"Invalid value for parameter `source`: {source}. Please choose from one of: {valid_sources}" + ) + self.source = source + if tool is None: + self.tool = "sketch" if source == "canvas" else "editor" + else: + self.tool = tool + self.invert_colors = invert_colors + self.streaming = streaming + self.show_download_button = show_download_button + if streaming and source != "webcam": + raise ValueError("Image streaming only available if source is 'webcam'.") + self.select: EventListenerMethod + """ + Event listener for when the user clicks on a pixel within the image. + Uses event data gradio.SelectData to carry `index` to refer to the [x, y] coordinates of the clicked pixel. + See EventData documentation on how to use this event data. + """ + self.show_share_button = ( + (utils.get_space() is not None) + if show_share_button is None + else show_share_button + ) + IOComponent.__init__( + self, + label=label, + every=every, + show_label=show_label, + container=container, + scale=scale, + min_width=min_width, + interactive=interactive, + visible=visible, + elem_id=elem_id, + elem_classes=elem_classes, + value=value, + **kwargs, + ) + TokenInterpretable.__init__(self) + + def get_config(self): + return { + "image_mode": self.image_mode, + "shape": self.shape, + "height": self.height, + "width": self.width, + "source": self.source, + "tool": self.tool, + "value": self.value, + "streaming": self.streaming, + "mirror_webcam": self.mirror_webcam, + "brush_radius": self.brush_radius, + "brush_color": self.brush_color, + "mask_opacity": self.mask_opacity, + "selectable": self.selectable, + "show_share_button": self.show_share_button, + "show_download_button": self.show_download_button, + **IOComponent.get_config(self), + } + + @staticmethod + def update( + value: Any | Literal[_Keywords.NO_VALUE] | None = _Keywords.NO_VALUE, + height: int | None = None, + width: int | None = None, + label: str | None = None, + show_label: bool | None = None, + show_download_button: bool | None = None, + container: bool | None = None, + scale: int | None = None, + min_width: int | None = None, + interactive: bool | None = None, + visible: bool | None = None, + brush_radius: float | None = None, + brush_color: str | None = None, + mask_opacity: float | None = None, + show_share_button: bool | None = None, + ): + return { + "height": height, + "width": width, + "label": label, + "show_label": show_label, + "show_download_button": show_download_button, + "container": container, + "scale": scale, + "min_width": min_width, + "interactive": interactive, + "visible": visible, + "value": value, + "brush_radius": brush_radius, + "brush_color": brush_color, + "mask_opacity": mask_opacity, + "show_share_button": show_share_button, + "__type__": "update", + } + + def _format_image( + self, im: _Image.Image | None + ) -> np.ndarray | _Image.Image | str | None: + """Helper method to format an image based on self.type""" + if im is None: + return im + fmt = im.format + if self.type == "pil": + return im + elif self.type == "numpy": + return np.array(im) + elif self.type == "filepath": + path = self.pil_to_temp_file( + im, dir=self.DEFAULT_TEMP_DIR, format=fmt or "png" + ) + self.temp_files.add(path) + return path + else: + raise ValueError( + "Unknown type: " + + str(self.type) + + ". Please choose from: 'numpy', 'pil', 'filepath'." + ) + + def preprocess( + self, x: str | dict[str, str] + ) -> np.ndarray | _Image.Image | str | dict | None: + """ + Parameters: + x: base64 url data, or (if tool == "sketch") a dict of image and mask base64 url data + Returns: + image in requested format, or (if tool == "sketch") a dict of image and mask in requested format + """ + if x is None: + return x + + mask = None + + if self.tool == "sketch" and self.source in ["upload", "webcam"]: + if isinstance(x, dict): + x, mask = x["image"], x["mask"] + + assert isinstance(x, str) + im = processing_utils.decode_base64_to_image(x) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + im = im.convert(self.image_mode) + if self.shape is not None: + im = processing_utils.resize_and_crop(im, self.shape) + if self.invert_colors: + im = PIL.ImageOps.invert(im) + if ( + self.source == "webcam" + and self.mirror_webcam is True + and self.tool != "color-sketch" + ): + im = PIL.ImageOps.mirror(im) + + if self.tool == "sketch" and self.source in ["upload", "webcam"]: + if mask is not None: + mask_im = processing_utils.decode_base64_to_image(mask) + if mask_im.mode == "RGBA": # whiten any opaque pixels in the mask + alpha_data = mask_im.getchannel("A").convert("L") + mask_im = _Image.merge("RGB", [alpha_data, alpha_data, alpha_data]) + return { + "image": self._format_image(im), + "mask": self._format_image(mask_im), + } + else: + return { + "image": self._format_image(im), + "mask": None, + } + + return self._format_image(im) + + def postprocess( + self, y: np.ndarray | _Image.Image | str | Path | None + ) -> str | None: + """ + Parameters: + y: image as a numpy array, PIL Image, string/Path filepath, or string URL + Returns: + base64 url data + """ + if y is None: + return None + if isinstance(y, np.ndarray): + return processing_utils.encode_array_to_base64(y) + elif isinstance(y, _Image.Image): + return processing_utils.encode_pil_to_base64(y) + elif isinstance(y, (str, Path)): + return client_utils.encode_url_or_file_to_base64(y) + else: + raise ValueError("Cannot process this value as an Image") + + def set_interpret_parameters(self, segments: int = 16): + """ + Calculates interpretation score of image subsections by splitting the image into subsections, then using a "leave one out" method to calculate the score of each subsection by whiting out the subsection and measuring the delta of the output value. + Parameters: + segments: Number of interpretation segments to split image into. + """ + self.interpretation_segments = segments + return self + + def _segment_by_slic(self, x): + """ + Helper method that segments an image into superpixels using slic. + Parameters: + x: base64 representation of an image + """ + x = processing_utils.decode_base64_to_image(x) + if self.shape is not None: + x = processing_utils.resize_and_crop(x, self.shape) + resized_and_cropped_image = np.array(x) + try: + from skimage.segmentation import slic + except (ImportError, ModuleNotFoundError) as err: + raise ValueError( + "Error: running this interpretation for images requires scikit-image, please install it first." + ) from err + try: + segments_slic = slic( + resized_and_cropped_image, + self.interpretation_segments, + compactness=10, + sigma=1, + start_label=1, + ) + except TypeError: # For skimage 0.16 and older + segments_slic = slic( + resized_and_cropped_image, + self.interpretation_segments, + compactness=10, + sigma=1, + ) + return segments_slic, resized_and_cropped_image + + def tokenize(self, x): + """ + Segments image into tokens, masks, and leave-one-out-tokens + Parameters: + x: base64 representation of an image + Returns: + tokens: list of tokens, used by the get_masked_input() method + leave_one_out_tokens: list of left-out tokens, used by the get_interpretation_neighbors() method + masks: list of masks, used by the get_interpretation_neighbors() method + """ + segments_slic, resized_and_cropped_image = self._segment_by_slic(x) + tokens, masks, leave_one_out_tokens = [], [], [] + replace_color = np.mean(resized_and_cropped_image, axis=(0, 1)) + for segment_value in np.unique(segments_slic): + mask = segments_slic == segment_value + image_screen = np.copy(resized_and_cropped_image) + image_screen[segments_slic == segment_value] = replace_color + leave_one_out_tokens.append( + processing_utils.encode_array_to_base64(image_screen) + ) + token = np.copy(resized_and_cropped_image) + token[segments_slic != segment_value] = 0 + tokens.append(token) + masks.append(mask) + return tokens, leave_one_out_tokens, masks + + def get_masked_inputs(self, tokens, binary_mask_matrix): + masked_inputs = [] + for binary_mask_vector in binary_mask_matrix: + masked_input = np.zeros_like(tokens[0], dtype=int) + for token, b in zip(tokens, binary_mask_vector): + masked_input = masked_input + token * int(b) + masked_inputs.append(processing_utils.encode_array_to_base64(masked_input)) + return masked_inputs + + def get_interpretation_scores( + self, x, neighbors, scores, masks, tokens=None, **kwargs + ) -> list[list[float]]: + """ + Returns: + A 2D array representing the interpretation score of each pixel of the image. + """ + x = processing_utils.decode_base64_to_image(x) + if self.shape is not None: + x = processing_utils.resize_and_crop(x, self.shape) + x = np.array(x) + output_scores = np.zeros((x.shape[0], x.shape[1])) + + for score, mask in zip(scores, masks): + output_scores += score * mask + + max_val, min_val = np.max(output_scores), np.min(output_scores) + if max_val > 0: + output_scores = (output_scores - min_val) / (max_val - min_val) + return output_scores.tolist() + + def style(self, *, height: int | None = None, width: int | None = None, **kwargs): + """ + This method is deprecated. Please set these arguments in the constructor instead. + """ + warn_style_method_deprecation() + if height is not None: + self.height = height + if width is not None: + self.width = width + return self + + def check_streamable(self): + if self.source != "webcam": + raise ValueError("Image streaming only available if source is 'webcam'.") + + def as_example(self, input_data: str | None) -> str: + if input_data is None: + return "" + elif ( + self.root_url + ): # If an externally hosted image, don't convert to absolute path + return input_data + return str(utils.abspath(input_data)) + + +all_components = [] + +if not hasattr(Block, 'original__init__'): + Block.original_init = Block.__init__ + + +def blk_ini(self, *args, **kwargs): + all_components.append(self) + return Block.original_init(self, *args, **kwargs) + + +Block.__init__ = blk_ini + + +gradio.routes.asyncio = importlib.reload(gradio.routes.asyncio) + +if not hasattr(gradio.routes.asyncio, 'original_wait_for'): + gradio.routes.asyncio.original_wait_for = gradio.routes.asyncio.wait_for + + +def patched_wait_for(fut, timeout): + del timeout + return gradio.routes.asyncio.original_wait_for(fut, timeout=65535) + + +gradio.routes.asyncio.wait_for = patched_wait_for + diff --git a/modules/html.py b/modules/html.py new file mode 100644 index 0000000000000000000000000000000000000000..769151a9ff86e460d69d3598fcac0481d59cf17b --- /dev/null +++ b/modules/html.py @@ -0,0 +1,146 @@ +css = ''' +.loader-container { + display: flex; /* Use flex to align items horizontally */ + align-items: center; /* Center items vertically within the container */ + white-space: nowrap; /* Prevent line breaks within the container */ +} + +.loader { + border: 8px solid #f3f3f3; /* Light grey */ + border-top: 8px solid #3498db; /* Blue */ + border-radius: 50%; + width: 30px; + height: 30px; + animation: spin 2s linear infinite; +} + +@keyframes spin { + 0% { transform: rotate(0deg); } + 100% { transform: rotate(360deg); } +} + +/* Style the progress bar */ +progress { + appearance: none; /* Remove default styling */ + height: 20px; /* Set the height of the progress bar */ + border-radius: 5px; /* Round the corners of the progress bar */ + background-color: #f3f3f3; /* Light grey background */ + width: 100%; +} + +/* Style the progress bar container */ +.progress-container { + margin-left: 20px; + margin-right: 20px; + flex-grow: 1; /* Allow the progress container to take up remaining space */ +} + +/* Set the color of the progress bar fill */ +progress::-webkit-progress-value { + background-color: #3498db; /* Blue color for the fill */ +} + +progress::-moz-progress-bar { + background-color: #3498db; /* Blue color for the fill in Firefox */ +} + +/* Style the text on the progress bar */ +progress::after { + content: attr(value '%'); /* Display the progress value followed by '%' */ + position: absolute; + top: 50%; + left: 50%; + transform: translate(-50%, -50%); + color: white; /* Set text color */ + font-size: 14px; /* Set font size */ +} + +/* Style other texts */ +.loader-container > span { + margin-left: 5px; /* Add spacing between the progress bar and the text */ +} + +.progress-bar > .generating { + display: none !important; +} + +.progress-bar{ + height: 30px !important; +} + +.type_row{ + height: 80px !important; +} + +.type_row_half{ + height: 32px !important; +} + +.scroll-hide{ + resize: none !important; +} + +.refresh_button{ + border: none !important; + background: none !important; + font-size: none !important; + box-shadow: none !important; +} + +.advanced_check_row{ + width: 250px !important; +} + +.min_check{ + min-width: min(1px, 100%) !important; +} + +.resizable_area { + resize: vertical; + overflow: auto !important; +} + +.aspect_ratios label { + width: 140px !important; +} + +.aspect_ratios label span { + white-space: nowrap !important; +} + +.aspect_ratios label input { + margin-left: -5px !important; +} + +.lora_enable label { + height: 100%; +} + +.lora_enable label input { + margin: auto; +} + +.lora_enable label span { + display: none; +} + +@-moz-document url-prefix() { + .lora_weight input[type=number] { + width: 80px; + } +} + +''' +progress_html = ''' +
+
+
+ +
+ *text* +
+''' + + +def make_progress_html(number, text): + return progress_html.replace('*number*', str(number)).replace('*text*', text) diff --git a/modules/inpaint_worker.py b/modules/inpaint_worker.py new file mode 100644 index 0000000000000000000000000000000000000000..43a7ae23e9bd2cebda69b94013bf1661bd8fd952 --- /dev/null +++ b/modules/inpaint_worker.py @@ -0,0 +1,264 @@ +import torch +import numpy as np + +from PIL import Image, ImageFilter +from modules.util import resample_image, set_image_shape_ceil, get_image_shape_ceil +from modules.upscaler import perform_upscale +import cv2 + + +inpaint_head_model = None + + +class InpaintHead(torch.nn.Module): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.head = torch.nn.Parameter(torch.empty(size=(320, 5, 3, 3), device='cpu')) + + def __call__(self, x): + x = torch.nn.functional.pad(x, (1, 1, 1, 1), "replicate") + return torch.nn.functional.conv2d(input=x, weight=self.head) + + +current_task = None + + +def box_blur(x, k): + x = Image.fromarray(x) + x = x.filter(ImageFilter.BoxBlur(k)) + return np.array(x) + + +def max_filter_opencv(x, ksize=3): + # Use OpenCV maximum filter + # Make sure the input type is int16 + return cv2.dilate(x, np.ones((ksize, ksize), dtype=np.int16)) + + +def morphological_open(x): + # Convert array to int16 type via threshold operation + x_int16 = np.zeros_like(x, dtype=np.int16) + x_int16[x > 127] = 256 + + for i in range(32): + # Use int16 type to avoid overflow + maxed = max_filter_opencv(x_int16, ksize=3) - 8 + x_int16 = np.maximum(maxed, x_int16) + + # Clip negative values to 0 and convert back to uint8 type + x_uint8 = np.clip(x_int16, 0, 255).astype(np.uint8) + return x_uint8 + + +def up255(x, t=0): + y = np.zeros_like(x).astype(np.uint8) + y[x > t] = 255 + return y + + +def imsave(x, path): + x = Image.fromarray(x) + x.save(path) + + +def regulate_abcd(x, a, b, c, d): + H, W = x.shape[:2] + if a < 0: + a = 0 + if a > H: + a = H + if b < 0: + b = 0 + if b > H: + b = H + if c < 0: + c = 0 + if c > W: + c = W + if d < 0: + d = 0 + if d > W: + d = W + return int(a), int(b), int(c), int(d) + + +def compute_initial_abcd(x): + indices = np.where(x) + a = np.min(indices[0]) + b = np.max(indices[0]) + c = np.min(indices[1]) + d = np.max(indices[1]) + abp = (b + a) // 2 + abm = (b - a) // 2 + cdp = (d + c) // 2 + cdm = (d - c) // 2 + l = int(max(abm, cdm) * 1.15) + a = abp - l + b = abp + l + 1 + c = cdp - l + d = cdp + l + 1 + a, b, c, d = regulate_abcd(x, a, b, c, d) + return a, b, c, d + + +def solve_abcd(x, a, b, c, d, k): + k = float(k) + assert 0.0 <= k <= 1.0 + + H, W = x.shape[:2] + if k == 1.0: + return 0, H, 0, W + while True: + if b - a >= H * k and d - c >= W * k: + break + + add_h = (b - a) < (d - c) + add_w = not add_h + + if b - a == H: + add_w = True + + if d - c == W: + add_h = True + + if add_h: + a -= 1 + b += 1 + + if add_w: + c -= 1 + d += 1 + + a, b, c, d = regulate_abcd(x, a, b, c, d) + return a, b, c, d + + +def fooocus_fill(image, mask): + current_image = image.copy() + raw_image = image.copy() + area = np.where(mask < 127) + store = raw_image[area] + + for k, repeats in [(512, 2), (256, 2), (128, 4), (64, 4), (33, 8), (15, 8), (5, 16), (3, 16)]: + for _ in range(repeats): + current_image = box_blur(current_image, k) + current_image[area] = store + + return current_image + + +class InpaintWorker: + def __init__(self, image, mask, use_fill=True, k=0.618): + a, b, c, d = compute_initial_abcd(mask > 0) + a, b, c, d = solve_abcd(mask, a, b, c, d, k=k) + + # interested area + self.interested_area = (a, b, c, d) + self.interested_mask = mask[a:b, c:d] + self.interested_image = image[a:b, c:d] + + # super resolution + if get_image_shape_ceil(self.interested_image) < 1024: + self.interested_image = perform_upscale(self.interested_image) + + # resize to make images ready for diffusion + self.interested_image = set_image_shape_ceil(self.interested_image, 1024) + self.interested_fill = self.interested_image.copy() + H, W, C = self.interested_image.shape + + # process mask + self.interested_mask = up255(resample_image(self.interested_mask, W, H), t=127) + + # compute filling + if use_fill: + self.interested_fill = fooocus_fill(self.interested_image, self.interested_mask) + + # soft pixels + self.mask = morphological_open(mask) + self.image = image + + # ending + self.latent = None + self.latent_after_swap = None + self.swapped = False + self.latent_mask = None + self.inpaint_head_feature = None + return + + def load_latent(self, latent_fill, latent_mask, latent_swap=None): + self.latent = latent_fill + self.latent_mask = latent_mask + self.latent_after_swap = latent_swap + return + + def patch(self, inpaint_head_model_path, inpaint_latent, inpaint_latent_mask, model): + global inpaint_head_model + + if inpaint_head_model is None: + inpaint_head_model = InpaintHead() + sd = torch.load(inpaint_head_model_path, map_location='cpu') + inpaint_head_model.load_state_dict(sd) + + feed = torch.cat([ + inpaint_latent_mask, + model.model.process_latent_in(inpaint_latent) + ], dim=1) + + inpaint_head_model.to(device=feed.device, dtype=feed.dtype) + inpaint_head_feature = inpaint_head_model(feed) + + def input_block_patch(h, transformer_options): + if transformer_options["block"][1] == 0: + h = h + inpaint_head_feature.to(h) + return h + + m = model.clone() + m.set_model_input_block_patch(input_block_patch) + return m + + def swap(self): + if self.swapped: + return + + if self.latent is None: + return + + if self.latent_after_swap is None: + return + + self.latent, self.latent_after_swap = self.latent_after_swap, self.latent + self.swapped = True + return + + def unswap(self): + if not self.swapped: + return + + if self.latent is None: + return + + if self.latent_after_swap is None: + return + + self.latent, self.latent_after_swap = self.latent_after_swap, self.latent + self.swapped = False + return + + def color_correction(self, img): + fg = img.astype(np.float32) + bg = self.image.copy().astype(np.float32) + w = self.mask[:, :, None].astype(np.float32) / 255.0 + y = fg * w + bg * (1 - w) + return y.clip(0, 255).astype(np.uint8) + + def post_process(self, img): + a, b, c, d = self.interested_area + content = resample_image(img, d - c, b - a) + result = self.image.copy() + result[a:b, c:d] = content + result = self.color_correction(result) + return result + + def visualize_mask_processing(self): + return [self.interested_fill, self.interested_mask, self.interested_image] + diff --git a/modules/launch_util.py b/modules/launch_util.py new file mode 100644 index 0000000000000000000000000000000000000000..b483d5158ca5eeeff6f385b1a94990f9e5f6e871 --- /dev/null +++ b/modules/launch_util.py @@ -0,0 +1,103 @@ +import os +import importlib +import importlib.util +import subprocess +import sys +import re +import logging +import importlib.metadata +import packaging.version +from packaging.requirements import Requirement + + + + +logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) # sshh... +logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) + +re_requirement = re.compile(r"\s*([-\w]+)\s*(?:==\s*([-+.\w]+))?\s*") + +python = sys.executable +default_command_live = (os.environ.get('LAUNCH_LIVE_OUTPUT') == "1") +index_url = os.environ.get('INDEX_URL', "") + +modules_path = os.path.dirname(os.path.realpath(__file__)) +script_path = os.path.dirname(modules_path) + + +def is_installed(package): + try: + spec = importlib.util.find_spec(package) + except ModuleNotFoundError: + return False + + return spec is not None + + +def run(command, desc=None, errdesc=None, custom_env=None, live: bool = default_command_live) -> str: + if desc is not None: + print(desc) + + run_kwargs = { + "args": command, + "shell": True, + "env": os.environ if custom_env is None else custom_env, + "encoding": 'utf8', + "errors": 'ignore', + } + + if not live: + run_kwargs["stdout"] = run_kwargs["stderr"] = subprocess.PIPE + + result = subprocess.run(**run_kwargs) + + if result.returncode != 0: + error_bits = [ + f"{errdesc or 'Error running command'}.", + f"Command: {command}", + f"Error code: {result.returncode}", + ] + if result.stdout: + error_bits.append(f"stdout: {result.stdout}") + if result.stderr: + error_bits.append(f"stderr: {result.stderr}") + raise RuntimeError("\n".join(error_bits)) + + return (result.stdout or "") + + +def run_pip(command, desc=None, live=default_command_live): + try: + index_url_line = f' --index-url {index_url}' if index_url != '' else '' + return run(f'"{python}" -m pip {command} --prefer-binary{index_url_line}', desc=f"Installing {desc}", + errdesc=f"Couldn't install {desc}", live=live) + except Exception as e: + print(e) + print(f'CMD Failed {desc}: {command}') + return None + + +def requirements_met(requirements_file): + with open(requirements_file, "r", encoding="utf8") as file: + for line in file: + line = line.strip() + if line == "" or line.startswith('#'): + continue + + requirement = Requirement(line) + package = requirement.name + + try: + version_installed = importlib.metadata.version(package) + installed_version = packaging.version.parse(version_installed) + + # Check if the installed version satisfies the requirement + if installed_version not in requirement.specifier: + print(f"Version mismatch for {package}: Installed version {version_installed} does not meet requirement {requirement}") + return False + except Exception as e: + print(f"Error checking version for {package}: {e}") + return False + + return True + diff --git a/modules/localization.py b/modules/localization.py new file mode 100644 index 0000000000000000000000000000000000000000..b21d4a564d134ac0be00d83c7005627d601d206e --- /dev/null +++ b/modules/localization.py @@ -0,0 +1,60 @@ +import json +import os + + +current_translation = {} +localization_root = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'language') + + +def localization_js(filename): + global current_translation + + if isinstance(filename, str): + full_name = os.path.abspath(os.path.join(localization_root, filename + '.json')) + if os.path.exists(full_name): + try: + with open(full_name, encoding='utf-8') as f: + current_translation = json.load(f) + assert isinstance(current_translation, dict) + for k, v in current_translation.items(): + assert isinstance(k, str) + assert isinstance(v, str) + except Exception as e: + print(str(e)) + print(f'Failed to load localization file {full_name}') + + # current_translation = {k: 'XXX' for k in current_translation.keys()} # use this to see if all texts are covered + + return f"window.localization = {json.dumps(current_translation)}" + + +def dump_english_config(components): + all_texts = [] + for c in components: + label = getattr(c, 'label', None) + value = getattr(c, 'value', None) + choices = getattr(c, 'choices', None) + info = getattr(c, 'info', None) + + if isinstance(label, str): + all_texts.append(label) + if isinstance(value, str): + all_texts.append(value) + if isinstance(info, str): + all_texts.append(info) + if isinstance(choices, list): + for x in choices: + if isinstance(x, str): + all_texts.append(x) + if isinstance(x, tuple): + for y in x: + if isinstance(y, str): + all_texts.append(y) + + config_dict = {k: k for k in all_texts if k != "" and 'progress-container' not in k} + full_name = os.path.abspath(os.path.join(localization_root, 'en.json')) + + with open(full_name, "w", encoding="utf-8") as json_file: + json.dump(config_dict, json_file, indent=4) + + return diff --git a/modules/lora.py b/modules/lora.py new file mode 100644 index 0000000000000000000000000000000000000000..088545c708943aa8e51e8b2bfd32b2a9968b639f --- /dev/null +++ b/modules/lora.py @@ -0,0 +1,152 @@ +def match_lora(lora, to_load): + patch_dict = {} + loaded_keys = set() + for x in to_load: + real_load_key = to_load[x] + if real_load_key in lora: + patch_dict[real_load_key] = ('fooocus', lora[real_load_key]) + loaded_keys.add(real_load_key) + continue + + alpha_name = "{}.alpha".format(x) + alpha = None + if alpha_name in lora.keys(): + alpha = lora[alpha_name].item() + loaded_keys.add(alpha_name) + + regular_lora = "{}.lora_up.weight".format(x) + diffusers_lora = "{}_lora.up.weight".format(x) + transformers_lora = "{}.lora_linear_layer.up.weight".format(x) + A_name = None + + if regular_lora in lora.keys(): + A_name = regular_lora + B_name = "{}.lora_down.weight".format(x) + mid_name = "{}.lora_mid.weight".format(x) + elif diffusers_lora in lora.keys(): + A_name = diffusers_lora + B_name = "{}_lora.down.weight".format(x) + mid_name = None + elif transformers_lora in lora.keys(): + A_name = transformers_lora + B_name ="{}.lora_linear_layer.down.weight".format(x) + mid_name = None + + if A_name is not None: + mid = None + if mid_name is not None and mid_name in lora.keys(): + mid = lora[mid_name] + loaded_keys.add(mid_name) + patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid)) + loaded_keys.add(A_name) + loaded_keys.add(B_name) + + + ######## loha + hada_w1_a_name = "{}.hada_w1_a".format(x) + hada_w1_b_name = "{}.hada_w1_b".format(x) + hada_w2_a_name = "{}.hada_w2_a".format(x) + hada_w2_b_name = "{}.hada_w2_b".format(x) + hada_t1_name = "{}.hada_t1".format(x) + hada_t2_name = "{}.hada_t2".format(x) + if hada_w1_a_name in lora.keys(): + hada_t1 = None + hada_t2 = None + if hada_t1_name in lora.keys(): + hada_t1 = lora[hada_t1_name] + hada_t2 = lora[hada_t2_name] + loaded_keys.add(hada_t1_name) + loaded_keys.add(hada_t2_name) + + patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2)) + loaded_keys.add(hada_w1_a_name) + loaded_keys.add(hada_w1_b_name) + loaded_keys.add(hada_w2_a_name) + loaded_keys.add(hada_w2_b_name) + + + ######## lokr + lokr_w1_name = "{}.lokr_w1".format(x) + lokr_w2_name = "{}.lokr_w2".format(x) + lokr_w1_a_name = "{}.lokr_w1_a".format(x) + lokr_w1_b_name = "{}.lokr_w1_b".format(x) + lokr_t2_name = "{}.lokr_t2".format(x) + lokr_w2_a_name = "{}.lokr_w2_a".format(x) + lokr_w2_b_name = "{}.lokr_w2_b".format(x) + + lokr_w1 = None + if lokr_w1_name in lora.keys(): + lokr_w1 = lora[lokr_w1_name] + loaded_keys.add(lokr_w1_name) + + lokr_w2 = None + if lokr_w2_name in lora.keys(): + lokr_w2 = lora[lokr_w2_name] + loaded_keys.add(lokr_w2_name) + + lokr_w1_a = None + if lokr_w1_a_name in lora.keys(): + lokr_w1_a = lora[lokr_w1_a_name] + loaded_keys.add(lokr_w1_a_name) + + lokr_w1_b = None + if lokr_w1_b_name in lora.keys(): + lokr_w1_b = lora[lokr_w1_b_name] + loaded_keys.add(lokr_w1_b_name) + + lokr_w2_a = None + if lokr_w2_a_name in lora.keys(): + lokr_w2_a = lora[lokr_w2_a_name] + loaded_keys.add(lokr_w2_a_name) + + lokr_w2_b = None + if lokr_w2_b_name in lora.keys(): + lokr_w2_b = lora[lokr_w2_b_name] + loaded_keys.add(lokr_w2_b_name) + + lokr_t2 = None + if lokr_t2_name in lora.keys(): + lokr_t2 = lora[lokr_t2_name] + loaded_keys.add(lokr_t2_name) + + if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None): + patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2)) + + #glora + a1_name = "{}.a1.weight".format(x) + a2_name = "{}.a2.weight".format(x) + b1_name = "{}.b1.weight".format(x) + b2_name = "{}.b2.weight".format(x) + if a1_name in lora: + patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha)) + loaded_keys.add(a1_name) + loaded_keys.add(a2_name) + loaded_keys.add(b1_name) + loaded_keys.add(b2_name) + + w_norm_name = "{}.w_norm".format(x) + b_norm_name = "{}.b_norm".format(x) + w_norm = lora.get(w_norm_name, None) + b_norm = lora.get(b_norm_name, None) + + if w_norm is not None: + loaded_keys.add(w_norm_name) + patch_dict[to_load[x]] = ("diff", (w_norm,)) + if b_norm is not None: + loaded_keys.add(b_norm_name) + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (b_norm,)) + + diff_name = "{}.diff".format(x) + diff_weight = lora.get(diff_name, None) + if diff_weight is not None: + patch_dict[to_load[x]] = ("diff", (diff_weight,)) + loaded_keys.add(diff_name) + + diff_bias_name = "{}.diff_b".format(x) + diff_bias = lora.get(diff_bias_name, None) + if diff_bias is not None: + patch_dict["{}.bias".format(to_load[x][:-len(".weight")])] = ("diff", (diff_bias,)) + loaded_keys.add(diff_bias_name) + + remaining_dict = {x: y for x, y in lora.items() if x not in loaded_keys} + return patch_dict, remaining_dict diff --git a/modules/meta_parser.py b/modules/meta_parser.py new file mode 100644 index 0000000000000000000000000000000000000000..546c093fa008da831fb17b0e6a2cc256467315b2 --- /dev/null +++ b/modules/meta_parser.py @@ -0,0 +1,573 @@ +import json +import os +import re +from abc import ABC, abstractmethod +from pathlib import Path + +import gradio as gr +from PIL import Image + +import fooocus_version +import modules.config +import modules.sdxl_styles +from modules.flags import MetadataScheme, Performance, Steps +from modules.flags import SAMPLERS, CIVITAI_NO_KARRAS +from modules.util import quote, unquote, extract_styles_from_prompt, is_json, get_file_from_folder_list, calculate_sha256 + +re_param_code = r'\s*(\w[\w \-/]+):\s*("(?:\\.|[^\\"])+"|[^,]*)(?:,|$)' +re_param = re.compile(re_param_code) +re_imagesize = re.compile(r"^(\d+)x(\d+)$") + +hash_cache = {} + + +def load_parameter_button_click(raw_metadata: dict | str, is_generating: bool): + loaded_parameter_dict = raw_metadata + if isinstance(raw_metadata, str): + loaded_parameter_dict = json.loads(raw_metadata) + assert isinstance(loaded_parameter_dict, dict) + + results = [len(loaded_parameter_dict) > 0, 1] + + get_str('prompt', 'Prompt', loaded_parameter_dict, results) + get_str('negative_prompt', 'Negative Prompt', loaded_parameter_dict, results) + get_list('styles', 'Styles', loaded_parameter_dict, results) + get_str('performance', 'Performance', loaded_parameter_dict, results) + get_steps('steps', 'Steps', loaded_parameter_dict, results) + get_float('overwrite_switch', 'Overwrite Switch', loaded_parameter_dict, results) + get_resolution('resolution', 'Resolution', loaded_parameter_dict, results) + get_float('guidance_scale', 'Guidance Scale', loaded_parameter_dict, results) + get_float('sharpness', 'Sharpness', loaded_parameter_dict, results) + get_adm_guidance('adm_guidance', 'ADM Guidance', loaded_parameter_dict, results) + get_str('refiner_swap_method', 'Refiner Swap Method', loaded_parameter_dict, results) + get_float('adaptive_cfg', 'CFG Mimicking from TSNR', loaded_parameter_dict, results) + get_str('base_model', 'Base Model', loaded_parameter_dict, results) + get_str('refiner_model', 'Refiner Model', loaded_parameter_dict, results) + get_float('refiner_switch', 'Refiner Switch', loaded_parameter_dict, results) + get_str('sampler', 'Sampler', loaded_parameter_dict, results) + get_str('scheduler', 'Scheduler', loaded_parameter_dict, results) + get_seed('seed', 'Seed', loaded_parameter_dict, results) + + if is_generating: + results.append(gr.update()) + else: + results.append(gr.update(visible=True)) + + results.append(gr.update(visible=False)) + + get_freeu('freeu', 'FreeU', loaded_parameter_dict, results) + + for i in range(modules.config.default_max_lora_number): + get_lora(f'lora_combined_{i + 1}', f'LoRA {i + 1}', loaded_parameter_dict, results) + + return results + + +def get_str(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert isinstance(h, str) + results.append(h) + except: + results.append(gr.update()) + + +def get_list(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + h = eval(h) + assert isinstance(h, list) + results.append(h) + except: + results.append(gr.update()) + + +def get_float(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = float(h) + results.append(h) + except: + results.append(gr.update()) + + +def get_steps(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = int(h) + # if not in steps or in steps and performance is not the same + if h not in iter(Steps) or Steps(h).name.casefold() != source_dict.get('performance', '').replace(' ', '_').casefold(): + results.append(h) + return + results.append(-1) + except: + results.append(-1) + + +def get_resolution(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + width, height = eval(h) + formatted = modules.config.add_ratio(f'{width}*{height}') + if formatted in modules.config.available_aspect_ratios: + results.append(formatted) + results.append(-1) + results.append(-1) + else: + results.append(gr.update()) + results.append(int(width)) + results.append(int(height)) + except: + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + + +def get_seed(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + assert h is not None + h = int(h) + results.append(False) + results.append(h) + except: + results.append(gr.update()) + results.append(gr.update()) + + +def get_adm_guidance(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + p, n, e = eval(h) + results.append(float(p)) + results.append(float(n)) + results.append(float(e)) + except: + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + + +def get_freeu(key: str, fallback: str | None, source_dict: dict, results: list, default=None): + try: + h = source_dict.get(key, source_dict.get(fallback, default)) + b1, b2, s1, s2 = eval(h) + results.append(True) + results.append(float(b1)) + results.append(float(b2)) + results.append(float(s1)) + results.append(float(s2)) + except: + results.append(False) + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + results.append(gr.update()) + + +def get_lora(key: str, fallback: str | None, source_dict: dict, results: list): + try: + n, w = source_dict.get(key, source_dict.get(fallback)).split(' : ') + w = float(w) + results.append(True) + results.append(n) + results.append(w) + except: + results.append(True) + results.append('None') + results.append(1) + + +def get_sha256(filepath): + global hash_cache + if filepath not in hash_cache: + hash_cache[filepath] = calculate_sha256(filepath) + + return hash_cache[filepath] + + +def parse_meta_from_preset(preset_content): + assert isinstance(preset_content, dict) + preset_prepared = {} + items = preset_content + + for settings_key, meta_key in modules.config.possible_preset_keys.items(): + if settings_key == "default_loras": + loras = getattr(modules.config, settings_key) + if settings_key in items: + loras = items[settings_key] + for index, lora in enumerate(loras[:5]): + preset_prepared[f'lora_combined_{index + 1}'] = ' : '.join(map(str, lora)) + elif settings_key == "default_aspect_ratio": + if settings_key in items and items[settings_key] is not None: + default_aspect_ratio = items[settings_key] + width, height = default_aspect_ratio.split('*') + else: + default_aspect_ratio = getattr(modules.config, settings_key) + width, height = default_aspect_ratio.split('×') + height = height[:height.index(" ")] + preset_prepared[meta_key] = (width, height) + else: + preset_prepared[meta_key] = items[settings_key] if settings_key in items and items[ + settings_key] is not None else getattr(modules.config, settings_key) + + if settings_key == "default_styles" or settings_key == "default_aspect_ratio": + preset_prepared[meta_key] = str(preset_prepared[meta_key]) + + return preset_prepared + + +class MetadataParser(ABC): + def __init__(self): + self.raw_prompt: str = '' + self.full_prompt: str = '' + self.raw_negative_prompt: str = '' + self.full_negative_prompt: str = '' + self.steps: int = 30 + self.base_model_name: str = '' + self.base_model_hash: str = '' + self.refiner_model_name: str = '' + self.refiner_model_hash: str = '' + self.loras: list = [] + + @abstractmethod + def get_scheme(self) -> MetadataScheme: + raise NotImplementedError + + @abstractmethod + def parse_json(self, metadata: dict | str) -> dict: + raise NotImplementedError + + @abstractmethod + def parse_string(self, metadata: dict) -> str: + raise NotImplementedError + + def set_data(self, raw_prompt, full_prompt, raw_negative_prompt, full_negative_prompt, steps, base_model_name, + refiner_model_name, loras): + self.raw_prompt = raw_prompt + self.full_prompt = full_prompt + self.raw_negative_prompt = raw_negative_prompt + self.full_negative_prompt = full_negative_prompt + self.steps = steps + self.base_model_name = Path(base_model_name).stem + + base_model_path = get_file_from_folder_list(base_model_name, modules.config.paths_checkpoints) + self.base_model_hash = get_sha256(base_model_path) + + if refiner_model_name not in ['', 'None']: + self.refiner_model_name = Path(refiner_model_name).stem + refiner_model_path = get_file_from_folder_list(refiner_model_name, modules.config.paths_checkpoints) + self.refiner_model_hash = get_sha256(refiner_model_path) + + self.loras = [] + for (lora_name, lora_weight) in loras: + if lora_name != 'None': + lora_path = get_file_from_folder_list(lora_name, modules.config.paths_loras) + lora_hash = get_sha256(lora_path) + self.loras.append((Path(lora_name).stem, lora_weight, lora_hash)) + + +class A1111MetadataParser(MetadataParser): + def get_scheme(self) -> MetadataScheme: + return MetadataScheme.A1111 + + fooocus_to_a1111 = { + 'raw_prompt': 'Raw prompt', + 'raw_negative_prompt': 'Raw negative prompt', + 'negative_prompt': 'Negative prompt', + 'styles': 'Styles', + 'performance': 'Performance', + 'steps': 'Steps', + 'sampler': 'Sampler', + 'scheduler': 'Scheduler', + 'guidance_scale': 'CFG scale', + 'seed': 'Seed', + 'resolution': 'Size', + 'sharpness': 'Sharpness', + 'adm_guidance': 'ADM Guidance', + 'refiner_swap_method': 'Refiner Swap Method', + 'adaptive_cfg': 'Adaptive CFG', + 'overwrite_switch': 'Overwrite Switch', + 'freeu': 'FreeU', + 'base_model': 'Model', + 'base_model_hash': 'Model hash', + 'refiner_model': 'Refiner', + 'refiner_model_hash': 'Refiner hash', + 'lora_hashes': 'Lora hashes', + 'lora_weights': 'Lora weights', + 'created_by': 'User', + 'version': 'Version' + } + + def parse_json(self, metadata: str) -> dict: + metadata_prompt = '' + metadata_negative_prompt = '' + + done_with_prompt = False + + *lines, lastline = metadata.strip().split("\n") + if len(re_param.findall(lastline)) < 3: + lines.append(lastline) + lastline = '' + + for line in lines: + line = line.strip() + if line.startswith(f"{self.fooocus_to_a1111['negative_prompt']}:"): + done_with_prompt = True + line = line[len(f"{self.fooocus_to_a1111['negative_prompt']}:"):].strip() + if done_with_prompt: + metadata_negative_prompt += ('' if metadata_negative_prompt == '' else "\n") + line + else: + metadata_prompt += ('' if metadata_prompt == '' else "\n") + line + + found_styles, prompt, negative_prompt = extract_styles_from_prompt(metadata_prompt, metadata_negative_prompt) + + data = { + 'prompt': prompt, + 'negative_prompt': negative_prompt + } + + for k, v in re_param.findall(lastline): + try: + if v != '' and v[0] == '"' and v[-1] == '"': + v = unquote(v) + + m = re_imagesize.match(v) + if m is not None: + data['resolution'] = str((m.group(1), m.group(2))) + else: + data[list(self.fooocus_to_a1111.keys())[list(self.fooocus_to_a1111.values()).index(k)]] = v + except Exception: + print(f"Error parsing \"{k}: {v}\"") + + # workaround for multiline prompts + if 'raw_prompt' in data: + data['prompt'] = data['raw_prompt'] + raw_prompt = data['raw_prompt'].replace("\n", ', ') + if metadata_prompt != raw_prompt and modules.sdxl_styles.fooocus_expansion not in found_styles: + found_styles.append(modules.sdxl_styles.fooocus_expansion) + + if 'raw_negative_prompt' in data: + data['negative_prompt'] = data['raw_negative_prompt'] + + data['styles'] = str(found_styles) + + # try to load performance based on steps, fallback for direct A1111 imports + if 'steps' in data and 'performance' not in data: + try: + data['performance'] = Performance[Steps(int(data['steps'])).name].value + except ValueError | KeyError: + pass + + if 'sampler' in data: + data['sampler'] = data['sampler'].replace(' Karras', '') + # get key + for k, v in SAMPLERS.items(): + if v == data['sampler']: + data['sampler'] = k + break + + for key in ['base_model', 'refiner_model']: + if key in data: + for filename in modules.config.model_filenames: + path = Path(filename) + if data[key] == path.stem: + data[key] = filename + break + + if 'lora_hashes' in data: + lora_filenames = modules.config.lora_filenames.copy() + if modules.config.sdxl_lcm_lora in lora_filenames: + lora_filenames.remove(modules.config.sdxl_lcm_lora) + for li, lora in enumerate(data['lora_hashes'].split(', ')): + lora_name, lora_hash, lora_weight = lora.split(': ') + for filename in lora_filenames: + path = Path(filename) + if lora_name == path.stem: + data[f'lora_combined_{li + 1}'] = f'{filename} : {lora_weight}' + break + + return data + + def parse_string(self, metadata: dict) -> str: + data = {k: v for _, k, v in metadata} + + width, height = eval(data['resolution']) + + sampler = data['sampler'] + scheduler = data['scheduler'] + if sampler in SAMPLERS and SAMPLERS[sampler] != '': + sampler = SAMPLERS[sampler] + if sampler not in CIVITAI_NO_KARRAS and scheduler == 'karras': + sampler += f' Karras' + + generation_params = { + self.fooocus_to_a1111['steps']: self.steps, + self.fooocus_to_a1111['sampler']: sampler, + self.fooocus_to_a1111['seed']: data['seed'], + self.fooocus_to_a1111['resolution']: f'{width}x{height}', + self.fooocus_to_a1111['guidance_scale']: data['guidance_scale'], + self.fooocus_to_a1111['sharpness']: data['sharpness'], + self.fooocus_to_a1111['adm_guidance']: data['adm_guidance'], + self.fooocus_to_a1111['base_model']: Path(data['base_model']).stem, + self.fooocus_to_a1111['base_model_hash']: self.base_model_hash, + + self.fooocus_to_a1111['performance']: data['performance'], + self.fooocus_to_a1111['scheduler']: scheduler, + # workaround for multiline prompts + self.fooocus_to_a1111['raw_prompt']: self.raw_prompt, + self.fooocus_to_a1111['raw_negative_prompt']: self.raw_negative_prompt, + } + + if self.refiner_model_name not in ['', 'None']: + generation_params |= { + self.fooocus_to_a1111['refiner_model']: self.refiner_model_name, + self.fooocus_to_a1111['refiner_model_hash']: self.refiner_model_hash + } + + for key in ['adaptive_cfg', 'overwrite_switch', 'refiner_swap_method', 'freeu']: + if key in data: + generation_params[self.fooocus_to_a1111[key]] = data[key] + + lora_hashes = [] + for index, (lora_name, lora_weight, lora_hash) in enumerate(self.loras): + # workaround for Fooocus not knowing LoRA name in LoRA metadata + lora_hashes.append(f'{lora_name}: {lora_hash}: {lora_weight}') + lora_hashes_string = ', '.join(lora_hashes) + + generation_params |= { + self.fooocus_to_a1111['lora_hashes']: lora_hashes_string, + self.fooocus_to_a1111['version']: data['version'] + } + + if modules.config.metadata_created_by != '': + generation_params[self.fooocus_to_a1111['created_by']] = modules.config.metadata_created_by + + generation_params_text = ", ".join( + [k if k == v else f'{k}: {quote(v)}' for k, v in generation_params.items() if + v is not None]) + positive_prompt_resolved = ', '.join(self.full_prompt) + negative_prompt_resolved = ', '.join(self.full_negative_prompt) + negative_prompt_text = f"\nNegative prompt: {negative_prompt_resolved}" if negative_prompt_resolved else "" + return f"{positive_prompt_resolved}{negative_prompt_text}\n{generation_params_text}".strip() + + +class FooocusMetadataParser(MetadataParser): + def get_scheme(self) -> MetadataScheme: + return MetadataScheme.FOOOCUS + + def parse_json(self, metadata: dict) -> dict: + model_filenames = modules.config.model_filenames.copy() + lora_filenames = modules.config.lora_filenames.copy() + if modules.config.sdxl_lcm_lora in lora_filenames: + lora_filenames.remove(modules.config.sdxl_lcm_lora) + + for key, value in metadata.items(): + if value in ['', 'None']: + continue + if key in ['base_model', 'refiner_model']: + metadata[key] = self.replace_value_with_filename(key, value, model_filenames) + elif key.startswith('lora_combined_'): + metadata[key] = self.replace_value_with_filename(key, value, lora_filenames) + else: + continue + + return metadata + + def parse_string(self, metadata: list) -> str: + for li, (label, key, value) in enumerate(metadata): + # remove model folder paths from metadata + if key.startswith('lora_combined_'): + name, weight = value.split(' : ') + name = Path(name).stem + value = f'{name} : {weight}' + metadata[li] = (label, key, value) + + res = {k: v for _, k, v in metadata} + + res['full_prompt'] = self.full_prompt + res['full_negative_prompt'] = self.full_negative_prompt + res['steps'] = self.steps + res['base_model'] = self.base_model_name + res['base_model_hash'] = self.base_model_hash + + if self.refiner_model_name not in ['', 'None']: + res['refiner_model'] = self.refiner_model_name + res['refiner_model_hash'] = self.refiner_model_hash + + res['loras'] = self.loras + + if modules.config.metadata_created_by != '': + res['created_by'] = modules.config.metadata_created_by + + return json.dumps(dict(sorted(res.items()))) + + @staticmethod + def replace_value_with_filename(key, value, filenames): + for filename in filenames: + path = Path(filename) + if key.startswith('lora_combined_'): + name, weight = value.split(' : ') + if name == path.stem: + return f'{filename} : {weight}' + elif value == path.stem: + return filename + + +def get_metadata_parser(metadata_scheme: MetadataScheme) -> MetadataParser: + match metadata_scheme: + case MetadataScheme.FOOOCUS: + return FooocusMetadataParser() + case MetadataScheme.A1111: + return A1111MetadataParser() + case _: + raise NotImplementedError + + +def read_info_from_image(filepath) -> tuple[str | None, MetadataScheme | None]: + with Image.open(filepath) as image: + items = (image.info or {}).copy() + + parameters = items.pop('parameters', None) + metadata_scheme = items.pop('fooocus_scheme', None) + exif = items.pop('exif', None) + + if parameters is not None and is_json(parameters): + parameters = json.loads(parameters) + elif exif is not None: + exif = image.getexif() + # 0x9286 = UserComment + parameters = exif.get(0x9286, None) + # 0x927C = MakerNote + metadata_scheme = exif.get(0x927C, None) + + if is_json(parameters): + parameters = json.loads(parameters) + + try: + metadata_scheme = MetadataScheme(metadata_scheme) + except ValueError: + metadata_scheme = None + + # broad fallback + if isinstance(parameters, dict): + metadata_scheme = MetadataScheme.FOOOCUS + + if isinstance(parameters, str): + metadata_scheme = MetadataScheme.A1111 + + return parameters, metadata_scheme + + +def get_exif(metadata: str | None, metadata_scheme: str): + exif = Image.Exif() + # tags see see https://github.com/python-pillow/Pillow/blob/9.2.x/src/PIL/ExifTags.py + # 0x9286 = UserComment + exif[0x9286] = metadata + # 0x0131 = Software + exif[0x0131] = 'Fooocus v' + fooocus_version.version + # 0x927C = MakerNote + exif[0x927C] = metadata_scheme + return exif \ No newline at end of file diff --git a/modules/model_loader.py b/modules/model_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..8ba336a915ae234b7cd5f9f2576d4edf779738ba --- /dev/null +++ b/modules/model_loader.py @@ -0,0 +1,26 @@ +import os +from urllib.parse import urlparse +from typing import Optional + + +def load_file_from_url( + url: str, + *, + model_dir: str, + progress: bool = True, + file_name: Optional[str] = None, +) -> str: + """Download a file from `url` into `model_dir`, using the file present if possible. + + Returns the path to the downloaded file. + """ + os.makedirs(model_dir, exist_ok=True) + if not file_name: + parts = urlparse(url) + file_name = os.path.basename(parts.path) + cached_file = os.path.abspath(os.path.join(model_dir, file_name)) + if not os.path.exists(cached_file): + print(f'Downloading: "{url}" to {cached_file}\n') + from torch.hub import download_url_to_file + download_url_to_file(url, cached_file, progress=progress) + return cached_file diff --git a/modules/ops.py b/modules/ops.py new file mode 100644 index 0000000000000000000000000000000000000000..ee0e775634314d1b71811258cff87b2178e1c740 --- /dev/null +++ b/modules/ops.py @@ -0,0 +1,19 @@ +import torch +import contextlib + + +@contextlib.contextmanager +def use_patched_ops(operations): + op_names = ['Linear', 'Conv2d', 'Conv3d', 'GroupNorm', 'LayerNorm'] + backups = {op_name: getattr(torch.nn, op_name) for op_name in op_names} + + try: + for op_name in op_names: + setattr(torch.nn, op_name, getattr(operations, op_name)) + + yield + + finally: + for op_name in op_names: + setattr(torch.nn, op_name, backups[op_name]) + return diff --git a/modules/patch.py b/modules/patch.py new file mode 100644 index 0000000000000000000000000000000000000000..3c2dd8f477902e68a467e8f89888934a762f4bb1 --- /dev/null +++ b/modules/patch.py @@ -0,0 +1,513 @@ +import os +import torch +import time +import math +import ldm_patched.modules.model_base +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.modules.model_management +import modules.anisotropic as anisotropic +import ldm_patched.ldm.modules.attention +import ldm_patched.k_diffusion.sampling +import ldm_patched.modules.sd1_clip +import modules.inpaint_worker as inpaint_worker +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.ldm.modules.diffusionmodules.model +import ldm_patched.modules.sd +import ldm_patched.controlnet.cldm +import ldm_patched.modules.model_patcher +import ldm_patched.modules.samplers +import ldm_patched.modules.args_parser +import warnings +import safetensors.torch +import modules.constants as constants + +from ldm_patched.modules.samplers import calc_cond_uncond_batch +from ldm_patched.k_diffusion.sampling import BatchedBrownianTree +from ldm_patched.ldm.modules.diffusionmodules.openaimodel import forward_timestep_embed, apply_control +from modules.patch_precision import patch_all_precision +from modules.patch_clip import patch_all_clip + + +class PatchSettings: + def __init__(self, + sharpness=2.0, + adm_scaler_end=0.3, + positive_adm_scale=1.5, + negative_adm_scale=0.8, + controlnet_softness=0.25, + adaptive_cfg=7.0): + self.sharpness = sharpness + self.adm_scaler_end = adm_scaler_end + self.positive_adm_scale = positive_adm_scale + self.negative_adm_scale = negative_adm_scale + self.controlnet_softness = controlnet_softness + self.adaptive_cfg = adaptive_cfg + self.global_diffusion_progress = 0 + self.eps_record = None + + +patch_settings = {} + + +def calculate_weight_patched(self, patches, weight, key): + for p in patches: + alpha = p[0] + v = p[1] + strength_model = p[2] + + if strength_model != 1.0: + weight *= strength_model + + if isinstance(v, list): + v = (self.calculate_weight(v[1:], v[0].clone(), key),) + + if len(v) == 1: + patch_type = "diff" + elif len(v) == 2: + patch_type = v[0] + v = v[1] + + if patch_type == "diff": + w1 = v[0] + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lora": + mat1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + mat2 = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) + if v[2] is not None: + alpha *= v[2] / mat2.shape[0] + if v[3] is not None: + mat3 = ldm_patched.modules.model_management.cast_to_device(v[3], weight.device, torch.float32) + final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]] + mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), + mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1) + try: + weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape( + weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "fooocus": + w1 = ldm_patched.modules.model_management.cast_to_device(v[0], weight.device, torch.float32) + w_min = ldm_patched.modules.model_management.cast_to_device(v[1], weight.device, torch.float32) + w_max = ldm_patched.modules.model_management.cast_to_device(v[2], weight.device, torch.float32) + w1 = (w1 / 255.0) * (w_max - w_min) + w_min + if alpha != 0.0: + if w1.shape != weight.shape: + print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) + else: + weight += alpha * ldm_patched.modules.model_management.cast_to_device(w1, weight.device, weight.dtype) + elif patch_type == "lokr": + w1 = v[0] + w2 = v[1] + w1_a = v[3] + w1_b = v[4] + w2_a = v[5] + w2_b = v[6] + t2 = v[7] + dim = None + + if w1 is None: + dim = w1_b.shape[0] + w1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1_b, weight.device, torch.float32)) + else: + w1 = ldm_patched.modules.model_management.cast_to_device(w1, weight.device, torch.float32) + + if w2 is None: + dim = w2_b.shape[0] + if t2 is None: + w2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32)) + else: + w2 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2_a, weight.device, torch.float32)) + else: + w2 = ldm_patched.modules.model_management.cast_to_device(w2, weight.device, torch.float32) + + if len(w2.shape) == 4: + w1 = w1.unsqueeze(2).unsqueeze(2) + if v[2] is not None and dim is not None: + alpha *= v[2] / dim + + try: + weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "loha": + w1a = v[0] + w1b = v[1] + if v[2] is not None: + alpha *= v[2] / w1b.shape[0] + w2a = v[3] + w2b = v[4] + if v[5] is not None: # cp decomposition + t1 = v[5] + t2 = v[6] + m1 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t1, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32)) + + m2 = torch.einsum('i j k l, j r, i p -> p r k l', + ldm_patched.modules.model_management.cast_to_device(t2, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32)) + else: + m1 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w1a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w1b, weight.device, torch.float32)) + m2 = torch.mm(ldm_patched.modules.model_management.cast_to_device(w2a, weight.device, torch.float32), + ldm_patched.modules.model_management.cast_to_device(w2b, weight.device, torch.float32)) + + try: + weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype) + except Exception as e: + print("ERROR", key, e) + elif patch_type == "glora": + if v[4] is not None: + alpha *= v[4] / v[0].shape[0] + + a1 = ldm_patched.modules.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32) + a2 = ldm_patched.modules.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32) + b1 = ldm_patched.modules.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32) + b2 = ldm_patched.modules.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32) + + weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype) + else: + print("patch type not recognized", patch_type, key) + + return weight + + +class BrownianTreeNoiseSamplerPatched: + transform = None + tree = None + + @staticmethod + def global_init(x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False): + if ldm_patched.modules.model_management.directml_enabled: + cpu = True + + t0, t1 = transform(torch.as_tensor(sigma_min)), transform(torch.as_tensor(sigma_max)) + + BrownianTreeNoiseSamplerPatched.transform = transform + BrownianTreeNoiseSamplerPatched.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu) + + def __init__(self, *args, **kwargs): + pass + + @staticmethod + def __call__(sigma, sigma_next): + transform = BrownianTreeNoiseSamplerPatched.transform + tree = BrownianTreeNoiseSamplerPatched.tree + + t0, t1 = transform(torch.as_tensor(sigma)), transform(torch.as_tensor(sigma_next)) + return tree(t0, t1) / (t1 - t0).abs().sqrt() + + +def compute_cfg(uncond, cond, cfg_scale, t): + pid = os.getpid() + mimic_cfg = float(patch_settings[pid].adaptive_cfg) + real_cfg = float(cfg_scale) + + real_eps = uncond + real_cfg * (cond - uncond) + + if cfg_scale > patch_settings[pid].adaptive_cfg: + mimicked_eps = uncond + mimic_cfg * (cond - uncond) + return real_eps * t + mimicked_eps * (1 - t) + else: + return real_eps + + +def patched_sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options=None, seed=None): + pid = os.getpid() + + if math.isclose(cond_scale, 1.0) and not model_options.get("disable_cfg1_optimization", False): + final_x0 = calc_cond_uncond_batch(model, cond, None, x, timestep, model_options)[0] + + if patch_settings[pid].eps_record is not None: + patch_settings[pid].eps_record = ((x - final_x0) / timestep).cpu() + + return final_x0 + + positive_x0, negative_x0 = calc_cond_uncond_batch(model, cond, uncond, x, timestep, model_options) + + positive_eps = x - positive_x0 + negative_eps = x - negative_x0 + + alpha = 0.001 * patch_settings[pid].sharpness * patch_settings[pid].global_diffusion_progress + + positive_eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0) + positive_eps_degraded_weighted = positive_eps_degraded * alpha + positive_eps * (1.0 - alpha) + + final_eps = compute_cfg(uncond=negative_eps, cond=positive_eps_degraded_weighted, + cfg_scale=cond_scale, t=patch_settings[pid].global_diffusion_progress) + + if patch_settings[pid].eps_record is not None: + patch_settings[pid].eps_record = (final_eps / timestep).cpu() + + return x - final_eps + + +def round_to_64(x): + h = float(x) + h = h / 64.0 + h = round(h) + h = int(h) + h = h * 64 + return h + + +def sdxl_encode_adm_patched(self, **kwargs): + clip_pooled = ldm_patched.modules.model_base.sdxl_pooled(kwargs, self.noise_augmentor) + width = kwargs.get("width", 1024) + height = kwargs.get("height", 1024) + target_width = width + target_height = height + pid = os.getpid() + + if kwargs.get("prompt_type", "") == "negative": + width = float(width) * patch_settings[pid].negative_adm_scale + height = float(height) * patch_settings[pid].negative_adm_scale + elif kwargs.get("prompt_type", "") == "positive": + width = float(width) * patch_settings[pid].positive_adm_scale + height = float(height) * patch_settings[pid].positive_adm_scale + + def embedder(number_list): + h = self.embedder(torch.tensor(number_list, dtype=torch.float32)) + h = torch.flatten(h).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1) + return h + + width, height = int(width), int(height) + target_width, target_height = round_to_64(target_width), round_to_64(target_height) + + adm_emphasized = embedder([height, width, 0, 0, target_height, target_width]) + adm_consistent = embedder([target_height, target_width, 0, 0, target_height, target_width]) + + clip_pooled = clip_pooled.to(adm_emphasized) + final_adm = torch.cat((clip_pooled, adm_emphasized, clip_pooled, adm_consistent), dim=1) + + return final_adm + + +def patched_KSamplerX0Inpaint_forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None): + if inpaint_worker.current_task is not None: + latent_processor = self.inner_model.inner_model.process_latent_in + inpaint_latent = latent_processor(inpaint_worker.current_task.latent).to(x) + inpaint_mask = inpaint_worker.current_task.latent_mask.to(x) + + if getattr(self, 'energy_generator', None) is None: + # avoid bad results by using different seeds. + self.energy_generator = torch.Generator(device='cpu').manual_seed((seed + 1) % constants.MAX_SEED) + + energy_sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(x.shape) - 1)) + current_energy = torch.randn( + x.size(), dtype=x.dtype, generator=self.energy_generator, device="cpu").to(x) * energy_sigma + x = x * inpaint_mask + (inpaint_latent + current_energy) * (1.0 - inpaint_mask) + + out = self.inner_model(x, sigma, + cond=cond, + uncond=uncond, + cond_scale=cond_scale, + model_options=model_options, + seed=seed) + + out = out * inpaint_mask + inpaint_latent * (1.0 - inpaint_mask) + else: + out = self.inner_model(x, sigma, + cond=cond, + uncond=uncond, + cond_scale=cond_scale, + model_options=model_options, + seed=seed) + return out + + +def timed_adm(y, timesteps): + if isinstance(y, torch.Tensor) and int(y.dim()) == 2 and int(y.shape[1]) == 5632: + y_mask = (timesteps > 999.0 * (1.0 - float(patch_settings[os.getpid()].adm_scaler_end))).to(y)[..., None] + y_with_adm = y[..., :2816].clone() + y_without_adm = y[..., 2816:].clone() + return y_with_adm * y_mask + y_without_adm * (1.0 - y_mask) + return y + + +def patched_cldm_forward(self, x, hint, timesteps, context, y=None, **kwargs): + t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + pid = os.getpid() + + guided_hint = self.input_hint_block(hint, emb, context) + + y = timed_adm(y, timesteps) + + outs = [] + + hs = [] + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for module, zero_conv in zip(self.input_blocks, self.zero_convs): + if guided_hint is not None: + h = module(h, emb, context) + h += guided_hint + guided_hint = None + else: + h = module(h, emb, context) + outs.append(zero_conv(h, emb, context)) + + h = self.middle_block(h, emb, context) + outs.append(self.middle_block_out(h, emb, context)) + + if patch_settings[pid].controlnet_softness > 0: + for i in range(10): + k = 1.0 - float(i) / 9.0 + outs[i] = outs[i] * (1.0 - patch_settings[pid].controlnet_softness * k) + + return outs + + +def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs): + self.current_step = 1.0 - timesteps.to(x) / 999.0 + patch_settings[os.getpid()].global_diffusion_progress = float(self.current_step.detach().cpu().numpy().tolist()[0]) + + y = timed_adm(y, timesteps) + + transformer_options["original_shape"] = list(x.shape) + transformer_options["transformer_index"] = 0 + transformer_patches = transformer_options.get("patches", {}) + + num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames) + image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator) + time_context = kwargs.get("time_context", None) + + assert (y is not None) == ( + self.num_classes is not None + ), "must specify y if and only if the model is class-conditional" + hs = [] + t_emb = ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype) + emb = self.time_embed(t_emb) + + if self.num_classes is not None: + assert y.shape[0] == x.shape[0] + emb = emb + self.label_emb(y) + + h = x + for id, module in enumerate(self.input_blocks): + transformer_options["block"] = ("input", id) + h = forward_timestep_embed(module, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'input') + if "input_block_patch" in transformer_patches: + patch = transformer_patches["input_block_patch"] + for p in patch: + h = p(h, transformer_options) + + hs.append(h) + if "input_block_patch_after_skip" in transformer_patches: + patch = transformer_patches["input_block_patch_after_skip"] + for p in patch: + h = p(h, transformer_options) + + transformer_options["block"] = ("middle", 0) + h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = apply_control(h, control, 'middle') + + for id, module in enumerate(self.output_blocks): + transformer_options["block"] = ("output", id) + hsp = hs.pop() + hsp = apply_control(hsp, control, 'output') + + if "output_block_patch" in transformer_patches: + patch = transformer_patches["output_block_patch"] + for p in patch: + h, hsp = p(h, hsp, transformer_options) + + h = torch.cat([h, hsp], dim=1) + del hsp + if len(hs) > 0: + output_shape = hs[-1].shape + else: + output_shape = None + h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator) + h = h.type(x.dtype) + if self.predict_codebook_ids: + return self.id_predictor(h) + else: + return self.out(h) + + +def patched_load_models_gpu(*args, **kwargs): + execution_start_time = time.perf_counter() + y = ldm_patched.modules.model_management.load_models_gpu_origin(*args, **kwargs) + moving_time = time.perf_counter() - execution_start_time + if moving_time > 0.1: + print(f'[Fooocus Model Management] Moving model(s) has taken {moving_time:.2f} seconds') + return y + + +def build_loaded(module, loader_name): + original_loader_name = loader_name + '_origin' + + if not hasattr(module, original_loader_name): + setattr(module, original_loader_name, getattr(module, loader_name)) + + original_loader = getattr(module, original_loader_name) + + def loader(*args, **kwargs): + result = None + try: + result = original_loader(*args, **kwargs) + except Exception as e: + result = None + exp = str(e) + '\n' + for path in list(args) + list(kwargs.values()): + if isinstance(path, str): + if os.path.exists(path): + exp += f'File corrupted: {path} \n' + corrupted_backup_file = path + '.corrupted' + if os.path.exists(corrupted_backup_file): + os.remove(corrupted_backup_file) + os.replace(path, corrupted_backup_file) + if os.path.exists(path): + os.remove(path) + exp += f'Fooocus has tried to move the corrupted file to {corrupted_backup_file} \n' + exp += f'You may try again now and Fooocus will download models again. \n' + raise ValueError(exp) + return result + + setattr(module, loader_name, loader) + return + + +def patch_all(): + if ldm_patched.modules.model_management.directml_enabled: + ldm_patched.modules.model_management.lowvram_available = True + ldm_patched.modules.model_management.OOM_EXCEPTION = Exception + + patch_all_precision() + patch_all_clip() + + if not hasattr(ldm_patched.modules.model_management, 'load_models_gpu_origin'): + ldm_patched.modules.model_management.load_models_gpu_origin = ldm_patched.modules.model_management.load_models_gpu + + ldm_patched.modules.model_management.load_models_gpu = patched_load_models_gpu + ldm_patched.modules.model_patcher.ModelPatcher.calculate_weight = calculate_weight_patched + ldm_patched.controlnet.cldm.ControlNet.forward = patched_cldm_forward + ldm_patched.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward + ldm_patched.modules.model_base.SDXL.encode_adm = sdxl_encode_adm_patched + ldm_patched.modules.samplers.KSamplerX0Inpaint.forward = patched_KSamplerX0Inpaint_forward + ldm_patched.k_diffusion.sampling.BrownianTreeNoiseSampler = BrownianTreeNoiseSamplerPatched + ldm_patched.modules.samplers.sampling_function = patched_sampling_function + + warnings.filterwarnings(action='ignore', module='torchsde') + + build_loaded(safetensors.torch, 'load_file') + build_loaded(torch, 'load') + + return diff --git a/modules/patch_clip.py b/modules/patch_clip.py new file mode 100644 index 0000000000000000000000000000000000000000..06b7f01bb857b01995ff7b0326813f98f92ea76d --- /dev/null +++ b/modules/patch_clip.py @@ -0,0 +1,195 @@ +# Consistent with Kohya/A1111 to reduce differences between model training and inference. + +import os +import torch +import ldm_patched.controlnet.cldm +import ldm_patched.k_diffusion.sampling +import ldm_patched.ldm.modules.attention +import ldm_patched.ldm.modules.diffusionmodules.model +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.modules.args_parser +import ldm_patched.modules.model_base +import ldm_patched.modules.model_management +import ldm_patched.modules.model_patcher +import ldm_patched.modules.samplers +import ldm_patched.modules.sd +import ldm_patched.modules.sd1_clip +import ldm_patched.modules.clip_vision +import ldm_patched.modules.ops as ops + +from modules.ops import use_patched_ops +from transformers import CLIPTextModel, CLIPTextConfig, modeling_utils, CLIPVisionConfig, CLIPVisionModelWithProjection + + +def patched_encode_token_weights(self, token_weight_pairs): + to_encode = list() + max_token_len = 0 + has_weights = False + for x in token_weight_pairs: + tokens = list(map(lambda a: a[0], x)) + max_token_len = max(len(tokens), max_token_len) + has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x)) + to_encode.append(tokens) + + sections = len(to_encode) + if has_weights or sections == 0: + to_encode.append(ldm_patched.modules.sd1_clip.gen_empty_tokens(self.special_tokens, max_token_len)) + + out, pooled = self.encode(to_encode) + if pooled is not None: + first_pooled = pooled[0:1].to(ldm_patched.modules.model_management.intermediate_device()) + else: + first_pooled = pooled + + output = [] + for k in range(0, sections): + z = out[k:k + 1] + if has_weights: + original_mean = z.mean() + z_empty = out[-1] + for i in range(len(z)): + for j in range(len(z[i])): + weight = token_weight_pairs[k][j][1] + if weight != 1.0: + z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j] + new_mean = z.mean() + z = z * (original_mean / new_mean) + output.append(z) + + if len(output) == 0: + return out[-1:].to(ldm_patched.modules.model_management.intermediate_device()), first_pooled + return torch.cat(output, dim=-2).to(ldm_patched.modules.model_management.intermediate_device()), first_pooled + + +def patched_SDClipModel__init__(self, max_length=77, freeze=True, layer="last", layer_idx=None, + textmodel_json_config=None, dtype=None, special_tokens=None, + layer_norm_hidden_state=True, **kwargs): + torch.nn.Module.__init__(self) + assert layer in self.LAYERS + + if special_tokens is None: + special_tokens = {"start": 49406, "end": 49407, "pad": 49407} + + if textmodel_json_config is None: + textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(ldm_patched.modules.sd1_clip.__file__)), + "sd1_clip_config.json") + + config = CLIPTextConfig.from_json_file(textmodel_json_config) + self.num_layers = config.num_hidden_layers + + with use_patched_ops(ops.manual_cast): + with modeling_utils.no_init_weights(): + self.transformer = CLIPTextModel(config) + + if dtype is not None: + self.transformer.to(dtype) + + self.transformer.text_model.embeddings.to(torch.float32) + + if freeze: + self.freeze() + + self.max_length = max_length + self.layer = layer + self.layer_idx = None + self.special_tokens = special_tokens + self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1])) + self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055)) + self.enable_attention_masks = False + + self.layer_norm_hidden_state = layer_norm_hidden_state + if layer == "hidden": + assert layer_idx is not None + assert abs(layer_idx) < self.num_layers + self.clip_layer(layer_idx) + self.layer_default = (self.layer, self.layer_idx) + + +def patched_SDClipModel_forward(self, tokens): + backup_embeds = self.transformer.get_input_embeddings() + device = backup_embeds.weight.device + tokens = self.set_up_textual_embeddings(tokens, backup_embeds) + tokens = torch.LongTensor(tokens).to(device) + + attention_mask = None + if self.enable_attention_masks: + attention_mask = torch.zeros_like(tokens) + max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 + for x in range(attention_mask.shape[0]): + for y in range(attention_mask.shape[1]): + attention_mask[x, y] = 1 + if tokens[x, y] == max_token: + break + + outputs = self.transformer(input_ids=tokens, attention_mask=attention_mask, + output_hidden_states=self.layer == "hidden") + self.transformer.set_input_embeddings(backup_embeds) + + if self.layer == "last": + z = outputs.last_hidden_state + elif self.layer == "pooled": + z = outputs.pooler_output[:, None, :] + else: + z = outputs.hidden_states[self.layer_idx] + if self.layer_norm_hidden_state: + z = self.transformer.text_model.final_layer_norm(z) + + if hasattr(outputs, "pooler_output"): + pooled_output = outputs.pooler_output.float() + else: + pooled_output = None + + if self.text_projection is not None and pooled_output is not None: + pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float() + + return z.float(), pooled_output + + +def patched_ClipVisionModel__init__(self, json_config): + config = CLIPVisionConfig.from_json_file(json_config) + + self.load_device = ldm_patched.modules.model_management.text_encoder_device() + self.offload_device = ldm_patched.modules.model_management.text_encoder_offload_device() + + if ldm_patched.modules.model_management.should_use_fp16(self.load_device, prioritize_performance=False): + self.dtype = torch.float16 + else: + self.dtype = torch.float32 + + with use_patched_ops(ops.manual_cast): + with modeling_utils.no_init_weights(): + self.model = CLIPVisionModelWithProjection(config) + + self.model.to(self.dtype) + self.patcher = ldm_patched.modules.model_patcher.ModelPatcher( + self.model, + load_device=self.load_device, + offload_device=self.offload_device + ) + + +def patched_ClipVisionModel_encode_image(self, image): + ldm_patched.modules.model_management.load_model_gpu(self.patcher) + pixel_values = ldm_patched.modules.clip_vision.clip_preprocess(image.to(self.load_device)) + outputs = self.model(pixel_values=pixel_values, output_hidden_states=True) + + for k in outputs: + t = outputs[k] + if t is not None: + if k == 'hidden_states': + outputs["penultimate_hidden_states"] = t[-2].to(ldm_patched.modules.model_management.intermediate_device()) + outputs["hidden_states"] = None + else: + outputs[k] = t.to(ldm_patched.modules.model_management.intermediate_device()) + + return outputs + + +def patch_all_clip(): + ldm_patched.modules.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = patched_encode_token_weights + ldm_patched.modules.sd1_clip.SDClipModel.__init__ = patched_SDClipModel__init__ + ldm_patched.modules.sd1_clip.SDClipModel.forward = patched_SDClipModel_forward + ldm_patched.modules.clip_vision.ClipVisionModel.__init__ = patched_ClipVisionModel__init__ + ldm_patched.modules.clip_vision.ClipVisionModel.encode_image = patched_ClipVisionModel_encode_image + return diff --git a/modules/patch_precision.py b/modules/patch_precision.py new file mode 100644 index 0000000000000000000000000000000000000000..83569bdd15f5ab0cac2c57353626c4e843bd264d --- /dev/null +++ b/modules/patch_precision.py @@ -0,0 +1,60 @@ +# Consistent with Kohya to reduce differences between model training and inference. + +import torch +import math +import einops +import numpy as np + +import ldm_patched.ldm.modules.diffusionmodules.openaimodel +import ldm_patched.modules.model_sampling +import ldm_patched.modules.sd1_clip + +from ldm_patched.ldm.modules.diffusionmodules.util import make_beta_schedule + + +def patched_timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False): + # Consistent with Kohya to reduce differences between model training and inference. + + if not repeat_only: + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + else: + embedding = einops.repeat(timesteps, 'b -> b d', d=dim) + return embedding + + +def patched_register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000, + linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3): + # Consistent with Kohya to reduce differences between model training and inference. + + if given_betas is not None: + betas = given_betas + else: + betas = make_beta_schedule( + beta_schedule, + timesteps, + linear_start=linear_start, + linear_end=linear_end, + cosine_s=cosine_s) + + alphas = 1. - betas + alphas_cumprod = np.cumprod(alphas, axis=0) + timesteps, = betas.shape + self.num_timesteps = int(timesteps) + self.linear_start = linear_start + self.linear_end = linear_end + sigmas = torch.tensor(((1 - alphas_cumprod) / alphas_cumprod) ** 0.5, dtype=torch.float32) + self.set_sigmas(sigmas) + return + + +def patch_all_precision(): + ldm_patched.ldm.modules.diffusionmodules.openaimodel.timestep_embedding = patched_timestep_embedding + ldm_patched.modules.model_sampling.ModelSamplingDiscrete._register_schedule = patched_register_schedule + return diff --git a/modules/private_logger.py b/modules/private_logger.py new file mode 100644 index 0000000000000000000000000000000000000000..01e570a7d96375a15a81b6f07a678b1f7eda743e --- /dev/null +++ b/modules/private_logger.py @@ -0,0 +1,130 @@ +import os +import args_manager +import modules.config +import json +import urllib.parse + +from PIL import Image +from PIL.PngImagePlugin import PngInfo +from modules.util import generate_temp_filename +from modules.meta_parser import MetadataParser, get_exif + +log_cache = {} + + +def get_current_html_path(output_format=None): + output_format = output_format if output_format else modules.config.default_output_format + date_string, local_temp_filename, only_name = generate_temp_filename(folder=modules.config.path_outputs, + extension=output_format) + html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html') + return html_name + + +def log(img, metadata, metadata_parser: MetadataParser | None = None, output_format=None) -> str: + path_outputs = args_manager.args.temp_path if args_manager.args.disable_image_log else modules.config.path_outputs + output_format = output_format if output_format else modules.config.default_output_format + date_string, local_temp_filename, only_name = generate_temp_filename(folder=path_outputs, extension=output_format) + os.makedirs(os.path.dirname(local_temp_filename), exist_ok=True) + + parsed_parameters = metadata_parser.parse_string(metadata.copy()) if metadata_parser is not None else '' + image = Image.fromarray(img) + + if output_format == 'png': + if parsed_parameters != '': + pnginfo = PngInfo() + pnginfo.add_text('parameters', parsed_parameters) + pnginfo.add_text('fooocus_scheme', metadata_parser.get_scheme().value) + else: + pnginfo = None + image.save(local_temp_filename, pnginfo=pnginfo) + elif output_format == 'jpg': + image.save(local_temp_filename, quality=95, optimize=True, progressive=True, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif()) + elif output_format == 'webp': + image.save(local_temp_filename, quality=95, lossless=False, exif=get_exif(parsed_parameters, metadata_parser.get_scheme().value) if metadata_parser else Image.Exif()) + else: + image.save(local_temp_filename) + + if args_manager.args.disable_image_log: + return local_temp_filename + + html_name = os.path.join(os.path.dirname(local_temp_filename), 'log.html') + + css_styles = ( + "" + ) + + js = ( + """""" + ) + + begin_part = f"Fooocus Log {date_string}{css_styles}{js}

Fooocus Log {date_string} (private)

\n

Metadata is embedded if enabled in the config or developer debug mode. You can find the information for each image in line Metadata Scheme.

\n\n" + end_part = f'\n' + + middle_part = log_cache.get(html_name, "") + + if middle_part == "": + if os.path.exists(html_name): + existing_split = open(html_name, 'r', encoding='utf-8').read().split('') + if len(existing_split) == 3: + middle_part = existing_split[1] + else: + middle_part = existing_split[0] + + div_name = only_name.replace('.', '_') + item = f"
\n" + item += f"" + item += "" + item += "
{only_name}
" + for label, key, value in metadata: + value_txt = str(value).replace('\n', '
') + item += f"\n" + item += "
{label}{value_txt}
" + + js_txt = urllib.parse.quote(json.dumps({k: v for _, k, v in metadata}, indent=0), safe='') + item += f"
" + + item += "
\n\n" + + middle_part = item + middle_part + + with open(html_name, 'w', encoding='utf-8') as f: + f.write(begin_part + middle_part + end_part) + + print(f'Image generated with private log at: {html_name}') + + log_cache[html_name] = middle_part + + return local_temp_filename diff --git a/modules/sample_hijack.py b/modules/sample_hijack.py new file mode 100644 index 0000000000000000000000000000000000000000..5936a096d9f0afaac0a672f72cee5f84b23496ad --- /dev/null +++ b/modules/sample_hijack.py @@ -0,0 +1,184 @@ +import torch +import ldm_patched.modules.samplers +import ldm_patched.modules.model_management + +from collections import namedtuple +from ldm_patched.contrib.external_custom_sampler import SDTurboScheduler +from ldm_patched.k_diffusion import sampling as k_diffusion_sampling +from ldm_patched.modules.samplers import normal_scheduler, simple_scheduler, ddim_scheduler +from ldm_patched.modules.model_base import SDXLRefiner, SDXL +from ldm_patched.modules.conds import CONDRegular +from ldm_patched.modules.sample import get_additional_models, get_models_from_cond, cleanup_additional_models +from ldm_patched.modules.samplers import resolve_areas_and_cond_masks, wrap_model, calculate_start_end_timesteps, \ + create_cond_with_same_area_if_none, pre_run_control, apply_empty_x_to_equal_area, encode_model_conds + + +current_refiner = None +refiner_switch_step = -1 + + +@torch.no_grad() +@torch.inference_mode() +def clip_separate_inner(c, p, target_model=None, target_clip=None): + if target_model is None or isinstance(target_model, SDXLRefiner): + c = c[..., -1280:].clone() + elif isinstance(target_model, SDXL): + c = c.clone() + else: + p = None + c = c[..., :768].clone() + + final_layer_norm = target_clip.cond_stage_model.clip_l.transformer.text_model.final_layer_norm + + final_layer_norm_origin_device = final_layer_norm.weight.device + final_layer_norm_origin_dtype = final_layer_norm.weight.dtype + + c_origin_device = c.device + c_origin_dtype = c.dtype + + final_layer_norm.to(device='cpu', dtype=torch.float32) + c = c.to(device='cpu', dtype=torch.float32) + + c = torch.chunk(c, int(c.size(1)) // 77, 1) + c = [final_layer_norm(ci) for ci in c] + c = torch.cat(c, dim=1) + + final_layer_norm.to(device=final_layer_norm_origin_device, dtype=final_layer_norm_origin_dtype) + c = c.to(device=c_origin_device, dtype=c_origin_dtype) + return c, p + + +@torch.no_grad() +@torch.inference_mode() +def clip_separate(cond, target_model=None, target_clip=None): + results = [] + + for c, px in cond: + p = px.get('pooled_output', None) + c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip) + p = {} if p is None else {'pooled_output': p.clone()} + results.append([c, p]) + + return results + + +@torch.no_grad() +@torch.inference_mode() +def clip_separate_after_preparation(cond, target_model=None, target_clip=None): + results = [] + + for x in cond: + p = x.get('pooled_output', None) + c = x['model_conds']['c_crossattn'].cond + + c, p = clip_separate_inner(c, p, target_model=target_model, target_clip=target_clip) + + result = {'model_conds': {'c_crossattn': CONDRegular(c)}} + + if p is not None: + result['pooled_output'] = p.clone() + + results.append(result) + + return results + + +@torch.no_grad() +@torch.inference_mode() +def sample_hacked(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None): + global current_refiner + + positive = positive[:] + negative = negative[:] + + resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device) + resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device) + + model_wrap = wrap_model(model) + + calculate_start_end_timesteps(model, negative) + calculate_start_end_timesteps(model, positive) + + if latent_image is not None: + latent_image = model.process_latent_in(latent_image) + + if hasattr(model, 'extra_conds'): + positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) + negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) + + #make sure each cond area has an opposite one with the same area + for c in positive: + create_cond_with_same_area_if_none(negative, c) + for c in negative: + create_cond_with_same_area_if_none(positive, c) + + # pre_run_control(model, negative + positive) + pre_run_control(model, positive) # negative is not necessary in Fooocus, 0.5s faster. + + apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x]) + apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x]) + + extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed} + + if current_refiner is not None and hasattr(current_refiner.model, 'extra_conds'): + positive_refiner = clip_separate_after_preparation(positive, target_model=current_refiner.model) + negative_refiner = clip_separate_after_preparation(negative, target_model=current_refiner.model) + + positive_refiner = encode_model_conds(current_refiner.model.extra_conds, positive_refiner, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask) + negative_refiner = encode_model_conds(current_refiner.model.extra_conds, negative_refiner, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask) + + def refiner_switch(): + cleanup_additional_models(set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))) + + extra_args["cond"] = positive_refiner + extra_args["uncond"] = negative_refiner + + # clear ip-adapter for refiner + extra_args['model_options'] = {k: {} if k == 'transformer_options' else v for k, v in extra_args['model_options'].items()} + + models, inference_memory = get_additional_models(positive_refiner, negative_refiner, current_refiner.model_dtype()) + ldm_patched.modules.model_management.load_models_gpu( + [current_refiner] + models, + model.memory_required([noise.shape[0] * 2] + list(noise.shape[1:])) + inference_memory) + + model_wrap.inner_model = current_refiner.model + print('Refiner Swapped') + return + + def callback_wrap(step, x0, x, total_steps): + if step == refiner_switch_step and current_refiner is not None: + refiner_switch() + if callback is not None: + # residual_noise_preview = x - x0 + # residual_noise_preview /= residual_noise_preview.std() + # residual_noise_preview *= x0.std() + callback(step, x0, x, total_steps) + + samples = sampler.sample(model_wrap, sigmas, extra_args, callback_wrap, noise, latent_image, denoise_mask, disable_pbar) + return model.process_latent_out(samples.to(torch.float32)) + + +@torch.no_grad() +@torch.inference_mode() +def calculate_sigmas_scheduler_hacked(model, scheduler_name, steps): + if scheduler_name == "karras": + sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "exponential": + sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max)) + elif scheduler_name == "normal": + sigmas = normal_scheduler(model, steps) + elif scheduler_name == "simple": + sigmas = simple_scheduler(model, steps) + elif scheduler_name == "ddim_uniform": + sigmas = ddim_scheduler(model, steps) + elif scheduler_name == "sgm_uniform": + sigmas = normal_scheduler(model, steps, sgm=True) + elif scheduler_name == "turbo": + sigmas = SDTurboScheduler().get_sigmas(namedtuple('Patcher', ['model'])(model=model), steps=steps, denoise=1.0)[0] + else: + raise TypeError("error invalid scheduler") + return sigmas + + +ldm_patched.modules.samplers.calculate_sigmas_scheduler = calculate_sigmas_scheduler_hacked +ldm_patched.modules.samplers.sample = sample_hacked diff --git a/modules/sdxl_styles.py b/modules/sdxl_styles.py new file mode 100644 index 0000000000000000000000000000000000000000..2a310024cdd0f96cb20341f811a50146000b586b --- /dev/null +++ b/modules/sdxl_styles.py @@ -0,0 +1,117 @@ +import os +import re +import json +import math + +from modules.util import get_files_from_folder + + +# cannot use modules.config - validators causing circular imports +styles_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../sdxl_styles/')) +wildcards_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '../wildcards/')) +wildcards_max_bfs_depth = 64 + + +def normalize_key(k): + k = k.replace('-', ' ') + words = k.split(' ') + words = [w[:1].upper() + w[1:].lower() for w in words] + k = ' '.join(words) + k = k.replace('3d', '3D') + k = k.replace('Sai', 'SAI') + k = k.replace('Mre', 'MRE') + k = k.replace('(s', '(S') + return k + + +styles = {} + +styles_files = get_files_from_folder(styles_path, ['.json']) + +for x in ['sdxl_styles_fooocus.json', + 'sdxl_styles_sai.json', + 'sdxl_styles_mre.json', + 'sdxl_styles_twri.json', + 'sdxl_styles_diva.json', + 'sdxl_styles_marc_k3nt3l.json']: + if x in styles_files: + styles_files.remove(x) + styles_files.append(x) + +for styles_file in styles_files: + try: + with open(os.path.join(styles_path, styles_file), encoding='utf-8') as f: + for entry in json.load(f): + name = normalize_key(entry['name']) + prompt = entry['prompt'] if 'prompt' in entry else '' + negative_prompt = entry['negative_prompt'] if 'negative_prompt' in entry else '' + styles[name] = (prompt, negative_prompt) + except Exception as e: + print(str(e)) + print(f'Failed to load style file {styles_file}') + +style_keys = list(styles.keys()) +fooocus_expansion = "Fooocus V2" +legal_style_names = [fooocus_expansion] + style_keys + + +def apply_style(style, positive): + p, n = styles[style] + return p.replace('{prompt}', positive).splitlines(), n.splitlines() + + +def apply_wildcards(wildcard_text, rng, directory=wildcards_path): + for _ in range(wildcards_max_bfs_depth): + placeholders = re.findall(r'__([\w-]+)__', wildcard_text) + if len(placeholders) == 0: + return wildcard_text + + print(f'[Wildcards] processing: {wildcard_text}') + for placeholder in placeholders: + try: + words = open(os.path.join(directory, f'{placeholder}.txt'), encoding='utf-8').read().splitlines() + words = [x for x in words if x != ''] + assert len(words) > 0 + wildcard_text = wildcard_text.replace(f'__{placeholder}__', rng.choice(words), 1) + except: + print(f'[Wildcards] Warning: {placeholder}.txt missing or empty. ' + f'Using "{placeholder}" as a normal word.') + wildcard_text = wildcard_text.replace(f'__{placeholder}__', placeholder) + print(f'[Wildcards] {wildcard_text}') + + print(f'[Wildcards] BFS stack overflow. Current text: {wildcard_text}') + return wildcard_text + +def get_words(arrays, totalMult, index): + if(len(arrays) == 1): + return [arrays[0].split(',')[index]] + else: + words = arrays[0].split(',') + word = words[index % len(words)] + index -= index % len(words) + index /= len(words) + index = math.floor(index) + return [word] + get_words(arrays[1:], math.floor(totalMult/len(words)), index) + + +def apply_arrays(text, index): + arrays = re.findall(r'\[\[(.*?)\]\]', text) + if len(arrays) == 0: + return text + + print(f'[Arrays] processing: {text}') + mult = 1 + for arr in arrays: + words = arr.split(',') + mult *= len(words) + + index %= mult + chosen_words = get_words(arrays, mult, index) + + i = 0 + for arr in arrays: + text = text.replace(f'[[{arr}]]', chosen_words[i], 1) + i = i+1 + + return text + diff --git a/modules/style_sorter.py b/modules/style_sorter.py new file mode 100644 index 0000000000000000000000000000000000000000..49142bc7926e06ee29f5678de1a9acc13dac5b70 --- /dev/null +++ b/modules/style_sorter.py @@ -0,0 +1,59 @@ +import os +import gradio as gr +import modules.localization as localization +import json + + +all_styles = [] + + +def try_load_sorted_styles(style_names, default_selected): + global all_styles + + all_styles = style_names + + try: + if os.path.exists('sorted_styles.json'): + with open('sorted_styles.json', 'rt', encoding='utf-8') as fp: + sorted_styles = [] + for x in json.load(fp): + if x in all_styles: + sorted_styles.append(x) + for x in all_styles: + if x not in sorted_styles: + sorted_styles.append(x) + all_styles = sorted_styles + except Exception as e: + print('Load style sorting failed.') + print(e) + + unselected = [y for y in all_styles if y not in default_selected] + all_styles = default_selected + unselected + + return + + +def sort_styles(selected): + global all_styles + unselected = [y for y in all_styles if y not in selected] + sorted_styles = selected + unselected + try: + with open('sorted_styles.json', 'wt', encoding='utf-8') as fp: + json.dump(sorted_styles, fp, indent=4) + except Exception as e: + print('Write style sorting failed.') + print(e) + all_styles = sorted_styles + return gr.CheckboxGroup.update(choices=sorted_styles) + + +def localization_key(x): + return x + localization.current_translation.get(x, '') + + +def search_styles(selected, query): + unselected = [y for y in all_styles if y not in selected] + matched = [y for y in unselected if query.lower() in localization_key(y).lower()] if len(query.replace(' ', '')) > 0 else [] + unmatched = [y for y in unselected if y not in matched] + sorted_styles = matched + selected + unmatched + return gr.CheckboxGroup.update(choices=sorted_styles) diff --git a/modules/ui_gradio_extensions.py b/modules/ui_gradio_extensions.py new file mode 100644 index 0000000000000000000000000000000000000000..bebf9f8ca7860c700f52ea5d3d3586917f17d34b --- /dev/null +++ b/modules/ui_gradio_extensions.py @@ -0,0 +1,67 @@ +# based on https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/v1.6.0/modules/ui_gradio_extensions.py + +import os +import gradio as gr +import args_manager + +from modules.localization import localization_js + + +GradioTemplateResponseOriginal = gr.routes.templates.TemplateResponse + +modules_path = os.path.dirname(os.path.realpath(__file__)) +script_path = os.path.dirname(modules_path) + + +def webpath(fn): + if fn.startswith(script_path): + web_path = os.path.relpath(fn, script_path).replace('\\', '/') + else: + web_path = os.path.abspath(fn) + + return f'file={web_path}?{os.path.getmtime(fn)}' + + +def javascript_html(): + script_js_path = webpath('javascript/script.js') + context_menus_js_path = webpath('javascript/contextMenus.js') + localization_js_path = webpath('javascript/localization.js') + zoom_js_path = webpath('javascript/zoom.js') + edit_attention_js_path = webpath('javascript/edit-attention.js') + viewer_js_path = webpath('javascript/viewer.js') + image_viewer_js_path = webpath('javascript/imageviewer.js') + samples_path = webpath(os.path.abspath('./sdxl_styles/samples/fooocus_v2.jpg')) + head = f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + head += f'\n' + + if args_manager.args.theme: + head += f'\n' + + return head + + +def css_html(): + style_css_path = webpath('css/style.css') + head = f'' + return head + + +def reload_javascript(): + js = javascript_html() + css = css_html() + + def template_response(*args, **kwargs): + res = GradioTemplateResponseOriginal(*args, **kwargs) + res.body = res.body.replace(b'', f'{js}'.encode("utf8")) + res.body = res.body.replace(b'', f'{css}'.encode("utf8")) + res.init_headers() + return res + + gr.routes.templates.TemplateResponse = template_response diff --git a/modules/upscaler.py b/modules/upscaler.py new file mode 100644 index 0000000000000000000000000000000000000000..974e4f37c8756df56b9e64143bddff1f1378bc83 --- /dev/null +++ b/modules/upscaler.py @@ -0,0 +1,34 @@ +import os +import torch +import modules.core as core + +from ldm_patched.pfn.architecture.RRDB import RRDBNet as ESRGAN +from ldm_patched.contrib.external_upscale_model import ImageUpscaleWithModel +from collections import OrderedDict +from modules.config import path_upscale_models + +model_filename = os.path.join(path_upscale_models, 'fooocus_upscaler_s409985e5.bin') +opImageUpscaleWithModel = ImageUpscaleWithModel() +model = None + + +def perform_upscale(img): + global model + + print(f'Upscaling image with shape {str(img.shape)} ...') + + if model is None: + sd = torch.load(model_filename) + sdo = OrderedDict() + for k, v in sd.items(): + sdo[k.replace('residual_block_', 'RDB')] = v + del sd + model = ESRGAN(sdo) + model.cpu() + model.eval() + + img = core.numpy_to_pytorch(img) + img = opImageUpscaleWithModel.upscale(model, img)[0] + img = core.pytorch_to_numpy(img)[0] + + return img diff --git a/modules/util.py b/modules/util.py new file mode 100644 index 0000000000000000000000000000000000000000..c7923ec8260286d4bcf858a250b0e75c7e51d97d --- /dev/null +++ b/modules/util.py @@ -0,0 +1,362 @@ +import typing + +import numpy as np +import datetime +import random +import math +import os +import cv2 +import json + +from PIL import Image +from hashlib import sha256 + +import modules.sdxl_styles + +LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS) +HASH_SHA256_LENGTH = 10 + +def erode_or_dilate(x, k): + k = int(k) + if k > 0: + return cv2.dilate(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=k) + if k < 0: + return cv2.erode(x, kernel=np.ones(shape=(3, 3), dtype=np.uint8), iterations=-k) + return x + + +def resample_image(im, width, height): + im = Image.fromarray(im) + im = im.resize((int(width), int(height)), resample=LANCZOS) + return np.array(im) + + +def resize_image(im, width, height, resize_mode=1): + """ + Resizes an image with the specified resize_mode, width, and height. + + Args: + resize_mode: The mode to use when resizing the image. + 0: Resize the image to the specified width and height. + 1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess. + 2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. + im: The image to resize. + width: The width to resize the image to. + height: The height to resize the image to. + """ + + im = Image.fromarray(im) + + def resize(im, w, h): + return im.resize((w, h), resample=LANCZOS) + + if resize_mode == 0: + res = resize(im, width, height) + + elif resize_mode == 1: + ratio = width / height + src_ratio = im.width / im.height + + src_w = width if ratio > src_ratio else im.width * height // im.height + src_h = height if ratio <= src_ratio else im.height * width // im.width + + resized = resize(im, src_w, src_h) + res = Image.new("RGB", (width, height)) + res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) + + else: + ratio = width / height + src_ratio = im.width / im.height + + src_w = width if ratio < src_ratio else im.width * height // im.height + src_h = height if ratio >= src_ratio else im.height * width // im.width + + resized = resize(im, src_w, src_h) + res = Image.new("RGB", (width, height)) + res.paste(resized, box=(width // 2 - src_w // 2, height // 2 - src_h // 2)) + + if ratio < src_ratio: + fill_height = height // 2 - src_h // 2 + if fill_height > 0: + res.paste(resized.resize((width, fill_height), box=(0, 0, width, 0)), box=(0, 0)) + res.paste(resized.resize((width, fill_height), box=(0, resized.height, width, resized.height)), box=(0, fill_height + src_h)) + elif ratio > src_ratio: + fill_width = width // 2 - src_w // 2 + if fill_width > 0: + res.paste(resized.resize((fill_width, height), box=(0, 0, 0, height)), box=(0, 0)) + res.paste(resized.resize((fill_width, height), box=(resized.width, 0, resized.width, height)), box=(fill_width + src_w, 0)) + + return np.array(res) + + +def get_shape_ceil(h, w): + return math.ceil(((h * w) ** 0.5) / 64.0) * 64.0 + + +def get_image_shape_ceil(im): + H, W = im.shape[:2] + return get_shape_ceil(H, W) + + +def set_image_shape_ceil(im, shape_ceil): + shape_ceil = float(shape_ceil) + + H_origin, W_origin, _ = im.shape + H, W = H_origin, W_origin + + for _ in range(256): + current_shape_ceil = get_shape_ceil(H, W) + if abs(current_shape_ceil - shape_ceil) < 0.1: + break + k = shape_ceil / current_shape_ceil + H = int(round(float(H) * k / 64.0) * 64) + W = int(round(float(W) * k / 64.0) * 64) + + if H == H_origin and W == W_origin: + return im + + return resample_image(im, width=W, height=H) + + +def HWC3(x): + assert x.dtype == np.uint8 + if x.ndim == 2: + x = x[:, :, None] + assert x.ndim == 3 + H, W, C = x.shape + assert C == 1 or C == 3 or C == 4 + if C == 3: + return x + if C == 1: + return np.concatenate([x, x, x], axis=2) + if C == 4: + color = x[:, :, 0:3].astype(np.float32) + alpha = x[:, :, 3:4].astype(np.float32) / 255.0 + y = color * alpha + 255.0 * (1.0 - alpha) + y = y.clip(0, 255).astype(np.uint8) + return y + + +def remove_empty_str(items, default=None): + items = [x for x in items if x != ""] + if len(items) == 0 and default is not None: + return [default] + return items + + +def join_prompts(*args, **kwargs): + prompts = [str(x) for x in args if str(x) != ""] + if len(prompts) == 0: + return "" + if len(prompts) == 1: + return prompts[0] + return ', '.join(prompts) + + +def generate_temp_filename(folder='./outputs/', extension='png'): + current_time = datetime.datetime.now() + date_string = current_time.strftime("%Y-%m-%d") + time_string = current_time.strftime("%Y-%m-%d_%H-%M-%S") + random_number = random.randint(1000, 9999) + filename = f"{time_string}_{random_number}.{extension}" + result = os.path.join(folder, date_string, filename) + return date_string, os.path.abspath(result), filename + + +def get_files_from_folder(folder_path, exensions=None, name_filter=None): + if not os.path.isdir(folder_path): + raise ValueError("Folder path is not a valid directory.") + + filenames = [] + + for root, dirs, files in os.walk(folder_path, topdown=False): + relative_path = os.path.relpath(root, folder_path) + if relative_path == ".": + relative_path = "" + for filename in sorted(files, key=lambda s: s.casefold()): + _, file_extension = os.path.splitext(filename) + if (exensions is None or file_extension.lower() in exensions) and (name_filter is None or name_filter in _): + path = os.path.join(relative_path, filename) + filenames.append(path) + + return filenames + + +def calculate_sha256(filename, length=HASH_SHA256_LENGTH) -> str: + hash_sha256 = sha256() + blksize = 1024 * 1024 + + with open(filename, "rb") as f: + for chunk in iter(lambda: f.read(blksize), b""): + hash_sha256.update(chunk) + + res = hash_sha256.hexdigest() + return res[:length] if length else res + + +def quote(text): + if ',' not in str(text) and '\n' not in str(text) and ':' not in str(text): + return text + + return json.dumps(text, ensure_ascii=False) + + +def unquote(text): + if len(text) == 0 or text[0] != '"' or text[-1] != '"': + return text + + try: + return json.loads(text) + except Exception: + return text + + +def unwrap_style_text_from_prompt(style_text, prompt): + """ + Checks the prompt to see if the style text is wrapped around it. If so, + returns True plus the prompt text without the style text. Otherwise, returns + False with the original prompt. + + Note that the "cleaned" version of the style text is only used for matching + purposes here. It isn't returned; the original style text is not modified. + """ + stripped_prompt = prompt + stripped_style_text = style_text + if "{prompt}" in stripped_style_text: + # Work out whether the prompt is wrapped in the style text. If so, we + # return True and the "inner" prompt text that isn't part of the style. + try: + left, right = stripped_style_text.split("{prompt}", 2) + except ValueError as e: + # If the style text has multple "{prompt}"s, we can't split it into + # two parts. This is an error, but we can't do anything about it. + print(f"Unable to compare style text to prompt:\n{style_text}") + print(f"Error: {e}") + return False, prompt, '' + + left_pos = stripped_prompt.find(left) + right_pos = stripped_prompt.find(right) + if 0 <= left_pos < right_pos: + real_prompt = stripped_prompt[left_pos + len(left):right_pos] + prompt = stripped_prompt.replace(left + real_prompt + right, '', 1) + if prompt.startswith(", "): + prompt = prompt[2:] + if prompt.endswith(", "): + prompt = prompt[:-2] + return True, prompt, real_prompt + else: + # Work out whether the given prompt starts with the style text. If so, we + # return True and the prompt text up to where the style text starts. + if stripped_prompt.endswith(stripped_style_text): + prompt = stripped_prompt[: len(stripped_prompt) - len(stripped_style_text)] + if prompt.endswith(", "): + prompt = prompt[:-2] + return True, prompt, prompt + + return False, prompt, '' + + +def extract_original_prompts(style, prompt, negative_prompt): + """ + Takes a style and compares it to the prompt and negative prompt. If the style + matches, returns True plus the prompt and negative prompt with the style text + removed. Otherwise, returns False with the original prompt and negative prompt. + """ + if not style.prompt and not style.negative_prompt: + return False, prompt, negative_prompt + + match_positive, extracted_positive, real_prompt = unwrap_style_text_from_prompt( + style.prompt, prompt + ) + if not match_positive: + return False, prompt, negative_prompt, '' + + match_negative, extracted_negative, _ = unwrap_style_text_from_prompt( + style.negative_prompt, negative_prompt + ) + if not match_negative: + return False, prompt, negative_prompt, '' + + return True, extracted_positive, extracted_negative, real_prompt + + +def extract_styles_from_prompt(prompt, negative_prompt): + extracted = [] + applicable_styles = [] + + for style_name, (style_prompt, style_negative_prompt) in modules.sdxl_styles.styles.items(): + applicable_styles.append(PromptStyle(name=style_name, prompt=style_prompt, negative_prompt=style_negative_prompt)) + + real_prompt = '' + + while True: + found_style = None + + for style in applicable_styles: + is_match, new_prompt, new_neg_prompt, new_real_prompt = extract_original_prompts( + style, prompt, negative_prompt + ) + if is_match: + found_style = style + prompt = new_prompt + negative_prompt = new_neg_prompt + if real_prompt == '' and new_real_prompt != '' and new_real_prompt != prompt: + real_prompt = new_real_prompt + break + + if not found_style: + break + + applicable_styles.remove(found_style) + extracted.append(found_style.name) + + # add prompt expansion if not all styles could be resolved + if prompt != '': + if real_prompt != '': + extracted.append(modules.sdxl_styles.fooocus_expansion) + else: + # find real_prompt when only prompt expansion is selected + first_word = prompt.split(', ')[0] + first_word_positions = [i for i in range(len(prompt)) if prompt.startswith(first_word, i)] + if len(first_word_positions) > 1: + real_prompt = prompt[:first_word_positions[-1]] + extracted.append(modules.sdxl_styles.fooocus_expansion) + if real_prompt.endswith(', '): + real_prompt = real_prompt[:-2] + + return list(reversed(extracted)), real_prompt, negative_prompt + + +class PromptStyle(typing.NamedTuple): + name: str + prompt: str + negative_prompt: str + + +def is_json(data: str) -> bool: + try: + loaded_json = json.loads(data) + assert isinstance(loaded_json, dict) + except (ValueError, AssertionError): + return False + return True + + +def get_file_from_folder_list(name, folders): + for folder in folders: + filename = os.path.abspath(os.path.realpath(os.path.join(folder, name))) + if os.path.isfile(filename): + return filename + + return os.path.abspath(os.path.realpath(os.path.join(folders[0], name))) + + +def ordinal_suffix(number: int) -> str: + return 'th' if 10 <= number % 100 <= 20 else {1: 'st', 2: 'nd', 3: 'rd'}.get(number % 10, 'th') + + +def makedirs_with_log(path): + try: + os.makedirs(path, exist_ok=True) + except OSError as error: + print(f'Directory {path} could not be created, reason: {error}') diff 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+ 1.0 + ] + ], + "default_cfg_scale": 3.0, + "default_sample_sharpness": 2.0, + "default_sampler": "dpmpp_2m_sde_gpu", + "default_scheduler": "karras", + "default_performance": "Speed", + "default_prompt": "", + "default_prompt_negative": "unrealistic, saturated, high contrast, big nose, painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label", + "default_styles": [ + "Fooocus V2", + "Fooocus Photograph", + "Fooocus Negative" + ], + "default_aspect_ratio": "896*1152", + "checkpoint_downloads": { + "realisticStockPhoto_v20.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/realisticStockPhoto_v20.safetensors" + }, + "embeddings_downloads": {}, + "lora_downloads": { + "SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors": "https://huggingface.co/lllyasviel/fav_models/resolve/main/fav/SDXL_FILM_PHOTOGRAPHY_STYLE_BetaV0.4.safetensors" + }, + "previous_default_models": ["realisticStockPhoto_v10.safetensors"] +} \ No newline at end of file diff --git a/presets/sai.json b/presets/sai.json new file mode 100644 index 0000000000000000000000000000000000000000..fecf047bfcbefa6747cc1908f495322c09ad71bd --- /dev/null +++ b/presets/sai.json @@ -0,0 +1,48 @@ +{ + "default_model": "sd_xl_base_1.0_0.9vae.safetensors", + "default_refiner": "sd_xl_refiner_1.0_0.9vae.safetensors", + "default_refiner_switch": 0.75, + "default_loras": [ + [ + "sd_xl_offset_example-lora_1.0.safetensors", + 0.5 + ], + [ + "None", + 1.0 + ], + [ + "None", + 1.0 + ], + [ + "None", + 1.0 + ], + [ + "None", + 1.0 + ] + ], + "default_cfg_scale": 7.0, + "default_sample_sharpness": 2.0, + "default_sampler": "dpmpp_2m_sde_gpu", + "default_scheduler": "karras", + "default_performance": "Speed", + "default_prompt": "", + "default_prompt_negative": "", + "default_styles": [ + "Fooocus V2", + "Fooocus Cinematic" + ], + "default_aspect_ratio": "1152*896", + "checkpoint_downloads": { + "sd_xl_base_1.0_0.9vae.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0_0.9vae.safetensors", + "sd_xl_refiner_1.0_0.9vae.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-refiner-1.0/resolve/main/sd_xl_refiner_1.0_0.9vae.safetensors" + }, + "embeddings_downloads": {}, + "lora_downloads": { + "sd_xl_offset_example-lora_1.0.safetensors": "https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_offset_example-lora_1.0.safetensors" + }, + "previous_default_models": [] +} \ No newline at end of file diff --git a/readme.md b/readme.md new file mode 100644 index 0000000000000000000000000000000000000000..4bcc43fc3fd1308086da23b23a8803e4131b95f6 --- /dev/null +++ b/readme.md @@ -0,0 +1,444 @@ +--- +title: APIFo +app_file: webui.py +sdk: gradio +sdk_version: 4.20.1 +--- +
+ + +**Non-cherry-picked** random batch by just typing two words "forest elf", + +without any parameter tweaking, without any strange prompt tags. + +See also **non-cherry-picked** generalization and diversity tests [here](https://github.com/lllyasviel/Fooocus/discussions/2067) and [here](https://github.com/lllyasviel/Fooocus/discussions/808) and [here](https://github.com/lllyasviel/Fooocus/discussions/679) and [here](https://github.com/lllyasviel/Fooocus/discussions/679#realistic). + +In the entire open source community, only Fooocus can achieve this level of **non-cherry-picked** quality. + +
+ + +# Fooocus + +Fooocus is an image generating software (based on [Gradio](https://www.gradio.app/)). + +Fooocus is a rethinking of Stable Diffusion and Midjourney’s designs: + +* Learned from Stable Diffusion, the software is offline, open source, and free. + +* Learned from Midjourney, the manual tweaking is not needed, and users only need to focus on the prompts and images. + +Fooocus has included and automated [lots of inner optimizations and quality improvements](#tech_list). Users can forget all those difficult technical parameters, and just enjoy the interaction between human and computer to "explore new mediums of thought and expanding the imaginative powers of the human species" `[1]`. + +Fooocus has simplified the installation. Between pressing "download" and generating the first image, the number of needed mouse clicks is strictly limited to less than 3. Minimal GPU memory requirement is 4GB (Nvidia). + +`[1]` David Holz, 2019. + +**Recently many fake websites exist on Google when you search “fooocus”. Do not trust those – here is the only official source of Fooocus.** + +## [Installing Fooocus](#download) + +# Moving from Midjourney to Fooocus + +Using Fooocus is as easy as (probably easier than) Midjourney – but this does not mean we lack functionality. Below are the details. + +| Midjourney | Fooocus | +| - | - | +| High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Unknown method) | High-quality text-to-image without needing much prompt engineering or parameter tuning.
(Fooocus has an offline GPT-2 based prompt processing engine and lots of sampling improvements so that results are always beautiful, no matter if your prompt is as short as “house in garden” or as long as 1000 words) | +| V1 V2 V3 V4 | Input Image -> Upscale or Variation -> Vary (Subtle) / Vary (Strong)| +| U1 U2 U3 U4 | Input Image -> Upscale or Variation -> Upscale (1.5x) / Upscale (2x) | +| Inpaint / Up / Down / Left / Right (Pan) | Input Image -> Inpaint or Outpaint -> Inpaint / Up / Down / Left / Right
(Fooocus uses its own inpaint algorithm and inpaint models so that results are more satisfying than all other software that uses standard SDXL inpaint method/model) | +| Image Prompt | Input Image -> Image Prompt
(Fooocus uses its own image prompt algorithm so that result quality and prompt understanding are more satisfying than all other software that uses standard SDXL methods like standard IP-Adapters or Revisions) | +| --style | Advanced -> Style | +| --stylize | Advanced -> Advanced -> Guidance | +| --niji | [Multiple launchers: "run.bat", "run_anime.bat", and "run_realistic.bat".](https://github.com/lllyasviel/Fooocus/discussions/679)
Fooocus support SDXL models on Civitai
(You can google search “Civitai” if you do not know about it) | +| --quality | Advanced -> Quality | +| --repeat | Advanced -> Image Number | +| Multi Prompts (::) | Just use multiple lines of prompts | +| Prompt Weights | You can use " I am (happy:1.5)".
Fooocus uses A1111's reweighting algorithm so that results are better than ComfyUI if users directly copy prompts from Civitai. (Because if prompts are written in ComfyUI's reweighting, users are less likely to copy prompt texts as they prefer dragging files)
To use embedding, you can use "(embedding:file_name:1.1)" | +| --no | Advanced -> Negative Prompt | +| --ar | Advanced -> Aspect Ratios | +| InsightFace | Input Image -> Image Prompt -> Advanced -> FaceSwap | +| Describe | Input Image -> Describe | + +We also have a few things borrowed from the best parts of LeonardoAI: + +| LeonardoAI | Fooocus | +| - | - | +| Prompt Magic | Advanced -> Style -> Fooocus V2 | +| Advanced Sampler Parameters (like Contrast/Sharpness/etc) | Advanced -> Advanced -> Sampling Sharpness / etc | +| User-friendly ControlNets | Input Image -> Image Prompt -> Advanced | + +Fooocus also developed many "fooocus-only" features for advanced users to get perfect results. [Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117) + +# Download + +### Windows + +You can directly download Fooocus with: + +**[>>> Click here to download <<<](https://github.com/lllyasviel/Fooocus/releases/download/release/Fooocus_win64_2-1-831.7z)** + +After you download the file, please uncompress it and then run the "run.bat". + +![image](https://github.com/lllyasviel/Fooocus/assets/19834515/c49269c4-c274-4893-b368-047c401cc58c) + +The first time you launch the software, it will automatically download models: + +1. It will download [default models](#models) to the folder "Fooocus\models\checkpoints" given different presets. You can download them in advance if you do not want automatic download. +2. Note that if you use inpaint, at the first time you inpaint an image, it will download [Fooocus's own inpaint control model from here](https://huggingface.co/lllyasviel/fooocus_inpaint/resolve/main/inpaint_v26.fooocus.patch) as the file "Fooocus\models\inpaint\inpaint_v26.fooocus.patch" (the size of this file is 1.28GB). + +After Fooocus 2.1.60, you will also have `run_anime.bat` and `run_realistic.bat`. They are different model presets (and require different models, but they will be automatically downloaded). [Check here for more details](https://github.com/lllyasviel/Fooocus/discussions/679). + +![image](https://github.com/lllyasviel/Fooocus/assets/19834515/d386f817-4bd7-490c-ad89-c1e228c23447) + +If you already have these files, you can copy them to the above locations to speed up installation. + +Note that if you see **"MetadataIncompleteBuffer" or "PytorchStreamReader"**, then your model files are corrupted. Please download models again. + +Below is a test on a relatively low-end laptop with **16GB System RAM** and **6GB VRAM** (Nvidia 3060 laptop). The speed on this machine is about 1.35 seconds per iteration. Pretty impressive – nowadays laptops with 3060 are usually at very acceptable price. + +![image](https://github.com/lllyasviel/Fooocus/assets/19834515/938737a5-b105-4f19-b051-81356cb7c495) + +Besides, recently many other software report that Nvidia driver above 532 is sometimes 10x slower than Nvidia driver 531. If your generation time is very long, consider download [Nvidia Driver 531 Laptop](https://www.nvidia.com/download/driverResults.aspx/199991/en-us/) or [Nvidia Driver 531 Desktop](https://www.nvidia.com/download/driverResults.aspx/199990/en-us/). + +Note that the minimal requirement is **4GB Nvidia GPU memory (4GB VRAM)** and **8GB system memory (8GB RAM)**. This requires using Microsoft’s Virtual Swap technique, which is automatically enabled by your Windows installation in most cases, so you often do not need to do anything about it. However, if you are not sure, or if you manually turned it off (would anyone really do that?), or **if you see any "RuntimeError: CPUAllocator"**, you can enable it here: + +
+Click here to see the image instructions. + +![image](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9) + +**And make sure that you have at least 40GB free space on each drive if you still see "RuntimeError: CPUAllocator" !** + +
+ +Please open an issue if you use similar devices but still cannot achieve acceptable performances. + +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + +See also the common problems and troubleshoots [here](troubleshoot.md). + +### Colab + +(Last tested - 2023 Dec 12) + +| Colab | Info +| --- | --- | +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lllyasviel/Fooocus/blob/main/fooocus_colab.ipynb) | Fooocus Official + +In Colab, you can modify the last line to `!python entry_with_update.py --share` or `!python entry_with_update.py --preset anime --share` or `!python entry_with_update.py --preset realistic --share` for Fooocus Default/Anime/Realistic Edition. + +Note that this Colab will disable refiner by default because Colab free's resources are relatively limited (and some "big" features like image prompt may cause free-tier Colab to disconnect). We make sure that basic text-to-image is always working on free-tier Colab. + +Thanks to [camenduru](https://github.com/camenduru)! + +### Linux (Using Anaconda) + +If you want to use Anaconda/Miniconda, you can + + git clone https://github.com/lllyasviel/Fooocus.git + cd Fooocus + conda env create -f environment.yaml + conda activate fooocus + pip install -r requirements_versions.txt + +Then download the models: download [default models](#models) to the folder "Fooocus\models\checkpoints". **Or let Fooocus automatically download the models** using the launcher: + + conda activate fooocus + python entry_with_update.py + +Or, if you want to open a remote port, use + + conda activate fooocus + python entry_with_update.py --listen + +Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. + +### Linux (Using Python Venv) + +Your Linux needs to have **Python 3.10** installed, and let's say your Python can be called with the command **python3** with your venv system working; you can + + git clone https://github.com/lllyasviel/Fooocus.git + cd Fooocus + python3 -m venv fooocus_env + source fooocus_env/bin/activate + pip install -r requirements_versions.txt + +See the above sections for model downloads. You can launch the software with: + + source fooocus_env/bin/activate + python entry_with_update.py + +Or, if you want to open a remote port, use + + source fooocus_env/bin/activate + python entry_with_update.py --listen + +Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. + +### Linux (Using native system Python) + +If you know what you are doing, and your Linux already has **Python 3.10** installed, and your Python can be called with the command **python3** (and Pip with **pip3**), you can + + git clone https://github.com/lllyasviel/Fooocus.git + cd Fooocus + pip3 install -r requirements_versions.txt + +See the above sections for model downloads. You can launch the software with: + + python3 entry_with_update.py + +Or, if you want to open a remote port, use + + python3 entry_with_update.py --listen + +Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. + +### Linux (AMD GPUs) + +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + +Same with the above instructions. You need to change torch to the AMD version + + pip uninstall torch torchvision torchaudio torchtext functorch xformers + pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.6 + +AMD is not intensively tested, however. The AMD support is in beta. + +Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. + +### Windows (AMD GPUs) + +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + +Same with Windows. Download the software and edit the content of `run.bat` as: + + .\python_embeded\python.exe -m pip uninstall torch torchvision torchaudio torchtext functorch xformers -y + .\python_embeded\python.exe -m pip install torch-directml + .\python_embeded\python.exe -s Fooocus\entry_with_update.py --directml + pause + +Then run the `run.bat`. + +AMD is not intensively tested, however. The AMD support is in beta. + +For AMD, use `.\python_embeded\python.exe entry_with_update.py --directml --preset anime` or `.\python_embeded\python.exe entry_with_update.py --directml --preset realistic` for Fooocus Anime/Realistic Edition. + +### Mac + +Note that the [minimal requirement](#minimal-requirement) for different platforms is different. + +Mac is not intensively tested. Below is an unofficial guideline for using Mac. You can discuss problems [here](https://github.com/lllyasviel/Fooocus/pull/129). + +You can install Fooocus on Apple Mac silicon (M1 or M2) with macOS 'Catalina' or a newer version. Fooocus runs on Apple silicon computers via [PyTorch](https://pytorch.org/get-started/locally/) MPS device acceleration. Mac Silicon computers don't come with a dedicated graphics card, resulting in significantly longer image processing times compared to computers with dedicated graphics cards. + +1. Install the conda package manager and pytorch nightly. Read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide for instructions. Make sure pytorch recognizes your MPS device. +1. Open the macOS Terminal app and clone this repository with `git clone https://github.com/lllyasviel/Fooocus.git`. +1. Change to the new Fooocus directory, `cd Fooocus`. +1. Create a new conda environment, `conda env create -f environment.yaml`. +1. Activate your new conda environment, `conda activate fooocus`. +1. Install the packages required by Fooocus, `pip install -r requirements_versions.txt`. +1. Launch Fooocus by running `python entry_with_update.py`. (Some Mac M2 users may need `python entry_with_update.py --disable-offload-from-vram` to speed up model loading/unloading.) The first time you run Fooocus, it will automatically download the Stable Diffusion SDXL models and will take a significant amount of time, depending on your internet connection. + +Use `python entry_with_update.py --preset anime` or `python entry_with_update.py --preset realistic` for Fooocus Anime/Realistic Edition. + +### Docker + +See [docker.md](docker.md) + +### Download Previous Version + +See the guidelines [here](https://github.com/lllyasviel/Fooocus/discussions/1405). + +## Minimal Requirement + +Below is the minimal requirement for running Fooocus locally. If your device capability is lower than this spec, you may not be able to use Fooocus locally. (Please let us know, in any case, if your device capability is lower but Fooocus still works.) + +| Operating System | GPU | Minimal GPU Memory | Minimal System Memory | [System Swap](troubleshoot.md) | Note | +|-------------------|------------------------------|------------------------------|---------------------------|--------------------------------|----------------------------------------------------------------------------| +| Windows/Linux | Nvidia RTX 4XXX | 4GB | 8GB | Required | fastest | +| Windows/Linux | Nvidia RTX 3XXX | 4GB | 8GB | Required | usually faster than RTX 2XXX | +| Windows/Linux | Nvidia RTX 2XXX | 4GB | 8GB | Required | usually faster than GTX 1XXX | +| Windows/Linux | Nvidia GTX 1XXX | 8GB (* 6GB uncertain) | 8GB | Required | only marginally faster than CPU | +| Windows/Linux | Nvidia GTX 9XX | 8GB | 8GB | Required | faster or slower than CPU | +| Windows/Linux | Nvidia GTX < 9XX | Not supported | / | / | / | +| Windows | AMD GPU | 8GB (updated 2023 Dec 30) | 8GB | Required | via DirectML (* ROCm is on hold), about 3x slower than Nvidia RTX 3XXX | +| Linux | AMD GPU | 8GB | 8GB | Required | via ROCm, about 1.5x slower than Nvidia RTX 3XXX | +| Mac | M1/M2 MPS | Shared | Shared | Shared | about 9x slower than Nvidia RTX 3XXX | +| Windows/Linux/Mac | only use CPU | 0GB | 32GB | Required | about 17x slower than Nvidia RTX 3XXX | + +* AMD GPU ROCm (on hold): The AMD is still working on supporting ROCm on Windows. + +* Nvidia GTX 1XXX 6GB uncertain: Some people report 6GB success on GTX 10XX, but some other people report failure cases. + +*Note that Fooocus is only for extremely high quality image generating. We will not support smaller models to reduce the requirement and sacrifice result quality.* + +## Troubleshoot + +See the common problems [here](troubleshoot.md). + +## Default Models + + +Given different goals, the default models and configs of Fooocus are different: + +| Task | Windows | Linux args | Main Model | Refiner | Config | +| --- | --- | --- | --- | --- |--------------------------------------------------------------------------------| +| General | run.bat | | juggernautXL_v8Rundiffusion | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/default.json) | +| Realistic | run_realistic.bat | --preset realistic | realisticStockPhoto_v20 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/realistic.json) | +| Anime | run_anime.bat | --preset anime | animaPencilXL_v100 | not used | [here](https://github.com/lllyasviel/Fooocus/blob/main/presets/anime.json) | + +Note that the download is **automatic** - you do not need to do anything if the internet connection is okay. However, you can download them manually if you (or move them from somewhere else) have your own preparation. + +## UI Access and Authentication +In addition to running on localhost, Fooocus can also expose its UI in two ways: +* Local UI listener: use `--listen` (specify port e.g. with `--port 8888`). +* API access: use `--share` (registers an endpoint at `.gradio.live`). + +In both ways the access is unauthenticated by default. You can add basic authentication by creating a file called `auth.json` in the main directory, which contains a list of JSON objects with the keys `user` and `pass` (see example in [auth-example.json](./auth-example.json)). + +## List of "Hidden" Tricks + + +The below things are already inside the software, and **users do not need to do anything about these**. + +1. GPT2-based [prompt expansion as a dynamic style "Fooocus V2".](https://github.com/lllyasviel/Fooocus/discussions/117#raw) (similar to Midjourney's hidden pre-processing and "raw" mode, or the LeonardoAI's Prompt Magic). +2. Native refiner swap inside one single k-sampler. The advantage is that the refiner model can now reuse the base model's momentum (or ODE's history parameters) collected from k-sampling to achieve more coherent sampling. In Automatic1111's high-res fix and ComfyUI's node system, the base model and refiner use two independent k-samplers, which means the momentum is largely wasted, and the sampling continuity is broken. Fooocus uses its own advanced k-diffusion sampling that ensures seamless, native, and continuous swap in a refiner setup. (Update Aug 13: Actually, I discussed this with Automatic1111 several days ago, and it seems that the “native refiner swap inside one single k-sampler” is [merged]( https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371) into the dev branch of webui. Great!) +3. Negative ADM guidance. Because the highest resolution level of XL Base does not have cross attentions, the positive and negative signals for XL's highest resolution level cannot receive enough contrasts during the CFG sampling, causing the results to look a bit plastic or overly smooth in certain cases. Fortunately, since the XL's highest resolution level is still conditioned on image aspect ratios (ADM), we can modify the adm on the positive/negative side to compensate for the lack of CFG contrast in the highest resolution level. (Update Aug 16, the IOS App [Draw Things](https://apps.apple.com/us/app/draw-things-ai-generation/id6444050820) will support Negative ADM Guidance. Great!) +4. We implemented a carefully tuned variation of Section 5.1 of ["Improving Sample Quality of Diffusion Models Using Self-Attention Guidance"](https://arxiv.org/pdf/2210.00939.pdf). The weight is set to very low, but this is Fooocus's final guarantee to make sure that the XL will never yield an overly smooth or plastic appearance (examples [here](https://github.com/lllyasviel/Fooocus/discussions/117#sharpness)). This can almost eliminate all cases for which XL still occasionally produces overly smooth results, even with negative ADM guidance. (Update 2023 Aug 18, the Gaussian kernel of SAG is changed to an anisotropic kernel for better structure preservation and fewer artifacts.) +5. We modified the style templates a bit and added the "cinematic-default". +6. We tested the "sd_xl_offset_example-lora_1.0.safetensors" and it seems that when the lora weight is below 0.5, the results are always better than XL without lora. +7. The parameters of samplers are carefully tuned. +8. Because XL uses positional encoding for generation resolution, images generated by several fixed resolutions look a bit better than those from arbitrary resolutions (because the positional encoding is not very good at handling int numbers that are unseen during training). This suggests that the resolutions in UI may be hard coded for best results. +9. Separated prompts for two different text encoders seem unnecessary. Separated prompts for the base model and refiner may work, but the effects are random, and we refrain from implementing this. +10. The DPM family seems well-suited for XL since XL sometimes generates overly smooth texture, but the DPM family sometimes generates overly dense detail in texture. Their joint effect looks neutral and appealing to human perception. +11. A carefully designed system for balancing multiple styles as well as prompt expansion. +12. Using automatic1111's method to normalize prompt emphasizing. This significantly improves results when users directly copy prompts from civitai. +13. The joint swap system of the refiner now also supports img2img and upscale in a seamless way. +14. CFG Scale and TSNR correction (tuned for SDXL) when CFG is bigger than 10. + +## Customization + +After the first time you run Fooocus, a config file will be generated at `Fooocus\config.txt`. This file can be edited to change the model path or default parameters. + +For example, an edited `Fooocus\config.txt` (this file will be generated after the first launch) may look like this: + +```json +{ + "path_checkpoints": "D:\\Fooocus\\models\\checkpoints", + "path_loras": "D:\\Fooocus\\models\\loras", + "path_embeddings": "D:\\Fooocus\\models\\embeddings", + "path_vae_approx": "D:\\Fooocus\\models\\vae_approx", + "path_upscale_models": "D:\\Fooocus\\models\\upscale_models", + "path_inpaint": "D:\\Fooocus\\models\\inpaint", + "path_controlnet": "D:\\Fooocus\\models\\controlnet", + "path_clip_vision": "D:\\Fooocus\\models\\clip_vision", + "path_fooocus_expansion": "D:\\Fooocus\\models\\prompt_expansion\\fooocus_expansion", + "path_outputs": "D:\\Fooocus\\outputs", + "default_model": "realisticStockPhoto_v10.safetensors", + "default_refiner": "", + "default_loras": [["lora_filename_1.safetensors", 0.5], ["lora_filename_2.safetensors", 0.5]], + "default_cfg_scale": 3.0, + "default_sampler": "dpmpp_2m", + "default_scheduler": "karras", + "default_negative_prompt": "low quality", + "default_positive_prompt": "", + "default_styles": [ + "Fooocus V2", + "Fooocus Photograph", + "Fooocus Negative" + ] +} +``` + +Many other keys, formats, and examples are in `Fooocus\config_modification_tutorial.txt` (this file will be generated after the first launch). + +Consider twice before you really change the config. If you find yourself breaking things, just delete `Fooocus\config.txt`. Fooocus will go back to default. + +A safer way is just to try "run_anime.bat" or "run_realistic.bat" - they should already be good enough for different tasks. + +~Note that `user_path_config.txt` is deprecated and will be removed soon.~ (Edit: it is already removed.) + +### All CMD Flags + +``` +entry_with_update.py [-h] [--listen [IP]] [--port PORT] + [--disable-header-check [ORIGIN]] + [--web-upload-size WEB_UPLOAD_SIZE] + [--external-working-path PATH [PATH ...]] + [--output-path OUTPUT_PATH] [--temp-path TEMP_PATH] + [--cache-path CACHE_PATH] [--in-browser] + [--disable-in-browser] [--gpu-device-id DEVICE_ID] + [--async-cuda-allocation | --disable-async-cuda-allocation] + [--disable-attention-upcast] [--all-in-fp32 | --all-in-fp16] + [--unet-in-bf16 | --unet-in-fp16 | --unet-in-fp8-e4m3fn | --unet-in-fp8-e5m2] + [--vae-in-fp16 | --vae-in-fp32 | --vae-in-bf16] + [--clip-in-fp8-e4m3fn | --clip-in-fp8-e5m2 | --clip-in-fp16 | --clip-in-fp32] + [--directml [DIRECTML_DEVICE]] [--disable-ipex-hijack] + [--preview-option [none,auto,fast,taesd]] + [--attention-split | --attention-quad | --attention-pytorch] + [--disable-xformers] + [--always-gpu | --always-high-vram | --always-normal-vram | + --always-low-vram | --always-no-vram | --always-cpu [CPU_NUM_THREADS]] + [--always-offload-from-vram] [--disable-server-log] + [--debug-mode] [--is-windows-embedded-python] + [--disable-server-info] [--share] [--preset PRESET] + [--language LANGUAGE] [--disable-offload-from-vram] + [--theme THEME] [--disable-image-log] +``` + +## Advanced Features + +[Click here to browse the advanced features.](https://github.com/lllyasviel/Fooocus/discussions/117) + +Fooocus also has many community forks, just like SD-WebUI's [vladmandic/automatic](https://github.com/vladmandic/automatic) and [anapnoe/stable-diffusion-webui-ux](https://github.com/anapnoe/stable-diffusion-webui-ux), for enthusiastic users who want to try! + +| Fooocus' forks | +| - | +| [fenneishi/Fooocus-Control](https://github.com/fenneishi/Fooocus-Control)
[runew0lf/RuinedFooocus](https://github.com/runew0lf/RuinedFooocus)
[MoonRide303/Fooocus-MRE](https://github.com/MoonRide303/Fooocus-MRE)
[metercai/SimpleSDXL](https://github.com/metercai/SimpleSDXL)
and so on ... | + +See also [About Forking and Promotion of Forks](https://github.com/lllyasviel/Fooocus/discussions/699). + +## Thanks + +Special thanks to [twri](https://github.com/twri) and [3Diva](https://github.com/3Diva) and [Marc K3nt3L](https://github.com/K3nt3L) for creating additional SDXL styles available in Fooocus. Thanks [daswer123](https://github.com/daswer123) for contributing the Canvas Zoom! + +## Update Log + +The log is [here](update_log.md). + +## Localization/Translation/I18N + +**We need your help!** Please help translate Fooocus into international languages. + +You can put json files in the `language` folder to translate the user interface. + +For example, below is the content of `Fooocus/language/example.json`: + +```json +{ + "Generate": "生成", + "Input Image": "入力画像", + "Advanced": "고급", + "SAI 3D Model": "SAI 3D Modèle" +} +``` + +If you add `--language example` arg, Fooocus will read `Fooocus/language/example.json` to translate the UI. + +For example, you can edit the ending line of Windows `run.bat` as + + .\python_embeded\python.exe -s Fooocus\entry_with_update.py --language example + +Or `run_anime.bat` as + + .\python_embeded\python.exe -s Fooocus\entry_with_update.py --language example --preset anime + +Or `run_realistic.bat` as + + .\python_embeded\python.exe -s Fooocus\entry_with_update.py --language example --preset realistic + +For practical translation, you may create your own file like `Fooocus/language/jp.json` or `Fooocus/language/cn.json` and then use flag `--language jp` or `--language cn`. Apparently, these files do not exist now. **We need your help to create these files!** + +Note that if no `--language` is given and at the same time `Fooocus/language/default.json` exists, Fooocus will always load `Fooocus/language/default.json` for translation. 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0000000000000000000000000000000000000000..d5afcb47688125afd173d2f093a55506ecb09b8b Binary files /dev/null and b/sdxl_styles/samples/whimsical_and_playful.jpg differ diff --git a/sdxl_styles/sdxl_styles_diva.json b/sdxl_styles/sdxl_styles_diva.json new file mode 100644 index 0000000000000000000000000000000000000000..ea552f4b92737f8d6369fb443210cf7446eed113 --- /dev/null +++ b/sdxl_styles/sdxl_styles_diva.json @@ -0,0 +1,397 @@ +[ + { + "name": "cinematic-diva", + "prompt": "UHD, 8K, ultra detailed, a cinematic photograph of {prompt}, beautiful lighting, great composition", + "negative_prompt": "ugly, deformed, noisy, blurry, NSFW" + }, + { + "name": "Abstract Expressionism", + "prompt": "Abstract Expressionism Art, {prompt}, High contrast, minimalistic, colorful, stark, dramatic, expressionism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" + }, + { + "name": "Academia", + "prompt": "Academia, {prompt}, preppy Ivy League style, stark, dramatic, chic boarding school, academia", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt" + }, + { + "name": "Action Figure", + "prompt": "Action Figure, {prompt}, plastic collectable action figure, collectable toy action figure", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Adorable 3D Character", + "prompt": "Adorable 3D Character, {prompt}, 3D render, adorable character, 3D art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, grunge, sloppy, unkempt, photograph, photo, realistic" + }, + { + "name": "Adorable Kawaii", + "prompt": "Adorable Kawaii, {prompt}, pretty, cute, adorable, kawaii", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, gothic, dark, moody, monochromatic" + }, + { + "name": "Art Deco", + "prompt": "Art Deco, {prompt}, sleek, geometric forms, art deco style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Art Nouveau", + "prompt": "Art Nouveau, beautiful art, {prompt}, sleek, organic forms, long, sinuous, art nouveau style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, industrial, mechanical" + }, + { + "name": "Astral Aura", + "prompt": "Astral Aura, {prompt}, astral, colorful aura, vibrant energy", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Avant-garde", + "prompt": "Avant-garde, {prompt}, unusual, experimental, avant-garde art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Baroque", + "prompt": "Baroque, {prompt}, dramatic, exuberant, grandeur, baroque art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Bauhaus-Style Poster", + "prompt": "Bauhaus-Style Poster, {prompt}, simple geometric shapes, clean lines, primary colors, Bauhaus-Style Poster", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Blueprint Schematic Drawing", + "prompt": "Blueprint Schematic Drawing, {prompt}, technical drawing, blueprint, schematic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Caricature", + "prompt": "Caricature, {prompt}, exaggerated, comical, caricature", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic" + }, + { + "name": "Cel Shaded Art", + "prompt": "Cel Shaded Art, {prompt}, 2D, flat color, toon shading, cel shaded style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Character Design Sheet", + "prompt": "Character Design Sheet, {prompt}, character reference sheet, character turn around", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Classicism Art", + "prompt": "Classicism Art, {prompt}, inspired by Roman and Greek culture, clarity, harmonious, classicism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Color Field Painting", + "prompt": "Color Field Painting, {prompt}, abstract, simple, geometic, color field painting style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Colored Pencil Art", + "prompt": "Colored Pencil Art, {prompt}, colored pencil strokes, light color, visible paper texture, colored pencil art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Conceptual Art", + "prompt": "Conceptual Art, {prompt}, concept art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Constructivism", + "prompt": "Constructivism Art, {prompt}, minimalistic, geometric forms, constructivism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Cubism", + "prompt": "Cubism Art, {prompt}, flat geometric forms, cubism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Dadaism", + "prompt": "Dadaism Art, {prompt}, satirical, nonsensical, dadaism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Dark Fantasy", + "prompt": "Dark Fantasy Art, {prompt}, dark, moody, dark fantasy style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, bright, sunny" + }, + { + "name": "Dark Moody Atmosphere", + "prompt": "Dark Moody Atmosphere, {prompt}, dramatic, mysterious, dark moody atmosphere", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, vibrant, colorful, bright" + }, + { + "name": "DMT Art Style", + "prompt": "DMT Art Style, {prompt}, bright colors, surreal visuals, swirling patterns, DMT art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Doodle Art", + "prompt": "Doodle Art Style, {prompt}, drawing, freeform, swirling patterns, doodle art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Double Exposure", + "prompt": "Double Exposure Style, {prompt}, double image ghost effect, image combination, double exposure style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Dripping Paint Splatter Art", + "prompt": "Dripping Paint Splatter Art, {prompt}, dramatic, paint drips, splatters, dripping paint", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Expressionism", + "prompt": "Expressionism Art Style, {prompt}, movement, contrast, emotional, exaggerated forms, expressionism art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Faded Polaroid Photo", + "prompt": "Faded Polaroid Photo, {prompt}, analog, old faded photo, old polaroid", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, vibrant, colorful" + }, + { + "name": "Fauvism", + "prompt": "Fauvism Art, {prompt}, painterly, bold colors, textured brushwork, fauvism art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Flat 2D Art", + "prompt": "Flat 2D Art, {prompt}, simple flat color, 2-dimensional, Flat 2D Art Style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic" + }, + { + "name": "Fortnite Art Style", + "prompt": "Fortnite Art Style, {prompt}, 3D cartoon, colorful, Fortnite Art Style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic" + }, + { + "name": "Futurism", + "prompt": "Futurism Art Style, {prompt}, dynamic, dramatic, Futurism Art Style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Glitchcore", + "prompt": "Glitchcore Art Style, {prompt}, dynamic, dramatic, distorted, vibrant colors, glitchcore art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Glo-fi", + "prompt": "Glo-fi Art Style, {prompt}, dynamic, dramatic, vibrant colors, glo-fi art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Googie Art Style", + "prompt": "Googie Art Style, {prompt}, dynamic, dramatic, 1950's futurism, bold boomerang angles, Googie art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Graffiti Art", + "prompt": "Graffiti Art Style, {prompt}, dynamic, dramatic, vibrant colors, graffiti art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Harlem Renaissance Art", + "prompt": "Harlem Renaissance Art Style, {prompt}, dynamic, dramatic, 1920s African American culture, Harlem Renaissance art style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "High Fashion", + "prompt": "High Fashion, {prompt}, dynamic, dramatic, haute couture, elegant, ornate clothing, High Fashion", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Idyllic", + "prompt": "Idyllic, {prompt}, peaceful, happy, pleasant, happy, harmonious, picturesque, charming", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Impressionism", + "prompt": "Impressionism, {prompt}, painterly, small brushstrokes, visible brushstrokes, impressionistic style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Infographic Drawing", + "prompt": "Infographic Drawing, {prompt}, diagram, infographic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Ink Dripping Drawing", + "prompt": "Ink Dripping Drawing, {prompt}, ink drawing, dripping ink", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, colorful, vibrant" + }, + { + "name": "Japanese Ink Drawing", + "prompt": "Japanese Ink Drawing, {prompt}, ink drawing, inkwash, Japanese Ink Drawing", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, colorful, vibrant" + }, + { + "name": "Knolling Photography", + "prompt": "Knolling Photography, {prompt}, flat lay photography, object arrangment, knolling photography", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Light Cheery Atmosphere", + "prompt": "Light Cheery Atmosphere, {prompt}, happy, joyful, cheerful, carefree, gleeful, lighthearted, pleasant atmosphere", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, monochromatic, dark, moody" + }, + { + "name": "Logo Design", + "prompt": "Logo Design, {prompt}, dynamic graphic art, vector art, minimalist, professional logo design", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Luxurious Elegance", + "prompt": "Luxurious Elegance, {prompt}, extravagant, ornate, designer, opulent, picturesque, lavish", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Macro Photography", + "prompt": "Macro Photography, {prompt}, close-up, macro 100mm, macro photography", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Mandola Art", + "prompt": "Mandola art style, {prompt}, complex, circular design, mandola", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Marker Drawing", + "prompt": "Marker Drawing, {prompt}, bold marker lines, visibile paper texture, marker drawing", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photograph, realistic" + }, + { + "name": "Medievalism", + "prompt": "Medievalism, {prompt}, inspired by The Middle Ages, medieval art, elaborate patterns and decoration, Medievalism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Minimalism", + "prompt": "Minimalism, {prompt}, abstract, simple geometic shapes, hard edges, sleek contours, Minimalism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Neo-Baroque", + "prompt": "Neo-Baroque, {prompt}, ornate and elaborate, dynaimc, Neo-Baroque", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Neo-Byzantine", + "prompt": "Neo-Byzantine, {prompt}, grand decorative religious style, Orthodox Christian inspired, Neo-Byzantine", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Neo-Futurism", + "prompt": "Neo-Futurism, {prompt}, high-tech, curves, spirals, flowing lines, idealistic future, Neo-Futurism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Neo-Impressionism", + "prompt": "Neo-Impressionism, {prompt}, tiny dabs of color, Pointillism, painterly, Neo-Impressionism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photograph, realistic" + }, + { + "name": "Neo-Rococo", + "prompt": "Neo-Rococo, {prompt}, curved forms, naturalistic ornamentation, elaborate, decorative, gaudy, Neo-Rococo", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Neoclassicism", + "prompt": "Neoclassicism, {prompt}, ancient Rome and Greece inspired, idealic, sober colors, Neoclassicism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Op Art", + "prompt": "Op Art, {prompt}, optical illusion, abstract, geometric pattern, impression of movement, Op Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Ornate and Intricate", + "prompt": "Ornate and Intricate, {prompt}, decorative, highly detailed, elaborate, ornate, intricate", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Pencil Sketch Drawing", + "prompt": "Pencil Sketch Drawing, {prompt}, black and white drawing, graphite drawing", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Pop Art 2", + "prompt": "Pop Art, {prompt}, vivid colors, flat color, 2D, strong lines, Pop Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic" + }, + { + "name": "Rococo", + "prompt": "Rococo, {prompt}, flamboyant, pastel colors, curved lines, elaborate detail, Rococo", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Silhouette Art", + "prompt": "Silhouette Art, {prompt}, high contrast, well defined, Silhouette Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Simple Vector Art", + "prompt": "Simple Vector Art, {prompt}, 2D flat, simple shapes, minimalistic, professional graphic, flat color, high contrast, Simple Vector Art", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, 3D, photo, realistic" + }, + { + "name": "Sketchup", + "prompt": "Sketchup, {prompt}, CAD, professional design, Sketchup", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, photograph" + }, + { + "name": "Steampunk 2", + "prompt": "Steampunk, {prompt}, retrofuturistic science fantasy, steam-powered tech, vintage industry, gears, neo-victorian, steampunk", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Surrealism", + "prompt": "Surrealism, {prompt}, expressive, dramatic, organic lines and forms, dreamlike and mysterious, Surrealism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic" + }, + { + "name": "Suprematism", + "prompt": "Suprematism, {prompt}, abstract, limited color palette, geometric forms, Suprematism", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realistic" + }, + { + "name": "Terragen", + "prompt": "Terragen, {prompt}, beautiful massive landscape, epic scenery, Terragen", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Tranquil Relaxing Atmosphere", + "prompt": "Tranquil Relaxing Atmosphere, {prompt}, calming style, soothing colors, peaceful, idealic, Tranquil Relaxing Atmosphere", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, oversaturated" + }, + { + "name": "Sticker Designs", + "prompt": "Vector Art Stickers, {prompt}, professional vector design, sticker designs, Sticker Sheet", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Vibrant Rim Light", + "prompt": "Vibrant Rim Light, {prompt}, bright rim light, high contrast, bold edge light", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Volumetric Lighting", + "prompt": "Volumetric Lighting, {prompt}, light depth, dramatic atmospheric lighting, Volumetric Lighting", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast" + }, + { + "name": "Watercolor 2", + "prompt": "Watercolor style painting, {prompt}, visible paper texture, colorwash, watercolor", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, photo, realistic" + }, + { + "name": "Whimsical and Playful", + "prompt": "Whimsical and Playful, {prompt}, imaginative, fantastical, bight colors, stylized, happy, Whimsical and Playful", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, drab, boring, moody" + } +] diff --git a/sdxl_styles/sdxl_styles_fooocus.json b/sdxl_styles/sdxl_styles_fooocus.json new file mode 100644 index 0000000000000000000000000000000000000000..81d6442e6ea3d070a0c88d02596b6daa050cb12e --- /dev/null +++ b/sdxl_styles/sdxl_styles_fooocus.json @@ -0,0 +1,30 @@ +[ + { + "name": "Fooocus Enhance", + "negative_prompt": "(worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)" + }, + { + "name": "Fooocus Sharp", + "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, 4k epic detailed, shot on kodak, 35mm photo, sharp focus, high budget, cinemascope, moody, epic, gorgeous, film grain, grainy", + "negative_prompt": "anime, cartoon, graphic, (blur, blurry, bokeh), text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" + }, + { + "name": "Fooocus Masterpiece", + "prompt": "(masterpiece), (best quality), (ultra-detailed), {prompt}, illustration, disheveled hair, detailed eyes, perfect composition, moist skin, intricate details, earrings, by wlop", + "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair,extra digit, fewer digits, cropped, worst quality, low quality" + }, + { + "name": "Fooocus Photograph", + "prompt": "photograph {prompt}, 50mm . cinematic 4k epic detailed 4k epic detailed photograph shot on kodak detailed cinematic hbo dark moody, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage", + "negative_prompt": "Brad Pitt, bokeh, depth of field, blurry, cropped, regular face, saturated, contrast, deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck" + }, + { + "name": "Fooocus Negative", + "negative_prompt": "deformed, bad anatomy, disfigured, poorly drawn face, mutated, extra limb, ugly, poorly drawn hands, missing limb, floating limbs, disconnected limbs, disconnected head, malformed hands, long neck, mutated hands and fingers, bad hands, missing fingers, cropped, worst quality, low quality, mutation, poorly drawn, huge calf, bad hands, fused hand, missing hand, disappearing arms, disappearing thigh, disappearing calf, disappearing legs, missing fingers, fused fingers, abnormal eye proportion, Abnormal hands, abnormal legs, abnormal feet, abnormal fingers, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch" + }, + { + "name": "Fooocus Cinematic", + "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", + "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" + } +] diff --git a/sdxl_styles/sdxl_styles_marc_k3nt3l.json b/sdxl_styles/sdxl_styles_marc_k3nt3l.json new file mode 100644 index 0000000000000000000000000000000000000000..fbbe1a24930f5372e7f73c601d652a5584dcdbeb --- /dev/null +++ b/sdxl_styles/sdxl_styles_marc_k3nt3l.json @@ -0,0 +1,312 @@ +[ + { + "name": "MK Chromolithography", + "prompt": "Chromolithograph {prompt}. Vibrant colors, intricate details, rich color saturation, meticulous registration, multi-layered printing, decorative elements, historical charm, artistic reproductions, commercial posters, nostalgic, ornate compositions.", + "negative_prompt": "monochromatic, simple designs, limited color palette, imprecise registration, minimalistic, modern aesthetic, digital appearance." + }, + { + "name": "MK Cross Processing Print", + "prompt": "Cross processing print {prompt}. Experimental color shifts, unconventional tonalities, vibrant and surreal hues, heightened contrasts, unpredictable results, artistic unpredictability, retro and vintage feel, dynamic color interplay, abstract and dreamlike.", + "negative_prompt": "predictable color tones, traditional processing, realistic color representation, subdued contrasts, standard photographic aesthetics." + }, + { + "name": "MK Dufaycolor Photograph", + "prompt": "Dufaycolor photograph {prompt}. Vintage color palette, distinctive color rendering, soft and dreamy atmosphere, historical charm, unique color process, grainy texture, evocative mood, nostalgic aesthetic, hand-tinted appearance, artistic patina.", + "negative_prompt": "modern color reproduction, hyperrealistic tones, sharp and clear details, digital precision, contemporary aesthetic." + }, + { + "name": "MK Herbarium", + "prompt": "Herbarium drawing{prompt}. Botanical accuracy, old botanical book illustration, detailed illustrations, pressed plants, delicate and precise linework, scientific documentation, meticulous presentation, educational purpose, organic compositions, timeless aesthetic, naturalistic beauty.", + "negative_prompt": "abstract representation, vibrant colors, artistic interpretation, chaotic compositions, fantastical elements, digital appearance." + }, + { + "name": "MK Punk Collage", + "prompt": "punk collage style {prompt} . mixed media, papercut,textured paper, overlapping, ripped posters, safety pins, chaotic layers, graffiti-style elements, anarchy symbols, vintage photos, cut-and-paste aesthetic, bold typography, distorted images, political messages, urban decay, distressed textures, newspaper clippings, spray paint, rebellious icons, DIY spirit, vivid colors, punk band logos, edgy and raw compositions, ", + "negative_prompt": "conventional,blurry, noisy, low contrast" + }, + { + "name": "MK mosaic", + "prompt": "mosaic style {prompt} . fragmented, assembled, colorful, highly detailed", + "negative_prompt": "whole, unbroken, monochrome" + }, + { + "name": "MK Van Gogh", + "prompt": "Oil painting by Van Gogh {prompt} . Expressive, impasto, swirling brushwork, vibrant, brush strokes, Brushstroke-heavy, Textured, Impasto, Colorful, Dynamic, Bold, Distinctive, Vibrant, Whirling, Expressive, Dramatic, Swirling, Layered, Intense, Contrastive, Atmospheric, Luminous, Textural, Evocative, SpiraledVan Gogh style", + "negative_prompt": "realistic, photorealistic, calm, straight lines, signature, frame, text, watermark" + }, + { + "name": "MK Coloring Book", + "prompt": "centered black and white high contrast line drawing, coloring book style,{prompt} . monochrome, blank white background", + "negative_prompt": "greyscale, gradients,shadows,shadow, colored, Red, Blue, Yellow, Green, Orange, Purple, Pink, Brown, Gray, Beige, Turquoise, Lavender, Cyan, Magenta, Olive, Indigo, black background" + }, + { + "name": "MK Singer Sargent", + "prompt": "Oil painting by John Singer Sargent {prompt}. Elegant, refined, masterful technique,realistic portrayal, subtle play of light, captivating expression, rich details, harmonious colors, skillful composition, brush strokes, chiaroscuro.", + "negative_prompt": "realistic, photorealistic, abstract, overly stylized, excessive contrasts, distorted,bright colors,disorder." + }, + { + "name": "MK Pollock", + "prompt": "Oil painting by Jackson Pollock {prompt}. Abstract expressionism, drip painting, chaotic composition, energetic, spontaneous, unconventional technique, dynamic, bold, distinctive, vibrant, intense, expressive, energetic, layered, non-representational, gestural.", + "negative_prompt": "(realistic:1.5), (photorealistic:1.5), representational, calm, ordered composition, precise lines, detailed forms, subdued colors, quiet, static, traditional, figurative." + }, + { + "name": "MK Basquiat", + "prompt": "Artwork by Jean-Michel Basquiat {prompt}. Neo-expressionism, street art influence, graffiti-inspired, raw, energetic, bold colors, dynamic composition, chaotic, layered, textural, expressive, spontaneous, distinctive, symbolic,energetic brushstrokes.", + "negative_prompt": "(realistic:1.5), (photorealistic:1.5), calm, precise lines, conventional composition, subdued" + }, + { + "name": "MK Andy Warhol", + "prompt": "Artwork in the style of Andy Warhol {prompt}. Pop art, vibrant colors, bold compositions, repetition of iconic imagery, celebrity culture, commercial aesthetics, mass production influence, stylized simplicity, cultural commentary, graphical elements, distinctive portraits.", + "negative_prompt": "subdued colors, realistic, lack of repetition, minimalistic." + }, + { + "name": "MK Halftone print", + "prompt": "Halftone print of {prompt}. Dot matrix pattern, grayscale tones, vintage aesthetic, newspaper print vibe, stylized dots, visual texture, black and white contrasts, retro appearance, artistic pointillism,pop culture, (Roy Lichtenstein style:1.5).", + "negative_prompt": "smooth gradients, continuous tones, vibrant colors." + }, + { + "name": "MK Gond Painting", + "prompt": "Gond painting {prompt}. Intricate patterns, vibrant colors, detailed motifs, nature-inspired themes, tribal folklore, fine lines, intricate detailing, storytelling compositions, mystical and folkloric, cultural richness.", + "negative_prompt": "monochromatic, abstract shapes, minimalistic." + }, + { + "name": "MK Albumen Print", + "prompt": "Albumen print {prompt}. Sepia tones, fine details, subtle tonal gradations, delicate highlights, vintage aesthetic, soft and muted atmosphere, historical charm, rich textures, meticulous craftsmanship, classic photographic technique, vignetting.", + "negative_prompt": "vibrant colors, high contrast, modern, digital appearance, sharp details, contemporary style." + }, + { + "name": "MK Aquatint Print", + "prompt": "Aquatint print {prompt}. Soft tonal gradations, atmospheric effects, velvety textures, rich contrasts, fine details, etching process, delicate lines, nuanced shading, expressive and moody atmosphere, artistic depth.", + "negative_prompt": "sharp contrasts, bold lines, minimalistic." + }, + { + "name": "MK Anthotype Print", + "prompt": "Anthotype print {prompt}. Monochrome dye, soft and muted colors, organic textures, ephemeral and delicate appearance, low details, watercolor canvas, low contrast, overexposed, silhouette, textured paper.", + "negative_prompt": "vibrant synthetic dyes, bold and saturated colors." + }, + { + "name": "MK Inuit Carving", + "prompt": "A sculpture made of ivory, {prompt} made of . Sculptures, Inuit art style, intricate carvings, natural materials, storytelling motifs, arctic wildlife themes, symbolic representations, cultural traditions, earthy tones, harmonious compositions, spiritual and mythological elements.", + "negative_prompt": "abstract, vibrant colors." + }, + { + "name": "MK Bromoil Print", + "prompt": "Bromoil print {prompt}. Painterly effects, sepia tones, textured surfaces, rich contrasts, expressive brushwork, tonal variations, vintage aesthetic, atmospheric mood, handmade quality, artistic experimentation, darkroom craftsmanship, vignetting.", + "negative_prompt": "smooth surfaces, minimal brushwork, contemporary digital appearance." + }, + { + "name": "MK Calotype Print", + "prompt": "Calotype print {prompt}. Soft focus, subtle tonal range, paper negative process, fine details, vintage aesthetic, artistic experimentation, atmospheric mood, early photographic charm, handmade quality, vignetting.", + "negative_prompt": "sharp focus, bold contrasts, modern aesthetic, digital photography." + }, + { + "name": "MK Color Sketchnote", + "prompt": "Color sketchnote {prompt}. Hand-drawn elements, vibrant colors, visual hierarchy, playful illustrations, varied typography, graphic icons, organic and dynamic layout, personalized touches, creative expression, engaging storytelling.", + "negative_prompt": "monochromatic, geometric layout." + }, + { + "name": "MK Cibulak Porcelain", + "prompt": "A sculpture made of blue pattern porcelain of {prompt}. Classic design, blue and white color scheme, intricate detailing, floral motifs, onion-shaped elements, historical charm, rococo, white ware, cobalt blue, underglaze pattern, fine craftsmanship, traditional elegance, delicate patterns, vintage aesthetic, Meissen, Blue Onion pattern, Cibulak.", + "negative_prompt": "tea, teapot, cup, teacup,bright colors, bold and modern design, absence of intricate detailing, lack of floral motifs, non-traditional shapes." + }, + { + "name": "MK Alcohol Ink Art", + "prompt": "Alcohol ink art {prompt}. Fluid and vibrant colors, unpredictable patterns, organic textures, translucent layers, abstract compositions, ethereal and dreamy effects, free-flowing movement, expressive brushstrokes, contemporary aesthetic, wet textured paper.", + "negative_prompt": "monochromatic, controlled patterns." + }, + { + "name": "MK One Line Art", + "prompt": "One line art {prompt}. Continuous and unbroken black line, minimalistic, simplicity, economical use of space, flowing and dynamic, symbolic representations, contemporary aesthetic, evocative and abstract, white background.", + "negative_prompt": "disjointed lines, complexity, complex detailing." + }, + { + "name": "MK Blacklight Paint", + "prompt": "Blacklight paint {prompt}. Fluorescent pigments, vibrant and surreal colors, ethereal glow, otherworldly effects, dynamic and psychedelic compositions, neon aesthetics, transformative in ultraviolet light, contemporary and experimental.", + "negative_prompt": "muted colors, traditional and realistic compositions." + }, + { + "name": "MK Carnival Glass", + "prompt": "A sculpture made of Carnival glass, {prompt}. Iridescent surfaces, vibrant colors, intricate patterns, opalescent hues, reflective and prismatic effects, Art Nouveau and Art Deco influences, vintage charm, intricate detailing, lustrous and luminous appearance, Carnival Glass style.", + "negative_prompt": "non-iridescent surfaces, muted colors, absence of intricate patterns, lack of opalescent hues, modern and minimalist aesthetic." + }, + { + "name": "MK Cyanotype Print", + "prompt": "Cyanotype print {prompt}. Prussian blue tones, distinctive coloration, high contrast, blueprint aesthetics, atmospheric mood, sun-exposed paper, silhouette effects, delicate details, historical charm, handmade and experimental quality.", + "negative_prompt": "vibrant colors, low contrast, modern and polished appearance." + }, + { + "name": "MK Cross-Stitching", + "prompt": "Cross-stitching {prompt}. Intricate patterns, embroidery thread, sewing, fine details, precise stitches, textile artistry, symmetrical designs, varied color palette, traditional and contemporary motifs, handmade and crafted,canvas, nostalgic charm.", + "negative_prompt": "paper, paint, ink, photography." + }, + { + "name": "MK Encaustic Paint", + "prompt": "Encaustic paint {prompt}. Textured surfaces, translucent layers, luminous quality, wax medium, rich color saturation, fluid and organic shapes, contemporary and historical influences, mixed media elements, atmospheric depth.", + "negative_prompt": "flat surfaces, opaque layers, lack of wax medium, muted color palette, absence of textured surfaces, non-mixed media." + }, + { + "name": "MK Embroidery", + "prompt": "Embroidery {prompt}. Intricate stitching, embroidery thread, fine details, varied thread textures, textile artistry, embellished surfaces, diverse color palette, traditional and contemporary motifs, handmade and crafted, tactile and ornate.", + "negative_prompt": "minimalist, monochromatic." + }, + { + "name": "MK Gyotaku", + "prompt": "Gyotaku {prompt}. Fish impressions, realistic details, ink rubbings, textured surfaces, traditional Japanese art form, nature-inspired compositions, artistic representation of marine life, black and white contrasts, cultural significance.", + "negative_prompt": "photography." + }, + { + "name": "MK Luminogram", + "prompt": "Luminogram {prompt}. Photogram technique, ethereal and abstract effects, light and shadow interplay, luminous quality, experimental process, direct light exposure, unique and unpredictable results, artistic experimentation.", + "negative_prompt": "" + }, + { + "name": "MK Lite Brite Art", + "prompt": "Lite Brite art {prompt}. Luminous and colorful designs, pixelated compositions, retro aesthetic, glowing effects, creative patterns, interactive and playful, nostalgic charm, vibrant and dynamic arrangements.", + "negative_prompt": "monochromatic." + }, + { + "name": "MK Mokume-gane", + "prompt": "Mokume-gane {prompt}. Wood-grain patterns, mixed metal layers, intricate and organic designs, traditional Japanese metalwork, harmonious color combinations, artisanal craftsmanship, unique and layered textures, cultural and historical significance.", + "negative_prompt": "uniform metal surfaces." + }, + { + "name": "Pebble Art", + "prompt": "a sculpture made of peebles, {prompt}. Pebble art style,natural materials, textured surfaces, balanced compositions, organic forms, harmonious arrangements, tactile and 3D effects, beach-inspired aesthetic, creative storytelling, artisanal craftsmanship.", + "negative_prompt": "non-natural materials, lack of textured surfaces, imbalanced compositions, absence of organic forms, non-tactile appearance." + }, + { + "name": "MK Palekh", + "prompt": "Palekh art {prompt}. Miniature paintings, intricate details, vivid colors, folkloric themes, lacquer finish, storytelling compositions, symbolic elements, Russian folklore influence, cultural and historical significance.", + "negative_prompt": "large-scale paintings." + }, + { + "name": "MK Suminagashi", + "prompt": "Suminagashi {prompt}. Floating ink patterns, marbled effects, delicate and ethereal designs, water-based ink, fluid and unpredictable compositions, meditative process, monochromatic or subtle color palette, Japanese artistic tradition.", + "negative_prompt": "vibrant and bold color palette." + }, + { + "name": "MK Scrimshaw", + "prompt": "A Scrimshaw engraving of {prompt}. Intricate engravings on a spermwhale's teeth, marine motifs, detailed scenes, nautical themes, black and white contrasts, historical craftsmanship, artisanal carving, storytelling compositions, maritime heritage.", + "negative_prompt": "colorful, modern." + }, + { + "name": "MK Shibori", + "prompt": "Shibori {prompt}. Textured fabric, intricate patterns, resist-dyeing technique, indigo or vibrant colors, organic and flowing designs, Japanese textile art, cultural tradition, tactile and visual interest.", + "negative_prompt": "monochromatic." + }, + { + "name": "MK Vitreous Enamel", + "prompt": "A sculpture made of Vitreous enamel {prompt}. Smooth and glossy surfaces, vibrant colors, glass-like finish, durable and resilient, intricate detailing, traditional and contemporary applications, artistic craftsmanship, jewelry and decorative objects, , Vitreous enamel, colored glass.", + "negative_prompt": "rough surfaces, muted colors." + }, + { + "name": "MK Ukiyo-e", + "prompt": "Ukiyo-e {prompt}. Woodblock prints, vibrant colors, intricate details, depictions of landscapes, kabuki actors, beautiful women, cultural scenes, traditional Japanese art, artistic craftsmanship, historical significance.", + "negative_prompt": "absence of woodblock prints, muted colors, lack of intricate details, non-traditional Japanese themes, absence of cultural scenes." + }, + { + "name": "MK vintage-airline-poster", + "prompt": "vintage airline poster {prompt} . classic aviation fonts, pastel colors, elegant aircraft illustrations, scenic destinations, distressed textures, retro travel allure", + "negative_prompt": "modern fonts, bold colors, hyper-realistic, sleek design" + }, + { + "name": "MK vintage-travel-poster", + "prompt": "vintage travel poster {prompt} . retro fonts, muted colors, scenic illustrations, iconic landmarks, distressed textures, nostalgic vibes", + "negative_prompt": "modern fonts, vibrant colors, hyper-realistic, sleek design" + }, + { + "name": "MK bauhaus-style", + "prompt": "Bauhaus-inspired {prompt} . minimalism, geometric precision, primary colors, sans-serif typography, asymmetry, functional design", + "negative_prompt": "ornate, intricate, excessive detail, complex patterns, serif typography" + }, + { + "name": "MK afrofuturism", + "prompt": "Afrofuturism illustration {prompt} . vibrant colors, futuristic elements, cultural symbolism, cosmic imagery, dynamic patterns, empowering narratives", + "negative_prompt": "monochromatic" + }, + { + "name": "MK atompunk", + "prompt": "Atompunk illustation, {prompt} . retro-futuristic, atomic age aesthetics, sleek lines, metallic textures, futuristic technology, optimism, energy", + "negative_prompt": "organic, natural textures, rustic, dystopian" + }, + { + "name": "MK constructivism", + "prompt": "Constructivism {prompt} . geometric abstraction, bold colors, industrial aesthetics, dynamic compositions, utilitarian design, revolutionary spirit", + "negative_prompt": "organic shapes, muted colors, ornate elements, traditional" + }, + { + "name": "MK chicano-art", + "prompt": "Chicano art {prompt} . bold colors, cultural symbolism, muralism, lowrider aesthetics, barrio life, political messages, social activism, Mexico", + "negative_prompt": "monochromatic, minimalist, mainstream aesthetics" + }, + { + "name": "MK de-stijl", + "prompt": "De Stijl Art {prompt} . neoplasticism, primary colors, geometric abstraction, horizontal and vertical lines, simplicity, harmony, utopian ideals", + "negative_prompt": "complex patterns, muted colors, ornate elements, asymmetry" + }, + { + "name": "MK dayak-art", + "prompt": "Dayak art sculpture of {prompt} . intricate patterns, nature-inspired motifs, vibrant colors, traditional craftsmanship, cultural symbolism, storytelling", + "negative_prompt": "minimalist, monochromatic, modern" + }, + { + "name": "MK fayum-portrait", + "prompt": "Fayum portrait {prompt} . encaustic painting, realistic facial features, warm earth tones, serene expressions, ancient Egyptian influences", + "negative_prompt": "abstract, vibrant colors, exaggerated features, modern" + }, + { + "name": "MK illuminated-manuscript", + "prompt": "Illuminated manuscript {prompt} . intricate calligraphy, rich colors, detailed illustrations, gold leaf accents, ornate borders, religious, historical, medieval", + "negative_prompt": "modern typography, minimalist design, monochromatic, abstract themes" + }, + { + "name": "MK kalighat-painting", + "prompt": "Kalighat painting {prompt} . bold lines, vibrant colors, narrative storytelling, cultural motifs, flat compositions, expressive characters", + "negative_prompt": "subdued colors, intricate details, realistic portrayal, modern aesthetics" + }, + { + "name": "MK madhubani-painting", + "prompt": "Madhubani painting {prompt} . intricate patterns, vibrant colors, nature-inspired motifs, cultural storytelling, symmetry, folk art aesthetics", + "negative_prompt": "abstract, muted colors, minimalistic design, modern aesthetics" + }, + { + "name": "MK pictorialism", + "prompt": "Pictorialism illustration{prompt} . soft focus, atmospheric effects, artistic interpretation, tonality, muted colors, evocative storytelling", + "negative_prompt": "sharp focus, high contrast, realistic depiction, vivid colors" + }, + { + "name": "MK pichwai-painting", + "prompt": "Pichwai painting {prompt} . intricate detailing, vibrant colors, religious themes, nature motifs, devotional storytelling, gold leaf accents", + "negative_prompt": "minimalist, subdued colors, abstract design" + }, + { + "name": "MK patachitra-painting", + "prompt": "Patachitra painting {prompt} . bold outlines, vibrant colors, intricate detailing, mythological themes, storytelling, traditional craftsmanship", + "negative_prompt": "subdued colors, minimalistic, abstract, modern aesthetics" + }, + { + "name": "MK samoan-art-inspired", + "prompt": "Samoan art-inspired wooden sculpture {prompt} . traditional motifs, natural elements, bold colors, cultural symbolism, storytelling, craftsmanship", + "negative_prompt": "modern aesthetics, minimalist, abstract" + }, + { + "name": "MK tlingit-art", + "prompt": "Tlingit art {prompt} . formline design, natural elements, animal motifs, bold colors, cultural storytelling, traditional craftsmanship, Alaska traditional art, (totem:1.5)", + "negative_prompt": "" + }, + { + "name": "MK adnate-style", + "prompt": "Painting by Adnate {prompt} . realistic portraits, street art, large-scale murals, subdued color palette, social narratives", + "negative_prompt": "abstract, vibrant colors, small-scale art" + }, + { + "name": "MK ron-english-style", + "prompt": "Painting by Ron English {prompt} . pop-surrealism, cultural subversion, iconic mash-ups, vibrant and bold colors, satirical commentary", + "negative_prompt": "traditional, monochromatic" + }, + { + "name": "MK shepard-fairey-style", + "prompt": "Painting by Shepard Fairey {prompt} . street art, political activism, iconic stencils, bold typography, high contrast, red, black, and white color palette", + "negative_prompt": "traditional, muted colors" + } +] diff --git a/sdxl_styles/sdxl_styles_mre.json b/sdxl_styles/sdxl_styles_mre.json new file mode 100644 index 0000000000000000000000000000000000000000..c18c3100984ac7e2582d6d46db6f30aba23911b1 --- /dev/null +++ b/sdxl_styles/sdxl_styles_mre.json @@ -0,0 +1,112 @@ +[ + { + "name": "mre-cinematic-dynamic", + "prompt": "epic cinematic shot of dynamic {prompt} in motion. main subject of high budget action movie. raw photo, motion blur. best quality, high resolution", + "negative_prompt": "static, still, motionless, sluggish. drawing, painting, illustration, rendered. low budget. low quality, low resolution" + }, + { + "name": "mre-spontaneous-picture", + "prompt": "spontaneous picture of {prompt}, taken by talented amateur. best quality, high resolution. magical moment, natural look. simple but good looking", + "negative_prompt": "overthinked. low quality, low resolution" + }, + { + "name": "mre-artistic-vision", + "prompt": "powerful artistic vision of {prompt}. breathtaking masterpiece made by great artist. best quality, high resolution", + "negative_prompt": "insignificant, flawed, made by bad artist. low quality, low resolution" + }, + { + "name": "mre-dark-dream", + "prompt": "dark and unsettling dream showing {prompt}. best quality, high resolution. created by genius but depressed mad artist. grim beauty", + "negative_prompt": "naive, cheerful. comfortable, casual, boring, cliche. low quality, low resolution" + }, + { + "name": "mre-gloomy-art", + "prompt": "astonishing gloomy art made mainly of shadows and lighting, forming {prompt}. masterful usage of lighting, shadows and chiaroscuro. made by black-hearted artist, drawing from darkness. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-bad-dream", + "prompt": "picture from really bad dream about terrifying {prompt}, true horror. bone-chilling vision. mad world that shouldn't exist. best quality, high resolution", + "negative_prompt": "nice dream, pleasant experience. low quality, low resolution" + }, + { + "name": "mre-underground", + "prompt": "uncanny caliginous vision of {prompt}, created by remarkable underground artist. best quality, high resolution. raw and brutal art, careless but impressive style. inspired by darkness and chaos", + "negative_prompt": "photography, mainstream, civilized. low quality, low resolution" + }, + { + "name": "mre-surreal-painting", + "prompt": "surreal painting representing strange vision of {prompt}. harmonious madness, synergy with chance. unique artstyle, mindbending art, magical surrealism. best quality, high resolution", + "negative_prompt": "photography, illustration, drawing. realistic, possible. logical, sane. low quality, low resolution" + }, + { + "name": "mre-dynamic-illustration", + "prompt": "insanely dynamic illustration of {prompt}. best quality, high resolution. crazy artstyle, careless brushstrokes, emotional and fun", + "negative_prompt": "photography, realistic. static, still, slow, boring. low quality, low resolution" + }, + { + "name": "mre-undead-art", + "prompt": "long forgotten art created by undead artist illustrating {prompt}, tribute to the death and decay. miserable art of the damned. wretched and decaying world. best quality, high resolution", + "negative_prompt": "alive, playful, living. low quality, low resolution" + }, + { + "name": "mre-elemental-art", + "prompt": "art illustrating insane amounts of raging elemental energy turning into {prompt}, avatar of elements. magical surrealism, wizardry. best quality, high resolution", + "negative_prompt": "photography, realistic, real. low quality, low resolution" + }, + { + "name": "mre-space-art", + "prompt": "winner of inter-galactic art contest illustrating {prompt}, symbol of the interstellar singularity. best quality, high resolution. artstyle previously unseen in the whole galaxy", + "negative_prompt": "created by human race, low quality, low resolution" + }, + { + "name": "mre-ancient-illustration", + "prompt": "sublime ancient illustration of {prompt}, predating human civilization. crude and simple, but also surprisingly beautiful artwork, made by genius primeval artist. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-brave-art", + "prompt": "brave, shocking, and brutally true art showing {prompt}. inspired by courage and unlimited creativity. truth found in chaos. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-heroic-fantasy", + "prompt": "heroic fantasy painting of {prompt}, in the dangerous fantasy world. airbrush over oil on canvas. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-dark-cyberpunk", + "prompt": "dark cyberpunk illustration of brutal {prompt} in a world without hope, ruled by ruthless criminal corporations. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-lyrical-geometry", + "prompt": "geometric and lyrical abstraction painting presenting {prompt}. oil on metal. best quality, high resolution", + "negative_prompt": "photography, realistic, drawing, rendered. low quality, low resolution" + }, + { + "name": "mre-sumi-e-symbolic", + "prompt": "big long brushstrokes of deep black sumi-e turning into symbolic painting of {prompt}. master level raw art. best quality, high resolution", + "negative_prompt": "photography, rendered. low quality, low resolution" + }, + { + "name": "mre-sumi-e-detailed", + "prompt": "highly detailed black sumi-e painting of {prompt}. in-depth study of perfection, created by a master. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-manga", + "prompt": "manga artwork presenting {prompt}. created by japanese manga artist. highly emotional. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-anime", + "prompt": "anime artwork illustrating {prompt}. created by japanese anime studio. highly emotional. best quality, high resolution", + "negative_prompt": "low quality, low resolution" + }, + { + "name": "mre-comic", + "prompt": "breathtaking illustration from adult comic book presenting {prompt}. fabulous artwork. best quality, high resolution", + "negative_prompt": "deformed, ugly, low quality, low resolution" + } +] diff --git a/sdxl_styles/sdxl_styles_sai.json b/sdxl_styles/sdxl_styles_sai.json new file mode 100644 index 0000000000000000000000000000000000000000..a705b2431bf09735b276d26afbb8d55e9516ffcc --- /dev/null +++ b/sdxl_styles/sdxl_styles_sai.json @@ -0,0 +1,87 @@ +[ + { + "name": "sai-3d-model", + "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting", + "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting" + }, + { + "name": "sai-analog film", + "prompt": "analog film photo {prompt} . faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage", + "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured" + }, + { + "name": "sai-anime", + "prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed", + "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast" + }, + { + "name": "sai-cinematic", + "prompt": "cinematic film still {prompt} . shallow depth of field, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy", + "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" + }, + { + "name": "sai-comic book", + "prompt": "comic {prompt} . graphic illustration, comic art, graphic novel art, vibrant, highly detailed", + "negative_prompt": "photograph, deformed, glitch, noisy, realistic, stock photo" + }, + { + "name": "sai-craft clay", + "prompt": "play-doh style {prompt} . sculpture, clay art, centered composition, Claymation", + "negative_prompt": "sloppy, messy, grainy, highly detailed, ultra textured, photo" + }, + { + "name": "sai-digital art", + "prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed", + "negative_prompt": "photo, photorealistic, realism, ugly" + }, + { + "name": "sai-enhance", + "prompt": "breathtaking {prompt} . award-winning, professional, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy" + }, + { + "name": "sai-fantasy art", + "prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy", + "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white" + }, + { + "name": "sai-isometric", + "prompt": "isometric style {prompt} . vibrant, beautiful, crisp, detailed, ultra detailed, intricate", + "negative_prompt": "deformed, mutated, ugly, disfigured, blur, blurry, noise, noisy, realistic, photographic" + }, + { + "name": "sai-line art", + "prompt": "line art drawing {prompt} . professional, sleek, modern, minimalist, graphic, line art, vector graphics", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, blurry, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, mutated, realism, realistic, impressionism, expressionism, oil, acrylic" + }, + { + "name": "sai-lowpoly", + "prompt": "low-poly style {prompt} . low-poly game art, polygon mesh, jagged, blocky, wireframe edges, centered composition", + "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo" + }, + { + "name": "sai-neonpunk", + "prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional", + "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured" + }, + { + "name": "sai-origami", + "prompt": "origami style {prompt} . paper art, pleated paper, folded, origami art, pleats, cut and fold, centered composition", + "negative_prompt": "noisy, sloppy, messy, grainy, highly detailed, ultra textured, photo" + }, + { + "name": "sai-photographic", + "prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed", + "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly" + }, + { + "name": "sai-pixel art", + "prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics", + "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic" + }, + { + "name": "sai-texture", + "prompt": "texture {prompt} top down close-up", + "negative_prompt": "ugly, deformed, noisy, blurry" + } +] \ No newline at end of file diff --git a/sdxl_styles/sdxl_styles_twri.json b/sdxl_styles/sdxl_styles_twri.json new file mode 100644 index 0000000000000000000000000000000000000000..687a19d2bf97d7d9f40d46fd4d89fec4a9f057b6 --- /dev/null +++ b/sdxl_styles/sdxl_styles_twri.json @@ -0,0 +1,447 @@ +[ + { + "name": "ads-advertising", + "prompt": "advertising poster style {prompt} . Professional, modern, product-focused, commercial, eye-catching, highly detailed", + "negative_prompt": "noisy, blurry, amateurish, sloppy, unattractive" + }, + { + "name": "ads-automotive", + "prompt": "automotive advertisement style {prompt} . sleek, dynamic, professional, commercial, vehicle-focused, high-resolution, highly detailed", + "negative_prompt": "noisy, blurry, unattractive, sloppy, unprofessional" + }, + { + "name": "ads-corporate", + "prompt": "corporate branding style {prompt} . professional, clean, modern, sleek, minimalist, business-oriented, highly detailed", + "negative_prompt": "noisy, blurry, grungy, sloppy, cluttered, disorganized" + }, + { + "name": "ads-fashion editorial", + "prompt": "fashion editorial style {prompt} . high fashion, trendy, stylish, editorial, magazine style, professional, highly detailed", + "negative_prompt": "outdated, blurry, noisy, unattractive, sloppy" + }, + { + "name": "ads-food photography", + "prompt": "food photography style {prompt} . appetizing, professional, culinary, high-resolution, commercial, highly detailed", + "negative_prompt": "unappetizing, sloppy, unprofessional, noisy, blurry" + }, + { + "name": "ads-gourmet food photography", + "prompt": "gourmet food photo of {prompt} . soft natural lighting, macro details, vibrant colors, fresh ingredients, glistening textures, bokeh background, styled plating, wooden tabletop, garnished, tantalizing, editorial quality", + "negative_prompt": "cartoon, anime, sketch, grayscale, dull, overexposed, cluttered, messy plate, deformed" + }, + { + "name": "ads-luxury", + "prompt": "luxury product style {prompt} . elegant, sophisticated, high-end, luxurious, professional, highly detailed", + "negative_prompt": "cheap, noisy, blurry, unattractive, amateurish" + }, + { + "name": "ads-real estate", + "prompt": "real estate photography style {prompt} . professional, inviting, well-lit, high-resolution, property-focused, commercial, highly detailed", + "negative_prompt": "dark, blurry, unappealing, noisy, unprofessional" + }, + { + "name": "ads-retail", + "prompt": "retail packaging style {prompt} . vibrant, enticing, commercial, product-focused, eye-catching, professional, highly detailed", + "negative_prompt": "noisy, blurry, amateurish, sloppy, unattractive" + }, + { + "name": "artstyle-abstract", + "prompt": "abstract style {prompt} . non-representational, colors and shapes, expression of feelings, imaginative, highly detailed", + "negative_prompt": "realistic, photographic, figurative, concrete" + }, + { + "name": "artstyle-abstract expressionism", + "prompt": "abstract expressionist painting {prompt} . energetic brushwork, bold colors, abstract forms, expressive, emotional", + "negative_prompt": "realistic, photorealistic, low contrast, plain, simple, monochrome" + }, + { + "name": "artstyle-art deco", + "prompt": "art deco style {prompt} . geometric shapes, bold colors, luxurious, elegant, decorative, symmetrical, ornate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist" + }, + { + "name": "artstyle-art nouveau", + "prompt": "art nouveau style {prompt} . elegant, decorative, curvilinear forms, nature-inspired, ornate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, modernist, minimalist" + }, + { + "name": "artstyle-constructivist", + "prompt": "constructivist style {prompt} . geometric shapes, bold colors, dynamic composition, propaganda art style", + "negative_prompt": "realistic, photorealistic, low contrast, plain, simple, abstract expressionism" + }, + { + "name": "artstyle-cubist", + "prompt": "cubist artwork {prompt} . geometric shapes, abstract, innovative, revolutionary", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" + }, + { + "name": "artstyle-expressionist", + "prompt": "expressionist {prompt} . raw, emotional, dynamic, distortion for emotional effect, vibrant, use of unusual colors, detailed", + "negative_prompt": "realism, symmetry, quiet, calm, photo" + }, + { + "name": "artstyle-graffiti", + "prompt": "graffiti style {prompt} . street art, vibrant, urban, detailed, tag, mural", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" + }, + { + "name": "artstyle-hyperrealism", + "prompt": "hyperrealistic art {prompt} . extremely high-resolution details, photographic, realism pushed to extreme, fine texture, incredibly lifelike", + "negative_prompt": "simplified, abstract, unrealistic, impressionistic, low resolution" + }, + { + "name": "artstyle-impressionist", + "prompt": "impressionist painting {prompt} . loose brushwork, vibrant color, light and shadow play, captures feeling over form", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" + }, + { + "name": "artstyle-pointillism", + "prompt": "pointillism style {prompt} . composed entirely of small, distinct dots of color, vibrant, highly detailed", + "negative_prompt": "line drawing, smooth shading, large color fields, simplistic" + }, + { + "name": "artstyle-pop art", + "prompt": "pop Art style {prompt} . bright colors, bold outlines, popular culture themes, ironic or kitsch", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, minimalist" + }, + { + "name": "artstyle-psychedelic", + "prompt": "psychedelic style {prompt} . vibrant colors, swirling patterns, abstract forms, surreal, trippy", + "negative_prompt": "monochrome, black and white, low contrast, realistic, photorealistic, plain, simple" + }, + { + "name": "artstyle-renaissance", + "prompt": "renaissance style {prompt} . realistic, perspective, light and shadow, religious or mythological themes, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, modernist, minimalist, abstract" + }, + { + "name": "artstyle-steampunk", + "prompt": "steampunk style {prompt} . antique, mechanical, brass and copper tones, gears, intricate, detailed", + "negative_prompt": "deformed, glitch, noisy, low contrast, anime, photorealistic" + }, + { + "name": "artstyle-surrealist", + "prompt": "surrealist art {prompt} . dreamlike, mysterious, provocative, symbolic, intricate, detailed", + "negative_prompt": "anime, photorealistic, realistic, deformed, glitch, noisy, low contrast" + }, + { + "name": "artstyle-typography", + "prompt": "typographic art {prompt} . stylized, intricate, detailed, artistic, text-based", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" + }, + { + "name": "artstyle-watercolor", + "prompt": "watercolor painting {prompt} . vibrant, beautiful, painterly, detailed, textural, artistic", + "negative_prompt": "anime, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" + }, + { + "name": "futuristic-biomechanical", + "prompt": "biomechanical style {prompt} . blend of organic and mechanical elements, futuristic, cybernetic, detailed, intricate", + "negative_prompt": "natural, rustic, primitive, organic, simplistic" + }, + { + "name": "futuristic-biomechanical cyberpunk", + "prompt": "biomechanical cyberpunk {prompt} . cybernetics, human-machine fusion, dystopian, organic meets artificial, dark, intricate, highly detailed", + "negative_prompt": "natural, colorful, deformed, sketch, low contrast, watercolor" + }, + { + "name": "futuristic-cybernetic", + "prompt": "cybernetic style {prompt} . futuristic, technological, cybernetic enhancements, robotics, artificial intelligence themes", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval" + }, + { + "name": "futuristic-cybernetic robot", + "prompt": "cybernetic robot {prompt} . android, AI, machine, metal, wires, tech, futuristic, highly detailed", + "negative_prompt": "organic, natural, human, sketch, watercolor, low contrast" + }, + { + "name": "futuristic-cyberpunk cityscape", + "prompt": "cyberpunk cityscape {prompt} . neon lights, dark alleys, skyscrapers, futuristic, vibrant colors, high contrast, highly detailed", + "negative_prompt": "natural, rural, deformed, low contrast, black and white, sketch, watercolor" + }, + { + "name": "futuristic-futuristic", + "prompt": "futuristic style {prompt} . sleek, modern, ultramodern, high tech, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vintage, antique" + }, + { + "name": "futuristic-retro cyberpunk", + "prompt": "retro cyberpunk {prompt} . 80's inspired, synthwave, neon, vibrant, detailed, retro futurism", + "negative_prompt": "modern, desaturated, black and white, realism, low contrast" + }, + { + "name": "futuristic-retro futurism", + "prompt": "retro-futuristic {prompt} . vintage sci-fi, 50s and 60s style, atomic age, vibrant, highly detailed", + "negative_prompt": "contemporary, realistic, rustic, primitive" + }, + { + "name": "futuristic-sci-fi", + "prompt": "sci-fi style {prompt} . futuristic, technological, alien worlds, space themes, advanced civilizations", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, historical, medieval" + }, + { + "name": "futuristic-vaporwave", + "prompt": "vaporwave style {prompt} . retro aesthetic, cyberpunk, vibrant, neon colors, vintage 80s and 90s style, highly detailed", + "negative_prompt": "monochrome, muted colors, realism, rustic, minimalist, dark" + }, + { + "name": "game-bubble bobble", + "prompt": "Bubble Bobble style {prompt} . 8-bit, cute, pixelated, fantasy, vibrant, reminiscent of Bubble Bobble game", + "negative_prompt": "realistic, modern, photorealistic, violent, horror" + }, + { + "name": "game-cyberpunk game", + "prompt": "cyberpunk game style {prompt} . neon, dystopian, futuristic, digital, vibrant, detailed, high contrast, reminiscent of cyberpunk genre video games", + "negative_prompt": "historical, natural, rustic, low detailed" + }, + { + "name": "game-fighting game", + "prompt": "fighting game style {prompt} . dynamic, vibrant, action-packed, detailed character design, reminiscent of fighting video games", + "negative_prompt": "peaceful, calm, minimalist, photorealistic" + }, + { + "name": "game-gta", + "prompt": "GTA-style artwork {prompt} . satirical, exaggerated, pop art style, vibrant colors, iconic characters, action-packed", + "negative_prompt": "realistic, black and white, low contrast, impressionist, cubist, noisy, blurry, deformed" + }, + { + "name": "game-mario", + "prompt": "Super Mario style {prompt} . vibrant, cute, cartoony, fantasy, playful, reminiscent of Super Mario series", + "negative_prompt": "realistic, modern, horror, dystopian, violent" + }, + { + "name": "game-minecraft", + "prompt": "Minecraft style {prompt} . blocky, pixelated, vibrant colors, recognizable characters and objects, game assets", + "negative_prompt": "smooth, realistic, detailed, photorealistic, noise, blurry, deformed" + }, + { + "name": "game-pokemon", + "prompt": "Pokémon style {prompt} . vibrant, cute, anime, fantasy, reminiscent of Pokémon series", + "negative_prompt": "realistic, modern, horror, dystopian, violent" + }, + { + "name": "game-retro arcade", + "prompt": "retro arcade style {prompt} . 8-bit, pixelated, vibrant, classic video game, old school gaming, reminiscent of 80s and 90s arcade games", + "negative_prompt": "modern, ultra-high resolution, photorealistic, 3D" + }, + { + "name": "game-retro game", + "prompt": "retro game art {prompt} . 16-bit, vibrant colors, pixelated, nostalgic, charming, fun", + "negative_prompt": "realistic, photorealistic, 35mm film, deformed, glitch, low contrast, noisy" + }, + { + "name": "game-rpg fantasy game", + "prompt": "role-playing game (RPG) style fantasy {prompt} . detailed, vibrant, immersive, reminiscent of high fantasy RPG games", + "negative_prompt": "sci-fi, modern, urban, futuristic, low detailed" + }, + { + "name": "game-strategy game", + "prompt": "strategy game style {prompt} . overhead view, detailed map, units, reminiscent of real-time strategy video games", + "negative_prompt": "first-person view, modern, photorealistic" + }, + { + "name": "game-streetfighter", + "prompt": "Street Fighter style {prompt} . vibrant, dynamic, arcade, 2D fighting game, highly detailed, reminiscent of Street Fighter series", + "negative_prompt": "3D, realistic, modern, photorealistic, turn-based strategy" + }, + { + "name": "game-zelda", + "prompt": "Legend of Zelda style {prompt} . vibrant, fantasy, detailed, epic, heroic, reminiscent of The Legend of Zelda series", + "negative_prompt": "sci-fi, modern, realistic, horror" + }, + { + "name": "misc-architectural", + "prompt": "architectural style {prompt} . clean lines, geometric shapes, minimalist, modern, architectural drawing, highly detailed", + "negative_prompt": "curved lines, ornate, baroque, abstract, grunge" + }, + { + "name": "misc-disco", + "prompt": "disco-themed {prompt} . vibrant, groovy, retro 70s style, shiny disco balls, neon lights, dance floor, highly detailed", + "negative_prompt": "minimalist, rustic, monochrome, contemporary, simplistic" + }, + { + "name": "misc-dreamscape", + "prompt": "dreamscape {prompt} . surreal, ethereal, dreamy, mysterious, fantasy, highly detailed", + "negative_prompt": "realistic, concrete, ordinary, mundane" + }, + { + "name": "misc-dystopian", + "prompt": "dystopian style {prompt} . bleak, post-apocalyptic, somber, dramatic, highly detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, cheerful, optimistic, vibrant, colorful" + }, + { + "name": "misc-fairy tale", + "prompt": "fairy tale {prompt} . magical, fantastical, enchanting, storybook style, highly detailed", + "negative_prompt": "realistic, modern, ordinary, mundane" + }, + { + "name": "misc-gothic", + "prompt": "gothic style {prompt} . dark, mysterious, haunting, dramatic, ornate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, cheerful, optimistic" + }, + { + "name": "misc-grunge", + "prompt": "grunge style {prompt} . textured, distressed, vintage, edgy, punk rock vibe, dirty, noisy", + "negative_prompt": "smooth, clean, minimalist, sleek, modern, photorealistic" + }, + { + "name": "misc-horror", + "prompt": "horror-themed {prompt} . eerie, unsettling, dark, spooky, suspenseful, grim, highly detailed", + "negative_prompt": "cheerful, bright, vibrant, light-hearted, cute" + }, + { + "name": "misc-kawaii", + "prompt": "kawaii style {prompt} . cute, adorable, brightly colored, cheerful, anime influence, highly detailed", + "negative_prompt": "dark, scary, realistic, monochrome, abstract" + }, + { + "name": "misc-lovecraftian", + "prompt": "lovecraftian horror {prompt} . eldritch, cosmic horror, unknown, mysterious, surreal, highly detailed", + "negative_prompt": "light-hearted, mundane, familiar, simplistic, realistic" + }, + { + "name": "misc-macabre", + "prompt": "macabre style {prompt} . dark, gothic, grim, haunting, highly detailed", + "negative_prompt": "bright, cheerful, light-hearted, cartoonish, cute" + }, + { + "name": "misc-manga", + "prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style" + }, + { + "name": "misc-metropolis", + "prompt": "metropolis-themed {prompt} . urban, cityscape, skyscrapers, modern, futuristic, highly detailed", + "negative_prompt": "rural, natural, rustic, historical, simple" + }, + { + "name": "misc-minimalist", + "prompt": "minimalist style {prompt} . simple, clean, uncluttered, modern, elegant", + "negative_prompt": "ornate, complicated, highly detailed, cluttered, disordered, messy, noisy" + }, + { + "name": "misc-monochrome", + "prompt": "monochrome {prompt} . black and white, contrast, tone, texture, detailed", + "negative_prompt": "colorful, vibrant, noisy, blurry, deformed" + }, + { + "name": "misc-nautical", + "prompt": "nautical-themed {prompt} . sea, ocean, ships, maritime, beach, marine life, highly detailed", + "negative_prompt": "landlocked, desert, mountains, urban, rustic" + }, + { + "name": "misc-space", + "prompt": "space-themed {prompt} . cosmic, celestial, stars, galaxies, nebulas, planets, science fiction, highly detailed", + "negative_prompt": "earthly, mundane, ground-based, realism" + }, + { + "name": "misc-stained glass", + "prompt": "stained glass style {prompt} . vibrant, beautiful, translucent, intricate, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" + }, + { + "name": "misc-techwear fashion", + "prompt": "techwear fashion {prompt} . futuristic, cyberpunk, urban, tactical, sleek, dark, highly detailed", + "negative_prompt": "vintage, rural, colorful, low contrast, realism, sketch, watercolor" + }, + { + "name": "misc-tribal", + "prompt": "tribal style {prompt} . indigenous, ethnic, traditional patterns, bold, natural colors, highly detailed", + "negative_prompt": "modern, futuristic, minimalist, pastel" + }, + { + "name": "misc-zentangle", + "prompt": "zentangle {prompt} . intricate, abstract, monochrome, patterns, meditative, highly detailed", + "negative_prompt": "colorful, representative, simplistic, large fields of color" + }, + { + "name": "papercraft-collage", + "prompt": "collage style {prompt} . mixed media, layered, textural, detailed, artistic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic" + }, + { + "name": "papercraft-flat papercut", + "prompt": "flat papercut style {prompt} . silhouette, clean cuts, paper, sharp edges, minimalist, color block", + "negative_prompt": "3D, high detail, noise, grainy, blurry, painting, drawing, photo, disfigured" + }, + { + "name": "papercraft-kirigami", + "prompt": "kirigami representation of {prompt} . 3D, paper folding, paper cutting, Japanese, intricate, symmetrical, precision, clean lines", + "negative_prompt": "painting, drawing, 2D, noisy, blurry, deformed" + }, + { + "name": "papercraft-paper mache", + "prompt": "paper mache representation of {prompt} . 3D, sculptural, textured, handmade, vibrant, fun", + "negative_prompt": "2D, flat, photo, sketch, digital art, deformed, noisy, blurry" + }, + { + "name": "papercraft-paper quilling", + "prompt": "paper quilling art of {prompt} . intricate, delicate, curling, rolling, shaping, coiling, loops, 3D, dimensional, ornamental", + "negative_prompt": "photo, painting, drawing, 2D, flat, deformed, noisy, blurry" + }, + { + "name": "papercraft-papercut collage", + "prompt": "papercut collage of {prompt} . mixed media, textured paper, overlapping, asymmetrical, abstract, vibrant", + "negative_prompt": "photo, 3D, realistic, drawing, painting, high detail, disfigured" + }, + { + "name": "papercraft-papercut shadow box", + "prompt": "3D papercut shadow box of {prompt} . layered, dimensional, depth, silhouette, shadow, papercut, handmade, high contrast", + "negative_prompt": "painting, drawing, photo, 2D, flat, high detail, blurry, noisy, disfigured" + }, + { + "name": "papercraft-stacked papercut", + "prompt": "stacked papercut art of {prompt} . 3D, layered, dimensional, depth, precision cut, stacked layers, papercut, high contrast", + "negative_prompt": "2D, flat, noisy, blurry, painting, drawing, photo, deformed" + }, + { + "name": "papercraft-thick layered papercut", + "prompt": "thick layered papercut art of {prompt} . deep 3D, volumetric, dimensional, depth, thick paper, high stack, heavy texture, tangible layers", + "negative_prompt": "2D, flat, thin paper, low stack, smooth texture, painting, drawing, photo, deformed" + }, + { + "name": "photo-alien", + "prompt": "alien-themed {prompt} . extraterrestrial, cosmic, otherworldly, mysterious, sci-fi, highly detailed", + "negative_prompt": "earthly, mundane, common, realistic, simple" + }, + { + "name": "photo-film noir", + "prompt": "film noir style {prompt} . monochrome, high contrast, dramatic shadows, 1940s style, mysterious, cinematic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, vibrant, colorful" + }, + { + "name": "photo-glamour", + "prompt": "glamorous photo {prompt} . high fashion, luxurious, extravagant, stylish, sensual, opulent, elegance, stunning beauty, professional, high contrast, detailed", + "negative_prompt": "ugly, deformed, noisy, blurry, distorted, grainy, sketch, low contrast, dull, plain, modest" + }, + { + "name": "photo-hdr", + "prompt": "HDR photo of {prompt} . High dynamic range, vivid, rich details, clear shadows and highlights, realistic, intense, enhanced contrast, highly detailed", + "negative_prompt": "flat, low contrast, oversaturated, underexposed, overexposed, blurred, noisy" + }, + { + "name": "photo-iphone photographic", + "prompt": "iphone photo {prompt} . large depth of field, deep depth of field, highly detailed", + "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, shallow depth of field, bokeh" + }, + { + "name": "photo-long exposure", + "prompt": "long exposure photo of {prompt} . Blurred motion, streaks of light, surreal, dreamy, ghosting effect, highly detailed", + "negative_prompt": "static, noisy, deformed, shaky, abrupt, flat, low contrast" + }, + { + "name": "photo-neon noir", + "prompt": "neon noir {prompt} . cyberpunk, dark, rainy streets, neon signs, high contrast, low light, vibrant, highly detailed", + "negative_prompt": "bright, sunny, daytime, low contrast, black and white, sketch, watercolor" + }, + { + "name": "photo-silhouette", + "prompt": "silhouette style {prompt} . high contrast, minimalistic, black and white, stark, dramatic", + "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, color, realism, photorealistic" + }, + { + "name": "photo-tilt-shift", + "prompt": "tilt-shift photo of {prompt} . selective focus, miniature effect, blurred background, highly detailed, vibrant, perspective control", + "negative_prompt": "blurry, noisy, deformed, flat, low contrast, unrealistic, oversaturated, underexposed" + } +] \ No newline at end of file diff --git a/shared.py b/shared.py new file mode 100644 index 0000000000000000000000000000000000000000..21a2a864bb2e139cc12a336cebcec2fbf8bfd36b --- /dev/null +++ b/shared.py @@ -0,0 +1 @@ +gradio_root = None \ No newline at end of file diff --git a/troubleshoot.md b/troubleshoot.md new file mode 100644 index 0000000000000000000000000000000000000000..7e079742d7218ba718b32a13a0f0645df6c8f622 --- /dev/null +++ b/troubleshoot.md @@ -0,0 +1,178 @@ +Below are many common problems that people encountered: + +### RuntimeError: CPUAllocator + +See also the section: **System Swap** + +### Model loaded, then paused, then nothing happens + +See also the section: **System Swap** + +### Segmentation Fault + +See also the section: **System Swap** + +### Aborted + +See also the section: **System Swap** + +### core dumped + +See also the section: **System Swap** + +### Killed + +See also the section: **System Swap** + +### ^C, then quit + +See also the section: **System Swap** + +### adm 2816, then stuck + +See also the section: **System Swap** + +### Connection errored out + +See also the section: **System Swap** + +### Error 1006 + +See also the section: **System Swap** + +### WinError 10060 + +See also the section: **System Swap** + +### Read timed out + +See also the section: **System Swap** + +### No error, but the console close in a flash. Cannot find any error. + +See also the section: **System Swap** + +### Model loading is extremely slow (more than 1 minute) + +See also the section: **System Swap** + +### System Swap + +All above problems are caused by the fact that you do not have enough System Swap. + +Please make sure that you have at least 40GB System Swap. In fact, it does not need so much Swap, but 40Gb should be safe for you to run Fooocus in 100% success. + +(If you have more than 64GB RAM, then *perhaps* you do not need any System Swap, but we are not exactly sure about this.) + +Also, if your system swap is on HDD, the speed of model loading will be very slow. Please try best to put system swap on SSD. + +If you are using Linux/Mac, please follow your provider's instructions to set Swap Space. Herein, the "provider" refers to Ubuntu official, CentOS official, Mac official, etc. + +If you are using Windows, you can set Swap here: + +![swap](https://github.com/lllyasviel/Fooocus/assets/19834515/2a06b130-fe9b-4504-94f1-2763be4476e9) + +If you use both HDD and SSD, you *may* test some settings on the above step 7 to try best to put swap area on SSD, so that the speed of model loading will be faster. + +**Important: Microsoft Windows 10/11 by default automate system swap for you so that you do not need to touch this dangerous setting. If you do not have enough system swap, just make sure that you have at least 40GB free space on each disk.** The Microsoft Windows 10/11 will automatically make swap areas for you. + +Also, if you obtain Microsoft Windows 10/11 from some unofficial Chinese or Russian provider, they may have modified the default setting of system swap to advertise some "Enhanced Windows 10/11" (but actually they are just making things worse rather than improve things). In those cases, you may need to manually check if your system swap setting is consistent to the above screenshot. + +Finally, note that you need to restart computer to activate any changes in system swap. + +### MetadataIncompleteBuffer + +See also the section: **Model corrupted** + +### PytorchStreamReader failed + +See also the section: **Model corrupted** + +### Model corrupted + +If you see Model Corrupted, then your model is corrupted. Fooocus will re-download corrupted models for you if your internet connection is good. Otherwise, you may also manually download models. You can find model url and their local location in the console each time a model download is requested. + +### UserWarning: The operator 'aten::std_mean.correction' is not currently supported on the DML + +This is a warning that you can ignore. + +### Torch not compiled with CUDA enabled + +You are not following the official installation guide. + +Please do not trust those wrong tutorials on the internet, and please only trust the official installation guide. + +### subprocess-exited-with-error + +Please use python 3.10 + +Also, you are not following the official installation guide. + +Please do not trust those wrong tutorials on the internet, and please only trust the official installation guide. + +### SSL: CERTIFICATE_VERIFY_FAILED + +Are you living in China? If yes, please consider turn off VPN, and/or try to download models manually. + +If you get this error elsewhere in the world, then you may need to look at [this search](https://www.google.com/search?q=SSL+Certificate+Error). We cannot give very specific guide to fix this since the cause can vary a lot. + +### CUDA kernel errors might be asynchronously reported at some other API call + +A very small amount of devices does have this problem. The cause can be complicated but usually can be resolved after following these steps: + +1. Make sure that you are using official version and latest version installed from [here](https://github.com/lllyasviel/Fooocus#download). (Some forks and other versions are more likely to cause this problem.) +2. Upgrade your Nvidia driver to the latest version. (Usually the version of your Nvidia driver should be 53X, not 3XX or 4XX.) +3. If things still do not work, then perhaps it is a problem with CUDA 12. You can use CUDA 11 and Xformers to try to solve this problem. We have prepared all files for you, and please do NOT install any CUDA or other environment on you own. The only one official way to do this is: (1) Backup and delete your `python_embeded` folder (near the `run.bat`); (2) Download the "previous_old_xformers_env.7z" from the [release page](https://github.com/lllyasviel/Fooocus/releases/tag/release), decompress it, and put the newly extracted `python_embeded` folder near your `run.bat`; (3) run Fooocus. +4. If it still does not work, please open an issue for us to take a look. + +### Found no NVIDIA driver on your system + +Please upgrade your Nvidia Driver. + +If you are using AMD, please follow official installation guide. + +### NVIDIA driver too old + +Please upgrade your Nvidia Driver. + +### I am using Mac, the speed is very slow. + +Some MAC users may need `--disable-offload-from-vram` to speed up model loading. + +Besides, the current support for MAC is very experimental, and we encourage users to also try Diffusionbee or Drawingthings: they are developed only for MAC. + +### I am using Nvidia with 8GB VRAM, I get CUDA Out Of Memory + +It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). + +### I am using Nvidia with 6GB VRAM, I get CUDA Out Of Memory + +It is very likely a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). + +### I am using Nvidia with 4GB VRAM with Float16 support, like RTX 3050, I get CUDA Out Of Memory + +It is a BUG. Please let us know as soon as possible. Please make an issue. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). + +### I am using Nvidia with 4GB VRAM without Float16 support, like GTX 960, I get CUDA Out Of Memory + +Supporting GPU with 4GB VRAM without fp16 is extremely difficult, and you may not be able to use SDXL. However, you may still make an issue and let us know. You may try SD1.5 in Automatic1111 or other software for your device. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). + +### I am using AMD GPU on Windows, I get CUDA Out Of Memory + +Current AMD support is very experimental for Windows. If you see this, then perhaps you cannot use Fooocus on this device on Windows. + +However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. + +Besides, the AMD support on Linux is slightly better because it will use ROCM. You may also try it if you are willing to change OS to linux. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). + +### I am using AMD GPU on Linux, I get CUDA Out Of Memory + +Current AMD support for Linux is better than that for Windows, but still, very experimental. However, if you re able to run SDXL on this same device on any other software, please let us know immediately, and we will support it as soon as possible. If no other software can enable your device to run SDXL on Windows, then we also do not have much to help. See also [minimal requirements](https://github.com/lllyasviel/Fooocus/tree/main?tab=readme-ov-file#minimal-requirement). + +### I tried flags like --lowvram or --gpu-only or --bf16 or so on, and things are not getting any better? + +Please remove these flags if you are mislead by some wrong tutorials. In most cases these flags are making things worse and introducing more problems. + +### Fooocus suddenly becomes very slow and I have not changed anything + +Are you accidentally running two Fooocus at the same time? diff --git a/update_log.md b/update_log.md new file mode 100644 index 0000000000000000000000000000000000000000..322c19c122ba50038ee297cade7fd89b82203ec4 --- /dev/null +++ b/update_log.md @@ -0,0 +1,632 @@ +# [2.2.1](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.1) + +* Fix some small bugs (e.g. image grid, upscale fast 2x, LoRA weight width in Firefox) +* Allow prompt weights in array syntax +* Add steps override and metadata scheme to history log + +# [2.2.0](https://github.com/lllyasviel/Fooocus/releases/tag/2.2.0) + +* Isolate every image generation to truly allow multi-user usage +* Add array support, changes the main prompt when increasing the image number. Syntax: `[[red, green, blue]] flower` +* Add optional metadata to images, allowing you to regenerate and modify them later with the same parameters +* Now supports native PNG, JPG and WEBP image generation +* Add Docker support + +# [2.1.865](https://github.com/lllyasviel/Fooocus/releases/tag/2.1.865) + +* Various bugfixes +* Add authentication to --listen + +# 2.1.864 + +* New model list. See also discussions. + +# 2.1.861 (requested update) + +(2023 Dec 21) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid or late Jan. However, you may still see updates if other collaborators are fixing bugs or solving problems. + +* Show image preview in Style when mouse hover. + +# 2.1.860 (requested update) + +* Allow upload inpaint mask in developer mode. + +# 2.1.857 (requested update) + +* Begin to support 8GB AMD GPU on Windows. + +# 2.1.854 + +* Add a button to copy parameters to clipboard in log. +* Allow users to load parameters directly by pasting parameters to prompt. + +# 2.1.853 + +* Add Marc K3nt3L's styles. Thanks [Marc K3nt3L](https://github.com/K3nt3L)! + +# 2.1.852 + +* New Log System: Log system now uses tables. If this is breaking some other browser extension or javascript developments, see also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405). + +# 2.1.846 + +* Many users reported that image quality is different from 2.1.824. We reviewed all codes and fixed several precision problems in 2.1.846. + +# 2.1.843 + +* Many improvements to Canvas. Thanks CanvasZoom author! + +# 2.1.841 + +* Backend maintain. +* Fix some potential frozen after model mismatch. +* Fix crash when cfg=1 when using anime preset. +* Added some guidelines for troubleshoot the "CUDA kernel errors asynchronously" problem. +* Fix inpaint device problem in `--always-gpu` mode. + +# 2.1.839 + +* Maintained some computation codes in backend for efficiency. +* Added a note about Seed Breaking Change. + +**Seed Breaking Change**: Note that 2.1.825-2.1.839 is seed breaking change. The computation float point is changed and some seeds may give slightly different results. The minor change in 2.1.825-2.1.839 do not influence image quality. See also [use previous version](https://github.com/lllyasviel/Fooocus/discussions/1405). + +# 2.1.837 + +* Fix some precision-related problems. + +# 2.1.836 + +* Avoid blip tokenizer download from torch hub + +# 2.1.831 + +* Input Image -> Describe (Midjourney Describe) + +# 2.1.830 + +* SegmindVega support. + +# 2.1.829 + +* Change SDE tree to CPU on AMD/DirectMl to avoid potential problems. + +# 2.1.828 + +* Allow to disable gradio analytics. +* Use html table in log. +* fix some SSL problems. + +# 2.1.826 + +* New backend. +* FP8 support (see also the new cmd flag list in Readme, eg, --unet-in-fp8-e4m3fn and --unet-in-fp8-e5m2). +* Fix some MPS problems. +* GLoRA support. +* Turbo scheduler. + +# 2.1.823 + +(2023 Nov 26) Hi all, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads. See you soon and we will come back in mid December. However, you may still see updates if other collaborators are fixing bugs or solving problems. + +* Fix some potential problem when LoRAs has clip keys and user want to load those LoRAs to refiners. + +# 2.1.822 + +* New inpaint system (inpaint beta test ends). + +# 2.1.821 + +* New UI for LoRAs. +* Improved preset system: normalized preset keys and file names. +* Improved session system: now multiple users can use one Fooocus at the same time without seeing others' results. +* Improved some computation related to model precision. +* Improved config loading system with user-friendly prints. + +# 2.1.820 + +* support "--disable-image-log" to prevent writing images and logs to hard drive. + +# 2.1.819 + +* Allow disabling preview in dev tools. + +# 2.1.818 + +* Fix preset lora failed to load when the weight is exactly one. + +# 2.1.817 + +* support "--theme dark" and "--theme light". +* added preset files "default" and "lcm", these presets exist but will not create launcher files (will not be exposed to users) to keep entry clean. The "--preset lcm" is equivalent to select "Extreme Speed" in UI, but will likely to make some online service deploying easier. + +# 2.1.815 + +* Multiple loras in preset. + +# 2.1.814 + +* Allow using previous preset of official SAI SDXL by modify the args to '--preset sai'. ~Note that this preset will set inpaint engine back to previous v1 to get same results like before. To change the inpaint engine to v2.6, use the dev tools -> inpaint engine -> v2.6.~ (update: it is not needed now after some tests.) + +# 2.1.813 + +* Allow preset to set default inpaint engine. + +# 2.1.812 + +* Allow preset to set default performance. +* heunpp2 sampler. + +# 2.1.810 + +* Added hints to config_modification_tutorial.txt +* Removed user hacked aspect ratios in I18N english templates, but it will still be read like before. +* fix some style sorting problem again (perhaps should try Gradio 4.0 later). +* Refreshed I18N english templates with more keys. + +# 2.1.809 + +* fix some sorting problem. + +# 2.1.808 + +* Aspect ratios now show aspect ratios. +* Added style search. +* Added style sorting/ordering/favorites. + +# 2.1.807 + +* Click on image to see it in full screen. + +# 2.1.806 + +* Fix some lora problems related to clip. + +# 2.1.805 + +* Responsive UI for small screens. +* Added skip preprocessor in dev tools. + +# 2.1.802 + +* Default inpaint engine changed to v2.6. You can still use inpaint engine v1 in dev tools. +* Fix some VRAM problems. + +# 2.1.799 + +* Added 'Extreme Speed' performance mode (based on LCM). The previous complicated settings are not needed now. + +# 2.1.798 + +* added lcm scheduler - LCM may need to set both sampler and scheduler to "lcm". Other than that, see the description in 2.1.782 logs. + +# 2.1.797 + +* fixed some dependency problems with facexlib and filterpy. + +# 2.1.793 + +* Added many javascripts to improve user experience. Now users with small screen will always see full canvas without needing to scroll. + +# 2.1.790 + +* Face swap (in line with Midjourney InsightFace): Input Image -> Image Prompt -> Advanced -> FaceSwap +* The performance is super high. Use it carefully and never use it in any illegal things! +* This implementation will crop faces for you and you do NOT need to crop faces before feeding images into Fooocus. (If you previously manually crop faces from images for other software, you do not need to do that now in Fooocus.) + +# 2.1.788 + +* Fixed some math problems in previous versions. +* Inpaint engine v2.6 join the beta test of Fooocus inpaint models. Use it in dev tools -> inpaint engine -> v2.6 . + +# 2.1.785 + +* The `user_path_config.txt` is deprecated since 2.1.785. If you are using it right now, please use the new `config.txt` instead. See also the new documentation in the Readme. +* The paths in `user_path_config.txt` will still be loaded in recent versions, but it will be removed soon. +* We use very user-friendly method to automatically transfer your path settings from `user_path_config.txt` to `config.txt` and usually you do not need to do anything. +* The new `config.txt` will never save default values so the default value changes in scripts will not be prevented by old config files. + +# 2.1.782 + +2.1.782 is mainly an update for a new LoRA system that supports both SDXL loras and SD1.5 loras. + +Now when you load a lora, the following things will happen: + +1. try to load the lora to the base model, if failed (model mismatch), then try to load the lora to refiner. +2. try to load the lora to refiner, if failed (model mismatch) then do nothing. + +In this way, Fooocus 2.1.782 can benefit from all models and loras from CivitAI with both SDXL and SD1.5 ecosystem, using the unique Fooocus swap algorithm, to achieve extremely high quality results (although the default setting is already very high quality), especially in some anime use cases, if users really want to play with all these things. + +Recently the community also developed LCM loras. Users can use it by setting the sampler as 'LCM', scheduler as 'sgm_uniform' (Update in 2.1.798: scheduler should also be "lcm"), the forced overwrite of sampling step as 4 to 8, and CFG guidance as 1.0, in dev tools. Do not forget to change the LCM lora weight to 1.0 (many people forget this and report failure cases). Also, set refiner to None. If LCM's feedback in the artists community is good (not the feedback in the programmer community of Stable Diffusion), Fooocus may add some other shortcuts in the future. + +# 2.1.781 + +(2023 Oct 26) Hi all, the feature updating of Fooocus will (really, really, this time) be paused for about two or three weeks because we really have some other workloads. Thanks for the passion of you all (and we in fact have kept updating even after last pausing announcement a week ago, because of many great feedbacks) - see you soon and we will come back in mid November. However, you may still see updates if other collaborators are fixing bugs or solving problems. + +* Disable refiner to speed up when new users mistakenly set same model to base and refiner. + +# 2.1.779 + +* Disable image grid by default because many users reports performance issues. For example, https://github.com/lllyasviel/Fooocus/issues/829 and so on. The image grid will cause problem when user hard drive is not super fast, or when user internet connection is not very good (eg, run in remote). The option is moved to dev tools if users want to use it. We will take a look at it later. + +# 2.1.776 + +* Support Ctrl+Up/Down Arrow to change prompt emphasizing weights. + +# 2.1.750 + +* New UI: now you can get each image during generating. + +# 2.1.743 + +* Improved GPT2 by removing some tokens that may corrupt styles. + +# 2.1.741 + +Style Updates: + +* "Default (Slightly Cinematic)" as renamed to "Fooocus Cinematic". +* "Default (Slightly Cinematic)" is canceled from default style selections. +* Added "Fooocus Sharp". This style combines many CivitAI prompts that reduces SDXL blurry and improves sharpness in a relatively natural way. +* Added "Fooocus Enhance". This style mainly use the very popular [default negative prompts from JuggernautXL](https://civitai.com/models/133005) and some other enhancing words. JuggernautXL's negative prompt has been proved to be very effective in many recent image posts on CivitAI to improve JuggernautXL and many other models. +* "Fooocus Sharp" and "Fooocus Enhance" and "Fooocus V2" becomes the new default set of styles. +* Removed the default text in the "negative prompt" input area since it is not necessary now. +* You can reproduce previous results by using "Fooocus Cinematic". +* "Fooocus Sharp" and "Fooocus Enhance" may undergo minor revision in future updates. + +# 2.1.739 + +* Added support for authentication in --share mode (via auth.json). + +# 2.1.737 + +* Allowed customizing resolutions in config. + +Modifying this will make results worse if you do not understand how Positional Encoding works. + +You have been warned. + +If you do not know why numbers must be transformed with many Sin and Cos functions (yes, those Trigonometric functions that you learn in junior high school) before they are fed to SDXL, we do not encourage you to change this - you will become a victim of Positional Encoding. You are likely to suffer from an easy-to-fail tool, rather than getting more control. + +Your knowledge gained from SD1.5 (for example, resolution numbers divided by 8 or 64 are good enough for UNet) does not work in SDXL. The SDXL uses Positional Encoding. The SD1.5 does not use Positional Encoding. They are completely different. + +Your knowledge gained from other resources (for example, resolutions around 1024 are good enough for SDXL) is wrong. The SDXL uses Positional Encoding. People who say "all resolutions around 1024 are good" do not understand what is Positional Encoding. They are not intentionally misleading. They are just not aware of the fact that SDXL is using Positional Encoding. + +The number 1152 must be exactly 1152, not 1152-1, not 1152+1, not 1152-8, not 1152+8. The number 1152 must be exactly 1152. Just Google what is a Positional Encoding. + +Again, if you do not understand how Positional Encoding works, just do not change the resolution numbers. + +# 2.1.735 + +* Fixed many problems related to torch autocast. + +# 2.1.733 + +* Increased allowed random seed range. + +# 2.1.728 + +* Fixed some potential numerical problems since 2.1.723 + +# 2.1.723 + +* Improve Fooocus Anime a bit by using better SD1.5 refining formulation. + +# 2.1.722 + +* Now it is possible to translate 100% all texts in the UI. + +# 2.1.721 + +* Added language/en.json to make translation easier. + +# 2.1.720 + +* Added Canvas Zoom to inpaint canvas +* Fixed the problem that image will be cropped in UI when the uploaded image is too wide. + +# 2.1.719 + +* I18N + +# 2.1.718 + +* Corrected handling dash in wildcard names, more wildcards (extended-color). + +# 2.1.717 + +* Corrected displaying multi-line prompts in Private Log. + +# 2.1.716 + +* Added support for nested wildcards, more wildcards (flower, color_flower). + +# 2.1.714 + +* Fixed resolution problems. + +# 2.1.712 + +* Cleaned up Private Log (most users won't need information about raw prompts). + +# 2.1.711 + +* Added more information about prompts in Private Log. +* Made wildcards in negative prompt use different seed. + +# 2.1.710 + +* Added information about wildcards usage in console log. + +# 2.1.709 + +* Allowed changing default values of advanced checkbox and image number. + +# 2.1.707 + +* Updated Gradio to v3.41.2. + +# 2.1.703 + +* Fixed many previous problems related to inpaint. + +# 2.1.702 + +* Corrected reading empty negative prompt from config (it shouldn't turn into None). + +# 2.1.701 + +* Updated FreeU node to v2 (gives less overcooked results). + +# 2.1.699 + +* Disabled smart memory management (solves some memory issues). + +# 2.1.698 + +* Added support for loading model files from subfolders. + +# 2.1.696 + +* Improved wildcards implementation (using same wildcard multiple times will now return different values). + +**(2023 Oct 18) Again, the feature updating of Fooocus will be paused for about two or three weeks because we have some other workloads - we will come back in early or mid November. However, you may still see updates if other collaborators are fixing bugs or solving problems.** + +# 2.1.695 (requested emergency bug fix) + +* Reduced 3.4GB RAM use when swapping base model. +* Reduced 372MB VRAM use in VAE decoding after using control model in image prompt. +* Note that Official ComfyUI (d44a2de) will run out of VRAM when using sdxl and control-lora on 2060 6GB that does not support float16 at resolution 1024. Fooocus 2.1.695 succeeded in outputting images without OOM using exactly same devices. + +(2023 Oct 17) Announcement of update being paused. + +# 2.1.693 + +* Putting custom styles before pre-defined styles. +* Avoided the consusion between Fooocus Anime preset and Fooocus Anime style (Fooocus Anime style is renamed to Fooocus Masterpiece because it does not make images Anime-looking if not using with Fooocus Anime preset). +* Fixed some minor bugs in Fooocus Anime preset's prompt emphasizing of commas. +* Supported and documented embedding grammar (and wildcards grammar). +* This release is a relative stable version and many features are determined now. + +# 2.1.687 + +* Added support for wildcards (using files from wildcards folder - try prompts like `__color__ sports car` with different seeds). + +# 2.1.682 + +* Added support for custom styles (loaded from JSON files placed in sdxl_styles folder). + +# 2.1.681 + +* Added support for generate hotkey (CTRL+ENTER). +* Added support for generate forever (RMB on Generate button). +* Added support for playing sound when generation is finished ('notification.ogg' or 'notification.mp3'). + +# 2.1.62 + +* Preset system. Added anime and realistic support. + +# 2.1.52 + +* removed pygit2 dependency (expect auto update) so that people will never have permission denied problems. + +# 2.1.50 + +* Begin to support sd1.5 as refiner. This method scale sigmas given SD15/Xl latent scale and is probably the most correct way to do it. I am going to write a discussion soon. + +# 2.1.25 + +AMD support on Linux and Windows. + +# 2.1.0 + +* Image Prompt +* Finished the "Moving from Midjourney" Table + +# 2.0.85 + +* Speed Up Again + +# 2.0.80 + +* Improved the scheduling of ADM guidance and CFG mimicking for better visual quality in high frequency domain and small objects. + +# 2.0.80 + +* Rework many patches and some UI details. +* Speed up processing. +* Move Colab to independent branch. +* Implemented CFG Scale and TSNR correction when CFG is bigger than 10. +* Implemented Developer Mode with more options to debug. + +### 2.0.72 + +(2023 sep 21) The feature updating of Fooocus will be paused for about two or three weeks because we have some events and travelling - we will come back in early or mid October. + +### 2.0.72 + +* Allow users to choose path of models. + +### 2.0.65 + +* Inpaint model released. + +### 2.0.50 + +* Variation/Upscale (Midjourney Toolbar) implemented. + +### 2.0.16 + +* Virtual memory system implemented. Now Colab can run both base model and refiner model with 7.8GB RAM + 5.3GB VRAM, and it never crashes. +* If you are lucky enough to read this line, keep in mind that ComfyUI cannot do this. This is very reasonable that Fooocus is more optimized because it only need to handle a fixed pipeline, but ComfyUI need to consider arbitrary pipelines. +* But if we just consider the optimization of this fixed workload, after 2.0.16, Fooocus has become the most optimized SDXL app, outperforming ComfyUI. + +### 2.0.0 + +* V2 released. +* completely rewrite text processing pipeline (higher image quality and prompt understanding). +* support multi-style. +* In 100 tests (prompts written by ChatGPT), V2 default results outperform V1 default results in 87 cases, evaluated by two human. +* In 100 tests (prompts written by ChatGPT), V2 prompt understanding outperform V1 prompt understanding in 81 cases, evaluated by two human, in both default setting and multi/single style mode. +* Because the above number is above 80%, we view this as a major update and directly jump to 2.0.0. +* Some other things are renamed. + +### 1.0.67 + +* Use dynamic weighting and lower weights for prompt expansion. + +### 1.0.64 + +* Fixed a small OOM problem. + +### 1.0.62 + +* Change prompt expansion to suffix mode for better balance of semantic and style (and debugging). + +### 1.0.60 + +* Tune the balance between style and Prompt Expansion. + +### 1.0.56 + +* Begin to use magic split. + +### 1.0.55 + +* Minor changes of Prompt Expansion. + +### 1.0.52 + +* Reduce the semantic corruption of Prompt Expansion. + +### 1.0.51 + +* Speed up Prompt Expansion a bit. + +### 1.0.50 + +* Prompt expansion and a "Raw mode" to turn it off (similar to Midjourney's "raw"). + +### 1.0.45 + +* Reworked SAG, removed unnecessary patch +* Reworked anisotropic filters for faster compute. +* Replaced with guided anisotropic filter for less distortion. + +### 1.0.41 + +(The update of Fooocus will be paused for a period of time for AUTOMATIC1111 sd-webui 1.6.X, and some features will also be implemented as webui extensions) + +### 1.0.40 + +* Behaviors reverted to 1.0.36 again (refiner steps). The 1.0.36 is too perfect and too typical; beating 1.0.36 is just impossible. + +### 1.0.39 + +* Reverted unstable changes between 1.0.37 and 1.0.38 . +* Increased refiner steps to half of sampling steps. + +### 1.0.36 + +* Change gaussian kernel to anisotropic kernel. + +### 1.0.34 + +* Random seed restoring. + +### 1.0.33 + +* Hide items in log when images are removed. + +### 1.0.32 + +* Fooocus private log + +### 1.0.31 + +* Fix typo and UI. + +### 1.0.29 + +* Added "Advanced->Advanced->Advanced" block for future development. + +### 1.0.29 + +* Fix overcook problem in 1.0.28 + +### 1.0.28 + +* SAG implemented + +### 1.0.27 + +* Fix small problem in textbox css + +### 1.0.25 + +* support sys.argv --listen --share --port + +### 1.0.24 + +* Taller input textbox. + +### 1.0.23 + +* Added some hints on linux after UI start so users know the App does not fail. + +### 1.0.20 + +* Support linux. + +### 1.0.20 + +* Speed-up text encoder. + +### 1.0.20 + +* Re-write UI to use async codes: (1) for faster start, and (2) for better live preview. +* Removed opencv dependency +* Plan to support Linux soon + +### 1.0.19 + +* Unlock to allow changing model. + +### 1.0.17 + +* Change default model to SDXL-1.0-vae-0.9. (This means the models will be downloaded again, but we should do it as early as possible so that all new users only need to download once. Really sorry for day-0 users. But frankly this is not too late considering that the project is just publicly available in less than 24 hours - if it has been a week then we will prefer more lightweight tricks to update.) + +### 1.0.16 + +* Implemented "Fooocus/outputs" folder for saving user results. +* Ignored cv2 errors when preview fails. +* Mentioned future AMD support in Readme. +* Created this log. + +### 1.0.15 + +Publicly available. + +### 1.0.0 + +Initial Version. diff --git a/webui.py b/webui.py new file mode 100644 index 0000000000000000000000000000000000000000..80b1a3d8ba2aeff1a748283c22af19b6147ca2a1 --- /dev/null +++ b/webui.py @@ -0,0 +1,688 @@ +import gradio as gr +import random +import os +import json +import time +import shared +import modules.config +import fooocus_version +import modules.html +import modules.async_worker as worker +import modules.constants as constants +import modules.flags as flags +import modules.gradio_hijack as grh +import modules.style_sorter as style_sorter +import modules.meta_parser +import args_manager +import copy + +from modules.sdxl_styles import legal_style_names +from modules.private_logger import get_current_html_path +from modules.ui_gradio_extensions import reload_javascript +from modules.auth import auth_enabled, check_auth +from modules.util import is_json + +def get_task(*args): + args = list(args) + args.pop(0) + + return worker.AsyncTask(args=args) + +def generate_clicked(task): + import ldm_patched.modules.model_management as model_management + + with model_management.interrupt_processing_mutex: + model_management.interrupt_processing = False + # outputs=[progress_html, progress_window, progress_gallery, gallery] + execution_start_time = time.perf_counter() + finished = False + + yield gr.update(visible=True, value=modules.html.make_progress_html(1, 'Waiting for task to start ...')), \ + gr.update(visible=True, value=None), \ + gr.update(visible=False, value=None), \ + gr.update(visible=False) + + worker.async_tasks.append(task) + + while not finished: + time.sleep(0.01) + if len(task.yields) > 0: + flag, product = task.yields.pop(0) + if flag == 'preview': + + # help bad internet connection by skipping duplicated preview + if len(task.yields) > 0: # if we have the next item + if task.yields[0][0] == 'preview': # if the next item is also a preview + # print('Skipped one preview for better internet connection.') + continue + + percentage, title, image = product + yield gr.update(visible=True, value=modules.html.make_progress_html(percentage, title)), \ + gr.update(visible=True, value=image) if image is not None else gr.update(), \ + gr.update(), \ + gr.update(visible=False) + if flag == 'results': + yield gr.update(visible=True), \ + gr.update(visible=True), \ + gr.update(visible=True, value=product), \ + gr.update(visible=False) + if flag == 'finish': + yield gr.update(visible=False), \ + gr.update(visible=False), \ + gr.update(visible=False), \ + gr.update(visible=True, value=product) + finished = True + + # delete Fooocus temp images, only keep gradio temp images + if args_manager.args.disable_image_log: + for filepath in product: + if isinstance(filepath, str) and os.path.exists(filepath): + os.remove(filepath) + + execution_time = time.perf_counter() - execution_start_time + print(f'Total time: {execution_time:.2f} seconds') + return + + +reload_javascript() + +title = f'Fooocus {fooocus_version.version}' + +if isinstance(args_manager.args.preset, str): + title += ' ' + args_manager.args.preset + +shared.gradio_root = gr.Blocks( + title=title, + css=modules.html.css).queue() + +with shared.gradio_root: + currentTask = gr.State(worker.AsyncTask(args=[])) + with gr.Row(): + with gr.Column(scale=2): + with gr.Row(): + progress_window = grh.Image(label='Preview', show_label=True, visible=False, height=768, + elem_classes=['main_view']) + progress_gallery = gr.Gallery(label='Finished Images', show_label=True, object_fit='contain', + height=768, visible=False, elem_classes=['main_view', 'image_gallery']) + progress_html = gr.HTML(value=modules.html.make_progress_html(32, 'Progress 32%'), visible=False, + elem_id='progress-bar', elem_classes='progress-bar') + gallery = gr.Gallery(label='Gallery', show_label=False, object_fit='contain', visible=True, height=768, + elem_classes=['resizable_area', 'main_view', 'final_gallery', 'image_gallery'], + elem_id='final_gallery') + with gr.Row(elem_classes='type_row'): + with gr.Column(scale=17): + prompt = gr.Textbox(show_label=False, placeholder="Type prompt here or paste parameters.", elem_id='positive_prompt', + container=False, autofocus=True, elem_classes='type_row', lines=1024) + + default_prompt = modules.config.default_prompt + if isinstance(default_prompt, str) and default_prompt != '': + shared.gradio_root.load(lambda: default_prompt, outputs=prompt) + + with gr.Column(scale=3, min_width=0): + generate_button = gr.Button(label="Generate", value="Generate", elem_classes='type_row', elem_id='generate_button', visible=True) + load_parameter_button = gr.Button(label="Load Parameters", value="Load Parameters", elem_classes='type_row', elem_id='load_parameter_button', visible=False) + skip_button = gr.Button(label="Skip", value="Skip", elem_classes='type_row_half', visible=False) + stop_button = gr.Button(label="Stop", value="Stop", elem_classes='type_row_half', elem_id='stop_button', visible=False) + + def stop_clicked(currentTask): + import ldm_patched.modules.model_management as model_management + currentTask.last_stop = 'stop' + if (currentTask.processing): + model_management.interrupt_current_processing() + return currentTask + + def skip_clicked(currentTask): + import ldm_patched.modules.model_management as model_management + currentTask.last_stop = 'skip' + if (currentTask.processing): + model_management.interrupt_current_processing() + return currentTask + + stop_button.click(stop_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False, _js='cancelGenerateForever') + skip_button.click(skip_clicked, inputs=currentTask, outputs=currentTask, queue=False, show_progress=False) + with gr.Row(elem_classes='advanced_check_row'): + input_image_checkbox = gr.Checkbox(label='Input Image', value=False, container=False, elem_classes='min_check') + advanced_checkbox = gr.Checkbox(label='Advanced', value=modules.config.default_advanced_checkbox, container=False, elem_classes='min_check') + with gr.Row(visible=False) as image_input_panel: + with gr.Tabs(): + with gr.TabItem(label='Upscale or Variation') as uov_tab: + with gr.Row(): + with gr.Column(): + uov_input_image = grh.Image(label='Drag above image to here', source='upload', type='numpy') + with gr.Column(): + uov_method = gr.Radio(label='Upscale or Variation:', choices=flags.uov_list, value=flags.disabled) + gr.HTML('\U0001F4D4 Document') + with gr.TabItem(label='Image Prompt') as ip_tab: + with gr.Row(): + ip_images = [] + ip_types = [] + ip_stops = [] + ip_weights = [] + ip_ctrls = [] + ip_ad_cols = [] + for _ in range(flags.controlnet_image_count): + with gr.Column(): + ip_image = grh.Image(label='Image', source='upload', type='numpy', show_label=False, height=300) + ip_images.append(ip_image) + ip_ctrls.append(ip_image) + with gr.Column(visible=False) as ad_col: + with gr.Row(): + default_end, default_weight = flags.default_parameters[flags.default_ip] + + ip_stop = gr.Slider(label='Stop At', minimum=0.0, maximum=1.0, step=0.001, value=default_end) + ip_stops.append(ip_stop) + ip_ctrls.append(ip_stop) + + ip_weight = gr.Slider(label='Weight', minimum=0.0, maximum=2.0, step=0.001, value=default_weight) + ip_weights.append(ip_weight) + ip_ctrls.append(ip_weight) + + ip_type = gr.Radio(label='Type', choices=flags.ip_list, value=flags.default_ip, container=False) + ip_types.append(ip_type) + ip_ctrls.append(ip_type) + + ip_type.change(lambda x: flags.default_parameters[x], inputs=[ip_type], outputs=[ip_stop, ip_weight], queue=False, show_progress=False) + ip_ad_cols.append(ad_col) + ip_advanced = gr.Checkbox(label='Advanced', value=False, container=False) + gr.HTML('* \"Image Prompt\" is powered by Fooocus Image Mixture Engine (v1.0.1). \U0001F4D4 Document') + + def ip_advance_checked(x): + return [gr.update(visible=x)] * len(ip_ad_cols) + \ + [flags.default_ip] * len(ip_types) + \ + [flags.default_parameters[flags.default_ip][0]] * len(ip_stops) + \ + [flags.default_parameters[flags.default_ip][1]] * len(ip_weights) + + ip_advanced.change(ip_advance_checked, inputs=ip_advanced, + outputs=ip_ad_cols + ip_types + ip_stops + ip_weights, + queue=False, show_progress=False) + with gr.TabItem(label='Inpaint or Outpaint') as inpaint_tab: + with gr.Row(): + inpaint_input_image = grh.Image(label='Drag inpaint or outpaint image to here', source='upload', type='numpy', tool='sketch', height=500, brush_color="#FFFFFF", elem_id='inpaint_canvas') + inpaint_mask_image = grh.Image(label='Mask Upload', source='upload', type='numpy', height=500, visible=False) + + with gr.Row(): + inpaint_additional_prompt = gr.Textbox(placeholder="Describe what you want to inpaint.", elem_id='inpaint_additional_prompt', label='Inpaint Additional Prompt', visible=False) + outpaint_selections = gr.CheckboxGroup(choices=['Left', 'Right', 'Top', 'Bottom'], value=[], label='Outpaint Direction') + inpaint_mode = gr.Dropdown(choices=modules.flags.inpaint_options, value=modules.flags.inpaint_option_default, label='Method') + example_inpaint_prompts = gr.Dataset(samples=modules.config.example_inpaint_prompts, label='Additional Prompt Quick List', components=[inpaint_additional_prompt], visible=False) + gr.HTML('* Powered by Fooocus Inpaint Engine \U0001F4D4 Document') + example_inpaint_prompts.click(lambda x: x[0], inputs=example_inpaint_prompts, outputs=inpaint_additional_prompt, show_progress=False, queue=False) + with gr.TabItem(label='Describe') as desc_tab: + with gr.Row(): + with gr.Column(): + desc_input_image = grh.Image(label='Drag any image to here', source='upload', type='numpy') + with gr.Column(): + desc_method = gr.Radio( + label='Content Type', + choices=[flags.desc_type_photo, flags.desc_type_anime], + value=flags.desc_type_photo) + desc_btn = gr.Button(value='Describe this Image into Prompt') + gr.HTML('\U0001F4D4 Document') + with gr.TabItem(label='Metadata') as load_tab: + with gr.Column(): + metadata_input_image = grh.Image(label='Drag any image generated by Fooocus here', source='upload', type='filepath') + metadata_json = gr.JSON(label='Metadata') + metadata_import_button = gr.Button(value='Apply Metadata') + + def trigger_metadata_preview(filepath): + parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath) + + results = {} + if parameters is not None: + results['parameters'] = parameters + + if isinstance(metadata_scheme, flags.MetadataScheme): + results['metadata_scheme'] = metadata_scheme.value + + return results + + metadata_input_image.upload(trigger_metadata_preview, inputs=metadata_input_image, + outputs=metadata_json, queue=False, show_progress=True) + + switch_js = "(x) => {if(x){viewer_to_bottom(100);viewer_to_bottom(500);}else{viewer_to_top();} return x;}" + down_js = "() => {viewer_to_bottom();}" + + input_image_checkbox.change(lambda x: gr.update(visible=x), inputs=input_image_checkbox, + outputs=image_input_panel, queue=False, show_progress=False, _js=switch_js) + ip_advanced.change(lambda: None, queue=False, show_progress=False, _js=down_js) + + current_tab = gr.Textbox(value='uov', visible=False) + uov_tab.select(lambda: 'uov', outputs=current_tab, queue=False, _js=down_js, show_progress=False) + inpaint_tab.select(lambda: 'inpaint', outputs=current_tab, queue=False, _js=down_js, show_progress=False) + ip_tab.select(lambda: 'ip', outputs=current_tab, queue=False, _js=down_js, show_progress=False) + desc_tab.select(lambda: 'desc', outputs=current_tab, queue=False, _js=down_js, show_progress=False) + + with gr.Column(scale=1, visible=modules.config.default_advanced_checkbox) as advanced_column: + with gr.Tab(label='Setting'): + performance_selection = gr.Radio(label='Performance', + choices=modules.flags.performance_selections, + value=modules.config.default_performance) + aspect_ratios_selection = gr.Radio(label='Aspect Ratios', choices=modules.config.available_aspect_ratios, + value=modules.config.default_aspect_ratio, info='width × height', + elem_classes='aspect_ratios') + image_number = gr.Slider(label='Image Number', minimum=1, maximum=modules.config.default_max_image_number, step=1, value=modules.config.default_image_number) + + output_format = gr.Radio(label='Output Format', + choices=modules.flags.output_formats, + value=modules.config.default_output_format) + + negative_prompt = gr.Textbox(label='Negative Prompt', show_label=True, placeholder="Type prompt here.", + info='Describing what you do not want to see.', lines=2, + elem_id='negative_prompt', + value=modules.config.default_prompt_negative) + seed_random = gr.Checkbox(label='Random', value=True) + image_seed = gr.Textbox(label='Seed', value=0, max_lines=1, visible=False) # workaround for https://github.com/gradio-app/gradio/issues/5354 + + def random_checked(r): + return gr.update(visible=not r) + + def refresh_seed(r, seed_string): + if r: + return random.randint(constants.MIN_SEED, constants.MAX_SEED) + else: + try: + seed_value = int(seed_string) + if constants.MIN_SEED <= seed_value <= constants.MAX_SEED: + return seed_value + except ValueError: + pass + return random.randint(constants.MIN_SEED, constants.MAX_SEED) + + seed_random.change(random_checked, inputs=[seed_random], outputs=[image_seed], + queue=False, show_progress=False) + + def update_history_link(): + if args_manager.args.disable_image_log: + return gr.update(value='') + + return gr.update(value=f'\U0001F4DA History Log') + + history_link = gr.HTML() + shared.gradio_root.load(update_history_link, outputs=history_link, queue=False, show_progress=False) + + with gr.Tab(label='Style'): + style_sorter.try_load_sorted_styles( + style_names=legal_style_names, + default_selected=modules.config.default_styles) + + style_search_bar = gr.Textbox(show_label=False, container=False, + placeholder="\U0001F50E Type here to search styles ...", + value="", + label='Search Styles') + style_selections = gr.CheckboxGroup(show_label=False, container=False, + choices=copy.deepcopy(style_sorter.all_styles), + value=copy.deepcopy(modules.config.default_styles), + label='Selected Styles', + elem_classes=['style_selections']) + gradio_receiver_style_selections = gr.Textbox(elem_id='gradio_receiver_style_selections', visible=False) + + shared.gradio_root.load(lambda: gr.update(choices=copy.deepcopy(style_sorter.all_styles)), + outputs=style_selections) + + style_search_bar.change(style_sorter.search_styles, + inputs=[style_selections, style_search_bar], + outputs=style_selections, + queue=False, + show_progress=False).then( + lambda: None, _js='()=>{refresh_style_localization();}') + + gradio_receiver_style_selections.input(style_sorter.sort_styles, + inputs=style_selections, + outputs=style_selections, + queue=False, + show_progress=False).then( + lambda: None, _js='()=>{refresh_style_localization();}') + + with gr.Tab(label='Model'): + with gr.Group(): + with gr.Row(): + base_model = gr.Dropdown(label='Base Model (SDXL only)', choices=modules.config.model_filenames, value=modules.config.default_base_model_name, show_label=True) + refiner_model = gr.Dropdown(label='Refiner (SDXL or SD 1.5)', choices=['None'] + modules.config.model_filenames, value=modules.config.default_refiner_model_name, show_label=True) + + refiner_switch = gr.Slider(label='Refiner Switch At', minimum=0.1, maximum=1.0, step=0.0001, + info='Use 0.4 for SD1.5 realistic models; ' + 'or 0.667 for SD1.5 anime models; ' + 'or 0.8 for XL-refiners; ' + 'or any value for switching two SDXL models.', + value=modules.config.default_refiner_switch, + visible=modules.config.default_refiner_model_name != 'None') + + refiner_model.change(lambda x: gr.update(visible=x != 'None'), + inputs=refiner_model, outputs=refiner_switch, show_progress=False, queue=False) + + with gr.Group(): + lora_ctrls = [] + + for i, (n, v) in enumerate(modules.config.default_loras): + with gr.Row(): + lora_enabled = gr.Checkbox(label='Enable', value=True, + elem_classes=['lora_enable', 'min_check'], scale=1) + lora_model = gr.Dropdown(label=f'LoRA {i + 1}', + choices=['None'] + modules.config.lora_filenames, value=n, + elem_classes='lora_model', scale=5) + lora_weight = gr.Slider(label='Weight', minimum=modules.config.default_loras_min_weight, + maximum=modules.config.default_loras_max_weight, step=0.01, value=v, + elem_classes='lora_weight', scale=5) + lora_ctrls += [lora_enabled, lora_model, lora_weight] + + with gr.Row(): + model_refresh = gr.Button(label='Refresh', value='\U0001f504 Refresh All Files', variant='secondary', elem_classes='refresh_button') + with gr.Tab(label='Advanced'): + guidance_scale = gr.Slider(label='Guidance Scale', minimum=1.0, maximum=30.0, step=0.01, + value=modules.config.default_cfg_scale, + info='Higher value means style is cleaner, vivider, and more artistic.') + sharpness = gr.Slider(label='Image Sharpness', minimum=0.0, maximum=30.0, step=0.001, + value=modules.config.default_sample_sharpness, + info='Higher value means image and texture are sharper.') + gr.HTML('\U0001F4D4 Document') + dev_mode = gr.Checkbox(label='Developer Debug Mode', value=False, container=False) + + with gr.Column(visible=False) as dev_tools: + with gr.Tab(label='Debug Tools'): + adm_scaler_positive = gr.Slider(label='Positive ADM Guidance Scaler', minimum=0.1, maximum=3.0, + step=0.001, value=1.5, info='The scaler multiplied to positive ADM (use 1.0 to disable). ') + adm_scaler_negative = gr.Slider(label='Negative ADM Guidance Scaler', minimum=0.1, maximum=3.0, + step=0.001, value=0.8, info='The scaler multiplied to negative ADM (use 1.0 to disable). ') + adm_scaler_end = gr.Slider(label='ADM Guidance End At Step', minimum=0.0, maximum=1.0, + step=0.001, value=0.3, + info='When to end the guidance from positive/negative ADM. ') + + refiner_swap_method = gr.Dropdown(label='Refiner swap method', value=flags.refiner_swap_method, + choices=['joint', 'separate', 'vae']) + + adaptive_cfg = gr.Slider(label='CFG Mimicking from TSNR', minimum=1.0, maximum=30.0, step=0.01, + value=modules.config.default_cfg_tsnr, + info='Enabling Fooocus\'s implementation of CFG mimicking for TSNR ' + '(effective when real CFG > mimicked CFG).') + sampler_name = gr.Dropdown(label='Sampler', choices=flags.sampler_list, + value=modules.config.default_sampler) + scheduler_name = gr.Dropdown(label='Scheduler', choices=flags.scheduler_list, + value=modules.config.default_scheduler) + + generate_image_grid = gr.Checkbox(label='Generate Image Grid for Each Batch', + info='(Experimental) This may cause performance problems on some computers and certain internet conditions.', + value=False) + + overwrite_step = gr.Slider(label='Forced Overwrite of Sampling Step', + minimum=-1, maximum=200, step=1, + value=modules.config.default_overwrite_step, + info='Set as -1 to disable. For developer debugging.') + overwrite_switch = gr.Slider(label='Forced Overwrite of Refiner Switch Step', + minimum=-1, maximum=200, step=1, + value=modules.config.default_overwrite_switch, + info='Set as -1 to disable. For developer debugging.') + overwrite_width = gr.Slider(label='Forced Overwrite of Generating Width', + minimum=-1, maximum=2048, step=1, value=-1, + info='Set as -1 to disable. For developer debugging. ' + 'Results will be worse for non-standard numbers that SDXL is not trained on.') + overwrite_height = gr.Slider(label='Forced Overwrite of Generating Height', + minimum=-1, maximum=2048, step=1, value=-1, + info='Set as -1 to disable. For developer debugging. ' + 'Results will be worse for non-standard numbers that SDXL is not trained on.') + overwrite_vary_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Vary"', + minimum=-1, maximum=1.0, step=0.001, value=-1, + info='Set as negative number to disable. For developer debugging.') + overwrite_upscale_strength = gr.Slider(label='Forced Overwrite of Denoising Strength of "Upscale"', + minimum=-1, maximum=1.0, step=0.001, value=-1, + info='Set as negative number to disable. For developer debugging.') + disable_preview = gr.Checkbox(label='Disable Preview', value=False, + info='Disable preview during generation.') + disable_intermediate_results = gr.Checkbox(label='Disable Intermediate Results', + value=modules.config.default_performance == 'Extreme Speed', + interactive=modules.config.default_performance != 'Extreme Speed', + info='Disable intermediate results during generation, only show final gallery.') + disable_seed_increment = gr.Checkbox(label='Disable seed increment', + info='Disable automatic seed increment when image number is > 1.', + value=False) + + if not args_manager.args.disable_metadata: + save_metadata_to_images = gr.Checkbox(label='Save Metadata to Images', value=modules.config.default_save_metadata_to_images, + info='Adds parameters to generated images allowing manual regeneration.') + metadata_scheme = gr.Radio(label='Metadata Scheme', choices=flags.metadata_scheme, value=modules.config.default_metadata_scheme, + info='Image Prompt parameters are not included. Use png and a1111 for compatibility with Civitai.', + visible=modules.config.default_save_metadata_to_images) + + save_metadata_to_images.change(lambda x: gr.update(visible=x), inputs=[save_metadata_to_images], outputs=[metadata_scheme], + queue=False, show_progress=False) + + with gr.Tab(label='Control'): + debugging_cn_preprocessor = gr.Checkbox(label='Debug Preprocessors', value=False, + info='See the results from preprocessors.') + skipping_cn_preprocessor = gr.Checkbox(label='Skip Preprocessors', value=False, + info='Do not preprocess images. (Inputs are already canny/depth/cropped-face/etc.)') + + mixing_image_prompt_and_vary_upscale = gr.Checkbox(label='Mixing Image Prompt and Vary/Upscale', + value=False) + mixing_image_prompt_and_inpaint = gr.Checkbox(label='Mixing Image Prompt and Inpaint', + value=False) + + controlnet_softness = gr.Slider(label='Softness of ControlNet', minimum=0.0, maximum=1.0, + step=0.001, value=0.25, + info='Similar to the Control Mode in A1111 (use 0.0 to disable). ') + + with gr.Tab(label='Canny'): + canny_low_threshold = gr.Slider(label='Canny Low Threshold', minimum=1, maximum=255, + step=1, value=64) + canny_high_threshold = gr.Slider(label='Canny High Threshold', minimum=1, maximum=255, + step=1, value=128) + + with gr.Tab(label='Inpaint'): + debugging_inpaint_preprocessor = gr.Checkbox(label='Debug Inpaint Preprocessing', value=False) + inpaint_disable_initial_latent = gr.Checkbox(label='Disable initial latent in inpaint', value=False) + inpaint_engine = gr.Dropdown(label='Inpaint Engine', + value=modules.config.default_inpaint_engine_version, + choices=flags.inpaint_engine_versions, + info='Version of Fooocus inpaint model') + inpaint_strength = gr.Slider(label='Inpaint Denoising Strength', + minimum=0.0, maximum=1.0, step=0.001, value=1.0, + info='Same as the denoising strength in A1111 inpaint. ' + 'Only used in inpaint, not used in outpaint. ' + '(Outpaint always use 1.0)') + inpaint_respective_field = gr.Slider(label='Inpaint Respective Field', + minimum=0.0, maximum=1.0, step=0.001, value=0.618, + info='The area to inpaint. ' + 'Value 0 is same as "Only Masked" in A1111. ' + 'Value 1 is same as "Whole Image" in A1111. ' + 'Only used in inpaint, not used in outpaint. ' + '(Outpaint always use 1.0)') + inpaint_erode_or_dilate = gr.Slider(label='Mask Erode or Dilate', + minimum=-64, maximum=64, step=1, value=0, + info='Positive value will make white area in the mask larger, ' + 'negative value will make white area smaller.' + '(default is 0, always process before any mask invert)') + inpaint_mask_upload_checkbox = gr.Checkbox(label='Enable Mask Upload', value=False) + invert_mask_checkbox = gr.Checkbox(label='Invert Mask', value=False) + + inpaint_ctrls = [debugging_inpaint_preprocessor, inpaint_disable_initial_latent, inpaint_engine, + inpaint_strength, inpaint_respective_field, + inpaint_mask_upload_checkbox, invert_mask_checkbox, inpaint_erode_or_dilate] + + inpaint_mask_upload_checkbox.change(lambda x: gr.update(visible=x), + inputs=inpaint_mask_upload_checkbox, + outputs=inpaint_mask_image, queue=False, show_progress=False) + + with gr.Tab(label='FreeU'): + freeu_enabled = gr.Checkbox(label='Enabled', value=False) + freeu_b1 = gr.Slider(label='B1', minimum=0, maximum=2, step=0.01, value=1.01) + freeu_b2 = gr.Slider(label='B2', minimum=0, maximum=2, step=0.01, value=1.02) + freeu_s1 = gr.Slider(label='S1', minimum=0, maximum=4, step=0.01, value=0.99) + freeu_s2 = gr.Slider(label='S2', minimum=0, maximum=4, step=0.01, value=0.95) + freeu_ctrls = [freeu_enabled, freeu_b1, freeu_b2, freeu_s1, freeu_s2] + + def dev_mode_checked(r): + return gr.update(visible=r) + + + dev_mode.change(dev_mode_checked, inputs=[dev_mode], outputs=[dev_tools], + queue=False, show_progress=False) + + def model_refresh_clicked(): + modules.config.update_all_model_names() + results = [gr.update(choices=modules.config.model_filenames)] + results += [gr.update(choices=['None'] + modules.config.model_filenames)] + for i in range(modules.config.default_max_lora_number): + results += [gr.update(interactive=True), gr.update(choices=['None'] + modules.config.lora_filenames), gr.update()] + return results + + model_refresh.click(model_refresh_clicked, [], [base_model, refiner_model] + lora_ctrls, + queue=False, show_progress=False) + + performance_selection.change(lambda x: [gr.update(interactive=x != 'Extreme Speed')] * 11 + + [gr.update(visible=x != 'Extreme Speed')] * 1 + + [gr.update(interactive=x != 'Extreme Speed', value=x == 'Extreme Speed', )] * 1, + inputs=performance_selection, + outputs=[ + guidance_scale, sharpness, adm_scaler_end, adm_scaler_positive, + adm_scaler_negative, refiner_switch, refiner_model, sampler_name, + scheduler_name, adaptive_cfg, refiner_swap_method, negative_prompt, disable_intermediate_results + ], queue=False, show_progress=False) + + output_format.input(lambda x: gr.update(output_format=x), inputs=output_format) + + advanced_checkbox.change(lambda x: gr.update(visible=x), advanced_checkbox, advanced_column, + queue=False, show_progress=False) \ + .then(fn=lambda: None, _js='refresh_grid_delayed', queue=False, show_progress=False) + + def inpaint_mode_change(mode): + assert mode in modules.flags.inpaint_options + + # inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, + # inpaint_disable_initial_latent, inpaint_engine, + # inpaint_strength, inpaint_respective_field + + if mode == modules.flags.inpaint_option_detail: + return [ + gr.update(visible=True), gr.update(visible=False, value=[]), + gr.Dataset.update(visible=True, samples=modules.config.example_inpaint_prompts), + False, 'None', 0.5, 0.0 + ] + + if mode == modules.flags.inpaint_option_modify: + return [ + gr.update(visible=True), gr.update(visible=False, value=[]), + gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts), + True, modules.config.default_inpaint_engine_version, 1.0, 0.0 + ] + + return [ + gr.update(visible=False, value=''), gr.update(visible=True), + gr.Dataset.update(visible=False, samples=modules.config.example_inpaint_prompts), + False, modules.config.default_inpaint_engine_version, 1.0, 0.618 + ] + + inpaint_mode.input(inpaint_mode_change, inputs=inpaint_mode, outputs=[ + inpaint_additional_prompt, outpaint_selections, example_inpaint_prompts, + inpaint_disable_initial_latent, inpaint_engine, + inpaint_strength, inpaint_respective_field + ], show_progress=False, queue=False) + + ctrls = [currentTask, generate_image_grid] + ctrls += [ + prompt, negative_prompt, style_selections, + performance_selection, aspect_ratios_selection, image_number, output_format, image_seed, sharpness, guidance_scale + ] + + ctrls += [base_model, refiner_model, refiner_switch] + lora_ctrls + ctrls += [input_image_checkbox, current_tab] + ctrls += [uov_method, uov_input_image] + ctrls += [outpaint_selections, inpaint_input_image, inpaint_additional_prompt, inpaint_mask_image] + ctrls += [disable_preview, disable_intermediate_results, disable_seed_increment] + ctrls += [adm_scaler_positive, adm_scaler_negative, adm_scaler_end, adaptive_cfg] + ctrls += [sampler_name, scheduler_name] + ctrls += [overwrite_step, overwrite_switch, overwrite_width, overwrite_height, overwrite_vary_strength] + ctrls += [overwrite_upscale_strength, mixing_image_prompt_and_vary_upscale, mixing_image_prompt_and_inpaint] + ctrls += [debugging_cn_preprocessor, skipping_cn_preprocessor, canny_low_threshold, canny_high_threshold] + ctrls += [refiner_swap_method, controlnet_softness] + ctrls += freeu_ctrls + ctrls += inpaint_ctrls + + if not args_manager.args.disable_metadata: + ctrls += [save_metadata_to_images, metadata_scheme] + + ctrls += ip_ctrls + + state_is_generating = gr.State(False) + + def parse_meta(raw_prompt_txt, is_generating): + loaded_json = None + if is_json(raw_prompt_txt): + loaded_json = json.loads(raw_prompt_txt) + + if loaded_json is None: + if is_generating: + return gr.update(), gr.update(), gr.update() + else: + return gr.update(), gr.update(visible=True), gr.update(visible=False) + + return json.dumps(loaded_json), gr.update(visible=False), gr.update(visible=True) + + prompt.input(parse_meta, inputs=[prompt, state_is_generating], outputs=[prompt, generate_button, load_parameter_button], queue=False, show_progress=False) + + load_data_outputs = [advanced_checkbox, image_number, prompt, negative_prompt, style_selections, + performance_selection, overwrite_step, overwrite_switch, aspect_ratios_selection, + overwrite_width, overwrite_height, guidance_scale, sharpness, adm_scaler_positive, + adm_scaler_negative, adm_scaler_end, refiner_swap_method, adaptive_cfg, base_model, + refiner_model, refiner_switch, sampler_name, scheduler_name, seed_random, image_seed, + generate_button, load_parameter_button] + freeu_ctrls + lora_ctrls + + load_parameter_button.click(modules.meta_parser.load_parameter_button_click, inputs=[prompt, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=False) + + def trigger_metadata_import(filepath, state_is_generating): + parameters, metadata_scheme = modules.meta_parser.read_info_from_image(filepath) + if parameters is None: + print('Could not find metadata in the image!') + parsed_parameters = {} + else: + metadata_parser = modules.meta_parser.get_metadata_parser(metadata_scheme) + parsed_parameters = metadata_parser.parse_json(parameters) + + return modules.meta_parser.load_parameter_button_click(parsed_parameters, state_is_generating) + + + metadata_import_button.click(trigger_metadata_import, inputs=[metadata_input_image, state_is_generating], outputs=load_data_outputs, queue=False, show_progress=True) \ + .then(style_sorter.sort_styles, inputs=style_selections, outputs=style_selections, queue=False, show_progress=False) + + generate_button.click(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), [], True), + outputs=[stop_button, skip_button, generate_button, gallery, state_is_generating]) \ + .then(fn=refresh_seed, inputs=[seed_random, image_seed], outputs=image_seed) \ + .then(fn=get_task, inputs=ctrls, outputs=currentTask) \ + .then(fn=generate_clicked, inputs=currentTask, outputs=[progress_html, progress_window, progress_gallery, gallery]) \ + .then(lambda: (gr.update(visible=True, interactive=True), gr.update(visible=False, interactive=False), gr.update(visible=False, interactive=False), False), + outputs=[generate_button, stop_button, skip_button, state_is_generating]) \ + .then(fn=update_history_link, outputs=history_link) \ + .then(fn=lambda: None, _js='playNotification').then(fn=lambda: None, _js='refresh_grid_delayed') + + for notification_file in ['notification.ogg', 'notification.mp3']: + if os.path.exists(notification_file): + gr.Audio(interactive=False, value=notification_file, elem_id='audio_notification', visible=False) + break + + def trigger_describe(mode, img): + if mode == flags.desc_type_photo: + from extras.interrogate import default_interrogator as default_interrogator_photo + return default_interrogator_photo(img), ["Fooocus V2", "Fooocus Enhance", "Fooocus Sharp"] + if mode == flags.desc_type_anime: + from extras.wd14tagger import default_interrogator as default_interrogator_anime + return default_interrogator_anime(img), ["Fooocus V2", "Fooocus Masterpiece"] + return mode, ["Fooocus V2"] + + desc_btn.click(trigger_describe, inputs=[desc_method, desc_input_image], + outputs=[prompt, style_selections], show_progress=True, queue=True) + + +def dump_default_english_config(): + from modules.localization import dump_english_config + dump_english_config(grh.all_components) + + +# dump_default_english_config() + +shared.gradio_root.launch( + inbrowser=args_manager.args.in_browser, + server_name=args_manager.args.listen, + server_port=args_manager.args.port, + share=args_manager.args.share, + auth=check_auth if (args_manager.args.share or args_manager.args.listen) and auth_enabled else None, + allowed_paths=[modules.config.path_outputs], + blocked_paths=[constants.AUTH_FILENAME] +) diff --git a/wildcards/animal.txt b/wildcards/animal.txt new file mode 100644 index 0000000000000000000000000000000000000000..3c479daa0969d10f4a77b133d0fe94024e4f02a9 --- /dev/null +++ b/wildcards/animal.txt @@ -0,0 +1,100 @@ +Alligator +Ant +Antelope +Armadillo +Badger +Bat +Bear +Beaver +Bison +Boar +Bobcat +Bull +Camel +Chameleon +Cheetah +Chicken +Chihuahua +Chimpanzee +Chinchilla +Chipmunk +Komodo Dragon +Cow +Coyote +Crocodile +Crow +Deer +Dinosaur +Dolphin +Donkey +Duck +Eagle +Eel +Elephant +Elk +Emu +Falcon +Ferret +Flamingo +Flying Squirrel +Giraffe +Goose +Guinea pig +Hawk +Hedgehog +Hippopotamus +Horse +Hummingbird +Hyena +Jackal +Jaguar +Jellyfish +Kangaroo +King Cobra +Koala bear +Leopard +Lion +Lizard +Magpie +Marten +Meerkat +Mole +Monkey +Moose +Mouse +Octopus +Okapi +Orangutan +Ostrich +Otter +Owl +Panda +Pangolin +Panther +Penguin +Pig +Porcupine +Possum +Puma +Quokka +Rabbit +Raccoon +Raven +Reindeer +Rhinoceros +Seal +Shark +Sheep +Snail +Snake +Sparrow +Spider +Squirrel +Swallow +Tiger +Walrus +Whale +Wolf +Wombat +Yak +Zebra diff --git a/wildcards/artist.txt b/wildcards/artist.txt new file mode 100644 index 0000000000000000000000000000000000000000..be64c2d3d0d795af40ef694cf85be80a90996975 --- /dev/null +++ b/wildcards/artist.txt @@ -0,0 +1,2765 @@ +A. J. Casson +Aaron Douglas +Aaron Horkey +Aaron Jasinski +Aaron Siskind +Abbott Fuller Graves +Abbott Handerson Thayer +Abigail Larson +Abraham Pether +Abram Efimovich Arkhipov +Adam Elsheimer +Adam Hughes +Adam Martinakis +Adi Granov +Adolf Hirémy-Hirschl +Adolph Gottlieb +Adolph Menzel +Adonna Khare +Adriaen van Ostade +Adriaen van Utrecht +Adrian Donoghue +Adrian Ghenie +Adrian Smith +Adrian Tomine +Adrianus Eversen +Afarin Sajedi +Agnes Cecile +Agnes Lawrence Pelton +Agnes Martin +Agostino Arrivabene +Akihiko Yoshida +Akira Toriyama +Akos Major +Al Capp +Al Feldstein +Al Williamson +Alain Laboile +Alan Bean +Alan Kenny +Alan Lee +Alan Moore +Alan Parry +Alasdair McLellan +Albert Benois +Albert Bierstadt +Albert Dubois-Pillet +Albert Edelfelt +Albert Gleizes +Albert Goodwin +Albert Joseph Moore +Albert Lynch +Albert Marquet +Albert Pinkham Ryder +Alberto Biasi +Alberto Burri +Alberto Giacometti +Alberto Seveso +Alberto Sughi +Albrecht Anker +Albrecht Dürer +Alec Soth +Alejandro Burdisio +Aleksey Savrasov +Aleksi Briclot +Alena Aenami +Alessandro Allori +Alessandro Gottardo +Alessio Albi +Alex Alemany +Alex Andreev +Alex Colville +Alex Garant +Alex Grey +Alex Gross +Alex Hirsch +Alex Horley +Alex Howitt +Alex Katz +Alex Maleev +Alex Prager +Alex Ross +Alex Russell Flint +Alex Timmermans +Alex Toth +Alexander Archipenko +Alexander Jansson +Alexander McQueen +Alexander Millar +Alexandr Averin +Alexandre Benois +Alexandre Cabanel +Alexandre Calame +Alexandre-Évariste Fragonard +Alexej von Jawlensky +Alfred Augustus Glendening +Alfred Cheney Johnston +Alfred Eisenstaedt +Alfred Guillou +Alfred Kubin +Alfred Munnings +Alfred Parsons +Alfred Sisley +Alfred Stevens +Alfredo Jaar +Algernon Blackwood +Alice Bailly +Alice Neel +Alice Pasquini +Alice Rahon +Alison Bechdel +Aliza Razell +Allie Brosh +Alma Woodsey Thomas +Alois Arnegger +Alphonse Mucha +Alphonse Osbert +Alson Skinner Clark +Alvar Aalto +Álvaro Siza +Alvin Langdon Coburn +Alyssa Monks +Amanda Clark +Amanda Sage +Amandine van Ray +Ambrosius Benson +Ambrosius Bosschaert +Amedeo Modigliani +Amy Earles +Amy Judd +Amy Sillman +Anato Finnstark +Anatoly Metlan +Anders Zorn +Ando Fuchs +Andre de Dienes +Andre Derain +André Kertész +Andre Kohn +André Lhote +André Masson +Andre Norton +Andre-Charles Boulle +Andrea Kowch +Andrea Mantegna +Andreas Achenbach +Andreas Franke +Andreas Levers +Andreas Rocha +Andreas Vesalius +Andrei Markin +Andrew Atroshenko +Andrew Ferez +Andrew Macara +Andrew Robinson +Andrew Wyeth +Andrey Remnev +Android Jones +Andy Fairhurst +Andy Goldsworthy +Andy Kehoe +Andy Singer +Andy Warhol +Angela Barrett +Angelina Wrona +Angus McKie +Anja Millen +Anja Percival +Anka Zhuravleva +Ann Stookey +Anna Ancher +Anna Bocek +Anna Dittmann +Anne Bachelier +Anne Brigman +Anne Dewailly +Anne Geddes +Anne McCaffrey +Anne Packard +Anne Rothenstein +Anne Stokes +Anne Truitt +Anne-Louis Girodet +Anni Albers +Annibale Carracci +Annick Bouvattier +Annie Leibovitz +Annie Soudain +Annie Swynnerton +Ansel Adams +Anselm Kiefer +Antanas Sutkus +Anthony Thieme +Anthony van Dyck +Antoine Blanchard +Anton Fadeev +Anton Mauve +Anton Otto Fischer +Anton Pieck +Anton Raphael Mengs +Anton Semenov +Antonello da Messina +Antoni Gaudí +Antonio Canova +Antonio J. Manzanedo +Antonio Mancini +Antonio Mora +Antony Gormley +Apollinary Vasnetsov +Apollonia Saintclair +Aquirax Uno +Archibald Thorburn +Aries Moross +Aristarkh Lentulov +Aristide Maillol +Armand Guillaumin +Armand Point +Arnold Böcklin +Aron Demetz +Aron Wiesenfeld +Arshile Gorky +Art Frahm +Art Spiegelman +Artemisia Gentileschi +Arthur Adams +Arthur Boyd +Arthur Dove +Arthur Elgort +Arthur Hacker +Arthur Hughes +Arthur Lismer +Arthur Rackham +Arthur Sarnoff +Arthur Stanley Wilkinson +Arthur Streeton +Arthur Tress +Arthur Wardle +Artur Bordalo +Arturo Souto +Ary Scheffer +Asaf Hanuka +Asger Jorn +Asher Brown Durand +Ashley Wood +Atelier Olschinsky +Atey Ghailan +Aubrey Beardsley +Audrey Kawasaki +August Friedrich Schenck +August Macke +August Sander +Auguste Herbin +Auguste Toulmouche +Augustus Edwin Mulready +Augustus John +Austin Briggs +Austin Osman Spare +Ayami Kojima +Barbara Hepworth +Barbara Kruger +Barbara Stauffacher Solomon +Barbara Takenaga +Barclay Shaw +Barkley L. Hendricks +Barnett Newman +Barry McGee +Barry Windsor Smith +Bart Sears +Bartolomé Esteban Murillo +Basil Gogos +Bastien Lecouffe-Deharme +Bayard Wu +Beatrix Potter +Beauford Delaney +Becky Cloonan +Bella Kotak +Ben Aronson +Ben Goossens +Ben Hatke +Ben Nicholson +Ben Quilty +Ben Shahn +Ben Templesmith +Benedick Bana +Benoit B. Mandelbrot +Bernard Buffet +Bernardo Bellotto +Bernardo Strozzi +Berndnaut Smilde +Bernie Wrightson +Bert Hardy +Bert Stern +Berthe Morisot +Bertil Nilsson +Beth Conklin +Bettina Rheims +Bhupen Khakhar +Bill Brandt +Bill Brauer +Bill Carman +Bill Durgin +Bill Gekas +Bill Henson +Bill Jacklin +Bill Medcalf +Bill Sienkiewicz +Bill Traylor +Bill Viola +Bill Ward +Bill Watterson +Billy Childish +Bjarke Ingels +Bo Bartlett +Bo Chen +Bob Byerley +Bob Clampett +Bob Eggleton +Bob Ross +Bojan Jevtic +Boris Grigoriev +Boris Groh +Boris Kustodiev +Boris Vallejo +Brad Kunkle +Brad Rigney +Brandon Mably +Brandon Woelfel +Brent Heighton +Brett Weston +Brett Whiteley +Brian Bolland +Brian Despain +Brian Froud +Brian K. Vaughan +Brian Kesinger +Brian M. Viveros +Brian Mashburn +Brian Stelfreeze +Brian Sum +Briana Mora +Brice Marden +Bridget Bate Tichenor +Bridget Riley +Briton Rivière +Brooke DiDonato +Brooke Shaden +Brothers Grimm +Brothers Hildebrandt +Bruce Davidson +Bruce Munro +Bruce Nauman +Bruce Pennington +Bruce Timm +Bruno Catalano +Bruno Munari +Bruno Walpoth +Bryan Hitch +Buckminster Fuller +Burt Glinn +Butcher Billy +C. R. W. Nevinson +Cai Guo-Qiang +Caia Koopman +Callie Fink +Camilla d'Errico +Camille Claudel +Camille Corot +Camille Pissarro +Camille Walala +Candido Portinari +Caras Ionut +Carel Willink +Carl Barks +Carl Gustav Carus +Carl Holsoe +Carl Kleiner +Carl Larsson +Carl Moll +Carl Spitzweg +Carlo Crivelli +Carlo Dolci +Carlo Scarpa +Carlos Cruz-Diez +Carlos Schwabe +Carne Griffiths +Carolina Herrera +Carolyn Blish +Carrie Ann Baade +Carrie Graber +Carrie Mae Weems +Carsten Holler +Carsten Meyerdierks +Casey Baugh +Casey Childs +Casey Weldon +Caspar David Friedrich +Catherine Hyde +Catherine Nolin +Cathy Wilkes +Catrin Welz-Stein +Cecil Beaton +Cecilia Beaux +Cecily Brown +Cedric Seaut +Cerith Wyn Evans +CFA Voysey +Chad Knight +Chaïm Soutine +Chantal Joffe +Charles Addams +Charles Angrand +Charles Blackman +Charles Burns +Charles Camoin +Charles Courtney Curran +Charles Demuth +Charles Dwyer +Charles E. 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Wyeth +N/A Affandi +N/A Archillect +N/A Artgerm +N/A Balthus +N/A Banksy +N/A Beeple +N/A Bordalo II +N/A Brassaï +N/A Bronzino +N/A Byam Shaw +N/A Canaletto +N/A Caravaggio +N/A Craola +N/A Domenichino +N/A Duccio +N/A El Greco +N/A Giorgione +N/A Giotto +N/A Guercino +N/A Hergé +N/A Hiroshige +N/A HUSH +N/A Kunisada +N/A Kurzgesagt +N/A Masaccio +N/A Michelangelo +N/A Moebius +N/A OSGEMEOS +N/A Parmigianino +N/A Pinturicchio +N/A Pontormo +N/A Raphael +N/A RHADS +N/A ROA +N/A Rone +N/A Sardax +N/A Sparth +N/A teamLab +N/A theCHAMBA +N/A Tintoretto +N/A Titian +N/A Tom of Finland +N/A Yuumei +Nadav Kander +Nam June Paik +Nan Goldin +Naoki Urasawa +Naoko Takeuchi +Naomi Okubo +Naomi Tydeman +Naoto Hattori +Natalia Drepina +Natalia Goncharova +Natalia Rak +Natalie Shau +Nathan Coley +Nathan Spoor +Nathan Wirth +ND Stevenson +Neal Adams +Neil Gaiman +Neil Welliver +Nele Zirnite +Nell Dorr +Nelleke Pieters +Nicholas Hely Hutchinson +Nicholas Hilliard +Nicholas Hughes +Nicholas Roerich +Nick Alm +Nick Knight +Nick Veasey +Nick Walker +Nickolas Muray +Nicola Samori +Nicolaes Maes +Nicolaes van Verendael +Nicolas Bruno +Nicolas de Stael +Nicolas Delort +Nicolas Mignard +Nicolas Poussin +Nicole Eisenman +Nicoletta Ceccoli +Nigel van Wieck +Nike Savvas +Nikolai Ge +Nikolai Lockertsen +Nikolay Makovsky +Nikolina Petolas +Nina Leen +Njideka Akunyili Crosby +Noah Bradley +Nobuyoshi Araki +Noell Oszvald +Nora Heysen +Noriyoshi Ohrai +Norman Ackroyd +Norman Bluhm +Norman Cornish +Norman Foster +Norman Lindsay +Norman Parkinson +Norman Rockwell +Octavio Ocampo +Odd Nerdrum +Odilon Redon +Ohara Koson +Okuda San Miguel +Olafur Eliasson +Oleg Oprisco +Oleg Shuplyak +Olive Cotton +Oliver Jeffers +Oliver Wetter +Olivia Locher +Olivier Bonhomme +Olivier Valsecchi +Orazio Gentileschi +Osamu Tezuka +Oscar Niemeyer +Oskar Fischinger +Oskar Kokoschka +Oskar Schlemmer +Ossip Zadkine +Oswaldo Guayasamin +Otto Dix +Otto Marseus van Schrieck +Otto Piene +Pablo Picasso +Pam Hawkes +Pamela Colman Smith +Paolo Roversi +Paolo Soleri +Paolo Uccello +Paolo Veronese +Pascale Campion +Pat Steir +Patrice Murciano +Patricia Piccinini +Patricia Polacco +Patrick Brown +Patrick Caulfield +Patrick Demarchelier +Patrick Dougherty +Patrick Heron +Patrick McHale +Patrick Nagel +Patrick Woodroffe +Patty Maher +Paul Barson +Paul Cadmus +Paul Catherall +Paul Cézanne +Paul Chabas +Paul Corfield +Paul Delvaux +Paul Fusco +Paul Gauguin +Paul Gustav Fischer +Paul Hedley +Paul Henry +Paul Klee +Paul Laffoley +Paul Lehr +Paul Lovering +Paul Nash +Paul Pelletier +Paul Poiret +Paul Rader +Paul Rand +Paul Ranson +Paul Rudolph +Paul Sérusier +Paul Signac +Paul Strand +Paul Wonner +Paula Modersohn-Becker +Paula Rego +Paula Scher +Paulus Potter +Pawel Kuczynski +Peder Balke +Peder Mork Monsted +Pegi Nicol MacLeod +Pendleton Ward +Percy Tarrant +Peregrine Heathcote +Perry Rhodan +Pete Turner +Peter Andrew Jones +Peter Bagge +Peter Basch +Peter Blake +Peter Coulson +Peter Doig +Peter Eisenman +Peter Elson +Peter Gric +Peter Holme III +Peter Howson +Peter Kemp +Peter Lely +Peter Lindbergh +Peter Lippmann +Peter Max +Peter Milligan +Peter Mitchev +Peter Mohrbacher +Peter Paul Rubens +Peter Saville +Peter Sculthorpe +Peter Sedgley +Peter Wileman +Peter Zumthor +Petra Cortright +Petrina Hicks +Phil Foglio +Phil Jimenez +Phil Koch +Phil Noto +Philip Guston +Philip McKay +Philip Pearlstein +Philip Taaffe +Philip Treacy +Philip Wilson Steer +Philip-Lorca diCorcia +Philipp Otto Runge +Philippe Buchet +Philippe de Champaigne +Philippe Druillet +Philippe Halsman +Philippe Parreno +Phoebe Anna Traquair +Phyllida Barlow +Piero della Francesca +Piero di Cosimo +Pierre Bonnard +Pierre Huyghe +Pierre Pellegrini +Pierre Puvis de Chavannes +Pierre Soulages +Pierre-Auguste Renoir +Piet Hein Eek +Piet Mondrian +Pieter Aertsen +Pieter Bruegel the Elder +Pieter Brueghel the Younger +Pieter Claesz +Pieter de Hooch +Pieter Hugo +Pieter Jansz Saenredam +Pieter Nason +Pieter-Jansz van Asch +Pietro Antonio Rotari +Pietro da Cortona +Pietro da Rimini +Pino Daeni +Pipilotti Rist +PJ Crook +Pol Ledent +Polixeni Papapetrou +Pompeo Batoni +Posuka Demizu +Prudence Heward +Q Hayashida +Qian Xuan +Quentin Blake +Quint Buchholz +R. Kenton Nelson +Rachel Maclean +Rachel Ruysch +Rachel Whiteread +Rafael Albuquerque +Rafael Zabaleta +Ragnar Kjartansson +Raimonds Staprans +Raimundo de Madrazo y Garreta +Raina Telgemeier +Ralph Bakshi +Ralph Blakelock +Ralph Horsley +Ralph McQuarrie +Ralph Steadman +Ramon Casas +Randolph Caldecott +Randolph Stanley Hewton +Range Murata +Raoul De Keyser +Raoul Dufy +Raphael Kirchner +Raphael Lacoste +Raphael Soyer +Raphaelle Peale +Rashad Alakbarov +Ravi Zupa +Ray Caesar +Ray Collins +Ray Donley +Ray Eames +Ray Metzker +Raymond Briggs +Raymond Duchamp-Villon +Raymond Leech +Raymond Pettibon +Raymond Swanland +Raynald Leclerc +Rebeca Saray +Rebecca Guay +Rebecca Louise Law +Rebecca Sugar +Regina Valluzzi +Reginald Marsh +Rembrandt van Rijn +Remedios Varo +Ren Hang +Renato Guttuso +Rene Burri +René Lalique +Rene Magritte +Reylia Slaby +Ricardo Bofill +Richard Anderson +Richard Bergh +Richard Billingham +Richard Burlet +Richard Corben +Richard Dadd +Richard Deacon +Richard Diebenkorn +Richard Doyle +Richard E. Miller +Richard Eurich +Richard Hamilton +Richard Lindner +Richard Long +Richard McGuire +Richard Meier +Richard Misrach +Richard Mosse +Richard Parkes Bonington +Richard Phillips +Richard Pousette-Dart +Richard S. Johnson +Richard Scarry +Richard Schmid +Richard Serra +Richard Tuttle +Rick Amor +Rick Guidice +Rick Owens +Rimel Neffati +Rinko Kawauchi +Rob Browning +Rob Gonsalves +Rob Guillory +Rob Hefferan +Rob Liefeld +Robby Cavanaugh +Robert Antoine Pinchon +Robert Bateman +Robert Bissell +Robert Campin +Robert Crumb +Robert Delaunay +Robert Gillmor +Robert Hagan +Robert Henri +Robert Indiana +Robert Irwin +Robert Mapplethorpe +Robert McCall +Robert McGinnis +Robert Motherwell +Robert Rauschenberg +Robert S. Duncanson +Robert Smithson +Robert Stivers +Robert Vonnoh +Robert Williams +Roberto Ferri +Roberto Matta +Rockwell Kent +Rodney Matthews +Rodney Smith +Roger de La Fresnaye +Roger Deakins +Roger Dean +Rogier van der Weyden +Roland Topor +Rolf Armstrong +Romaine Brooks +Roman Vishniac +Romare Bearden +Romero Britto +Romina Ressia +Ron Arad +Ron English +Ron Mueck +Ron Walotsky +Ronald Balfour +Ronald Searle +Ronald Wimberly +Roni Horn +Rosa Bonheur +Rosalba Carriera +Rose Wylie +Ross Tran +Roxy Paine +Roy DeCarava +Roy Lichtenstein +Roz Chast +Ruan Jia +Rudolf Ernst +Rudy Siswanto +Rufino Tamayo +Rui Palha +Rumiko Takahashi +Rupert Bunny +Rupert Vandervell +Rupi Kaur +Ruslan Lobanov +Russ Mills +Russell Dauterman +Ruth Bernhard +Ruth Orkin +Ryan Hewett +Ryan McGinley +Ryan Ottley +Ryan Stegman +Ryohei Fuke +Ryohei Hase +Ryoji Ikeda +Sabbas Apterus +Sacha Goldberger +Sailor Moon +Sally Mann +Sally Storch +Salomon van Ruysdael +Salvador Dalí +Salvator Rosa +Sam Bosma +Sam Francis +Sam Gilliam +Sam Guay +Sam Kieth +Sam Spratt +Sam Toft +Samantha Keely Smith +Samuel Melton Fisher +Samuel Peploe +Samuel van Hoogstraten +Sandra Chevrier +Sandra Dieckmann +Sandro Botticelli +Sandy Skoglund +Sanford Robinson Gifford +Santiago Calatrava +Santiago Caruso +Santiago Rusinol +Sarah Andersen +Sarah Lucas +Sarah Morris +Sarah Sze +Satoshi Kon +Saturno Butto +Saul Leiter +Saul Steinberg +Scarlett Hooft Graafland +Scott Kolins +Scott Listfield +Scott McCloud +Scott Naismith +Scott Rohlfs +Sean Scully +Sean Yoro +Sebastian Errazuriz +Sebastiano del Piombo +Serge Attukwei Clottey +Serge Marshennikov +Serge Najjar +Sergey Musin +Sergio Aragonés +Sergio Larraín +Sergio Toppi +Seth Globepainter +Shaun Tan +Sheila Hicks +Shepard Fairey +Sherree Valentine Daines +Sherry Akrami +Shigenori Soejima +Shilin Huang +Shinji Aramaki +Shintaro Kago +Shirin Neshat +Shohei Otomo +Shotaro Ishinomori +Shozo Shimamoto +Shuzo Oshimi +Sidney Nolan +Sidney Sime +Signe Vilstrup +Silvestro Lega +Simeon Solomon +Simon Birch +Simon Bisley +Simon Prades +Simon Stalenhag +Simon Vouet +Simone Bianchi +Sir James Guthrie +Siya Oum +Skottie Young +Slawomir Maniak +Slim Aarons +Sofonisba Anguissola +Sol LeWitt +Sonia Delaunay +Sopheap Pich +Sophie Anderson +Sou Fujimoto +Spencer Tunick +Squeak Carnwath +Stan Berenstain +Stanhope Forbes +Stanisław Szukalski +Stanisław Wyspiański +Stanley Donwood +Stanley Kubrick +Stanley Spencer +Stanley William Hayter +Stasia Burrington +Stefan Gesell +Stephan Martinière +Stephanie Rew +Stephen Darbishire +Stephen Gammell +Stephen Hillenburg +Stephen Mackey +Stephen Ormandy +Stephen Shore +Stephen Shortridge +Stephen Wiltshire +Stephen Youll +Steve Argyle +Steve Dillon +Steve Ditko +Steve Epting +Steve Hanks +Steve Henderson +Steve Hillier +Steve Lieber +Steve McCurry +Steve Sack +Steven Holl +Steven Klein +Steven Meisel +Steven Outram +Storm Thorgerson +Stuart Davis +Stuart Haygarth +Stuart Immonen +Subodh Gupta +Sudersan Pattnaik +Sui Ishida +Susan Seddon Boulet +Suzanne Valadon +Sven Nordqvist +Sverre Fehn +Syd Mead +Sydney Prior Hall +Tadao Ando +Taiyō Matsumoto +Takashi Murakami +Takato Yamamoto +Takeshi Obata +Talbot Hughes +Tamara de Lempicka +Tami Bone +Tanya Shatseva +Tara McPherson +Taras Loboda +Tarsila do Amaral +Tatiana Suarez +Tatsuo Miyajima +Tatsuro Kiuchi +Taylor Wessing +Ted Nasmith +Temmie Chang +Terada Katsuya +Terry Dodson +Terry O'Neill +Terry Oakes +Terry Redlin +Tetsuya Nomura +Teun Hocks +Tex Avery +Theo van Doesburg +Theo van Rysselberghe +Theodor Kittelsen +Théodore Chassériau +Théodore Géricault +Theodore Robinson +Théophile Steinlen +Thiago Valdi +Thom Mayne +Thomas Barbey +Thomas Benjamin Kennington +Thomas Birch +Thomas Blackshear +Thomas Cole +Thomas Dewing +Thomas Dodd +Thomas Eakins +Thomas Edwin Mostyn +Thomas Gainsborough +Thomas Hart Benton +Thomas Heatherwick +Thomas Kinkade +Thomas Lawrence +Thomas Leuthard +Thomas Moran +Thomas Nast +Thomas Saliot +Thomas Sully +Thomas W Schaller +Thomas Wrede +Thurston Hopkins +Tiago Hoisel +Tibor Nagy +Tiffany Bozic +Tim Burton +Tim Doyle +Tim Etchells +Tim Hildebrandt +Tim Holtz +Tim Okamura +Tim Shumate +Tim Walker +Tim White +Timothy Easton +Titus Kaphar +TJ Drysdale +Toby Fox +Todd Hido +Todd McFarlane +Todd Nauck +Todd Schorr +Tokujin Yoshioka +Tom Bagshaw +Tom Chambers +Tom Everhart +Tom Fruin +Tom Grummett +Tom Hammick +Tom Killion +Tom Roberts +Tom Thomson +Tom Wesselmann +Tom Whalen +Tomás Saraceno +Tomasz Alen Kopera +Tomer Hanuka +Tomma Abts +Tomokazu Matsuyama +Ton Dubbeldam +Toni Frissell +Tony Conrad +Tony Cragg +Tony DiTerlizzi +Tony Fitzpatrick +Tony Moore +Tony Orrico +Tony Oursler +Tony Sart +Tooth Wu +Toshiko Okanoue +Toshio Saeki +Tove Jansson +Toyo Ito +Tracey Emin +Tracie Grimwood +Tran Nguyen +Travis Louie +Trevor Brown +Trish Mistric +Tristan Eaton +Troy Brooks +Truls Espedal +Tsutomu Nihei +Tyler Edlin +Tyler Rayburn +Tyler Shields +Ub Iwerks +Uemura Shoen +Umberto Boccioni +Ursula von Rydingsvard +Utagawa Kuniyoshi +Valentin de Boulogne +Valentin Rekunenko +Valentin Serov +Valerie Hegarty +Valerio Olgiati +Vanessa Beecroft +Vanessa Bell +Vania Zouravliov +Vasily Vereshchagin +Verner Panton +Veronika Pinke +Vicente Romero Redondo +Victo Ngai +Victor Brauner +Victor Enrich +Victor Horta +Victor Moscoso +Victor Nizovtsev +Victor Pasmore +Victor Vasarely +Victoria Crowe +Victoria Selbach +Vija Celmins +Vik Muniz +Viktor Vasnetsov +Vilhelm Hammershoi +Vincent Callebaut +Vincent Desiderio +Vincent Di Fate +Vincent van Gogh +Virgil Finlay +Virginia Frances Sterrett +Vito Acconci +Vittorio Matteo Corcos +Vittorio Reggianini +Vivian Maier +Viviane Sassen +Vivienne Westwood +Vladimir Kush +Vladimir Tatlin +Vladimir Volegov +Vytautas Kairiukstis +W. Eugene Smith +W. Heath Robinson +Wade Guyton +Wadim Kashin +Walker Evans +Walt Disney +Walt Kelly +Walter Crane +Walter Ernest Webster +Walter Gropius +Walter Langley +Walter Launt Palmer +Walter Percy Day +Walter Sickert +Wangechi Mutu +Warren Ellis +Warwick Goble +Wassily Kandinsky +Wayne Barlowe +Wayne Thiebaud +Wendy Froud +Wes Anderson +Wifredo Lam +Wilhelmina Barns-Graham +Will Barnet +Will Eisner +Willard Metcalf +Willem Basse +Willem Claesz. Heda +Willem de Kooning +Willem Haenraets +Willem Kalf +Willem van Aelst +Willem van Haecht +Willi Baumeister +William Blake +William Dyce +William Eggleston +William Etty +William Gropper +William Henry Hunt +William Henry Margetson +William Hogarth +William Holman Hunt +William James Glackens +William Kay Blacklock +William Kentridge +William Klein +William Langson Lathrop +William Larkin +William Morris +William Nicholson +William Oxer +William Powell Frith +William Russell Flint +William S. Burroughs +William Stanley Haseltine +William Steig +William Stout +William Timlin +William Trost Richards +William Turner +William Wegman +William Wendt +William Whitaker +William Wray +William Zorach +William-Adolphe Bouguereau +Wim Delvoye +Wim Wenders +Winifred Knights +Winslow Homer +Winsor McCay +Wlad Safronow +Wolfgang Suschitzky +Wong Kar-wai +Worthington Whittredge +Wu Guanzhong +Xiaofei Yue +Xu Bing +Yaacov Agam +Yanjun Cheng +Yasuo Kuniyoshi +Yasushi Nirasawa +Yayoi Kusama +Yiannis Moralis +Yigal Ozeri +Yinka Shonibare +Yoann Lossel +Yoh Nagao +Yoji Shinkawa +Yoshitaka Amano +Yoshiyuki Tomino +Yosuke Ueno +Yuko Shimizu +Yuri Shwedoff +Yusuke Murata +Yves Klein +Yves Tanguy +Yvonne Coomber +Zack Snyder +Zaha Hadid +Zdzisław Beksiński +Zena Holloway +Zhang Jingna +Zhang Kechun +Zhichao Cai +Zinaida Serebriakova +Zoe Buckman diff --git a/wildcards/color.txt b/wildcards/color.txt new file mode 100644 index 0000000000000000000000000000000000000000..467898638cec80b44589f0d70e1bd555d2b28ac1 --- /dev/null +++ b/wildcards/color.txt @@ -0,0 +1,17 @@ +aqua +black +blue +fuchsia +gray +green +lime +maroon +navy +olive +orange +purple +red +silver +teal +white +yellow diff --git a/wildcards/color_flower.txt b/wildcards/color_flower.txt new file mode 100644 index 0000000000000000000000000000000000000000..bce7a3c6c9015e635794cde4f85b95bb99344044 --- /dev/null +++ b/wildcards/color_flower.txt @@ -0,0 +1 @@ +__color__ __flower__ diff --git a/wildcards/extended-color.txt b/wildcards/extended-color.txt new file mode 100644 index 0000000000000000000000000000000000000000..04cf7af72a6a85a01f523ccc8a37a4a9c399e98c --- /dev/null +++ b/wildcards/extended-color.txt @@ -0,0 +1,147 @@ +aliceblue +antiquewhite +aqua +aquamarine +azure +beige +bisque +black +blanchedalmond +blue +blueviolet +brown +burlywood +cadetblue +chartreuse +chocolate +coral +cornflowerblue +cornsilk +crimson +cyan +darkblue +darkcyan +darkgoldenrod +darkgray +darkgreen +darkgrey +darkkhaki +darkmagenta +darkolivegreen +darkorange +darkorchid +darkred +darksalmon +darkseagreen +darkslateblue +darkslategray +darkslategrey +darkturquoise +darkviolet +deeppink +deepskyblue +dimgray +dimgrey +dodgerblue +firebrick +floralwhite +forestgreen +fuchsia +gainsboro +ghostwhite +gold +goldenrod +gray +green +greenyellow +grey +honeydew +hotpink +indianred +indigo +ivory +khaki +lavender +lavenderblush +lawngreen +lemonchiffon +lightblue +lightcoral +lightcyan +lightgoldenrodyellow +lightgray +lightgreen +lightgrey +lightpink +lightsalmon +lightseagreen +lightskyblue +lightslategray +lightslategrey +lightsteelblue +lightyellow +lime +limegreen +linen +magenta +maroon +mediumaquamarine +mediumblue +mediumorchid +mediumpurple +mediumseagreen +mediumslateblue +mediumspringgreen +mediumturquoise +mediumvioletred +midnightblue +mintcream +mistyrose +moccasin +navajowhite +navy +oldlace +olive +olivedrab +orange +orangered +orchid +palegoldenrod +palegreen +paleturquoise +palevioletred +papayawhip +peachpuff +peru +pink +plum +powderblue +purple +red +rosybrown +royalblue +saddlebrown +salmon +sandybrown +seagreen +seashell +sienna +silver +skyblue +slateblue +slategray +slategrey +snow +springgreen +steelblue +tan +teal +thistle +tomato +turquoise +violet +wheat +white +whitesmoke +yellow +yellowgreen diff --git a/wildcards/flower.txt b/wildcards/flower.txt new file mode 100644 index 0000000000000000000000000000000000000000..bb3e984b6f2a9e3d661e084627001f3d42209d5c --- /dev/null +++ b/wildcards/flower.txt @@ -0,0 +1,250 @@ +Acacia +Achillea +Adam's-needle +African Boxwood +African Lily +Agapanthus +Ageratum +Ageratum houstonim +Allium +Alpina +Alstroemeria +Amaranthus hypochondriacus +Amaryllis +Ammi majus +Anconitum +Anemone +Anigozanthus +Annual Delphinium +Anthurium +Antirrhinum majus +Artichoke thistle +Asparagus +Aster +Astilbe +Baby's Breath +Bachelor's Button +Banksia +Bellflower +Big Flax +Bighead Knapweed +Billy Buttons +Bird of Paradise +Blazing Star +Blue Lace Flower +Boronia +Bouvardia +Boxwood African +Diosma +Buckthorn Variegated +Buddleia +Bupleurum +Butterfly Bush +Butterfly Orchid +Calla Lily +Campanula +Candytuft +Canterbury Bells +Carnation +Carthamus +Casa Blanca +Caspia +Cattleya +Celosia +Celosia argenta +Centaurea cyanus +Chamelaucium +Chimney Bells +Chrysanthemum +Chrysanthemum x morifolium +Clarkia +Cockscomb Crested +Coffee Bean Berry +Common Myrtle +Common Yarrow +Cone Flower +Consolida ambigua +Convallaria +Cordyline +Cosmos +Cornflower +Craspedia +Curly Willow +Cymbidium +Cymbidium Orchid +Daffodil +Dahlia +Daisy Mums +Delphinium Belladonna +Delphinium Pacific Giant +Dendrobium +Dendrobium Orchid +Dianthus barbatus +Dianthus caryophyllus +Dianthus caryophyllus nana +Erica spp +Eucalyptus seeded +Eucalyptus silver dollar +Eustoma grandiflorum +False Bird of Paradise +False Spirea +Farewell-To-Spring +Fernleaf Yarrow +Feverfew +Flamingo Flower +Flax New Zealand +Floss Flower +Foxtail Fern +Freesia +Freesia x hybrida +Fuji Mums +Gardenia +Gay Feather +Genista +Gerbera +Gerbera Ruby Red +Ginger +Gladiolus +Gladiolus hybrid nanus +Goat's Beard +Godetia +Golden Rod +Guersney Lily +Gyp +Gypsophila paniculata +Hanging Helicona +Heath +Heather +Helianthus annuus +Heliconia spp. +Hippeastrum +Hydrangea +Iberis amara +Inca Lily +Iris +Japhette Orchid +Jonquil +Knapweed +Lace fern +Larkspur +Lathyrus odoratus +Lavandula +Lavender +Liatris +Lilac +Lily +Lilly-of-the-Valley +Lily Casa Blanca +Lily of the Field +Lily of the Nile +Lily Stargazer +Limonium +Lisianthus +Marguerite daisy +Mattholia incana +Melaleuca +Memosa +Misty Blue Limonium +Moluccella laevis +Monkshood +Montbretia +Monte Cassino +Moon orchid +Musa +Myrsine +Myrtle +Myrtus +Nephrolepis +Nerine +Nerine Lily +Nigella +Ornithogalum +Paeonia +Painted Tongue +Paper Reed +Papyrus lion's head +Peony +Peruvian Lily +Phalaenopsis +Philodendron +Phlox +Pincushion Flower +Pink Mink +Pitt +Pittosporum +Pixie Carnation +Polianthes tuberosa +Pompon Chrysanthemum +Poppy Anemone +Porium +Pussy Willow +Queen Anne's Lace +Ranunculus +Red Ribbons +Rice flower +Rose +Rose Bridal Pink +Rose Bridal White +Rose Champagne +Rose Diadem +Rose Emblem +Rose Kardinal +Rose Lady Liberty +Rose Lavanda +Rose Osiana +Rose Royalty +Safari Sunset +Safflower +Sage Perennial +Salix +Salmon Reagan +Sansevieria +Saponaria +Satin Flowers +Saxicola +Scabiosa +Schinus +Sea lavender +Shell Flowers +Snake Plant +Snapdragon +Solidago +Solidaster +Speedwell +Spider Lily +Spider Mums +Spray Carnation +Sprengeri Fern +Star of Bethlehem +Statice +Stenamezon +Stephanotis +Strawberry banksia +Strawflower +Summer poinsettia +Summer's Darling +Sunflower +Sweet Pea +Sweet William +Sword Fern +Syringa vulgaris +Tailflowers +Tassel flower +Thouroughwax +Throatwort +Tracelium +Tree Fern +Trumpet Lily +Tuberose +Tulip +Tulipa +Veronica +Wattle +Waxflower +Wild Plantain +Willow curly +Windflower +Wolfsbane +Zantedeschia +Zinna +Zinnia elegans diff --git a/wildcards/nationality.txt b/wildcards/nationality.txt new file mode 100644 index 0000000000000000000000000000000000000000..a8ae3a57732b15137752df54fdb930f92b6024b6 --- /dev/null +++ b/wildcards/nationality.txt @@ -0,0 +1,195 @@ +Afghan +Albanian +Algerian +American +Andorran +Angolan +Antiguans +Argentine +Armenian +Australian +Austrian +Azerbaijani +Bahamian +Bahraini +Bangladeshi +Barbadian +Barbudans +Batswana +Belarusian +Belgian +Belizean +Beninese +Bhutanese +Bolivian +Bosnian +Brazilian +British +Bruneian +Bulgarian +Burkinabe +Burmese +Burundian +Cambodian +Cameroonian +Canadian +Cape Verdean +Central African +Chadian +Chilean +Chinese +Colombian +Comoran +Congolese +Costa Rican +Croatian +Cuban +Cypriot +Czech +Danish +Djibouti +Dominican +Dutch +East Timorese +Ecuadorean +Egyptian +Emirati +Equatorial Guinean +Eritrean +Estonian +Ethiopian +Fijian +Filipino +Finnish +French +Gabonese +Gambian +Georgian +German +Ghanaian +Greek +Grenadian +Guatemalan +Guinea-Bissauan +Guinean +Guyanese +Haitian +Herzegovinian +Honduran +Hungarian +Icelander +Indian +Indonesian +Iranian +Iraqi +Irish +Israeli +Italian +Ivorian +Jamaican +Japanese +Jordanian +Kazakhstani +Kenyan +Kittian and Nevisian +Kuwaiti +Kyrgyz +Laotian +Latvian +Lebanese +Liberian +Libyan +Liechtensteiner +Lithuanian +Luxembourger +Macedonian +Malagasy +Malawian +Malaysian +Maldivan +Malian +Maltese +Marshallese +Mauritanian +Mauritian +Mexican +Micronesian +Moldovan +Monacan +Mongolian +Montenegrin +Moroccan +Mosotho +Motswana +Mozambican +Namibian +Nauruan +Nepalese +New Zealander +Nicaraguan +Nigerian +Nigerien +North Korean +Northern Irish +Norwegian +Omani +Pakistani +Palauan +Palestinian +Panamanian +Papua New Guinean +Paraguayan +Peruvian +Polish +Portuguese +Qatari +Romanian +Russian +Rwandan +Saint Lucian +Salvadoran +Samoan +San Marinese +Sao Tomean +Saudi +Scottish +Senegalese +Serbian +Seychellois +Sierra Leonean +Singaporean +Slovakian +Slovenian +Solomon Islander +Somali +South African +South Korean +Spanish +Sri Lankan +Sudanese +Surinamer +Swazi +Swedish +Swiss +Syrian +Taiwanese +Tajik +Tanzanian +Thai +Togolese +Tongan +Trinidadian or Tobagonian +Tunisian +Turkish +Tuvaluan +Ugandan +Ukrainian +Uruguayan +Uzbekistani +Vanuatuan +Venezuelan +Vietnamese +Welsh +Yemenite +Zambian +Zimbabwean