Upload folder using huggingface_hub

.eslintignore

@@ -0,0 +1,4 @@
+extensions
+extensions-disabled
+repositories
+venv

.eslintrc.js

@@ -0,0 +1,91 @@
+/* global module */
+module.exports = {
+    env: {
+        browser: true,
+        es2021: true,
+    },
+    extends: "eslint:recommended",
+    parserOptions: {
+        ecmaVersion: "latest",
+    },
+    rules: {
+        "arrow-spacing": "error",
+        "block-spacing": "error",
+        "brace-style": "error",
+        "comma-dangle": ["error", "only-multiline"],
+        "comma-spacing": "error",
+        "comma-style": ["error", "last"],
+        "curly": ["error", "multi-line", "consistent"],
+        "eol-last": "error",
+        "func-call-spacing": "error",
+        "function-call-argument-newline": ["error", "consistent"],
+        "function-paren-newline": ["error", "consistent"],
+        "indent": ["error", 4],
+        "key-spacing": "error",
+        "keyword-spacing": "error",
+        "linebreak-style": ["error", "unix"],
+        "no-extra-semi": "error",
+        "no-mixed-spaces-and-tabs": "error",
+        "no-multi-spaces": "error",
+        "no-redeclare": ["error", {builtinGlobals: false}],
+        "no-trailing-spaces": "error",
+        "no-unused-vars": "off",
+        "no-whitespace-before-property": "error",
+        "object-curly-newline": ["error", {consistent: true, multiline: true}],
+        "object-curly-spacing": ["error", "never"],
+        "operator-linebreak": ["error", "after"],
+        "quote-props": ["error", "consistent-as-needed"],
+        "semi": ["error", "always"],
+        "semi-spacing": "error",
+        "semi-style": ["error", "last"],
+        "space-before-blocks": "error",
+        "space-before-function-paren": ["error", "never"],
+        "space-in-parens": ["error", "never"],
+        "space-infix-ops": "error",
+        "space-unary-ops": "error",
+        "switch-colon-spacing": "error",
+        "template-curly-spacing": ["error", "never"],
+        "unicode-bom": "error",
+    },
+    globals: {
+        //script.js
+        gradioApp: "readonly",
+        executeCallbacks: "readonly",
+        onAfterUiUpdate: "readonly",
+        onOptionsChanged: "readonly",
+        onUiLoaded: "readonly",
+        onUiUpdate: "readonly",
+        uiCurrentTab: "writable",
+        uiElementInSight: "readonly",
+        uiElementIsVisible: "readonly",
+        //ui.js
+        opts: "writable",
+        all_gallery_buttons: "readonly",
+        selected_gallery_button: "readonly",
+        selected_gallery_index: "readonly",
+        switch_to_txt2img: "readonly",
+        switch_to_img2img_tab: "readonly",
+        switch_to_img2img: "readonly",
+        switch_to_sketch: "readonly",
+        switch_to_inpaint: "readonly",
+        switch_to_inpaint_sketch: "readonly",
+        switch_to_extras: "readonly",
+        get_tab_index: "readonly",
+        create_submit_args: "readonly",
+        restart_reload: "readonly",
+        updateInput: "readonly",
+        //extraNetworks.js
+        requestGet: "readonly",
+        popup: "readonly",
+        // from python
+        localization: "readonly",
+        // progrssbar.js
+        randomId: "readonly",
+        requestProgress: "readonly",
+        // imageviewer.js
+        modalPrevImage: "readonly",
+        modalNextImage: "readonly",
+        // token-counters.js
+        setupTokenCounters: "readonly",
+    }
+};

.git-blame-ignore-revs

@@ -0,0 +1,2 @@
+# Apply ESlint
+9c54b78d9dde5601e916f308d9a9d6953ec39430

.gitattributes

@@ -33,3 +33,32 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+extensions/Stable-Diffusion-Webui-Civitai-Helper/img/all_in_one.png filter=lfs diff=lfs merge=lfs -text
+models/Lora/141887\?type=Model filter=lfs diff=lfs merge=lfs -text
+repositories/BLIP/BLIP.gif filter=lfs diff=lfs merge=lfs -text
+repositories/generative-models/assets/sdxl_report.pdf filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-inpainting/merged-leopards.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/d2i.gif filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img01.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/depth2img02.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0000.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0004.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/depth2img/merged-0005.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-in.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/img2img/upscaling-out.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/stable-unclip/unclip-variations_noise.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0001.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0002.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0003.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0004.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0005.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/768/merged-0006.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0001.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0003.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0005.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0006.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/txt2img/merged-0007.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/merged-dog.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/sampled-bear-x4.png filter=lfs diff=lfs merge=lfs -text
+repositories/stable-diffusion-stability-ai/assets/stable-samples/upscaling/snow-leopard-x4.png filter=lfs diff=lfs merge=lfs -text

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,138 @@
+name: Bug Report
+description: You think somethings is broken in the UI
+title: "[Bug]: "
+labels: ["bug-report"]
+
+body:
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed 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, don't forget to fill "What OS..." and "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
+    validations:
+      required: true
+  - type: textarea
+    id: steps
+    attributes:
+      label: Steps to reproduce the problem
+      description: Please provide us with precise step by step information on how to reproduce the bug
+      value: |
+        1. Go to .... 
+        2. Press ....
+        3. ...
+    validations:
+      required: true
+  - type: textarea
+    id: what-should
+    attributes:
+      label: What should have happened?
+      description: Tell what you think the normal behavior should be
+    validations:
+      required: true
+  - type: input
+    id: commit
+    attributes:
+      label: Version or Commit where the problem happens
+      description: "Which webui version or commit are you running ? (Do not write *Latest Version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Version: v1.2.3** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)"
+    validations:
+      required: true
+  - type: dropdown
+    id: py-version
+    attributes:
+      label: What Python version are you running on ?
+      multiple: false
+      options:
+        - Python 3.10.x
+        - Python 3.11.x (above, no supported yet)
+        - Python 3.9.x (below, no recommended)
+  - type: dropdown
+    id: platforms
+    attributes:
+      label: What platforms do you use to access the UI ?
+      multiple: true
+      options:
+        - Windows
+        - Linux
+        - MacOS
+        - iOS
+        - Android
+        - Other/Cloud
+  - type: dropdown
+    id: device
+    attributes:
+        label: What device are you running WebUI on?
+        multiple: true
+        options:
+        - Nvidia GPUs (RTX 20 above)
+        - Nvidia GPUs (GTX 16 below)
+        - AMD GPUs (RX 6000 above)
+        - AMD GPUs (RX 5000 below)
+        - CPU
+        - Other GPUs
+  - type: dropdown
+    id: cross_attention_opt
+    attributes:
+      label: Cross attention optimization
+      description: What cross attention optimization are you using, Settings -> Optimizations -> Cross attention optimization
+      multiple: false
+      options:
+        - Automatic
+        - xformers
+        - sdp-no-mem
+        - sdp
+        - Doggettx
+        - V1 
+        - InvokeAI
+        - "None "
+    validations:
+      required: true
+  - type: dropdown
+    id: browsers
+    attributes:
+      label: What browsers do you use to access the UI ?
+      multiple: true
+      options:
+        - Mozilla Firefox
+        - Google Chrome
+        - Brave
+        - Apple Safari
+        - Microsoft Edge
+  - type: textarea
+    id: cmdargs
+    attributes:
+      label: Command Line Arguments
+      description: Are you using any launching parameters/command line arguments (modified webui-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
+      render: Shell
+    validations:
+      required: true
+  - type: textarea
+    id: extensions
+    attributes:
+      label: List of extensions
+      description: Are you using any extensions other than built-ins? If yes, provide a list, you can copy it at "Extensions" tab. Write "No" otherwise.
+    validations:
+      required: true
+  - 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 your bug happened. 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.

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,5 @@
+blank_issues_enabled: false
+contact_links:
+  - name: WebUI Community Support
+    url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
+    about: Please ask and answer questions here.

.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"]
+
+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.

.github/pull_request_template.md

@@ -0,0 +1,15 @@
+## Description
+
+* a simple description of what you're trying to accomplish
+* a summary of changes in code
+* which issues it fixes, if any
+
+## Screenshots/videos:
+
+
+## Checklist:
+
+- [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
+- [ ] I have performed a self-review of my own code
+- [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style)
+- [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests)

.github/workflows/on_pull_request.yaml

@@ -0,0 +1,38 @@
+name: Linter
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  lint-python:
+    name: ruff
+    runs-on: ubuntu-latest
+    if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+          # NB: there's no cache: pip here since we're not installing anything
+          #     from the requirements.txt file(s) in the repository; it's faster
+          #     not to have GHA download an (at the time of writing) 4 GB cache
+          #     of PyTorch and other dependencies.
+      - name: Install Ruff
+        run: pip install ruff==0.0.272
+      - name: Run Ruff
+        run: ruff .
+  lint-js:
+    name: eslint
+    runs-on: ubuntu-latest
+    if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Install Node.js
+        uses: actions/setup-node@v3
+        with:
+          node-version: 18
+      - run: npm i --ci
+      - run: npm run lint

.github/workflows/run_tests.yaml

@@ -0,0 +1,73 @@
+name: Tests
+
+on:
+  - push
+  - pull_request
+
+jobs:
+  test:
+    name: tests on CPU with empty model
+    runs-on: ubuntu-latest
+    if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.10.6
+          cache: pip
+          cache-dependency-path: |
+            **/requirements*txt
+            launch.py
+      - name: Install test dependencies
+        run: pip install wait-for-it -r requirements-test.txt
+        env:
+          PIP_DISABLE_PIP_VERSION_CHECK: "1"
+          PIP_PROGRESS_BAR: "off"
+      - name: Setup environment
+        run: python launch.py --skip-torch-cuda-test --exit
+        env:
+          PIP_DISABLE_PIP_VERSION_CHECK: "1"
+          PIP_PROGRESS_BAR: "off"
+          TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
+          WEBUI_LAUNCH_LIVE_OUTPUT: "1"
+          PYTHONUNBUFFERED: "1"
+      - name: Start test server
+        run: >
+          python -m coverage run
+          --data-file=.coverage.server
+          launch.py
+          --skip-prepare-environment
+          --skip-torch-cuda-test
+          --test-server
+          --do-not-download-clip
+          --no-half
+          --disable-opt-split-attention
+          --use-cpu all
+          --api-server-stop
+          2>&1 | tee output.txt &
+      - name: Run tests
+        run: |
+          wait-for-it --service 127.0.0.1:7860 -t 600
+          python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
+      - name: Kill test server
+        if: always()
+        run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
+      - name: Show coverage
+        run: |
+          python -m coverage combine .coverage*
+          python -m coverage report -i
+          python -m coverage html -i
+      - name: Upload main app output
+        uses: actions/upload-artifact@v3
+        if: always()
+        with:
+          name: output
+          path: output.txt
+      - name: Upload coverage HTML
+        uses: actions/upload-artifact@v3
+        if: always()
+        with:
+          name: htmlcov
+          path: htmlcov

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

.github/workflows/warns_merge_master.yml

@@ -0,0 +1,19 @@
+name: Pull requests can't target master branch
+
+"on":
+  pull_request:
+    types:
+      - opened
+      - synchronize
+      - reopened
+    branches:
+      - master
+
+jobs:
+  check:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Warning marge into master
+        run: |
+          echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch."
+          exit 1

.gitignore

@@ -0,0 +1,39 @@
+__pycache__
+*.ckpt
+*.safetensors
+*.pth
+/ESRGAN/*
+/SwinIR/*
+/repositories
+/venv
+/tmp
+/model.ckpt
+/models/**/*
+/GFPGANv1.3.pth
+/gfpgan/weights/*.pth
+/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.mp3
+/SwinIR
+/textual_inversion
+.vscode
+/extensions
+/test/stdout.txt
+/test/stderr.txt
+/cache.json*
+/config_states/
+/node_modules
+/package-lock.json
+/.coverage*

.ipynb_checkpoints/webui-user-checkpoint.sh

@@ -0,0 +1,48 @@
+# #!/bin/bash
+#########################################################
+# Uncomment and change the variables below to your need:#
+#########################################################
+
+# Install directory without trailing slash
+install_dir="/workspace"
+
+# Name of the subdirectory
+#clone_dir="stable-diffusion-webui"
+
+# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS="--medvram --opt-split-attention"
+export COMMANDLINE_ARGS="--xformers --port 3000 --listen --enable-insecure-extension-access --no-half-vae --share"
+#export XFORMERS_PACKAGE="xformers==0.0.17.dev447"
+
+# python3 executable
+#python_cmd="python3"
+
+# git executable
+#export GIT="git"
+
+# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)
+venv_dir="/workspace/venv"
+
+# script to launch to start the app
+#export LAUNCH_SCRIPT="launch.py"
+
+# install command for torch
+# export TORCH_COMMAND="pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118"
+
+# Requirements file to use for stable-diffusion-webui
+#export REQS_FILE="./extensions/sd_dreambooth_extension/requirements.txt"
+
+# Fixed git repos
+#export K_DIFFUSION_PACKAGE=""
+#export GFPGAN_PACKAGE=""
+
+# Fixed git commits
+#export STABLE_DIFFUSION_COMMIT_HASH=""
+#export TAMING_TRANSFORMERS_COMMIT_HASH=""
+#export CODEFORMER_COMMIT_HASH=""
+#export BLIP_COMMIT_HASH=""
+
+# Uncomment to enable accelerated launch
+#export ACCELERATE="True"
+
+###########################################
+

.pylintrc

@@ -0,0 +1,3 @@
+# See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
+[MESSAGES CONTROL]
+disable=C,R,W,E,I

CHANGELOG.md

@@ -0,0 +1,352 @@
+## 1.5.1
+
+### Minor:
+ * support parsing text encoder blocks in some new LoRAs
+ * delete scale checker script due to user demand
+
+### Extensions and API:
+ * add postprocess_batch_list script callback
+
+### Bug Fixes:
+ * fix TI training for SD1
+ * fix reload altclip model error
+ * prepend the pythonpath instead of overriding it
+ * fix typo in SD_WEBUI_RESTARTING
+ * if txt2img/img2img raises an exception, finally call state.end()
+ * fix composable diffusion weight parsing
+ * restyle Startup profile for black users
+ * fix webui not launching with --nowebui
+ * catch exception for non git extensions
+ * fix some options missing from /sdapi/v1/options
+ * fix for extension update status always saying "unknown"
+ * fix display of extra network cards that have `<>` in the name
+ * update lora extension to work with python 3.8
+
+
+## 1.5.0
+
+### Features:
+ * SD XL support
+ * user metadata system for custom networks
+ * extended Lora metadata editor: set activation text, default weight, view tags, training info
+ * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
+ * show github stars for extenstions
+ * img2img batch mode can read extra stuff from png info
+ * img2img batch works with subdirectories
+ * hotkeys to move prompt elements: alt+left/right
+ * restyle time taken/VRAM display
+ * add textual inversion hashes to infotext
+ * optimization: cache git extension repo information
+ * move generate button next to the generated picture for mobile clients
+ * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface
+ * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds
+
+### Minor:
+ * checkbox to check/uncheck all extensions in the Installed tab
+ * add gradio user to infotext and to filename patterns
+ * allow gif for extra network previews
+ * add options to change colors in grid
+ * use natural sort for items in extra networks
+ * Mac: use empty_cache() from torch 2 to clear VRAM
+ * added automatic support for installing the right libraries for Navi3 (AMD)
+ * add option SWIN_torch_compile to accelerate SwinIR upscale
+ * suppress printing TI embedding info at start to console by default
+ * speedup extra networks listing
+ * added `[none]` filename token.
+ * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)
+ * add always_discard_next_to_last_sigma option to XYZ plot
+ * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.
+ 
+### Extensions and API:
+ * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop
+ * allow Script to have custom metaclass
+ * add model exists status check /sdapi/v1/options
+ * rename --add-stop-route to --api-server-stop
+ * add `before_hr` script callback
+ * add callback `after_extra_networks_activate`
+ * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable
+ * return http 404 when thumb file not found
+ * allow replacing extensions index with environment variable
+ 
+### Bug Fixes:
+ * fix for catch errors when retrieving extension index #11290
+ * fix very slow loading speed of .safetensors files when reading from network drives
+ * API cache cleanup
+ * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode
+ * fix warning of 'has_mps' deprecated from PyTorch
+ * fix problem with extra network saving images as previews losing generation info
+ * fix throwing exception when trying to resize image with I;16 mode
+ * fix for #11534: canvas zoom and pan extension hijacking shortcut keys
+ * fixed launch script to be runnable from any directory
+ * don't add "Seed Resize: -1x-1" to API image metadata
+ * correctly remove end parenthesis with ctrl+up/down
+ * fixing --subpath on newer gradio version
+ * fix: check fill size none zero when resize  (fixes #11425)
+ * use submit and blur for quick settings textbox
+ * save img2img batch with images.save_image()
+ * prevent running preload.py for disabled extensions
+ * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included
+
+
+## 1.4.1
+
+### Bug Fixes:
+ * add queue lock for refresh-checkpoints
+
+## 1.4.0
+
+### Features:
+ * zoom controls for inpainting
+ * run basic torch calculation at startup in parallel to reduce the performance impact of first generation
+ * option to pad prompt/neg prompt to be same length
+ * remove taming_transformers dependency
+ * custom k-diffusion scheduler settings
+ * add an option to show selected settings in main txt2img/img2img UI
+ * sysinfo tab in settings
+ * infer styles from prompts when pasting params into the UI
+ * an option to control the behavior of the above
+
+### Minor:
+ * bump Gradio to 3.32.0
+ * bump xformers to 0.0.20
+ * Add option to disable token counters
+ * tooltip fixes & optimizations
+ * make it possible to configure filename for the zip download
+ * `[vae_filename]` pattern for filenames
+ * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE
+ * change UI reorder setting to multiselect
+ * read version info form CHANGELOG.md if git version info is not available
+ * link footer API to Wiki when API is not active
+ * persistent conds cache (opt-in optimization)
+ 
+### Extensions:
+ * After installing extensions, webui properly restarts the process rather than reloads the UI 
+ * Added VAE listing to web API. Via: /sdapi/v1/sd-vae
+ * custom unet support
+ * Add onAfterUiUpdate callback
+ * refactor EmbeddingDatabase.register_embedding() to allow unregistering
+ * add before_process callback for scripts
+ * add ability for alwayson scripts to specify section and let user reorder those sections
+ 
+### Bug Fixes:
+ * Fix dragging text to prompt
+ * fix incorrect quoting for infotext values with colon in them
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
+ * Fix s_min_uncond default type int
+ * Fix for #10643 (Inpainting mask sometimes not working)
+ * fix bad styling for thumbs view in extra networks #10639
+ * fix for empty list of optimizations #10605
+ * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility
+ * fix --ui-debug-mode exit
+ * patch GitPython to not use leaky persistent processes
+ * fix duplicate Cross attention optimization after UI reload
+ * torch.cuda.is_available() check for SdOptimizationXformers
+ * fix hires fix using wrong conds in second pass if using Loras.
+ * handle exception when parsing generation parameters from png info
+ * fix upcast attention dtype error
+ * forcing Torch Version to 1.13.1 for RX 5000 series GPUs
+ * split mask blur into X and Y components, patch Outpainting MK2 accordingly
+ * don't die when a LoRA is a broken symlink
+ * allow activation of Generate Forever during generation
+
+
+## 1.3.2
+
+### Bug Fixes:
+ * fix files served out of tmp directory even if they are saved to disk
+ * fix postprocessing overwriting parameters
+
+## 1.3.1
+
+### Features:
+ * revert default cross attention optimization to Doggettx
+
+### Bug Fixes:
+ * fix bug: LoRA don't apply on dropdown list sd_lora
+ * fix png info always added even if setting is not enabled
+ * fix some fields not applying in xyz plot
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
+ * fix lora hashes not being added properly to infotex if there is only one lora
+ * fix --use-cpu failing to work properly at startup
+ * make --disable-opt-split-attention command line option work again
+
+## 1.3.0
+
+### Features:
+ * add UI to edit defaults
+ * token merging (via dbolya/tomesd)
+ * settings tab rework: add a lot of additional explanations and links
+ * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup
+ * update extensions table: show branch, show date in separate column, and show version from tags if available
+ * TAESD - another option for cheap live previews
+ * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
+ * calculate hashes for Lora
+ * add lora hashes to infotext
+ * when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
+ * select cross attention optimization from UI
+
+### Minor:
+ * bump Gradio to 3.31.0
+ * bump PyTorch to 2.0.1 for macOS and Linux AMD
+ * allow setting defaults for elements in extensions' tabs
+ * allow selecting file type for live previews
+ * show "Loading..." for extra networks when displaying for the first time
+ * suppress ENSD infotext for samplers that don't use it
+ * clientside optimizations
+ * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories
+ * allow whitespace in styles.csv
+ * add option to reorder tabs
+ * move some functionality (swap resolution and set seed to -1) to client
+ * option to specify editor height for img2img
+ * button to copy image resolution into img2img width/height sliders
+ * switch from pyngrok to ngrok-py
+ * lazy-load images in extra networks UI
+ * set "Navigate image viewer with gamepad" option to false by default, by request
+ * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
+ * allow hiding buttons in ui-config.json
+
+### Extensions:
+ * add /sdapi/v1/script-info api
+ * use Ruff to lint Python code
+ * use ESlint to lint Javascript code
+ * add/modify CFG callbacks for Self-Attention Guidance extension
+ * add command and endpoint for graceful server stopping
+ * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
+ * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously)
+ * add /sdapi/v1/refresh-loras api checkpoint post request
+ * tests overhaul
+
+### Bug Fixes:
+ * fix an issue preventing the program from starting if the user specifies a bad Gradio theme
+ * fix broken prompts from file script
+ * fix symlink scanning for extra networks
+ * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS
+ * allow web UI to be ran fully offline
+ * fix inability to run with --freeze-settings
+ * fix inability to merge checkpoint without adding metadata
+ * fix extra networks' save preview image not adding infotext for jpeg/webm
+ * remove blinking effect from text in hires fix and scale resolution preview
+ * make links to `http://<...>.git` extensions work in the extension tab
+ * fix bug with webui hanging at startup due to hanging git process
+
+
+## 1.2.1
+
+### Features:
+ * add an option to always refer to LoRA by filenames
+
+### Bug Fixes:
+ * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none
+ * fix upscalers disappearing after the user reloads UI
+ * allow bf16 in safe unpickler (resolves problems with loading some LoRAs)
+ * allow web UI to be ran fully offline
+ * fix localizations not working
+ * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'`
+
+## 1.2.0
+
+### Features:
+ * do not wait for Stable Diffusion model to load at startup
+ * add filename patterns: `[denoising]`
+ * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
+ * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metdata of the file, if present, instead of filename (both can be used to activate LoRA)
+ * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
+ * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
+ * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
+ * add version to infotext, footer and console output when starting
+ * add links to wiki for filename pattern settings
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
+
+### Minor:
+ * bump Gradio to 3.29.0
+ * bump PyTorch to 2.0.1
+ * `--subpath` option for gradio for use with reverse proxy
+ * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
+ * do not apply localizations if there are none (possible frontend optimization)
+ * add extra `None` option for VAE in XYZ plot
+ * print error to console when batch processing in img2img fails
+ * create HTML for extra network pages only on demand
+ * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc
+ * put infotext options into their own category in settings tab
+ * do not show licenses page when user selects Show all pages in settings
+
+### Extensions:
+ * tooltip localization support
+ * add API method to get LoRA models with prompt
+
+### Bug Fixes:
+ * re-add `/docs` endpoint
+ * fix gamepad navigation
+ * make the lightbox fullscreen image function properly
+ * fix squished thumbnails in extras tab
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
+ * fix webui showing the same image if you configure the generation to always save results into same file
+ * fix bug with upscalers not working properly
+ * fix MPS on PyTorch 2.0.1, Intel Macs
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
+ * fix prompts from file script failing to read contents from a drag/drop file
+
+
+## 1.1.1
+### Bug Fixes:
+ * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle
+
+## 1.1.0
+### Features:
+ * switch to PyTorch 2.0.0 (except for AMD GPUs)
+ * visual improvements to custom code scripts
+ * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducability
+ * automatically select current word when adjusting weight with ctrl+up/down
+ * add dropdowns for X/Y/Z plot
+ * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
+ * support Gradio's theme API
+ * use TCMalloc on Linux by default; possible fix for memory leaks
+ * add optimization option to remove negative conditioning at low sigma values #9177
+ * embed model merge metadata in .safetensors file
+ * extension settings backup/restore feature #9169
+ * add "resize by" and "resize to" tabs to img2img
+ * add option "keep original size" to textual inversion images preprocess
+ * image viewer scrolling via analog stick
+ * button to restore the progress from session lost / tab reload
+
+### Minor:
+ * bump Gradio to 3.28.1
+ * change "scale to" to sliders in Extras tab
+ * add labels to tool buttons to make it possible to hide them
+ * add tiled inference support for ScuNET
+ * add branch support for extension installation
+ * change Linux installation script to install into current directory rather than `/home/username`
+ * sort textual inversion embeddings by name (case-insensitive)
+ * allow styles.csv to be symlinked or mounted in docker
+ * remove the "do not add watermark to images" option
+ * make selected tab configurable with UI config
+ * make the extra networks UI fixed height and scrollable
+ * add `disable_tls_verify` arg for use with self-signed certs
+
+### Extensions:
+ * add reload callback
+ * add `is_hr_pass` field for processing
+
+### Bug Fixes:
+ * fix broken batch image processing on 'Extras/Batch Process' tab
+ * add "None" option to extra networks dropdowns
+ * fix FileExistsError for CLIP Interrogator
+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319
+ * fix disappearing live previews and progressbar during slow tasks
+ * fix fullscreen image view not working properly in some cases
+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
+ * fix prompt schedule for second order samplers
+ * fix image mask/composite for weird resolutions #9628
+ * use correct images for previews when using AND (see #9491)
+ * one broken image in img2img batch won't stop all processing
+ * fix image orientation bug in train/preprocess
+ * fix Ngrok recreating tunnels every reload
+ * fix `--realesrgan-models-path` and `--ldsr-models-path` not working
+ * fix `--skip-install` not working
+ * use SAMPLE file format in Outpainting Mk2 & Poorman
+ * do not fail all LoRAs if some have failed to load when making a picture
+
+## 1.0.0
+  * everything

CODEOWNERS

@@ -0,0 +1,12 @@
+*       @AUTOMATIC1111
+
+# if you were managing a localization and were removed from this file, this is because
+# the intended way to do localizations now is via extensions. See:
+# https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
+# Make a repo with your localization and since you are still listed as a collaborator
+# you can add it to the wiki page yourself. This change is because some people complained
+# the git commit log is cluttered with things unrelated to almost everyone and
+# because I believe this is the best overall for the project to handle localizations almost
+# entirely without my oversight.
+
+

LICENSE.txt

@@ -0,0 +1,663 @@
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README.md

@@ -1,12 +1,179 @@
 ---
-title: SD Webui
-emoji: 👁
-colorFrom: blue
-colorTo: red
+title: SD_webui
+app_file: webui.py
 sdk: gradio
 sdk_version: 3.40.1
-app_file: app.py
-pinned: false
 ---
+# Stable Diffusion web UI
+A browser interface based on Gradio library for Stable Diffusion.
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+![](screenshot.png)
+
+## Features
+[Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
+- Original txt2img and img2img modes
+- One click install and run script (but you still must install python and git)
+- Outpainting
+- Inpainting
+- Color Sketch
+- Prompt Matrix
+- Stable Diffusion Upscale
+- Attention, specify parts of text that the model should pay more attention to
+    - a man in a `((tuxedo))` - will pay more attention to tuxedo
+    - a man in a `(tuxedo:1.21)` - alternative syntax
+    - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user)
+- Loopback, run img2img processing multiple times
+- X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
+- Textual Inversion
+    - have as many embeddings as you want and use any names you like for them
+    - use multiple embeddings with different numbers of vectors per token
+    - works with half precision floating point numbers
+    - train embeddings on 8GB (also reports of 6GB working)
+- Extras tab with:
+    - GFPGAN, neural network that fixes faces
+    - CodeFormer, face restoration tool as an alternative to GFPGAN
+    - RealESRGAN, neural network upscaler
+    - ESRGAN, neural network upscaler with a lot of third party models
+    - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
+    - LDSR, Latent diffusion super resolution upscaling
+- Resizing aspect ratio options
+- Sampling method selection
+    - Adjust sampler eta values (noise multiplier)
+    - More advanced noise setting options
+- Interrupt processing at any time
+- 4GB video card support (also reports of 2GB working)
+- Correct seeds for batches
+- Live prompt token length validation
+- Generation parameters
+     - parameters you used to generate images are saved with that image
+     - in PNG chunks for PNG, in EXIF for JPEG
+     - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
+     - can be disabled in settings
+     - drag and drop an image/text-parameters to promptbox
+- Read Generation Parameters Button, loads parameters in promptbox to UI
+- Settings page
+- Running arbitrary python code from UI (must run with `--allow-code` to enable)
+- Mouseover hints for most UI elements
+- Possible to change defaults/mix/max/step values for UI elements via text config
+- Tiling support, a checkbox to create images that can be tiled like textures
+- Progress bar and live image generation preview
+    - Can use a separate neural network to produce previews with almost none VRAM or compute requirement
+- Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
+- Styles, a way to save part of prompt and easily apply them via dropdown later
+- Variations, a way to generate same image but with tiny differences
+- Seed resizing, a way to generate same image but at slightly different resolution
+- CLIP interrogator, a button that tries to guess prompt from an image
+- Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
+- Batch Processing, process a group of files using img2img
+- Img2img Alternative, reverse Euler method of cross attention control
+- Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
+- Reloading checkpoints on the fly
+- Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
+- [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
+- [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
+     - separate prompts using uppercase `AND`
+     - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
+- No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
+- DeepDanbooru integration, creates danbooru style tags for anime prompts
+- [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
+- via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
+- Generate forever option
+- Training tab
+     - hypernetworks and embeddings options
+     - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
+- Clip skip
+- Hypernetworks
+- Loras (same as Hypernetworks but more pretty)
+- A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt 
+- Can select to load a different VAE from settings screen
+- Estimated completion time in progress bar
+- API
+- Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
+- via extension: [Aesthetic Gradients](https://github.com/AUTOMATIC1111/stable-diffusion-webui-aesthetic-gradients), a way to generate images with a specific aesthetic by using clip images embeds (implementation of [https://github.com/vicgalle/stable-diffusion-aesthetic-gradients](https://github.com/vicgalle/stable-diffusion-aesthetic-gradients))
+- [Stable Diffusion 2.0](https://github.com/Stability-AI/stablediffusion) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#stable-diffusion-20) for instructions
+- [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
+- Now without any bad letters!
+- Load checkpoints in safetensors format
+- Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
+- Now with a license!
+- Reorder elements in the UI from settings screen
+
+## Installation and Running
+Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for both [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended) and [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
+
+Alternatively, use online services (like Google Colab):
+
+- [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
+
+### Installation on Windows 10/11 with NVidia-GPUs using release package
+1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
+2. Run `update.bat`.
+3. Run `run.bat`.
+> For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
+
+### Automatic Installation on Windows
+1. Install [Python 3.10.6](https://www.python.org/downloads/release/python-3106/) (Newer version of Python does not support torch), checking "Add Python to PATH".
+2. Install [git](https://git-scm.com/download/win).
+3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
+4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
+
+### Automatic Installation on Linux
+1. Install the dependencies:
+```bash
+# Debian-based:
+sudo apt install wget git python3 python3-venv
+# Red Hat-based:
+sudo dnf install wget git python3
+# Arch-based:
+sudo pacman -S wget git python3
+```
+2. Navigate to the directory you would like the webui to be installed and execute the following command:
+```bash
+bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
+```
+3. Run `webui.sh`.
+4. Check `webui-user.sh` for options.
+### Installation on Apple Silicon
+
+Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
+
+## Contributing
+Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
+
+## Documentation
+
+The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
+
+For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).
+
+## Credits
+Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
+
+- Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
+- k-diffusion - https://github.com/crowsonkb/k-diffusion.git
+- GFPGAN - https://github.com/TencentARC/GFPGAN.git
+- CodeFormer - https://github.com/sczhou/CodeFormer
+- ESRGAN - https://github.com/xinntao/ESRGAN
+- SwinIR - https://github.com/JingyunLiang/SwinIR
+- Swin2SR - https://github.com/mv-lab/swin2sr
+- LDSR - https://github.com/Hafiidz/latent-diffusion
+- MiDaS - https://github.com/isl-org/MiDaS
+- Ideas for optimizations - https://github.com/basujindal/stable-diffusion
+- Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
+- Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
+- Sub-quadratic Cross Attention layer optimization - Alex Birch (https://github.com/Birch-san/diffusers/pull/1), Amin Rezaei (https://github.com/AminRezaei0x443/memory-efficient-attention)
+- Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
+- Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
+- Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
+- CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
+- Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
+- xformers - https://github.com/facebookresearch/xformers
+- DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
+- Sampling in float32 precision from a float16 UNet - marunine for the idea, Birch-san for the example Diffusers implementation (https://github.com/Birch-san/diffusers-play/tree/92feee6)
+- Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
+- Security advice - RyotaK
+- UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
+- TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
+- LyCORIS - KohakuBlueleaf
+- Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
+- (You)

cache.json

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+        "lora/sd_xl_offset_example-lora_1.0": {
+            "mtime": 1690358783.0,
+            "value": {
+                "ss_adaptive_noise_scale": "None",
+                "ss_base_model_version": "sdxl_base_v0-9",
+                "ss_cache_latents": "True",
+                "ss_caption_dropout_every_n_epochs": "0",
+                "ss_caption_dropout_rate": "0.0",
+                "ss_caption_tag_dropout_rate": "0.0",
+                "ss_clip_skip": "None",
+                "ss_dataset_dirs": {
+                    "": {
+                        "n_repeats": 1,
+                        "img_count": 7412
+                    }
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+                "ss_datasets": "[{\"is_dreambooth\": true, \"batch_size_per_device\": 1, \"num_train_images\": 7412, \"num_reg_images\": 0, \"resolution\": [1024, 1024], \"enable_bucket\": false, \"min_bucket_reso\": null, \"max_bucket_reso\": null, \"tag_frequency\": {\"\": {\"contrast\": 7412}}, \"bucket_info\": null, \"subsets\": [{\"img_count\": 7412, \"num_repeats\": 1, \"color_aug\": false, \"flip_aug\": false, \"random_crop\": false, \"shuffle_caption\": false, \"keep_tokens\": 1, \"image_dir\": \"\", \"class_tokens\": \"contrast\", \"is_reg\": false}]}]",
+                "ss_epoch": "4",
+                "ss_face_crop_aug_range": "None",
+                "ss_full_fp16": "False",
+                "ss_gradient_accumulation_steps": "1",
+                "ss_gradient_checkpointing": "False",
+                "ss_learning_rate": "0.0009",
+                "ss_lowram": "False",
+                "ss_lr_scheduler": "cosine_with_restarts",
+                "ss_lr_warmup_steps": "100",
+                "ss_max_grad_norm": "1.0",
+                "ss_max_token_length": "None",
+                "ss_max_train_steps": "7750",
+                "ss_min_snr_gamma": "None",
+                "ss_mixed_precision": "fp16",
+                "ss_multires_noise_discount": "0.3",
+                "ss_multires_noise_iterations": "None",
+                "ss_network_alpha": "1",
+                "ss_network_args": {
+                    "conv_dim": "8"
+                },
+                "ss_network_dim": "8",
+                "ss_network_dropout": "None",
+                "ss_network_module": "networks.lora",
+                "ss_new_sd_model_hash": "a0f13b7eb4f4807f6863db3da874cb01e3cd0d5e2c481b6b01b8ea4a3139542c",
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+                "ss_num_epochs": "50",
+                "ss_num_reg_images": "0",
+                "ss_num_train_images": "7412",
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+                "ss_output_name": "offset_0.2",
+                "ss_prior_loss_weight": "1.0",
+                "ss_scale_weight_norms": "None",
+                "ss_sd_model_hash": "b1facb5b",
+                "ss_sd_model_name": "SDXL_1-0.safetensors",
+                "ss_sd_scripts_commit_hash": "71a6d49d0663fbdeacab11c1050c33384695122b",
+                "ss_seed": "42",
+                "ss_session_id": "2452006521",
+                "ss_steps": "620",
+                "ss_tag_frequency": {
+                    "": {
+                        "contrast": 7412
+                    }
+                },
+                "ss_text_encoder_lr": "None",
+                "ss_training_comment": "None",
+                "ss_training_finished_at": "1689972654.070616",
+                "ss_training_started_at": "1689970447.43792",
+                "ss_unet_lr": "None",
+                "ss_v2": "False",
+                "sshs_legacy_hash": "fec84cf7",
+                "sshs_model_hash": "8e3e833226b356a1bb9688b472e8e36315cd8a656f1a2d576bc13edd251392dd",
+                "modelspec.sai_model_spec": "1.0.0",
+                "modelspec.architecture": "stable-diffusion-xl-v1-base/lora",
+                "modelspec.title": "SDXL 1.0 Official Offset Example LoRA",
+                "modelspec.author": "StabilityAI",
+                "modelspec.description": "This is an example LoRA for SDXL 1.0 (Base) that adds Offset Noise to the model, trained by KaliYuga for StabilityAI. When applied, it will extend the image's contrast (range of brightness to darkness), which is particularly popular for producing very dark or nighttime images. At low percentages, it improves contrast and perceived image quality; at higher percentages it can be a powerful tool to produce perfect inky black. This small file (50 megabytes) demonstrates the power of LoRA on SDXL and produces a clear visual upgrade to the base model without needing to replace the 6.5 gigabyte full model. The LoRA was heavily trained with the keyword `contrasts`, which can be used alter the high-contrast effect of offset noise.",
+                "modelspec.usage_hint": "Recommended strength: 50% (0.5). The keyword `contrasts` may alter the effect.",
+                "modelspec.date": "2023-07-26",
+                "modelspec.resolution": "1024x1024",
+                "modelspec.prediction_type": "epsilon",
+                "modelspec.license": "CreativeML Open RAIL++-M License",
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SIB95x3rym/ljW4LdVBrknUvJJdDtpUrRbl1KbAQaOtufvMRmsLUIdrLmtG7uvMmDdvT0qzJ4e1TVBBLZWzzRyHAdRkA+/tThdMc2nFmZofijU/DE7/YXDROctE3Qn1puoeMb7Ubl3dBGZDzzmu/0D4O32phZ792twY2DRkfMkgPH1BFcT4o8E6noGutpzQPOSpeN41PzqOTj6V0ezW7Rz+2klyxZW0b7JLqKS6lHJdRxsHaMyrFFgH+Nj29hya+m/A+uWWq6X5VmIY44+FS3iKRgegJ5P5Cvl2PSZVeP+0bS+jib5kCRffx15NfR/wkYS+HzJb6TDplop2pCiksx/vF2+9n24rSNrWMp3bud+sYVtx5PpT/AC98bAn73U+1B+bOKgmn2WgcjBMbUEjInRFaVeFH7uNfYdaxk1RStxZyj7rldw6SD1H54pb26Wx0xxy8sdsQoHXOMk/nXKWFxNdeENPvGzHJJuMmDj5iegzRe+gJaXI/EFnJdxp5FyrXEQIhlY9F757EgdPc+1ctZ6P4yV2/s2KG2c/xSsPLKjpkmrusfaYH8tzsiOMeX0Ue57//AF6itb68cnbO72wBwwJbB9B7+9QkovY1u3Hc5u41vWjq/wDZPiXTdLv45Ac4iCSDtlWGMc1yOtqNRvtUlj3Abw67+ckDmup8exXt5qlnPawKoii2kxrgn2NczCX89pHyGfhwe1OTCK0OVIyKbnFWbmIw3UsTDBDH8qrMK0MRAc0oFNFSCkNBjimmnGk7UITG0UUUwClFJQOtAAaTvT6bjmkAtFFITQB9rTarbafYi7vpAvHyqTyfpXmPivxrdanJHb2p8q3duQp5x71l+LtUutWmaWJz9mhzGoB6H1rnNFZ7vWmgkOcR8ZriqVG9EdtKmlZs05Jzc7kQfKvBPrWDqWnzpACkbHe2AccV2Xh7Q5Ly5lt/MVSHwc16Xp3hW2it1SaFWA6gjj61FGlKWvQ0rV4w93qeYeB/hvf3F9DfXsSeXG3MbjOa9p0vw/YaTE0drAkSEltijgE9cVpWttHbW4SNQAB2qOd9nQ4Nd8YqK0OCUnJ3YpRVzwBVOaC2nmV5YkYp91mXJBPFQzX+3huM96oXWqJHHuZwOeAKHIXLc0nsrW/kS3kgieNecFQQBWi/kWkSooWNeiqorO8POZ4prg9GO1fpV+8QmCVkAMmOM0XugSsczrPjzSdFuryK4hvGt7KNGvLuOLMdvv8Aug8gknI4GTWvj+0NMgntpVeF0V43Xo6HByPwrzjxH4V1nxBc3Ma/aZtJurmG6cW0iiWOSNdpRlYjcpwCDngjpXofhuG7tfDqLqCrFLvdhCH3+UueELfxEDqRxmkmmNpo8v8AFniW5s9futOTaWkJUMW4QDg/mK4ofEm4itItOs9Lint9MDSTPcXGwSYPb8xgDmtH4hW0+m+IbjVLhGEM25oCf4j6YrkPCfgPxD4mhlkg0vz7K4yfOMyxhGz1BNF7MdtDe/4WFd69IIpdPjgaXoIRvBH4+1d1pE1nHaxefKqTbeFkUofoAateF/hjZeG5Vv8AUpI7i8VNkMEXKQr3yx+8x7ngelct4/8AEwutQ+wxJGY4Dgjrz9aLvdhpsi94l1qB7V4kTcCOW4rzF2JmY56mr9xdmVVnt1244lhJyPqKz2+aTcOh5qJO5UdDH1kYu1PPKisw10GsQNLaLKnIXqK54MG6GtIvQiS1GkU8UhFFNkoD1pRShaSgY0ikp5FNIoEJRnmkNIvWgCQUu2lUVPHFmk3YaVyuy1GauvCcVUdcGhO4NWPWvEEGreH/ABDc2ccLT2s7blwM9a1NC8PXz6kjfZXRyvJPbNWbD4h6fqYha+i23UeN6gZ/KvVfCd5Ya1ZC9syskLcKwGOlL2MWveWqBVpL4XuTaH4as7FBO1uouCPmfua25BtGFqfGBgVBL0NNJJWQNt6sLeYsu1hgiqWoSPnCAGl+0pbh3dgAB3pkkH2xg7M6RKMkLwW/+tSbGlc5nWRqclo6WlqZrgcqFOB+JPSsPTNG1q91NYdUaKGH7zJHKHLe1dRrupnyvsOm5M5GGKqWCj14qTw7oFzZRia4dlLDJVuXY+rensB0rmu51LLY63BU6XM3q9jprO3S1tUhQAKo4FI0y+YYyeT0qZWzH9Kz7ohW3EH1yO1dZxlqK1SElgACaz9X1G0060c3EypGiktk4rRtphPbRyhw4I6g5ryf4zWdxqVpY2Gn7/Ou7tI2YyYVGPAJ9Ae/uKTdtSoRTdmzh21Q+KvEdxBcyJcWccheMH7rR57V6X4dvtP0LThZWrBYUHmBBzsB/pXm50CPwhZ3Ftbi4bUXiAkvlwSOcNtDHisS6ur25mV0e7iJIIO9BjjH3V65/nmhPl1YP3tj0nxN8QbEwS21nNM8zIQHiwqqffPNeVOUG93Yu7nLO1R3kxgAAEckoG44IyPw/wAis5bi5eUb4lUnpu5//XUybYkrGh5LSr+6Vgo71WlIVjGnLfxGtyysri8RUafYWXKrjaD+Paq9xpiQCRcFJEOCH6qfQ/40co0zMcBrdkPcVyU6GOZl64PUd66ieXZnjleorntRKNc70/iGacRyK6Pzg1LtzzVenpKV68iqILAX5aYRzUqurpxTCKAGUEUGl7UARMKEFKelCUCJ41yRWraWu8ZxVC1Xc4FdZpVnvQnHasKsrG1ONzJmsiq9Kx7mHaTxXfzWK7MEVyeqW3luwxUU56l1IaXO5+G+ged4qjM8fmKgO5SOOa+k7Gzt7G1WK3iWJB/CowK8z+EFqH0uS5Nm6lnP79yPnHbHsK9TPTjivQrW5rI83C8zppyGPIR1OKrXNyqxE5+g7k0+aMN/Hg1nR/JqUaynjnb6E1zydkdcVdkdvp3m3H2y/O7acxw5+Vfc+prH8S+IxHFLa27kSyjZlTyM1j/EXxdc6fJDp9gCGYh3kHcelReB7Rda1v7XcHcYVDqCep9cen1rjnNyfs4dep6VOkoQ9rU2XQ7PwjoR0nTEadme4kALljk10RxmlIwMDiq0pdFLL82O1dkIqEVFHnTm5ycpbsmPAOKoXjIYmDbcY5LdKii1q1lwDIqknbgnoemK5Txd4stNDtvNnkUICeD3Ipt2JSbdg1Tx7Y+GrwWflvcnhp0U/NGD0/HA6e4rP8ReFIPHtvHq1rq0gtAuViiG1g455PXINedWdjrd5a6l4kuLNWS9Jl8pz8+zsR6fSsXS/Hl7oz79LvDERndFIMq3swrGNa8mmtDrlh+WKknqbetrqVmiR3dwl+F3IGlGJFHpuH3vesbzJlPFvBGXU4YL0I/x6VX1Lxvqur3ESJawPcP8iLGhZnY+g9c1u3PgrUrXSI73Xr4q7KS1vFgBP9nI6n1qpVFHUzhRlN2OXbThel2s0K3KNuNtIcAn0B/hb0I4P166ei2EVyA8Mu1lP76CVRwfcdAc9xjPcA1lSXtvbXsU9tvzJD5Tl23ZIOM+/b9ajmuZLqY3KSeW5BWRgeo6HPuOPqMGrU09SJUmnY9AktB9iU28ZWeLOYz1I749a5XXL/dcKzEb3hCSH1IOAT+GKr2vii7s7ZrG/cyMgxDcdx6BvUe9c/fX0mpXfnSfLIRyB3rRyi1oYqMovUju7skEryyjAPqKx5H3tkdKtXT4G2qfXmoRYUUUUxCgkHIOKkSbs1RUUATsRTQaYrcYNKDQAppUpppyUCL9iP3orudIwI8e1cLZnEgrstJlwg+lctY6aWxsz42VyWuKPMaukmm+SuW1qbLtzWdPcuex9YeFtMj0nw/a2sagbUGcdzWjPKQCFBJqvpM6zaXCyHgoMGrLofLIHBPU16EtW7nDBJRSRlXE08YPQ+mDWBruqSwabLcxcTwDzAPcVsXMRVvkV5DknOa5zxDEx0+bKk/KcjuK56l7HRStzJkXiAWuq6Lb6lEFMckYkVseozWz8OtDbT7O4v5G3Ndbdny42qO1edeBtXW5sLvw3LJmS2cyW+erRk8j8DXuWnhU0+BE6IgXHpXPQp/vXJ9DsxFVqioLZks0qxIWY4FY0+oSyuy2yb1A5csFUfia17iFJEO4Z9jXHa39rtg0VnHZpCQWke4cgRj1wOoP4V2M89HGeNYTHI2o2OqK17G4kMEXMXHqR1P6VwuixXvj3X0k1FibS2YkoehOcnNdjqV6uya0udZgkuGUo9lY23lLH7k9Sa8t07xDfeHb25s7VS0juVAXqawnd3SOqlypps9Q+Ivi+DStMTRdNCid48SyDoi9OPc14W0E+palHBZwPLPKQkccYyzGu4tvCl5rzm51G7YySHJii5OT2LV6R4e8N6N4JiaZIl+1uuHnblh7D0H86iNRR1W/4G9Sm5JR6fiR+APANt4RsxqeqiJ9UZcls5EI/ug+vqa5Dx741TULme0s8mLoeeAf7w/qKm8e+OJLhTYWspRf4yD2rzrTtK1HxHcGDT4XaMH95Mfuj6mqS5tWQ3ye7HcqRvLdyxW9uC77iSfTkV1UumW+j6a7TPmWY5x6H/8AUfyrWg0jTvCdkXkZXuccn3rita1dr2csxJH8K54ppuT02IsoK8tyDzNxKu2VA+UnqR2qI8ybgcHsarI0zAsVJQ9DjpUqtmtbWML33I70fvORtJGcVVq3dkNGuT8wPH0qpVrYzaswpaKKYgpKWigBOlOPrTacv3cUAGacpqOlBoAuW74YGum026Cgc1yUbYNadrc7QMmsqkbmlOVjqJ7sbetczqM2925qeW9ymM1lXEu4mopwsypy0PsDw9qdsNOggjdSFG3IPcV0wAZQexFfM3h/xAdO1u1nvbyWOxiByqc7u4GPc19H6ReLqOk290n3ZUDAZ6V6Fenyu55uEre0jZ9B00OeSxAHpXNa5CkcUjxsz4HzoRyB611VxkoQvHua5+fTcbrkTSZIzyQB+Vc01dHbF2Z87+I9SXRvFEGqWK+VNBKC23gOueQR9K+k/C+qR6npSTROGR8Mv0Iryb4h+H9Kv9NnuZU8m6iUklAOaxvhl48g0FoNMn8ySNjtFxM+1U9lHU/U4HtWMF7yaOiTvFp/I+jZX2LwNzdh61yPiHTTeR/ZGnSKa8Ro5bk8lU6sFH0GBnpXVWtxFeWyXETB0dchgc5rL1LZEXlEJlkVCwGP84rexzJnnuqrp3h+2NvaW0iyH5muSN3n8ctk8EjuOD6V4prcf2PX5bkBB5vzoyHgj29K9u15NQa1lDWxjki5cW43GDIyAydHUjuOeCK8j8V24ntRMfK8xPmV4jlGHt/geaymtTam9DofA+s2kUjXN9KESMcA1Q8a+ORqGsSjT2xahAFbPfua89S8dV2huD2qN2LKSeB/Os1S7m7xGmm5u+GNBm8Y+I1tGdlt0HmTvnnb/ia9U8UXWn+CdCttPsIgiCMhcY5YHqfUkGvOvCOpjw/G9xGwWV+prP8AFPiF9buRLIWKp3J6n2oa5nYcZKMbvcoarrE17IZZmJz0XNW/CvhO68SXfmyZjs0OZJTxn2FQeHtDfW75ZLjK2yn5vf2rste8S2+laeNN0zamFx8vAFU3b3YmaXN70jA8VjTbW+W000/uY1+ZT2Pc5rkHlJdthwueKdc3DTyMxYnJyT61DWkY2RjUnzPQOpyaWkxS1ZmFFFFABRRRQAUL1xRQOCKADFFTMgPNREYNACqamR8d6r0uaVgLLS+9QO2abk0UWC509nd718mX7ynvXunwU1/7RYXWjTMQ8chli3vksv8AFgdgDivH/E2hPp063cC5ifkMO1dN8F7or8RbXzHx59vKij1OAfy4Nd1VNRcWeThpRlJVIdT6NuCOFPRjiuT8Rao1tFgkKAMHnj6H0rq2IZ8N3yVHuK838eq8SFoZFE0qsAjeo6VxSdlc9aKvJIw5hpviuG+0rU0kCIQEmhfbIvGQfcexrxe6in8P6/PYsY5JbWTb5iRrlx1ByQccV6rcSQax4bV7Wb7LqAj2pOnDK47N614jI832mX7Q7NNvIkZjkk55yazpqxrVdz6i+EfjA+IrW5spXZpbaNW6ZVR0wCODXeamzR2spRC77cqg7kc/0rwH9n5yvjC8UD71m27LnP3l6L0/HrX0HelhC5VcttIGe1bSMEeU+J7jWtUnt5bHNvZvlGmx8649exGcivMdZtxby3EMs6zFs72QYBPevX/FMWrNHGlpOtvAZyN6jkI6jGR0IzkH657V5L4i0r+yZGhcSCXJBy25PbB9Kzkro1g7M88CBZ2AzgHvU4UELk9+lJKoWRs9c1GXOc+lLca0NNEL7VJ+UVes9DiuhJNPIu0cKua503G0cswHsamg1MQMrAMdpDAHpmpcX0KU431Ou1C9TRNMFpbYEhGCR2rh7qdpHIJJJ+8at6lrMmpXHnPGqt6L0zWYTk5PU04RtuFSd9EFLRRWhiFFFFABRRRQAUUUUAFBooPSgC0OUFQOOanU/ulNQydTQBHSgUlPAoASin7aaRSA9Q0bVVv9MbTdRG4OuI3PrXNWU1x4X8UwXMe4PbTrKvONwU5x9D0p2g38M6LDKQrDofeus1vQDqOkpfIM3EABOP4lr1WvaQTW589GTw9Zxasn/Vz17R/iLpfibTWu7HjULUCSSwZgHPqF/vVwfxJ8Q2GpGxu7a7iW26Sb2w8ZPYr17H8q8n1Mm2dJIC0b4yGU4IP1rCnlkmlaSV2d2OSzHJNcFWmk7Hs4es5xUjp73V7gOZbC58uKRORjmQg/exXJsT5jFiSSck+prprS08600oNgBoJGJ+hNc9IuZzgA8050lGKsTSrucpKT2/zf+R638AQ3/CZSFXKxm1kymSdxyOcdh2r6JutxjbHp3r5y+D7PpPjbRDcjDalFL5YJwQu04Y+xIwo49a+jZHBWQgghTj8RUTVmjWnLmTfmcRrLzBxGZgu9FkAZchXBIXPsQCDXifimVTqbJCzLGTxEWJC89BnsDXqXj68a20+QxuVnLIqsOoIJOa8Sv7qSfUTLKPm3biPT6VEnpY1jvcytStnikJK8ZrNyetdFq9xb3cYkjI+YZyD1rnSdpx1rKDujWasyJx3pnTinuCOvHtTK0MgxRRRTAKKKKACiiigAooooAKKKKACiiigCVHxFj0qNmyaSkNABUsfNRVInFAFjb8tRMMGnbzimMalDY5ZHgl3oSDmvQvCPjFRttbtvk6Ant/8AWrn/ABH4dNg5urU+ZaOchh2rmwHjcMhIYeldilOhKzPOcaWLpqSep6d4s8PxpAby32tA/wAw287c/wBK80nQo5Haus8P+LSsJ0/UPmhcY5rP1bSCJHltj5kJORj0rSqlUjzwMMM5Yebp1PkbGlLnSNOlCgmOxnPP1Nc3p9n9v1KC1HBnlSL6bmA/rXWafCV8EtKeGW28vnsC5JrF8EgTeNNLXji6RufY5omvgTFRlrVkuj/zHeIdRd/Hd3cWkrwJaTCG1CHG0R/KoHoBjJNfRvgjxxa+K/CM9yJM3lggW8O3buYgneB6Ng18pahKZNTvXJzulfH4sc1r+GfEOp+HLt7nTp9hkjMUkbDckqnqGHf+nauZvmkz0oWhBeh6/wDEvU4z4Z0nUoo9y3O2Zc8YGc4P1/rXjWoXgRXlxtaTPlqP4Qa6HXfHkmv6VYWl1YpG9kpQ+U2I3APy/L2xXD3c7XM/mOcsRg1nKPc0jJPYiSR0GAx2+lTxtujbsQQTj0NV6kgG52XIGVPX86FuEtgkA7dO9MIx9KkIMgyOnZQM4prqR1X8zQwT6DKKKKRQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUopKcKAFzTSadTSKAPVbq1ey+HV69yeWYbM15YruxVQCzHgADkmum8SeMbjWLNNMjULaxt26sa3vA9pYaTE2ozQQvew/NJcXX+qth22j+J67qi9tUUYPRHkUG8LRcqi1k9jBm8LjRLKG+8RiSFpuYbJOJHHqx/hFUpdatVIFtp7QKOm2Un881P411eXVNflMmofb1iYhbjGA/wBB2Fc95hHasZzUHywOunSdSKnU1b6bHoUV4154DuJmXbuIG1OgwTWH4EZU8aaXI33RdJ/hWjZSFfhvMxI5fHT3NUfBFq0us28wGfLcSfka3leUoehw0rU6dbtdmBq8Zg1m+jPVLhwc/wC8aIZAFGBW58RbIWXjjUgv3Jn85fo3Nc3E+OK5Je7No9OPv00ydxyevJqvIoDtjpzVwMhUc8kiorlBHOVPbP8AOiSCD1sVcHGakgfy7iJz0DDP0pJDwKjPSo2ZtutS4fkLR5+VHIpkwynH1NJKwZ5CDwwDDH05pm4tHiqbM0upHRRRUGoUUUtACUUtFACUUUUAFFFFABRQaKACiiigApwptKDQA6kNGaSgD//Z",
+                "modelspec.hash_sha256": "0x8e3e833226b356a1bb9688b472e8e36315cd8a656f1a2d576bc13edd251392dd"
+            }
+        },
+        "lora/szdnpp_lora_v1": {
+            "mtime": 1692334745.0,
+            "value": {
+                "ss_network_dim": "128",
+                "ss_num_epochs": "10",
+                "ss_reg_dataset_dirs": {},
+                "ss_face_crop_aug_range": "None",
+                "ss_sd_model_name": "runwayml/stable-diffusion-v1-5",
+                "ss_max_token_length": "None",
+                "ss_multires_noise_discount": "0.3",
+                "ss_max_train_steps": "3200",
+                "ss_caption_dropout_every_n_epochs": "0",
+                "ss_seed": "420420420",
+                "ss_learning_rate": "None",
+                "ss_training_comment": "sszdnpp",
+                "ss_num_train_images": "320",
+                "ss_dataset_dirs": {
+                    "10_szdnpp woman": {
+                        "n_repeats": 10,
+                        "img_count": 32
+                    }
+                },
+                "sshs_model_hash": "f4edac6000fcbc1d33ab59c681605d77da2e0fcf656893791abcd2adc6277d53",
+                "modelspec.implementation": "diffusers",
+                "ss_steps": "3200",
+                "ss_tag_frequency": {
+                    "10_szdnpp woman": {
+                        "szdnpp woman": 32
+                    },
+                    "4_woman": {},
+                    "6_woman": {}
+                },
+                "ss_keep_tokens": "0",
+                "modelspec.resolution": "768x768",
+                "ss_min_bucket_reso": "128",
+                "ss_bucket_no_upscale": "True",
+                "ss_color_aug": "False",
+                "ss_cache_latents": "True",
+                "ss_unet_lr": "None",
+                "ss_session_id": "243271588",
+                "modelspec.sai_model_spec": "1.0.0",
+                "ss_network_alpha": "64.0",
+                "ss_gradient_accumulation_steps": "1",
+                "modelspec.title": "szdnpp_lora_v1",
+                "ss_num_reg_images": "0",
+                "ss_lr_warmup_steps": "0",
+                "ss_network_dropout": "None",
+                "ss_random_crop": "False",
+                "ss_epoch": "10",
+                "ss_prior_loss_weight": "1.0",
+                "ss_text_encoder_lr": "None",
+                "ss_lr_scheduler": "adafactor:0.0001",
+                "sshs_legacy_hash": "d7f0f78b",
+                "ss_full_fp16": "False",
+                "ss_flip_aug": "False",
+                "ss_max_bucket_reso": "2048",
+                "ss_bucket_info": {
+                    "buckets": {
+                        "0": {
+                            "resolution": [
+                                512,
+                                512
+                            ],
+                            "count": 320
+                        }
+                    },
+                    "mean_img_ar_error": 0.0
+                },
+                "ss_adaptive_noise_scale": "None",
+                "ss_max_grad_norm": "1.0",
+                "ss_zero_terminal_snr": "False",
+                "ss_optimizer": "transformers.optimization.Adafactor(relative_step=True)",
+                "ss_num_batches_per_epoch": "320",
+                "ss_sd_scripts_commit_hash": "ee37a5311d3591caffa8c7ebe798ec2bb870ed87",
+                "ss_min_snr_gamma": "None",
+                "ss_batch_size_per_device": "1",
+                "ss_gradient_checkpointing": "False",
+                "ss_network_module": "networks.lora",
+                "modelspec.prediction_type": "epsilon",
+                "ss_scale_weight_norms": "None",
+                "ss_multires_noise_iterations": "None",
+                "ss_output_name": "szdnpp_lora_v1",
+                "ss_total_batch_size": "1",
+                "ss_shuffle_caption": "False",
+                "modelspec.date": "2023-08-18T04:58:07",
+                "ss_caption_tag_dropout_rate": "0.0",
+                "ss_clip_skip": "None",
+                "ss_resolution": "(768, 768)",
+                "ss_caption_dropout_rate": "0.0",
+                "ss_v2": "False",
+                "ss_noise_offset": "0.0",
+                "ss_enable_bucket": "True",
+                "ss_mixed_precision": "fp16",
+                "ss_lowram": "False",
+                "ss_training_started_at": "1692333214.5576196",
+                "modelspec.architecture": "stable-diffusion-v1/lora",
+                "ss_base_model_version": "sd_v1",
+                "ss_training_finished_at": "1692334687.9210682"
+            }
+        }
+    },
+    "extensions-git": {
+        "Stable-Diffusion-Webui-Civitai-Helper": {
+            "mtime": 1692335674.0,
+            "value": {
+                "remote": "https://github.com/butaixianran/Stable-Diffusion-Webui-Civitai-Helper.git",
+                "commit_date": 1684842802,
+                "branch": "main",
+                "commit_hash": "920ca3267f789a72e3225c99f069bf0be0db795d",
+                "version": "920ca326"
+            }
+        },
+        "Hypernetwork-MonkeyPatch-Extension": {
+            "mtime": 1692335697.0,
+            "value": {
+                "remote": "https://github.com/aria1th/Hypernetwork-MonkeyPatch-Extension.git",
+                "commit_date": 1690546730,
+                "branch": "main",
+                "commit_hash": "bd47167526e59c9cf3fdd8e8de364dbae96550df",
+                "version": "bd471675"
+            }
+        }
+    }
+}

config.json

@@ -0,0 +1,234 @@
+{
+    "samples_save": true,
+    "samples_format": "png",
+    "samples_filename_pattern": "",
+    "save_images_add_number": true,
+    "grid_save": true,
+    "grid_format": "png",
+    "grid_extended_filename": false,
+    "grid_only_if_multiple": true,
+    "grid_prevent_empty_spots": false,
+    "grid_zip_filename_pattern": "",
+    "n_rows": -1,
+    "font": "",
+    "grid_text_active_color": "#000000",
+    "grid_text_inactive_color": "#999999",
+    "grid_background_color": "#ffffff",
+    "enable_pnginfo": true,
+    "save_txt": false,
+    "save_images_before_face_restoration": false,
+    "save_images_before_highres_fix": false,
+    "save_images_before_color_correction": false,
+    "save_mask": false,
+    "save_mask_composite": false,
+    "jpeg_quality": 80,
+    "webp_lossless": false,
+    "export_for_4chan": true,
+    "img_downscale_threshold": 4.0,
+    "target_side_length": 4000,
+    "img_max_size_mp": 200,
+    "use_original_name_batch": true,
+    "use_upscaler_name_as_suffix": false,
+    "save_selected_only": true,
+    "save_init_img": false,
+    "temp_dir": "",
+    "clean_temp_dir_at_start": false,
+    "outdir_samples": "",
+    "outdir_txt2img_samples": "outputs/txt2img-images",
+    "outdir_img2img_samples": "outputs/img2img-images",
+    "outdir_extras_samples": "outputs/extras-images",
+    "outdir_grids": "",
+    "outdir_txt2img_grids": "outputs/txt2img-grids",
+    "outdir_img2img_grids": "outputs/img2img-grids",
+    "outdir_save": "log/images",
+    "outdir_init_images": "outputs/init-images",
+    "save_to_dirs": true,
+    "grid_save_to_dirs": true,
+    "use_save_to_dirs_for_ui": false,
+    "directories_filename_pattern": "[date]",
+    "directories_max_prompt_words": 8,
+    "ESRGAN_tile": 192,
+    "ESRGAN_tile_overlap": 8,
+    "realesrgan_enabled_models": [
+        "R-ESRGAN 4x+",
+        "R-ESRGAN 4x+ Anime6B"
+    ],
+    "upscaler_for_img2img": null,
+    "face_restoration_model": "CodeFormer",
+    "code_former_weight": 0.5,
+    "face_restoration_unload": false,
+    "show_warnings": false,
+    "memmon_poll_rate": 8,
+    "samples_log_stdout": false,
+    "multiple_tqdm": true,
+    "print_hypernet_extra": false,
+    "list_hidden_files": true,
+    "disable_mmap_load_safetensors": false,
+    "unload_models_when_training": false,
+    "pin_memory": false,
+    "save_optimizer_state": false,
+    "save_training_settings_to_txt": true,
+    "dataset_filename_word_regex": "",
+    "dataset_filename_join_string": " ",
+    "training_image_repeats_per_epoch": 1,
+    "training_write_csv_every": 500,
+    "training_xattention_optimizations": false,
+    "training_enable_tensorboard": false,
+    "training_tensorboard_save_images": false,
+    "training_tensorboard_flush_every": 120,
+    "sd_model_checkpoint": "v1-5-pruned-emaonly.safetensors [6ce0161689]",
+    "sd_checkpoint_cache": 0,
+    "sd_vae_checkpoint_cache": 0,
+    "sd_vae": "Automatic",
+    "sd_vae_as_default": true,
+    "sd_unet": "Automatic",
+    "inpainting_mask_weight": 1.0,
+    "initial_noise_multiplier": 1.0,
+    "img2img_color_correction": false,
+    "img2img_fix_steps": false,
+    "img2img_background_color": "#ffffff",
+    "enable_quantization": false,
+    "enable_emphasis": true,
+    "enable_batch_seeds": true,
+    "comma_padding_backtrack": 20,
+    "CLIP_stop_at_last_layers": 1,
+    "upcast_attn": false,
+    "auto_vae_precision": true,
+    "randn_source": "GPU",
+    "sdxl_crop_top": 0,
+    "sdxl_crop_left": 0,
+    "sdxl_refiner_low_aesthetic_score": 2.5,
+    "sdxl_refiner_high_aesthetic_score": 6.0,
+    "cross_attention_optimization": "Automatic",
+    "s_min_uncond": 0.0,
+    "token_merging_ratio": 0.0,
+    "token_merging_ratio_img2img": 0.0,
+    "token_merging_ratio_hr": 0.0,
+    "pad_cond_uncond": false,
+    "experimental_persistent_cond_cache": false,
+    "use_old_emphasis_implementation": false,
+    "use_old_karras_scheduler_sigmas": false,
+    "no_dpmpp_sde_batch_determinism": false,
+    "use_old_hires_fix_width_height": false,
+    "dont_fix_second_order_samplers_schedule": false,
+    "hires_fix_use_firstpass_conds": false,
+    "interrogate_keep_models_in_memory": false,
+    "interrogate_return_ranks": false,
+    "interrogate_clip_num_beams": 1,
+    "interrogate_clip_min_length": 24,
+    "interrogate_clip_max_length": 48,
+    "interrogate_clip_dict_limit": 1500,
+    "interrogate_clip_skip_categories": [],
+    "interrogate_deepbooru_score_threshold": 0.5,
+    "deepbooru_sort_alpha": true,
+    "deepbooru_use_spaces": true,
+    "deepbooru_escape": true,
+    "deepbooru_filter_tags": "",
+    "extra_networks_show_hidden_directories": true,
+    "extra_networks_hidden_models": "When searched",
+    "extra_networks_default_multiplier": 1.0,
+    "extra_networks_card_width": 0,
+    "extra_networks_card_height": 0,
+    "extra_networks_card_text_scale": 1.0,
+    "extra_networks_card_show_desc": true,
+    "extra_networks_add_text_separator": " ",
+    "ui_extra_networks_tab_reorder": "",
+    "textual_inversion_print_at_load": false,
+    "textual_inversion_add_hashes_to_infotext": true,
+    "sd_hypernetwork": "None",
+    "localization": "None",
+    "gradio_theme": "Default",
+    "img2img_editor_height": 720,
+    "return_grid": true,
+    "return_mask": false,
+    "return_mask_composite": false,
+    "do_not_show_images": false,
+    "send_seed": true,
+    "send_size": true,
+    "js_modal_lightbox": true,
+    "js_modal_lightbox_initially_zoomed": true,
+    "js_modal_lightbox_gamepad": false,
+    "js_modal_lightbox_gamepad_repeat": 250,
+    "show_progress_in_title": true,
+    "samplers_in_dropdown": true,
+    "dimensions_and_batch_together": true,
+    "keyedit_precision_attention": 0.1,
+    "keyedit_precision_extra": 0.05,
+    "keyedit_delimiters": ".,\\/!?%^*;:{}=`~()",
+    "keyedit_move": true,
+    "quicksettings_list": [
+        "sd_model_checkpoint",
+        "sd_vae",
+        "lora_preferred_name"
+    ],
+    "ui_tab_order": [],
+    "hidden_tabs": [],
+    "ui_reorder_list": [],
+    "hires_fix_show_sampler": false,
+    "hires_fix_show_prompts": false,
+    "disable_token_counters": false,
+    "add_model_hash_to_info": true,
+    "add_model_name_to_info": true,
+    "add_user_name_to_info": false,
+    "add_version_to_infotext": true,
+    "disable_weights_auto_swap": true,
+    "infotext_styles": "Apply if any",
+    "show_progressbar": true,
+    "live_previews_enable": true,
+    "live_previews_image_format": "png",
+    "show_progress_grid": true,
+    "show_progress_every_n_steps": 10,
+    "show_progress_type": "Approx NN",
+    "live_preview_content": "Prompt",
+    "live_preview_refresh_period": 1000,
+    "hide_samplers": [],
+    "eta_ddim": 0.0,
+    "eta_ancestral": 1.0,
+    "ddim_discretize": "uniform",
+    "s_churn": 0.0,
+    "s_tmin": 0.0,
+    "s_noise": 1.0,
+    "k_sched_type": "Automatic",
+    "sigma_min": 0.0,
+    "sigma_max": 0.0,
+    "rho": 0.0,
+    "eta_noise_seed_delta": 0,
+    "always_discard_next_to_last_sigma": false,
+    "uni_pc_variant": "bh1",
+    "uni_pc_skip_type": "time_uniform",
+    "uni_pc_order": 3,
+    "uni_pc_lower_order_final": true,
+    "postprocessing_enable_in_main_ui": [],
+    "postprocessing_operation_order": [],
+    "upscaling_max_images_in_cache": 5,
+    "disabled_extensions": [],
+    "disable_all_extensions": "none",
+    "restore_config_state_file": "",
+    "sd_checkpoint_hash": "6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa",
+    "ldsr_steps": 100,
+    "ldsr_cached": false,
+    "SCUNET_tile": 256,
+    "SCUNET_tile_overlap": 8,
+    "SWIN_tile": 192,
+    "SWIN_tile_overlap": 8,
+    "SWIN_torch_compile": false,
+    "lora_functional": false,
+    "sd_lora": "None",
+    "lora_preferred_name": "Alias from file",
+    "lora_add_hashes_to_infotext": true,
+    "lora_show_all": false,
+    "lora_hide_unknown_for_versions": [],
+    "extra_options": [],
+    "extra_options_accordion": false,
+    "canvas_hotkey_zoom": "Alt",
+    "canvas_hotkey_adjust": "Ctrl",
+    "canvas_hotkey_move": "F",
+    "canvas_hotkey_fullscreen": "S",
+    "canvas_hotkey_reset": "R",
+    "canvas_hotkey_overlap": "O",
+    "canvas_show_tooltip": true,
+    "canvas_blur_prompt": false,
+    "canvas_disabled_functions": [
+        "Overlap"
+    ]
+}

configs/alt-diffusion-inference.yaml

@@ -0,0 +1,72 @@
+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: modules.xlmr.BertSeriesModelWithTransformation
+      params:
+        name: "XLMR-Large"

configs/instruct-pix2pix.yaml

@@ -0,0 +1,98 @@
+# File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
+# See more details in LICENSE.
+
+model:
+  base_learning_rate: 1.0e-04
+  target: modules.models.diffusion.ddpm_edit.LatentDiffusion
+  params:
+    linear_start: 0.00085
+    linear_end: 0.0120
+    num_timesteps_cond: 1
+    log_every_t: 200
+    timesteps: 1000
+    first_stage_key: edited
+    cond_stage_key: edit
+    # image_size: 64
+    # image_size: 32
+    image_size: 16
+    channels: 4
+    cond_stage_trainable: false   # Note: different from the one we trained before
+    conditioning_key: hybrid
+    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: [ 0 ]
+        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: 8
+        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
+
+data:
+  target: main.DataModuleFromConfig
+  params:
+    batch_size: 128
+    num_workers: 1
+    wrap: false
+    validation:
+      target: edit_dataset.EditDataset
+      params:
+        path: data/clip-filtered-dataset
+        cache_dir:  data/
+        cache_name: data_10k
+        split: val
+        min_text_sim: 0.2
+        min_image_sim: 0.75
+        min_direction_sim: 0.2
+        max_samples_per_prompt: 1
+        min_resize_res: 512
+        max_resize_res: 512
+        crop_res: 512
+        output_as_edit: False
+        real_input: True

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

configs/v1-inpainting-inference.yaml

@@ -0,0 +1,70 @@
+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

environment-wsl2.yaml

@@ -0,0 +1,11 @@
+name: automatic
+channels:
+  - pytorch
+  - defaults
+dependencies:
+  - python=3.10
+  - pip=23.0
+  - cudatoolkit=11.8
+  - pytorch=2.0
+  - torchvision=0.15
+  - numpy=1.23

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -0,0 +1,250 @@
+import os
+import gc
+import time
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from einops import rearrange, repeat
+from omegaconf import OmegaConf
+import safetensors.torch
+
+from ldm.models.diffusion.ddim import DDIMSampler
+from ldm.util import instantiate_from_config, ismap
+from modules import shared, sd_hijack, devices
+
+cached_ldsr_model: torch.nn.Module = None
+
+
+# Create LDSR Class
+class LDSR:
+    def load_model_from_config(self, half_attention):
+        global cached_ldsr_model
+
+        if shared.opts.ldsr_cached and cached_ldsr_model is not None:
+            print("Loading model from cache")
+            model: torch.nn.Module = cached_ldsr_model
+        else:
+            print(f"Loading model from {self.modelPath}")
+            _, extension = os.path.splitext(self.modelPath)
+            if extension.lower() == ".safetensors":
+                pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
+            else:
+                pl_sd = torch.load(self.modelPath, map_location="cpu")
+            sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
+            config = OmegaConf.load(self.yamlPath)
+            config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
+            model: torch.nn.Module = instantiate_from_config(config.model)
+            model.load_state_dict(sd, strict=False)
+            model = model.to(shared.device)
+            if half_attention:
+                model = model.half()
+            if shared.cmd_opts.opt_channelslast:
+                model = model.to(memory_format=torch.channels_last)
+
+            sd_hijack.model_hijack.hijack(model) # apply optimization
+            model.eval()
+
+            if shared.opts.ldsr_cached:
+                cached_ldsr_model = model
+
+        return {"model": model}
+
+    def __init__(self, model_path, yaml_path):
+        self.modelPath = model_path
+        self.yamlPath = yaml_path
+
+    @staticmethod
+    def run(model, selected_path, custom_steps, eta):
+        example = get_cond(selected_path)
+
+        n_runs = 1
+        guider = None
+        ckwargs = None
+        ddim_use_x0_pred = False
+        temperature = 1.
+        eta = eta
+        custom_shape = None
+
+        height, width = example["image"].shape[1:3]
+        split_input = height >= 128 and width >= 128
+
+        if split_input:
+            ks = 128
+            stride = 64
+            vqf = 4  #
+            model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
+                                        "vqf": vqf,
+                                        "patch_distributed_vq": True,
+                                        "tie_braker": False,
+                                        "clip_max_weight": 0.5,
+                                        "clip_min_weight": 0.01,
+                                        "clip_max_tie_weight": 0.5,
+                                        "clip_min_tie_weight": 0.01}
+        else:
+            if hasattr(model, "split_input_params"):
+                delattr(model, "split_input_params")
+
+        x_t = None
+        logs = None
+        for _ in range(n_runs):
+            if custom_shape is not None:
+                x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
+                x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
+
+            logs = make_convolutional_sample(example, model,
+                                             custom_steps=custom_steps,
+                                             eta=eta, quantize_x0=False,
+                                             custom_shape=custom_shape,
+                                             temperature=temperature, noise_dropout=0.,
+                                             corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
+                                             ddim_use_x0_pred=ddim_use_x0_pred
+                                             )
+        return logs
+
+    def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
+        model = self.load_model_from_config(half_attention)
+
+        # Run settings
+        diffusion_steps = int(steps)
+        eta = 1.0
+
+
+        gc.collect()
+        devices.torch_gc()
+
+        im_og = image
+        width_og, height_og = im_og.size
+        # If we can adjust the max upscale size, then the 4 below should be our variable
+        down_sample_rate = target_scale / 4
+        wd = width_og * down_sample_rate
+        hd = height_og * down_sample_rate
+        width_downsampled_pre = int(np.ceil(wd))
+        height_downsampled_pre = int(np.ceil(hd))
+
+        if down_sample_rate != 1:
+            print(
+                f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
+            im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
+        else:
+            print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
+
+        # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
+        pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
+        im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
+
+        logs = self.run(model["model"], im_padded, diffusion_steps, eta)
+
+        sample = logs["sample"]
+        sample = sample.detach().cpu()
+        sample = torch.clamp(sample, -1., 1.)
+        sample = (sample + 1.) / 2. * 255
+        sample = sample.numpy().astype(np.uint8)
+        sample = np.transpose(sample, (0, 2, 3, 1))
+        a = Image.fromarray(sample[0])
+
+        # remove padding
+        a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
+
+        del model
+        gc.collect()
+        devices.torch_gc()
+
+        return a
+
+
+def get_cond(selected_path):
+    example = {}
+    up_f = 4
+    c = selected_path.convert('RGB')
+    c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
+    c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
+                                                    antialias=True)
+    c_up = rearrange(c_up, '1 c h w -> 1 h w c')
+    c = rearrange(c, '1 c h w -> 1 h w c')
+    c = 2. * c - 1.
+
+    c = c.to(shared.device)
+    example["LR_image"] = c
+    example["image"] = c_up
+
+    return example
+
+
+@torch.no_grad()
+def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
+                    mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
+                    corrector_kwargs=None, x_t=None
+                    ):
+    ddim = DDIMSampler(model)
+    bs = shape[0]
+    shape = shape[1:]
+    print(f"Sampling with eta = {eta}; steps: {steps}")
+    samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
+                                         normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
+                                         mask=mask, x0=x0, temperature=temperature, verbose=False,
+                                         score_corrector=score_corrector,
+                                         corrector_kwargs=corrector_kwargs, x_t=x_t)
+
+    return samples, intermediates
+
+
+@torch.no_grad()
+def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize_x0=False, custom_shape=None, temperature=1., noise_dropout=0., corrector=None,
+                              corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
+    log = {}
+
+    z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
+                                        return_first_stage_outputs=True,
+                                        force_c_encode=not (hasattr(model, 'split_input_params')
+                                                            and model.cond_stage_key == 'coordinates_bbox'),
+                                        return_original_cond=True)
+
+    if custom_shape is not None:
+        z = torch.randn(custom_shape)
+        print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
+
+    z0 = None
+
+    log["input"] = x
+    log["reconstruction"] = xrec
+
+    if ismap(xc):
+        log["original_conditioning"] = model.to_rgb(xc)
+        if hasattr(model, 'cond_stage_key'):
+            log[model.cond_stage_key] = model.to_rgb(xc)
+
+    else:
+        log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
+        if model.cond_stage_model:
+            log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
+            if model.cond_stage_key == 'class_label':
+                log[model.cond_stage_key] = xc[model.cond_stage_key]
+
+    with model.ema_scope("Plotting"):
+        t0 = time.time()
+
+        sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
+                                                eta=eta,
+                                                quantize_x0=quantize_x0, mask=None, x0=z0,
+                                                temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
+                                                x_t=x_T)
+        t1 = time.time()
+
+        if ddim_use_x0_pred:
+            sample = intermediates['pred_x0'][-1]
+
+    x_sample = model.decode_first_stage(sample)
+
+    try:
+        x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
+        log["sample_noquant"] = x_sample_noquant
+        log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
+    except Exception:
+        pass
+
+    log["sample"] = x_sample
+    log["time"] = t1 - t0
+
+    return log

extensions-builtin/LDSR/preload.py

@@ -0,0 +1,6 @@
+import os
+from modules import paths
+
+
+def preload(parser):
+    parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(paths.models_path, 'LDSR'))

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -0,0 +1,68 @@
+import os
+
+from modules.modelloader import load_file_from_url
+from modules.upscaler import Upscaler, UpscalerData
+from ldsr_model_arch import LDSR
+from modules import shared, script_callbacks, errors
+import sd_hijack_autoencoder  # noqa: F401
+import sd_hijack_ddpm_v1  # noqa: F401
+
+
+class UpscalerLDSR(Upscaler):
+    def __init__(self, user_path):
+        self.name = "LDSR"
+        self.user_path = user_path
+        self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
+        self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
+        super().__init__()
+        scaler_data = UpscalerData("LDSR", None, self)
+        self.scalers = [scaler_data]
+
+    def load_model(self, path: str):
+        # Remove incorrect project.yaml file if too big
+        yaml_path = os.path.join(self.model_path, "project.yaml")
+        old_model_path = os.path.join(self.model_path, "model.pth")
+        new_model_path = os.path.join(self.model_path, "model.ckpt")
+
+        local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
+        local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
+        local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
+        local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
+
+        if os.path.exists(yaml_path):
+            statinfo = os.stat(yaml_path)
+            if statinfo.st_size >= 10485760:
+                print("Removing invalid LDSR YAML file.")
+                os.remove(yaml_path)
+
+        if os.path.exists(old_model_path):
+            print("Renaming model from model.pth to model.ckpt")
+            os.rename(old_model_path, new_model_path)
+
+        if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
+            model = local_safetensors_path
+        else:
+            model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
+
+        yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
+
+        return LDSR(model, yaml)
+
+    def do_upscale(self, img, path):
+        try:
+            ldsr = self.load_model(path)
+        except Exception:
+            errors.report(f"Failed loading LDSR model {path}", exc_info=True)
+            return img
+        ddim_steps = shared.opts.ldsr_steps
+        return ldsr.super_resolution(img, ddim_steps, self.scale)
+
+
+def on_ui_settings():
+    import gradio as gr
+
+    shared.opts.add_option("ldsr_steps", shared.OptionInfo(100, "LDSR processing steps. Lower = faster", gr.Slider, {"minimum": 1, "maximum": 200, "step": 1}, section=('upscaling', "Upscaling")))
+    shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
+
+
+script_callbacks.on_ui_settings(on_ui_settings)

extensions-builtin/LDSR/sd_hijack_autoencoder.py

@@ -0,0 +1,293 @@
+# The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
+# The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
+# As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
+import numpy as np
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from torch.optim.lr_scheduler import LambdaLR
+
+from ldm.modules.ema import LitEma
+from vqvae_quantize import VectorQuantizer2 as VectorQuantizer
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.util import instantiate_from_config
+
+import ldm.models.autoencoder
+from packaging import version
+
+class VQModel(pl.LightningModule):
+    def __init__(self,
+                 ddconfig,
+                 lossconfig,
+                 n_embed,
+                 embed_dim,
+                 ckpt_path=None,
+                 ignore_keys=None,
+                 image_key="image",
+                 colorize_nlabels=None,
+                 monitor=None,
+                 batch_resize_range=None,
+                 scheduler_config=None,
+                 lr_g_factor=1.0,
+                 remap=None,
+                 sane_index_shape=False, # tell vector quantizer to return indices as bhw
+                 use_ema=False
+                 ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.n_embed = n_embed
+        self.image_key = image_key
+        self.encoder = Encoder(**ddconfig)
+        self.decoder = Decoder(**ddconfig)
+        self.loss = instantiate_from_config(lossconfig)
+        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
+                                        remap=remap,
+                                        sane_index_shape=sane_index_shape)
+        self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
+        self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+        if colorize_nlabels is not None:
+            assert type(colorize_nlabels)==int
+            self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+        if monitor is not None:
+            self.monitor = monitor
+        self.batch_resize_range = batch_resize_range
+        if self.batch_resize_range is not None:
+            print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
+
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
+        self.scheduler_config = scheduler_config
+        self.lr_g_factor = lr_g_factor
+
+    @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:
+                print(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:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=None):
+        sd = torch.load(path, map_location="cpu")["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys or []:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if missing:
+            print(f"Missing Keys: {missing}")
+        if unexpected:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self)
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        quant, emb_loss, info = self.quantize(h)
+        return quant, emb_loss, info
+
+    def encode_to_prequant(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, quant):
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+    def decode_code(self, code_b):
+        quant_b = self.quantize.embed_code(code_b)
+        dec = self.decode(quant_b)
+        return dec
+
+    def forward(self, input, return_pred_indices=False):
+        quant, diff, (_,_,ind) = self.encode(input)
+        dec = self.decode(quant)
+        if return_pred_indices:
+            return dec, diff, ind
+        return dec, diff
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+        if self.batch_resize_range is not None:
+            lower_size = self.batch_resize_range[0]
+            upper_size = self.batch_resize_range[1]
+            if self.global_step <= 4:
+                # do the first few batches with max size to avoid later oom
+                new_resize = upper_size
+            else:
+                new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
+            if new_resize != x.shape[2]:
+                x = F.interpolate(x, size=new_resize, mode="bicubic")
+            x = x.detach()
+        return x
+
+    def training_step(self, batch, batch_idx, optimizer_idx):
+        # https://github.com/pytorch/pytorch/issues/37142
+        # try not to fool the heuristics
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+
+        if optimizer_idx == 0:
+            # autoencode
+            aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train",
+                                            predicted_indices=ind)
+
+            self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return aeloss
+
+        if optimizer_idx == 1:
+            # discriminator
+            discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
+                                            last_layer=self.get_last_layer(), split="train")
+            self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
+            return discloss
+
+    def validation_step(self, batch, batch_idx):
+        log_dict = self._validation_step(batch, batch_idx)
+        with self.ema_scope():
+            self._validation_step(batch, batch_idx, suffix="_ema")
+        return log_dict
+
+    def _validation_step(self, batch, batch_idx, suffix=""):
+        x = self.get_input(batch, self.image_key)
+        xrec, qloss, ind = self(x, return_pred_indices=True)
+        aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
+                                        self.global_step,
+                                        last_layer=self.get_last_layer(),
+                                        split="val"+suffix,
+                                        predicted_indices=ind
+                                        )
+
+        discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
+                                            self.global_step,
+                                            last_layer=self.get_last_layer(),
+                                            split="val"+suffix,
+                                            predicted_indices=ind
+                                            )
+        rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log(f"val{suffix}/rec_loss", rec_loss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        self.log(f"val{suffix}/aeloss", aeloss,
+                   prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
+        if version.parse(pl.__version__) >= version.parse('1.4.0'):
+            del log_dict_ae[f"val{suffix}/rec_loss"]
+        self.log_dict(log_dict_ae)
+        self.log_dict(log_dict_disc)
+        return self.log_dict
+
+    def configure_optimizers(self):
+        lr_d = self.learning_rate
+        lr_g = self.lr_g_factor*self.learning_rate
+        print("lr_d", lr_d)
+        print("lr_g", lr_g)
+        opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
+                                  list(self.decoder.parameters())+
+                                  list(self.quantize.parameters())+
+                                  list(self.quant_conv.parameters())+
+                                  list(self.post_quant_conv.parameters()),
+                                  lr=lr_g, betas=(0.5, 0.9))
+        opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+                                    lr=lr_d, betas=(0.5, 0.9))
+
+        if self.scheduler_config is not None:
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+                {
+                    'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                },
+            ]
+            return [opt_ae, opt_disc], scheduler
+        return [opt_ae, opt_disc], []
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
+        log = {}
+        x = self.get_input(batch, self.image_key)
+        x = x.to(self.device)
+        if only_inputs:
+            log["inputs"] = x
+            return log
+        xrec, _ = self(x)
+        if x.shape[1] > 3:
+            # colorize with random projection
+            assert xrec.shape[1] > 3
+            x = self.to_rgb(x)
+            xrec = self.to_rgb(xrec)
+        log["inputs"] = x
+        log["reconstructions"] = xrec
+        if plot_ema:
+            with self.ema_scope():
+                xrec_ema, _ = self(x)
+                if x.shape[1] > 3:
+                    xrec_ema = self.to_rgb(xrec_ema)
+                log["reconstructions_ema"] = xrec_ema
+        return log
+
+    def to_rgb(self, x):
+        assert self.image_key == "segmentation"
+        if not hasattr(self, "colorize"):
+            self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+        x = F.conv2d(x, weight=self.colorize)
+        x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+        return x
+
+
+class VQModelInterface(VQModel):
+    def __init__(self, embed_dim, *args, **kwargs):
+        super().__init__(*args, embed_dim=embed_dim, **kwargs)
+        self.embed_dim = embed_dim
+
+    def encode(self, x):
+        h = self.encoder(x)
+        h = self.quant_conv(h)
+        return h
+
+    def decode(self, h, force_not_quantize=False):
+        # also go through quantization layer
+        if not force_not_quantize:
+            quant, emb_loss, info = self.quantize(h)
+        else:
+            quant = h
+        quant = self.post_quant_conv(quant)
+        dec = self.decoder(quant)
+        return dec
+
+ldm.models.autoencoder.VQModel = VQModel
+ldm.models.autoencoder.VQModelInterface = VQModelInterface

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -0,0 +1,1443 @@
+# This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
+# Original filename: ldm/models/diffusion/ddpm.py
+# The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
+# Some models such as LDSR require VQ to work correctly
+# The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager
+from functools import partial
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+import ldm.models.diffusion.ddpm
+
+__conditioning_keys__ = {'concat': 'c_concat',
+                         'crossattn': 'c_crossattn',
+                         'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+    """Overwrite model.train with this function to make sure train/eval mode
+    does not change anymore."""
+    return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+    return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPMV1(pl.LightningModule):
+    # classic DDPM with Gaussian diffusion, in image space
+    def __init__(self,
+                 unet_config,
+                 timesteps=1000,
+                 beta_schedule="linear",
+                 loss_type="l2",
+                 ckpt_path=None,
+                 ignore_keys=None,
+                 load_only_unet=False,
+                 monitor="val/loss",
+                 use_ema=True,
+                 first_stage_key="image",
+                 image_size=256,
+                 channels=3,
+                 log_every_t=100,
+                 clip_denoised=True,
+                 linear_start=1e-4,
+                 linear_end=2e-2,
+                 cosine_s=8e-3,
+                 given_betas=None,
+                 original_elbo_weight=0.,
+                 v_posterior=0.,  # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+                 l_simple_weight=1.,
+                 conditioning_key=None,
+                 parameterization="eps",  # all assuming fixed variance schedules
+                 scheduler_config=None,
+                 use_positional_encodings=False,
+                 learn_logvar=False,
+                 logvar_init=0.,
+                 ):
+        super().__init__()
+        assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
+        self.parameterization = parameterization
+        print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+        self.cond_stage_model = None
+        self.clip_denoised = clip_denoised
+        self.log_every_t = log_every_t
+        self.first_stage_key = first_stage_key
+        self.image_size = image_size  # try conv?
+        self.channels = channels
+        self.use_positional_encodings = use_positional_encodings
+        self.model = DiffusionWrapperV1(unet_config, conditioning_key)
+        count_params(self.model, verbose=True)
+        self.use_ema = use_ema
+        if self.use_ema:
+            self.model_ema = LitEma(self.model)
+            print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+        self.use_scheduler = scheduler_config is not None
+        if self.use_scheduler:
+            self.scheduler_config = scheduler_config
+
+        self.v_posterior = v_posterior
+        self.original_elbo_weight = original_elbo_weight
+        self.l_simple_weight = l_simple_weight
+
+        if monitor is not None:
+            self.monitor = monitor
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
+
+        self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+                               linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+        self.loss_type = loss_type
+
+        self.learn_logvar = learn_logvar
+        self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+        if self.learn_logvar:
+            self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+
+
+    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 exists(given_betas):
+            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)
+        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)))
+
+        # calculations for posterior q(x_{t-1} | x_t, x_0)
+        posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+                    1. - alphas_cumprod) + self.v_posterior * betas
+        # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+        self.register_buffer('posterior_variance', to_torch(posterior_variance))
+        # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+        self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+        self.register_buffer('posterior_mean_coef1', to_torch(
+            betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+        self.register_buffer('posterior_mean_coef2', to_torch(
+            (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+        if self.parameterization == "eps":
+            lvlb_weights = self.betas ** 2 / (
+                        2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+        elif self.parameterization == "x0":
+            lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+        else:
+            raise NotImplementedError("mu not supported")
+        # TODO how to choose this term
+        lvlb_weights[0] = lvlb_weights[1]
+        self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+        assert not torch.isnan(self.lvlb_weights).all()
+
+    @contextmanager
+    def ema_scope(self, context=None):
+        if self.use_ema:
+            self.model_ema.store(self.model.parameters())
+            self.model_ema.copy_to(self.model)
+            if context is not None:
+                print(f"{context}: Switched to EMA weights")
+        try:
+            yield None
+        finally:
+            if self.use_ema:
+                self.model_ema.restore(self.model.parameters())
+                if context is not None:
+                    print(f"{context}: Restored training weights")
+
+    def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
+        sd = torch.load(path, map_location="cpu")
+        if "state_dict" in list(sd.keys()):
+            sd = sd["state_dict"]
+        keys = list(sd.keys())
+        for k in keys:
+            for ik in ignore_keys or []:
+                if k.startswith(ik):
+                    print("Deleting key {} from state_dict.".format(k))
+                    del sd[k]
+        missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+            sd, strict=False)
+        print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+        if missing:
+            print(f"Missing Keys: {missing}")
+        if unexpected:
+            print(f"Unexpected Keys: {unexpected}")
+
+    def q_mean_variance(self, x_start, t):
+        """
+        Get the distribution q(x_t | x_0).
+        :param x_start: the [N x C x ...] tensor of noiseless inputs.
+        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+        """
+        mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+        variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+        log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+        return mean, variance, log_variance
+
+    def predict_start_from_noise(self, x_t, t, noise):
+        return (
+                extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+                extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+        )
+
+    def q_posterior(self, x_start, x_t, t):
+        posterior_mean = (
+                extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+                extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+        )
+        posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+        posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+        return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+    def p_mean_variance(self, x, t, clip_denoised: bool):
+        model_out = self.model(x, t)
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+        b, *_, device = *x.shape, x.device
+        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+        noise = noise_like(x.shape, device, repeat_noise)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def p_sample_loop(self, shape, return_intermediates=False):
+        device = self.betas.device
+        b = shape[0]
+        img = torch.randn(shape, device=device)
+        intermediates = [img]
+        for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+            img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+                                clip_denoised=self.clip_denoised)
+            if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+                intermediates.append(img)
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, batch_size=16, return_intermediates=False):
+        image_size = self.image_size
+        channels = self.channels
+        return self.p_sample_loop((batch_size, channels, image_size, image_size),
+                                  return_intermediates=return_intermediates)
+
+    def q_sample(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+                extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+    def get_loss(self, pred, target, mean=True):
+        if self.loss_type == 'l1':
+            loss = (target - pred).abs()
+            if mean:
+                loss = loss.mean()
+        elif self.loss_type == 'l2':
+            if mean:
+                loss = torch.nn.functional.mse_loss(target, pred)
+            else:
+                loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+        else:
+            raise NotImplementedError("unknown loss type '{loss_type}'")
+
+        return loss
+
+    def p_losses(self, x_start, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_out = self.model(x_noisy, t)
+
+        loss_dict = {}
+        if self.parameterization == "eps":
+            target = noise
+        elif self.parameterization == "x0":
+            target = x_start
+        else:
+            raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
+
+        loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+        log_prefix = 'train' if self.training else 'val'
+
+        loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+        loss_simple = loss.mean() * self.l_simple_weight
+
+        loss_vlb = (self.lvlb_weights[t] * loss).mean()
+        loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+        loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+        loss_dict.update({f'{log_prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def forward(self, x, *args, **kwargs):
+        # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+        # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        return self.p_losses(x, t, *args, **kwargs)
+
+    def get_input(self, batch, k):
+        x = batch[k]
+        if len(x.shape) == 3:
+            x = x[..., None]
+        x = rearrange(x, 'b h w c -> b c h w')
+        x = x.to(memory_format=torch.contiguous_format).float()
+        return x
+
+    def shared_step(self, batch):
+        x = self.get_input(batch, self.first_stage_key)
+        loss, loss_dict = self(x)
+        return loss, loss_dict
+
+    def training_step(self, batch, batch_idx):
+        loss, loss_dict = self.shared_step(batch)
+
+        self.log_dict(loss_dict, prog_bar=True,
+                      logger=True, on_step=True, on_epoch=True)
+
+        self.log("global_step", self.global_step,
+                 prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        if self.use_scheduler:
+            lr = self.optimizers().param_groups[0]['lr']
+            self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+        return loss
+
+    @torch.no_grad()
+    def validation_step(self, batch, batch_idx):
+        _, loss_dict_no_ema = self.shared_step(batch)
+        with self.ema_scope():
+            _, loss_dict_ema = self.shared_step(batch)
+            loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+        self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+        self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+    def on_train_batch_end(self, *args, **kwargs):
+        if self.use_ema:
+            self.model_ema(self.model)
+
+    def _get_rows_from_list(self, samples):
+        n_imgs_per_row = len(samples)
+        denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+        log = {}
+        x = self.get_input(batch, self.first_stage_key)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        x = x.to(self.device)[:N]
+        log["inputs"] = x
+
+        # get diffusion row
+        diffusion_row = []
+        x_start = x[:n_row]
+
+        for t in range(self.num_timesteps):
+            if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                t = t.to(self.device).long()
+                noise = torch.randn_like(x_start)
+                x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+                diffusion_row.append(x_noisy)
+
+        log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+            log["samples"] = samples
+            log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.learn_logvar:
+            params = params + [self.logvar]
+        opt = torch.optim.AdamW(params, lr=lr)
+        return opt
+
+
+class LatentDiffusionV1(DDPMV1):
+    """main class"""
+    def __init__(self,
+                 first_stage_config,
+                 cond_stage_config,
+                 num_timesteps_cond=None,
+                 cond_stage_key="image",
+                 cond_stage_trainable=False,
+                 concat_mode=True,
+                 cond_stage_forward=None,
+                 conditioning_key=None,
+                 scale_factor=1.0,
+                 scale_by_std=False,
+                 *args, **kwargs):
+        self.num_timesteps_cond = default(num_timesteps_cond, 1)
+        self.scale_by_std = scale_by_std
+        assert self.num_timesteps_cond <= kwargs['timesteps']
+        # for backwards compatibility after implementation of DiffusionWrapper
+        if conditioning_key is None:
+            conditioning_key = 'concat' if concat_mode else 'crossattn'
+        if cond_stage_config == '__is_unconditional__':
+            conditioning_key = None
+        ckpt_path = kwargs.pop("ckpt_path", None)
+        ignore_keys = kwargs.pop("ignore_keys", [])
+        super().__init__(*args, conditioning_key=conditioning_key, **kwargs)
+        self.concat_mode = concat_mode
+        self.cond_stage_trainable = cond_stage_trainable
+        self.cond_stage_key = cond_stage_key
+        try:
+            self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+        except Exception:
+            self.num_downs = 0
+        if not scale_by_std:
+            self.scale_factor = scale_factor
+        else:
+            self.register_buffer('scale_factor', torch.tensor(scale_factor))
+        self.instantiate_first_stage(first_stage_config)
+        self.instantiate_cond_stage(cond_stage_config)
+        self.cond_stage_forward = cond_stage_forward
+        self.clip_denoised = False
+        self.bbox_tokenizer = None
+
+        self.restarted_from_ckpt = False
+        if ckpt_path is not None:
+            self.init_from_ckpt(ckpt_path, ignore_keys)
+            self.restarted_from_ckpt = True
+
+    def make_cond_schedule(self, ):
+        self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+        ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+        self.cond_ids[:self.num_timesteps_cond] = ids
+
+    @rank_zero_only
+    @torch.no_grad()
+    def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+        # only for very first batch
+        if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+            assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+            # set rescale weight to 1./std of encodings
+            print("### USING STD-RESCALING ###")
+            x = super().get_input(batch, self.first_stage_key)
+            x = x.to(self.device)
+            encoder_posterior = self.encode_first_stage(x)
+            z = self.get_first_stage_encoding(encoder_posterior).detach()
+            del self.scale_factor
+            self.register_buffer('scale_factor', 1. / z.flatten().std())
+            print(f"setting self.scale_factor to {self.scale_factor}")
+            print("### USING STD-RESCALING ###")
+
+    def register_schedule(self,
+                          given_betas=None, beta_schedule="linear", timesteps=1000,
+                          linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+        super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+        self.shorten_cond_schedule = self.num_timesteps_cond > 1
+        if self.shorten_cond_schedule:
+            self.make_cond_schedule()
+
+    def instantiate_first_stage(self, config):
+        model = instantiate_from_config(config)
+        self.first_stage_model = model.eval()
+        self.first_stage_model.train = disabled_train
+        for param in self.first_stage_model.parameters():
+            param.requires_grad = False
+
+    def instantiate_cond_stage(self, config):
+        if not self.cond_stage_trainable:
+            if config == "__is_first_stage__":
+                print("Using first stage also as cond stage.")
+                self.cond_stage_model = self.first_stage_model
+            elif config == "__is_unconditional__":
+                print(f"Training {self.__class__.__name__} as an unconditional model.")
+                self.cond_stage_model = None
+                # self.be_unconditional = True
+            else:
+                model = instantiate_from_config(config)
+                self.cond_stage_model = model.eval()
+                self.cond_stage_model.train = disabled_train
+                for param in self.cond_stage_model.parameters():
+                    param.requires_grad = False
+        else:
+            assert config != '__is_first_stage__'
+            assert config != '__is_unconditional__'
+            model = instantiate_from_config(config)
+            self.cond_stage_model = model
+
+    def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+        denoise_row = []
+        for zd in tqdm(samples, desc=desc):
+            denoise_row.append(self.decode_first_stage(zd.to(self.device),
+                                                            force_not_quantize=force_no_decoder_quantization))
+        n_imgs_per_row = len(denoise_row)
+        denoise_row = torch.stack(denoise_row)  # n_log_step, n_row, C, H, W
+        denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+        denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+        denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+        return denoise_grid
+
+    def get_first_stage_encoding(self, encoder_posterior):
+        if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+            z = encoder_posterior.sample()
+        elif isinstance(encoder_posterior, torch.Tensor):
+            z = encoder_posterior
+        else:
+            raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+        return self.scale_factor * z
+
+    def get_learned_conditioning(self, c):
+        if self.cond_stage_forward is None:
+            if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+                c = self.cond_stage_model.encode(c)
+                if isinstance(c, DiagonalGaussianDistribution):
+                    c = c.mode()
+            else:
+                c = self.cond_stage_model(c)
+        else:
+            assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+            c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+        return c
+
+    def meshgrid(self, h, w):
+        y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+        x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+        arr = torch.cat([y, x], dim=-1)
+        return arr
+
+    def delta_border(self, h, w):
+        """
+        :param h: height
+        :param w: width
+        :return: normalized distance to image border,
+         wtith min distance = 0 at border and max dist = 0.5 at image center
+        """
+        lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+        arr = self.meshgrid(h, w) / lower_right_corner
+        dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+        dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+        edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+        return edge_dist
+
+    def get_weighting(self, h, w, Ly, Lx, device):
+        weighting = self.delta_border(h, w)
+        weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+                               self.split_input_params["clip_max_weight"], )
+        weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+        if self.split_input_params["tie_braker"]:
+            L_weighting = self.delta_border(Ly, Lx)
+            L_weighting = torch.clip(L_weighting,
+                                     self.split_input_params["clip_min_tie_weight"],
+                                     self.split_input_params["clip_max_tie_weight"])
+
+            L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+            weighting = weighting * L_weighting
+        return weighting
+
+    def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1):  # todo load once not every time, shorten code
+        """
+        :param x: img of size (bs, c, h, w)
+        :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+        """
+        bs, nc, h, w = x.shape
+
+        # number of crops in image
+        Ly = (h - kernel_size[0]) // stride[0] + 1
+        Lx = (w - kernel_size[1]) // stride[1] + 1
+
+        if uf == 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+            weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h, w)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+        elif uf > 1 and df == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+                                dilation=1, padding=0,
+                                stride=(stride[0] * uf, stride[1] * uf))
+            fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h * uf, w * uf)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+        elif df > 1 and uf == 1:
+            fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+            unfold = torch.nn.Unfold(**fold_params)
+
+            fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+                                dilation=1, padding=0,
+                                stride=(stride[0] // df, stride[1] // df))
+            fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+            weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+            normalization = fold(weighting).view(1, 1, h // df, w // df)  # normalizes the overlap
+            weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+        else:
+            raise NotImplementedError
+
+        return fold, unfold, normalization, weighting
+
+    @torch.no_grad()
+    def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+                  cond_key=None, return_original_cond=False, bs=None):
+        x = super().get_input(batch, k)
+        if bs is not None:
+            x = x[:bs]
+        x = x.to(self.device)
+        encoder_posterior = self.encode_first_stage(x)
+        z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+        if self.model.conditioning_key is not None:
+            if cond_key is None:
+                cond_key = self.cond_stage_key
+            if cond_key != self.first_stage_key:
+                if cond_key in ['caption', 'coordinates_bbox']:
+                    xc = batch[cond_key]
+                elif cond_key == 'class_label':
+                    xc = batch
+                else:
+                    xc = super().get_input(batch, cond_key).to(self.device)
+            else:
+                xc = x
+            if not self.cond_stage_trainable or force_c_encode:
+                if isinstance(xc, dict) or isinstance(xc, list):
+                    # import pudb; pudb.set_trace()
+                    c = self.get_learned_conditioning(xc)
+                else:
+                    c = self.get_learned_conditioning(xc.to(self.device))
+            else:
+                c = xc
+            if bs is not None:
+                c = c[:bs]
+
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                ckey = __conditioning_keys__[self.model.conditioning_key]
+                c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+        else:
+            c = None
+            xc = None
+            if self.use_positional_encodings:
+                pos_x, pos_y = self.compute_latent_shifts(batch)
+                c = {'pos_x': pos_x, 'pos_y': pos_y}
+        out = [z, c]
+        if return_first_stage_outputs:
+            xrec = self.decode_first_stage(z)
+            out.extend([x, xrec])
+        if return_original_cond:
+            out.append(xc)
+        return out
+
+    @torch.no_grad()
+    def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    # same as above but without decorator
+    def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+        if predict_cids:
+            if z.dim() == 4:
+                z = torch.argmax(z.exp(), dim=1).long()
+            z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+            z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+        z = 1. / self.scale_factor * z
+
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                uf = self.split_input_params["vqf"]
+                bs, nc, h, w = z.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
+
+                z = unfold(z)  # (bn, nc * prod(**ks), L)
+                # 1. Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                # 2. apply model loop over last dim
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
+                                                                 force_not_quantize=predict_cids or force_not_quantize)
+                                   for i in range(z.shape[-1])]
+                else:
+
+                    output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
+                                   for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)  # # (bn, nc, ks[0], ks[1], L)
+                o = o * weighting
+                # Reverse 1. reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization  # norm is shape (1, 1, h, w)
+                return decoded
+            else:
+                if isinstance(self.first_stage_model, VQModelInterface):
+                    return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+                else:
+                    return self.first_stage_model.decode(z)
+
+        else:
+            if isinstance(self.first_stage_model, VQModelInterface):
+                return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
+            else:
+                return self.first_stage_model.decode(z)
+
+    @torch.no_grad()
+    def encode_first_stage(self, x):
+        if hasattr(self, "split_input_params"):
+            if self.split_input_params["patch_distributed_vq"]:
+                ks = self.split_input_params["ks"]  # eg. (128, 128)
+                stride = self.split_input_params["stride"]  # eg. (64, 64)
+                df = self.split_input_params["vqf"]
+                self.split_input_params['original_image_size'] = x.shape[-2:]
+                bs, nc, h, w = x.shape
+                if ks[0] > h or ks[1] > w:
+                    ks = (min(ks[0], h), min(ks[1], w))
+                    print("reducing Kernel")
+
+                if stride[0] > h or stride[1] > w:
+                    stride = (min(stride[0], h), min(stride[1], w))
+                    print("reducing stride")
+
+                fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
+                z = unfold(x)  # (bn, nc * prod(**ks), L)
+                # Reshape to img shape
+                z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
+                               for i in range(z.shape[-1])]
+
+                o = torch.stack(output_list, axis=-1)
+                o = o * weighting
+
+                # Reverse reshape to img shape
+                o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+                # stitch crops together
+                decoded = fold(o)
+                decoded = decoded / normalization
+                return decoded
+
+            else:
+                return self.first_stage_model.encode(x)
+        else:
+            return self.first_stage_model.encode(x)
+
+    def shared_step(self, batch, **kwargs):
+        x, c = self.get_input(batch, self.first_stage_key)
+        loss = self(x, c)
+        return loss
+
+    def forward(self, x, c, *args, **kwargs):
+        t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+        if self.model.conditioning_key is not None:
+            assert c is not None
+            if self.cond_stage_trainable:
+                c = self.get_learned_conditioning(c)
+            if self.shorten_cond_schedule:  # TODO: drop this option
+                tc = self.cond_ids[t].to(self.device)
+                c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+        return self.p_losses(x, c, t, *args, **kwargs)
+
+    def apply_model(self, x_noisy, t, cond, return_ids=False):
+
+        if isinstance(cond, dict):
+            # hybrid case, cond is exptected to be a dict
+            pass
+        else:
+            if not isinstance(cond, list):
+                cond = [cond]
+            key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+            cond = {key: cond}
+
+        if hasattr(self, "split_input_params"):
+            assert len(cond) == 1  # todo can only deal with one conditioning atm
+            assert not return_ids
+            ks = self.split_input_params["ks"]  # eg. (128, 128)
+            stride = self.split_input_params["stride"]  # eg. (64, 64)
+
+            h, w = x_noisy.shape[-2:]
+
+            fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
+
+            z = unfold(x_noisy)  # (bn, nc * prod(**ks), L)
+            # Reshape to img shape
+            z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+            z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
+
+            if self.cond_stage_key in ["image", "LR_image", "segmentation",
+                                       'bbox_img'] and self.model.conditioning_key:  # todo check for completeness
+                c_key = next(iter(cond.keys()))  # get key
+                c = next(iter(cond.values()))  # get value
+                assert (len(c) == 1)  # todo extend to list with more than one elem
+                c = c[0]  # get element
+
+                c = unfold(c)
+                c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1]))  # (bn, nc, ks[0], ks[1], L )
+
+                cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
+
+            elif self.cond_stage_key == 'coordinates_bbox':
+                assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
+
+                # assuming padding of unfold is always 0 and its dilation is always 1
+                n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
+                full_img_h, full_img_w = self.split_input_params['original_image_size']
+                # as we are operating on latents, we need the factor from the original image size to the
+                # spatial latent size to properly rescale the crops for regenerating the bbox annotations
+                num_downs = self.first_stage_model.encoder.num_resolutions - 1
+                rescale_latent = 2 ** (num_downs)
+
+                # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
+                # need to rescale the tl patch coordinates to be in between (0,1)
+                tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
+                                         rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
+                                        for patch_nr in range(z.shape[-1])]
+
+                # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
+                patch_limits = [(x_tl, y_tl,
+                                 rescale_latent * ks[0] / full_img_w,
+                                 rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
+                # patch_values = [(np.arange(x_tl,min(x_tl+ks, 1.)),np.arange(y_tl,min(y_tl+ks, 1.))) for x_tl, y_tl in tl_patch_coordinates]
+
+                # tokenize crop coordinates for the bounding boxes of the respective patches
+                patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
+                                      for bbox in patch_limits]  # list of length l with tensors of shape (1, 2)
+                print(patch_limits_tknzd[0].shape)
+                # cut tknzd crop position from conditioning
+                assert isinstance(cond, dict), 'cond must be dict to be fed into model'
+                cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
+                print(cut_cond.shape)
+
+                adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
+                adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
+                print(adapted_cond.shape)
+                adapted_cond = self.get_learned_conditioning(adapted_cond)
+                print(adapted_cond.shape)
+                adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
+                print(adapted_cond.shape)
+
+                cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
+
+            else:
+                cond_list = [cond for i in range(z.shape[-1])]  # Todo make this more efficient
+
+            # apply model by loop over crops
+            output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
+            assert not isinstance(output_list[0],
+                                  tuple)  # todo cant deal with multiple model outputs check this never happens
+
+            o = torch.stack(output_list, axis=-1)
+            o = o * weighting
+            # Reverse reshape to img shape
+            o = o.view((o.shape[0], -1, o.shape[-1]))  # (bn, nc * ks[0] * ks[1], L)
+            # stitch crops together
+            x_recon = fold(o) / normalization
+
+        else:
+            x_recon = self.model(x_noisy, t, **cond)
+
+        if isinstance(x_recon, tuple) and not return_ids:
+            return x_recon[0]
+        else:
+            return x_recon
+
+    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+        return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+               extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+    def _prior_bpd(self, x_start):
+        """
+        Get the prior KL term for the variational lower-bound, measured in
+        bits-per-dim.
+        This term can't be optimized, as it only depends on the encoder.
+        :param x_start: the [N x C x ...] tensor of inputs.
+        :return: a batch of [N] KL values (in bits), one per batch element.
+        """
+        batch_size = x_start.shape[0]
+        t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+        qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+        kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+        return mean_flat(kl_prior) / np.log(2.0)
+
+    def p_losses(self, x_start, cond, t, noise=None):
+        noise = default(noise, lambda: torch.randn_like(x_start))
+        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+        model_output = self.apply_model(x_noisy, t, cond)
+
+        loss_dict = {}
+        prefix = 'train' if self.training else 'val'
+
+        if self.parameterization == "x0":
+            target = x_start
+        elif self.parameterization == "eps":
+            target = noise
+        else:
+            raise NotImplementedError()
+
+        loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+        loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+        logvar_t = self.logvar[t].to(self.device)
+        loss = loss_simple / torch.exp(logvar_t) + logvar_t
+        # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+        if self.learn_logvar:
+            loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+            loss_dict.update({'logvar': self.logvar.data.mean()})
+
+        loss = self.l_simple_weight * loss.mean()
+
+        loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+        loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+        loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+        loss += (self.original_elbo_weight * loss_vlb)
+        loss_dict.update({f'{prefix}/loss': loss})
+
+        return loss, loss_dict
+
+    def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+                        return_x0=False, score_corrector=None, corrector_kwargs=None):
+        t_in = t
+        model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+        if score_corrector is not None:
+            assert self.parameterization == "eps"
+            model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+        if return_codebook_ids:
+            model_out, logits = model_out
+
+        if self.parameterization == "eps":
+            x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+        elif self.parameterization == "x0":
+            x_recon = model_out
+        else:
+            raise NotImplementedError()
+
+        if clip_denoised:
+            x_recon.clamp_(-1., 1.)
+        if quantize_denoised:
+            x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+        if return_codebook_ids:
+            return model_mean, posterior_variance, posterior_log_variance, logits
+        elif return_x0:
+            return model_mean, posterior_variance, posterior_log_variance, x_recon
+        else:
+            return model_mean, posterior_variance, posterior_log_variance
+
+    @torch.no_grad()
+    def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+                 return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+                 temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+        b, *_, device = *x.shape, x.device
+        outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+                                       return_codebook_ids=return_codebook_ids,
+                                       quantize_denoised=quantize_denoised,
+                                       return_x0=return_x0,
+                                       score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+        if return_codebook_ids:
+            raise DeprecationWarning("Support dropped.")
+            model_mean, _, model_log_variance, logits = outputs
+        elif return_x0:
+            model_mean, _, model_log_variance, x0 = outputs
+        else:
+            model_mean, _, model_log_variance = outputs
+
+        noise = noise_like(x.shape, device, repeat_noise) * temperature
+        if noise_dropout > 0.:
+            noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+        # no noise when t == 0
+        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+        if return_codebook_ids:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+        if return_x0:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+        else:
+            return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+    @torch.no_grad()
+    def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+                              img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+                              score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+                              log_every_t=None):
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        timesteps = self.num_timesteps
+        if batch_size is not None:
+            b = batch_size if batch_size is not None else shape[0]
+            shape = [batch_size] + list(shape)
+        else:
+            b = batch_size = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=self.device)
+        else:
+            img = x_T
+        intermediates = []
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                [x[:batch_size] for x in cond[key]] for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+                        total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+        if type(temperature) == float:
+            temperature = [temperature] * timesteps
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img, x0_partial = self.p_sample(img, cond, ts,
+                                            clip_denoised=self.clip_denoised,
+                                            quantize_denoised=quantize_denoised, return_x0=True,
+                                            temperature=temperature[i], noise_dropout=noise_dropout,
+                                            score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+            if mask is not None:
+                assert x0 is not None
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(x0_partial)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
+        return img, intermediates
+
+    @torch.no_grad()
+    def p_sample_loop(self, cond, shape, return_intermediates=False,
+                      x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+                      mask=None, x0=None, img_callback=None, start_T=None,
+                      log_every_t=None):
+
+        if not log_every_t:
+            log_every_t = self.log_every_t
+        device = self.betas.device
+        b = shape[0]
+        if x_T is None:
+            img = torch.randn(shape, device=device)
+        else:
+            img = x_T
+
+        intermediates = [img]
+        if timesteps is None:
+            timesteps = self.num_timesteps
+
+        if start_T is not None:
+            timesteps = min(timesteps, start_T)
+        iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+            range(0, timesteps))
+
+        if mask is not None:
+            assert x0 is not None
+            assert x0.shape[2:3] == mask.shape[2:3]  # spatial size has to match
+
+        for i in iterator:
+            ts = torch.full((b,), i, device=device, dtype=torch.long)
+            if self.shorten_cond_schedule:
+                assert self.model.conditioning_key != 'hybrid'
+                tc = self.cond_ids[ts].to(cond.device)
+                cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+            img = self.p_sample(img, cond, ts,
+                                clip_denoised=self.clip_denoised,
+                                quantize_denoised=quantize_denoised)
+            if mask is not None:
+                img_orig = self.q_sample(x0, ts)
+                img = img_orig * mask + (1. - mask) * img
+
+            if i % log_every_t == 0 or i == timesteps - 1:
+                intermediates.append(img)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
+
+        if return_intermediates:
+            return img, intermediates
+        return img
+
+    @torch.no_grad()
+    def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+               verbose=True, timesteps=None, quantize_denoised=False,
+               mask=None, x0=None, shape=None,**kwargs):
+        if shape is None:
+            shape = (batch_size, self.channels, self.image_size, self.image_size)
+        if cond is not None:
+            if isinstance(cond, dict):
+                cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+                [x[:batch_size] for x in cond[key]] for key in cond}
+            else:
+                cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+        return self.p_sample_loop(cond,
+                                  shape,
+                                  return_intermediates=return_intermediates, x_T=x_T,
+                                  verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+                                  mask=mask, x0=x0)
+
+    @torch.no_grad()
+    def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
+
+        if ddim:
+            ddim_sampler = DDIMSampler(self)
+            shape = (self.channels, self.image_size, self.image_size)
+            samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
+                                                        shape,cond,verbose=False,**kwargs)
+
+        else:
+            samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+                                                 return_intermediates=True,**kwargs)
+
+        return samples, intermediates
+
+
+    @torch.no_grad()
+    def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+                   quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+                   plot_diffusion_rows=True, **kwargs):
+
+        use_ddim = ddim_steps is not None
+
+        log = {}
+        z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+                                           return_first_stage_outputs=True,
+                                           force_c_encode=True,
+                                           return_original_cond=True,
+                                           bs=N)
+        N = min(x.shape[0], N)
+        n_row = min(x.shape[0], n_row)
+        log["inputs"] = x
+        log["reconstruction"] = xrec
+        if self.model.conditioning_key is not None:
+            if hasattr(self.cond_stage_model, "decode"):
+                xc = self.cond_stage_model.decode(c)
+                log["conditioning"] = xc
+            elif self.cond_stage_key in ["caption"]:
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
+                log["conditioning"] = xc
+            elif self.cond_stage_key == 'class_label':
+                xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
+                log['conditioning'] = xc
+            elif isimage(xc):
+                log["conditioning"] = xc
+            if ismap(xc):
+                log["original_conditioning"] = self.to_rgb(xc)
+
+        if plot_diffusion_rows:
+            # get diffusion row
+            diffusion_row = []
+            z_start = z[:n_row]
+            for t in range(self.num_timesteps):
+                if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+                    t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+                    t = t.to(self.device).long()
+                    noise = torch.randn_like(z_start)
+                    z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+                    diffusion_row.append(self.decode_first_stage(z_noisy))
+
+            diffusion_row = torch.stack(diffusion_row)  # n_log_step, n_row, C, H, W
+            diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+            diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+            diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+            log["diffusion_row"] = diffusion_grid
+
+        if sample:
+            # get denoise row
+            with self.ema_scope("Plotting"):
+                samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                         ddim_steps=ddim_steps,eta=ddim_eta)
+                # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+            x_samples = self.decode_first_stage(samples)
+            log["samples"] = x_samples
+            if plot_denoise_rows:
+                denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+                log["denoise_row"] = denoise_grid
+
+            if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+                    self.first_stage_model, IdentityFirstStage):
+                # also display when quantizing x0 while sampling
+                with self.ema_scope("Plotting Quantized Denoised"):
+                    samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
+                                                             ddim_steps=ddim_steps,eta=ddim_eta,
+                                                             quantize_denoised=True)
+                    # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+                    #                                      quantize_denoised=True)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_x0_quantized"] = x_samples
+
+            if inpaint:
+                # make a simple center square
+                h, w = z.shape[2], z.shape[3]
+                mask = torch.ones(N, h, w).to(self.device)
+                # zeros will be filled in
+                mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+                mask = mask[:, None, ...]
+                with self.ema_scope("Plotting Inpaint"):
+
+                    samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_inpainting"] = x_samples
+                log["mask"] = mask
+
+                # outpaint
+                with self.ema_scope("Plotting Outpaint"):
+                    samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
+                                                ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+                x_samples = self.decode_first_stage(samples.to(self.device))
+                log["samples_outpainting"] = x_samples
+
+        if plot_progressive_rows:
+            with self.ema_scope("Plotting Progressives"):
+                img, progressives = self.progressive_denoising(c,
+                                                               shape=(self.channels, self.image_size, self.image_size),
+                                                               batch_size=N)
+            prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+            log["progressive_row"] = prog_row
+
+        if return_keys:
+            if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+                return log
+            else:
+                return {key: log[key] for key in return_keys}
+        return log
+
+    def configure_optimizers(self):
+        lr = self.learning_rate
+        params = list(self.model.parameters())
+        if self.cond_stage_trainable:
+            print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+            params = params + list(self.cond_stage_model.parameters())
+        if self.learn_logvar:
+            print('Diffusion model optimizing logvar')
+            params.append(self.logvar)
+        opt = torch.optim.AdamW(params, lr=lr)
+        if self.use_scheduler:
+            assert 'target' in self.scheduler_config
+            scheduler = instantiate_from_config(self.scheduler_config)
+
+            print("Setting up LambdaLR scheduler...")
+            scheduler = [
+                {
+                    'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+                    'interval': 'step',
+                    'frequency': 1
+                }]
+            return [opt], scheduler
+        return opt
+
+    @torch.no_grad()
+    def to_rgb(self, x):
+        x = x.float()
+        if not hasattr(self, "colorize"):
+            self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+        x = nn.functional.conv2d(x, weight=self.colorize)
+        x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+        return x
+
+
+class DiffusionWrapperV1(pl.LightningModule):
+    def __init__(self, diff_model_config, conditioning_key):
+        super().__init__()
+        self.diffusion_model = instantiate_from_config(diff_model_config)
+        self.conditioning_key = conditioning_key
+        assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
+
+    def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
+        if self.conditioning_key is None:
+            out = self.diffusion_model(x, t)
+        elif self.conditioning_key == 'concat':
+            xc = torch.cat([x] + c_concat, dim=1)
+            out = self.diffusion_model(xc, t)
+        elif self.conditioning_key == 'crossattn':
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(x, t, context=cc)
+        elif self.conditioning_key == 'hybrid':
+            xc = torch.cat([x] + c_concat, dim=1)
+            cc = torch.cat(c_crossattn, 1)
+            out = self.diffusion_model(xc, t, context=cc)
+        elif self.conditioning_key == 'adm':
+            cc = c_crossattn[0]
+            out = self.diffusion_model(x, t, y=cc)
+        else:
+            raise NotImplementedError()
+
+        return out
+
+
+class Layout2ImgDiffusionV1(LatentDiffusionV1):
+    # TODO: move all layout-specific hacks to this class
+    def __init__(self, cond_stage_key, *args, **kwargs):
+        assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
+        super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
+
+    def log_images(self, batch, N=8, *args, **kwargs):
+        logs = super().log_images(*args, batch=batch, N=N, **kwargs)
+
+        key = 'train' if self.training else 'validation'
+        dset = self.trainer.datamodule.datasets[key]
+        mapper = dset.conditional_builders[self.cond_stage_key]
+
+        bbox_imgs = []
+        map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
+        for tknzd_bbox in batch[self.cond_stage_key][:N]:
+            bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
+            bbox_imgs.append(bboximg)
+
+        cond_img = torch.stack(bbox_imgs, dim=0)
+        logs['bbox_image'] = cond_img
+        return logs
+
+ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
+ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
+ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
+ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1

extensions-builtin/LDSR/vqvae_quantize.py

@@ -0,0 +1,147 @@
+# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
+# where the license is as follows:
+#
+# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
+#
+# 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./
+
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+
+
+class VectorQuantizer2(nn.Module):
+    """
+    Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
+    avoids costly matrix multiplications and allows for post-hoc remapping of indices.
+    """
+
+    # NOTE: due to a bug the beta term was applied to the wrong term. for
+    # backwards compatibility we use the buggy version by default, but you can
+    # specify legacy=False to fix it.
+    def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
+                 sane_index_shape=False, legacy=True):
+        super().__init__()
+        self.n_e = n_e
+        self.e_dim = e_dim
+        self.beta = beta
+        self.legacy = legacy
+
+        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)
+
+        self.remap = remap
+        if self.remap is not None:
+            self.register_buffer("used", torch.tensor(np.load(self.remap)))
+            self.re_embed = self.used.shape[0]
+            self.unknown_index = unknown_index  # "random" or "extra" or integer
+            if self.unknown_index == "extra":
+                self.unknown_index = self.re_embed
+                self.re_embed = self.re_embed + 1
+            print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
+                  f"Using {self.unknown_index} for unknown indices.")
+        else:
+            self.re_embed = n_e
+
+        self.sane_index_shape = sane_index_shape
+
+    def remap_to_used(self, inds):
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        match = (inds[:, :, None] == used[None, None, ...]).long()
+        new = match.argmax(-1)
+        unknown = match.sum(2) < 1
+        if self.unknown_index == "random":
+            new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
+        else:
+            new[unknown] = self.unknown_index
+        return new.reshape(ishape)
+
+    def unmap_to_all(self, inds):
+        ishape = inds.shape
+        assert len(ishape) > 1
+        inds = inds.reshape(ishape[0], -1)
+        used = self.used.to(inds)
+        if self.re_embed > self.used.shape[0]:  # extra token
+            inds[inds >= self.used.shape[0]] = 0  # simply set to zero
+        back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
+        return back.reshape(ishape)
+
+    def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
+        assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
+        assert rescale_logits is False, "Only for interface compatible with Gumbel"
+        assert return_logits is False, "Only for interface compatible with Gumbel"
+        # reshape z -> (batch, height, width, channel) and flatten
+        z = rearrange(z, 'b c h w -> b h w c').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.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
+
+        min_encoding_indices = torch.argmin(d, dim=1)
+        z_q = self.embedding(min_encoding_indices).view(z.shape)
+        perplexity = None
+        min_encodings = None
+
+        # compute loss for embedding
+        if not self.legacy:
+            loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
+                   torch.mean((z_q - z.detach()) ** 2)
+        else:
+            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()
+
+        # reshape back to match original input shape
+        z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
+
+        if self.remap is not None:
+            min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1)  # add batch axis
+            min_encoding_indices = self.remap_to_used(min_encoding_indices)
+            min_encoding_indices = min_encoding_indices.reshape(-1, 1)  # flatten
+
+        if self.sane_index_shape:
+            min_encoding_indices = min_encoding_indices.reshape(
+                z_q.shape[0], z_q.shape[2], z_q.shape[3])
+
+        return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
+
+    def get_codebook_entry(self, indices, shape):
+        # shape specifying (batch, height, width, channel)
+        if self.remap is not None:
+            indices = indices.reshape(shape[0], -1)  # add batch axis
+            indices = self.unmap_to_all(indices)
+            indices = indices.reshape(-1)  # flatten again
+
+        # get quantized latent vectors
+        z_q = self.embedding(indices)
+
+        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