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  1. .github/ISSUE_TEMPLATE/bug_report.yml +100 -0
  2. .github/ISSUE_TEMPLATE/config.yml +5 -0
  3. .github/ISSUE_TEMPLATE/feature_request.yml +40 -0
  4. .github/pull_request_template.md +28 -0
  5. .github/workflows/on_pull_request.yaml +39 -0
  6. .github/workflows/run_tests.yaml +29 -0
  7. .gitignore +36 -0
  8. .pylintrc +3 -0
  9. CHANGELOG.md +120 -0
  10. CODEOWNERS +12 -0
  11. LICENSE.txt +663 -0
  12. README.md +162 -0
  13. configs/alt-diffusion-inference.yaml +72 -0
  14. configs/instruct-pix2pix.yaml +98 -0
  15. configs/v1-inference.yaml +70 -0
  16. configs/v1-inpainting-inference.yaml +70 -0
  17. embeddings/Place Textual Inversion embeddings here.txt +0 -0
  18. environment-wsl2.yaml +11 -0
  19. extensions-builtin/LDSR/ldsr_model_arch.py +253 -0
  20. extensions-builtin/LDSR/preload.py +6 -0
  21. extensions-builtin/LDSR/scripts/ldsr_model.py +75 -0
  22. extensions-builtin/LDSR/sd_hijack_autoencoder.py +286 -0
  23. extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +1449 -0
  24. extensions-builtin/Lora/extra_networks_lora.py +27 -0
  25. extensions-builtin/Lora/lora.py +460 -0
  26. extensions-builtin/Lora/preload.py +6 -0
  27. extensions-builtin/Lora/scripts/lora_script.py +82 -0
  28. extensions-builtin/Lora/ui_extra_networks_lora.py +37 -0
  29. extensions-builtin/ScuNET/preload.py +6 -0
  30. extensions-builtin/ScuNET/scripts/scunet_model.py +140 -0
  31. extensions-builtin/ScuNET/scunet_model_arch.py +265 -0
  32. extensions-builtin/SwinIR/preload.py +6 -0
  33. extensions-builtin/SwinIR/scripts/swinir_model.py +178 -0
  34. extensions-builtin/SwinIR/swinir_model_arch.py +867 -0
  35. extensions-builtin/SwinIR/swinir_model_arch_v2.py +1017 -0
  36. extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js +42 -0
  37. extensions/put extensions here.txt +0 -0
  38. html/card-no-preview.png +0 -0
  39. html/extra-networks-card.html +15 -0
  40. html/extra-networks-no-cards.html +8 -0
  41. html/footer.html +13 -0
  42. html/image-update.svg +7 -0
  43. html/licenses.html +664 -0
  44. javascript/aspectRatioOverlay.js +111 -0
  45. javascript/contextMenus.js +166 -0
  46. javascript/dragdrop.js +97 -0
  47. javascript/edit-attention.js +120 -0
  48. javascript/extensions.js +71 -0
  49. javascript/extraNetworks.js +196 -0
  50. javascript/generationParams.js +33 -0
.github/ISSUE_TEMPLATE/bug_report.yml ADDED
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1
+ name: Bug Report
2
+ description: You think somethings is broken in the UI
3
+ title: "[Bug]: "
4
+ labels: ["bug-report"]
5
+
6
+ body:
7
+ - type: checkboxes
8
+ attributes:
9
+ label: Is there an existing issue for this?
10
+ 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.
11
+ options:
12
+ - label: I have searched the existing issues and checked the recent builds/commits
13
+ required: true
14
+ - type: markdown
15
+ attributes:
16
+ value: |
17
+ *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**
18
+ - type: textarea
19
+ id: what-did
20
+ attributes:
21
+ label: What happened?
22
+ description: Tell us what happened in a very clear and simple way
23
+ validations:
24
+ required: true
25
+ - type: textarea
26
+ id: steps
27
+ attributes:
28
+ label: Steps to reproduce the problem
29
+ description: Please provide us with precise step by step information on how to reproduce the bug
30
+ value: |
31
+ 1. Go to ....
32
+ 2. Press ....
33
+ 3. ...
34
+ validations:
35
+ required: true
36
+ - type: textarea
37
+ id: what-should
38
+ attributes:
39
+ label: What should have happened?
40
+ description: Tell what you think the normal behavior should be
41
+ validations:
42
+ required: true
43
+ - type: input
44
+ id: commit
45
+ attributes:
46
+ label: Commit where the problem happens
47
+ description: Which 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 **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
48
+ validations:
49
+ required: true
50
+ - type: dropdown
51
+ id: platforms
52
+ attributes:
53
+ label: What platforms do you use to access the UI ?
54
+ multiple: true
55
+ options:
56
+ - Windows
57
+ - Linux
58
+ - MacOS
59
+ - iOS
60
+ - Android
61
+ - Other/Cloud
62
+ - type: dropdown
63
+ id: browsers
64
+ attributes:
65
+ label: What browsers do you use to access the UI ?
66
+ multiple: true
67
+ options:
68
+ - Mozilla Firefox
69
+ - Google Chrome
70
+ - Brave
71
+ - Apple Safari
72
+ - Microsoft Edge
73
+ - type: textarea
74
+ id: cmdargs
75
+ attributes:
76
+ label: Command Line Arguments
77
+ description: Are you using any launching parameters/command line arguments (modified webui-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
78
+ render: Shell
79
+ validations:
80
+ required: true
81
+ - type: textarea
82
+ id: extensions
83
+ attributes:
84
+ label: List of extensions
85
+ 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.
86
+ validations:
87
+ required: true
88
+ - type: textarea
89
+ id: logs
90
+ attributes:
91
+ label: Console logs
92
+ 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.
93
+ render: Shell
94
+ validations:
95
+ required: true
96
+ - type: textarea
97
+ id: misc
98
+ attributes:
99
+ label: Additional information
100
+ description: Please provide us with any relevant additional info or context.
.github/ISSUE_TEMPLATE/config.yml ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ blank_issues_enabled: false
2
+ contact_links:
3
+ - name: WebUI Community Support
4
+ url: https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions
5
+ about: Please ask and answer questions here.
.github/ISSUE_TEMPLATE/feature_request.yml ADDED
@@ -0,0 +1,40 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Feature request
2
+ description: Suggest an idea for this project
3
+ title: "[Feature Request]: "
4
+ labels: ["enhancement"]
5
+
6
+ body:
7
+ - type: checkboxes
8
+ attributes:
9
+ label: Is there an existing issue for this?
10
+ 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.
11
+ options:
12
+ - label: I have searched the existing issues and checked the recent builds/commits
13
+ required: true
14
+ - type: markdown
15
+ attributes:
16
+ value: |
17
+ *Please fill this form with as much information as possible, provide screenshots and/or illustrations of the feature if possible*
18
+ - type: textarea
19
+ id: feature
20
+ attributes:
21
+ label: What would your feature do ?
22
+ description: Tell us about your feature in a very clear and simple way, and what problem it would solve
23
+ validations:
24
+ required: true
25
+ - type: textarea
26
+ id: workflow
27
+ attributes:
28
+ label: Proposed workflow
29
+ description: Please provide us with step by step information on how you'd like the feature to be accessed and used
30
+ value: |
31
+ 1. Go to ....
32
+ 2. Press ....
33
+ 3. ...
34
+ validations:
35
+ required: true
36
+ - type: textarea
37
+ id: misc
38
+ attributes:
39
+ label: Additional information
40
+ description: Add any other context or screenshots about the feature request here.
.github/pull_request_template.md ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Please read the [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing) before submitting a pull request!
2
+
3
+ If you have a large change, pay special attention to this paragraph:
4
+
5
+ > Before making changes, if you think that your feature will result in more than 100 lines changing, find me and talk to me about the feature you are proposing. It pains me to reject the hard work someone else did, but I won't add everything to the repo, and it's better if the rejection happens before you have to waste time working on the feature.
6
+
7
+ Otherwise, after making sure you're following the rules described in wiki page, remove this section and continue on.
8
+
9
+ **Describe what this pull request is trying to achieve.**
10
+
11
+ A clear and concise description of what you're trying to accomplish with this, so your intent doesn't have to be extracted from your code.
12
+
13
+ **Additional notes and description of your changes**
14
+
15
+ More technical discussion about your changes go here, plus anything that a maintainer might have to specifically take a look at, or be wary of.
16
+
17
+ **Environment this was tested in**
18
+
19
+ List the environment you have developed / tested this on. As per the contributing page, changes should be able to work on Windows out of the box.
20
+ - OS: [e.g. Windows, Linux]
21
+ - Browser: [e.g. chrome, safari]
22
+ - Graphics card: [e.g. NVIDIA RTX 2080 8GB, AMD RX 6600 8GB]
23
+
24
+ **Screenshots or videos of your changes**
25
+
26
+ If applicable, screenshots or a video showing off your changes. If it edits an existing UI, it should ideally contain a comparison of what used to be there, before your changes were made.
27
+
28
+ This is **required** for anything that touches the user interface.
.github/workflows/on_pull_request.yaml ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # See https://github.com/actions/starter-workflows/blob/1067f16ad8a1eac328834e4b0ae24f7d206f810d/ci/pylint.yml for original reference file
2
+ name: Run Linting/Formatting on Pull Requests
3
+
4
+ on:
5
+ - push
6
+ - pull_request
7
+ # See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#onpull_requestpull_request_targetbranchesbranches-ignore for syntax docs
8
+ # if you want to filter out branches, delete the `- pull_request` and uncomment these lines :
9
+ # pull_request:
10
+ # branches:
11
+ # - master
12
+ # branches-ignore:
13
+ # - development
14
+
15
+ jobs:
16
+ lint:
17
+ runs-on: ubuntu-latest
18
+ steps:
19
+ - name: Checkout Code
20
+ uses: actions/checkout@v3
21
+ - name: Set up Python 3.10
22
+ uses: actions/setup-python@v4
23
+ with:
24
+ python-version: 3.10.6
25
+ cache: pip
26
+ cache-dependency-path: |
27
+ **/requirements*txt
28
+ - name: Install PyLint
29
+ run: |
30
+ python -m pip install --upgrade pip
31
+ pip install pylint
32
+ # This lets PyLint check to see if it can resolve imports
33
+ - name: Install dependencies
34
+ run: |
35
+ export COMMANDLINE_ARGS="--skip-torch-cuda-test --exit"
36
+ python launch.py
37
+ - name: Analysing the code with pylint
38
+ run: |
39
+ pylint $(git ls-files '*.py')
.github/workflows/run_tests.yaml ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Run basic features tests on CPU with empty SD model
2
+
3
+ on:
4
+ - push
5
+ - pull_request
6
+
7
+ jobs:
8
+ test:
9
+ runs-on: ubuntu-latest
10
+ steps:
11
+ - name: Checkout Code
12
+ uses: actions/checkout@v3
13
+ - name: Set up Python 3.10
14
+ uses: actions/setup-python@v4
15
+ with:
16
+ python-version: 3.10.6
17
+ cache: pip
18
+ cache-dependency-path: |
19
+ **/requirements*txt
20
+ - name: Run tests
21
+ run: python launch.py --tests test --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test
22
+ - name: Upload main app stdout-stderr
23
+ uses: actions/upload-artifact@v3
24
+ if: always()
25
+ with:
26
+ name: stdout-stderr
27
+ path: |
28
+ test/stdout.txt
29
+ test/stderr.txt
.gitignore ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ __pycache__
2
+ *.ckpt
3
+ *.safetensors
4
+ *.pth
5
+ /ESRGAN/*
6
+ /SwinIR/*
7
+ /repositories
8
+ /venv
9
+ /tmp
10
+ /model.ckpt
11
+ /models/**/*
12
+ /GFPGANv1.3.pth
13
+ /gfpgan/weights/*.pth
14
+ /ui-config.json
15
+ /outputs
16
+ /config.json
17
+ /log
18
+ /webui.settings.bat
19
+ /embeddings
20
+ /styles.csv
21
+ /params.txt
22
+ /styles.csv.bak
23
+ /webui-user.bat
24
+ /webui-user.sh
25
+ /interrogate
26
+ /user.css
27
+ /.idea
28
+ notification.mp3
29
+ /SwinIR
30
+ /textual_inversion
31
+ .vscode
32
+ /extensions
33
+ /test/stdout.txt
34
+ /test/stderr.txt
35
+ /cache.json*
36
+ /config_states/
.pylintrc ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ # See https://pylint.pycqa.org/en/latest/user_guide/messages/message_control.html
2
+ [MESSAGES CONTROL]
3
+ disable=C,R,W,E,I
CHANGELOG.md ADDED
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1
+ ## 1.2.1
2
+
3
+ ### Features:
4
+ * add an option to always refer to lora by filenames
5
+
6
+ ### Bug Fixes:
7
+ * never refer to lora by an alias if multiple loras have same alias or the alias is called none
8
+ * fix upscalers disappearing after the user reloads UI
9
+ * allow bf16 in safe unpickler (resolves problems with loading some loras)
10
+ * allow web UI to be ran fully offline
11
+ * fix localizations not working
12
+ * fix error for loras: 'LatentDiffusion' object has no attribute 'lora_layer_mapping'
13
+
14
+ ## 1.2.0
15
+
16
+ ### Features:
17
+ * do not wait for stable diffusion model to load at startup
18
+ * add filename patterns: [denoising]
19
+ * directory hiding for extra networks: dirs starting with . will hide their cards on extra network tabs unless specifically searched for
20
+ * 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)
21
+ * Lora: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
22
+ * Lora: Fix some Loras not working (ones that have 3x3 convolution layer)
23
+ * Lora: add an option to use old method of applying loras (producing same results as with kohya-ss)
24
+ * add version to infotext, footer and console output when starting
25
+ * add links to wiki for filename pattern settings
26
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
27
+
28
+ ### Minor:
29
+ * gradio bumped to 3.29.0
30
+ * torch bumped to 2.0.1
31
+ * --subpath option for gradio for use with reverse proxy
32
+ * linux/OSX: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
33
+ * possible frontend optimization: do not apply localizations if there are none
34
+ * Add extra `None` option for VAE in XYZ plot
35
+ * print error to console when batch processing in img2img fails
36
+ * create HTML for extra network pages only on demand
37
+ * allow directories starting with . to still list their models for lora, checkpoints, etc
38
+ * put infotext options into their own category in settings tab
39
+ * do not show licenses page when user selects Show all pages in settings
40
+
41
+ ### Extensions:
42
+ * Tooltip localization support
43
+ * Add api method to get LoRA models with prompt
44
+
45
+ ### Bug Fixes:
46
+ * re-add /docs endpoint
47
+ * fix gamepad navigation
48
+ * make the lightbox fullscreen image function properly
49
+ * fix squished thumbnails in extras tab
50
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
51
+ * fix webui showing the same image if you configure the generation to always save results into same file
52
+ * fix bug with upscalers not working properly
53
+ * Fix MPS on PyTorch 2.0.1, Intel Macs
54
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
55
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
56
+ * fix prompts from file script failing to read contents from a drag/drop file
57
+
58
+
59
+ ## 1.1.1
60
+ ### Bug Fixes:
61
+ * fix an error that prevents running webui on torch<2.0 without --disable-safe-unpickle
62
+
63
+ ## 1.1.0
64
+ ### Features:
65
+ * switch to torch 2.0.0 (except for AMD GPUs)
66
+ * visual improvements to custom code scripts
67
+ * add filename patterns: [clip_skip], [hasprompt<>], [batch_number], [generation_number]
68
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducability
69
+ * automatically select current word when adjusting weight with ctrl+up/down
70
+ * add dropdowns for X/Y/Z plot
71
+ * setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
72
+ * support Gradio's theme API
73
+ * use TCMalloc on Linux by default; possible fix for memory leaks
74
+ * (optimization) option to remove negative conditioning at low sigma values #9177
75
+ * embed model merge metadata in .safetensors file
76
+ * extension settings backup/restore feature #9169
77
+ * add "resize by" and "resize to" tabs to img2img
78
+ * add option "keep original size" to textual inversion images preprocess
79
+ * image viewer scrolling via analog stick
80
+ * button to restore the progress from session lost / tab reload
81
+
82
+ ### Minor:
83
+ * gradio bumped to 3.28.1
84
+ * in extra tab, change extras "scale to" to sliders
85
+ * add labels to tool buttons to make it possible to hide them
86
+ * add tiled inference support for ScuNET
87
+ * add branch support for extension installation
88
+ * change linux installation script to insall into current directory rather than /home/username
89
+ * sort textual inversion embeddings by name (case insensitive)
90
+ * allow styles.csv to be symlinked or mounted in docker
91
+ * remove the "do not add watermark to images" option
92
+ * make selected tab configurable with UI config
93
+ * extra networks UI in now fixed height and scrollable
94
+ * add disable_tls_verify arg for use with self-signed certs
95
+
96
+ ### Extensions:
97
+ * Add reload callback
98
+ * add is_hr_pass field for processing
99
+
100
+ ### Bug Fixes:
101
+ * fix broken batch image processing on 'Extras/Batch Process' tab
102
+ * add "None" option to extra networks dropdowns
103
+ * fix FileExistsError for CLIP Interrogator
104
+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319
105
+ * fix disappearing live previews and progressbar during slow tasks
106
+ * fix fullscreen image view not working properly in some cases
107
+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
108
+ * fix prompt schedule for second order samplers
109
+ * fix image mask/composite for weird resolutions #9628
110
+ * use correct images for previews when using AND (see #9491)
111
+ * one broken image in img2img batch won't stop all processing
112
+ * fix image orientation bug in train/preprocess
113
+ * fix Ngrok recreating tunnels every reload
114
+ * fix --realesrgan-models-path and --ldsr-models-path not working
115
+ * fix --skip-install not working
116
+ * outpainting Mk2 & Poorman should use the SAMPLE file format to save images, not GRID file format
117
+ * do not fail all Loras if some have failed to load when making a picture
118
+
119
+ ## 1.0.0
120
+ * everything
CODEOWNERS ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ * @AUTOMATIC1111
2
+
3
+ # if you were managing a localization and were removed from this file, this is because
4
+ # the intended way to do localizations now is via extensions. See:
5
+ # https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Developing-extensions
6
+ # Make a repo with your localization and since you are still listed as a collaborator
7
+ # you can add it to the wiki page yourself. This change is because some people complained
8
+ # the git commit log is cluttered with things unrelated to almost everyone and
9
+ # because I believe this is the best overall for the project to handle localizations almost
10
+ # entirely without my oversight.
11
+
12
+
LICENSE.txt ADDED
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+ GNU AFFERO GENERAL PUBLIC LICENSE
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+ Version 3, 19 November 2007
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+ Copyright (c) 2023 AUTOMATIC1111
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+ For more information on this, and how to apply and follow the GNU AGPL, see
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+ <https://www.gnu.org/licenses/>.
README.md ADDED
@@ -0,0 +1,162 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Stable Diffusion web UI
2
+ A browser interface based on Gradio library for Stable Diffusion.
3
+
4
+ ![](screenshot.png)
5
+
6
+ ## Features
7
+ [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
8
+ - Original txt2img and img2img modes
9
+ - One click install and run script (but you still must install python and git)
10
+ - Outpainting
11
+ - Inpainting
12
+ - Color Sketch
13
+ - Prompt Matrix
14
+ - Stable Diffusion Upscale
15
+ - Attention, specify parts of text that the model should pay more attention to
16
+ - a man in a `((tuxedo))` - will pay more attention to tuxedo
17
+ - a man in a `(tuxedo:1.21)` - alternative syntax
18
+ - select text and press `Ctrl+Up` or `Ctrl+Down` to automatically adjust attention to selected text (code contributed by anonymous user)
19
+ - Loopback, run img2img processing multiple times
20
+ - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
21
+ - Textual Inversion
22
+ - have as many embeddings as you want and use any names you like for them
23
+ - use multiple embeddings with different numbers of vectors per token
24
+ - works with half precision floating point numbers
25
+ - train embeddings on 8GB (also reports of 6GB working)
26
+ - Extras tab with:
27
+ - GFPGAN, neural network that fixes faces
28
+ - CodeFormer, face restoration tool as an alternative to GFPGAN
29
+ - RealESRGAN, neural network upscaler
30
+ - ESRGAN, neural network upscaler with a lot of third party models
31
+ - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
32
+ - LDSR, Latent diffusion super resolution upscaling
33
+ - Resizing aspect ratio options
34
+ - Sampling method selection
35
+ - Adjust sampler eta values (noise multiplier)
36
+ - More advanced noise setting options
37
+ - Interrupt processing at any time
38
+ - 4GB video card support (also reports of 2GB working)
39
+ - Correct seeds for batches
40
+ - Live prompt token length validation
41
+ - Generation parameters
42
+ - parameters you used to generate images are saved with that image
43
+ - in PNG chunks for PNG, in EXIF for JPEG
44
+ - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
45
+ - can be disabled in settings
46
+ - drag and drop an image/text-parameters to promptbox
47
+ - Read Generation Parameters Button, loads parameters in promptbox to UI
48
+ - Settings page
49
+ - Running arbitrary python code from UI (must run with `--allow-code` to enable)
50
+ - Mouseover hints for most UI elements
51
+ - Possible to change defaults/mix/max/step values for UI elements via text config
52
+ - Tiling support, a checkbox to create images that can be tiled like textures
53
+ - Progress bar and live image generation preview
54
+ - Can use a separate neural network to produce previews with almost none VRAM or compute requirement
55
+ - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
56
+ - Styles, a way to save part of prompt and easily apply them via dropdown later
57
+ - Variations, a way to generate same image but with tiny differences
58
+ - Seed resizing, a way to generate same image but at slightly different resolution
59
+ - CLIP interrogator, a button that tries to guess prompt from an image
60
+ - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
61
+ - Batch Processing, process a group of files using img2img
62
+ - Img2img Alternative, reverse Euler method of cross attention control
63
+ - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
64
+ - Reloading checkpoints on the fly
65
+ - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
66
+ - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
67
+ - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
68
+ - separate prompts using uppercase `AND`
69
+ - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
70
+ - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
71
+ - DeepDanbooru integration, creates danbooru style tags for anime prompts
72
+ - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
73
+ - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
74
+ - Generate forever option
75
+ - Training tab
76
+ - hypernetworks and embeddings options
77
+ - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
78
+ - Clip skip
79
+ - Hypernetworks
80
+ - Loras (same as Hypernetworks but more pretty)
81
+ - A sparate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
82
+ - Can select to load a different VAE from settings screen
83
+ - Estimated completion time in progress bar
84
+ - API
85
+ - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
86
+ - 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))
87
+ - [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
88
+ - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
89
+ - Now without any bad letters!
90
+ - Load checkpoints in safetensors format
91
+ - Eased resolution restriction: generated image's domension must be a multiple of 8 rather than 64
92
+ - Now with a license!
93
+ - Reorder elements in the UI from settings screen
94
+
95
+ ## Installation and Running
96
+ 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.
97
+
98
+ Alternatively, use online services (like Google Colab):
99
+
100
+ - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
101
+
102
+ ### Automatic Installation on Windows
103
+ 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".
104
+ 2. Install [git](https://git-scm.com/download/win).
105
+ 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
106
+ 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
107
+
108
+ ### Automatic Installation on Linux
109
+ 1. Install the dependencies:
110
+ ```bash
111
+ # Debian-based:
112
+ sudo apt install wget git python3 python3-venv
113
+ # Red Hat-based:
114
+ sudo dnf install wget git python3
115
+ # Arch-based:
116
+ sudo pacman -S wget git python3
117
+ ```
118
+ 2. Navigate to the directory you would like the webui to be installed and execute the following command:
119
+ ```bash
120
+ bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
121
+ ```
122
+ 3. Run `webui.sh`.
123
+ 4. Check `webui-user.sh` for options.
124
+ ### Installation on Apple Silicon
125
+
126
+ Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
127
+
128
+ ## Contributing
129
+ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
130
+
131
+ ## Documentation
132
+ The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
133
+
134
+ ## Credits
135
+ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
136
+
137
+ - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
138
+ - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
139
+ - GFPGAN - https://github.com/TencentARC/GFPGAN.git
140
+ - CodeFormer - https://github.com/sczhou/CodeFormer
141
+ - ESRGAN - https://github.com/xinntao/ESRGAN
142
+ - SwinIR - https://github.com/JingyunLiang/SwinIR
143
+ - Swin2SR - https://github.com/mv-lab/swin2sr
144
+ - LDSR - https://github.com/Hafiidz/latent-diffusion
145
+ - MiDaS - https://github.com/isl-org/MiDaS
146
+ - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
147
+ - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
148
+ - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
149
+ - 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)
150
+ - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
151
+ - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
152
+ - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
153
+ - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
154
+ - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
155
+ - xformers - https://github.com/facebookresearch/xformers
156
+ - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
157
+ - 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)
158
+ - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
159
+ - Security advice - RyotaK
160
+ - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
161
+ - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
162
+ - (You)
configs/alt-diffusion-inference.yaml ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ base_learning_rate: 1.0e-04
3
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
4
+ params:
5
+ linear_start: 0.00085
6
+ linear_end: 0.0120
7
+ num_timesteps_cond: 1
8
+ log_every_t: 200
9
+ timesteps: 1000
10
+ first_stage_key: "jpg"
11
+ cond_stage_key: "txt"
12
+ image_size: 64
13
+ channels: 4
14
+ cond_stage_trainable: false # Note: different from the one we trained before
15
+ conditioning_key: crossattn
16
+ monitor: val/loss_simple_ema
17
+ scale_factor: 0.18215
18
+ use_ema: False
19
+
20
+ scheduler_config: # 10000 warmup steps
21
+ target: ldm.lr_scheduler.LambdaLinearScheduler
22
+ params:
23
+ warm_up_steps: [ 10000 ]
24
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
25
+ f_start: [ 1.e-6 ]
26
+ f_max: [ 1. ]
27
+ f_min: [ 1. ]
28
+
29
+ unet_config:
30
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
31
+ params:
32
+ image_size: 32 # unused
33
+ in_channels: 4
34
+ out_channels: 4
35
+ model_channels: 320
36
+ attention_resolutions: [ 4, 2, 1 ]
37
+ num_res_blocks: 2
38
+ channel_mult: [ 1, 2, 4, 4 ]
39
+ num_heads: 8
40
+ use_spatial_transformer: True
41
+ transformer_depth: 1
42
+ context_dim: 768
43
+ use_checkpoint: True
44
+ legacy: False
45
+
46
+ first_stage_config:
47
+ target: ldm.models.autoencoder.AutoencoderKL
48
+ params:
49
+ embed_dim: 4
50
+ monitor: val/rec_loss
51
+ ddconfig:
52
+ double_z: true
53
+ z_channels: 4
54
+ resolution: 256
55
+ in_channels: 3
56
+ out_ch: 3
57
+ ch: 128
58
+ ch_mult:
59
+ - 1
60
+ - 2
61
+ - 4
62
+ - 4
63
+ num_res_blocks: 2
64
+ attn_resolutions: []
65
+ dropout: 0.0
66
+ lossconfig:
67
+ target: torch.nn.Identity
68
+
69
+ cond_stage_config:
70
+ target: modules.xlmr.BertSeriesModelWithTransformation
71
+ params:
72
+ name: "XLMR-Large"
configs/instruct-pix2pix.yaml ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
2
+ # See more details in LICENSE.
3
+
4
+ model:
5
+ base_learning_rate: 1.0e-04
6
+ target: modules.models.diffusion.ddpm_edit.LatentDiffusion
7
+ params:
8
+ linear_start: 0.00085
9
+ linear_end: 0.0120
10
+ num_timesteps_cond: 1
11
+ log_every_t: 200
12
+ timesteps: 1000
13
+ first_stage_key: edited
14
+ cond_stage_key: edit
15
+ # image_size: 64
16
+ # image_size: 32
17
+ image_size: 16
18
+ channels: 4
19
+ cond_stage_trainable: false # Note: different from the one we trained before
20
+ conditioning_key: hybrid
21
+ monitor: val/loss_simple_ema
22
+ scale_factor: 0.18215
23
+ use_ema: false
24
+
25
+ scheduler_config: # 10000 warmup steps
26
+ target: ldm.lr_scheduler.LambdaLinearScheduler
27
+ params:
28
+ warm_up_steps: [ 0 ]
29
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
30
+ f_start: [ 1.e-6 ]
31
+ f_max: [ 1. ]
32
+ f_min: [ 1. ]
33
+
34
+ unet_config:
35
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
36
+ params:
37
+ image_size: 32 # unused
38
+ in_channels: 8
39
+ out_channels: 4
40
+ model_channels: 320
41
+ attention_resolutions: [ 4, 2, 1 ]
42
+ num_res_blocks: 2
43
+ channel_mult: [ 1, 2, 4, 4 ]
44
+ num_heads: 8
45
+ use_spatial_transformer: True
46
+ transformer_depth: 1
47
+ context_dim: 768
48
+ use_checkpoint: True
49
+ legacy: False
50
+
51
+ first_stage_config:
52
+ target: ldm.models.autoencoder.AutoencoderKL
53
+ params:
54
+ embed_dim: 4
55
+ monitor: val/rec_loss
56
+ ddconfig:
57
+ double_z: true
58
+ z_channels: 4
59
+ resolution: 256
60
+ in_channels: 3
61
+ out_ch: 3
62
+ ch: 128
63
+ ch_mult:
64
+ - 1
65
+ - 2
66
+ - 4
67
+ - 4
68
+ num_res_blocks: 2
69
+ attn_resolutions: []
70
+ dropout: 0.0
71
+ lossconfig:
72
+ target: torch.nn.Identity
73
+
74
+ cond_stage_config:
75
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
76
+
77
+ data:
78
+ target: main.DataModuleFromConfig
79
+ params:
80
+ batch_size: 128
81
+ num_workers: 1
82
+ wrap: false
83
+ validation:
84
+ target: edit_dataset.EditDataset
85
+ params:
86
+ path: data/clip-filtered-dataset
87
+ cache_dir: data/
88
+ cache_name: data_10k
89
+ split: val
90
+ min_text_sim: 0.2
91
+ min_image_sim: 0.75
92
+ min_direction_sim: 0.2
93
+ max_samples_per_prompt: 1
94
+ min_resize_res: 512
95
+ max_resize_res: 512
96
+ crop_res: 512
97
+ output_as_edit: False
98
+ real_input: True
configs/v1-inference.yaml ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ base_learning_rate: 1.0e-04
3
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
4
+ params:
5
+ linear_start: 0.00085
6
+ linear_end: 0.0120
7
+ num_timesteps_cond: 1
8
+ log_every_t: 200
9
+ timesteps: 1000
10
+ first_stage_key: "jpg"
11
+ cond_stage_key: "txt"
12
+ image_size: 64
13
+ channels: 4
14
+ cond_stage_trainable: false # Note: different from the one we trained before
15
+ conditioning_key: crossattn
16
+ monitor: val/loss_simple_ema
17
+ scale_factor: 0.18215
18
+ use_ema: False
19
+
20
+ scheduler_config: # 10000 warmup steps
21
+ target: ldm.lr_scheduler.LambdaLinearScheduler
22
+ params:
23
+ warm_up_steps: [ 10000 ]
24
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
25
+ f_start: [ 1.e-6 ]
26
+ f_max: [ 1. ]
27
+ f_min: [ 1. ]
28
+
29
+ unet_config:
30
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
31
+ params:
32
+ image_size: 32 # unused
33
+ in_channels: 4
34
+ out_channels: 4
35
+ model_channels: 320
36
+ attention_resolutions: [ 4, 2, 1 ]
37
+ num_res_blocks: 2
38
+ channel_mult: [ 1, 2, 4, 4 ]
39
+ num_heads: 8
40
+ use_spatial_transformer: True
41
+ transformer_depth: 1
42
+ context_dim: 768
43
+ use_checkpoint: True
44
+ legacy: False
45
+
46
+ first_stage_config:
47
+ target: ldm.models.autoencoder.AutoencoderKL
48
+ params:
49
+ embed_dim: 4
50
+ monitor: val/rec_loss
51
+ ddconfig:
52
+ double_z: true
53
+ z_channels: 4
54
+ resolution: 256
55
+ in_channels: 3
56
+ out_ch: 3
57
+ ch: 128
58
+ ch_mult:
59
+ - 1
60
+ - 2
61
+ - 4
62
+ - 4
63
+ num_res_blocks: 2
64
+ attn_resolutions: []
65
+ dropout: 0.0
66
+ lossconfig:
67
+ target: torch.nn.Identity
68
+
69
+ cond_stage_config:
70
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
configs/v1-inpainting-inference.yaml ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ base_learning_rate: 7.5e-05
3
+ target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
4
+ params:
5
+ linear_start: 0.00085
6
+ linear_end: 0.0120
7
+ num_timesteps_cond: 1
8
+ log_every_t: 200
9
+ timesteps: 1000
10
+ first_stage_key: "jpg"
11
+ cond_stage_key: "txt"
12
+ image_size: 64
13
+ channels: 4
14
+ cond_stage_trainable: false # Note: different from the one we trained before
15
+ conditioning_key: hybrid # important
16
+ monitor: val/loss_simple_ema
17
+ scale_factor: 0.18215
18
+ finetune_keys: null
19
+
20
+ scheduler_config: # 10000 warmup steps
21
+ target: ldm.lr_scheduler.LambdaLinearScheduler
22
+ params:
23
+ warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
24
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
25
+ f_start: [ 1.e-6 ]
26
+ f_max: [ 1. ]
27
+ f_min: [ 1. ]
28
+
29
+ unet_config:
30
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
31
+ params:
32
+ image_size: 32 # unused
33
+ in_channels: 9 # 4 data + 4 downscaled image + 1 mask
34
+ out_channels: 4
35
+ model_channels: 320
36
+ attention_resolutions: [ 4, 2, 1 ]
37
+ num_res_blocks: 2
38
+ channel_mult: [ 1, 2, 4, 4 ]
39
+ num_heads: 8
40
+ use_spatial_transformer: True
41
+ transformer_depth: 1
42
+ context_dim: 768
43
+ use_checkpoint: True
44
+ legacy: False
45
+
46
+ first_stage_config:
47
+ target: ldm.models.autoencoder.AutoencoderKL
48
+ params:
49
+ embed_dim: 4
50
+ monitor: val/rec_loss
51
+ ddconfig:
52
+ double_z: true
53
+ z_channels: 4
54
+ resolution: 256
55
+ in_channels: 3
56
+ out_ch: 3
57
+ ch: 128
58
+ ch_mult:
59
+ - 1
60
+ - 2
61
+ - 4
62
+ - 4
63
+ num_res_blocks: 2
64
+ attn_resolutions: []
65
+ dropout: 0.0
66
+ lossconfig:
67
+ target: torch.nn.Identity
68
+
69
+ cond_stage_config:
70
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
embeddings/Place Textual Inversion embeddings here.txt ADDED
File without changes
environment-wsl2.yaml ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: automatic
2
+ channels:
3
+ - pytorch
4
+ - defaults
5
+ dependencies:
6
+ - python=3.10
7
+ - pip=23.0
8
+ - cudatoolkit=11.8
9
+ - pytorch=2.0
10
+ - torchvision=0.15
11
+ - numpy=1.23
extensions-builtin/LDSR/ldsr_model_arch.py ADDED
@@ -0,0 +1,253 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import gc
3
+ import time
4
+
5
+ import numpy as np
6
+ import torch
7
+ import torchvision
8
+ from PIL import Image
9
+ from einops import rearrange, repeat
10
+ from omegaconf import OmegaConf
11
+ import safetensors.torch
12
+
13
+ from ldm.models.diffusion.ddim import DDIMSampler
14
+ from ldm.util import instantiate_from_config, ismap
15
+ from modules import shared, sd_hijack
16
+
17
+ cached_ldsr_model: torch.nn.Module = None
18
+
19
+
20
+ # Create LDSR Class
21
+ class LDSR:
22
+ def load_model_from_config(self, half_attention):
23
+ global cached_ldsr_model
24
+
25
+ if shared.opts.ldsr_cached and cached_ldsr_model is not None:
26
+ print("Loading model from cache")
27
+ model: torch.nn.Module = cached_ldsr_model
28
+ else:
29
+ print(f"Loading model from {self.modelPath}")
30
+ _, extension = os.path.splitext(self.modelPath)
31
+ if extension.lower() == ".safetensors":
32
+ pl_sd = safetensors.torch.load_file(self.modelPath, device="cpu")
33
+ else:
34
+ pl_sd = torch.load(self.modelPath, map_location="cpu")
35
+ sd = pl_sd["state_dict"] if "state_dict" in pl_sd else pl_sd
36
+ config = OmegaConf.load(self.yamlPath)
37
+ config.model.target = "ldm.models.diffusion.ddpm.LatentDiffusionV1"
38
+ model: torch.nn.Module = instantiate_from_config(config.model)
39
+ model.load_state_dict(sd, strict=False)
40
+ model = model.to(shared.device)
41
+ if half_attention:
42
+ model = model.half()
43
+ if shared.cmd_opts.opt_channelslast:
44
+ model = model.to(memory_format=torch.channels_last)
45
+
46
+ sd_hijack.model_hijack.hijack(model) # apply optimization
47
+ model.eval()
48
+
49
+ if shared.opts.ldsr_cached:
50
+ cached_ldsr_model = model
51
+
52
+ return {"model": model}
53
+
54
+ def __init__(self, model_path, yaml_path):
55
+ self.modelPath = model_path
56
+ self.yamlPath = yaml_path
57
+
58
+ @staticmethod
59
+ def run(model, selected_path, custom_steps, eta):
60
+ example = get_cond(selected_path)
61
+
62
+ n_runs = 1
63
+ guider = None
64
+ ckwargs = None
65
+ ddim_use_x0_pred = False
66
+ temperature = 1.
67
+ eta = eta
68
+ custom_shape = None
69
+
70
+ height, width = example["image"].shape[1:3]
71
+ split_input = height >= 128 and width >= 128
72
+
73
+ if split_input:
74
+ ks = 128
75
+ stride = 64
76
+ vqf = 4 #
77
+ model.split_input_params = {"ks": (ks, ks), "stride": (stride, stride),
78
+ "vqf": vqf,
79
+ "patch_distributed_vq": True,
80
+ "tie_braker": False,
81
+ "clip_max_weight": 0.5,
82
+ "clip_min_weight": 0.01,
83
+ "clip_max_tie_weight": 0.5,
84
+ "clip_min_tie_weight": 0.01}
85
+ else:
86
+ if hasattr(model, "split_input_params"):
87
+ delattr(model, "split_input_params")
88
+
89
+ x_t = None
90
+ logs = None
91
+ for n in range(n_runs):
92
+ if custom_shape is not None:
93
+ x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
94
+ x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])
95
+
96
+ logs = make_convolutional_sample(example, model,
97
+ custom_steps=custom_steps,
98
+ eta=eta, quantize_x0=False,
99
+ custom_shape=custom_shape,
100
+ temperature=temperature, noise_dropout=0.,
101
+ corrector=guider, corrector_kwargs=ckwargs, x_T=x_t,
102
+ ddim_use_x0_pred=ddim_use_x0_pred
103
+ )
104
+ return logs
105
+
106
+ def super_resolution(self, image, steps=100, target_scale=2, half_attention=False):
107
+ model = self.load_model_from_config(half_attention)
108
+
109
+ # Run settings
110
+ diffusion_steps = int(steps)
111
+ eta = 1.0
112
+
113
+ down_sample_method = 'Lanczos'
114
+
115
+ gc.collect()
116
+ if torch.cuda.is_available:
117
+ torch.cuda.empty_cache()
118
+
119
+ im_og = image
120
+ width_og, height_og = im_og.size
121
+ # If we can adjust the max upscale size, then the 4 below should be our variable
122
+ down_sample_rate = target_scale / 4
123
+ wd = width_og * down_sample_rate
124
+ hd = height_og * down_sample_rate
125
+ width_downsampled_pre = int(np.ceil(wd))
126
+ height_downsampled_pre = int(np.ceil(hd))
127
+
128
+ if down_sample_rate != 1:
129
+ print(
130
+ f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
131
+ im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
132
+ else:
133
+ print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
134
+
135
+ # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
136
+ pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
137
+ im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
138
+
139
+ logs = self.run(model["model"], im_padded, diffusion_steps, eta)
140
+
141
+ sample = logs["sample"]
142
+ sample = sample.detach().cpu()
143
+ sample = torch.clamp(sample, -1., 1.)
144
+ sample = (sample + 1.) / 2. * 255
145
+ sample = sample.numpy().astype(np.uint8)
146
+ sample = np.transpose(sample, (0, 2, 3, 1))
147
+ a = Image.fromarray(sample[0])
148
+
149
+ # remove padding
150
+ a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
151
+
152
+ del model
153
+ gc.collect()
154
+ if torch.cuda.is_available:
155
+ torch.cuda.empty_cache()
156
+
157
+ return a
158
+
159
+
160
+ def get_cond(selected_path):
161
+ example = dict()
162
+ up_f = 4
163
+ c = selected_path.convert('RGB')
164
+ c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
165
+ c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
166
+ antialias=True)
167
+ c_up = rearrange(c_up, '1 c h w -> 1 h w c')
168
+ c = rearrange(c, '1 c h w -> 1 h w c')
169
+ c = 2. * c - 1.
170
+
171
+ c = c.to(shared.device)
172
+ example["LR_image"] = c
173
+ example["image"] = c_up
174
+
175
+ return example
176
+
177
+
178
+ @torch.no_grad()
179
+ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
180
+ mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
181
+ corrector_kwargs=None, x_t=None
182
+ ):
183
+ ddim = DDIMSampler(model)
184
+ bs = shape[0]
185
+ shape = shape[1:]
186
+ print(f"Sampling with eta = {eta}; steps: {steps}")
187
+ samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
188
+ normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
189
+ mask=mask, x0=x0, temperature=temperature, verbose=False,
190
+ score_corrector=score_corrector,
191
+ corrector_kwargs=corrector_kwargs, x_t=x_t)
192
+
193
+ return samples, intermediates
194
+
195
+
196
+ @torch.no_grad()
197
+ 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,
198
+ corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
199
+ log = dict()
200
+
201
+ z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
202
+ return_first_stage_outputs=True,
203
+ force_c_encode=not (hasattr(model, 'split_input_params')
204
+ and model.cond_stage_key == 'coordinates_bbox'),
205
+ return_original_cond=True)
206
+
207
+ if custom_shape is not None:
208
+ z = torch.randn(custom_shape)
209
+ print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
210
+
211
+ z0 = None
212
+
213
+ log["input"] = x
214
+ log["reconstruction"] = xrec
215
+
216
+ if ismap(xc):
217
+ log["original_conditioning"] = model.to_rgb(xc)
218
+ if hasattr(model, 'cond_stage_key'):
219
+ log[model.cond_stage_key] = model.to_rgb(xc)
220
+
221
+ else:
222
+ log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
223
+ if model.cond_stage_model:
224
+ log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
225
+ if model.cond_stage_key == 'class_label':
226
+ log[model.cond_stage_key] = xc[model.cond_stage_key]
227
+
228
+ with model.ema_scope("Plotting"):
229
+ t0 = time.time()
230
+
231
+ sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
232
+ eta=eta,
233
+ quantize_x0=quantize_x0, mask=None, x0=z0,
234
+ temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
235
+ x_t=x_T)
236
+ t1 = time.time()
237
+
238
+ if ddim_use_x0_pred:
239
+ sample = intermediates['pred_x0'][-1]
240
+
241
+ x_sample = model.decode_first_stage(sample)
242
+
243
+ try:
244
+ x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
245
+ log["sample_noquant"] = x_sample_noquant
246
+ log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
247
+ except:
248
+ pass
249
+
250
+ log["sample"] = x_sample
251
+ log["time"] = t1 - t0
252
+
253
+ return log
extensions-builtin/LDSR/preload.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ import os
2
+ from modules import paths
3
+
4
+
5
+ def preload(parser):
6
+ 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 ADDED
@@ -0,0 +1,75 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import sys
3
+ import traceback
4
+
5
+ from basicsr.utils.download_util import load_file_from_url
6
+
7
+ from modules.upscaler import Upscaler, UpscalerData
8
+ from ldsr_model_arch import LDSR
9
+ from modules import shared, script_callbacks
10
+ import sd_hijack_autoencoder, sd_hijack_ddpm_v1
11
+
12
+
13
+ class UpscalerLDSR(Upscaler):
14
+ def __init__(self, user_path):
15
+ self.name = "LDSR"
16
+ self.user_path = user_path
17
+ self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
18
+ self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
19
+ super().__init__()
20
+ scaler_data = UpscalerData("LDSR", None, self)
21
+ self.scalers = [scaler_data]
22
+
23
+ def load_model(self, path: str):
24
+ # Remove incorrect project.yaml file if too big
25
+ yaml_path = os.path.join(self.model_path, "project.yaml")
26
+ old_model_path = os.path.join(self.model_path, "model.pth")
27
+ new_model_path = os.path.join(self.model_path, "model.ckpt")
28
+
29
+ local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
30
+ local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
31
+ local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
32
+ local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
33
+
34
+ if os.path.exists(yaml_path):
35
+ statinfo = os.stat(yaml_path)
36
+ if statinfo.st_size >= 10485760:
37
+ print("Removing invalid LDSR YAML file.")
38
+ os.remove(yaml_path)
39
+
40
+ if os.path.exists(old_model_path):
41
+ print("Renaming model from model.pth to model.ckpt")
42
+ os.rename(old_model_path, new_model_path)
43
+
44
+ if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
45
+ model = local_safetensors_path
46
+ else:
47
+ model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="model.ckpt", progress=True)
48
+
49
+ yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_path, file_name="project.yaml", progress=True)
50
+
51
+ try:
52
+ return LDSR(model, yaml)
53
+
54
+ except Exception:
55
+ print("Error importing LDSR:", file=sys.stderr)
56
+ print(traceback.format_exc(), file=sys.stderr)
57
+ return None
58
+
59
+ def do_upscale(self, img, path):
60
+ ldsr = self.load_model(path)
61
+ if ldsr is None:
62
+ print("NO LDSR!")
63
+ return img
64
+ ddim_steps = shared.opts.ldsr_steps
65
+ return ldsr.super_resolution(img, ddim_steps, self.scale)
66
+
67
+
68
+ def on_ui_settings():
69
+ import gradio as gr
70
+
71
+ 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")))
72
+ shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
73
+
74
+
75
+ script_callbacks.on_ui_settings(on_ui_settings)
extensions-builtin/LDSR/sd_hijack_autoencoder.py ADDED
@@ -0,0 +1,286 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
2
+ # The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
3
+ # As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
4
+
5
+ import torch
6
+ import pytorch_lightning as pl
7
+ import torch.nn.functional as F
8
+ from contextlib import contextmanager
9
+ from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
10
+ from ldm.modules.diffusionmodules.model import Encoder, Decoder
11
+ from ldm.util import instantiate_from_config
12
+
13
+ import ldm.models.autoencoder
14
+
15
+ class VQModel(pl.LightningModule):
16
+ def __init__(self,
17
+ ddconfig,
18
+ lossconfig,
19
+ n_embed,
20
+ embed_dim,
21
+ ckpt_path=None,
22
+ ignore_keys=[],
23
+ image_key="image",
24
+ colorize_nlabels=None,
25
+ monitor=None,
26
+ batch_resize_range=None,
27
+ scheduler_config=None,
28
+ lr_g_factor=1.0,
29
+ remap=None,
30
+ sane_index_shape=False, # tell vector quantizer to return indices as bhw
31
+ use_ema=False
32
+ ):
33
+ super().__init__()
34
+ self.embed_dim = embed_dim
35
+ self.n_embed = n_embed
36
+ self.image_key = image_key
37
+ self.encoder = Encoder(**ddconfig)
38
+ self.decoder = Decoder(**ddconfig)
39
+ self.loss = instantiate_from_config(lossconfig)
40
+ self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
41
+ remap=remap,
42
+ sane_index_shape=sane_index_shape)
43
+ self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
44
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
45
+ if colorize_nlabels is not None:
46
+ assert type(colorize_nlabels)==int
47
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
48
+ if monitor is not None:
49
+ self.monitor = monitor
50
+ self.batch_resize_range = batch_resize_range
51
+ if self.batch_resize_range is not None:
52
+ print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
53
+
54
+ self.use_ema = use_ema
55
+ if self.use_ema:
56
+ self.model_ema = LitEma(self)
57
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
58
+
59
+ if ckpt_path is not None:
60
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
61
+ self.scheduler_config = scheduler_config
62
+ self.lr_g_factor = lr_g_factor
63
+
64
+ @contextmanager
65
+ def ema_scope(self, context=None):
66
+ if self.use_ema:
67
+ self.model_ema.store(self.parameters())
68
+ self.model_ema.copy_to(self)
69
+ if context is not None:
70
+ print(f"{context}: Switched to EMA weights")
71
+ try:
72
+ yield None
73
+ finally:
74
+ if self.use_ema:
75
+ self.model_ema.restore(self.parameters())
76
+ if context is not None:
77
+ print(f"{context}: Restored training weights")
78
+
79
+ def init_from_ckpt(self, path, ignore_keys=list()):
80
+ sd = torch.load(path, map_location="cpu")["state_dict"]
81
+ keys = list(sd.keys())
82
+ for k in keys:
83
+ for ik in ignore_keys:
84
+ if k.startswith(ik):
85
+ print("Deleting key {} from state_dict.".format(k))
86
+ del sd[k]
87
+ missing, unexpected = self.load_state_dict(sd, strict=False)
88
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
89
+ if len(missing) > 0:
90
+ print(f"Missing Keys: {missing}")
91
+ print(f"Unexpected Keys: {unexpected}")
92
+
93
+ def on_train_batch_end(self, *args, **kwargs):
94
+ if self.use_ema:
95
+ self.model_ema(self)
96
+
97
+ def encode(self, x):
98
+ h = self.encoder(x)
99
+ h = self.quant_conv(h)
100
+ quant, emb_loss, info = self.quantize(h)
101
+ return quant, emb_loss, info
102
+
103
+ def encode_to_prequant(self, x):
104
+ h = self.encoder(x)
105
+ h = self.quant_conv(h)
106
+ return h
107
+
108
+ def decode(self, quant):
109
+ quant = self.post_quant_conv(quant)
110
+ dec = self.decoder(quant)
111
+ return dec
112
+
113
+ def decode_code(self, code_b):
114
+ quant_b = self.quantize.embed_code(code_b)
115
+ dec = self.decode(quant_b)
116
+ return dec
117
+
118
+ def forward(self, input, return_pred_indices=False):
119
+ quant, diff, (_,_,ind) = self.encode(input)
120
+ dec = self.decode(quant)
121
+ if return_pred_indices:
122
+ return dec, diff, ind
123
+ return dec, diff
124
+
125
+ def get_input(self, batch, k):
126
+ x = batch[k]
127
+ if len(x.shape) == 3:
128
+ x = x[..., None]
129
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
130
+ if self.batch_resize_range is not None:
131
+ lower_size = self.batch_resize_range[0]
132
+ upper_size = self.batch_resize_range[1]
133
+ if self.global_step <= 4:
134
+ # do the first few batches with max size to avoid later oom
135
+ new_resize = upper_size
136
+ else:
137
+ new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
138
+ if new_resize != x.shape[2]:
139
+ x = F.interpolate(x, size=new_resize, mode="bicubic")
140
+ x = x.detach()
141
+ return x
142
+
143
+ def training_step(self, batch, batch_idx, optimizer_idx):
144
+ # https://github.com/pytorch/pytorch/issues/37142
145
+ # try not to fool the heuristics
146
+ x = self.get_input(batch, self.image_key)
147
+ xrec, qloss, ind = self(x, return_pred_indices=True)
148
+
149
+ if optimizer_idx == 0:
150
+ # autoencode
151
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
152
+ last_layer=self.get_last_layer(), split="train",
153
+ predicted_indices=ind)
154
+
155
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
156
+ return aeloss
157
+
158
+ if optimizer_idx == 1:
159
+ # discriminator
160
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
161
+ last_layer=self.get_last_layer(), split="train")
162
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
163
+ return discloss
164
+
165
+ def validation_step(self, batch, batch_idx):
166
+ log_dict = self._validation_step(batch, batch_idx)
167
+ with self.ema_scope():
168
+ log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
169
+ return log_dict
170
+
171
+ def _validation_step(self, batch, batch_idx, suffix=""):
172
+ x = self.get_input(batch, self.image_key)
173
+ xrec, qloss, ind = self(x, return_pred_indices=True)
174
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
175
+ self.global_step,
176
+ last_layer=self.get_last_layer(),
177
+ split="val"+suffix,
178
+ predicted_indices=ind
179
+ )
180
+
181
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
182
+ self.global_step,
183
+ last_layer=self.get_last_layer(),
184
+ split="val"+suffix,
185
+ predicted_indices=ind
186
+ )
187
+ rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
188
+ self.log(f"val{suffix}/rec_loss", rec_loss,
189
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
190
+ self.log(f"val{suffix}/aeloss", aeloss,
191
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
192
+ if version.parse(pl.__version__) >= version.parse('1.4.0'):
193
+ del log_dict_ae[f"val{suffix}/rec_loss"]
194
+ self.log_dict(log_dict_ae)
195
+ self.log_dict(log_dict_disc)
196
+ return self.log_dict
197
+
198
+ def configure_optimizers(self):
199
+ lr_d = self.learning_rate
200
+ lr_g = self.lr_g_factor*self.learning_rate
201
+ print("lr_d", lr_d)
202
+ print("lr_g", lr_g)
203
+ opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
204
+ list(self.decoder.parameters())+
205
+ list(self.quantize.parameters())+
206
+ list(self.quant_conv.parameters())+
207
+ list(self.post_quant_conv.parameters()),
208
+ lr=lr_g, betas=(0.5, 0.9))
209
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
210
+ lr=lr_d, betas=(0.5, 0.9))
211
+
212
+ if self.scheduler_config is not None:
213
+ scheduler = instantiate_from_config(self.scheduler_config)
214
+
215
+ print("Setting up LambdaLR scheduler...")
216
+ scheduler = [
217
+ {
218
+ 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
219
+ 'interval': 'step',
220
+ 'frequency': 1
221
+ },
222
+ {
223
+ 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
224
+ 'interval': 'step',
225
+ 'frequency': 1
226
+ },
227
+ ]
228
+ return [opt_ae, opt_disc], scheduler
229
+ return [opt_ae, opt_disc], []
230
+
231
+ def get_last_layer(self):
232
+ return self.decoder.conv_out.weight
233
+
234
+ def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
235
+ log = dict()
236
+ x = self.get_input(batch, self.image_key)
237
+ x = x.to(self.device)
238
+ if only_inputs:
239
+ log["inputs"] = x
240
+ return log
241
+ xrec, _ = self(x)
242
+ if x.shape[1] > 3:
243
+ # colorize with random projection
244
+ assert xrec.shape[1] > 3
245
+ x = self.to_rgb(x)
246
+ xrec = self.to_rgb(xrec)
247
+ log["inputs"] = x
248
+ log["reconstructions"] = xrec
249
+ if plot_ema:
250
+ with self.ema_scope():
251
+ xrec_ema, _ = self(x)
252
+ if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
253
+ log["reconstructions_ema"] = xrec_ema
254
+ return log
255
+
256
+ def to_rgb(self, x):
257
+ assert self.image_key == "segmentation"
258
+ if not hasattr(self, "colorize"):
259
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
260
+ x = F.conv2d(x, weight=self.colorize)
261
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
262
+ return x
263
+
264
+
265
+ class VQModelInterface(VQModel):
266
+ def __init__(self, embed_dim, *args, **kwargs):
267
+ super().__init__(embed_dim=embed_dim, *args, **kwargs)
268
+ self.embed_dim = embed_dim
269
+
270
+ def encode(self, x):
271
+ h = self.encoder(x)
272
+ h = self.quant_conv(h)
273
+ return h
274
+
275
+ def decode(self, h, force_not_quantize=False):
276
+ # also go through quantization layer
277
+ if not force_not_quantize:
278
+ quant, emb_loss, info = self.quantize(h)
279
+ else:
280
+ quant = h
281
+ quant = self.post_quant_conv(quant)
282
+ dec = self.decoder(quant)
283
+ return dec
284
+
285
+ setattr(ldm.models.autoencoder, "VQModel", VQModel)
286
+ setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py ADDED
@@ -0,0 +1,1449 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # This script is copied from the compvis/stable-diffusion repo (aka the SD V1 repo)
2
+ # Original filename: ldm/models/diffusion/ddpm.py
3
+ # The purpose to reinstate the old DDPM logic which works with VQ, whereas the V2 one doesn't
4
+ # Some models such as LDSR require VQ to work correctly
5
+ # The classes are suffixed with "V1" and added back to the "ldm.models.diffusion.ddpm" module
6
+
7
+ import torch
8
+ import torch.nn as nn
9
+ import numpy as np
10
+ import pytorch_lightning as pl
11
+ from torch.optim.lr_scheduler import LambdaLR
12
+ from einops import rearrange, repeat
13
+ from contextlib import contextmanager
14
+ from functools import partial
15
+ from tqdm import tqdm
16
+ from torchvision.utils import make_grid
17
+ from pytorch_lightning.utilities.distributed import rank_zero_only
18
+
19
+ from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
20
+ from ldm.modules.ema import LitEma
21
+ from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
22
+ from ldm.models.autoencoder import VQModelInterface, IdentityFirstStage, AutoencoderKL
23
+ from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
24
+ from ldm.models.diffusion.ddim import DDIMSampler
25
+
26
+ import ldm.models.diffusion.ddpm
27
+
28
+ __conditioning_keys__ = {'concat': 'c_concat',
29
+ 'crossattn': 'c_crossattn',
30
+ 'adm': 'y'}
31
+
32
+
33
+ def disabled_train(self, mode=True):
34
+ """Overwrite model.train with this function to make sure train/eval mode
35
+ does not change anymore."""
36
+ return self
37
+
38
+
39
+ def uniform_on_device(r1, r2, shape, device):
40
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
41
+
42
+
43
+ class DDPMV1(pl.LightningModule):
44
+ # classic DDPM with Gaussian diffusion, in image space
45
+ def __init__(self,
46
+ unet_config,
47
+ timesteps=1000,
48
+ beta_schedule="linear",
49
+ loss_type="l2",
50
+ ckpt_path=None,
51
+ ignore_keys=[],
52
+ load_only_unet=False,
53
+ monitor="val/loss",
54
+ use_ema=True,
55
+ first_stage_key="image",
56
+ image_size=256,
57
+ channels=3,
58
+ log_every_t=100,
59
+ clip_denoised=True,
60
+ linear_start=1e-4,
61
+ linear_end=2e-2,
62
+ cosine_s=8e-3,
63
+ given_betas=None,
64
+ original_elbo_weight=0.,
65
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
66
+ l_simple_weight=1.,
67
+ conditioning_key=None,
68
+ parameterization="eps", # all assuming fixed variance schedules
69
+ scheduler_config=None,
70
+ use_positional_encodings=False,
71
+ learn_logvar=False,
72
+ logvar_init=0.,
73
+ ):
74
+ super().__init__()
75
+ assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
76
+ self.parameterization = parameterization
77
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
78
+ self.cond_stage_model = None
79
+ self.clip_denoised = clip_denoised
80
+ self.log_every_t = log_every_t
81
+ self.first_stage_key = first_stage_key
82
+ self.image_size = image_size # try conv?
83
+ self.channels = channels
84
+ self.use_positional_encodings = use_positional_encodings
85
+ self.model = DiffusionWrapperV1(unet_config, conditioning_key)
86
+ count_params(self.model, verbose=True)
87
+ self.use_ema = use_ema
88
+ if self.use_ema:
89
+ self.model_ema = LitEma(self.model)
90
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
91
+
92
+ self.use_scheduler = scheduler_config is not None
93
+ if self.use_scheduler:
94
+ self.scheduler_config = scheduler_config
95
+
96
+ self.v_posterior = v_posterior
97
+ self.original_elbo_weight = original_elbo_weight
98
+ self.l_simple_weight = l_simple_weight
99
+
100
+ if monitor is not None:
101
+ self.monitor = monitor
102
+ if ckpt_path is not None:
103
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
104
+
105
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
106
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
107
+
108
+ self.loss_type = loss_type
109
+
110
+ self.learn_logvar = learn_logvar
111
+ self.logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
112
+ if self.learn_logvar:
113
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
114
+
115
+
116
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
117
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
118
+ if exists(given_betas):
119
+ betas = given_betas
120
+ else:
121
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
122
+ cosine_s=cosine_s)
123
+ alphas = 1. - betas
124
+ alphas_cumprod = np.cumprod(alphas, axis=0)
125
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
126
+
127
+ timesteps, = betas.shape
128
+ self.num_timesteps = int(timesteps)
129
+ self.linear_start = linear_start
130
+ self.linear_end = linear_end
131
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
132
+
133
+ to_torch = partial(torch.tensor, dtype=torch.float32)
134
+
135
+ self.register_buffer('betas', to_torch(betas))
136
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
137
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
138
+
139
+ # calculations for diffusion q(x_t | x_{t-1}) and others
140
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
141
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
142
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
143
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
144
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
145
+
146
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
147
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
148
+ 1. - alphas_cumprod) + self.v_posterior * betas
149
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
150
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
151
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
152
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
153
+ self.register_buffer('posterior_mean_coef1', to_torch(
154
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
155
+ self.register_buffer('posterior_mean_coef2', to_torch(
156
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
157
+
158
+ if self.parameterization == "eps":
159
+ lvlb_weights = self.betas ** 2 / (
160
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
161
+ elif self.parameterization == "x0":
162
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
163
+ else:
164
+ raise NotImplementedError("mu not supported")
165
+ # TODO how to choose this term
166
+ lvlb_weights[0] = lvlb_weights[1]
167
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
168
+ assert not torch.isnan(self.lvlb_weights).all()
169
+
170
+ @contextmanager
171
+ def ema_scope(self, context=None):
172
+ if self.use_ema:
173
+ self.model_ema.store(self.model.parameters())
174
+ self.model_ema.copy_to(self.model)
175
+ if context is not None:
176
+ print(f"{context}: Switched to EMA weights")
177
+ try:
178
+ yield None
179
+ finally:
180
+ if self.use_ema:
181
+ self.model_ema.restore(self.model.parameters())
182
+ if context is not None:
183
+ print(f"{context}: Restored training weights")
184
+
185
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
186
+ sd = torch.load(path, map_location="cpu")
187
+ if "state_dict" in list(sd.keys()):
188
+ sd = sd["state_dict"]
189
+ keys = list(sd.keys())
190
+ for k in keys:
191
+ for ik in ignore_keys:
192
+ if k.startswith(ik):
193
+ print("Deleting key {} from state_dict.".format(k))
194
+ del sd[k]
195
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
196
+ sd, strict=False)
197
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
198
+ if len(missing) > 0:
199
+ print(f"Missing Keys: {missing}")
200
+ if len(unexpected) > 0:
201
+ print(f"Unexpected Keys: {unexpected}")
202
+
203
+ def q_mean_variance(self, x_start, t):
204
+ """
205
+ Get the distribution q(x_t | x_0).
206
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
207
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
208
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
209
+ """
210
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
211
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
212
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
213
+ return mean, variance, log_variance
214
+
215
+ def predict_start_from_noise(self, x_t, t, noise):
216
+ return (
217
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
218
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
219
+ )
220
+
221
+ def q_posterior(self, x_start, x_t, t):
222
+ posterior_mean = (
223
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
224
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
225
+ )
226
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
227
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
228
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
229
+
230
+ def p_mean_variance(self, x, t, clip_denoised: bool):
231
+ model_out = self.model(x, t)
232
+ if self.parameterization == "eps":
233
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
234
+ elif self.parameterization == "x0":
235
+ x_recon = model_out
236
+ if clip_denoised:
237
+ x_recon.clamp_(-1., 1.)
238
+
239
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
240
+ return model_mean, posterior_variance, posterior_log_variance
241
+
242
+ @torch.no_grad()
243
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
244
+ b, *_, device = *x.shape, x.device
245
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
246
+ noise = noise_like(x.shape, device, repeat_noise)
247
+ # no noise when t == 0
248
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
249
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
250
+
251
+ @torch.no_grad()
252
+ def p_sample_loop(self, shape, return_intermediates=False):
253
+ device = self.betas.device
254
+ b = shape[0]
255
+ img = torch.randn(shape, device=device)
256
+ intermediates = [img]
257
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
258
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
259
+ clip_denoised=self.clip_denoised)
260
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
261
+ intermediates.append(img)
262
+ if return_intermediates:
263
+ return img, intermediates
264
+ return img
265
+
266
+ @torch.no_grad()
267
+ def sample(self, batch_size=16, return_intermediates=False):
268
+ image_size = self.image_size
269
+ channels = self.channels
270
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
271
+ return_intermediates=return_intermediates)
272
+
273
+ def q_sample(self, x_start, t, noise=None):
274
+ noise = default(noise, lambda: torch.randn_like(x_start))
275
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
276
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
277
+
278
+ def get_loss(self, pred, target, mean=True):
279
+ if self.loss_type == 'l1':
280
+ loss = (target - pred).abs()
281
+ if mean:
282
+ loss = loss.mean()
283
+ elif self.loss_type == 'l2':
284
+ if mean:
285
+ loss = torch.nn.functional.mse_loss(target, pred)
286
+ else:
287
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
288
+ else:
289
+ raise NotImplementedError("unknown loss type '{loss_type}'")
290
+
291
+ return loss
292
+
293
+ def p_losses(self, x_start, t, noise=None):
294
+ noise = default(noise, lambda: torch.randn_like(x_start))
295
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
296
+ model_out = self.model(x_noisy, t)
297
+
298
+ loss_dict = {}
299
+ if self.parameterization == "eps":
300
+ target = noise
301
+ elif self.parameterization == "x0":
302
+ target = x_start
303
+ else:
304
+ raise NotImplementedError(f"Paramterization {self.parameterization} not yet supported")
305
+
306
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
307
+
308
+ log_prefix = 'train' if self.training else 'val'
309
+
310
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
311
+ loss_simple = loss.mean() * self.l_simple_weight
312
+
313
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
314
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
315
+
316
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
317
+
318
+ loss_dict.update({f'{log_prefix}/loss': loss})
319
+
320
+ return loss, loss_dict
321
+
322
+ def forward(self, x, *args, **kwargs):
323
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
324
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
325
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
326
+ return self.p_losses(x, t, *args, **kwargs)
327
+
328
+ def get_input(self, batch, k):
329
+ x = batch[k]
330
+ if len(x.shape) == 3:
331
+ x = x[..., None]
332
+ x = rearrange(x, 'b h w c -> b c h w')
333
+ x = x.to(memory_format=torch.contiguous_format).float()
334
+ return x
335
+
336
+ def shared_step(self, batch):
337
+ x = self.get_input(batch, self.first_stage_key)
338
+ loss, loss_dict = self(x)
339
+ return loss, loss_dict
340
+
341
+ def training_step(self, batch, batch_idx):
342
+ loss, loss_dict = self.shared_step(batch)
343
+
344
+ self.log_dict(loss_dict, prog_bar=True,
345
+ logger=True, on_step=True, on_epoch=True)
346
+
347
+ self.log("global_step", self.global_step,
348
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
349
+
350
+ if self.use_scheduler:
351
+ lr = self.optimizers().param_groups[0]['lr']
352
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
353
+
354
+ return loss
355
+
356
+ @torch.no_grad()
357
+ def validation_step(self, batch, batch_idx):
358
+ _, loss_dict_no_ema = self.shared_step(batch)
359
+ with self.ema_scope():
360
+ _, loss_dict_ema = self.shared_step(batch)
361
+ loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
362
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
363
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
364
+
365
+ def on_train_batch_end(self, *args, **kwargs):
366
+ if self.use_ema:
367
+ self.model_ema(self.model)
368
+
369
+ def _get_rows_from_list(self, samples):
370
+ n_imgs_per_row = len(samples)
371
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
372
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
373
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
374
+ return denoise_grid
375
+
376
+ @torch.no_grad()
377
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
378
+ log = dict()
379
+ x = self.get_input(batch, self.first_stage_key)
380
+ N = min(x.shape[0], N)
381
+ n_row = min(x.shape[0], n_row)
382
+ x = x.to(self.device)[:N]
383
+ log["inputs"] = x
384
+
385
+ # get diffusion row
386
+ diffusion_row = list()
387
+ x_start = x[:n_row]
388
+
389
+ for t in range(self.num_timesteps):
390
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
391
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
392
+ t = t.to(self.device).long()
393
+ noise = torch.randn_like(x_start)
394
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
395
+ diffusion_row.append(x_noisy)
396
+
397
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
398
+
399
+ if sample:
400
+ # get denoise row
401
+ with self.ema_scope("Plotting"):
402
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
403
+
404
+ log["samples"] = samples
405
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
406
+
407
+ if return_keys:
408
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
409
+ return log
410
+ else:
411
+ return {key: log[key] for key in return_keys}
412
+ return log
413
+
414
+ def configure_optimizers(self):
415
+ lr = self.learning_rate
416
+ params = list(self.model.parameters())
417
+ if self.learn_logvar:
418
+ params = params + [self.logvar]
419
+ opt = torch.optim.AdamW(params, lr=lr)
420
+ return opt
421
+
422
+
423
+ class LatentDiffusionV1(DDPMV1):
424
+ """main class"""
425
+ def __init__(self,
426
+ first_stage_config,
427
+ cond_stage_config,
428
+ num_timesteps_cond=None,
429
+ cond_stage_key="image",
430
+ cond_stage_trainable=False,
431
+ concat_mode=True,
432
+ cond_stage_forward=None,
433
+ conditioning_key=None,
434
+ scale_factor=1.0,
435
+ scale_by_std=False,
436
+ *args, **kwargs):
437
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
438
+ self.scale_by_std = scale_by_std
439
+ assert self.num_timesteps_cond <= kwargs['timesteps']
440
+ # for backwards compatibility after implementation of DiffusionWrapper
441
+ if conditioning_key is None:
442
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
443
+ if cond_stage_config == '__is_unconditional__':
444
+ conditioning_key = None
445
+ ckpt_path = kwargs.pop("ckpt_path", None)
446
+ ignore_keys = kwargs.pop("ignore_keys", [])
447
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
448
+ self.concat_mode = concat_mode
449
+ self.cond_stage_trainable = cond_stage_trainable
450
+ self.cond_stage_key = cond_stage_key
451
+ try:
452
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
453
+ except:
454
+ self.num_downs = 0
455
+ if not scale_by_std:
456
+ self.scale_factor = scale_factor
457
+ else:
458
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
459
+ self.instantiate_first_stage(first_stage_config)
460
+ self.instantiate_cond_stage(cond_stage_config)
461
+ self.cond_stage_forward = cond_stage_forward
462
+ self.clip_denoised = False
463
+ self.bbox_tokenizer = None
464
+
465
+ self.restarted_from_ckpt = False
466
+ if ckpt_path is not None:
467
+ self.init_from_ckpt(ckpt_path, ignore_keys)
468
+ self.restarted_from_ckpt = True
469
+
470
+ def make_cond_schedule(self, ):
471
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
472
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
473
+ self.cond_ids[:self.num_timesteps_cond] = ids
474
+
475
+ @rank_zero_only
476
+ @torch.no_grad()
477
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
478
+ # only for very first batch
479
+ 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:
480
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
481
+ # set rescale weight to 1./std of encodings
482
+ print("### USING STD-RESCALING ###")
483
+ x = super().get_input(batch, self.first_stage_key)
484
+ x = x.to(self.device)
485
+ encoder_posterior = self.encode_first_stage(x)
486
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
487
+ del self.scale_factor
488
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
489
+ print(f"setting self.scale_factor to {self.scale_factor}")
490
+ print("### USING STD-RESCALING ###")
491
+
492
+ def register_schedule(self,
493
+ given_betas=None, beta_schedule="linear", timesteps=1000,
494
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
495
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
496
+
497
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
498
+ if self.shorten_cond_schedule:
499
+ self.make_cond_schedule()
500
+
501
+ def instantiate_first_stage(self, config):
502
+ model = instantiate_from_config(config)
503
+ self.first_stage_model = model.eval()
504
+ self.first_stage_model.train = disabled_train
505
+ for param in self.first_stage_model.parameters():
506
+ param.requires_grad = False
507
+
508
+ def instantiate_cond_stage(self, config):
509
+ if not self.cond_stage_trainable:
510
+ if config == "__is_first_stage__":
511
+ print("Using first stage also as cond stage.")
512
+ self.cond_stage_model = self.first_stage_model
513
+ elif config == "__is_unconditional__":
514
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
515
+ self.cond_stage_model = None
516
+ # self.be_unconditional = True
517
+ else:
518
+ model = instantiate_from_config(config)
519
+ self.cond_stage_model = model.eval()
520
+ self.cond_stage_model.train = disabled_train
521
+ for param in self.cond_stage_model.parameters():
522
+ param.requires_grad = False
523
+ else:
524
+ assert config != '__is_first_stage__'
525
+ assert config != '__is_unconditional__'
526
+ model = instantiate_from_config(config)
527
+ self.cond_stage_model = model
528
+
529
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
530
+ denoise_row = []
531
+ for zd in tqdm(samples, desc=desc):
532
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
533
+ force_not_quantize=force_no_decoder_quantization))
534
+ n_imgs_per_row = len(denoise_row)
535
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
536
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
537
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
538
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
539
+ return denoise_grid
540
+
541
+ def get_first_stage_encoding(self, encoder_posterior):
542
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
543
+ z = encoder_posterior.sample()
544
+ elif isinstance(encoder_posterior, torch.Tensor):
545
+ z = encoder_posterior
546
+ else:
547
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
548
+ return self.scale_factor * z
549
+
550
+ def get_learned_conditioning(self, c):
551
+ if self.cond_stage_forward is None:
552
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
553
+ c = self.cond_stage_model.encode(c)
554
+ if isinstance(c, DiagonalGaussianDistribution):
555
+ c = c.mode()
556
+ else:
557
+ c = self.cond_stage_model(c)
558
+ else:
559
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
560
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
561
+ return c
562
+
563
+ def meshgrid(self, h, w):
564
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
565
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
566
+
567
+ arr = torch.cat([y, x], dim=-1)
568
+ return arr
569
+
570
+ def delta_border(self, h, w):
571
+ """
572
+ :param h: height
573
+ :param w: width
574
+ :return: normalized distance to image border,
575
+ wtith min distance = 0 at border and max dist = 0.5 at image center
576
+ """
577
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
578
+ arr = self.meshgrid(h, w) / lower_right_corner
579
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
580
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
581
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
582
+ return edge_dist
583
+
584
+ def get_weighting(self, h, w, Ly, Lx, device):
585
+ weighting = self.delta_border(h, w)
586
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
587
+ self.split_input_params["clip_max_weight"], )
588
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
589
+
590
+ if self.split_input_params["tie_braker"]:
591
+ L_weighting = self.delta_border(Ly, Lx)
592
+ L_weighting = torch.clip(L_weighting,
593
+ self.split_input_params["clip_min_tie_weight"],
594
+ self.split_input_params["clip_max_tie_weight"])
595
+
596
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
597
+ weighting = weighting * L_weighting
598
+ return weighting
599
+
600
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
601
+ """
602
+ :param x: img of size (bs, c, h, w)
603
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
604
+ """
605
+ bs, nc, h, w = x.shape
606
+
607
+ # number of crops in image
608
+ Ly = (h - kernel_size[0]) // stride[0] + 1
609
+ Lx = (w - kernel_size[1]) // stride[1] + 1
610
+
611
+ if uf == 1 and df == 1:
612
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
613
+ unfold = torch.nn.Unfold(**fold_params)
614
+
615
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
616
+
617
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
618
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
619
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
620
+
621
+ elif uf > 1 and df == 1:
622
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
623
+ unfold = torch.nn.Unfold(**fold_params)
624
+
625
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
626
+ dilation=1, padding=0,
627
+ stride=(stride[0] * uf, stride[1] * uf))
628
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
629
+
630
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
631
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
632
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
633
+
634
+ elif df > 1 and uf == 1:
635
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
636
+ unfold = torch.nn.Unfold(**fold_params)
637
+
638
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
639
+ dilation=1, padding=0,
640
+ stride=(stride[0] // df, stride[1] // df))
641
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
642
+
643
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
644
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
645
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
646
+
647
+ else:
648
+ raise NotImplementedError
649
+
650
+ return fold, unfold, normalization, weighting
651
+
652
+ @torch.no_grad()
653
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
654
+ cond_key=None, return_original_cond=False, bs=None):
655
+ x = super().get_input(batch, k)
656
+ if bs is not None:
657
+ x = x[:bs]
658
+ x = x.to(self.device)
659
+ encoder_posterior = self.encode_first_stage(x)
660
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
661
+
662
+ if self.model.conditioning_key is not None:
663
+ if cond_key is None:
664
+ cond_key = self.cond_stage_key
665
+ if cond_key != self.first_stage_key:
666
+ if cond_key in ['caption', 'coordinates_bbox']:
667
+ xc = batch[cond_key]
668
+ elif cond_key == 'class_label':
669
+ xc = batch
670
+ else:
671
+ xc = super().get_input(batch, cond_key).to(self.device)
672
+ else:
673
+ xc = x
674
+ if not self.cond_stage_trainable or force_c_encode:
675
+ if isinstance(xc, dict) or isinstance(xc, list):
676
+ # import pudb; pudb.set_trace()
677
+ c = self.get_learned_conditioning(xc)
678
+ else:
679
+ c = self.get_learned_conditioning(xc.to(self.device))
680
+ else:
681
+ c = xc
682
+ if bs is not None:
683
+ c = c[:bs]
684
+
685
+ if self.use_positional_encodings:
686
+ pos_x, pos_y = self.compute_latent_shifts(batch)
687
+ ckey = __conditioning_keys__[self.model.conditioning_key]
688
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
689
+
690
+ else:
691
+ c = None
692
+ xc = None
693
+ if self.use_positional_encodings:
694
+ pos_x, pos_y = self.compute_latent_shifts(batch)
695
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
696
+ out = [z, c]
697
+ if return_first_stage_outputs:
698
+ xrec = self.decode_first_stage(z)
699
+ out.extend([x, xrec])
700
+ if return_original_cond:
701
+ out.append(xc)
702
+ return out
703
+
704
+ @torch.no_grad()
705
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
706
+ if predict_cids:
707
+ if z.dim() == 4:
708
+ z = torch.argmax(z.exp(), dim=1).long()
709
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
710
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
711
+
712
+ z = 1. / self.scale_factor * z
713
+
714
+ if hasattr(self, "split_input_params"):
715
+ if self.split_input_params["patch_distributed_vq"]:
716
+ ks = self.split_input_params["ks"] # eg. (128, 128)
717
+ stride = self.split_input_params["stride"] # eg. (64, 64)
718
+ uf = self.split_input_params["vqf"]
719
+ bs, nc, h, w = z.shape
720
+ if ks[0] > h or ks[1] > w:
721
+ ks = (min(ks[0], h), min(ks[1], w))
722
+ print("reducing Kernel")
723
+
724
+ if stride[0] > h or stride[1] > w:
725
+ stride = (min(stride[0], h), min(stride[1], w))
726
+ print("reducing stride")
727
+
728
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
729
+
730
+ z = unfold(z) # (bn, nc * prod(**ks), L)
731
+ # 1. Reshape to img shape
732
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
733
+
734
+ # 2. apply model loop over last dim
735
+ if isinstance(self.first_stage_model, VQModelInterface):
736
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
737
+ force_not_quantize=predict_cids or force_not_quantize)
738
+ for i in range(z.shape[-1])]
739
+ else:
740
+
741
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
742
+ for i in range(z.shape[-1])]
743
+
744
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
745
+ o = o * weighting
746
+ # Reverse 1. reshape to img shape
747
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
748
+ # stitch crops together
749
+ decoded = fold(o)
750
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
751
+ return decoded
752
+ else:
753
+ if isinstance(self.first_stage_model, VQModelInterface):
754
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
755
+ else:
756
+ return self.first_stage_model.decode(z)
757
+
758
+ else:
759
+ if isinstance(self.first_stage_model, VQModelInterface):
760
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
761
+ else:
762
+ return self.first_stage_model.decode(z)
763
+
764
+ # same as above but without decorator
765
+ def differentiable_decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
766
+ if predict_cids:
767
+ if z.dim() == 4:
768
+ z = torch.argmax(z.exp(), dim=1).long()
769
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
770
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
771
+
772
+ z = 1. / self.scale_factor * z
773
+
774
+ if hasattr(self, "split_input_params"):
775
+ if self.split_input_params["patch_distributed_vq"]:
776
+ ks = self.split_input_params["ks"] # eg. (128, 128)
777
+ stride = self.split_input_params["stride"] # eg. (64, 64)
778
+ uf = self.split_input_params["vqf"]
779
+ bs, nc, h, w = z.shape
780
+ if ks[0] > h or ks[1] > w:
781
+ ks = (min(ks[0], h), min(ks[1], w))
782
+ print("reducing Kernel")
783
+
784
+ if stride[0] > h or stride[1] > w:
785
+ stride = (min(stride[0], h), min(stride[1], w))
786
+ print("reducing stride")
787
+
788
+ fold, unfold, normalization, weighting = self.get_fold_unfold(z, ks, stride, uf=uf)
789
+
790
+ z = unfold(z) # (bn, nc * prod(**ks), L)
791
+ # 1. Reshape to img shape
792
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
793
+
794
+ # 2. apply model loop over last dim
795
+ if isinstance(self.first_stage_model, VQModelInterface):
796
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i],
797
+ force_not_quantize=predict_cids or force_not_quantize)
798
+ for i in range(z.shape[-1])]
799
+ else:
800
+
801
+ output_list = [self.first_stage_model.decode(z[:, :, :, :, i])
802
+ for i in range(z.shape[-1])]
803
+
804
+ o = torch.stack(output_list, axis=-1) # # (bn, nc, ks[0], ks[1], L)
805
+ o = o * weighting
806
+ # Reverse 1. reshape to img shape
807
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
808
+ # stitch crops together
809
+ decoded = fold(o)
810
+ decoded = decoded / normalization # norm is shape (1, 1, h, w)
811
+ return decoded
812
+ else:
813
+ if isinstance(self.first_stage_model, VQModelInterface):
814
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
815
+ else:
816
+ return self.first_stage_model.decode(z)
817
+
818
+ else:
819
+ if isinstance(self.first_stage_model, VQModelInterface):
820
+ return self.first_stage_model.decode(z, force_not_quantize=predict_cids or force_not_quantize)
821
+ else:
822
+ return self.first_stage_model.decode(z)
823
+
824
+ @torch.no_grad()
825
+ def encode_first_stage(self, x):
826
+ if hasattr(self, "split_input_params"):
827
+ if self.split_input_params["patch_distributed_vq"]:
828
+ ks = self.split_input_params["ks"] # eg. (128, 128)
829
+ stride = self.split_input_params["stride"] # eg. (64, 64)
830
+ df = self.split_input_params["vqf"]
831
+ self.split_input_params['original_image_size'] = x.shape[-2:]
832
+ bs, nc, h, w = x.shape
833
+ if ks[0] > h or ks[1] > w:
834
+ ks = (min(ks[0], h), min(ks[1], w))
835
+ print("reducing Kernel")
836
+
837
+ if stride[0] > h or stride[1] > w:
838
+ stride = (min(stride[0], h), min(stride[1], w))
839
+ print("reducing stride")
840
+
841
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x, ks, stride, df=df)
842
+ z = unfold(x) # (bn, nc * prod(**ks), L)
843
+ # Reshape to img shape
844
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
845
+
846
+ output_list = [self.first_stage_model.encode(z[:, :, :, :, i])
847
+ for i in range(z.shape[-1])]
848
+
849
+ o = torch.stack(output_list, axis=-1)
850
+ o = o * weighting
851
+
852
+ # Reverse reshape to img shape
853
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
854
+ # stitch crops together
855
+ decoded = fold(o)
856
+ decoded = decoded / normalization
857
+ return decoded
858
+
859
+ else:
860
+ return self.first_stage_model.encode(x)
861
+ else:
862
+ return self.first_stage_model.encode(x)
863
+
864
+ def shared_step(self, batch, **kwargs):
865
+ x, c = self.get_input(batch, self.first_stage_key)
866
+ loss = self(x, c)
867
+ return loss
868
+
869
+ def forward(self, x, c, *args, **kwargs):
870
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
871
+ if self.model.conditioning_key is not None:
872
+ assert c is not None
873
+ if self.cond_stage_trainable:
874
+ c = self.get_learned_conditioning(c)
875
+ if self.shorten_cond_schedule: # TODO: drop this option
876
+ tc = self.cond_ids[t].to(self.device)
877
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
878
+ return self.p_losses(x, c, t, *args, **kwargs)
879
+
880
+ def _rescale_annotations(self, bboxes, crop_coordinates): # TODO: move to dataset
881
+ def rescale_bbox(bbox):
882
+ x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
883
+ y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
884
+ w = min(bbox[2] / crop_coordinates[2], 1 - x0)
885
+ h = min(bbox[3] / crop_coordinates[3], 1 - y0)
886
+ return x0, y0, w, h
887
+
888
+ return [rescale_bbox(b) for b in bboxes]
889
+
890
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
891
+
892
+ if isinstance(cond, dict):
893
+ # hybrid case, cond is exptected to be a dict
894
+ pass
895
+ else:
896
+ if not isinstance(cond, list):
897
+ cond = [cond]
898
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
899
+ cond = {key: cond}
900
+
901
+ if hasattr(self, "split_input_params"):
902
+ assert len(cond) == 1 # todo can only deal with one conditioning atm
903
+ assert not return_ids
904
+ ks = self.split_input_params["ks"] # eg. (128, 128)
905
+ stride = self.split_input_params["stride"] # eg. (64, 64)
906
+
907
+ h, w = x_noisy.shape[-2:]
908
+
909
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
910
+
911
+ z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
912
+ # Reshape to img shape
913
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
914
+ z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
915
+
916
+ if self.cond_stage_key in ["image", "LR_image", "segmentation",
917
+ 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
918
+ c_key = next(iter(cond.keys())) # get key
919
+ c = next(iter(cond.values())) # get value
920
+ assert (len(c) == 1) # todo extend to list with more than one elem
921
+ c = c[0] # get element
922
+
923
+ c = unfold(c)
924
+ c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
925
+
926
+ cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
927
+
928
+ elif self.cond_stage_key == 'coordinates_bbox':
929
+ assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
930
+
931
+ # assuming padding of unfold is always 0 and its dilation is always 1
932
+ n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
933
+ full_img_h, full_img_w = self.split_input_params['original_image_size']
934
+ # as we are operating on latents, we need the factor from the original image size to the
935
+ # spatial latent size to properly rescale the crops for regenerating the bbox annotations
936
+ num_downs = self.first_stage_model.encoder.num_resolutions - 1
937
+ rescale_latent = 2 ** (num_downs)
938
+
939
+ # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
940
+ # need to rescale the tl patch coordinates to be in between (0,1)
941
+ tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
942
+ rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
943
+ for patch_nr in range(z.shape[-1])]
944
+
945
+ # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
946
+ patch_limits = [(x_tl, y_tl,
947
+ rescale_latent * ks[0] / full_img_w,
948
+ rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
949
+ # 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]
950
+
951
+ # tokenize crop coordinates for the bounding boxes of the respective patches
952
+ patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
953
+ for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
954
+ print(patch_limits_tknzd[0].shape)
955
+ # cut tknzd crop position from conditioning
956
+ assert isinstance(cond, dict), 'cond must be dict to be fed into model'
957
+ cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
958
+ print(cut_cond.shape)
959
+
960
+ adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
961
+ adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
962
+ print(adapted_cond.shape)
963
+ adapted_cond = self.get_learned_conditioning(adapted_cond)
964
+ print(adapted_cond.shape)
965
+ adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
966
+ print(adapted_cond.shape)
967
+
968
+ cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
969
+
970
+ else:
971
+ cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
972
+
973
+ # apply model by loop over crops
974
+ output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
975
+ assert not isinstance(output_list[0],
976
+ tuple) # todo cant deal with multiple model outputs check this never happens
977
+
978
+ o = torch.stack(output_list, axis=-1)
979
+ o = o * weighting
980
+ # Reverse reshape to img shape
981
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
982
+ # stitch crops together
983
+ x_recon = fold(o) / normalization
984
+
985
+ else:
986
+ x_recon = self.model(x_noisy, t, **cond)
987
+
988
+ if isinstance(x_recon, tuple) and not return_ids:
989
+ return x_recon[0]
990
+ else:
991
+ return x_recon
992
+
993
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
994
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
995
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
996
+
997
+ def _prior_bpd(self, x_start):
998
+ """
999
+ Get the prior KL term for the variational lower-bound, measured in
1000
+ bits-per-dim.
1001
+ This term can't be optimized, as it only depends on the encoder.
1002
+ :param x_start: the [N x C x ...] tensor of inputs.
1003
+ :return: a batch of [N] KL values (in bits), one per batch element.
1004
+ """
1005
+ batch_size = x_start.shape[0]
1006
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
1007
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
1008
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
1009
+ return mean_flat(kl_prior) / np.log(2.0)
1010
+
1011
+ def p_losses(self, x_start, cond, t, noise=None):
1012
+ noise = default(noise, lambda: torch.randn_like(x_start))
1013
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
1014
+ model_output = self.apply_model(x_noisy, t, cond)
1015
+
1016
+ loss_dict = {}
1017
+ prefix = 'train' if self.training else 'val'
1018
+
1019
+ if self.parameterization == "x0":
1020
+ target = x_start
1021
+ elif self.parameterization == "eps":
1022
+ target = noise
1023
+ else:
1024
+ raise NotImplementedError()
1025
+
1026
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
1027
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
1028
+
1029
+ logvar_t = self.logvar[t].to(self.device)
1030
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
1031
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
1032
+ if self.learn_logvar:
1033
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
1034
+ loss_dict.update({'logvar': self.logvar.data.mean()})
1035
+
1036
+ loss = self.l_simple_weight * loss.mean()
1037
+
1038
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
1039
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
1040
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
1041
+ loss += (self.original_elbo_weight * loss_vlb)
1042
+ loss_dict.update({f'{prefix}/loss': loss})
1043
+
1044
+ return loss, loss_dict
1045
+
1046
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
1047
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
1048
+ t_in = t
1049
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
1050
+
1051
+ if score_corrector is not None:
1052
+ assert self.parameterization == "eps"
1053
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
1054
+
1055
+ if return_codebook_ids:
1056
+ model_out, logits = model_out
1057
+
1058
+ if self.parameterization == "eps":
1059
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
1060
+ elif self.parameterization == "x0":
1061
+ x_recon = model_out
1062
+ else:
1063
+ raise NotImplementedError()
1064
+
1065
+ if clip_denoised:
1066
+ x_recon.clamp_(-1., 1.)
1067
+ if quantize_denoised:
1068
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
1069
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
1070
+ if return_codebook_ids:
1071
+ return model_mean, posterior_variance, posterior_log_variance, logits
1072
+ elif return_x0:
1073
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
1074
+ else:
1075
+ return model_mean, posterior_variance, posterior_log_variance
1076
+
1077
+ @torch.no_grad()
1078
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
1079
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
1080
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
1081
+ b, *_, device = *x.shape, x.device
1082
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
1083
+ return_codebook_ids=return_codebook_ids,
1084
+ quantize_denoised=quantize_denoised,
1085
+ return_x0=return_x0,
1086
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
1087
+ if return_codebook_ids:
1088
+ raise DeprecationWarning("Support dropped.")
1089
+ model_mean, _, model_log_variance, logits = outputs
1090
+ elif return_x0:
1091
+ model_mean, _, model_log_variance, x0 = outputs
1092
+ else:
1093
+ model_mean, _, model_log_variance = outputs
1094
+
1095
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
1096
+ if noise_dropout > 0.:
1097
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
1098
+ # no noise when t == 0
1099
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
1100
+
1101
+ if return_codebook_ids:
1102
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
1103
+ if return_x0:
1104
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
1105
+ else:
1106
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
1107
+
1108
+ @torch.no_grad()
1109
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
1110
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
1111
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
1112
+ log_every_t=None):
1113
+ if not log_every_t:
1114
+ log_every_t = self.log_every_t
1115
+ timesteps = self.num_timesteps
1116
+ if batch_size is not None:
1117
+ b = batch_size if batch_size is not None else shape[0]
1118
+ shape = [batch_size] + list(shape)
1119
+ else:
1120
+ b = batch_size = shape[0]
1121
+ if x_T is None:
1122
+ img = torch.randn(shape, device=self.device)
1123
+ else:
1124
+ img = x_T
1125
+ intermediates = []
1126
+ if cond is not None:
1127
+ if isinstance(cond, dict):
1128
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
1129
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
1130
+ else:
1131
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
1132
+
1133
+ if start_T is not None:
1134
+ timesteps = min(timesteps, start_T)
1135
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
1136
+ total=timesteps) if verbose else reversed(
1137
+ range(0, timesteps))
1138
+ if type(temperature) == float:
1139
+ temperature = [temperature] * timesteps
1140
+
1141
+ for i in iterator:
1142
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
1143
+ if self.shorten_cond_schedule:
1144
+ assert self.model.conditioning_key != 'hybrid'
1145
+ tc = self.cond_ids[ts].to(cond.device)
1146
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
1147
+
1148
+ img, x0_partial = self.p_sample(img, cond, ts,
1149
+ clip_denoised=self.clip_denoised,
1150
+ quantize_denoised=quantize_denoised, return_x0=True,
1151
+ temperature=temperature[i], noise_dropout=noise_dropout,
1152
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
1153
+ if mask is not None:
1154
+ assert x0 is not None
1155
+ img_orig = self.q_sample(x0, ts)
1156
+ img = img_orig * mask + (1. - mask) * img
1157
+
1158
+ if i % log_every_t == 0 or i == timesteps - 1:
1159
+ intermediates.append(x0_partial)
1160
+ if callback: callback(i)
1161
+ if img_callback: img_callback(img, i)
1162
+ return img, intermediates
1163
+
1164
+ @torch.no_grad()
1165
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
1166
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
1167
+ mask=None, x0=None, img_callback=None, start_T=None,
1168
+ log_every_t=None):
1169
+
1170
+ if not log_every_t:
1171
+ log_every_t = self.log_every_t
1172
+ device = self.betas.device
1173
+ b = shape[0]
1174
+ if x_T is None:
1175
+ img = torch.randn(shape, device=device)
1176
+ else:
1177
+ img = x_T
1178
+
1179
+ intermediates = [img]
1180
+ if timesteps is None:
1181
+ timesteps = self.num_timesteps
1182
+
1183
+ if start_T is not None:
1184
+ timesteps = min(timesteps, start_T)
1185
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
1186
+ range(0, timesteps))
1187
+
1188
+ if mask is not None:
1189
+ assert x0 is not None
1190
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
1191
+
1192
+ for i in iterator:
1193
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
1194
+ if self.shorten_cond_schedule:
1195
+ assert self.model.conditioning_key != 'hybrid'
1196
+ tc = self.cond_ids[ts].to(cond.device)
1197
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
1198
+
1199
+ img = self.p_sample(img, cond, ts,
1200
+ clip_denoised=self.clip_denoised,
1201
+ quantize_denoised=quantize_denoised)
1202
+ if mask is not None:
1203
+ img_orig = self.q_sample(x0, ts)
1204
+ img = img_orig * mask + (1. - mask) * img
1205
+
1206
+ if i % log_every_t == 0 or i == timesteps - 1:
1207
+ intermediates.append(img)
1208
+ if callback: callback(i)
1209
+ if img_callback: img_callback(img, i)
1210
+
1211
+ if return_intermediates:
1212
+ return img, intermediates
1213
+ return img
1214
+
1215
+ @torch.no_grad()
1216
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
1217
+ verbose=True, timesteps=None, quantize_denoised=False,
1218
+ mask=None, x0=None, shape=None,**kwargs):
1219
+ if shape is None:
1220
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
1221
+ if cond is not None:
1222
+ if isinstance(cond, dict):
1223
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
1224
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
1225
+ else:
1226
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
1227
+ return self.p_sample_loop(cond,
1228
+ shape,
1229
+ return_intermediates=return_intermediates, x_T=x_T,
1230
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
1231
+ mask=mask, x0=x0)
1232
+
1233
+ @torch.no_grad()
1234
+ def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
1235
+
1236
+ if ddim:
1237
+ ddim_sampler = DDIMSampler(self)
1238
+ shape = (self.channels, self.image_size, self.image_size)
1239
+ samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
1240
+ shape,cond,verbose=False,**kwargs)
1241
+
1242
+ else:
1243
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
1244
+ return_intermediates=True,**kwargs)
1245
+
1246
+ return samples, intermediates
1247
+
1248
+
1249
+ @torch.no_grad()
1250
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
1251
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
1252
+ plot_diffusion_rows=True, **kwargs):
1253
+
1254
+ use_ddim = ddim_steps is not None
1255
+
1256
+ log = dict()
1257
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
1258
+ return_first_stage_outputs=True,
1259
+ force_c_encode=True,
1260
+ return_original_cond=True,
1261
+ bs=N)
1262
+ N = min(x.shape[0], N)
1263
+ n_row = min(x.shape[0], n_row)
1264
+ log["inputs"] = x
1265
+ log["reconstruction"] = xrec
1266
+ if self.model.conditioning_key is not None:
1267
+ if hasattr(self.cond_stage_model, "decode"):
1268
+ xc = self.cond_stage_model.decode(c)
1269
+ log["conditioning"] = xc
1270
+ elif self.cond_stage_key in ["caption"]:
1271
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
1272
+ log["conditioning"] = xc
1273
+ elif self.cond_stage_key == 'class_label':
1274
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
1275
+ log['conditioning'] = xc
1276
+ elif isimage(xc):
1277
+ log["conditioning"] = xc
1278
+ if ismap(xc):
1279
+ log["original_conditioning"] = self.to_rgb(xc)
1280
+
1281
+ if plot_diffusion_rows:
1282
+ # get diffusion row
1283
+ diffusion_row = list()
1284
+ z_start = z[:n_row]
1285
+ for t in range(self.num_timesteps):
1286
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
1287
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
1288
+ t = t.to(self.device).long()
1289
+ noise = torch.randn_like(z_start)
1290
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
1291
+ diffusion_row.append(self.decode_first_stage(z_noisy))
1292
+
1293
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
1294
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
1295
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
1296
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
1297
+ log["diffusion_row"] = diffusion_grid
1298
+
1299
+ if sample:
1300
+ # get denoise row
1301
+ with self.ema_scope("Plotting"):
1302
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
1303
+ ddim_steps=ddim_steps,eta=ddim_eta)
1304
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
1305
+ x_samples = self.decode_first_stage(samples)
1306
+ log["samples"] = x_samples
1307
+ if plot_denoise_rows:
1308
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
1309
+ log["denoise_row"] = denoise_grid
1310
+
1311
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
1312
+ self.first_stage_model, IdentityFirstStage):
1313
+ # also display when quantizing x0 while sampling
1314
+ with self.ema_scope("Plotting Quantized Denoised"):
1315
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
1316
+ ddim_steps=ddim_steps,eta=ddim_eta,
1317
+ quantize_denoised=True)
1318
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
1319
+ # quantize_denoised=True)
1320
+ x_samples = self.decode_first_stage(samples.to(self.device))
1321
+ log["samples_x0_quantized"] = x_samples
1322
+
1323
+ if inpaint:
1324
+ # make a simple center square
1325
+ b, h, w = z.shape[0], z.shape[2], z.shape[3]
1326
+ mask = torch.ones(N, h, w).to(self.device)
1327
+ # zeros will be filled in
1328
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
1329
+ mask = mask[:, None, ...]
1330
+ with self.ema_scope("Plotting Inpaint"):
1331
+
1332
+ samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
1333
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
1334
+ x_samples = self.decode_first_stage(samples.to(self.device))
1335
+ log["samples_inpainting"] = x_samples
1336
+ log["mask"] = mask
1337
+
1338
+ # outpaint
1339
+ with self.ema_scope("Plotting Outpaint"):
1340
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
1341
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
1342
+ x_samples = self.decode_first_stage(samples.to(self.device))
1343
+ log["samples_outpainting"] = x_samples
1344
+
1345
+ if plot_progressive_rows:
1346
+ with self.ema_scope("Plotting Progressives"):
1347
+ img, progressives = self.progressive_denoising(c,
1348
+ shape=(self.channels, self.image_size, self.image_size),
1349
+ batch_size=N)
1350
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
1351
+ log["progressive_row"] = prog_row
1352
+
1353
+ if return_keys:
1354
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
1355
+ return log
1356
+ else:
1357
+ return {key: log[key] for key in return_keys}
1358
+ return log
1359
+
1360
+ def configure_optimizers(self):
1361
+ lr = self.learning_rate
1362
+ params = list(self.model.parameters())
1363
+ if self.cond_stage_trainable:
1364
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
1365
+ params = params + list(self.cond_stage_model.parameters())
1366
+ if self.learn_logvar:
1367
+ print('Diffusion model optimizing logvar')
1368
+ params.append(self.logvar)
1369
+ opt = torch.optim.AdamW(params, lr=lr)
1370
+ if self.use_scheduler:
1371
+ assert 'target' in self.scheduler_config
1372
+ scheduler = instantiate_from_config(self.scheduler_config)
1373
+
1374
+ print("Setting up LambdaLR scheduler...")
1375
+ scheduler = [
1376
+ {
1377
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
1378
+ 'interval': 'step',
1379
+ 'frequency': 1
1380
+ }]
1381
+ return [opt], scheduler
1382
+ return opt
1383
+
1384
+ @torch.no_grad()
1385
+ def to_rgb(self, x):
1386
+ x = x.float()
1387
+ if not hasattr(self, "colorize"):
1388
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
1389
+ x = nn.functional.conv2d(x, weight=self.colorize)
1390
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
1391
+ return x
1392
+
1393
+
1394
+ class DiffusionWrapperV1(pl.LightningModule):
1395
+ def __init__(self, diff_model_config, conditioning_key):
1396
+ super().__init__()
1397
+ self.diffusion_model = instantiate_from_config(diff_model_config)
1398
+ self.conditioning_key = conditioning_key
1399
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
1400
+
1401
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
1402
+ if self.conditioning_key is None:
1403
+ out = self.diffusion_model(x, t)
1404
+ elif self.conditioning_key == 'concat':
1405
+ xc = torch.cat([x] + c_concat, dim=1)
1406
+ out = self.diffusion_model(xc, t)
1407
+ elif self.conditioning_key == 'crossattn':
1408
+ cc = torch.cat(c_crossattn, 1)
1409
+ out = self.diffusion_model(x, t, context=cc)
1410
+ elif self.conditioning_key == 'hybrid':
1411
+ xc = torch.cat([x] + c_concat, dim=1)
1412
+ cc = torch.cat(c_crossattn, 1)
1413
+ out = self.diffusion_model(xc, t, context=cc)
1414
+ elif self.conditioning_key == 'adm':
1415
+ cc = c_crossattn[0]
1416
+ out = self.diffusion_model(x, t, y=cc)
1417
+ else:
1418
+ raise NotImplementedError()
1419
+
1420
+ return out
1421
+
1422
+
1423
+ class Layout2ImgDiffusionV1(LatentDiffusionV1):
1424
+ # TODO: move all layout-specific hacks to this class
1425
+ def __init__(self, cond_stage_key, *args, **kwargs):
1426
+ assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
1427
+ super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
1428
+
1429
+ def log_images(self, batch, N=8, *args, **kwargs):
1430
+ logs = super().log_images(batch=batch, N=N, *args, **kwargs)
1431
+
1432
+ key = 'train' if self.training else 'validation'
1433
+ dset = self.trainer.datamodule.datasets[key]
1434
+ mapper = dset.conditional_builders[self.cond_stage_key]
1435
+
1436
+ bbox_imgs = []
1437
+ map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
1438
+ for tknzd_bbox in batch[self.cond_stage_key][:N]:
1439
+ bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
1440
+ bbox_imgs.append(bboximg)
1441
+
1442
+ cond_img = torch.stack(bbox_imgs, dim=0)
1443
+ logs['bbox_image'] = cond_img
1444
+ return logs
1445
+
1446
+ setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
1447
+ setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
1448
+ setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
1449
+ setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
extensions-builtin/Lora/extra_networks_lora.py ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from modules import extra_networks, shared
2
+ import lora
3
+
4
+
5
+ class ExtraNetworkLora(extra_networks.ExtraNetwork):
6
+ def __init__(self):
7
+ super().__init__('lora')
8
+
9
+ def activate(self, p, params_list):
10
+ additional = shared.opts.sd_lora
11
+
12
+ if additional != "None" and additional in lora.available_loras and len([x for x in params_list if x.items[0] == additional]) == 0:
13
+ p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
14
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
15
+
16
+ names = []
17
+ multipliers = []
18
+ for params in params_list:
19
+ assert len(params.items) > 0
20
+
21
+ names.append(params.items[0])
22
+ multipliers.append(float(params.items[1]) if len(params.items) > 1 else 1.0)
23
+
24
+ lora.load_loras(names, multipliers)
25
+
26
+ def deactivate(self, p):
27
+ pass
extensions-builtin/Lora/lora.py ADDED
@@ -0,0 +1,460 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import glob
2
+ import os
3
+ import re
4
+ import torch
5
+ from typing import Union
6
+
7
+ from modules import shared, devices, sd_models, errors, scripts
8
+
9
+ metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
10
+
11
+ re_digits = re.compile(r"\d+")
12
+ re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
13
+ re_compiled = {}
14
+
15
+ suffix_conversion = {
16
+ "attentions": {},
17
+ "resnets": {
18
+ "conv1": "in_layers_2",
19
+ "conv2": "out_layers_3",
20
+ "time_emb_proj": "emb_layers_1",
21
+ "conv_shortcut": "skip_connection",
22
+ }
23
+ }
24
+
25
+
26
+ def convert_diffusers_name_to_compvis(key, is_sd2):
27
+ def match(match_list, regex_text):
28
+ regex = re_compiled.get(regex_text)
29
+ if regex is None:
30
+ regex = re.compile(regex_text)
31
+ re_compiled[regex_text] = regex
32
+
33
+ r = re.match(regex, key)
34
+ if not r:
35
+ return False
36
+
37
+ match_list.clear()
38
+ match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
39
+ return True
40
+
41
+ m = []
42
+
43
+ if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
44
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
45
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
46
+
47
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
48
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
49
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
50
+
51
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
52
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
53
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
54
+
55
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
56
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
57
+
58
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
59
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
60
+
61
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
62
+ if is_sd2:
63
+ if 'mlp_fc1' in m[1]:
64
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
65
+ elif 'mlp_fc2' in m[1]:
66
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
67
+ else:
68
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
69
+
70
+ return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
71
+
72
+ return key
73
+
74
+
75
+ class LoraOnDisk:
76
+ def __init__(self, name, filename):
77
+ self.name = name
78
+ self.filename = filename
79
+ self.metadata = {}
80
+
81
+ _, ext = os.path.splitext(filename)
82
+ if ext.lower() == ".safetensors":
83
+ try:
84
+ self.metadata = sd_models.read_metadata_from_safetensors(filename)
85
+ except Exception as e:
86
+ errors.display(e, f"reading lora {filename}")
87
+
88
+ if self.metadata:
89
+ m = {}
90
+ for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
91
+ m[k] = v
92
+
93
+ self.metadata = m
94
+
95
+ self.ssmd_cover_images = self.metadata.pop('ssmd_cover_images', None) # those are cover images and they are too big to display in UI as text
96
+ self.alias = self.metadata.get('ss_output_name', self.name)
97
+
98
+
99
+ class LoraModule:
100
+ def __init__(self, name):
101
+ self.name = name
102
+ self.multiplier = 1.0
103
+ self.modules = {}
104
+ self.mtime = None
105
+
106
+
107
+ class LoraUpDownModule:
108
+ def __init__(self):
109
+ self.up = None
110
+ self.down = None
111
+ self.alpha = None
112
+
113
+
114
+ def assign_lora_names_to_compvis_modules(sd_model):
115
+ lora_layer_mapping = {}
116
+
117
+ for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
118
+ lora_name = name.replace(".", "_")
119
+ lora_layer_mapping[lora_name] = module
120
+ module.lora_layer_name = lora_name
121
+
122
+ for name, module in shared.sd_model.model.named_modules():
123
+ lora_name = name.replace(".", "_")
124
+ lora_layer_mapping[lora_name] = module
125
+ module.lora_layer_name = lora_name
126
+
127
+ sd_model.lora_layer_mapping = lora_layer_mapping
128
+
129
+
130
+ def load_lora(name, filename):
131
+ lora = LoraModule(name)
132
+ lora.mtime = os.path.getmtime(filename)
133
+
134
+ sd = sd_models.read_state_dict(filename)
135
+
136
+ # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
137
+ if not hasattr(shared.sd_model, 'lora_layer_mapping'):
138
+ assign_lora_names_to_compvis_modules(shared.sd_model)
139
+
140
+ keys_failed_to_match = {}
141
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
142
+
143
+ for key_diffusers, weight in sd.items():
144
+ key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
145
+ key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
146
+
147
+ sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
148
+
149
+ if sd_module is None:
150
+ m = re_x_proj.match(key)
151
+ if m:
152
+ sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
153
+
154
+ if sd_module is None:
155
+ keys_failed_to_match[key_diffusers] = key
156
+ continue
157
+
158
+ lora_module = lora.modules.get(key, None)
159
+ if lora_module is None:
160
+ lora_module = LoraUpDownModule()
161
+ lora.modules[key] = lora_module
162
+
163
+ if lora_key == "alpha":
164
+ lora_module.alpha = weight.item()
165
+ continue
166
+
167
+ if type(sd_module) == torch.nn.Linear:
168
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
169
+ elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
170
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
171
+ elif type(sd_module) == torch.nn.MultiheadAttention:
172
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
173
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
174
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
175
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
176
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
177
+ else:
178
+ print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
179
+ continue
180
+ assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'
181
+
182
+ with torch.no_grad():
183
+ module.weight.copy_(weight)
184
+
185
+ module.to(device=devices.cpu, dtype=devices.dtype)
186
+
187
+ if lora_key == "lora_up.weight":
188
+ lora_module.up = module
189
+ elif lora_key == "lora_down.weight":
190
+ lora_module.down = module
191
+ else:
192
+ assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
193
+
194
+ if len(keys_failed_to_match) > 0:
195
+ print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
196
+
197
+ return lora
198
+
199
+
200
+ def load_loras(names, multipliers=None):
201
+ already_loaded = {}
202
+
203
+ for lora in loaded_loras:
204
+ if lora.name in names:
205
+ already_loaded[lora.name] = lora
206
+
207
+ loaded_loras.clear()
208
+
209
+ loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
210
+ if any([x is None for x in loras_on_disk]):
211
+ list_available_loras()
212
+
213
+ loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
214
+
215
+ for i, name in enumerate(names):
216
+ lora = already_loaded.get(name, None)
217
+
218
+ lora_on_disk = loras_on_disk[i]
219
+ if lora_on_disk is not None:
220
+ if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
221
+ try:
222
+ lora = load_lora(name, lora_on_disk.filename)
223
+ except Exception as e:
224
+ errors.display(e, f"loading Lora {lora_on_disk.filename}")
225
+ continue
226
+
227
+ if lora is None:
228
+ print(f"Couldn't find Lora with name {name}")
229
+ continue
230
+
231
+ lora.multiplier = multipliers[i] if multipliers else 1.0
232
+ loaded_loras.append(lora)
233
+
234
+
235
+ def lora_calc_updown(lora, module, target):
236
+ with torch.no_grad():
237
+ up = module.up.weight.to(target.device, dtype=target.dtype)
238
+ down = module.down.weight.to(target.device, dtype=target.dtype)
239
+
240
+ if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
241
+ updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
242
+ elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
243
+ updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
244
+ else:
245
+ updown = up @ down
246
+
247
+ updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
248
+
249
+ return updown
250
+
251
+
252
+ def lora_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
253
+ weights_backup = getattr(self, "lora_weights_backup", None)
254
+
255
+ if weights_backup is None:
256
+ return
257
+
258
+ if isinstance(self, torch.nn.MultiheadAttention):
259
+ self.in_proj_weight.copy_(weights_backup[0])
260
+ self.out_proj.weight.copy_(weights_backup[1])
261
+ else:
262
+ self.weight.copy_(weights_backup)
263
+
264
+
265
+ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
266
+ """
267
+ Applies the currently selected set of Loras to the weights of torch layer self.
268
+ If weights already have this particular set of loras applied, does nothing.
269
+ If not, restores orginal weights from backup and alters weights according to loras.
270
+ """
271
+
272
+ lora_layer_name = getattr(self, 'lora_layer_name', None)
273
+ if lora_layer_name is None:
274
+ return
275
+
276
+ current_names = getattr(self, "lora_current_names", ())
277
+ wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
278
+
279
+ weights_backup = getattr(self, "lora_weights_backup", None)
280
+ if weights_backup is None:
281
+ if isinstance(self, torch.nn.MultiheadAttention):
282
+ weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
283
+ else:
284
+ weights_backup = self.weight.to(devices.cpu, copy=True)
285
+
286
+ self.lora_weights_backup = weights_backup
287
+
288
+ if current_names != wanted_names:
289
+ lora_restore_weights_from_backup(self)
290
+
291
+ for lora in loaded_loras:
292
+ module = lora.modules.get(lora_layer_name, None)
293
+ if module is not None and hasattr(self, 'weight'):
294
+ self.weight += lora_calc_updown(lora, module, self.weight)
295
+ continue
296
+
297
+ module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
298
+ module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
299
+ module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
300
+ module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
301
+
302
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
303
+ updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
304
+ updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
305
+ updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
306
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
307
+
308
+ self.in_proj_weight += updown_qkv
309
+ self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
310
+ continue
311
+
312
+ if module is None:
313
+ continue
314
+
315
+ print(f'failed to calculate lora weights for layer {lora_layer_name}')
316
+
317
+ setattr(self, "lora_current_names", wanted_names)
318
+
319
+
320
+ def lora_forward(module, input, original_forward):
321
+ """
322
+ Old way of applying Lora by executing operations during layer's forward.
323
+ Stacking many loras this way results in big performance degradation.
324
+ """
325
+
326
+ if len(loaded_loras) == 0:
327
+ return original_forward(module, input)
328
+
329
+ input = devices.cond_cast_unet(input)
330
+
331
+ lora_restore_weights_from_backup(module)
332
+ lora_reset_cached_weight(module)
333
+
334
+ res = original_forward(module, input)
335
+
336
+ lora_layer_name = getattr(module, 'lora_layer_name', None)
337
+ for lora in loaded_loras:
338
+ module = lora.modules.get(lora_layer_name, None)
339
+ if module is None:
340
+ continue
341
+
342
+ module.up.to(device=devices.device)
343
+ module.down.to(device=devices.device)
344
+
345
+ res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
346
+
347
+ return res
348
+
349
+
350
+ def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
351
+ setattr(self, "lora_current_names", ())
352
+ setattr(self, "lora_weights_backup", None)
353
+
354
+
355
+ def lora_Linear_forward(self, input):
356
+ if shared.opts.lora_functional:
357
+ return lora_forward(self, input, torch.nn.Linear_forward_before_lora)
358
+
359
+ lora_apply_weights(self)
360
+
361
+ return torch.nn.Linear_forward_before_lora(self, input)
362
+
363
+
364
+ def lora_Linear_load_state_dict(self, *args, **kwargs):
365
+ lora_reset_cached_weight(self)
366
+
367
+ return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
368
+
369
+
370
+ def lora_Conv2d_forward(self, input):
371
+ if shared.opts.lora_functional:
372
+ return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora)
373
+
374
+ lora_apply_weights(self)
375
+
376
+ return torch.nn.Conv2d_forward_before_lora(self, input)
377
+
378
+
379
+ def lora_Conv2d_load_state_dict(self, *args, **kwargs):
380
+ lora_reset_cached_weight(self)
381
+
382
+ return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
383
+
384
+
385
+ def lora_MultiheadAttention_forward(self, *args, **kwargs):
386
+ lora_apply_weights(self)
387
+
388
+ return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
389
+
390
+
391
+ def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
392
+ lora_reset_cached_weight(self)
393
+
394
+ return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
395
+
396
+
397
+ def list_available_loras():
398
+ available_loras.clear()
399
+ available_lora_aliases.clear()
400
+ forbidden_lora_aliases.clear()
401
+ forbidden_lora_aliases.update({"none": 1})
402
+
403
+ os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
404
+
405
+ candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
406
+ for filename in sorted(candidates, key=str.lower):
407
+ if os.path.isdir(filename):
408
+ continue
409
+
410
+ name = os.path.splitext(os.path.basename(filename))[0]
411
+ entry = LoraOnDisk(name, filename)
412
+
413
+ available_loras[name] = entry
414
+
415
+ if entry.alias in available_lora_aliases:
416
+ forbidden_lora_aliases[entry.alias.lower()] = 1
417
+
418
+ available_lora_aliases[name] = entry
419
+ available_lora_aliases[entry.alias] = entry
420
+
421
+
422
+ re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
423
+
424
+
425
+ def infotext_pasted(infotext, params):
426
+ if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
427
+ return # if the other extension is active, it will handle those fields, no need to do anything
428
+
429
+ added = []
430
+
431
+ for k, v in params.items():
432
+ if not k.startswith("AddNet Model "):
433
+ continue
434
+
435
+ num = k[13:]
436
+
437
+ if params.get("AddNet Module " + num) != "LoRA":
438
+ continue
439
+
440
+ name = params.get("AddNet Model " + num)
441
+ if name is None:
442
+ continue
443
+
444
+ m = re_lora_name.match(name)
445
+ if m:
446
+ name = m.group(1)
447
+
448
+ multiplier = params.get("AddNet Weight A " + num, "1.0")
449
+
450
+ added.append(f"<lora:{name}:{multiplier}>")
451
+
452
+ if added:
453
+ params["Prompt"] += "\n" + "".join(added)
454
+
455
+ available_loras = {}
456
+ available_lora_aliases = {}
457
+ forbidden_lora_aliases = {}
458
+ loaded_loras = []
459
+
460
+ list_available_loras()
extensions-builtin/Lora/preload.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ import os
2
+ from modules import paths
3
+
4
+
5
+ def preload(parser):
6
+ parser.add_argument("--lora-dir", type=str, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
extensions-builtin/Lora/scripts/lora_script.py ADDED
@@ -0,0 +1,82 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import gradio as gr
3
+ from fastapi import FastAPI
4
+
5
+ import lora
6
+ import extra_networks_lora
7
+ import ui_extra_networks_lora
8
+ from modules import script_callbacks, ui_extra_networks, extra_networks, shared
9
+
10
+ def unload():
11
+ torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
12
+ torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
13
+ torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
14
+ torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
15
+ torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
16
+ torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
17
+
18
+
19
+ def before_ui():
20
+ ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
21
+ extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())
22
+
23
+
24
+ if not hasattr(torch.nn, 'Linear_forward_before_lora'):
25
+ torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
26
+
27
+ if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
28
+ torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
29
+
30
+ if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
31
+ torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
32
+
33
+ if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
34
+ torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
35
+
36
+ if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
37
+ torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
38
+
39
+ if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
40
+ torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
41
+
42
+ torch.nn.Linear.forward = lora.lora_Linear_forward
43
+ torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
44
+ torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
45
+ torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
46
+ torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
47
+ torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
48
+
49
+ script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
50
+ script_callbacks.on_script_unloaded(unload)
51
+ script_callbacks.on_before_ui(before_ui)
52
+ script_callbacks.on_infotext_pasted(lora.infotext_pasted)
53
+
54
+
55
+ shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
56
+ "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
57
+ "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
58
+ }))
59
+
60
+
61
+ shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
62
+ "lora_functional": shared.OptionInfo(False, "Lora: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
63
+ }))
64
+
65
+
66
+ def create_lora_json(obj: lora.LoraOnDisk):
67
+ return {
68
+ "name": obj.name,
69
+ "alias": obj.alias,
70
+ "path": obj.filename,
71
+ "metadata": obj.metadata,
72
+ }
73
+
74
+
75
+ def api_loras(_: gr.Blocks, app: FastAPI):
76
+ @app.get("/sdapi/v1/loras")
77
+ async def get_loras():
78
+ return [create_lora_json(obj) for obj in lora.available_loras.values()]
79
+
80
+
81
+ script_callbacks.on_app_started(api_loras)
82
+
extensions-builtin/Lora/ui_extra_networks_lora.py ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import os
3
+ import lora
4
+
5
+ from modules import shared, ui_extra_networks
6
+
7
+
8
+ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
9
+ def __init__(self):
10
+ super().__init__('Lora')
11
+
12
+ def refresh(self):
13
+ lora.list_available_loras()
14
+
15
+ def list_items(self):
16
+ for name, lora_on_disk in lora.available_loras.items():
17
+ path, ext = os.path.splitext(lora_on_disk.filename)
18
+
19
+ if shared.opts.lora_preferred_name == "Filename" or lora_on_disk.alias.lower() in lora.forbidden_lora_aliases:
20
+ alias = name
21
+ else:
22
+ alias = lora_on_disk.alias
23
+
24
+ yield {
25
+ "name": name,
26
+ "filename": path,
27
+ "preview": self.find_preview(path),
28
+ "description": self.find_description(path),
29
+ "search_term": self.search_terms_from_path(lora_on_disk.filename),
30
+ "prompt": json.dumps(f"<lora:{alias}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
31
+ "local_preview": f"{path}.{shared.opts.samples_format}",
32
+ "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
33
+ }
34
+
35
+ def allowed_directories_for_previews(self):
36
+ return [shared.cmd_opts.lora_dir]
37
+
extensions-builtin/ScuNET/preload.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ import os
2
+ from modules import paths
3
+
4
+
5
+ def preload(parser):
6
+ parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(paths.models_path, 'ScuNET'))
extensions-builtin/ScuNET/scripts/scunet_model.py ADDED
@@ -0,0 +1,140 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os.path
2
+ import sys
3
+ import traceback
4
+
5
+ import PIL.Image
6
+ import numpy as np
7
+ import torch
8
+ from tqdm import tqdm
9
+
10
+ from basicsr.utils.download_util import load_file_from_url
11
+
12
+ import modules.upscaler
13
+ from modules import devices, modelloader
14
+ from scunet_model_arch import SCUNet as net
15
+ from modules.shared import opts
16
+ from modules import images
17
+
18
+
19
+ class UpscalerScuNET(modules.upscaler.Upscaler):
20
+ def __init__(self, dirname):
21
+ self.name = "ScuNET"
22
+ self.model_name = "ScuNET GAN"
23
+ self.model_name2 = "ScuNET PSNR"
24
+ self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
25
+ self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
26
+ self.user_path = dirname
27
+ super().__init__()
28
+ model_paths = self.find_models(ext_filter=[".pth"])
29
+ scalers = []
30
+ add_model2 = True
31
+ for file in model_paths:
32
+ if "http" in file:
33
+ name = self.model_name
34
+ else:
35
+ name = modelloader.friendly_name(file)
36
+ if name == self.model_name2 or file == self.model_url2:
37
+ add_model2 = False
38
+ try:
39
+ scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
40
+ scalers.append(scaler_data)
41
+ except Exception:
42
+ print(f"Error loading ScuNET model: {file}", file=sys.stderr)
43
+ print(traceback.format_exc(), file=sys.stderr)
44
+ if add_model2:
45
+ scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
46
+ scalers.append(scaler_data2)
47
+ self.scalers = scalers
48
+
49
+ @staticmethod
50
+ @torch.no_grad()
51
+ def tiled_inference(img, model):
52
+ # test the image tile by tile
53
+ h, w = img.shape[2:]
54
+ tile = opts.SCUNET_tile
55
+ tile_overlap = opts.SCUNET_tile_overlap
56
+ if tile == 0:
57
+ return model(img)
58
+
59
+ device = devices.get_device_for('scunet')
60
+ assert tile % 8 == 0, "tile size should be a multiple of window_size"
61
+ sf = 1
62
+
63
+ stride = tile - tile_overlap
64
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
65
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
66
+ E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
67
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device)
68
+
69
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
70
+ for h_idx in h_idx_list:
71
+
72
+ for w_idx in w_idx_list:
73
+
74
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
75
+
76
+ out_patch = model(in_patch)
77
+ out_patch_mask = torch.ones_like(out_patch)
78
+
79
+ E[
80
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
81
+ ].add_(out_patch)
82
+ W[
83
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
84
+ ].add_(out_patch_mask)
85
+ pbar.update(1)
86
+ output = E.div_(W)
87
+
88
+ return output
89
+
90
+ def do_upscale(self, img: PIL.Image.Image, selected_file):
91
+
92
+ torch.cuda.empty_cache()
93
+
94
+ model = self.load_model(selected_file)
95
+ if model is None:
96
+ print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
97
+ return img
98
+
99
+ device = devices.get_device_for('scunet')
100
+ tile = opts.SCUNET_tile
101
+ h, w = img.height, img.width
102
+ np_img = np.array(img)
103
+ np_img = np_img[:, :, ::-1] # RGB to BGR
104
+ np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
105
+ torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
106
+
107
+ if tile > h or tile > w:
108
+ _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
109
+ _img[:, :, :h, :w] = torch_img # pad image
110
+ torch_img = _img
111
+
112
+ torch_output = self.tiled_inference(torch_img, model).squeeze(0)
113
+ torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
114
+ np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
115
+ del torch_img, torch_output
116
+ torch.cuda.empty_cache()
117
+
118
+ output = np_output.transpose((1, 2, 0)) # CHW to HWC
119
+ output = output[:, :, ::-1] # BGR to RGB
120
+ return PIL.Image.fromarray((output * 255).astype(np.uint8))
121
+
122
+ def load_model(self, path: str):
123
+ device = devices.get_device_for('scunet')
124
+ if "http" in path:
125
+ filename = load_file_from_url(url=self.model_url, model_dir=self.model_path, file_name="%s.pth" % self.name,
126
+ progress=True)
127
+ else:
128
+ filename = path
129
+ if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
130
+ print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
131
+ return None
132
+
133
+ model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
134
+ model.load_state_dict(torch.load(filename), strict=True)
135
+ model.eval()
136
+ for k, v in model.named_parameters():
137
+ v.requires_grad = False
138
+ model = model.to(device)
139
+
140
+ return model
extensions-builtin/ScuNET/scunet_model_arch.py ADDED
@@ -0,0 +1,265 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -*- coding: utf-8 -*-
2
+ import numpy as np
3
+ import torch
4
+ import torch.nn as nn
5
+ from einops import rearrange
6
+ from einops.layers.torch import Rearrange
7
+ from timm.models.layers import trunc_normal_, DropPath
8
+
9
+
10
+ class WMSA(nn.Module):
11
+ """ Self-attention module in Swin Transformer
12
+ """
13
+
14
+ def __init__(self, input_dim, output_dim, head_dim, window_size, type):
15
+ super(WMSA, self).__init__()
16
+ self.input_dim = input_dim
17
+ self.output_dim = output_dim
18
+ self.head_dim = head_dim
19
+ self.scale = self.head_dim ** -0.5
20
+ self.n_heads = input_dim // head_dim
21
+ self.window_size = window_size
22
+ self.type = type
23
+ self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
24
+
25
+ self.relative_position_params = nn.Parameter(
26
+ torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads))
27
+
28
+ self.linear = nn.Linear(self.input_dim, self.output_dim)
29
+
30
+ trunc_normal_(self.relative_position_params, std=.02)
31
+ self.relative_position_params = torch.nn.Parameter(
32
+ self.relative_position_params.view(2 * window_size - 1, 2 * window_size - 1, self.n_heads).transpose(1,
33
+ 2).transpose(
34
+ 0, 1))
35
+
36
+ def generate_mask(self, h, w, p, shift):
37
+ """ generating the mask of SW-MSA
38
+ Args:
39
+ shift: shift parameters in CyclicShift.
40
+ Returns:
41
+ attn_mask: should be (1 1 w p p),
42
+ """
43
+ # supporting square.
44
+ attn_mask = torch.zeros(h, w, p, p, p, p, dtype=torch.bool, device=self.relative_position_params.device)
45
+ if self.type == 'W':
46
+ return attn_mask
47
+
48
+ s = p - shift
49
+ attn_mask[-1, :, :s, :, s:, :] = True
50
+ attn_mask[-1, :, s:, :, :s, :] = True
51
+ attn_mask[:, -1, :, :s, :, s:] = True
52
+ attn_mask[:, -1, :, s:, :, :s] = True
53
+ attn_mask = rearrange(attn_mask, 'w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)')
54
+ return attn_mask
55
+
56
+ def forward(self, x):
57
+ """ Forward pass of Window Multi-head Self-attention module.
58
+ Args:
59
+ x: input tensor with shape of [b h w c];
60
+ attn_mask: attention mask, fill -inf where the value is True;
61
+ Returns:
62
+ output: tensor shape [b h w c]
63
+ """
64
+ if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
65
+ x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
66
+ h_windows = x.size(1)
67
+ w_windows = x.size(2)
68
+ # square validation
69
+ # assert h_windows == w_windows
70
+
71
+ x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
72
+ qkv = self.embedding_layer(x)
73
+ q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
74
+ sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
75
+ # Adding learnable relative embedding
76
+ sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
77
+ # Using Attn Mask to distinguish different subwindows.
78
+ if self.type != 'W':
79
+ attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
80
+ sim = sim.masked_fill_(attn_mask, float("-inf"))
81
+
82
+ probs = nn.functional.softmax(sim, dim=-1)
83
+ output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
84
+ output = rearrange(output, 'h b w p c -> b w p (h c)')
85
+ output = self.linear(output)
86
+ output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
87
+
88
+ if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
89
+ dims=(1, 2))
90
+ return output
91
+
92
+ def relative_embedding(self):
93
+ cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
94
+ relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
95
+ # negative is allowed
96
+ return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
97
+
98
+
99
+ class Block(nn.Module):
100
+ def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
101
+ """ SwinTransformer Block
102
+ """
103
+ super(Block, self).__init__()
104
+ self.input_dim = input_dim
105
+ self.output_dim = output_dim
106
+ assert type in ['W', 'SW']
107
+ self.type = type
108
+ if input_resolution <= window_size:
109
+ self.type = 'W'
110
+
111
+ self.ln1 = nn.LayerNorm(input_dim)
112
+ self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
113
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
114
+ self.ln2 = nn.LayerNorm(input_dim)
115
+ self.mlp = nn.Sequential(
116
+ nn.Linear(input_dim, 4 * input_dim),
117
+ nn.GELU(),
118
+ nn.Linear(4 * input_dim, output_dim),
119
+ )
120
+
121
+ def forward(self, x):
122
+ x = x + self.drop_path(self.msa(self.ln1(x)))
123
+ x = x + self.drop_path(self.mlp(self.ln2(x)))
124
+ return x
125
+
126
+
127
+ class ConvTransBlock(nn.Module):
128
+ def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
129
+ """ SwinTransformer and Conv Block
130
+ """
131
+ super(ConvTransBlock, self).__init__()
132
+ self.conv_dim = conv_dim
133
+ self.trans_dim = trans_dim
134
+ self.head_dim = head_dim
135
+ self.window_size = window_size
136
+ self.drop_path = drop_path
137
+ self.type = type
138
+ self.input_resolution = input_resolution
139
+
140
+ assert self.type in ['W', 'SW']
141
+ if self.input_resolution <= self.window_size:
142
+ self.type = 'W'
143
+
144
+ self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
145
+ self.type, self.input_resolution)
146
+ self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
147
+ self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
148
+
149
+ self.conv_block = nn.Sequential(
150
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
151
+ nn.ReLU(True),
152
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
153
+ )
154
+
155
+ def forward(self, x):
156
+ conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
157
+ conv_x = self.conv_block(conv_x) + conv_x
158
+ trans_x = Rearrange('b c h w -> b h w c')(trans_x)
159
+ trans_x = self.trans_block(trans_x)
160
+ trans_x = Rearrange('b h w c -> b c h w')(trans_x)
161
+ res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
162
+ x = x + res
163
+
164
+ return x
165
+
166
+
167
+ class SCUNet(nn.Module):
168
+ # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
169
+ def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
170
+ super(SCUNet, self).__init__()
171
+ if config is None:
172
+ config = [2, 2, 2, 2, 2, 2, 2]
173
+ self.config = config
174
+ self.dim = dim
175
+ self.head_dim = 32
176
+ self.window_size = 8
177
+
178
+ # drop path rate for each layer
179
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
180
+
181
+ self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
182
+
183
+ begin = 0
184
+ self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
185
+ 'W' if not i % 2 else 'SW', input_resolution)
186
+ for i in range(config[0])] + \
187
+ [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
188
+
189
+ begin += config[0]
190
+ self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
191
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
192
+ for i in range(config[1])] + \
193
+ [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
194
+
195
+ begin += config[1]
196
+ self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
197
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
198
+ for i in range(config[2])] + \
199
+ [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
200
+
201
+ begin += config[2]
202
+ self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
203
+ 'W' if not i % 2 else 'SW', input_resolution // 8)
204
+ for i in range(config[3])]
205
+
206
+ begin += config[3]
207
+ self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
208
+ [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
209
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
210
+ for i in range(config[4])]
211
+
212
+ begin += config[4]
213
+ self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
214
+ [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
215
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
216
+ for i in range(config[5])]
217
+
218
+ begin += config[5]
219
+ self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
220
+ [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
221
+ 'W' if not i % 2 else 'SW', input_resolution)
222
+ for i in range(config[6])]
223
+
224
+ self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
225
+
226
+ self.m_head = nn.Sequential(*self.m_head)
227
+ self.m_down1 = nn.Sequential(*self.m_down1)
228
+ self.m_down2 = nn.Sequential(*self.m_down2)
229
+ self.m_down3 = nn.Sequential(*self.m_down3)
230
+ self.m_body = nn.Sequential(*self.m_body)
231
+ self.m_up3 = nn.Sequential(*self.m_up3)
232
+ self.m_up2 = nn.Sequential(*self.m_up2)
233
+ self.m_up1 = nn.Sequential(*self.m_up1)
234
+ self.m_tail = nn.Sequential(*self.m_tail)
235
+ # self.apply(self._init_weights)
236
+
237
+ def forward(self, x0):
238
+
239
+ h, w = x0.size()[-2:]
240
+ paddingBottom = int(np.ceil(h / 64) * 64 - h)
241
+ paddingRight = int(np.ceil(w / 64) * 64 - w)
242
+ x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
243
+
244
+ x1 = self.m_head(x0)
245
+ x2 = self.m_down1(x1)
246
+ x3 = self.m_down2(x2)
247
+ x4 = self.m_down3(x3)
248
+ x = self.m_body(x4)
249
+ x = self.m_up3(x + x4)
250
+ x = self.m_up2(x + x3)
251
+ x = self.m_up1(x + x2)
252
+ x = self.m_tail(x + x1)
253
+
254
+ x = x[..., :h, :w]
255
+
256
+ return x
257
+
258
+ def _init_weights(self, m):
259
+ if isinstance(m, nn.Linear):
260
+ trunc_normal_(m.weight, std=.02)
261
+ if m.bias is not None:
262
+ nn.init.constant_(m.bias, 0)
263
+ elif isinstance(m, nn.LayerNorm):
264
+ nn.init.constant_(m.bias, 0)
265
+ nn.init.constant_(m.weight, 1.0)
extensions-builtin/SwinIR/preload.py ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ import os
2
+ from modules import paths
3
+
4
+
5
+ def preload(parser):
6
+ parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(paths.models_path, 'SwinIR'))
extensions-builtin/SwinIR/scripts/swinir_model.py ADDED
@@ -0,0 +1,178 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import contextlib
2
+ import os
3
+
4
+ import numpy as np
5
+ import torch
6
+ from PIL import Image
7
+ from basicsr.utils.download_util import load_file_from_url
8
+ from tqdm import tqdm
9
+
10
+ from modules import modelloader, devices, script_callbacks, shared
11
+ from modules.shared import cmd_opts, opts, state
12
+ from swinir_model_arch import SwinIR as net
13
+ from swinir_model_arch_v2 import Swin2SR as net2
14
+ from modules.upscaler import Upscaler, UpscalerData
15
+
16
+
17
+ device_swinir = devices.get_device_for('swinir')
18
+
19
+
20
+ class UpscalerSwinIR(Upscaler):
21
+ def __init__(self, dirname):
22
+ self.name = "SwinIR"
23
+ self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
24
+ "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
25
+ "-L_x4_GAN.pth "
26
+ self.model_name = "SwinIR 4x"
27
+ self.user_path = dirname
28
+ super().__init__()
29
+ scalers = []
30
+ model_files = self.find_models(ext_filter=[".pt", ".pth"])
31
+ for model in model_files:
32
+ if "http" in model:
33
+ name = self.model_name
34
+ else:
35
+ name = modelloader.friendly_name(model)
36
+ model_data = UpscalerData(name, model, self)
37
+ scalers.append(model_data)
38
+ self.scalers = scalers
39
+
40
+ def do_upscale(self, img, model_file):
41
+ model = self.load_model(model_file)
42
+ if model is None:
43
+ return img
44
+ model = model.to(device_swinir, dtype=devices.dtype)
45
+ img = upscale(img, model)
46
+ try:
47
+ torch.cuda.empty_cache()
48
+ except:
49
+ pass
50
+ return img
51
+
52
+ def load_model(self, path, scale=4):
53
+ if "http" in path:
54
+ dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
55
+ filename = load_file_from_url(url=path, model_dir=self.model_path, file_name=dl_name, progress=True)
56
+ else:
57
+ filename = path
58
+ if filename is None or not os.path.exists(filename):
59
+ return None
60
+ if filename.endswith(".v2.pth"):
61
+ model = net2(
62
+ upscale=scale,
63
+ in_chans=3,
64
+ img_size=64,
65
+ window_size=8,
66
+ img_range=1.0,
67
+ depths=[6, 6, 6, 6, 6, 6],
68
+ embed_dim=180,
69
+ num_heads=[6, 6, 6, 6, 6, 6],
70
+ mlp_ratio=2,
71
+ upsampler="nearest+conv",
72
+ resi_connection="1conv",
73
+ )
74
+ params = None
75
+ else:
76
+ model = net(
77
+ upscale=scale,
78
+ in_chans=3,
79
+ img_size=64,
80
+ window_size=8,
81
+ img_range=1.0,
82
+ depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
83
+ embed_dim=240,
84
+ num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
85
+ mlp_ratio=2,
86
+ upsampler="nearest+conv",
87
+ resi_connection="3conv",
88
+ )
89
+ params = "params_ema"
90
+
91
+ pretrained_model = torch.load(filename)
92
+ if params is not None:
93
+ model.load_state_dict(pretrained_model[params], strict=True)
94
+ else:
95
+ model.load_state_dict(pretrained_model, strict=True)
96
+ return model
97
+
98
+
99
+ def upscale(
100
+ img,
101
+ model,
102
+ tile=None,
103
+ tile_overlap=None,
104
+ window_size=8,
105
+ scale=4,
106
+ ):
107
+ tile = tile or opts.SWIN_tile
108
+ tile_overlap = tile_overlap or opts.SWIN_tile_overlap
109
+
110
+
111
+ img = np.array(img)
112
+ img = img[:, :, ::-1]
113
+ img = np.moveaxis(img, 2, 0) / 255
114
+ img = torch.from_numpy(img).float()
115
+ img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
116
+ with torch.no_grad(), devices.autocast():
117
+ _, _, h_old, w_old = img.size()
118
+ h_pad = (h_old // window_size + 1) * window_size - h_old
119
+ w_pad = (w_old // window_size + 1) * window_size - w_old
120
+ img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
121
+ img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
122
+ output = inference(img, model, tile, tile_overlap, window_size, scale)
123
+ output = output[..., : h_old * scale, : w_old * scale]
124
+ output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
125
+ if output.ndim == 3:
126
+ output = np.transpose(
127
+ output[[2, 1, 0], :, :], (1, 2, 0)
128
+ ) # CHW-RGB to HCW-BGR
129
+ output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
130
+ return Image.fromarray(output, "RGB")
131
+
132
+
133
+ def inference(img, model, tile, tile_overlap, window_size, scale):
134
+ # test the image tile by tile
135
+ b, c, h, w = img.size()
136
+ tile = min(tile, h, w)
137
+ assert tile % window_size == 0, "tile size should be a multiple of window_size"
138
+ sf = scale
139
+
140
+ stride = tile - tile_overlap
141
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
142
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
143
+ E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
144
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
145
+
146
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
147
+ for h_idx in h_idx_list:
148
+ if state.interrupted or state.skipped:
149
+ break
150
+
151
+ for w_idx in w_idx_list:
152
+ if state.interrupted or state.skipped:
153
+ break
154
+
155
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
156
+ out_patch = model(in_patch)
157
+ out_patch_mask = torch.ones_like(out_patch)
158
+
159
+ E[
160
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
161
+ ].add_(out_patch)
162
+ W[
163
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
164
+ ].add_(out_patch_mask)
165
+ pbar.update(1)
166
+ output = E.div_(W)
167
+
168
+ return output
169
+
170
+
171
+ def on_ui_settings():
172
+ import gradio as gr
173
+
174
+ shared.opts.add_option("SWIN_tile", shared.OptionInfo(192, "Tile size for all SwinIR.", gr.Slider, {"minimum": 16, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")))
175
+ shared.opts.add_option("SWIN_tile_overlap", shared.OptionInfo(8, "Tile overlap, in pixels for SwinIR. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}, section=('upscaling', "Upscaling")))
176
+
177
+
178
+ script_callbacks.on_ui_settings(on_ui_settings)
extensions-builtin/SwinIR/swinir_model_arch.py ADDED
@@ -0,0 +1,867 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -----------------------------------------------------------------------------------
2
+ # SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
3
+ # Originally Written by Ze Liu, Modified by Jingyun Liang.
4
+ # -----------------------------------------------------------------------------------
5
+
6
+ import math
7
+ import torch
8
+ import torch.nn as nn
9
+ import torch.nn.functional as F
10
+ import torch.utils.checkpoint as checkpoint
11
+ from timm.models.layers import DropPath, to_2tuple, trunc_normal_
12
+
13
+
14
+ class Mlp(nn.Module):
15
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
16
+ super().__init__()
17
+ out_features = out_features or in_features
18
+ hidden_features = hidden_features or in_features
19
+ self.fc1 = nn.Linear(in_features, hidden_features)
20
+ self.act = act_layer()
21
+ self.fc2 = nn.Linear(hidden_features, out_features)
22
+ self.drop = nn.Dropout(drop)
23
+
24
+ def forward(self, x):
25
+ x = self.fc1(x)
26
+ x = self.act(x)
27
+ x = self.drop(x)
28
+ x = self.fc2(x)
29
+ x = self.drop(x)
30
+ return x
31
+
32
+
33
+ def window_partition(x, window_size):
34
+ """
35
+ Args:
36
+ x: (B, H, W, C)
37
+ window_size (int): window size
38
+
39
+ Returns:
40
+ windows: (num_windows*B, window_size, window_size, C)
41
+ """
42
+ B, H, W, C = x.shape
43
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
44
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
45
+ return windows
46
+
47
+
48
+ def window_reverse(windows, window_size, H, W):
49
+ """
50
+ Args:
51
+ windows: (num_windows*B, window_size, window_size, C)
52
+ window_size (int): Window size
53
+ H (int): Height of image
54
+ W (int): Width of image
55
+
56
+ Returns:
57
+ x: (B, H, W, C)
58
+ """
59
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
60
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
61
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
62
+ return x
63
+
64
+
65
+ class WindowAttention(nn.Module):
66
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
67
+ It supports both of shifted and non-shifted window.
68
+
69
+ Args:
70
+ dim (int): Number of input channels.
71
+ window_size (tuple[int]): The height and width of the window.
72
+ num_heads (int): Number of attention heads.
73
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
74
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
75
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
76
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
77
+ """
78
+
79
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
80
+
81
+ super().__init__()
82
+ self.dim = dim
83
+ self.window_size = window_size # Wh, Ww
84
+ self.num_heads = num_heads
85
+ head_dim = dim // num_heads
86
+ self.scale = qk_scale or head_dim ** -0.5
87
+
88
+ # define a parameter table of relative position bias
89
+ self.relative_position_bias_table = nn.Parameter(
90
+ torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH
91
+
92
+ # get pair-wise relative position index for each token inside the window
93
+ coords_h = torch.arange(self.window_size[0])
94
+ coords_w = torch.arange(self.window_size[1])
95
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
96
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
97
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
98
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
99
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
100
+ relative_coords[:, :, 1] += self.window_size[1] - 1
101
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
102
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
103
+ self.register_buffer("relative_position_index", relative_position_index)
104
+
105
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
106
+ self.attn_drop = nn.Dropout(attn_drop)
107
+ self.proj = nn.Linear(dim, dim)
108
+
109
+ self.proj_drop = nn.Dropout(proj_drop)
110
+
111
+ trunc_normal_(self.relative_position_bias_table, std=.02)
112
+ self.softmax = nn.Softmax(dim=-1)
113
+
114
+ def forward(self, x, mask=None):
115
+ """
116
+ Args:
117
+ x: input features with shape of (num_windows*B, N, C)
118
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
119
+ """
120
+ B_, N, C = x.shape
121
+ qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
122
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
123
+
124
+ q = q * self.scale
125
+ attn = (q @ k.transpose(-2, -1))
126
+
127
+ relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
128
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
129
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
130
+ attn = attn + relative_position_bias.unsqueeze(0)
131
+
132
+ if mask is not None:
133
+ nW = mask.shape[0]
134
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
135
+ attn = attn.view(-1, self.num_heads, N, N)
136
+ attn = self.softmax(attn)
137
+ else:
138
+ attn = self.softmax(attn)
139
+
140
+ attn = self.attn_drop(attn)
141
+
142
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
143
+ x = self.proj(x)
144
+ x = self.proj_drop(x)
145
+ return x
146
+
147
+ def extra_repr(self) -> str:
148
+ return f'dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}'
149
+
150
+ def flops(self, N):
151
+ # calculate flops for 1 window with token length of N
152
+ flops = 0
153
+ # qkv = self.qkv(x)
154
+ flops += N * self.dim * 3 * self.dim
155
+ # attn = (q @ k.transpose(-2, -1))
156
+ flops += self.num_heads * N * (self.dim // self.num_heads) * N
157
+ # x = (attn @ v)
158
+ flops += self.num_heads * N * N * (self.dim // self.num_heads)
159
+ # x = self.proj(x)
160
+ flops += N * self.dim * self.dim
161
+ return flops
162
+
163
+
164
+ class SwinTransformerBlock(nn.Module):
165
+ r""" Swin Transformer Block.
166
+
167
+ Args:
168
+ dim (int): Number of input channels.
169
+ input_resolution (tuple[int]): Input resolution.
170
+ num_heads (int): Number of attention heads.
171
+ window_size (int): Window size.
172
+ shift_size (int): Shift size for SW-MSA.
173
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
174
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
175
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
176
+ drop (float, optional): Dropout rate. Default: 0.0
177
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
178
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
179
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
180
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
181
+ """
182
+
183
+ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
184
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
185
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm):
186
+ super().__init__()
187
+ self.dim = dim
188
+ self.input_resolution = input_resolution
189
+ self.num_heads = num_heads
190
+ self.window_size = window_size
191
+ self.shift_size = shift_size
192
+ self.mlp_ratio = mlp_ratio
193
+ if min(self.input_resolution) <= self.window_size:
194
+ # if window size is larger than input resolution, we don't partition windows
195
+ self.shift_size = 0
196
+ self.window_size = min(self.input_resolution)
197
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
198
+
199
+ self.norm1 = norm_layer(dim)
200
+ self.attn = WindowAttention(
201
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
202
+ qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
203
+
204
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
205
+ self.norm2 = norm_layer(dim)
206
+ mlp_hidden_dim = int(dim * mlp_ratio)
207
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
208
+
209
+ if self.shift_size > 0:
210
+ attn_mask = self.calculate_mask(self.input_resolution)
211
+ else:
212
+ attn_mask = None
213
+
214
+ self.register_buffer("attn_mask", attn_mask)
215
+
216
+ def calculate_mask(self, x_size):
217
+ # calculate attention mask for SW-MSA
218
+ H, W = x_size
219
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
220
+ h_slices = (slice(0, -self.window_size),
221
+ slice(-self.window_size, -self.shift_size),
222
+ slice(-self.shift_size, None))
223
+ w_slices = (slice(0, -self.window_size),
224
+ slice(-self.window_size, -self.shift_size),
225
+ slice(-self.shift_size, None))
226
+ cnt = 0
227
+ for h in h_slices:
228
+ for w in w_slices:
229
+ img_mask[:, h, w, :] = cnt
230
+ cnt += 1
231
+
232
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
233
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
234
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
235
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
236
+
237
+ return attn_mask
238
+
239
+ def forward(self, x, x_size):
240
+ H, W = x_size
241
+ B, L, C = x.shape
242
+ # assert L == H * W, "input feature has wrong size"
243
+
244
+ shortcut = x
245
+ x = self.norm1(x)
246
+ x = x.view(B, H, W, C)
247
+
248
+ # cyclic shift
249
+ if self.shift_size > 0:
250
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
251
+ else:
252
+ shifted_x = x
253
+
254
+ # partition windows
255
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
256
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
257
+
258
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
259
+ if self.input_resolution == x_size:
260
+ attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
261
+ else:
262
+ attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
263
+
264
+ # merge windows
265
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
266
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
267
+
268
+ # reverse cyclic shift
269
+ if self.shift_size > 0:
270
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
271
+ else:
272
+ x = shifted_x
273
+ x = x.view(B, H * W, C)
274
+
275
+ # FFN
276
+ x = shortcut + self.drop_path(x)
277
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
278
+
279
+ return x
280
+
281
+ def extra_repr(self) -> str:
282
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
283
+ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
284
+
285
+ def flops(self):
286
+ flops = 0
287
+ H, W = self.input_resolution
288
+ # norm1
289
+ flops += self.dim * H * W
290
+ # W-MSA/SW-MSA
291
+ nW = H * W / self.window_size / self.window_size
292
+ flops += nW * self.attn.flops(self.window_size * self.window_size)
293
+ # mlp
294
+ flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
295
+ # norm2
296
+ flops += self.dim * H * W
297
+ return flops
298
+
299
+
300
+ class PatchMerging(nn.Module):
301
+ r""" Patch Merging Layer.
302
+
303
+ Args:
304
+ input_resolution (tuple[int]): Resolution of input feature.
305
+ dim (int): Number of input channels.
306
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
307
+ """
308
+
309
+ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
310
+ super().__init__()
311
+ self.input_resolution = input_resolution
312
+ self.dim = dim
313
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
314
+ self.norm = norm_layer(4 * dim)
315
+
316
+ def forward(self, x):
317
+ """
318
+ x: B, H*W, C
319
+ """
320
+ H, W = self.input_resolution
321
+ B, L, C = x.shape
322
+ assert L == H * W, "input feature has wrong size"
323
+ assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
324
+
325
+ x = x.view(B, H, W, C)
326
+
327
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
328
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
329
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
330
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
331
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
332
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
333
+
334
+ x = self.norm(x)
335
+ x = self.reduction(x)
336
+
337
+ return x
338
+
339
+ def extra_repr(self) -> str:
340
+ return f"input_resolution={self.input_resolution}, dim={self.dim}"
341
+
342
+ def flops(self):
343
+ H, W = self.input_resolution
344
+ flops = H * W * self.dim
345
+ flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
346
+ return flops
347
+
348
+
349
+ class BasicLayer(nn.Module):
350
+ """ A basic Swin Transformer layer for one stage.
351
+
352
+ Args:
353
+ dim (int): Number of input channels.
354
+ input_resolution (tuple[int]): Input resolution.
355
+ depth (int): Number of blocks.
356
+ num_heads (int): Number of attention heads.
357
+ window_size (int): Local window size.
358
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
359
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
360
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
361
+ drop (float, optional): Dropout rate. Default: 0.0
362
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
363
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
364
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
365
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
366
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
367
+ """
368
+
369
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
370
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
371
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
372
+
373
+ super().__init__()
374
+ self.dim = dim
375
+ self.input_resolution = input_resolution
376
+ self.depth = depth
377
+ self.use_checkpoint = use_checkpoint
378
+
379
+ # build blocks
380
+ self.blocks = nn.ModuleList([
381
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
382
+ num_heads=num_heads, window_size=window_size,
383
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
384
+ mlp_ratio=mlp_ratio,
385
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
386
+ drop=drop, attn_drop=attn_drop,
387
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
388
+ norm_layer=norm_layer)
389
+ for i in range(depth)])
390
+
391
+ # patch merging layer
392
+ if downsample is not None:
393
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
394
+ else:
395
+ self.downsample = None
396
+
397
+ def forward(self, x, x_size):
398
+ for blk in self.blocks:
399
+ if self.use_checkpoint:
400
+ x = checkpoint.checkpoint(blk, x, x_size)
401
+ else:
402
+ x = blk(x, x_size)
403
+ if self.downsample is not None:
404
+ x = self.downsample(x)
405
+ return x
406
+
407
+ def extra_repr(self) -> str:
408
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
409
+
410
+ def flops(self):
411
+ flops = 0
412
+ for blk in self.blocks:
413
+ flops += blk.flops()
414
+ if self.downsample is not None:
415
+ flops += self.downsample.flops()
416
+ return flops
417
+
418
+
419
+ class RSTB(nn.Module):
420
+ """Residual Swin Transformer Block (RSTB).
421
+
422
+ Args:
423
+ dim (int): Number of input channels.
424
+ input_resolution (tuple[int]): Input resolution.
425
+ depth (int): Number of blocks.
426
+ num_heads (int): Number of attention heads.
427
+ window_size (int): Local window size.
428
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
429
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
430
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
431
+ drop (float, optional): Dropout rate. Default: 0.0
432
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
433
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
434
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
435
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
436
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
437
+ img_size: Input image size.
438
+ patch_size: Patch size.
439
+ resi_connection: The convolutional block before residual connection.
440
+ """
441
+
442
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
443
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
444
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
445
+ img_size=224, patch_size=4, resi_connection='1conv'):
446
+ super(RSTB, self).__init__()
447
+
448
+ self.dim = dim
449
+ self.input_resolution = input_resolution
450
+
451
+ self.residual_group = BasicLayer(dim=dim,
452
+ input_resolution=input_resolution,
453
+ depth=depth,
454
+ num_heads=num_heads,
455
+ window_size=window_size,
456
+ mlp_ratio=mlp_ratio,
457
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
458
+ drop=drop, attn_drop=attn_drop,
459
+ drop_path=drop_path,
460
+ norm_layer=norm_layer,
461
+ downsample=downsample,
462
+ use_checkpoint=use_checkpoint)
463
+
464
+ if resi_connection == '1conv':
465
+ self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
466
+ elif resi_connection == '3conv':
467
+ # to save parameters and memory
468
+ self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
469
+ nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
470
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
471
+ nn.Conv2d(dim // 4, dim, 3, 1, 1))
472
+
473
+ self.patch_embed = PatchEmbed(
474
+ img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
475
+ norm_layer=None)
476
+
477
+ self.patch_unembed = PatchUnEmbed(
478
+ img_size=img_size, patch_size=patch_size, in_chans=0, embed_dim=dim,
479
+ norm_layer=None)
480
+
481
+ def forward(self, x, x_size):
482
+ return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
483
+
484
+ def flops(self):
485
+ flops = 0
486
+ flops += self.residual_group.flops()
487
+ H, W = self.input_resolution
488
+ flops += H * W * self.dim * self.dim * 9
489
+ flops += self.patch_embed.flops()
490
+ flops += self.patch_unembed.flops()
491
+
492
+ return flops
493
+
494
+
495
+ class PatchEmbed(nn.Module):
496
+ r""" Image to Patch Embedding
497
+
498
+ Args:
499
+ img_size (int): Image size. Default: 224.
500
+ patch_size (int): Patch token size. Default: 4.
501
+ in_chans (int): Number of input image channels. Default: 3.
502
+ embed_dim (int): Number of linear projection output channels. Default: 96.
503
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
504
+ """
505
+
506
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
507
+ super().__init__()
508
+ img_size = to_2tuple(img_size)
509
+ patch_size = to_2tuple(patch_size)
510
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
511
+ self.img_size = img_size
512
+ self.patch_size = patch_size
513
+ self.patches_resolution = patches_resolution
514
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
515
+
516
+ self.in_chans = in_chans
517
+ self.embed_dim = embed_dim
518
+
519
+ if norm_layer is not None:
520
+ self.norm = norm_layer(embed_dim)
521
+ else:
522
+ self.norm = None
523
+
524
+ def forward(self, x):
525
+ x = x.flatten(2).transpose(1, 2) # B Ph*Pw C
526
+ if self.norm is not None:
527
+ x = self.norm(x)
528
+ return x
529
+
530
+ def flops(self):
531
+ flops = 0
532
+ H, W = self.img_size
533
+ if self.norm is not None:
534
+ flops += H * W * self.embed_dim
535
+ return flops
536
+
537
+
538
+ class PatchUnEmbed(nn.Module):
539
+ r""" Image to Patch Unembedding
540
+
541
+ Args:
542
+ img_size (int): Image size. Default: 224.
543
+ patch_size (int): Patch token size. Default: 4.
544
+ in_chans (int): Number of input image channels. Default: 3.
545
+ embed_dim (int): Number of linear projection output channels. Default: 96.
546
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
547
+ """
548
+
549
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
550
+ super().__init__()
551
+ img_size = to_2tuple(img_size)
552
+ patch_size = to_2tuple(patch_size)
553
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
554
+ self.img_size = img_size
555
+ self.patch_size = patch_size
556
+ self.patches_resolution = patches_resolution
557
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
558
+
559
+ self.in_chans = in_chans
560
+ self.embed_dim = embed_dim
561
+
562
+ def forward(self, x, x_size):
563
+ B, HW, C = x.shape
564
+ x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
565
+ return x
566
+
567
+ def flops(self):
568
+ flops = 0
569
+ return flops
570
+
571
+
572
+ class Upsample(nn.Sequential):
573
+ """Upsample module.
574
+
575
+ Args:
576
+ scale (int): Scale factor. Supported scales: 2^n and 3.
577
+ num_feat (int): Channel number of intermediate features.
578
+ """
579
+
580
+ def __init__(self, scale, num_feat):
581
+ m = []
582
+ if (scale & (scale - 1)) == 0: # scale = 2^n
583
+ for _ in range(int(math.log(scale, 2))):
584
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
585
+ m.append(nn.PixelShuffle(2))
586
+ elif scale == 3:
587
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
588
+ m.append(nn.PixelShuffle(3))
589
+ else:
590
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
591
+ super(Upsample, self).__init__(*m)
592
+
593
+
594
+ class UpsampleOneStep(nn.Sequential):
595
+ """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
596
+ Used in lightweight SR to save parameters.
597
+
598
+ Args:
599
+ scale (int): Scale factor. Supported scales: 2^n and 3.
600
+ num_feat (int): Channel number of intermediate features.
601
+
602
+ """
603
+
604
+ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
605
+ self.num_feat = num_feat
606
+ self.input_resolution = input_resolution
607
+ m = []
608
+ m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
609
+ m.append(nn.PixelShuffle(scale))
610
+ super(UpsampleOneStep, self).__init__(*m)
611
+
612
+ def flops(self):
613
+ H, W = self.input_resolution
614
+ flops = H * W * self.num_feat * 3 * 9
615
+ return flops
616
+
617
+
618
+ class SwinIR(nn.Module):
619
+ r""" SwinIR
620
+ A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
621
+
622
+ Args:
623
+ img_size (int | tuple(int)): Input image size. Default 64
624
+ patch_size (int | tuple(int)): Patch size. Default: 1
625
+ in_chans (int): Number of input image channels. Default: 3
626
+ embed_dim (int): Patch embedding dimension. Default: 96
627
+ depths (tuple(int)): Depth of each Swin Transformer layer.
628
+ num_heads (tuple(int)): Number of attention heads in different layers.
629
+ window_size (int): Window size. Default: 7
630
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
631
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
632
+ qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
633
+ drop_rate (float): Dropout rate. Default: 0
634
+ attn_drop_rate (float): Attention dropout rate. Default: 0
635
+ drop_path_rate (float): Stochastic depth rate. Default: 0.1
636
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
637
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
638
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True
639
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
640
+ upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
641
+ img_range: Image range. 1. or 255.
642
+ upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
643
+ resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
644
+ """
645
+
646
+ def __init__(self, img_size=64, patch_size=1, in_chans=3,
647
+ embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
648
+ window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
649
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
650
+ norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
651
+ use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
652
+ **kwargs):
653
+ super(SwinIR, self).__init__()
654
+ num_in_ch = in_chans
655
+ num_out_ch = in_chans
656
+ num_feat = 64
657
+ self.img_range = img_range
658
+ if in_chans == 3:
659
+ rgb_mean = (0.4488, 0.4371, 0.4040)
660
+ self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
661
+ else:
662
+ self.mean = torch.zeros(1, 1, 1, 1)
663
+ self.upscale = upscale
664
+ self.upsampler = upsampler
665
+ self.window_size = window_size
666
+
667
+ #####################################################################################################
668
+ ################################### 1, shallow feature extraction ###################################
669
+ self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
670
+
671
+ #####################################################################################################
672
+ ################################### 2, deep feature extraction ######################################
673
+ self.num_layers = len(depths)
674
+ self.embed_dim = embed_dim
675
+ self.ape = ape
676
+ self.patch_norm = patch_norm
677
+ self.num_features = embed_dim
678
+ self.mlp_ratio = mlp_ratio
679
+
680
+ # split image into non-overlapping patches
681
+ self.patch_embed = PatchEmbed(
682
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
683
+ norm_layer=norm_layer if self.patch_norm else None)
684
+ num_patches = self.patch_embed.num_patches
685
+ patches_resolution = self.patch_embed.patches_resolution
686
+ self.patches_resolution = patches_resolution
687
+
688
+ # merge non-overlapping patches into image
689
+ self.patch_unembed = PatchUnEmbed(
690
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
691
+ norm_layer=norm_layer if self.patch_norm else None)
692
+
693
+ # absolute position embedding
694
+ if self.ape:
695
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
696
+ trunc_normal_(self.absolute_pos_embed, std=.02)
697
+
698
+ self.pos_drop = nn.Dropout(p=drop_rate)
699
+
700
+ # stochastic depth
701
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
702
+
703
+ # build Residual Swin Transformer blocks (RSTB)
704
+ self.layers = nn.ModuleList()
705
+ for i_layer in range(self.num_layers):
706
+ layer = RSTB(dim=embed_dim,
707
+ input_resolution=(patches_resolution[0],
708
+ patches_resolution[1]),
709
+ depth=depths[i_layer],
710
+ num_heads=num_heads[i_layer],
711
+ window_size=window_size,
712
+ mlp_ratio=self.mlp_ratio,
713
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
714
+ drop=drop_rate, attn_drop=attn_drop_rate,
715
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
716
+ norm_layer=norm_layer,
717
+ downsample=None,
718
+ use_checkpoint=use_checkpoint,
719
+ img_size=img_size,
720
+ patch_size=patch_size,
721
+ resi_connection=resi_connection
722
+
723
+ )
724
+ self.layers.append(layer)
725
+ self.norm = norm_layer(self.num_features)
726
+
727
+ # build the last conv layer in deep feature extraction
728
+ if resi_connection == '1conv':
729
+ self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
730
+ elif resi_connection == '3conv':
731
+ # to save parameters and memory
732
+ self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
733
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
734
+ nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
735
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
736
+ nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
737
+
738
+ #####################################################################################################
739
+ ################################ 3, high quality image reconstruction ################################
740
+ if self.upsampler == 'pixelshuffle':
741
+ # for classical SR
742
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
743
+ nn.LeakyReLU(inplace=True))
744
+ self.upsample = Upsample(upscale, num_feat)
745
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
746
+ elif self.upsampler == 'pixelshuffledirect':
747
+ # for lightweight SR (to save parameters)
748
+ self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
749
+ (patches_resolution[0], patches_resolution[1]))
750
+ elif self.upsampler == 'nearest+conv':
751
+ # for real-world SR (less artifacts)
752
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
753
+ nn.LeakyReLU(inplace=True))
754
+ self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
755
+ if self.upscale == 4:
756
+ self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
757
+ self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
758
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
759
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
760
+ else:
761
+ # for image denoising and JPEG compression artifact reduction
762
+ self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
763
+
764
+ self.apply(self._init_weights)
765
+
766
+ def _init_weights(self, m):
767
+ if isinstance(m, nn.Linear):
768
+ trunc_normal_(m.weight, std=.02)
769
+ if isinstance(m, nn.Linear) and m.bias is not None:
770
+ nn.init.constant_(m.bias, 0)
771
+ elif isinstance(m, nn.LayerNorm):
772
+ nn.init.constant_(m.bias, 0)
773
+ nn.init.constant_(m.weight, 1.0)
774
+
775
+ @torch.jit.ignore
776
+ def no_weight_decay(self):
777
+ return {'absolute_pos_embed'}
778
+
779
+ @torch.jit.ignore
780
+ def no_weight_decay_keywords(self):
781
+ return {'relative_position_bias_table'}
782
+
783
+ def check_image_size(self, x):
784
+ _, _, h, w = x.size()
785
+ mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
786
+ mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
787
+ x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
788
+ return x
789
+
790
+ def forward_features(self, x):
791
+ x_size = (x.shape[2], x.shape[3])
792
+ x = self.patch_embed(x)
793
+ if self.ape:
794
+ x = x + self.absolute_pos_embed
795
+ x = self.pos_drop(x)
796
+
797
+ for layer in self.layers:
798
+ x = layer(x, x_size)
799
+
800
+ x = self.norm(x) # B L C
801
+ x = self.patch_unembed(x, x_size)
802
+
803
+ return x
804
+
805
+ def forward(self, x):
806
+ H, W = x.shape[2:]
807
+ x = self.check_image_size(x)
808
+
809
+ self.mean = self.mean.type_as(x)
810
+ x = (x - self.mean) * self.img_range
811
+
812
+ if self.upsampler == 'pixelshuffle':
813
+ # for classical SR
814
+ x = self.conv_first(x)
815
+ x = self.conv_after_body(self.forward_features(x)) + x
816
+ x = self.conv_before_upsample(x)
817
+ x = self.conv_last(self.upsample(x))
818
+ elif self.upsampler == 'pixelshuffledirect':
819
+ # for lightweight SR
820
+ x = self.conv_first(x)
821
+ x = self.conv_after_body(self.forward_features(x)) + x
822
+ x = self.upsample(x)
823
+ elif self.upsampler == 'nearest+conv':
824
+ # for real-world SR
825
+ x = self.conv_first(x)
826
+ x = self.conv_after_body(self.forward_features(x)) + x
827
+ x = self.conv_before_upsample(x)
828
+ x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
829
+ if self.upscale == 4:
830
+ x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
831
+ x = self.conv_last(self.lrelu(self.conv_hr(x)))
832
+ else:
833
+ # for image denoising and JPEG compression artifact reduction
834
+ x_first = self.conv_first(x)
835
+ res = self.conv_after_body(self.forward_features(x_first)) + x_first
836
+ x = x + self.conv_last(res)
837
+
838
+ x = x / self.img_range + self.mean
839
+
840
+ return x[:, :, :H*self.upscale, :W*self.upscale]
841
+
842
+ def flops(self):
843
+ flops = 0
844
+ H, W = self.patches_resolution
845
+ flops += H * W * 3 * self.embed_dim * 9
846
+ flops += self.patch_embed.flops()
847
+ for i, layer in enumerate(self.layers):
848
+ flops += layer.flops()
849
+ flops += H * W * 3 * self.embed_dim * self.embed_dim
850
+ flops += self.upsample.flops()
851
+ return flops
852
+
853
+
854
+ if __name__ == '__main__':
855
+ upscale = 4
856
+ window_size = 8
857
+ height = (1024 // upscale // window_size + 1) * window_size
858
+ width = (720 // upscale // window_size + 1) * window_size
859
+ model = SwinIR(upscale=2, img_size=(height, width),
860
+ window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
861
+ embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
862
+ print(model)
863
+ print(height, width, model.flops() / 1e9)
864
+
865
+ x = torch.randn((1, 3, height, width))
866
+ x = model(x)
867
+ print(x.shape)
extensions-builtin/SwinIR/swinir_model_arch_v2.py ADDED
@@ -0,0 +1,1017 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # -----------------------------------------------------------------------------------
2
+ # Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/
3
+ # Written by Conde and Choi et al.
4
+ # -----------------------------------------------------------------------------------
5
+
6
+ import math
7
+ import numpy as np
8
+ import torch
9
+ import torch.nn as nn
10
+ import torch.nn.functional as F
11
+ import torch.utils.checkpoint as checkpoint
12
+ from timm.models.layers import DropPath, to_2tuple, trunc_normal_
13
+
14
+
15
+ class Mlp(nn.Module):
16
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
17
+ super().__init__()
18
+ out_features = out_features or in_features
19
+ hidden_features = hidden_features or in_features
20
+ self.fc1 = nn.Linear(in_features, hidden_features)
21
+ self.act = act_layer()
22
+ self.fc2 = nn.Linear(hidden_features, out_features)
23
+ self.drop = nn.Dropout(drop)
24
+
25
+ def forward(self, x):
26
+ x = self.fc1(x)
27
+ x = self.act(x)
28
+ x = self.drop(x)
29
+ x = self.fc2(x)
30
+ x = self.drop(x)
31
+ return x
32
+
33
+
34
+ def window_partition(x, window_size):
35
+ """
36
+ Args:
37
+ x: (B, H, W, C)
38
+ window_size (int): window size
39
+ Returns:
40
+ windows: (num_windows*B, window_size, window_size, C)
41
+ """
42
+ B, H, W, C = x.shape
43
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
44
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
45
+ return windows
46
+
47
+
48
+ def window_reverse(windows, window_size, H, W):
49
+ """
50
+ Args:
51
+ windows: (num_windows*B, window_size, window_size, C)
52
+ window_size (int): Window size
53
+ H (int): Height of image
54
+ W (int): Width of image
55
+ Returns:
56
+ x: (B, H, W, C)
57
+ """
58
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
59
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
60
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
61
+ return x
62
+
63
+ class WindowAttention(nn.Module):
64
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
65
+ It supports both of shifted and non-shifted window.
66
+ Args:
67
+ dim (int): Number of input channels.
68
+ window_size (tuple[int]): The height and width of the window.
69
+ num_heads (int): Number of attention heads.
70
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
71
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
72
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
73
+ pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
74
+ """
75
+
76
+ def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.,
77
+ pretrained_window_size=[0, 0]):
78
+
79
+ super().__init__()
80
+ self.dim = dim
81
+ self.window_size = window_size # Wh, Ww
82
+ self.pretrained_window_size = pretrained_window_size
83
+ self.num_heads = num_heads
84
+
85
+ self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)
86
+
87
+ # mlp to generate continuous relative position bias
88
+ self.cpb_mlp = nn.Sequential(nn.Linear(2, 512, bias=True),
89
+ nn.ReLU(inplace=True),
90
+ nn.Linear(512, num_heads, bias=False))
91
+
92
+ # get relative_coords_table
93
+ relative_coords_h = torch.arange(-(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32)
94
+ relative_coords_w = torch.arange(-(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32)
95
+ relative_coords_table = torch.stack(
96
+ torch.meshgrid([relative_coords_h,
97
+ relative_coords_w])).permute(1, 2, 0).contiguous().unsqueeze(0) # 1, 2*Wh-1, 2*Ww-1, 2
98
+ if pretrained_window_size[0] > 0:
99
+ relative_coords_table[:, :, :, 0] /= (pretrained_window_size[0] - 1)
100
+ relative_coords_table[:, :, :, 1] /= (pretrained_window_size[1] - 1)
101
+ else:
102
+ relative_coords_table[:, :, :, 0] /= (self.window_size[0] - 1)
103
+ relative_coords_table[:, :, :, 1] /= (self.window_size[1] - 1)
104
+ relative_coords_table *= 8 # normalize to -8, 8
105
+ relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
106
+ torch.abs(relative_coords_table) + 1.0) / np.log2(8)
107
+
108
+ self.register_buffer("relative_coords_table", relative_coords_table)
109
+
110
+ # get pair-wise relative position index for each token inside the window
111
+ coords_h = torch.arange(self.window_size[0])
112
+ coords_w = torch.arange(self.window_size[1])
113
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
114
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
115
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
116
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
117
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
118
+ relative_coords[:, :, 1] += self.window_size[1] - 1
119
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
120
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
121
+ self.register_buffer("relative_position_index", relative_position_index)
122
+
123
+ self.qkv = nn.Linear(dim, dim * 3, bias=False)
124
+ if qkv_bias:
125
+ self.q_bias = nn.Parameter(torch.zeros(dim))
126
+ self.v_bias = nn.Parameter(torch.zeros(dim))
127
+ else:
128
+ self.q_bias = None
129
+ self.v_bias = None
130
+ self.attn_drop = nn.Dropout(attn_drop)
131
+ self.proj = nn.Linear(dim, dim)
132
+ self.proj_drop = nn.Dropout(proj_drop)
133
+ self.softmax = nn.Softmax(dim=-1)
134
+
135
+ def forward(self, x, mask=None):
136
+ """
137
+ Args:
138
+ x: input features with shape of (num_windows*B, N, C)
139
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
140
+ """
141
+ B_, N, C = x.shape
142
+ qkv_bias = None
143
+ if self.q_bias is not None:
144
+ qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))
145
+ qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
146
+ qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
147
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
148
+
149
+ # cosine attention
150
+ attn = (F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1))
151
+ logit_scale = torch.clamp(self.logit_scale, max=torch.log(torch.tensor(1. / 0.01)).to(self.logit_scale.device)).exp()
152
+ attn = attn * logit_scale
153
+
154
+ relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(-1, self.num_heads)
155
+ relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(
156
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH
157
+ relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
158
+ relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
159
+ attn = attn + relative_position_bias.unsqueeze(0)
160
+
161
+ if mask is not None:
162
+ nW = mask.shape[0]
163
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
164
+ attn = attn.view(-1, self.num_heads, N, N)
165
+ attn = self.softmax(attn)
166
+ else:
167
+ attn = self.softmax(attn)
168
+
169
+ attn = self.attn_drop(attn)
170
+
171
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
172
+ x = self.proj(x)
173
+ x = self.proj_drop(x)
174
+ return x
175
+
176
+ def extra_repr(self) -> str:
177
+ return f'dim={self.dim}, window_size={self.window_size}, ' \
178
+ f'pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}'
179
+
180
+ def flops(self, N):
181
+ # calculate flops for 1 window with token length of N
182
+ flops = 0
183
+ # qkv = self.qkv(x)
184
+ flops += N * self.dim * 3 * self.dim
185
+ # attn = (q @ k.transpose(-2, -1))
186
+ flops += self.num_heads * N * (self.dim // self.num_heads) * N
187
+ # x = (attn @ v)
188
+ flops += self.num_heads * N * N * (self.dim // self.num_heads)
189
+ # x = self.proj(x)
190
+ flops += N * self.dim * self.dim
191
+ return flops
192
+
193
+ class SwinTransformerBlock(nn.Module):
194
+ r""" Swin Transformer Block.
195
+ Args:
196
+ dim (int): Number of input channels.
197
+ input_resolution (tuple[int]): Input resulotion.
198
+ num_heads (int): Number of attention heads.
199
+ window_size (int): Window size.
200
+ shift_size (int): Shift size for SW-MSA.
201
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
202
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
203
+ drop (float, optional): Dropout rate. Default: 0.0
204
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
205
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
206
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
207
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
208
+ pretrained_window_size (int): Window size in pre-training.
209
+ """
210
+
211
+ def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
212
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
213
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm, pretrained_window_size=0):
214
+ super().__init__()
215
+ self.dim = dim
216
+ self.input_resolution = input_resolution
217
+ self.num_heads = num_heads
218
+ self.window_size = window_size
219
+ self.shift_size = shift_size
220
+ self.mlp_ratio = mlp_ratio
221
+ if min(self.input_resolution) <= self.window_size:
222
+ # if window size is larger than input resolution, we don't partition windows
223
+ self.shift_size = 0
224
+ self.window_size = min(self.input_resolution)
225
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
226
+
227
+ self.norm1 = norm_layer(dim)
228
+ self.attn = WindowAttention(
229
+ dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
230
+ qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop,
231
+ pretrained_window_size=to_2tuple(pretrained_window_size))
232
+
233
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
234
+ self.norm2 = norm_layer(dim)
235
+ mlp_hidden_dim = int(dim * mlp_ratio)
236
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
237
+
238
+ if self.shift_size > 0:
239
+ attn_mask = self.calculate_mask(self.input_resolution)
240
+ else:
241
+ attn_mask = None
242
+
243
+ self.register_buffer("attn_mask", attn_mask)
244
+
245
+ def calculate_mask(self, x_size):
246
+ # calculate attention mask for SW-MSA
247
+ H, W = x_size
248
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
249
+ h_slices = (slice(0, -self.window_size),
250
+ slice(-self.window_size, -self.shift_size),
251
+ slice(-self.shift_size, None))
252
+ w_slices = (slice(0, -self.window_size),
253
+ slice(-self.window_size, -self.shift_size),
254
+ slice(-self.shift_size, None))
255
+ cnt = 0
256
+ for h in h_slices:
257
+ for w in w_slices:
258
+ img_mask[:, h, w, :] = cnt
259
+ cnt += 1
260
+
261
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
262
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
263
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
264
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
265
+
266
+ return attn_mask
267
+
268
+ def forward(self, x, x_size):
269
+ H, W = x_size
270
+ B, L, C = x.shape
271
+ #assert L == H * W, "input feature has wrong size"
272
+
273
+ shortcut = x
274
+ x = x.view(B, H, W, C)
275
+
276
+ # cyclic shift
277
+ if self.shift_size > 0:
278
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
279
+ else:
280
+ shifted_x = x
281
+
282
+ # partition windows
283
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
284
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
285
+
286
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
287
+ if self.input_resolution == x_size:
288
+ attn_windows = self.attn(x_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
289
+ else:
290
+ attn_windows = self.attn(x_windows, mask=self.calculate_mask(x_size).to(x.device))
291
+
292
+ # merge windows
293
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
294
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
295
+
296
+ # reverse cyclic shift
297
+ if self.shift_size > 0:
298
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
299
+ else:
300
+ x = shifted_x
301
+ x = x.view(B, H * W, C)
302
+ x = shortcut + self.drop_path(self.norm1(x))
303
+
304
+ # FFN
305
+ x = x + self.drop_path(self.norm2(self.mlp(x)))
306
+
307
+ return x
308
+
309
+ def extra_repr(self) -> str:
310
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, " \
311
+ f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
312
+
313
+ def flops(self):
314
+ flops = 0
315
+ H, W = self.input_resolution
316
+ # norm1
317
+ flops += self.dim * H * W
318
+ # W-MSA/SW-MSA
319
+ nW = H * W / self.window_size / self.window_size
320
+ flops += nW * self.attn.flops(self.window_size * self.window_size)
321
+ # mlp
322
+ flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
323
+ # norm2
324
+ flops += self.dim * H * W
325
+ return flops
326
+
327
+ class PatchMerging(nn.Module):
328
+ r""" Patch Merging Layer.
329
+ Args:
330
+ input_resolution (tuple[int]): Resolution of input feature.
331
+ dim (int): Number of input channels.
332
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
333
+ """
334
+
335
+ def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
336
+ super().__init__()
337
+ self.input_resolution = input_resolution
338
+ self.dim = dim
339
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
340
+ self.norm = norm_layer(2 * dim)
341
+
342
+ def forward(self, x):
343
+ """
344
+ x: B, H*W, C
345
+ """
346
+ H, W = self.input_resolution
347
+ B, L, C = x.shape
348
+ assert L == H * W, "input feature has wrong size"
349
+ assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
350
+
351
+ x = x.view(B, H, W, C)
352
+
353
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
354
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
355
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
356
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
357
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
358
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
359
+
360
+ x = self.reduction(x)
361
+ x = self.norm(x)
362
+
363
+ return x
364
+
365
+ def extra_repr(self) -> str:
366
+ return f"input_resolution={self.input_resolution}, dim={self.dim}"
367
+
368
+ def flops(self):
369
+ H, W = self.input_resolution
370
+ flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
371
+ flops += H * W * self.dim // 2
372
+ return flops
373
+
374
+ class BasicLayer(nn.Module):
375
+ """ A basic Swin Transformer layer for one stage.
376
+ Args:
377
+ dim (int): Number of input channels.
378
+ input_resolution (tuple[int]): Input resolution.
379
+ depth (int): Number of blocks.
380
+ num_heads (int): Number of attention heads.
381
+ window_size (int): Local window size.
382
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
383
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
384
+ drop (float, optional): Dropout rate. Default: 0.0
385
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
386
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
387
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
388
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
389
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
390
+ pretrained_window_size (int): Local window size in pre-training.
391
+ """
392
+
393
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
394
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
395
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
396
+ pretrained_window_size=0):
397
+
398
+ super().__init__()
399
+ self.dim = dim
400
+ self.input_resolution = input_resolution
401
+ self.depth = depth
402
+ self.use_checkpoint = use_checkpoint
403
+
404
+ # build blocks
405
+ self.blocks = nn.ModuleList([
406
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
407
+ num_heads=num_heads, window_size=window_size,
408
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
409
+ mlp_ratio=mlp_ratio,
410
+ qkv_bias=qkv_bias,
411
+ drop=drop, attn_drop=attn_drop,
412
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
413
+ norm_layer=norm_layer,
414
+ pretrained_window_size=pretrained_window_size)
415
+ for i in range(depth)])
416
+
417
+ # patch merging layer
418
+ if downsample is not None:
419
+ self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
420
+ else:
421
+ self.downsample = None
422
+
423
+ def forward(self, x, x_size):
424
+ for blk in self.blocks:
425
+ if self.use_checkpoint:
426
+ x = checkpoint.checkpoint(blk, x, x_size)
427
+ else:
428
+ x = blk(x, x_size)
429
+ if self.downsample is not None:
430
+ x = self.downsample(x)
431
+ return x
432
+
433
+ def extra_repr(self) -> str:
434
+ return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
435
+
436
+ def flops(self):
437
+ flops = 0
438
+ for blk in self.blocks:
439
+ flops += blk.flops()
440
+ if self.downsample is not None:
441
+ flops += self.downsample.flops()
442
+ return flops
443
+
444
+ def _init_respostnorm(self):
445
+ for blk in self.blocks:
446
+ nn.init.constant_(blk.norm1.bias, 0)
447
+ nn.init.constant_(blk.norm1.weight, 0)
448
+ nn.init.constant_(blk.norm2.bias, 0)
449
+ nn.init.constant_(blk.norm2.weight, 0)
450
+
451
+ class PatchEmbed(nn.Module):
452
+ r""" Image to Patch Embedding
453
+ Args:
454
+ img_size (int): Image size. Default: 224.
455
+ patch_size (int): Patch token size. Default: 4.
456
+ in_chans (int): Number of input image channels. Default: 3.
457
+ embed_dim (int): Number of linear projection output channels. Default: 96.
458
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
459
+ """
460
+
461
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
462
+ super().__init__()
463
+ img_size = to_2tuple(img_size)
464
+ patch_size = to_2tuple(patch_size)
465
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
466
+ self.img_size = img_size
467
+ self.patch_size = patch_size
468
+ self.patches_resolution = patches_resolution
469
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
470
+
471
+ self.in_chans = in_chans
472
+ self.embed_dim = embed_dim
473
+
474
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
475
+ if norm_layer is not None:
476
+ self.norm = norm_layer(embed_dim)
477
+ else:
478
+ self.norm = None
479
+
480
+ def forward(self, x):
481
+ B, C, H, W = x.shape
482
+ # FIXME look at relaxing size constraints
483
+ # assert H == self.img_size[0] and W == self.img_size[1],
484
+ # f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
485
+ x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
486
+ if self.norm is not None:
487
+ x = self.norm(x)
488
+ return x
489
+
490
+ def flops(self):
491
+ Ho, Wo = self.patches_resolution
492
+ flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
493
+ if self.norm is not None:
494
+ flops += Ho * Wo * self.embed_dim
495
+ return flops
496
+
497
+ class RSTB(nn.Module):
498
+ """Residual Swin Transformer Block (RSTB).
499
+
500
+ Args:
501
+ dim (int): Number of input channels.
502
+ input_resolution (tuple[int]): Input resolution.
503
+ depth (int): Number of blocks.
504
+ num_heads (int): Number of attention heads.
505
+ window_size (int): Local window size.
506
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
507
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
508
+ drop (float, optional): Dropout rate. Default: 0.0
509
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
510
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
511
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
512
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
513
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
514
+ img_size: Input image size.
515
+ patch_size: Patch size.
516
+ resi_connection: The convolutional block before residual connection.
517
+ """
518
+
519
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size,
520
+ mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
521
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False,
522
+ img_size=224, patch_size=4, resi_connection='1conv'):
523
+ super(RSTB, self).__init__()
524
+
525
+ self.dim = dim
526
+ self.input_resolution = input_resolution
527
+
528
+ self.residual_group = BasicLayer(dim=dim,
529
+ input_resolution=input_resolution,
530
+ depth=depth,
531
+ num_heads=num_heads,
532
+ window_size=window_size,
533
+ mlp_ratio=mlp_ratio,
534
+ qkv_bias=qkv_bias,
535
+ drop=drop, attn_drop=attn_drop,
536
+ drop_path=drop_path,
537
+ norm_layer=norm_layer,
538
+ downsample=downsample,
539
+ use_checkpoint=use_checkpoint)
540
+
541
+ if resi_connection == '1conv':
542
+ self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
543
+ elif resi_connection == '3conv':
544
+ # to save parameters and memory
545
+ self.conv = nn.Sequential(nn.Conv2d(dim, dim // 4, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True),
546
+ nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
547
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
548
+ nn.Conv2d(dim // 4, dim, 3, 1, 1))
549
+
550
+ self.patch_embed = PatchEmbed(
551
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
552
+ norm_layer=None)
553
+
554
+ self.patch_unembed = PatchUnEmbed(
555
+ img_size=img_size, patch_size=patch_size, in_chans=dim, embed_dim=dim,
556
+ norm_layer=None)
557
+
558
+ def forward(self, x, x_size):
559
+ return self.patch_embed(self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))) + x
560
+
561
+ def flops(self):
562
+ flops = 0
563
+ flops += self.residual_group.flops()
564
+ H, W = self.input_resolution
565
+ flops += H * W * self.dim * self.dim * 9
566
+ flops += self.patch_embed.flops()
567
+ flops += self.patch_unembed.flops()
568
+
569
+ return flops
570
+
571
+ class PatchUnEmbed(nn.Module):
572
+ r""" Image to Patch Unembedding
573
+
574
+ Args:
575
+ img_size (int): Image size. Default: 224.
576
+ patch_size (int): Patch token size. Default: 4.
577
+ in_chans (int): Number of input image channels. Default: 3.
578
+ embed_dim (int): Number of linear projection output channels. Default: 96.
579
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
580
+ """
581
+
582
+ def __init__(self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
583
+ super().__init__()
584
+ img_size = to_2tuple(img_size)
585
+ patch_size = to_2tuple(patch_size)
586
+ patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]
587
+ self.img_size = img_size
588
+ self.patch_size = patch_size
589
+ self.patches_resolution = patches_resolution
590
+ self.num_patches = patches_resolution[0] * patches_resolution[1]
591
+
592
+ self.in_chans = in_chans
593
+ self.embed_dim = embed_dim
594
+
595
+ def forward(self, x, x_size):
596
+ B, HW, C = x.shape
597
+ x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1]) # B Ph*Pw C
598
+ return x
599
+
600
+ def flops(self):
601
+ flops = 0
602
+ return flops
603
+
604
+
605
+ class Upsample(nn.Sequential):
606
+ """Upsample module.
607
+
608
+ Args:
609
+ scale (int): Scale factor. Supported scales: 2^n and 3.
610
+ num_feat (int): Channel number of intermediate features.
611
+ """
612
+
613
+ def __init__(self, scale, num_feat):
614
+ m = []
615
+ if (scale & (scale - 1)) == 0: # scale = 2^n
616
+ for _ in range(int(math.log(scale, 2))):
617
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
618
+ m.append(nn.PixelShuffle(2))
619
+ elif scale == 3:
620
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
621
+ m.append(nn.PixelShuffle(3))
622
+ else:
623
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
624
+ super(Upsample, self).__init__(*m)
625
+
626
+ class Upsample_hf(nn.Sequential):
627
+ """Upsample module.
628
+
629
+ Args:
630
+ scale (int): Scale factor. Supported scales: 2^n and 3.
631
+ num_feat (int): Channel number of intermediate features.
632
+ """
633
+
634
+ def __init__(self, scale, num_feat):
635
+ m = []
636
+ if (scale & (scale - 1)) == 0: # scale = 2^n
637
+ for _ in range(int(math.log(scale, 2))):
638
+ m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
639
+ m.append(nn.PixelShuffle(2))
640
+ elif scale == 3:
641
+ m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
642
+ m.append(nn.PixelShuffle(3))
643
+ else:
644
+ raise ValueError(f'scale {scale} is not supported. ' 'Supported scales: 2^n and 3.')
645
+ super(Upsample_hf, self).__init__(*m)
646
+
647
+
648
+ class UpsampleOneStep(nn.Sequential):
649
+ """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
650
+ Used in lightweight SR to save parameters.
651
+
652
+ Args:
653
+ scale (int): Scale factor. Supported scales: 2^n and 3.
654
+ num_feat (int): Channel number of intermediate features.
655
+
656
+ """
657
+
658
+ def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
659
+ self.num_feat = num_feat
660
+ self.input_resolution = input_resolution
661
+ m = []
662
+ m.append(nn.Conv2d(num_feat, (scale ** 2) * num_out_ch, 3, 1, 1))
663
+ m.append(nn.PixelShuffle(scale))
664
+ super(UpsampleOneStep, self).__init__(*m)
665
+
666
+ def flops(self):
667
+ H, W = self.input_resolution
668
+ flops = H * W * self.num_feat * 3 * 9
669
+ return flops
670
+
671
+
672
+
673
+ class Swin2SR(nn.Module):
674
+ r""" Swin2SR
675
+ A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
676
+
677
+ Args:
678
+ img_size (int | tuple(int)): Input image size. Default 64
679
+ patch_size (int | tuple(int)): Patch size. Default: 1
680
+ in_chans (int): Number of input image channels. Default: 3
681
+ embed_dim (int): Patch embedding dimension. Default: 96
682
+ depths (tuple(int)): Depth of each Swin Transformer layer.
683
+ num_heads (tuple(int)): Number of attention heads in different layers.
684
+ window_size (int): Window size. Default: 7
685
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
686
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
687
+ drop_rate (float): Dropout rate. Default: 0
688
+ attn_drop_rate (float): Attention dropout rate. Default: 0
689
+ drop_path_rate (float): Stochastic depth rate. Default: 0.1
690
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
691
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
692
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True
693
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
694
+ upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
695
+ img_range: Image range. 1. or 255.
696
+ upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
697
+ resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
698
+ """
699
+
700
+ def __init__(self, img_size=64, patch_size=1, in_chans=3,
701
+ embed_dim=96, depths=[6, 6, 6, 6], num_heads=[6, 6, 6, 6],
702
+ window_size=7, mlp_ratio=4., qkv_bias=True,
703
+ drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
704
+ norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
705
+ use_checkpoint=False, upscale=2, img_range=1., upsampler='', resi_connection='1conv',
706
+ **kwargs):
707
+ super(Swin2SR, self).__init__()
708
+ num_in_ch = in_chans
709
+ num_out_ch = in_chans
710
+ num_feat = 64
711
+ self.img_range = img_range
712
+ if in_chans == 3:
713
+ rgb_mean = (0.4488, 0.4371, 0.4040)
714
+ self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
715
+ else:
716
+ self.mean = torch.zeros(1, 1, 1, 1)
717
+ self.upscale = upscale
718
+ self.upsampler = upsampler
719
+ self.window_size = window_size
720
+
721
+ #####################################################################################################
722
+ ################################### 1, shallow feature extraction ###################################
723
+ self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
724
+
725
+ #####################################################################################################
726
+ ################################### 2, deep feature extraction ######################################
727
+ self.num_layers = len(depths)
728
+ self.embed_dim = embed_dim
729
+ self.ape = ape
730
+ self.patch_norm = patch_norm
731
+ self.num_features = embed_dim
732
+ self.mlp_ratio = mlp_ratio
733
+
734
+ # split image into non-overlapping patches
735
+ self.patch_embed = PatchEmbed(
736
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
737
+ norm_layer=norm_layer if self.patch_norm else None)
738
+ num_patches = self.patch_embed.num_patches
739
+ patches_resolution = self.patch_embed.patches_resolution
740
+ self.patches_resolution = patches_resolution
741
+
742
+ # merge non-overlapping patches into image
743
+ self.patch_unembed = PatchUnEmbed(
744
+ img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim,
745
+ norm_layer=norm_layer if self.patch_norm else None)
746
+
747
+ # absolute position embedding
748
+ if self.ape:
749
+ self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
750
+ trunc_normal_(self.absolute_pos_embed, std=.02)
751
+
752
+ self.pos_drop = nn.Dropout(p=drop_rate)
753
+
754
+ # stochastic depth
755
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] # stochastic depth decay rule
756
+
757
+ # build Residual Swin Transformer blocks (RSTB)
758
+ self.layers = nn.ModuleList()
759
+ for i_layer in range(self.num_layers):
760
+ layer = RSTB(dim=embed_dim,
761
+ input_resolution=(patches_resolution[0],
762
+ patches_resolution[1]),
763
+ depth=depths[i_layer],
764
+ num_heads=num_heads[i_layer],
765
+ window_size=window_size,
766
+ mlp_ratio=self.mlp_ratio,
767
+ qkv_bias=qkv_bias,
768
+ drop=drop_rate, attn_drop=attn_drop_rate,
769
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
770
+ norm_layer=norm_layer,
771
+ downsample=None,
772
+ use_checkpoint=use_checkpoint,
773
+ img_size=img_size,
774
+ patch_size=patch_size,
775
+ resi_connection=resi_connection
776
+
777
+ )
778
+ self.layers.append(layer)
779
+
780
+ if self.upsampler == 'pixelshuffle_hf':
781
+ self.layers_hf = nn.ModuleList()
782
+ for i_layer in range(self.num_layers):
783
+ layer = RSTB(dim=embed_dim,
784
+ input_resolution=(patches_resolution[0],
785
+ patches_resolution[1]),
786
+ depth=depths[i_layer],
787
+ num_heads=num_heads[i_layer],
788
+ window_size=window_size,
789
+ mlp_ratio=self.mlp_ratio,
790
+ qkv_bias=qkv_bias,
791
+ drop=drop_rate, attn_drop=attn_drop_rate,
792
+ drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])], # no impact on SR results
793
+ norm_layer=norm_layer,
794
+ downsample=None,
795
+ use_checkpoint=use_checkpoint,
796
+ img_size=img_size,
797
+ patch_size=patch_size,
798
+ resi_connection=resi_connection
799
+
800
+ )
801
+ self.layers_hf.append(layer)
802
+
803
+ self.norm = norm_layer(self.num_features)
804
+
805
+ # build the last conv layer in deep feature extraction
806
+ if resi_connection == '1conv':
807
+ self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
808
+ elif resi_connection == '3conv':
809
+ # to save parameters and memory
810
+ self.conv_after_body = nn.Sequential(nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
811
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
812
+ nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
813
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
814
+ nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1))
815
+
816
+ #####################################################################################################
817
+ ################################ 3, high quality image reconstruction ################################
818
+ if self.upsampler == 'pixelshuffle':
819
+ # for classical SR
820
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
821
+ nn.LeakyReLU(inplace=True))
822
+ self.upsample = Upsample(upscale, num_feat)
823
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
824
+ elif self.upsampler == 'pixelshuffle_aux':
825
+ self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
826
+ self.conv_before_upsample = nn.Sequential(
827
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
828
+ nn.LeakyReLU(inplace=True))
829
+ self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
830
+ self.conv_after_aux = nn.Sequential(
831
+ nn.Conv2d(3, num_feat, 3, 1, 1),
832
+ nn.LeakyReLU(inplace=True))
833
+ self.upsample = Upsample(upscale, num_feat)
834
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
835
+
836
+ elif self.upsampler == 'pixelshuffle_hf':
837
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
838
+ nn.LeakyReLU(inplace=True))
839
+ self.upsample = Upsample(upscale, num_feat)
840
+ self.upsample_hf = Upsample_hf(upscale, num_feat)
841
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
842
+ self.conv_first_hf = nn.Sequential(nn.Conv2d(num_feat, embed_dim, 3, 1, 1),
843
+ nn.LeakyReLU(inplace=True))
844
+ self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
845
+ self.conv_before_upsample_hf = nn.Sequential(
846
+ nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
847
+ nn.LeakyReLU(inplace=True))
848
+ self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
849
+
850
+ elif self.upsampler == 'pixelshuffledirect':
851
+ # for lightweight SR (to save parameters)
852
+ self.upsample = UpsampleOneStep(upscale, embed_dim, num_out_ch,
853
+ (patches_resolution[0], patches_resolution[1]))
854
+ elif self.upsampler == 'nearest+conv':
855
+ # for real-world SR (less artifacts)
856
+ assert self.upscale == 4, 'only support x4 now.'
857
+ self.conv_before_upsample = nn.Sequential(nn.Conv2d(embed_dim, num_feat, 3, 1, 1),
858
+ nn.LeakyReLU(inplace=True))
859
+ self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
860
+ self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
861
+ self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
862
+ self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
863
+ self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
864
+ else:
865
+ # for image denoising and JPEG compression artifact reduction
866
+ self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
867
+
868
+ self.apply(self._init_weights)
869
+
870
+ def _init_weights(self, m):
871
+ if isinstance(m, nn.Linear):
872
+ trunc_normal_(m.weight, std=.02)
873
+ if isinstance(m, nn.Linear) and m.bias is not None:
874
+ nn.init.constant_(m.bias, 0)
875
+ elif isinstance(m, nn.LayerNorm):
876
+ nn.init.constant_(m.bias, 0)
877
+ nn.init.constant_(m.weight, 1.0)
878
+
879
+ @torch.jit.ignore
880
+ def no_weight_decay(self):
881
+ return {'absolute_pos_embed'}
882
+
883
+ @torch.jit.ignore
884
+ def no_weight_decay_keywords(self):
885
+ return {'relative_position_bias_table'}
886
+
887
+ def check_image_size(self, x):
888
+ _, _, h, w = x.size()
889
+ mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
890
+ mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
891
+ x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
892
+ return x
893
+
894
+ def forward_features(self, x):
895
+ x_size = (x.shape[2], x.shape[3])
896
+ x = self.patch_embed(x)
897
+ if self.ape:
898
+ x = x + self.absolute_pos_embed
899
+ x = self.pos_drop(x)
900
+
901
+ for layer in self.layers:
902
+ x = layer(x, x_size)
903
+
904
+ x = self.norm(x) # B L C
905
+ x = self.patch_unembed(x, x_size)
906
+
907
+ return x
908
+
909
+ def forward_features_hf(self, x):
910
+ x_size = (x.shape[2], x.shape[3])
911
+ x = self.patch_embed(x)
912
+ if self.ape:
913
+ x = x + self.absolute_pos_embed
914
+ x = self.pos_drop(x)
915
+
916
+ for layer in self.layers_hf:
917
+ x = layer(x, x_size)
918
+
919
+ x = self.norm(x) # B L C
920
+ x = self.patch_unembed(x, x_size)
921
+
922
+ return x
923
+
924
+ def forward(self, x):
925
+ H, W = x.shape[2:]
926
+ x = self.check_image_size(x)
927
+
928
+ self.mean = self.mean.type_as(x)
929
+ x = (x - self.mean) * self.img_range
930
+
931
+ if self.upsampler == 'pixelshuffle':
932
+ # for classical SR
933
+ x = self.conv_first(x)
934
+ x = self.conv_after_body(self.forward_features(x)) + x
935
+ x = self.conv_before_upsample(x)
936
+ x = self.conv_last(self.upsample(x))
937
+ elif self.upsampler == 'pixelshuffle_aux':
938
+ bicubic = F.interpolate(x, size=(H * self.upscale, W * self.upscale), mode='bicubic', align_corners=False)
939
+ bicubic = self.conv_bicubic(bicubic)
940
+ x = self.conv_first(x)
941
+ x = self.conv_after_body(self.forward_features(x)) + x
942
+ x = self.conv_before_upsample(x)
943
+ aux = self.conv_aux(x) # b, 3, LR_H, LR_W
944
+ x = self.conv_after_aux(aux)
945
+ x = self.upsample(x)[:, :, :H * self.upscale, :W * self.upscale] + bicubic[:, :, :H * self.upscale, :W * self.upscale]
946
+ x = self.conv_last(x)
947
+ aux = aux / self.img_range + self.mean
948
+ elif self.upsampler == 'pixelshuffle_hf':
949
+ # for classical SR with HF
950
+ x = self.conv_first(x)
951
+ x = self.conv_after_body(self.forward_features(x)) + x
952
+ x_before = self.conv_before_upsample(x)
953
+ x_out = self.conv_last(self.upsample(x_before))
954
+
955
+ x_hf = self.conv_first_hf(x_before)
956
+ x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
957
+ x_hf = self.conv_before_upsample_hf(x_hf)
958
+ x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
959
+ x = x_out + x_hf
960
+ x_hf = x_hf / self.img_range + self.mean
961
+
962
+ elif self.upsampler == 'pixelshuffledirect':
963
+ # for lightweight SR
964
+ x = self.conv_first(x)
965
+ x = self.conv_after_body(self.forward_features(x)) + x
966
+ x = self.upsample(x)
967
+ elif self.upsampler == 'nearest+conv':
968
+ # for real-world SR
969
+ x = self.conv_first(x)
970
+ x = self.conv_after_body(self.forward_features(x)) + x
971
+ x = self.conv_before_upsample(x)
972
+ x = self.lrelu(self.conv_up1(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
973
+ x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
974
+ x = self.conv_last(self.lrelu(self.conv_hr(x)))
975
+ else:
976
+ # for image denoising and JPEG compression artifact reduction
977
+ x_first = self.conv_first(x)
978
+ res = self.conv_after_body(self.forward_features(x_first)) + x_first
979
+ x = x + self.conv_last(res)
980
+
981
+ x = x / self.img_range + self.mean
982
+ if self.upsampler == "pixelshuffle_aux":
983
+ return x[:, :, :H*self.upscale, :W*self.upscale], aux
984
+
985
+ elif self.upsampler == "pixelshuffle_hf":
986
+ x_out = x_out / self.img_range + self.mean
987
+ return x_out[:, :, :H*self.upscale, :W*self.upscale], x[:, :, :H*self.upscale, :W*self.upscale], x_hf[:, :, :H*self.upscale, :W*self.upscale]
988
+
989
+ else:
990
+ return x[:, :, :H*self.upscale, :W*self.upscale]
991
+
992
+ def flops(self):
993
+ flops = 0
994
+ H, W = self.patches_resolution
995
+ flops += H * W * 3 * self.embed_dim * 9
996
+ flops += self.patch_embed.flops()
997
+ for i, layer in enumerate(self.layers):
998
+ flops += layer.flops()
999
+ flops += H * W * 3 * self.embed_dim * self.embed_dim
1000
+ flops += self.upsample.flops()
1001
+ return flops
1002
+
1003
+
1004
+ if __name__ == '__main__':
1005
+ upscale = 4
1006
+ window_size = 8
1007
+ height = (1024 // upscale // window_size + 1) * window_size
1008
+ width = (720 // upscale // window_size + 1) * window_size
1009
+ model = Swin2SR(upscale=2, img_size=(height, width),
1010
+ window_size=window_size, img_range=1., depths=[6, 6, 6, 6],
1011
+ embed_dim=60, num_heads=[6, 6, 6, 6], mlp_ratio=2, upsampler='pixelshuffledirect')
1012
+ print(model)
1013
+ print(height, width, model.flops() / 1e9)
1014
+
1015
+ x = torch.randn((1, 3, height, width))
1016
+ x = model(x)
1017
+ print(x.shape)
extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ // Stable Diffusion WebUI - Bracket checker
2
+ // By Hingashi no Florin/Bwin4L & @akx
3
+ // Counts open and closed brackets (round, square, curly) in the prompt and negative prompt text boxes in the txt2img and img2img tabs.
4
+ // If there's a mismatch, the keyword counter turns red and if you hover on it, a tooltip tells you what's wrong.
5
+
6
+ function checkBrackets(textArea, counterElt) {
7
+ var counts = {};
8
+ (textArea.value.match(/[(){}\[\]]/g) || []).forEach(bracket => {
9
+ counts[bracket] = (counts[bracket] || 0) + 1;
10
+ });
11
+ var errors = [];
12
+
13
+ function checkPair(open, close, kind) {
14
+ if (counts[open] !== counts[close]) {
15
+ errors.push(
16
+ `${open}...${close} - Detected ${counts[open] || 0} opening and ${counts[close] || 0} closing ${kind}.`
17
+ );
18
+ }
19
+ }
20
+
21
+ checkPair('(', ')', 'round brackets');
22
+ checkPair('[', ']', 'square brackets');
23
+ checkPair('{', '}', 'curly brackets');
24
+ counterElt.title = errors.join('\n');
25
+ counterElt.classList.toggle('error', errors.length !== 0);
26
+ }
27
+
28
+ function setupBracketChecking(id_prompt, id_counter) {
29
+ var textarea = gradioApp().querySelector("#" + id_prompt + " > label > textarea");
30
+ var counter = gradioApp().getElementById(id_counter)
31
+
32
+ if (textarea && counter) {
33
+ textarea.addEventListener("input", () => checkBrackets(textarea, counter));
34
+ }
35
+ }
36
+
37
+ onUiLoaded(function () {
38
+ setupBracketChecking('txt2img_prompt', 'txt2img_token_counter');
39
+ setupBracketChecking('txt2img_neg_prompt', 'txt2img_negative_token_counter');
40
+ setupBracketChecking('img2img_prompt', 'img2img_token_counter');
41
+ setupBracketChecking('img2img_neg_prompt', 'img2img_negative_token_counter');
42
+ });
extensions/put extensions here.txt ADDED
File without changes
html/card-no-preview.png ADDED
html/extra-networks-card.html ADDED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <div class='card' style={style} onclick={card_clicked}>
2
+ {metadata_button}
3
+
4
+ <div class='actions'>
5
+ <div class='additional'>
6
+ <ul>
7
+ <a href="#" title="replace preview image with currently selected in gallery" onclick={save_card_preview}>replace preview</a>
8
+ </ul>
9
+ <span style="display:none" class='search_term{serach_only}'>{search_term}</span>
10
+ </div>
11
+ <span class='name'>{name}</span>
12
+ <span class='description'>{description}</span>
13
+ </div>
14
+ </div>
15
+
html/extra-networks-no-cards.html ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ <div class='nocards'>
2
+ <h1>Nothing here. Add some content to the following directories:</h1>
3
+
4
+ <ul>
5
+ {dirs}
6
+ </ul>
7
+ </div>
8
+
html/footer.html ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <div>
2
+ <a href="/docs">API</a>
3
+  • 
4
+ <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui">Github</a>
5
+  • 
6
+ <a href="https://gradio.app">Gradio</a>
7
+  • 
8
+ <a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
9
+ </div>
10
+ <br />
11
+ <div class="versions">
12
+ {versions}
13
+ </div>
html/image-update.svg ADDED
html/licenses.html ADDED
@@ -0,0 +1,664 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <style>
2
+ #licenses h2 {font-size: 1.2em; font-weight: bold; margin-bottom: 0.2em;}
3
+ #licenses small {font-size: 0.95em; opacity: 0.85;}
4
+ #licenses pre { margin: 1em 0 2em 0;}
5
+ </style>
6
+
7
+ <h2><a href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">CodeFormer</a></h2>
8
+ <small>Parts of CodeFormer code had to be copied to be compatible with GFPGAN.</small>
9
+ <pre>
10
+ S-Lab License 1.0
11
+
12
+ Copyright 2022 S-Lab
13
+
14
+ Redistribution and use for non-commercial purpose in source and
15
+ binary forms, with or without modification, are permitted provided
16
+ that the following conditions are met:
17
+
18
+ 1. Redistributions of source code must retain the above copyright
19
+ notice, this list of conditions and the following disclaimer.
20
+
21
+ 2. Redistributions in binary form must reproduce the above copyright
22
+ notice, this list of conditions and the following disclaimer in
23
+ the documentation and/or other materials provided with the
24
+ distribution.
25
+
26
+ 3. Neither the name of the copyright holder nor the names of its
27
+ contributors may be used to endorse or promote products derived
28
+ from this software without specific prior written permission.
29
+
30
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
31
+ "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
32
+ LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
33
+ A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
34
+ HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
35
+ SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
36
+ LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
37
+ DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
38
+ THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
39
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
40
+ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
41
+
42
+ In the event that redistribution and/or use for commercial purpose in
43
+ source or binary forms, with or without modification is required,
44
+ please contact the contributor(s) of the work.
45
+ </pre>
46
+
47
+
48
+ <h2><a href="https://github.com/victorca25/iNNfer/blob/main/LICENSE">ESRGAN</a></h2>
49
+ <small>Code for architecture and reading models copied.</small>
50
+ <pre>
51
+ MIT License
52
+
53
+ Copyright (c) 2021 victorca25
54
+
55
+ Permission is hereby granted, free of charge, to any person obtaining a copy
56
+ of this software and associated documentation files (the "Software"), to deal
57
+ in the Software without restriction, including without limitation the rights
58
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
59
+ copies of the Software, and to permit persons to whom the Software is
60
+ furnished to do so, subject to the following conditions:
61
+
62
+ The above copyright notice and this permission notice shall be included in all
63
+ copies or substantial portions of the Software.
64
+
65
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
66
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
67
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
68
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
69
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
70
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
71
+ SOFTWARE.
72
+ </pre>
73
+
74
+ <h2><a href="https://github.com/xinntao/Real-ESRGAN/blob/master/LICENSE">Real-ESRGAN</a></h2>
75
+ <small>Some code is copied to support ESRGAN models.</small>
76
+ <pre>
77
+ BSD 3-Clause License
78
+
79
+ Copyright (c) 2021, Xintao Wang
80
+ All rights reserved.
81
+
82
+ Redistribution and use in source and binary forms, with or without
83
+ modification, are permitted provided that the following conditions are met:
84
+
85
+ 1. Redistributions of source code must retain the above copyright notice, this
86
+ list of conditions and the following disclaimer.
87
+
88
+ 2. Redistributions in binary form must reproduce the above copyright notice,
89
+ this list of conditions and the following disclaimer in the documentation
90
+ and/or other materials provided with the distribution.
91
+
92
+ 3. Neither the name of the copyright holder nor the names of its
93
+ contributors may be used to endorse or promote products derived from
94
+ this software without specific prior written permission.
95
+
96
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
97
+ AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
98
+ IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
99
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
100
+ FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
101
+ DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
102
+ SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
103
+ CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
104
+ OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
105
+ OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
106
+ </pre>
107
+
108
+ <h2><a href="https://github.com/invoke-ai/InvokeAI/blob/main/LICENSE">InvokeAI</a></h2>
109
+ <small>Some code for compatibility with OSX is taken from lstein's repository.</small>
110
+ <pre>
111
+ MIT License
112
+
113
+ Copyright (c) 2022 InvokeAI Team
114
+
115
+ Permission is hereby granted, free of charge, to any person obtaining a copy
116
+ of this software and associated documentation files (the "Software"), to deal
117
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+ incurred by, or claims asserted against, such Contributor by reason
610
+ of your accepting any such warranty or additional liability.
611
+
612
+ END OF TERMS AND CONDITIONS
613
+
614
+ APPENDIX: How to apply the Apache License to your work.
615
+
616
+ To apply the Apache License to your work, attach the following
617
+ boilerplate notice, with the fields enclosed by brackets "[]"
618
+ replaced with your own identifying information. (Don't include
619
+ the brackets!) The text should be enclosed in the appropriate
620
+ comment syntax for the file format. We also recommend that a
621
+ file or class name and description of purpose be included on the
622
+ same "printed page" as the copyright notice for easier
623
+ identification within third-party archives.
624
+
625
+ Copyright [yyyy] [name of copyright owner]
626
+
627
+ Licensed under the Apache License, Version 2.0 (the "License");
628
+ you may not use this file except in compliance with the License.
629
+ You may obtain a copy of the License at
630
+
631
+ http://www.apache.org/licenses/LICENSE-2.0
632
+
633
+ Unless required by applicable law or agreed to in writing, software
634
+ distributed under the License is distributed on an "AS IS" BASIS,
635
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
636
+ See the License for the specific language governing permissions and
637
+ limitations under the License.
638
+ </pre>
639
+
640
+ <h2><a href="https://github.com/explosion/curated-transformers/blob/main/LICENSE">Curated transformers</a></h2>
641
+ <small>The MPS workaround for nn.Linear on macOS 13.2.X is based on the MPS workaround for nn.Linear created by danieldk for Curated transformers</small>
642
+ <pre>
643
+ The MIT License (MIT)
644
+
645
+ Copyright (C) 2021 ExplosionAI GmbH
646
+
647
+ Permission is hereby granted, free of charge, to any person obtaining a copy
648
+ of this software and associated documentation files (the "Software"), to deal
649
+ in the Software without restriction, including without limitation the rights
650
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
651
+ copies of the Software, and to permit persons to whom the Software is
652
+ furnished to do so, subject to the following conditions:
653
+
654
+ The above copyright notice and this permission notice shall be included in
655
+ all copies or substantial portions of the Software.
656
+
657
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
658
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
659
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
660
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
661
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
662
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
663
+ THE SOFTWARE.
664
+ </pre>
javascript/aspectRatioOverlay.js ADDED
@@ -0,0 +1,111 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ let currentWidth = null;
3
+ let currentHeight = null;
4
+ let arFrameTimeout = setTimeout(function(){},0);
5
+
6
+ function dimensionChange(e, is_width, is_height){
7
+
8
+ if(is_width){
9
+ currentWidth = e.target.value*1.0
10
+ }
11
+ if(is_height){
12
+ currentHeight = e.target.value*1.0
13
+ }
14
+
15
+ var inImg2img = gradioApp().querySelector("#tab_img2img").style.display == "block";
16
+
17
+ if(!inImg2img){
18
+ return;
19
+ }
20
+
21
+ var targetElement = null;
22
+
23
+ var tabIndex = get_tab_index('mode_img2img')
24
+ if(tabIndex == 0){ // img2img
25
+ targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
26
+ } else if(tabIndex == 1){ //Sketch
27
+ targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
28
+ } else if(tabIndex == 2){ // Inpaint
29
+ targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
30
+ } else if(tabIndex == 3){ // Inpaint sketch
31
+ targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
32
+ }
33
+
34
+
35
+ if(targetElement){
36
+
37
+ var arPreviewRect = gradioApp().querySelector('#imageARPreview');
38
+ if(!arPreviewRect){
39
+ arPreviewRect = document.createElement('div')
40
+ arPreviewRect.id = "imageARPreview";
41
+ gradioApp().appendChild(arPreviewRect)
42
+ }
43
+
44
+
45
+
46
+ var viewportOffset = targetElement.getBoundingClientRect();
47
+
48
+ var viewportscale = Math.min( targetElement.clientWidth/targetElement.naturalWidth, targetElement.clientHeight/targetElement.naturalHeight )
49
+
50
+ var scaledx = targetElement.naturalWidth*viewportscale
51
+ var scaledy = targetElement.naturalHeight*viewportscale
52
+
53
+ var cleintRectTop = (viewportOffset.top+window.scrollY)
54
+ var cleintRectLeft = (viewportOffset.left+window.scrollX)
55
+ var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight/2)
56
+ var cleintRectCentreX = cleintRectLeft + (targetElement.clientWidth/2)
57
+
58
+ var arscale = Math.min( scaledx/currentWidth, scaledy/currentHeight )
59
+ var arscaledx = currentWidth*arscale
60
+ var arscaledy = currentHeight*arscale
61
+
62
+ var arRectTop = cleintRectCentreY-(arscaledy/2)
63
+ var arRectLeft = cleintRectCentreX-(arscaledx/2)
64
+ var arRectWidth = arscaledx
65
+ var arRectHeight = arscaledy
66
+
67
+ arPreviewRect.style.top = arRectTop+'px';
68
+ arPreviewRect.style.left = arRectLeft+'px';
69
+ arPreviewRect.style.width = arRectWidth+'px';
70
+ arPreviewRect.style.height = arRectHeight+'px';
71
+
72
+ clearTimeout(arFrameTimeout);
73
+ arFrameTimeout = setTimeout(function(){
74
+ arPreviewRect.style.display = 'none';
75
+ },2000);
76
+
77
+ arPreviewRect.style.display = 'block';
78
+
79
+ }
80
+
81
+ }
82
+
83
+
84
+ onUiUpdate(function(){
85
+ var arPreviewRect = gradioApp().querySelector('#imageARPreview');
86
+ if(arPreviewRect){
87
+ arPreviewRect.style.display = 'none';
88
+ }
89
+ var tabImg2img = gradioApp().querySelector("#tab_img2img");
90
+ if (tabImg2img) {
91
+ var inImg2img = tabImg2img.style.display == "block";
92
+ if(inImg2img){
93
+ let inputs = gradioApp().querySelectorAll('input');
94
+ inputs.forEach(function(e){
95
+ var is_width = e.parentElement.id == "img2img_width"
96
+ var is_height = e.parentElement.id == "img2img_height"
97
+
98
+ if((is_width || is_height) && !e.classList.contains('scrollwatch')){
99
+ e.addEventListener('input', function(e){dimensionChange(e, is_width, is_height)} )
100
+ e.classList.add('scrollwatch')
101
+ }
102
+ if(is_width){
103
+ currentWidth = e.value*1.0
104
+ }
105
+ if(is_height){
106
+ currentHeight = e.value*1.0
107
+ }
108
+ })
109
+ }
110
+ }
111
+ });
javascript/contextMenus.js ADDED
@@ -0,0 +1,166 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ contextMenuInit = function(){
3
+ let eventListenerApplied=false;
4
+ let menuSpecs = new Map();
5
+
6
+ const uid = function(){
7
+ return Date.now().toString(36) + Math.random().toString(36).substring(2);
8
+ }
9
+
10
+ function showContextMenu(event,element,menuEntries){
11
+ let posx = event.clientX + document.body.scrollLeft + document.documentElement.scrollLeft;
12
+ let posy = event.clientY + document.body.scrollTop + document.documentElement.scrollTop;
13
+
14
+ let oldMenu = gradioApp().querySelector('#context-menu')
15
+ if(oldMenu){
16
+ oldMenu.remove()
17
+ }
18
+
19
+ let baseStyle = window.getComputedStyle(uiCurrentTab)
20
+
21
+ const contextMenu = document.createElement('nav')
22
+ contextMenu.id = "context-menu"
23
+ contextMenu.style.background = baseStyle.background
24
+ contextMenu.style.color = baseStyle.color
25
+ contextMenu.style.fontFamily = baseStyle.fontFamily
26
+ contextMenu.style.top = posy+'px'
27
+ contextMenu.style.left = posx+'px'
28
+
29
+
30
+
31
+ const contextMenuList = document.createElement('ul')
32
+ contextMenuList.className = 'context-menu-items';
33
+ contextMenu.append(contextMenuList);
34
+
35
+ menuEntries.forEach(function(entry){
36
+ let contextMenuEntry = document.createElement('a')
37
+ contextMenuEntry.innerHTML = entry['name']
38
+ contextMenuEntry.addEventListener("click", function() {
39
+ entry['func']();
40
+ })
41
+ contextMenuList.append(contextMenuEntry);
42
+
43
+ })
44
+
45
+ gradioApp().appendChild(contextMenu)
46
+
47
+ let menuWidth = contextMenu.offsetWidth + 4;
48
+ let menuHeight = contextMenu.offsetHeight + 4;
49
+
50
+ let windowWidth = window.innerWidth;
51
+ let windowHeight = window.innerHeight;
52
+
53
+ if ( (windowWidth - posx) < menuWidth ) {
54
+ contextMenu.style.left = windowWidth - menuWidth + "px";
55
+ }
56
+
57
+ if ( (windowHeight - posy) < menuHeight ) {
58
+ contextMenu.style.top = windowHeight - menuHeight + "px";
59
+ }
60
+
61
+ }
62
+
63
+ function appendContextMenuOption(targetElementSelector,entryName,entryFunction){
64
+
65
+ var currentItems = menuSpecs.get(targetElementSelector)
66
+
67
+ if(!currentItems){
68
+ currentItems = []
69
+ menuSpecs.set(targetElementSelector,currentItems);
70
+ }
71
+ let newItem = {'id':targetElementSelector+'_'+uid(),
72
+ 'name':entryName,
73
+ 'func':entryFunction,
74
+ 'isNew':true}
75
+
76
+ currentItems.push(newItem)
77
+ return newItem['id']
78
+ }
79
+
80
+ function removeContextMenuOption(uid){
81
+ menuSpecs.forEach(function(v) {
82
+ let index = -1
83
+ v.forEach(function(e,ei){if(e['id']==uid){index=ei}})
84
+ if(index>=0){
85
+ v.splice(index, 1);
86
+ }
87
+ })
88
+ }
89
+
90
+ function addContextMenuEventListener(){
91
+ if(eventListenerApplied){
92
+ return;
93
+ }
94
+ gradioApp().addEventListener("click", function(e) {
95
+ if(! e.isTrusted){
96
+ return
97
+ }
98
+
99
+ let oldMenu = gradioApp().querySelector('#context-menu')
100
+ if(oldMenu){
101
+ oldMenu.remove()
102
+ }
103
+ });
104
+ gradioApp().addEventListener("contextmenu", function(e) {
105
+ let oldMenu = gradioApp().querySelector('#context-menu')
106
+ if(oldMenu){
107
+ oldMenu.remove()
108
+ }
109
+ menuSpecs.forEach(function(v,k) {
110
+ if(e.composedPath()[0].matches(k)){
111
+ showContextMenu(e,e.composedPath()[0],v)
112
+ e.preventDefault()
113
+ }
114
+ })
115
+ });
116
+ eventListenerApplied=true
117
+
118
+ }
119
+
120
+ return [appendContextMenuOption, removeContextMenuOption, addContextMenuEventListener]
121
+ }
122
+
123
+ initResponse = contextMenuInit();
124
+ appendContextMenuOption = initResponse[0];
125
+ removeContextMenuOption = initResponse[1];
126
+ addContextMenuEventListener = initResponse[2];
127
+
128
+ (function(){
129
+ //Start example Context Menu Items
130
+ let generateOnRepeat = function(genbuttonid,interruptbuttonid){
131
+ let genbutton = gradioApp().querySelector(genbuttonid);
132
+ let interruptbutton = gradioApp().querySelector(interruptbuttonid);
133
+ if(!interruptbutton.offsetParent){
134
+ genbutton.click();
135
+ }
136
+ clearInterval(window.generateOnRepeatInterval)
137
+ window.generateOnRepeatInterval = setInterval(function(){
138
+ if(!interruptbutton.offsetParent){
139
+ genbutton.click();
140
+ }
141
+ },
142
+ 500)
143
+ }
144
+
145
+ appendContextMenuOption('#txt2img_generate','Generate forever',function(){
146
+ generateOnRepeat('#txt2img_generate','#txt2img_interrupt');
147
+ })
148
+ appendContextMenuOption('#img2img_generate','Generate forever',function(){
149
+ generateOnRepeat('#img2img_generate','#img2img_interrupt');
150
+ })
151
+
152
+ let cancelGenerateForever = function(){
153
+ clearInterval(window.generateOnRepeatInterval)
154
+ }
155
+
156
+ appendContextMenuOption('#txt2img_interrupt','Cancel generate forever',cancelGenerateForever)
157
+ appendContextMenuOption('#txt2img_generate', 'Cancel generate forever',cancelGenerateForever)
158
+ appendContextMenuOption('#img2img_interrupt','Cancel generate forever',cancelGenerateForever)
159
+ appendContextMenuOption('#img2img_generate', 'Cancel generate forever',cancelGenerateForever)
160
+
161
+ })();
162
+ //End example Context Menu Items
163
+
164
+ onUiUpdate(function(){
165
+ addContextMenuEventListener()
166
+ });
javascript/dragdrop.js ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ // allows drag-dropping files into gradio image elements, and also pasting images from clipboard
2
+
3
+ function isValidImageList( files ) {
4
+ return files && files?.length === 1 && ['image/png', 'image/gif', 'image/jpeg'].includes(files[0].type);
5
+ }
6
+
7
+ function dropReplaceImage( imgWrap, files ) {
8
+ if ( ! isValidImageList( files ) ) {
9
+ return;
10
+ }
11
+
12
+ const tmpFile = files[0];
13
+
14
+ imgWrap.querySelector('.modify-upload button + button, .touch-none + div button + button')?.click();
15
+ const callback = () => {
16
+ const fileInput = imgWrap.querySelector('input[type="file"]');
17
+ if ( fileInput ) {
18
+ if ( files.length === 0 ) {
19
+ files = new DataTransfer();
20
+ files.items.add(tmpFile);
21
+ fileInput.files = files.files;
22
+ } else {
23
+ fileInput.files = files;
24
+ }
25
+ fileInput.dispatchEvent(new Event('change'));
26
+ }
27
+ };
28
+
29
+ if ( imgWrap.closest('#pnginfo_image') ) {
30
+ // special treatment for PNG Info tab, wait for fetch request to finish
31
+ const oldFetch = window.fetch;
32
+ window.fetch = async (input, options) => {
33
+ const response = await oldFetch(input, options);
34
+ if ( 'api/predict/' === input ) {
35
+ const content = await response.text();
36
+ window.fetch = oldFetch;
37
+ window.requestAnimationFrame( () => callback() );
38
+ return new Response(content, {
39
+ status: response.status,
40
+ statusText: response.statusText,
41
+ headers: response.headers
42
+ })
43
+ }
44
+ return response;
45
+ };
46
+ } else {
47
+ window.requestAnimationFrame( () => callback() );
48
+ }
49
+ }
50
+
51
+ window.document.addEventListener('dragover', e => {
52
+ const target = e.composedPath()[0];
53
+ const imgWrap = target.closest('[data-testid="image"]');
54
+ if ( !imgWrap && target.placeholder && target.placeholder.indexOf("Prompt") == -1) {
55
+ return;
56
+ }
57
+ e.stopPropagation();
58
+ e.preventDefault();
59
+ e.dataTransfer.dropEffect = 'copy';
60
+ });
61
+
62
+ window.document.addEventListener('drop', e => {
63
+ const target = e.composedPath()[0];
64
+ if (target.placeholder.indexOf("Prompt") == -1) {
65
+ return;
66
+ }
67
+ const imgWrap = target.closest('[data-testid="image"]');
68
+ if ( !imgWrap ) {
69
+ return;
70
+ }
71
+ e.stopPropagation();
72
+ e.preventDefault();
73
+ const files = e.dataTransfer.files;
74
+ dropReplaceImage( imgWrap, files );
75
+ });
76
+
77
+ window.addEventListener('paste', e => {
78
+ const files = e.clipboardData.files;
79
+ if ( ! isValidImageList( files ) ) {
80
+ return;
81
+ }
82
+
83
+ const visibleImageFields = [...gradioApp().querySelectorAll('[data-testid="image"]')]
84
+ .filter(el => uiElementIsVisible(el));
85
+ if ( ! visibleImageFields.length ) {
86
+ return;
87
+ }
88
+
89
+ const firstFreeImageField = visibleImageFields
90
+ .filter(el => el.querySelector('input[type=file]'))?.[0];
91
+
92
+ dropReplaceImage(
93
+ firstFreeImageField ?
94
+ firstFreeImageField :
95
+ visibleImageFields[visibleImageFields.length - 1]
96
+ , files );
97
+ });
javascript/edit-attention.js ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ function keyupEditAttention(event){
2
+ let target = event.originalTarget || event.composedPath()[0];
3
+ if (! target.matches("[id*='_toprow'] [id*='_prompt'] textarea")) return;
4
+ if (! (event.metaKey || event.ctrlKey)) return;
5
+
6
+ let isPlus = event.key == "ArrowUp"
7
+ let isMinus = event.key == "ArrowDown"
8
+ if (!isPlus && !isMinus) return;
9
+
10
+ let selectionStart = target.selectionStart;
11
+ let selectionEnd = target.selectionEnd;
12
+ let text = target.value;
13
+
14
+ function selectCurrentParenthesisBlock(OPEN, CLOSE){
15
+ if (selectionStart !== selectionEnd) return false;
16
+
17
+ // Find opening parenthesis around current cursor
18
+ const before = text.substring(0, selectionStart);
19
+ let beforeParen = before.lastIndexOf(OPEN);
20
+ if (beforeParen == -1) return false;
21
+ let beforeParenClose = before.lastIndexOf(CLOSE);
22
+ while (beforeParenClose !== -1 && beforeParenClose > beforeParen) {
23
+ beforeParen = before.lastIndexOf(OPEN, beforeParen - 1);
24
+ beforeParenClose = before.lastIndexOf(CLOSE, beforeParenClose - 1);
25
+ }
26
+
27
+ // Find closing parenthesis around current cursor
28
+ const after = text.substring(selectionStart);
29
+ let afterParen = after.indexOf(CLOSE);
30
+ if (afterParen == -1) return false;
31
+ let afterParenOpen = after.indexOf(OPEN);
32
+ while (afterParenOpen !== -1 && afterParen > afterParenOpen) {
33
+ afterParen = after.indexOf(CLOSE, afterParen + 1);
34
+ afterParenOpen = after.indexOf(OPEN, afterParenOpen + 1);
35
+ }
36
+ if (beforeParen === -1 || afterParen === -1) return false;
37
+
38
+ // Set the selection to the text between the parenthesis
39
+ const parenContent = text.substring(beforeParen + 1, selectionStart + afterParen);
40
+ const lastColon = parenContent.lastIndexOf(":");
41
+ selectionStart = beforeParen + 1;
42
+ selectionEnd = selectionStart + lastColon;
43
+ target.setSelectionRange(selectionStart, selectionEnd);
44
+ return true;
45
+ }
46
+
47
+ function selectCurrentWord(){
48
+ if (selectionStart !== selectionEnd) return false;
49
+ const delimiters = opts.keyedit_delimiters + " \r\n\t";
50
+
51
+ // seek backward until to find beggining
52
+ while (!delimiters.includes(text[selectionStart - 1]) && selectionStart > 0) {
53
+ selectionStart--;
54
+ }
55
+
56
+ // seek forward to find end
57
+ while (!delimiters.includes(text[selectionEnd]) && selectionEnd < text.length) {
58
+ selectionEnd++;
59
+ }
60
+
61
+ target.setSelectionRange(selectionStart, selectionEnd);
62
+ return true;
63
+ }
64
+
65
+ // If the user hasn't selected anything, let's select their current parenthesis block or word
66
+ if (!selectCurrentParenthesisBlock('<', '>') && !selectCurrentParenthesisBlock('(', ')')) {
67
+ selectCurrentWord();
68
+ }
69
+
70
+ event.preventDefault();
71
+
72
+ var closeCharacter = ')'
73
+ var delta = opts.keyedit_precision_attention
74
+
75
+ if (selectionStart > 0 && text[selectionStart - 1] == '<'){
76
+ closeCharacter = '>'
77
+ delta = opts.keyedit_precision_extra
78
+ } else if (selectionStart == 0 || text[selectionStart - 1] != "(") {
79
+
80
+ // do not include spaces at the end
81
+ while(selectionEnd > selectionStart && text[selectionEnd-1] == ' '){
82
+ selectionEnd -= 1;
83
+ }
84
+ if(selectionStart == selectionEnd){
85
+ return
86
+ }
87
+
88
+ text = text.slice(0, selectionStart) + "(" + text.slice(selectionStart, selectionEnd) + ":1.0)" + text.slice(selectionEnd);
89
+
90
+ selectionStart += 1;
91
+ selectionEnd += 1;
92
+ }
93
+
94
+ var end = text.slice(selectionEnd + 1).indexOf(closeCharacter) + 1;
95
+ var weight = parseFloat(text.slice(selectionEnd + 1, selectionEnd + 1 + end));
96
+ if (isNaN(weight)) return;
97
+
98
+ weight += isPlus ? delta : -delta;
99
+ weight = parseFloat(weight.toPrecision(12));
100
+ if(String(weight).length == 1) weight += ".0"
101
+
102
+ if (closeCharacter == ')' && weight == 1) {
103
+ text = text.slice(0, selectionStart - 1) + text.slice(selectionStart, selectionEnd) + text.slice(selectionEnd + 5);
104
+ selectionStart--;
105
+ selectionEnd--;
106
+ } else {
107
+ text = text.slice(0, selectionEnd + 1) + weight + text.slice(selectionEnd + 1 + end - 1);
108
+ }
109
+
110
+ target.focus();
111
+ target.value = text;
112
+ target.selectionStart = selectionStart;
113
+ target.selectionEnd = selectionEnd;
114
+
115
+ updateInput(target)
116
+ }
117
+
118
+ addEventListener('keydown', (event) => {
119
+ keyupEditAttention(event);
120
+ });
javascript/extensions.js ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ function extensions_apply(_disabled_list, _update_list, disable_all){
3
+ var disable = []
4
+ var update = []
5
+
6
+ gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
7
+ if(x.name.startsWith("enable_") && ! x.checked)
8
+ disable.push(x.name.substring(7))
9
+
10
+ if(x.name.startsWith("update_") && x.checked)
11
+ update.push(x.name.substring(7))
12
+ })
13
+
14
+ restart_reload()
15
+
16
+ return [JSON.stringify(disable), JSON.stringify(update), disable_all]
17
+ }
18
+
19
+ function extensions_check(){
20
+ var disable = []
21
+
22
+ gradioApp().querySelectorAll('#extensions input[type="checkbox"]').forEach(function(x){
23
+ if(x.name.startsWith("enable_") && ! x.checked)
24
+ disable.push(x.name.substring(7))
25
+ })
26
+
27
+ gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
28
+ x.innerHTML = "Loading..."
29
+ })
30
+
31
+
32
+ var id = randomId()
33
+ requestProgress(id, gradioApp().getElementById('extensions_installed_top'), null, function(){
34
+
35
+ })
36
+
37
+ return [id, JSON.stringify(disable)]
38
+ }
39
+
40
+ function install_extension_from_index(button, url){
41
+ button.disabled = "disabled"
42
+ button.value = "Installing..."
43
+
44
+ var textarea = gradioApp().querySelector('#extension_to_install textarea')
45
+ textarea.value = url
46
+ updateInput(textarea)
47
+
48
+ gradioApp().querySelector('#install_extension_button').click()
49
+ }
50
+
51
+ function config_state_confirm_restore(_, config_state_name, config_restore_type) {
52
+ if (config_state_name == "Current") {
53
+ return [false, config_state_name, config_restore_type];
54
+ }
55
+ let restored = "";
56
+ if (config_restore_type == "extensions") {
57
+ restored = "all saved extension versions";
58
+ } else if (config_restore_type == "webui") {
59
+ restored = "the webui version";
60
+ } else {
61
+ restored = "the webui version and all saved extension versions";
62
+ }
63
+ let confirmed = confirm("Are you sure you want to restore from this state?\nThis will reset " + restored + ".");
64
+ if (confirmed) {
65
+ restart_reload();
66
+ gradioApp().querySelectorAll('#extensions .extension_status').forEach(function(x){
67
+ x.innerHTML = "Loading..."
68
+ })
69
+ }
70
+ return [confirmed, config_state_name, config_restore_type];
71
+ }
javascript/extraNetworks.js ADDED
@@ -0,0 +1,196 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ function setupExtraNetworksForTab(tabname){
2
+ gradioApp().querySelector('#'+tabname+'_extra_tabs').classList.add('extra-networks')
3
+
4
+ var tabs = gradioApp().querySelector('#'+tabname+'_extra_tabs > div')
5
+ var search = gradioApp().querySelector('#'+tabname+'_extra_search textarea')
6
+ var refresh = gradioApp().getElementById(tabname+'_extra_refresh')
7
+
8
+ search.classList.add('search')
9
+ tabs.appendChild(search)
10
+ tabs.appendChild(refresh)
11
+
12
+ var applyFilter = function(){
13
+ var searchTerm = search.value.toLowerCase()
14
+
15
+ gradioApp().querySelectorAll('#'+tabname+'_extra_tabs div.card').forEach(function(elem){
16
+ var searchOnly = elem.querySelector('.search_only')
17
+ var text = elem.querySelector('.name').textContent.toLowerCase() + " " + elem.querySelector('.search_term').textContent.toLowerCase()
18
+
19
+ var visible = text.indexOf(searchTerm) != -1
20
+
21
+ if(searchOnly && searchTerm.length < 4){
22
+ visible = false
23
+ }
24
+
25
+ elem.style.display = visible ? "" : "none"
26
+ })
27
+ }
28
+
29
+ search.addEventListener("input", applyFilter);
30
+ applyFilter();
31
+
32
+ extraNetworksApplyFilter[tabname] = applyFilter;
33
+ }
34
+
35
+ function applyExtraNetworkFilter(tabname){
36
+ setTimeout(extraNetworksApplyFilter[tabname], 1);
37
+ }
38
+
39
+ var extraNetworksApplyFilter = {}
40
+ var activePromptTextarea = {};
41
+
42
+ function setupExtraNetworks(){
43
+ setupExtraNetworksForTab('txt2img')
44
+ setupExtraNetworksForTab('img2img')
45
+
46
+ function registerPrompt(tabname, id){
47
+ var textarea = gradioApp().querySelector("#" + id + " > label > textarea");
48
+
49
+ if (! activePromptTextarea[tabname]){
50
+ activePromptTextarea[tabname] = textarea
51
+ }
52
+
53
+ textarea.addEventListener("focus", function(){
54
+ activePromptTextarea[tabname] = textarea;
55
+ });
56
+ }
57
+
58
+ registerPrompt('txt2img', 'txt2img_prompt')
59
+ registerPrompt('txt2img', 'txt2img_neg_prompt')
60
+ registerPrompt('img2img', 'img2img_prompt')
61
+ registerPrompt('img2img', 'img2img_neg_prompt')
62
+ }
63
+
64
+ onUiLoaded(setupExtraNetworks)
65
+
66
+ var re_extranet = /<([^:]+:[^:]+):[\d\.]+>/;
67
+ var re_extranet_g = /\s+<([^:]+:[^:]+):[\d\.]+>/g;
68
+
69
+ function tryToRemoveExtraNetworkFromPrompt(textarea, text){
70
+ var m = text.match(re_extranet)
71
+ if(! m) return false
72
+
73
+ var partToSearch = m[1]
74
+ var replaced = false
75
+ var newTextareaText = textarea.value.replaceAll(re_extranet_g, function(found){
76
+ m = found.match(re_extranet);
77
+ if(m[1] == partToSearch){
78
+ replaced = true;
79
+ return ""
80
+ }
81
+ return found;
82
+ })
83
+
84
+ if(replaced){
85
+ textarea.value = newTextareaText
86
+ return true;
87
+ }
88
+
89
+ return false
90
+ }
91
+
92
+ function cardClicked(tabname, textToAdd, allowNegativePrompt){
93
+ var textarea = allowNegativePrompt ? activePromptTextarea[tabname] : gradioApp().querySelector("#" + tabname + "_prompt > label > textarea")
94
+
95
+ if(! tryToRemoveExtraNetworkFromPrompt(textarea, textToAdd)){
96
+ textarea.value = textarea.value + opts.extra_networks_add_text_separator + textToAdd
97
+ }
98
+
99
+ updateInput(textarea)
100
+ }
101
+
102
+ function saveCardPreview(event, tabname, filename){
103
+ var textarea = gradioApp().querySelector("#" + tabname + '_preview_filename > label > textarea')
104
+ var button = gradioApp().getElementById(tabname + '_save_preview')
105
+
106
+ textarea.value = filename
107
+ updateInput(textarea)
108
+
109
+ button.click()
110
+
111
+ event.stopPropagation()
112
+ event.preventDefault()
113
+ }
114
+
115
+ function extraNetworksSearchButton(tabs_id, event){
116
+ var searchTextarea = gradioApp().querySelector("#" + tabs_id + ' > div > textarea')
117
+ var button = event.target
118
+ var text = button.classList.contains("search-all") ? "" : button.textContent.trim()
119
+
120
+ searchTextarea.value = text
121
+ updateInput(searchTextarea)
122
+ }
123
+
124
+ var globalPopup = null;
125
+ var globalPopupInner = null;
126
+ function popup(contents){
127
+ if(! globalPopup){
128
+ globalPopup = document.createElement('div')
129
+ globalPopup.onclick = function(){ globalPopup.style.display = "none"; };
130
+ globalPopup.classList.add('global-popup');
131
+
132
+ var close = document.createElement('div')
133
+ close.classList.add('global-popup-close');
134
+ close.onclick = function(){ globalPopup.style.display = "none"; };
135
+ close.title = "Close";
136
+ globalPopup.appendChild(close)
137
+
138
+ globalPopupInner = document.createElement('div')
139
+ globalPopupInner.onclick = function(event){ event.stopPropagation(); return false; };
140
+ globalPopupInner.classList.add('global-popup-inner');
141
+ globalPopup.appendChild(globalPopupInner)
142
+
143
+ gradioApp().appendChild(globalPopup);
144
+ }
145
+
146
+ globalPopupInner.innerHTML = '';
147
+ globalPopupInner.appendChild(contents);
148
+
149
+ globalPopup.style.display = "flex";
150
+ }
151
+
152
+ function extraNetworksShowMetadata(text){
153
+ var elem = document.createElement('pre')
154
+ elem.classList.add('popup-metadata');
155
+ elem.textContent = text;
156
+
157
+ popup(elem);
158
+ }
159
+
160
+ function requestGet(url, data, handler, errorHandler){
161
+ var xhr = new XMLHttpRequest();
162
+ var args = Object.keys(data).map(function(k){ return encodeURIComponent(k) + '=' + encodeURIComponent(data[k]) }).join('&')
163
+ xhr.open("GET", url + "?" + args, true);
164
+
165
+ xhr.onreadystatechange = function () {
166
+ if (xhr.readyState === 4) {
167
+ if (xhr.status === 200) {
168
+ try {
169
+ var js = JSON.parse(xhr.responseText);
170
+ handler(js)
171
+ } catch (error) {
172
+ console.error(error);
173
+ errorHandler()
174
+ }
175
+ } else{
176
+ errorHandler()
177
+ }
178
+ }
179
+ };
180
+ var js = JSON.stringify(data);
181
+ xhr.send(js);
182
+ }
183
+
184
+ function extraNetworksRequestMetadata(event, extraPage, cardName){
185
+ var showError = function(){ extraNetworksShowMetadata("there was an error getting metadata"); }
186
+
187
+ requestGet("./sd_extra_networks/metadata", {"page": extraPage, "item": cardName}, function(data){
188
+ if(data && data.metadata){
189
+ extraNetworksShowMetadata(data.metadata)
190
+ } else{
191
+ showError()
192
+ }
193
+ }, showError)
194
+
195
+ event.stopPropagation()
196
+ }
javascript/generationParams.js ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ // attaches listeners to the txt2img and img2img galleries to update displayed generation param text when the image changes
2
+
3
+ let txt2img_gallery, img2img_gallery, modal = undefined;
4
+ onUiUpdate(function(){
5
+ if (!txt2img_gallery) {
6
+ txt2img_gallery = attachGalleryListeners("txt2img")
7
+ }
8
+ if (!img2img_gallery) {
9
+ img2img_gallery = attachGalleryListeners("img2img")
10
+ }
11
+ if (!modal) {
12
+ modal = gradioApp().getElementById('lightboxModal')
13
+ modalObserver.observe(modal, { attributes : true, attributeFilter : ['style'] });
14
+ }
15
+ });
16
+
17
+ let modalObserver = new MutationObserver(function(mutations) {
18
+ mutations.forEach(function(mutationRecord) {
19
+ let selectedTab = gradioApp().querySelector('#tabs div button.selected')?.innerText
20
+ if (mutationRecord.target.style.display === 'none' && (selectedTab === 'txt2img' || selectedTab === 'img2img'))
21
+ gradioApp().getElementById(selectedTab+"_generation_info_button")?.click()
22
+ });
23
+ });
24
+
25
+ function attachGalleryListeners(tab_name) {
26
+ var gallery = gradioApp().querySelector('#'+tab_name+'_gallery')
27
+ gallery?.addEventListener('click', () => gradioApp().getElementById(tab_name+"_generation_info_button").click());
28
+ gallery?.addEventListener('keydown', (e) => {
29
+ if (e.keyCode == 37 || e.keyCode == 39) // left or right arrow
30
+ gradioApp().getElementById(tab_name+"_generation_info_button").click()
31
+ });
32
+ return gallery;
33
+ }