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  1. .eslintignore +4 -0
  2. .eslintrc.js +91 -0
  3. .git-blame-ignore-revs +2 -0
  4. .github/ISSUE_TEMPLATE/bug_report.yml +138 -0
  5. .github/ISSUE_TEMPLATE/config.yml +5 -0
  6. .github/ISSUE_TEMPLATE/feature_request.yml +40 -0
  7. .github/pull_request_template.md +15 -0
  8. .github/workflows/on_pull_request.yaml +34 -0
  9. .github/workflows/run_tests.yaml +70 -0
  10. .gitignore +39 -0
  11. .pylintrc +3 -0
  12. CHANGELOG.md +262 -0
  13. CODEOWNERS +12 -0
  14. LICENSE.txt +663 -0
  15. README.md +169 -0
  16. configs/alt-diffusion-inference.yaml +72 -0
  17. configs/instruct-pix2pix.yaml +98 -0
  18. configs/v1-inference.yaml +70 -0
  19. configs/v1-inpainting-inference.yaml +70 -0
  20. environment-wsl2.yaml +11 -0
  21. extensions-builtin/LDSR/ldsr_model_arch.py +252 -0
  22. extensions-builtin/LDSR/preload.py +6 -0
  23. extensions-builtin/LDSR/scripts/ldsr_model.py +72 -0
  24. extensions-builtin/LDSR/sd_hijack_autoencoder.py +293 -0
  25. extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +1443 -0
  26. extensions-builtin/LDSR/vqvae_quantize.py +147 -0
  27. extensions-builtin/Lora/extra_networks_lora.py +45 -0
  28. extensions-builtin/Lora/lora.py +506 -0
  29. extensions-builtin/Lora/preload.py +6 -0
  30. extensions-builtin/Lora/scripts/lora_script.py +116 -0
  31. extensions-builtin/Lora/ui_extra_networks_lora.py +36 -0
  32. extensions-builtin/ScuNET/preload.py +6 -0
  33. extensions-builtin/ScuNET/scripts/scunet_model.py +148 -0
  34. extensions-builtin/ScuNET/scunet_model_arch.py +268 -0
  35. extensions-builtin/SwinIR/preload.py +6 -0
  36. extensions-builtin/SwinIR/scripts/swinir_model.py +177 -0
  37. extensions-builtin/SwinIR/swinir_model_arch.py +867 -0
  38. extensions-builtin/SwinIR/swinir_model_arch_v2.py +1017 -0
  39. extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js +748 -0
  40. extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py +13 -0
  41. extensions-builtin/canvas-zoom-and-pan/style.css +63 -0
  42. extensions-builtin/extra-options-section/scripts/extra_options_section.py +48 -0
  43. extensions-builtin/prompt-bracket-checker/javascript/prompt-bracket-checker.js +42 -0
  44. html/card-no-preview.png +0 -0
  45. html/extra-networks-card.html +14 -0
  46. html/extra-networks-no-cards.html +8 -0
  47. html/footer.html +15 -0
  48. html/image-update.svg +7 -0
  49. html/licenses.html +690 -0
  50. javascript/aspectRatioOverlay.js +113 -0
.eslintignore ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ extensions
2
+ extensions-disabled
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+ repositories
4
+ venv
.eslintrc.js ADDED
@@ -0,0 +1,91 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ /* global module */
2
+ module.exports = {
3
+ env: {
4
+ browser: true,
5
+ es2021: true,
6
+ },
7
+ extends: "eslint:recommended",
8
+ parserOptions: {
9
+ ecmaVersion: "latest",
10
+ },
11
+ rules: {
12
+ "arrow-spacing": "error",
13
+ "block-spacing": "error",
14
+ "brace-style": "error",
15
+ "comma-dangle": ["error", "only-multiline"],
16
+ "comma-spacing": "error",
17
+ "comma-style": ["error", "last"],
18
+ "curly": ["error", "multi-line", "consistent"],
19
+ "eol-last": "error",
20
+ "func-call-spacing": "error",
21
+ "function-call-argument-newline": ["error", "consistent"],
22
+ "function-paren-newline": ["error", "consistent"],
23
+ "indent": ["error", 4],
24
+ "key-spacing": "error",
25
+ "keyword-spacing": "error",
26
+ "linebreak-style": ["error", "unix"],
27
+ "no-extra-semi": "error",
28
+ "no-mixed-spaces-and-tabs": "error",
29
+ "no-multi-spaces": "error",
30
+ "no-redeclare": ["error", {builtinGlobals: false}],
31
+ "no-trailing-spaces": "error",
32
+ "no-unused-vars": "off",
33
+ "no-whitespace-before-property": "error",
34
+ "object-curly-newline": ["error", {consistent: true, multiline: true}],
35
+ "object-curly-spacing": ["error", "never"],
36
+ "operator-linebreak": ["error", "after"],
37
+ "quote-props": ["error", "consistent-as-needed"],
38
+ "semi": ["error", "always"],
39
+ "semi-spacing": "error",
40
+ "semi-style": ["error", "last"],
41
+ "space-before-blocks": "error",
42
+ "space-before-function-paren": ["error", "never"],
43
+ "space-in-parens": ["error", "never"],
44
+ "space-infix-ops": "error",
45
+ "space-unary-ops": "error",
46
+ "switch-colon-spacing": "error",
47
+ "template-curly-spacing": ["error", "never"],
48
+ "unicode-bom": "error",
49
+ },
50
+ globals: {
51
+ //script.js
52
+ gradioApp: "readonly",
53
+ executeCallbacks: "readonly",
54
+ onAfterUiUpdate: "readonly",
55
+ onOptionsChanged: "readonly",
56
+ onUiLoaded: "readonly",
57
+ onUiUpdate: "readonly",
58
+ uiCurrentTab: "writable",
59
+ uiElementInSight: "readonly",
60
+ uiElementIsVisible: "readonly",
61
+ //ui.js
62
+ opts: "writable",
63
+ all_gallery_buttons: "readonly",
64
+ selected_gallery_button: "readonly",
65
+ selected_gallery_index: "readonly",
66
+ switch_to_txt2img: "readonly",
67
+ switch_to_img2img_tab: "readonly",
68
+ switch_to_img2img: "readonly",
69
+ switch_to_sketch: "readonly",
70
+ switch_to_inpaint: "readonly",
71
+ switch_to_inpaint_sketch: "readonly",
72
+ switch_to_extras: "readonly",
73
+ get_tab_index: "readonly",
74
+ create_submit_args: "readonly",
75
+ restart_reload: "readonly",
76
+ updateInput: "readonly",
77
+ //extraNetworks.js
78
+ requestGet: "readonly",
79
+ popup: "readonly",
80
+ // from python
81
+ localization: "readonly",
82
+ // progrssbar.js
83
+ randomId: "readonly",
84
+ requestProgress: "readonly",
85
+ // imageviewer.js
86
+ modalPrevImage: "readonly",
87
+ modalNextImage: "readonly",
88
+ // token-counters.js
89
+ setupTokenCounters: "readonly",
90
+ }
91
+ };
.git-blame-ignore-revs ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ # Apply ESlint
2
+ 9c54b78d9dde5601e916f308d9a9d6953ec39430
.github/ISSUE_TEMPLATE/bug_report.yml ADDED
@@ -0,0 +1,138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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: Version or Commit where the problem happens
47
+ description: "Which webui version or commit are you running ? (Do not write *Latest Version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Version: v1.2.3** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)"
48
+ validations:
49
+ required: true
50
+ - type: dropdown
51
+ id: py-version
52
+ attributes:
53
+ label: What Python version are you running on ?
54
+ multiple: false
55
+ options:
56
+ - Python 3.10.x
57
+ - Python 3.11.x (above, no supported yet)
58
+ - Python 3.9.x (below, no recommended)
59
+ - type: dropdown
60
+ id: platforms
61
+ attributes:
62
+ label: What platforms do you use to access the UI ?
63
+ multiple: true
64
+ options:
65
+ - Windows
66
+ - Linux
67
+ - MacOS
68
+ - iOS
69
+ - Android
70
+ - Other/Cloud
71
+ - type: dropdown
72
+ id: device
73
+ attributes:
74
+ label: What device are you running WebUI on?
75
+ multiple: true
76
+ options:
77
+ - Nvidia GPUs (RTX 20 above)
78
+ - Nvidia GPUs (GTX 16 below)
79
+ - AMD GPUs (RX 6000 above)
80
+ - AMD GPUs (RX 5000 below)
81
+ - CPU
82
+ - Other GPUs
83
+ - type: dropdown
84
+ id: cross_attention_opt
85
+ attributes:
86
+ label: Cross attention optimization
87
+ description: What cross attention optimization are you using, Settings -> Optimizations -> Cross attention optimization
88
+ multiple: false
89
+ options:
90
+ - Automatic
91
+ - xformers
92
+ - sdp-no-mem
93
+ - sdp
94
+ - Doggettx
95
+ - V1
96
+ - InvokeAI
97
+ - "None "
98
+ validations:
99
+ required: true
100
+ - type: dropdown
101
+ id: browsers
102
+ attributes:
103
+ label: What browsers do you use to access the UI ?
104
+ multiple: true
105
+ options:
106
+ - Mozilla Firefox
107
+ - Google Chrome
108
+ - Brave
109
+ - Apple Safari
110
+ - Microsoft Edge
111
+ - type: textarea
112
+ id: cmdargs
113
+ attributes:
114
+ label: Command Line Arguments
115
+ description: Are you using any launching parameters/command line arguments (modified webui-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
116
+ render: Shell
117
+ validations:
118
+ required: true
119
+ - type: textarea
120
+ id: extensions
121
+ attributes:
122
+ label: List of extensions
123
+ 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.
124
+ validations:
125
+ required: true
126
+ - type: textarea
127
+ id: logs
128
+ attributes:
129
+ label: Console logs
130
+ 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.
131
+ render: Shell
132
+ validations:
133
+ required: true
134
+ - type: textarea
135
+ id: misc
136
+ attributes:
137
+ label: Additional information
138
+ 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 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ## Description
2
+
3
+ * a simple description of what you're trying to accomplish
4
+ * a summary of changes in code
5
+ * which issues it fixes, if any
6
+
7
+ ## Screenshots/videos:
8
+
9
+
10
+ ## Checklist:
11
+
12
+ - [ ] I have read [contributing wiki page](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
13
+ - [ ] I have performed a self-review of my own code
14
+ - [ ] My code follows the [style guidelines](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing#code-style)
15
+ - [ ] My code passes [tests](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Tests)
.github/workflows/on_pull_request.yaml ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Run Linting/Formatting on Pull Requests
2
+
3
+ on:
4
+ - push
5
+ - pull_request
6
+
7
+ jobs:
8
+ lint-python:
9
+ runs-on: ubuntu-latest
10
+ steps:
11
+ - name: Checkout Code
12
+ uses: actions/checkout@v3
13
+ - uses: actions/setup-python@v4
14
+ with:
15
+ python-version: 3.11
16
+ # NB: there's no cache: pip here since we're not installing anything
17
+ # from the requirements.txt file(s) in the repository; it's faster
18
+ # not to have GHA download an (at the time of writing) 4 GB cache
19
+ # of PyTorch and other dependencies.
20
+ - name: Install Ruff
21
+ run: pip install ruff==0.0.265
22
+ - name: Run Ruff
23
+ run: ruff .
24
+ lint-js:
25
+ runs-on: ubuntu-latest
26
+ steps:
27
+ - name: Checkout Code
28
+ uses: actions/checkout@v3
29
+ - name: Install Node.js
30
+ uses: actions/setup-node@v3
31
+ with:
32
+ node-version: 18
33
+ - run: npm i --ci
34
+ - run: npm run lint
.github/workflows/run_tests.yaml ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ launch.py
21
+ - name: Install test dependencies
22
+ run: pip install wait-for-it -r requirements-test.txt
23
+ env:
24
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
25
+ PIP_PROGRESS_BAR: "off"
26
+ - name: Setup environment
27
+ run: python launch.py --skip-torch-cuda-test --exit
28
+ env:
29
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
30
+ PIP_PROGRESS_BAR: "off"
31
+ TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
32
+ WEBUI_LAUNCH_LIVE_OUTPUT: "1"
33
+ PYTHONUNBUFFERED: "1"
34
+ - name: Start test server
35
+ run: >
36
+ python -m coverage run
37
+ --data-file=.coverage.server
38
+ launch.py
39
+ --skip-prepare-environment
40
+ --skip-torch-cuda-test
41
+ --test-server
42
+ --no-half
43
+ --disable-opt-split-attention
44
+ --use-cpu all
45
+ --add-stop-route
46
+ 2>&1 | tee output.txt &
47
+ - name: Run tests
48
+ run: |
49
+ wait-for-it --service 127.0.0.1:7860 -t 600
50
+ python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
51
+ - name: Kill test server
52
+ if: always()
53
+ run: curl -vv -XPOST http://127.0.0.1:7860/_stop && sleep 10
54
+ - name: Show coverage
55
+ run: |
56
+ python -m coverage combine .coverage*
57
+ python -m coverage report -i
58
+ python -m coverage html -i
59
+ - name: Upload main app output
60
+ uses: actions/upload-artifact@v3
61
+ if: always()
62
+ with:
63
+ name: output
64
+ path: output.txt
65
+ - name: Upload coverage HTML
66
+ uses: actions/upload-artifact@v3
67
+ if: always()
68
+ with:
69
+ name: htmlcov
70
+ path: htmlcov
.gitignore ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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/
37
+ /node_modules
38
+ /package-lock.json
39
+ /.coverage*
.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
@@ -0,0 +1,262 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ## 1.4.1
2
+
3
+ ### Bug Fixes:
4
+ * add queue lock for refresh-checkpoints
5
+
6
+ ## 1.4.0
7
+
8
+ ### Features:
9
+ * zoom controls for inpainting
10
+ * run basic torch calculation at startup in parallel to reduce the performance impact of first generation
11
+ * option to pad prompt/neg prompt to be same length
12
+ * remove taming_transformers dependency
13
+ * custom k-diffusion scheduler settings
14
+ * add an option to show selected settings in main txt2img/img2img UI
15
+ * sysinfo tab in settings
16
+ * infer styles from prompts when pasting params into the UI
17
+ * an option to control the behavior of the above
18
+
19
+ ### Minor:
20
+ * bump Gradio to 3.32.0
21
+ * bump xformers to 0.0.20
22
+ * Add option to disable token counters
23
+ * tooltip fixes & optimizations
24
+ * make it possible to configure filename for the zip download
25
+ * `[vae_filename]` pattern for filenames
26
+ * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE
27
+ * change UI reorder setting to multiselect
28
+ * read version info form CHANGELOG.md if git version info is not available
29
+ * link footer API to Wiki when API is not active
30
+ * persistent conds cache (opt-in optimization)
31
+
32
+ ### Extensions:
33
+ * After installing extensions, webui properly restarts the process rather than reloads the UI
34
+ * Added VAE listing to web API. Via: /sdapi/v1/sd-vae
35
+ * custom unet support
36
+ * Add onAfterUiUpdate callback
37
+ * refactor EmbeddingDatabase.register_embedding() to allow unregistering
38
+ * add before_process callback for scripts
39
+ * add ability for alwayson scripts to specify section and let user reorder those sections
40
+
41
+ ### Bug Fixes:
42
+ * Fix dragging text to prompt
43
+ * fix incorrect quoting for infotext values with colon in them
44
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
45
+ * Fix s_min_uncond default type int
46
+ * Fix for #10643 (Inpainting mask sometimes not working)
47
+ * fix bad styling for thumbs view in extra networks #10639
48
+ * fix for empty list of optimizations #10605
49
+ * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility
50
+ * fix --ui-debug-mode exit
51
+ * patch GitPython to not use leaky persistent processes
52
+ * fix duplicate Cross attention optimization after UI reload
53
+ * torch.cuda.is_available() check for SdOptimizationXformers
54
+ * fix hires fix using wrong conds in second pass if using Loras.
55
+ * handle exception when parsing generation parameters from png info
56
+ * fix upcast attention dtype error
57
+ * forcing Torch Version to 1.13.1 for RX 5000 series GPUs
58
+ * split mask blur into X and Y components, patch Outpainting MK2 accordingly
59
+ * don't die when a LoRA is a broken symlink
60
+ * allow activation of Generate Forever during generation
61
+
62
+
63
+ ## 1.3.2
64
+
65
+ ### Bug Fixes:
66
+ * fix files served out of tmp directory even if they are saved to disk
67
+ * fix postprocessing overwriting parameters
68
+
69
+ ## 1.3.1
70
+
71
+ ### Features:
72
+ * revert default cross attention optimization to Doggettx
73
+
74
+ ### Bug Fixes:
75
+ * fix bug: LoRA don't apply on dropdown list sd_lora
76
+ * fix png info always added even if setting is not enabled
77
+ * fix some fields not applying in xyz plot
78
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
79
+ * fix lora hashes not being added properly to infotex if there is only one lora
80
+ * fix --use-cpu failing to work properly at startup
81
+ * make --disable-opt-split-attention command line option work again
82
+
83
+ ## 1.3.0
84
+
85
+ ### Features:
86
+ * add UI to edit defaults
87
+ * token merging (via dbolya/tomesd)
88
+ * settings tab rework: add a lot of additional explanations and links
89
+ * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup
90
+ * update extensions table: show branch, show date in separate column, and show version from tags if available
91
+ * TAESD - another option for cheap live previews
92
+ * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
93
+ * calculate hashes for Lora
94
+ * add lora hashes to infotext
95
+ * when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
96
+ * select cross attention optimization from UI
97
+
98
+ ### Minor:
99
+ * bump Gradio to 3.31.0
100
+ * bump PyTorch to 2.0.1 for macOS and Linux AMD
101
+ * allow setting defaults for elements in extensions' tabs
102
+ * allow selecting file type for live previews
103
+ * show "Loading..." for extra networks when displaying for the first time
104
+ * suppress ENSD infotext for samplers that don't use it
105
+ * clientside optimizations
106
+ * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories
107
+ * allow whitespace in styles.csv
108
+ * add option to reorder tabs
109
+ * move some functionality (swap resolution and set seed to -1) to client
110
+ * option to specify editor height for img2img
111
+ * button to copy image resolution into img2img width/height sliders
112
+ * switch from pyngrok to ngrok-py
113
+ * lazy-load images in extra networks UI
114
+ * set "Navigate image viewer with gamepad" option to false by default, by request
115
+ * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
116
+ * allow hiding buttons in ui-config.json
117
+
118
+ ### Extensions:
119
+ * add /sdapi/v1/script-info api
120
+ * use Ruff to lint Python code
121
+ * use ESlint to lint Javascript code
122
+ * add/modify CFG callbacks for Self-Attention Guidance extension
123
+ * add command and endpoint for graceful server stopping
124
+ * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
125
+ * rework quoting for infotext items that have commas in them to use JSON (should be backwards compatible except for cases where it didn't work previously)
126
+ * add /sdapi/v1/refresh-loras api checkpoint post request
127
+ * tests overhaul
128
+
129
+ ### Bug Fixes:
130
+ * fix an issue preventing the program from starting if the user specifies a bad Gradio theme
131
+ * fix broken prompts from file script
132
+ * fix symlink scanning for extra networks
133
+ * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS
134
+ * allow web UI to be ran fully offline
135
+ * fix inability to run with --freeze-settings
136
+ * fix inability to merge checkpoint without adding metadata
137
+ * fix extra networks' save preview image not adding infotext for jpeg/webm
138
+ * remove blinking effect from text in hires fix and scale resolution preview
139
+ * make links to `http://<...>.git` extensions work in the extension tab
140
+ * fix bug with webui hanging at startup due to hanging git process
141
+
142
+
143
+ ## 1.2.1
144
+
145
+ ### Features:
146
+ * add an option to always refer to LoRA by filenames
147
+
148
+ ### Bug Fixes:
149
+ * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none
150
+ * fix upscalers disappearing after the user reloads UI
151
+ * allow bf16 in safe unpickler (resolves problems with loading some LoRAs)
152
+ * allow web UI to be ran fully offline
153
+ * fix localizations not working
154
+ * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'`
155
+
156
+ ## 1.2.0
157
+
158
+ ### Features:
159
+ * do not wait for Stable Diffusion model to load at startup
160
+ * add filename patterns: `[denoising]`
161
+ * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
162
+ * 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)
163
+ * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
164
+ * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
165
+ * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
166
+ * add version to infotext, footer and console output when starting
167
+ * add links to wiki for filename pattern settings
168
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
169
+
170
+ ### Minor:
171
+ * bump Gradio to 3.29.0
172
+ * bump PyTorch to 2.0.1
173
+ * `--subpath` option for gradio for use with reverse proxy
174
+ * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
175
+ * do not apply localizations if there are none (possible frontend optimization)
176
+ * add extra `None` option for VAE in XYZ plot
177
+ * print error to console when batch processing in img2img fails
178
+ * create HTML for extra network pages only on demand
179
+ * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc
180
+ * put infotext options into their own category in settings tab
181
+ * do not show licenses page when user selects Show all pages in settings
182
+
183
+ ### Extensions:
184
+ * tooltip localization support
185
+ * add API method to get LoRA models with prompt
186
+
187
+ ### Bug Fixes:
188
+ * re-add `/docs` endpoint
189
+ * fix gamepad navigation
190
+ * make the lightbox fullscreen image function properly
191
+ * fix squished thumbnails in extras tab
192
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
193
+ * fix webui showing the same image if you configure the generation to always save results into same file
194
+ * fix bug with upscalers not working properly
195
+ * fix MPS on PyTorch 2.0.1, Intel Macs
196
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
197
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
198
+ * fix prompts from file script failing to read contents from a drag/drop file
199
+
200
+
201
+ ## 1.1.1
202
+ ### Bug Fixes:
203
+ * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle
204
+
205
+ ## 1.1.0
206
+ ### Features:
207
+ * switch to PyTorch 2.0.0 (except for AMD GPUs)
208
+ * visual improvements to custom code scripts
209
+ * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
210
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducability
211
+ * automatically select current word when adjusting weight with ctrl+up/down
212
+ * add dropdowns for X/Y/Z plot
213
+ * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
214
+ * support Gradio's theme API
215
+ * use TCMalloc on Linux by default; possible fix for memory leaks
216
+ * add optimization option to remove negative conditioning at low sigma values #9177
217
+ * embed model merge metadata in .safetensors file
218
+ * extension settings backup/restore feature #9169
219
+ * add "resize by" and "resize to" tabs to img2img
220
+ * add option "keep original size" to textual inversion images preprocess
221
+ * image viewer scrolling via analog stick
222
+ * button to restore the progress from session lost / tab reload
223
+
224
+ ### Minor:
225
+ * bump Gradio to 3.28.1
226
+ * change "scale to" to sliders in Extras tab
227
+ * add labels to tool buttons to make it possible to hide them
228
+ * add tiled inference support for ScuNET
229
+ * add branch support for extension installation
230
+ * change Linux installation script to install into current directory rather than `/home/username`
231
+ * sort textual inversion embeddings by name (case-insensitive)
232
+ * allow styles.csv to be symlinked or mounted in docker
233
+ * remove the "do not add watermark to images" option
234
+ * make selected tab configurable with UI config
235
+ * make the extra networks UI fixed height and scrollable
236
+ * add `disable_tls_verify` arg for use with self-signed certs
237
+
238
+ ### Extensions:
239
+ * add reload callback
240
+ * add `is_hr_pass` field for processing
241
+
242
+ ### Bug Fixes:
243
+ * fix broken batch image processing on 'Extras/Batch Process' tab
244
+ * add "None" option to extra networks dropdowns
245
+ * fix FileExistsError for CLIP Interrogator
246
+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319
247
+ * fix disappearing live previews and progressbar during slow tasks
248
+ * fix fullscreen image view not working properly in some cases
249
+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
250
+ * fix prompt schedule for second order samplers
251
+ * fix image mask/composite for weird resolutions #9628
252
+ * use correct images for previews when using AND (see #9491)
253
+ * one broken image in img2img batch won't stop all processing
254
+ * fix image orientation bug in train/preprocess
255
+ * fix Ngrok recreating tunnels every reload
256
+ * fix `--realesrgan-models-path` and `--ldsr-models-path` not working
257
+ * fix `--skip-install` not working
258
+ * use SAMPLE file format in Outpainting Mk2 & Poorman
259
+ * do not fail all LoRAs if some have failed to load when making a picture
260
+
261
+ ## 1.0.0
262
+ * 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
@@ -0,0 +1,663 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ GNU AFFERO GENERAL PUBLIC LICENSE
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+ 9. Acceptance Not Required for Having Copies.
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+ 10. Automatic Licensing of Downstream Recipients.
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+ Each time you convey a covered work, the recipient automatically
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+ 11. Patents.
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+ A "contributor" is a copyright holder who authorizes use under this
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+ 12. No Surrender of Others' Freedom.
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+ the Program, the only way you could satisfy both those terms and this
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541
+
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+ 13. Remote Network Interaction; Use with the GNU General Public License.
543
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+ Notwithstanding any other provision of this License, if you modify the
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+
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+ 14. Revised Versions of this License.
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+
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+ The Free Software Foundation may publish revised and/or new versions of
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+
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+ Each version is given a distinguishing version number. If the
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+ If the Program specifies that a proxy can decide which future
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+
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+ later version.
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+
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+ 15. Disclaimer of Warranty.
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+ THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+ APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
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+ ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
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+ 16. Limitation of Liability.
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+
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+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
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+ WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
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+ DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
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+ PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
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+ EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
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+ SUCH DAMAGES.
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+
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+ 17. Interpretation of Sections 15 and 16.
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+
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+ If the disclaimer of warranty and limitation of liability provided
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+ above cannot be given local legal effect according to their terms,
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+ reviewing courts shall apply local law that most closely approximates
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+ an absolute waiver of all civil liability in connection with the
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+ Program, unless a warranty or assumption of liability accompanies a
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+ copy of the Program in return for a fee.
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+
621
+ END OF TERMS AND CONDITIONS
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+
623
+ How to Apply These Terms to Your New Programs
624
+
625
+ If you develop a new program, and you want it to be of the greatest
626
+ possible use to the public, the best way to achieve this is to make it
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+ free software which everyone can redistribute and change under these terms.
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+
629
+ To do so, attach the following notices to the program. It is safest
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+ to attach them to the start of each source file to most effectively
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+ state the exclusion of warranty; and each file should have at least
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+ the "copyright" line and a pointer to where the full notice is found.
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+
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+ <one line to give the program's name and a brief idea of what it does.>
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+ Copyright (C) <year> <name of author>
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+
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+ This program is free software: you can redistribute it and/or modify
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+ it under the terms of the GNU Affero General Public License as published by
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+ the Free Software Foundation, either version 3 of the License, or
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+ (at your option) any later version.
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+
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+ This program is distributed in the hope that it will be useful,
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+ but WITHOUT ANY WARRANTY; without even the implied warranty of
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+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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+ GNU Affero General Public License for more details.
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+
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+ You should have received a copy of the GNU Affero General Public License
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+ along with this program. If not, see <https://www.gnu.org/licenses/>.
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+
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+ Also add information on how to contact you by electronic and paper mail.
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+
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+ If your software can interact with users remotely through a computer
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+ network, you should also make sure that it provides a way for users to
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+ get its source. For example, if your program is a web application, its
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+ interface could display a "Source" link that leads users to an archive
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+ of the code. There are many ways you could offer source, and different
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+ solutions will be better for different programs; see section 13 for the
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+ specific requirements.
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+
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+ You should also get your employer (if you work as a programmer) or school,
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+ if any, to sign a "copyright disclaimer" for the program, if necessary.
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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 +1,169 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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` (or `Command+Up` or `Command+Down` if you're on a MacOS) 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
+ ### Installation on Windows 10/11 with NVidia-GPUs using release package
103
+ 1. Download `sd.webui.zip` from [v1.0.0-pre](https://github.com/AUTOMATIC1111/stable-diffusion-webui/releases/tag/v1.0.0-pre) and extract it's contents.
104
+ 2. Run `update.bat`.
105
+ 3. Run `run.bat`.
106
+ > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
107
+
108
+ ### Automatic Installation on Windows
109
+ 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".
110
+ 2. Install [git](https://git-scm.com/download/win).
111
+ 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
112
+ 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
113
+
114
+ ### Automatic Installation on Linux
115
+ 1. Install the dependencies:
116
+ ```bash
117
+ # Debian-based:
118
+ sudo apt install wget git python3 python3-venv
119
+ # Red Hat-based:
120
+ sudo dnf install wget git python3
121
+ # Arch-based:
122
+ sudo pacman -S wget git python3
123
+ ```
124
+ 2. Navigate to the directory you would like the webui to be installed and execute the following command:
125
+ ```bash
126
+ bash <(wget -qO- https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh)
127
+ ```
128
+ 3. Run `webui.sh`.
129
+ 4. Check `webui-user.sh` for options.
130
+ ### Installation on Apple Silicon
131
+
132
+ Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
133
+
134
+ ## Contributing
135
+ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
136
+
137
+ ## Documentation
138
+ The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
139
+
140
+ ## Credits
141
+ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
142
+
143
+ - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
144
+ - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
145
+ - GFPGAN - https://github.com/TencentARC/GFPGAN.git
146
+ - CodeFormer - https://github.com/sczhou/CodeFormer
147
+ - ESRGAN - https://github.com/xinntao/ESRGAN
148
+ - SwinIR - https://github.com/JingyunLiang/SwinIR
149
+ - Swin2SR - https://github.com/mv-lab/swin2sr
150
+ - LDSR - https://github.com/Hafiidz/latent-diffusion
151
+ - MiDaS - https://github.com/isl-org/MiDaS
152
+ - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
153
+ - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
154
+ - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
155
+ - 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)
156
+ - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
157
+ - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
158
+ - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
159
+ - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
160
+ - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
161
+ - xformers - https://github.com/facebookresearch/xformers
162
+ - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
163
+ - 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)
164
+ - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
165
+ - Security advice - RyotaK
166
+ - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
167
+ - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
168
+ - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
169
+ - (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
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 +1,252 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 _ 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
+
114
+ gc.collect()
115
+ if torch.cuda.is_available:
116
+ torch.cuda.empty_cache()
117
+
118
+ im_og = image
119
+ width_og, height_og = im_og.size
120
+ # If we can adjust the max upscale size, then the 4 below should be our variable
121
+ down_sample_rate = target_scale / 4
122
+ wd = width_og * down_sample_rate
123
+ hd = height_og * down_sample_rate
124
+ width_downsampled_pre = int(np.ceil(wd))
125
+ height_downsampled_pre = int(np.ceil(hd))
126
+
127
+ if down_sample_rate != 1:
128
+ print(
129
+ f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
130
+ im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
131
+ else:
132
+ print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
133
+
134
+ # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
135
+ pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
136
+ im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
137
+
138
+ logs = self.run(model["model"], im_padded, diffusion_steps, eta)
139
+
140
+ sample = logs["sample"]
141
+ sample = sample.detach().cpu()
142
+ sample = torch.clamp(sample, -1., 1.)
143
+ sample = (sample + 1.) / 2. * 255
144
+ sample = sample.numpy().astype(np.uint8)
145
+ sample = np.transpose(sample, (0, 2, 3, 1))
146
+ a = Image.fromarray(sample[0])
147
+
148
+ # remove padding
149
+ a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
150
+
151
+ del model
152
+ gc.collect()
153
+ if torch.cuda.is_available:
154
+ torch.cuda.empty_cache()
155
+
156
+ return a
157
+
158
+
159
+ def get_cond(selected_path):
160
+ example = {}
161
+ up_f = 4
162
+ c = selected_path.convert('RGB')
163
+ c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
164
+ c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
165
+ antialias=True)
166
+ c_up = rearrange(c_up, '1 c h w -> 1 h w c')
167
+ c = rearrange(c, '1 c h w -> 1 h w c')
168
+ c = 2. * c - 1.
169
+
170
+ c = c.to(shared.device)
171
+ example["LR_image"] = c
172
+ example["image"] = c_up
173
+
174
+ return example
175
+
176
+
177
+ @torch.no_grad()
178
+ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
179
+ mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
180
+ corrector_kwargs=None, x_t=None
181
+ ):
182
+ ddim = DDIMSampler(model)
183
+ bs = shape[0]
184
+ shape = shape[1:]
185
+ print(f"Sampling with eta = {eta}; steps: {steps}")
186
+ samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
187
+ normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
188
+ mask=mask, x0=x0, temperature=temperature, verbose=False,
189
+ score_corrector=score_corrector,
190
+ corrector_kwargs=corrector_kwargs, x_t=x_t)
191
+
192
+ return samples, intermediates
193
+
194
+
195
+ @torch.no_grad()
196
+ 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,
197
+ corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
198
+ log = {}
199
+
200
+ z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
201
+ return_first_stage_outputs=True,
202
+ force_c_encode=not (hasattr(model, 'split_input_params')
203
+ and model.cond_stage_key == 'coordinates_bbox'),
204
+ return_original_cond=True)
205
+
206
+ if custom_shape is not None:
207
+ z = torch.randn(custom_shape)
208
+ print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
209
+
210
+ z0 = None
211
+
212
+ log["input"] = x
213
+ log["reconstruction"] = xrec
214
+
215
+ if ismap(xc):
216
+ log["original_conditioning"] = model.to_rgb(xc)
217
+ if hasattr(model, 'cond_stage_key'):
218
+ log[model.cond_stage_key] = model.to_rgb(xc)
219
+
220
+ else:
221
+ log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
222
+ if model.cond_stage_model:
223
+ log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
224
+ if model.cond_stage_key == 'class_label':
225
+ log[model.cond_stage_key] = xc[model.cond_stage_key]
226
+
227
+ with model.ema_scope("Plotting"):
228
+ t0 = time.time()
229
+
230
+ sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
231
+ eta=eta,
232
+ quantize_x0=quantize_x0, mask=None, x0=z0,
233
+ temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
234
+ x_t=x_T)
235
+ t1 = time.time()
236
+
237
+ if ddim_use_x0_pred:
238
+ sample = intermediates['pred_x0'][-1]
239
+
240
+ x_sample = model.decode_first_stage(sample)
241
+
242
+ try:
243
+ x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
244
+ log["sample_noquant"] = x_sample_noquant
245
+ log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
246
+ except Exception:
247
+ pass
248
+
249
+ log["sample"] = x_sample
250
+ log["time"] = t1 - t0
251
+
252
+ 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 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ from basicsr.utils.download_util import load_file_from_url
4
+
5
+ from modules.upscaler import Upscaler, UpscalerData
6
+ from ldsr_model_arch import LDSR
7
+ from modules import shared, script_callbacks, errors
8
+ import sd_hijack_autoencoder # noqa: F401
9
+ import sd_hijack_ddpm_v1 # noqa: F401
10
+
11
+
12
+ class UpscalerLDSR(Upscaler):
13
+ def __init__(self, user_path):
14
+ self.name = "LDSR"
15
+ self.user_path = user_path
16
+ self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
17
+ self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
18
+ super().__init__()
19
+ scaler_data = UpscalerData("LDSR", None, self)
20
+ self.scalers = [scaler_data]
21
+
22
+ def load_model(self, path: str):
23
+ # Remove incorrect project.yaml file if too big
24
+ yaml_path = os.path.join(self.model_path, "project.yaml")
25
+ old_model_path = os.path.join(self.model_path, "model.pth")
26
+ new_model_path = os.path.join(self.model_path, "model.ckpt")
27
+
28
+ local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
29
+ local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
30
+ local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
31
+ local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
32
+
33
+ if os.path.exists(yaml_path):
34
+ statinfo = os.stat(yaml_path)
35
+ if statinfo.st_size >= 10485760:
36
+ print("Removing invalid LDSR YAML file.")
37
+ os.remove(yaml_path)
38
+
39
+ if os.path.exists(old_model_path):
40
+ print("Renaming model from model.pth to model.ckpt")
41
+ os.rename(old_model_path, new_model_path)
42
+
43
+ if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
44
+ model = local_safetensors_path
45
+ else:
46
+ model = local_ckpt_path if local_ckpt_path is not None else load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="model.ckpt", progress=True)
47
+
48
+ yaml = local_yaml_path if local_yaml_path is not None else load_file_from_url(url=self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml", progress=True)
49
+
50
+ try:
51
+ return LDSR(model, yaml)
52
+ except Exception:
53
+ errors.report("Error importing LDSR", exc_info=True)
54
+ return None
55
+
56
+ def do_upscale(self, img, path):
57
+ ldsr = self.load_model(path)
58
+ if ldsr is None:
59
+ print("NO LDSR!")
60
+ return img
61
+ ddim_steps = shared.opts.ldsr_steps
62
+ return ldsr.super_resolution(img, ddim_steps, self.scale)
63
+
64
+
65
+ def on_ui_settings():
66
+ import gradio as gr
67
+
68
+ 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")))
69
+ shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
70
+
71
+
72
+ script_callbacks.on_ui_settings(on_ui_settings)
extensions-builtin/LDSR/sd_hijack_autoencoder.py ADDED
@@ -0,0 +1,293 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ import numpy as np
5
+ import torch
6
+ import pytorch_lightning as pl
7
+ import torch.nn.functional as F
8
+ from contextlib import contextmanager
9
+
10
+ from torch.optim.lr_scheduler import LambdaLR
11
+
12
+ from ldm.modules.ema import LitEma
13
+ from vqvae_quantize import VectorQuantizer2 as VectorQuantizer
14
+ from ldm.modules.diffusionmodules.model import Encoder, Decoder
15
+ from ldm.util import instantiate_from_config
16
+
17
+ import ldm.models.autoencoder
18
+ from packaging import version
19
+
20
+ class VQModel(pl.LightningModule):
21
+ def __init__(self,
22
+ ddconfig,
23
+ lossconfig,
24
+ n_embed,
25
+ embed_dim,
26
+ ckpt_path=None,
27
+ ignore_keys=None,
28
+ image_key="image",
29
+ colorize_nlabels=None,
30
+ monitor=None,
31
+ batch_resize_range=None,
32
+ scheduler_config=None,
33
+ lr_g_factor=1.0,
34
+ remap=None,
35
+ sane_index_shape=False, # tell vector quantizer to return indices as bhw
36
+ use_ema=False
37
+ ):
38
+ super().__init__()
39
+ self.embed_dim = embed_dim
40
+ self.n_embed = n_embed
41
+ self.image_key = image_key
42
+ self.encoder = Encoder(**ddconfig)
43
+ self.decoder = Decoder(**ddconfig)
44
+ self.loss = instantiate_from_config(lossconfig)
45
+ self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25,
46
+ remap=remap,
47
+ sane_index_shape=sane_index_shape)
48
+ self.quant_conv = torch.nn.Conv2d(ddconfig["z_channels"], embed_dim, 1)
49
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
50
+ if colorize_nlabels is not None:
51
+ assert type(colorize_nlabels)==int
52
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
53
+ if monitor is not None:
54
+ self.monitor = monitor
55
+ self.batch_resize_range = batch_resize_range
56
+ if self.batch_resize_range is not None:
57
+ print(f"{self.__class__.__name__}: Using per-batch resizing in range {batch_resize_range}.")
58
+
59
+ self.use_ema = use_ema
60
+ if self.use_ema:
61
+ self.model_ema = LitEma(self)
62
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
63
+
64
+ if ckpt_path is not None:
65
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [])
66
+ self.scheduler_config = scheduler_config
67
+ self.lr_g_factor = lr_g_factor
68
+
69
+ @contextmanager
70
+ def ema_scope(self, context=None):
71
+ if self.use_ema:
72
+ self.model_ema.store(self.parameters())
73
+ self.model_ema.copy_to(self)
74
+ if context is not None:
75
+ print(f"{context}: Switched to EMA weights")
76
+ try:
77
+ yield None
78
+ finally:
79
+ if self.use_ema:
80
+ self.model_ema.restore(self.parameters())
81
+ if context is not None:
82
+ print(f"{context}: Restored training weights")
83
+
84
+ def init_from_ckpt(self, path, ignore_keys=None):
85
+ sd = torch.load(path, map_location="cpu")["state_dict"]
86
+ keys = list(sd.keys())
87
+ for k in keys:
88
+ for ik in ignore_keys or []:
89
+ if k.startswith(ik):
90
+ print("Deleting key {} from state_dict.".format(k))
91
+ del sd[k]
92
+ missing, unexpected = self.load_state_dict(sd, strict=False)
93
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
94
+ if missing:
95
+ print(f"Missing Keys: {missing}")
96
+ if unexpected:
97
+ print(f"Unexpected Keys: {unexpected}")
98
+
99
+ def on_train_batch_end(self, *args, **kwargs):
100
+ if self.use_ema:
101
+ self.model_ema(self)
102
+
103
+ def encode(self, x):
104
+ h = self.encoder(x)
105
+ h = self.quant_conv(h)
106
+ quant, emb_loss, info = self.quantize(h)
107
+ return quant, emb_loss, info
108
+
109
+ def encode_to_prequant(self, x):
110
+ h = self.encoder(x)
111
+ h = self.quant_conv(h)
112
+ return h
113
+
114
+ def decode(self, quant):
115
+ quant = self.post_quant_conv(quant)
116
+ dec = self.decoder(quant)
117
+ return dec
118
+
119
+ def decode_code(self, code_b):
120
+ quant_b = self.quantize.embed_code(code_b)
121
+ dec = self.decode(quant_b)
122
+ return dec
123
+
124
+ def forward(self, input, return_pred_indices=False):
125
+ quant, diff, (_,_,ind) = self.encode(input)
126
+ dec = self.decode(quant)
127
+ if return_pred_indices:
128
+ return dec, diff, ind
129
+ return dec, diff
130
+
131
+ def get_input(self, batch, k):
132
+ x = batch[k]
133
+ if len(x.shape) == 3:
134
+ x = x[..., None]
135
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
136
+ if self.batch_resize_range is not None:
137
+ lower_size = self.batch_resize_range[0]
138
+ upper_size = self.batch_resize_range[1]
139
+ if self.global_step <= 4:
140
+ # do the first few batches with max size to avoid later oom
141
+ new_resize = upper_size
142
+ else:
143
+ new_resize = np.random.choice(np.arange(lower_size, upper_size+16, 16))
144
+ if new_resize != x.shape[2]:
145
+ x = F.interpolate(x, size=new_resize, mode="bicubic")
146
+ x = x.detach()
147
+ return x
148
+
149
+ def training_step(self, batch, batch_idx, optimizer_idx):
150
+ # https://github.com/pytorch/pytorch/issues/37142
151
+ # try not to fool the heuristics
152
+ x = self.get_input(batch, self.image_key)
153
+ xrec, qloss, ind = self(x, return_pred_indices=True)
154
+
155
+ if optimizer_idx == 0:
156
+ # autoencode
157
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
158
+ last_layer=self.get_last_layer(), split="train",
159
+ predicted_indices=ind)
160
+
161
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=True)
162
+ return aeloss
163
+
164
+ if optimizer_idx == 1:
165
+ # discriminator
166
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, optimizer_idx, self.global_step,
167
+ last_layer=self.get_last_layer(), split="train")
168
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=True)
169
+ return discloss
170
+
171
+ def validation_step(self, batch, batch_idx):
172
+ log_dict = self._validation_step(batch, batch_idx)
173
+ with self.ema_scope():
174
+ self._validation_step(batch, batch_idx, suffix="_ema")
175
+ return log_dict
176
+
177
+ def _validation_step(self, batch, batch_idx, suffix=""):
178
+ x = self.get_input(batch, self.image_key)
179
+ xrec, qloss, ind = self(x, return_pred_indices=True)
180
+ aeloss, log_dict_ae = self.loss(qloss, x, xrec, 0,
181
+ self.global_step,
182
+ last_layer=self.get_last_layer(),
183
+ split="val"+suffix,
184
+ predicted_indices=ind
185
+ )
186
+
187
+ discloss, log_dict_disc = self.loss(qloss, x, xrec, 1,
188
+ self.global_step,
189
+ last_layer=self.get_last_layer(),
190
+ split="val"+suffix,
191
+ predicted_indices=ind
192
+ )
193
+ rec_loss = log_dict_ae[f"val{suffix}/rec_loss"]
194
+ self.log(f"val{suffix}/rec_loss", rec_loss,
195
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
196
+ self.log(f"val{suffix}/aeloss", aeloss,
197
+ prog_bar=True, logger=True, on_step=False, on_epoch=True, sync_dist=True)
198
+ if version.parse(pl.__version__) >= version.parse('1.4.0'):
199
+ del log_dict_ae[f"val{suffix}/rec_loss"]
200
+ self.log_dict(log_dict_ae)
201
+ self.log_dict(log_dict_disc)
202
+ return self.log_dict
203
+
204
+ def configure_optimizers(self):
205
+ lr_d = self.learning_rate
206
+ lr_g = self.lr_g_factor*self.learning_rate
207
+ print("lr_d", lr_d)
208
+ print("lr_g", lr_g)
209
+ opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
210
+ list(self.decoder.parameters())+
211
+ list(self.quantize.parameters())+
212
+ list(self.quant_conv.parameters())+
213
+ list(self.post_quant_conv.parameters()),
214
+ lr=lr_g, betas=(0.5, 0.9))
215
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
216
+ lr=lr_d, betas=(0.5, 0.9))
217
+
218
+ if self.scheduler_config is not None:
219
+ scheduler = instantiate_from_config(self.scheduler_config)
220
+
221
+ print("Setting up LambdaLR scheduler...")
222
+ scheduler = [
223
+ {
224
+ 'scheduler': LambdaLR(opt_ae, lr_lambda=scheduler.schedule),
225
+ 'interval': 'step',
226
+ 'frequency': 1
227
+ },
228
+ {
229
+ 'scheduler': LambdaLR(opt_disc, lr_lambda=scheduler.schedule),
230
+ 'interval': 'step',
231
+ 'frequency': 1
232
+ },
233
+ ]
234
+ return [opt_ae, opt_disc], scheduler
235
+ return [opt_ae, opt_disc], []
236
+
237
+ def get_last_layer(self):
238
+ return self.decoder.conv_out.weight
239
+
240
+ def log_images(self, batch, only_inputs=False, plot_ema=False, **kwargs):
241
+ log = {}
242
+ x = self.get_input(batch, self.image_key)
243
+ x = x.to(self.device)
244
+ if only_inputs:
245
+ log["inputs"] = x
246
+ return log
247
+ xrec, _ = self(x)
248
+ if x.shape[1] > 3:
249
+ # colorize with random projection
250
+ assert xrec.shape[1] > 3
251
+ x = self.to_rgb(x)
252
+ xrec = self.to_rgb(xrec)
253
+ log["inputs"] = x
254
+ log["reconstructions"] = xrec
255
+ if plot_ema:
256
+ with self.ema_scope():
257
+ xrec_ema, _ = self(x)
258
+ if x.shape[1] > 3:
259
+ xrec_ema = self.to_rgb(xrec_ema)
260
+ log["reconstructions_ema"] = xrec_ema
261
+ return log
262
+
263
+ def to_rgb(self, x):
264
+ assert self.image_key == "segmentation"
265
+ if not hasattr(self, "colorize"):
266
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
267
+ x = F.conv2d(x, weight=self.colorize)
268
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
269
+ return x
270
+
271
+
272
+ class VQModelInterface(VQModel):
273
+ def __init__(self, embed_dim, *args, **kwargs):
274
+ super().__init__(*args, embed_dim=embed_dim, **kwargs)
275
+ self.embed_dim = embed_dim
276
+
277
+ def encode(self, x):
278
+ h = self.encoder(x)
279
+ h = self.quant_conv(h)
280
+ return h
281
+
282
+ def decode(self, h, force_not_quantize=False):
283
+ # also go through quantization layer
284
+ if not force_not_quantize:
285
+ quant, emb_loss, info = self.quantize(h)
286
+ else:
287
+ quant = h
288
+ quant = self.post_quant_conv(quant)
289
+ dec = self.decoder(quant)
290
+ return dec
291
+
292
+ ldm.models.autoencoder.VQModel = VQModel
293
+ ldm.models.autoencoder.VQModelInterface = VQModelInterface
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py ADDED
@@ -0,0 +1,1443 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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=None,
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 or [], 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=None, 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 or []:
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 missing:
199
+ print(f"Missing Keys: {missing}")
200
+ if unexpected:
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 = {}
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 = []
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__(*args, conditioning_key=conditioning_key, **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 Exception:
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 apply_model(self, x_noisy, t, cond, return_ids=False):
881
+
882
+ if isinstance(cond, dict):
883
+ # hybrid case, cond is exptected to be a dict
884
+ pass
885
+ else:
886
+ if not isinstance(cond, list):
887
+ cond = [cond]
888
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
889
+ cond = {key: cond}
890
+
891
+ if hasattr(self, "split_input_params"):
892
+ assert len(cond) == 1 # todo can only deal with one conditioning atm
893
+ assert not return_ids
894
+ ks = self.split_input_params["ks"] # eg. (128, 128)
895
+ stride = self.split_input_params["stride"] # eg. (64, 64)
896
+
897
+ h, w = x_noisy.shape[-2:]
898
+
899
+ fold, unfold, normalization, weighting = self.get_fold_unfold(x_noisy, ks, stride)
900
+
901
+ z = unfold(x_noisy) # (bn, nc * prod(**ks), L)
902
+ # Reshape to img shape
903
+ z = z.view((z.shape[0], -1, ks[0], ks[1], z.shape[-1])) # (bn, nc, ks[0], ks[1], L )
904
+ z_list = [z[:, :, :, :, i] for i in range(z.shape[-1])]
905
+
906
+ if self.cond_stage_key in ["image", "LR_image", "segmentation",
907
+ 'bbox_img'] and self.model.conditioning_key: # todo check for completeness
908
+ c_key = next(iter(cond.keys())) # get key
909
+ c = next(iter(cond.values())) # get value
910
+ assert (len(c) == 1) # todo extend to list with more than one elem
911
+ c = c[0] # get element
912
+
913
+ c = unfold(c)
914
+ c = c.view((c.shape[0], -1, ks[0], ks[1], c.shape[-1])) # (bn, nc, ks[0], ks[1], L )
915
+
916
+ cond_list = [{c_key: [c[:, :, :, :, i]]} for i in range(c.shape[-1])]
917
+
918
+ elif self.cond_stage_key == 'coordinates_bbox':
919
+ assert 'original_image_size' in self.split_input_params, 'BoudingBoxRescaling is missing original_image_size'
920
+
921
+ # assuming padding of unfold is always 0 and its dilation is always 1
922
+ n_patches_per_row = int((w - ks[0]) / stride[0] + 1)
923
+ full_img_h, full_img_w = self.split_input_params['original_image_size']
924
+ # as we are operating on latents, we need the factor from the original image size to the
925
+ # spatial latent size to properly rescale the crops for regenerating the bbox annotations
926
+ num_downs = self.first_stage_model.encoder.num_resolutions - 1
927
+ rescale_latent = 2 ** (num_downs)
928
+
929
+ # get top left postions of patches as conforming for the bbbox tokenizer, therefore we
930
+ # need to rescale the tl patch coordinates to be in between (0,1)
931
+ tl_patch_coordinates = [(rescale_latent * stride[0] * (patch_nr % n_patches_per_row) / full_img_w,
932
+ rescale_latent * stride[1] * (patch_nr // n_patches_per_row) / full_img_h)
933
+ for patch_nr in range(z.shape[-1])]
934
+
935
+ # patch_limits are tl_coord, width and height coordinates as (x_tl, y_tl, h, w)
936
+ patch_limits = [(x_tl, y_tl,
937
+ rescale_latent * ks[0] / full_img_w,
938
+ rescale_latent * ks[1] / full_img_h) for x_tl, y_tl in tl_patch_coordinates]
939
+ # 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]
940
+
941
+ # tokenize crop coordinates for the bounding boxes of the respective patches
942
+ patch_limits_tknzd = [torch.LongTensor(self.bbox_tokenizer._crop_encoder(bbox))[None].to(self.device)
943
+ for bbox in patch_limits] # list of length l with tensors of shape (1, 2)
944
+ print(patch_limits_tknzd[0].shape)
945
+ # cut tknzd crop position from conditioning
946
+ assert isinstance(cond, dict), 'cond must be dict to be fed into model'
947
+ cut_cond = cond['c_crossattn'][0][..., :-2].to(self.device)
948
+ print(cut_cond.shape)
949
+
950
+ adapted_cond = torch.stack([torch.cat([cut_cond, p], dim=1) for p in patch_limits_tknzd])
951
+ adapted_cond = rearrange(adapted_cond, 'l b n -> (l b) n')
952
+ print(adapted_cond.shape)
953
+ adapted_cond = self.get_learned_conditioning(adapted_cond)
954
+ print(adapted_cond.shape)
955
+ adapted_cond = rearrange(adapted_cond, '(l b) n d -> l b n d', l=z.shape[-1])
956
+ print(adapted_cond.shape)
957
+
958
+ cond_list = [{'c_crossattn': [e]} for e in adapted_cond]
959
+
960
+ else:
961
+ cond_list = [cond for i in range(z.shape[-1])] # Todo make this more efficient
962
+
963
+ # apply model by loop over crops
964
+ output_list = [self.model(z_list[i], t, **cond_list[i]) for i in range(z.shape[-1])]
965
+ assert not isinstance(output_list[0],
966
+ tuple) # todo cant deal with multiple model outputs check this never happens
967
+
968
+ o = torch.stack(output_list, axis=-1)
969
+ o = o * weighting
970
+ # Reverse reshape to img shape
971
+ o = o.view((o.shape[0], -1, o.shape[-1])) # (bn, nc * ks[0] * ks[1], L)
972
+ # stitch crops together
973
+ x_recon = fold(o) / normalization
974
+
975
+ else:
976
+ x_recon = self.model(x_noisy, t, **cond)
977
+
978
+ if isinstance(x_recon, tuple) and not return_ids:
979
+ return x_recon[0]
980
+ else:
981
+ return x_recon
982
+
983
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
984
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
985
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
986
+
987
+ def _prior_bpd(self, x_start):
988
+ """
989
+ Get the prior KL term for the variational lower-bound, measured in
990
+ bits-per-dim.
991
+ This term can't be optimized, as it only depends on the encoder.
992
+ :param x_start: the [N x C x ...] tensor of inputs.
993
+ :return: a batch of [N] KL values (in bits), one per batch element.
994
+ """
995
+ batch_size = x_start.shape[0]
996
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
997
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
998
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
999
+ return mean_flat(kl_prior) / np.log(2.0)
1000
+
1001
+ def p_losses(self, x_start, cond, t, noise=None):
1002
+ noise = default(noise, lambda: torch.randn_like(x_start))
1003
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
1004
+ model_output = self.apply_model(x_noisy, t, cond)
1005
+
1006
+ loss_dict = {}
1007
+ prefix = 'train' if self.training else 'val'
1008
+
1009
+ if self.parameterization == "x0":
1010
+ target = x_start
1011
+ elif self.parameterization == "eps":
1012
+ target = noise
1013
+ else:
1014
+ raise NotImplementedError()
1015
+
1016
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
1017
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
1018
+
1019
+ logvar_t = self.logvar[t].to(self.device)
1020
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
1021
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
1022
+ if self.learn_logvar:
1023
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
1024
+ loss_dict.update({'logvar': self.logvar.data.mean()})
1025
+
1026
+ loss = self.l_simple_weight * loss.mean()
1027
+
1028
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
1029
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
1030
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
1031
+ loss += (self.original_elbo_weight * loss_vlb)
1032
+ loss_dict.update({f'{prefix}/loss': loss})
1033
+
1034
+ return loss, loss_dict
1035
+
1036
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
1037
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
1038
+ t_in = t
1039
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
1040
+
1041
+ if score_corrector is not None:
1042
+ assert self.parameterization == "eps"
1043
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
1044
+
1045
+ if return_codebook_ids:
1046
+ model_out, logits = model_out
1047
+
1048
+ if self.parameterization == "eps":
1049
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
1050
+ elif self.parameterization == "x0":
1051
+ x_recon = model_out
1052
+ else:
1053
+ raise NotImplementedError()
1054
+
1055
+ if clip_denoised:
1056
+ x_recon.clamp_(-1., 1.)
1057
+ if quantize_denoised:
1058
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
1059
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
1060
+ if return_codebook_ids:
1061
+ return model_mean, posterior_variance, posterior_log_variance, logits
1062
+ elif return_x0:
1063
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
1064
+ else:
1065
+ return model_mean, posterior_variance, posterior_log_variance
1066
+
1067
+ @torch.no_grad()
1068
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
1069
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
1070
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
1071
+ b, *_, device = *x.shape, x.device
1072
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
1073
+ return_codebook_ids=return_codebook_ids,
1074
+ quantize_denoised=quantize_denoised,
1075
+ return_x0=return_x0,
1076
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
1077
+ if return_codebook_ids:
1078
+ raise DeprecationWarning("Support dropped.")
1079
+ model_mean, _, model_log_variance, logits = outputs
1080
+ elif return_x0:
1081
+ model_mean, _, model_log_variance, x0 = outputs
1082
+ else:
1083
+ model_mean, _, model_log_variance = outputs
1084
+
1085
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
1086
+ if noise_dropout > 0.:
1087
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
1088
+ # no noise when t == 0
1089
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
1090
+
1091
+ if return_codebook_ids:
1092
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
1093
+ if return_x0:
1094
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
1095
+ else:
1096
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
1097
+
1098
+ @torch.no_grad()
1099
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
1100
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
1101
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
1102
+ log_every_t=None):
1103
+ if not log_every_t:
1104
+ log_every_t = self.log_every_t
1105
+ timesteps = self.num_timesteps
1106
+ if batch_size is not None:
1107
+ b = batch_size if batch_size is not None else shape[0]
1108
+ shape = [batch_size] + list(shape)
1109
+ else:
1110
+ b = batch_size = shape[0]
1111
+ if x_T is None:
1112
+ img = torch.randn(shape, device=self.device)
1113
+ else:
1114
+ img = x_T
1115
+ intermediates = []
1116
+ if cond is not None:
1117
+ if isinstance(cond, dict):
1118
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
1119
+ [x[:batch_size] for x in cond[key]] for key in cond}
1120
+ else:
1121
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
1122
+
1123
+ if start_T is not None:
1124
+ timesteps = min(timesteps, start_T)
1125
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
1126
+ total=timesteps) if verbose else reversed(
1127
+ range(0, timesteps))
1128
+ if type(temperature) == float:
1129
+ temperature = [temperature] * timesteps
1130
+
1131
+ for i in iterator:
1132
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
1133
+ if self.shorten_cond_schedule:
1134
+ assert self.model.conditioning_key != 'hybrid'
1135
+ tc = self.cond_ids[ts].to(cond.device)
1136
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
1137
+
1138
+ img, x0_partial = self.p_sample(img, cond, ts,
1139
+ clip_denoised=self.clip_denoised,
1140
+ quantize_denoised=quantize_denoised, return_x0=True,
1141
+ temperature=temperature[i], noise_dropout=noise_dropout,
1142
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
1143
+ if mask is not None:
1144
+ assert x0 is not None
1145
+ img_orig = self.q_sample(x0, ts)
1146
+ img = img_orig * mask + (1. - mask) * img
1147
+
1148
+ if i % log_every_t == 0 or i == timesteps - 1:
1149
+ intermediates.append(x0_partial)
1150
+ if callback:
1151
+ callback(i)
1152
+ if img_callback:
1153
+ img_callback(img, i)
1154
+ return img, intermediates
1155
+
1156
+ @torch.no_grad()
1157
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
1158
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
1159
+ mask=None, x0=None, img_callback=None, start_T=None,
1160
+ log_every_t=None):
1161
+
1162
+ if not log_every_t:
1163
+ log_every_t = self.log_every_t
1164
+ device = self.betas.device
1165
+ b = shape[0]
1166
+ if x_T is None:
1167
+ img = torch.randn(shape, device=device)
1168
+ else:
1169
+ img = x_T
1170
+
1171
+ intermediates = [img]
1172
+ if timesteps is None:
1173
+ timesteps = self.num_timesteps
1174
+
1175
+ if start_T is not None:
1176
+ timesteps = min(timesteps, start_T)
1177
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
1178
+ range(0, timesteps))
1179
+
1180
+ if mask is not None:
1181
+ assert x0 is not None
1182
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
1183
+
1184
+ for i in iterator:
1185
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
1186
+ if self.shorten_cond_schedule:
1187
+ assert self.model.conditioning_key != 'hybrid'
1188
+ tc = self.cond_ids[ts].to(cond.device)
1189
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
1190
+
1191
+ img = self.p_sample(img, cond, ts,
1192
+ clip_denoised=self.clip_denoised,
1193
+ quantize_denoised=quantize_denoised)
1194
+ if mask is not None:
1195
+ img_orig = self.q_sample(x0, ts)
1196
+ img = img_orig * mask + (1. - mask) * img
1197
+
1198
+ if i % log_every_t == 0 or i == timesteps - 1:
1199
+ intermediates.append(img)
1200
+ if callback:
1201
+ callback(i)
1202
+ if img_callback:
1203
+ img_callback(img, i)
1204
+
1205
+ if return_intermediates:
1206
+ return img, intermediates
1207
+ return img
1208
+
1209
+ @torch.no_grad()
1210
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
1211
+ verbose=True, timesteps=None, quantize_denoised=False,
1212
+ mask=None, x0=None, shape=None,**kwargs):
1213
+ if shape is None:
1214
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
1215
+ if cond is not None:
1216
+ if isinstance(cond, dict):
1217
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
1218
+ [x[:batch_size] for x in cond[key]] for key in cond}
1219
+ else:
1220
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
1221
+ return self.p_sample_loop(cond,
1222
+ shape,
1223
+ return_intermediates=return_intermediates, x_T=x_T,
1224
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
1225
+ mask=mask, x0=x0)
1226
+
1227
+ @torch.no_grad()
1228
+ def sample_log(self,cond,batch_size,ddim, ddim_steps,**kwargs):
1229
+
1230
+ if ddim:
1231
+ ddim_sampler = DDIMSampler(self)
1232
+ shape = (self.channels, self.image_size, self.image_size)
1233
+ samples, intermediates =ddim_sampler.sample(ddim_steps,batch_size,
1234
+ shape,cond,verbose=False,**kwargs)
1235
+
1236
+ else:
1237
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
1238
+ return_intermediates=True,**kwargs)
1239
+
1240
+ return samples, intermediates
1241
+
1242
+
1243
+ @torch.no_grad()
1244
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
1245
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
1246
+ plot_diffusion_rows=True, **kwargs):
1247
+
1248
+ use_ddim = ddim_steps is not None
1249
+
1250
+ log = {}
1251
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
1252
+ return_first_stage_outputs=True,
1253
+ force_c_encode=True,
1254
+ return_original_cond=True,
1255
+ bs=N)
1256
+ N = min(x.shape[0], N)
1257
+ n_row = min(x.shape[0], n_row)
1258
+ log["inputs"] = x
1259
+ log["reconstruction"] = xrec
1260
+ if self.model.conditioning_key is not None:
1261
+ if hasattr(self.cond_stage_model, "decode"):
1262
+ xc = self.cond_stage_model.decode(c)
1263
+ log["conditioning"] = xc
1264
+ elif self.cond_stage_key in ["caption"]:
1265
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["caption"])
1266
+ log["conditioning"] = xc
1267
+ elif self.cond_stage_key == 'class_label':
1268
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"])
1269
+ log['conditioning'] = xc
1270
+ elif isimage(xc):
1271
+ log["conditioning"] = xc
1272
+ if ismap(xc):
1273
+ log["original_conditioning"] = self.to_rgb(xc)
1274
+
1275
+ if plot_diffusion_rows:
1276
+ # get diffusion row
1277
+ diffusion_row = []
1278
+ z_start = z[:n_row]
1279
+ for t in range(self.num_timesteps):
1280
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
1281
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
1282
+ t = t.to(self.device).long()
1283
+ noise = torch.randn_like(z_start)
1284
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
1285
+ diffusion_row.append(self.decode_first_stage(z_noisy))
1286
+
1287
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
1288
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
1289
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
1290
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
1291
+ log["diffusion_row"] = diffusion_grid
1292
+
1293
+ if sample:
1294
+ # get denoise row
1295
+ with self.ema_scope("Plotting"):
1296
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
1297
+ ddim_steps=ddim_steps,eta=ddim_eta)
1298
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
1299
+ x_samples = self.decode_first_stage(samples)
1300
+ log["samples"] = x_samples
1301
+ if plot_denoise_rows:
1302
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
1303
+ log["denoise_row"] = denoise_grid
1304
+
1305
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
1306
+ self.first_stage_model, IdentityFirstStage):
1307
+ # also display when quantizing x0 while sampling
1308
+ with self.ema_scope("Plotting Quantized Denoised"):
1309
+ samples, z_denoise_row = self.sample_log(cond=c,batch_size=N,ddim=use_ddim,
1310
+ ddim_steps=ddim_steps,eta=ddim_eta,
1311
+ quantize_denoised=True)
1312
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
1313
+ # quantize_denoised=True)
1314
+ x_samples = self.decode_first_stage(samples.to(self.device))
1315
+ log["samples_x0_quantized"] = x_samples
1316
+
1317
+ if inpaint:
1318
+ # make a simple center square
1319
+ h, w = z.shape[2], z.shape[3]
1320
+ mask = torch.ones(N, h, w).to(self.device)
1321
+ # zeros will be filled in
1322
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
1323
+ mask = mask[:, None, ...]
1324
+ with self.ema_scope("Plotting Inpaint"):
1325
+
1326
+ samples, _ = self.sample_log(cond=c,batch_size=N,ddim=use_ddim, eta=ddim_eta,
1327
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
1328
+ x_samples = self.decode_first_stage(samples.to(self.device))
1329
+ log["samples_inpainting"] = x_samples
1330
+ log["mask"] = mask
1331
+
1332
+ # outpaint
1333
+ with self.ema_scope("Plotting Outpaint"):
1334
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,eta=ddim_eta,
1335
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
1336
+ x_samples = self.decode_first_stage(samples.to(self.device))
1337
+ log["samples_outpainting"] = x_samples
1338
+
1339
+ if plot_progressive_rows:
1340
+ with self.ema_scope("Plotting Progressives"):
1341
+ img, progressives = self.progressive_denoising(c,
1342
+ shape=(self.channels, self.image_size, self.image_size),
1343
+ batch_size=N)
1344
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
1345
+ log["progressive_row"] = prog_row
1346
+
1347
+ if return_keys:
1348
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
1349
+ return log
1350
+ else:
1351
+ return {key: log[key] for key in return_keys}
1352
+ return log
1353
+
1354
+ def configure_optimizers(self):
1355
+ lr = self.learning_rate
1356
+ params = list(self.model.parameters())
1357
+ if self.cond_stage_trainable:
1358
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
1359
+ params = params + list(self.cond_stage_model.parameters())
1360
+ if self.learn_logvar:
1361
+ print('Diffusion model optimizing logvar')
1362
+ params.append(self.logvar)
1363
+ opt = torch.optim.AdamW(params, lr=lr)
1364
+ if self.use_scheduler:
1365
+ assert 'target' in self.scheduler_config
1366
+ scheduler = instantiate_from_config(self.scheduler_config)
1367
+
1368
+ print("Setting up LambdaLR scheduler...")
1369
+ scheduler = [
1370
+ {
1371
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
1372
+ 'interval': 'step',
1373
+ 'frequency': 1
1374
+ }]
1375
+ return [opt], scheduler
1376
+ return opt
1377
+
1378
+ @torch.no_grad()
1379
+ def to_rgb(self, x):
1380
+ x = x.float()
1381
+ if not hasattr(self, "colorize"):
1382
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
1383
+ x = nn.functional.conv2d(x, weight=self.colorize)
1384
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
1385
+ return x
1386
+
1387
+
1388
+ class DiffusionWrapperV1(pl.LightningModule):
1389
+ def __init__(self, diff_model_config, conditioning_key):
1390
+ super().__init__()
1391
+ self.diffusion_model = instantiate_from_config(diff_model_config)
1392
+ self.conditioning_key = conditioning_key
1393
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm']
1394
+
1395
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None):
1396
+ if self.conditioning_key is None:
1397
+ out = self.diffusion_model(x, t)
1398
+ elif self.conditioning_key == 'concat':
1399
+ xc = torch.cat([x] + c_concat, dim=1)
1400
+ out = self.diffusion_model(xc, t)
1401
+ elif self.conditioning_key == 'crossattn':
1402
+ cc = torch.cat(c_crossattn, 1)
1403
+ out = self.diffusion_model(x, t, context=cc)
1404
+ elif self.conditioning_key == 'hybrid':
1405
+ xc = torch.cat([x] + c_concat, dim=1)
1406
+ cc = torch.cat(c_crossattn, 1)
1407
+ out = self.diffusion_model(xc, t, context=cc)
1408
+ elif self.conditioning_key == 'adm':
1409
+ cc = c_crossattn[0]
1410
+ out = self.diffusion_model(x, t, y=cc)
1411
+ else:
1412
+ raise NotImplementedError()
1413
+
1414
+ return out
1415
+
1416
+
1417
+ class Layout2ImgDiffusionV1(LatentDiffusionV1):
1418
+ # TODO: move all layout-specific hacks to this class
1419
+ def __init__(self, cond_stage_key, *args, **kwargs):
1420
+ assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
1421
+ super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
1422
+
1423
+ def log_images(self, batch, N=8, *args, **kwargs):
1424
+ logs = super().log_images(*args, batch=batch, N=N, **kwargs)
1425
+
1426
+ key = 'train' if self.training else 'validation'
1427
+ dset = self.trainer.datamodule.datasets[key]
1428
+ mapper = dset.conditional_builders[self.cond_stage_key]
1429
+
1430
+ bbox_imgs = []
1431
+ map_fn = lambda catno: dset.get_textual_label(dset.get_category_id(catno))
1432
+ for tknzd_bbox in batch[self.cond_stage_key][:N]:
1433
+ bboximg = mapper.plot(tknzd_bbox.detach().cpu(), map_fn, (256, 256))
1434
+ bbox_imgs.append(bboximg)
1435
+
1436
+ cond_img = torch.stack(bbox_imgs, dim=0)
1437
+ logs['bbox_image'] = cond_img
1438
+ return logs
1439
+
1440
+ ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
1441
+ ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
1442
+ ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
1443
+ ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1
extensions-builtin/LDSR/vqvae_quantize.py ADDED
@@ -0,0 +1,147 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
2
+ # where the license is as follows:
3
+ #
4
+ # Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
5
+ #
6
+ # Permission is hereby granted, free of charge, to any person obtaining a copy
7
+ # of this software and associated documentation files (the "Software"), to deal
8
+ # in the Software without restriction, including without limitation the rights
9
+ # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
10
+ # copies of the Software, and to permit persons to whom the Software is
11
+ # furnished to do so, subject to the following conditions:
12
+ #
13
+ # The above copyright notice and this permission notice shall be included in all
14
+ # copies or substantial portions of the Software.
15
+ #
16
+ # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
17
+ # EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
18
+ # MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
19
+ # IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
20
+ # DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
21
+ # OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
22
+ # OR OTHER DEALINGS IN THE SOFTWARE./
23
+
24
+ import torch
25
+ import torch.nn as nn
26
+ import numpy as np
27
+ from einops import rearrange
28
+
29
+
30
+ class VectorQuantizer2(nn.Module):
31
+ """
32
+ Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
33
+ avoids costly matrix multiplications and allows for post-hoc remapping of indices.
34
+ """
35
+
36
+ # NOTE: due to a bug the beta term was applied to the wrong term. for
37
+ # backwards compatibility we use the buggy version by default, but you can
38
+ # specify legacy=False to fix it.
39
+ def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
40
+ sane_index_shape=False, legacy=True):
41
+ super().__init__()
42
+ self.n_e = n_e
43
+ self.e_dim = e_dim
44
+ self.beta = beta
45
+ self.legacy = legacy
46
+
47
+ self.embedding = nn.Embedding(self.n_e, self.e_dim)
48
+ self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
49
+
50
+ self.remap = remap
51
+ if self.remap is not None:
52
+ self.register_buffer("used", torch.tensor(np.load(self.remap)))
53
+ self.re_embed = self.used.shape[0]
54
+ self.unknown_index = unknown_index # "random" or "extra" or integer
55
+ if self.unknown_index == "extra":
56
+ self.unknown_index = self.re_embed
57
+ self.re_embed = self.re_embed + 1
58
+ print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
59
+ f"Using {self.unknown_index} for unknown indices.")
60
+ else:
61
+ self.re_embed = n_e
62
+
63
+ self.sane_index_shape = sane_index_shape
64
+
65
+ def remap_to_used(self, inds):
66
+ ishape = inds.shape
67
+ assert len(ishape) > 1
68
+ inds = inds.reshape(ishape[0], -1)
69
+ used = self.used.to(inds)
70
+ match = (inds[:, :, None] == used[None, None, ...]).long()
71
+ new = match.argmax(-1)
72
+ unknown = match.sum(2) < 1
73
+ if self.unknown_index == "random":
74
+ new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
75
+ else:
76
+ new[unknown] = self.unknown_index
77
+ return new.reshape(ishape)
78
+
79
+ def unmap_to_all(self, inds):
80
+ ishape = inds.shape
81
+ assert len(ishape) > 1
82
+ inds = inds.reshape(ishape[0], -1)
83
+ used = self.used.to(inds)
84
+ if self.re_embed > self.used.shape[0]: # extra token
85
+ inds[inds >= self.used.shape[0]] = 0 # simply set to zero
86
+ back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
87
+ return back.reshape(ishape)
88
+
89
+ def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
90
+ assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
91
+ assert rescale_logits is False, "Only for interface compatible with Gumbel"
92
+ assert return_logits is False, "Only for interface compatible with Gumbel"
93
+ # reshape z -> (batch, height, width, channel) and flatten
94
+ z = rearrange(z, 'b c h w -> b h w c').contiguous()
95
+ z_flattened = z.view(-1, self.e_dim)
96
+ # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
97
+
98
+ d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
99
+ torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
100
+ torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
101
+
102
+ min_encoding_indices = torch.argmin(d, dim=1)
103
+ z_q = self.embedding(min_encoding_indices).view(z.shape)
104
+ perplexity = None
105
+ min_encodings = None
106
+
107
+ # compute loss for embedding
108
+ if not self.legacy:
109
+ loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
110
+ torch.mean((z_q - z.detach()) ** 2)
111
+ else:
112
+ loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
113
+ torch.mean((z_q - z.detach()) ** 2)
114
+
115
+ # preserve gradients
116
+ z_q = z + (z_q - z).detach()
117
+
118
+ # reshape back to match original input shape
119
+ z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
120
+
121
+ if self.remap is not None:
122
+ min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
123
+ min_encoding_indices = self.remap_to_used(min_encoding_indices)
124
+ min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
125
+
126
+ if self.sane_index_shape:
127
+ min_encoding_indices = min_encoding_indices.reshape(
128
+ z_q.shape[0], z_q.shape[2], z_q.shape[3])
129
+
130
+ return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
131
+
132
+ def get_codebook_entry(self, indices, shape):
133
+ # shape specifying (batch, height, width, channel)
134
+ if self.remap is not None:
135
+ indices = indices.reshape(shape[0], -1) # add batch axis
136
+ indices = self.unmap_to_all(indices)
137
+ indices = indices.reshape(-1) # flatten again
138
+
139
+ # get quantized latent vectors
140
+ z_q = self.embedding(indices)
141
+
142
+ if shape is not None:
143
+ z_q = z_q.view(shape)
144
+ # reshape back to match original input shape
145
+ z_q = z_q.permute(0, 3, 1, 2).contiguous()
146
+
147
+ return z_q
extensions-builtin/Lora/extra_networks_lora.py ADDED
@@ -0,0 +1,45 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 not any(x for x in params_list if x.items[0] == additional):
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 params.items
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
+ if shared.opts.lora_add_hashes_to_infotext:
27
+ lora_hashes = []
28
+ for item in lora.loaded_loras:
29
+ shorthash = item.lora_on_disk.shorthash
30
+ if not shorthash:
31
+ continue
32
+
33
+ alias = item.mentioned_name
34
+ if not alias:
35
+ continue
36
+
37
+ alias = alias.replace(":", "").replace(",", "")
38
+
39
+ lora_hashes.append(f"{alias}: {shorthash}")
40
+
41
+ if lora_hashes:
42
+ p.extra_generation_params["Lora hashes"] = ", ".join(lora_hashes)
43
+
44
+ def deactivate(self, p):
45
+ pass
extensions-builtin/Lora/lora.py ADDED
@@ -0,0 +1,506 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import re
3
+ import torch
4
+ from typing import Union
5
+
6
+ from modules import shared, devices, sd_models, errors, scripts, sd_hijack, hashes
7
+
8
+ metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
9
+
10
+ re_digits = re.compile(r"\d+")
11
+ re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
12
+ re_compiled = {}
13
+
14
+ suffix_conversion = {
15
+ "attentions": {},
16
+ "resnets": {
17
+ "conv1": "in_layers_2",
18
+ "conv2": "out_layers_3",
19
+ "time_emb_proj": "emb_layers_1",
20
+ "conv_shortcut": "skip_connection",
21
+ }
22
+ }
23
+
24
+
25
+ def convert_diffusers_name_to_compvis(key, is_sd2):
26
+ def match(match_list, regex_text):
27
+ regex = re_compiled.get(regex_text)
28
+ if regex is None:
29
+ regex = re.compile(regex_text)
30
+ re_compiled[regex_text] = regex
31
+
32
+ r = re.match(regex, key)
33
+ if not r:
34
+ return False
35
+
36
+ match_list.clear()
37
+ match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
38
+ return True
39
+
40
+ m = []
41
+
42
+ if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
43
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
44
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
45
+
46
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
47
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
48
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
49
+
50
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
51
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
52
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
53
+
54
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
55
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
56
+
57
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
58
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
59
+
60
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
61
+ if is_sd2:
62
+ if 'mlp_fc1' in m[1]:
63
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
64
+ elif 'mlp_fc2' in m[1]:
65
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
66
+ else:
67
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
68
+
69
+ return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
70
+
71
+ return key
72
+
73
+
74
+ class LoraOnDisk:
75
+ def __init__(self, name, filename):
76
+ self.name = name
77
+ self.filename = filename
78
+ self.metadata = {}
79
+ self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
80
+
81
+ if self.is_safetensors:
82
+ try:
83
+ self.metadata = sd_models.read_metadata_from_safetensors(filename)
84
+ except Exception as e:
85
+ errors.display(e, f"reading lora {filename}")
86
+
87
+ if self.metadata:
88
+ m = {}
89
+ for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
90
+ m[k] = v
91
+
92
+ self.metadata = m
93
+
94
+ 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
95
+ self.alias = self.metadata.get('ss_output_name', self.name)
96
+
97
+ self.hash = None
98
+ self.shorthash = None
99
+ self.set_hash(
100
+ self.metadata.get('sshs_model_hash') or
101
+ hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
102
+ ''
103
+ )
104
+
105
+ def set_hash(self, v):
106
+ self.hash = v
107
+ self.shorthash = self.hash[0:12]
108
+
109
+ if self.shorthash:
110
+ available_lora_hash_lookup[self.shorthash] = self
111
+
112
+ def read_hash(self):
113
+ if not self.hash:
114
+ self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
115
+
116
+ def get_alias(self):
117
+ if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in forbidden_lora_aliases:
118
+ return self.name
119
+ else:
120
+ return self.alias
121
+
122
+
123
+ class LoraModule:
124
+ def __init__(self, name, lora_on_disk: LoraOnDisk):
125
+ self.name = name
126
+ self.lora_on_disk = lora_on_disk
127
+ self.multiplier = 1.0
128
+ self.modules = {}
129
+ self.mtime = None
130
+
131
+ self.mentioned_name = None
132
+ """the text that was used to add lora to prompt - can be either name or an alias"""
133
+
134
+
135
+ class LoraUpDownModule:
136
+ def __init__(self):
137
+ self.up = None
138
+ self.down = None
139
+ self.alpha = None
140
+
141
+
142
+ def assign_lora_names_to_compvis_modules(sd_model):
143
+ lora_layer_mapping = {}
144
+
145
+ for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
146
+ lora_name = name.replace(".", "_")
147
+ lora_layer_mapping[lora_name] = module
148
+ module.lora_layer_name = lora_name
149
+
150
+ for name, module in shared.sd_model.model.named_modules():
151
+ lora_name = name.replace(".", "_")
152
+ lora_layer_mapping[lora_name] = module
153
+ module.lora_layer_name = lora_name
154
+
155
+ sd_model.lora_layer_mapping = lora_layer_mapping
156
+
157
+
158
+ def load_lora(name, lora_on_disk):
159
+ lora = LoraModule(name, lora_on_disk)
160
+ lora.mtime = os.path.getmtime(lora_on_disk.filename)
161
+
162
+ sd = sd_models.read_state_dict(lora_on_disk.filename)
163
+
164
+ # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
165
+ if not hasattr(shared.sd_model, 'lora_layer_mapping'):
166
+ assign_lora_names_to_compvis_modules(shared.sd_model)
167
+
168
+ keys_failed_to_match = {}
169
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping
170
+
171
+ for key_diffusers, weight in sd.items():
172
+ key_diffusers_without_lora_parts, lora_key = key_diffusers.split(".", 1)
173
+ key = convert_diffusers_name_to_compvis(key_diffusers_without_lora_parts, is_sd2)
174
+
175
+ sd_module = shared.sd_model.lora_layer_mapping.get(key, None)
176
+
177
+ if sd_module is None:
178
+ m = re_x_proj.match(key)
179
+ if m:
180
+ sd_module = shared.sd_model.lora_layer_mapping.get(m.group(1), None)
181
+
182
+ if sd_module is None:
183
+ keys_failed_to_match[key_diffusers] = key
184
+ continue
185
+
186
+ lora_module = lora.modules.get(key, None)
187
+ if lora_module is None:
188
+ lora_module = LoraUpDownModule()
189
+ lora.modules[key] = lora_module
190
+
191
+ if lora_key == "alpha":
192
+ lora_module.alpha = weight.item()
193
+ continue
194
+
195
+ if type(sd_module) == torch.nn.Linear:
196
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
197
+ elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:
198
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
199
+ elif type(sd_module) == torch.nn.MultiheadAttention:
200
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
201
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (1, 1):
202
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
203
+ elif type(sd_module) == torch.nn.Conv2d and weight.shape[2:] == (3, 3):
204
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (3, 3), bias=False)
205
+ else:
206
+ print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')
207
+ continue
208
+ raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")
209
+
210
+ with torch.no_grad():
211
+ module.weight.copy_(weight)
212
+
213
+ module.to(device=devices.cpu, dtype=devices.dtype)
214
+
215
+ if lora_key == "lora_up.weight":
216
+ lora_module.up = module
217
+ elif lora_key == "lora_down.weight":
218
+ lora_module.down = module
219
+ else:
220
+ raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")
221
+
222
+ if keys_failed_to_match:
223
+ print(f"Failed to match keys when loading Lora {lora_on_disk.filename}: {keys_failed_to_match}")
224
+
225
+ return lora
226
+
227
+
228
+ def load_loras(names, multipliers=None):
229
+ already_loaded = {}
230
+
231
+ for lora in loaded_loras:
232
+ if lora.name in names:
233
+ already_loaded[lora.name] = lora
234
+
235
+ loaded_loras.clear()
236
+
237
+ loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
238
+ if any(x is None for x in loras_on_disk):
239
+ list_available_loras()
240
+
241
+ loras_on_disk = [available_lora_aliases.get(name, None) for name in names]
242
+
243
+ failed_to_load_loras = []
244
+
245
+ for i, name in enumerate(names):
246
+ lora = already_loaded.get(name, None)
247
+
248
+ lora_on_disk = loras_on_disk[i]
249
+
250
+ if lora_on_disk is not None:
251
+ if lora is None or os.path.getmtime(lora_on_disk.filename) > lora.mtime:
252
+ try:
253
+ lora = load_lora(name, lora_on_disk)
254
+ except Exception as e:
255
+ errors.display(e, f"loading Lora {lora_on_disk.filename}")
256
+ continue
257
+
258
+ lora.mentioned_name = name
259
+
260
+ lora_on_disk.read_hash()
261
+
262
+ if lora is None:
263
+ failed_to_load_loras.append(name)
264
+ print(f"Couldn't find Lora with name {name}")
265
+ continue
266
+
267
+ lora.multiplier = multipliers[i] if multipliers else 1.0
268
+ loaded_loras.append(lora)
269
+
270
+ if failed_to_load_loras:
271
+ sd_hijack.model_hijack.comments.append("Failed to find Loras: " + ", ".join(failed_to_load_loras))
272
+
273
+
274
+ def lora_calc_updown(lora, module, target):
275
+ with torch.no_grad():
276
+ up = module.up.weight.to(target.device, dtype=target.dtype)
277
+ down = module.down.weight.to(target.device, dtype=target.dtype)
278
+
279
+ if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
280
+ updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
281
+ elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
282
+ updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
283
+ else:
284
+ updown = up @ down
285
+
286
+ updown = updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
287
+
288
+ return updown
289
+
290
+
291
+ def lora_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
292
+ weights_backup = getattr(self, "lora_weights_backup", None)
293
+
294
+ if weights_backup is None:
295
+ return
296
+
297
+ if isinstance(self, torch.nn.MultiheadAttention):
298
+ self.in_proj_weight.copy_(weights_backup[0])
299
+ self.out_proj.weight.copy_(weights_backup[1])
300
+ else:
301
+ self.weight.copy_(weights_backup)
302
+
303
+
304
+ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.MultiheadAttention]):
305
+ """
306
+ Applies the currently selected set of Loras to the weights of torch layer self.
307
+ If weights already have this particular set of loras applied, does nothing.
308
+ If not, restores orginal weights from backup and alters weights according to loras.
309
+ """
310
+
311
+ lora_layer_name = getattr(self, 'lora_layer_name', None)
312
+ if lora_layer_name is None:
313
+ return
314
+
315
+ current_names = getattr(self, "lora_current_names", ())
316
+ wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
317
+
318
+ weights_backup = getattr(self, "lora_weights_backup", None)
319
+ if weights_backup is None:
320
+ if isinstance(self, torch.nn.MultiheadAttention):
321
+ weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
322
+ else:
323
+ weights_backup = self.weight.to(devices.cpu, copy=True)
324
+
325
+ self.lora_weights_backup = weights_backup
326
+
327
+ if current_names != wanted_names:
328
+ lora_restore_weights_from_backup(self)
329
+
330
+ for lora in loaded_loras:
331
+ module = lora.modules.get(lora_layer_name, None)
332
+ if module is not None and hasattr(self, 'weight'):
333
+ self.weight += lora_calc_updown(lora, module, self.weight)
334
+ continue
335
+
336
+ module_q = lora.modules.get(lora_layer_name + "_q_proj", None)
337
+ module_k = lora.modules.get(lora_layer_name + "_k_proj", None)
338
+ module_v = lora.modules.get(lora_layer_name + "_v_proj", None)
339
+ module_out = lora.modules.get(lora_layer_name + "_out_proj", None)
340
+
341
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
342
+ updown_q = lora_calc_updown(lora, module_q, self.in_proj_weight)
343
+ updown_k = lora_calc_updown(lora, module_k, self.in_proj_weight)
344
+ updown_v = lora_calc_updown(lora, module_v, self.in_proj_weight)
345
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
346
+
347
+ self.in_proj_weight += updown_qkv
348
+ self.out_proj.weight += lora_calc_updown(lora, module_out, self.out_proj.weight)
349
+ continue
350
+
351
+ if module is None:
352
+ continue
353
+
354
+ print(f'failed to calculate lora weights for layer {lora_layer_name}')
355
+
356
+ self.lora_current_names = wanted_names
357
+
358
+
359
+ def lora_forward(module, input, original_forward):
360
+ """
361
+ Old way of applying Lora by executing operations during layer's forward.
362
+ Stacking many loras this way results in big performance degradation.
363
+ """
364
+
365
+ if len(loaded_loras) == 0:
366
+ return original_forward(module, input)
367
+
368
+ input = devices.cond_cast_unet(input)
369
+
370
+ lora_restore_weights_from_backup(module)
371
+ lora_reset_cached_weight(module)
372
+
373
+ res = original_forward(module, input)
374
+
375
+ lora_layer_name = getattr(module, 'lora_layer_name', None)
376
+ for lora in loaded_loras:
377
+ module = lora.modules.get(lora_layer_name, None)
378
+ if module is None:
379
+ continue
380
+
381
+ module.up.to(device=devices.device)
382
+ module.down.to(device=devices.device)
383
+
384
+ res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
385
+
386
+ return res
387
+
388
+
389
+ def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
390
+ self.lora_current_names = ()
391
+ self.lora_weights_backup = None
392
+
393
+
394
+ def lora_Linear_forward(self, input):
395
+ if shared.opts.lora_functional:
396
+ return lora_forward(self, input, torch.nn.Linear_forward_before_lora)
397
+
398
+ lora_apply_weights(self)
399
+
400
+ return torch.nn.Linear_forward_before_lora(self, input)
401
+
402
+
403
+ def lora_Linear_load_state_dict(self, *args, **kwargs):
404
+ lora_reset_cached_weight(self)
405
+
406
+ return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
407
+
408
+
409
+ def lora_Conv2d_forward(self, input):
410
+ if shared.opts.lora_functional:
411
+ return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora)
412
+
413
+ lora_apply_weights(self)
414
+
415
+ return torch.nn.Conv2d_forward_before_lora(self, input)
416
+
417
+
418
+ def lora_Conv2d_load_state_dict(self, *args, **kwargs):
419
+ lora_reset_cached_weight(self)
420
+
421
+ return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
422
+
423
+
424
+ def lora_MultiheadAttention_forward(self, *args, **kwargs):
425
+ lora_apply_weights(self)
426
+
427
+ return torch.nn.MultiheadAttention_forward_before_lora(self, *args, **kwargs)
428
+
429
+
430
+ def lora_MultiheadAttention_load_state_dict(self, *args, **kwargs):
431
+ lora_reset_cached_weight(self)
432
+
433
+ return torch.nn.MultiheadAttention_load_state_dict_before_lora(self, *args, **kwargs)
434
+
435
+
436
+ def list_available_loras():
437
+ available_loras.clear()
438
+ available_lora_aliases.clear()
439
+ forbidden_lora_aliases.clear()
440
+ available_lora_hash_lookup.clear()
441
+ forbidden_lora_aliases.update({"none": 1, "Addams": 1})
442
+
443
+ os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
444
+
445
+ candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
446
+ for filename in sorted(candidates, key=str.lower):
447
+ if os.path.isdir(filename):
448
+ continue
449
+
450
+ name = os.path.splitext(os.path.basename(filename))[0]
451
+ try:
452
+ entry = LoraOnDisk(name, filename)
453
+ except OSError: # should catch FileNotFoundError and PermissionError etc.
454
+ errors.report(f"Failed to load LoRA {name} from {filename}", exc_info=True)
455
+ continue
456
+
457
+ available_loras[name] = entry
458
+
459
+ if entry.alias in available_lora_aliases:
460
+ forbidden_lora_aliases[entry.alias.lower()] = 1
461
+
462
+ available_lora_aliases[name] = entry
463
+ available_lora_aliases[entry.alias] = entry
464
+
465
+
466
+ re_lora_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
467
+
468
+
469
+ def infotext_pasted(infotext, params):
470
+ if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
471
+ return # if the other extension is active, it will handle those fields, no need to do anything
472
+
473
+ added = []
474
+
475
+ for k in params:
476
+ if not k.startswith("AddNet Model "):
477
+ continue
478
+
479
+ num = k[13:]
480
+
481
+ if params.get("AddNet Module " + num) != "LoRA":
482
+ continue
483
+
484
+ name = params.get("AddNet Model " + num)
485
+ if name is None:
486
+ continue
487
+
488
+ m = re_lora_name.match(name)
489
+ if m:
490
+ name = m.group(1)
491
+
492
+ multiplier = params.get("AddNet Weight A " + num, "1.0")
493
+
494
+ added.append(f"<lora:{name}:{multiplier}>")
495
+
496
+ if added:
497
+ params["Prompt"] += "\n" + "".join(added)
498
+
499
+
500
+ available_loras = {}
501
+ available_lora_aliases = {}
502
+ available_lora_hash_lookup = {}
503
+ forbidden_lora_aliases = {}
504
+ loaded_loras = []
505
+
506
+ 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 +1,116 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+
3
+ import torch
4
+ import gradio as gr
5
+ from fastapi import FastAPI
6
+
7
+ import lora
8
+ import extra_networks_lora
9
+ import ui_extra_networks_lora
10
+ from modules import script_callbacks, ui_extra_networks, extra_networks, shared
11
+
12
+ def unload():
13
+ torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
14
+ torch.nn.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
15
+ torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
16
+ torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
17
+ torch.nn.MultiheadAttention.forward = torch.nn.MultiheadAttention_forward_before_lora
18
+ torch.nn.MultiheadAttention._load_from_state_dict = torch.nn.MultiheadAttention_load_state_dict_before_lora
19
+
20
+
21
+ def before_ui():
22
+ ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
23
+ extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())
24
+
25
+
26
+ if not hasattr(torch.nn, 'Linear_forward_before_lora'):
27
+ torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
28
+
29
+ if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
30
+ torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
31
+
32
+ if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
33
+ torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
34
+
35
+ if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
36
+ torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
37
+
38
+ if not hasattr(torch.nn, 'MultiheadAttention_forward_before_lora'):
39
+ torch.nn.MultiheadAttention_forward_before_lora = torch.nn.MultiheadAttention.forward
40
+
41
+ if not hasattr(torch.nn, 'MultiheadAttention_load_state_dict_before_lora'):
42
+ torch.nn.MultiheadAttention_load_state_dict_before_lora = torch.nn.MultiheadAttention._load_from_state_dict
43
+
44
+ torch.nn.Linear.forward = lora.lora_Linear_forward
45
+ torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
46
+ torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
47
+ torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
48
+ torch.nn.MultiheadAttention.forward = lora.lora_MultiheadAttention_forward
49
+ torch.nn.MultiheadAttention._load_from_state_dict = lora.lora_MultiheadAttention_load_state_dict
50
+
51
+ script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
52
+ script_callbacks.on_script_unloaded(unload)
53
+ script_callbacks.on_before_ui(before_ui)
54
+ script_callbacks.on_infotext_pasted(lora.infotext_pasted)
55
+
56
+
57
+ shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
58
+ "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None", *lora.available_loras]}, refresh=lora.list_available_loras),
59
+ "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
60
+ "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
61
+ }))
62
+
63
+
64
+ shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
65
+ "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"),
66
+ }))
67
+
68
+
69
+ def create_lora_json(obj: lora.LoraOnDisk):
70
+ return {
71
+ "name": obj.name,
72
+ "alias": obj.alias,
73
+ "path": obj.filename,
74
+ "metadata": obj.metadata,
75
+ }
76
+
77
+
78
+ def api_loras(_: gr.Blocks, app: FastAPI):
79
+ @app.get("/sdapi/v1/loras")
80
+ async def get_loras():
81
+ return [create_lora_json(obj) for obj in lora.available_loras.values()]
82
+
83
+ @app.post("/sdapi/v1/refresh-loras")
84
+ async def refresh_loras():
85
+ return lora.list_available_loras()
86
+
87
+
88
+ script_callbacks.on_app_started(api_loras)
89
+
90
+ re_lora = re.compile("<lora:([^:]+):")
91
+
92
+
93
+ def infotext_pasted(infotext, d):
94
+ hashes = d.get("Lora hashes")
95
+ if not hashes:
96
+ return
97
+
98
+ hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
99
+ hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
100
+
101
+ def lora_replacement(m):
102
+ alias = m.group(1)
103
+ shorthash = hashes.get(alias)
104
+ if shorthash is None:
105
+ return m.group(0)
106
+
107
+ lora_on_disk = lora.available_lora_hash_lookup.get(shorthash)
108
+ if lora_on_disk is None:
109
+ return m.group(0)
110
+
111
+ return f'<lora:{lora_on_disk.get_alias()}:'
112
+
113
+ d["Prompt"] = re.sub(re_lora, lora_replacement, d["Prompt"])
114
+
115
+
116
+ script_callbacks.on_infotext_pasted(infotext_pasted)
extensions-builtin/Lora/ui_extra_networks_lora.py ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 index, (name, lora_on_disk) in enumerate(lora.available_loras.items()):
17
+ path, ext = os.path.splitext(lora_on_disk.filename)
18
+
19
+ alias = lora_on_disk.get_alias()
20
+
21
+ yield {
22
+ "name": name,
23
+ "filename": path,
24
+ "preview": self.find_preview(path),
25
+ "description": self.find_description(path),
26
+ "search_term": self.search_terms_from_path(lora_on_disk.filename),
27
+ "prompt": json.dumps(f"<lora:{alias}:") + " + opts.extra_networks_default_multiplier + " + json.dumps(">"),
28
+ "local_preview": f"{path}.{shared.opts.samples_format}",
29
+ "metadata": json.dumps(lora_on_disk.metadata, indent=4) if lora_on_disk.metadata else None,
30
+ "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
31
+
32
+ }
33
+
34
+ def allowed_directories_for_previews(self):
35
+ return [shared.cmd_opts.lora_dir]
36
+
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 +1,148 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os.path
2
+ import sys
3
+
4
+ import PIL.Image
5
+ import numpy as np
6
+ import torch
7
+ from tqdm import tqdm
8
+
9
+ from basicsr.utils.download_util import load_file_from_url
10
+
11
+ import modules.upscaler
12
+ from modules import devices, modelloader, script_callbacks, errors
13
+ from scunet_model_arch import SCUNet as net
14
+
15
+ from modules.shared import opts
16
+
17
+
18
+ class UpscalerScuNET(modules.upscaler.Upscaler):
19
+ def __init__(self, dirname):
20
+ self.name = "ScuNET"
21
+ self.model_name = "ScuNET GAN"
22
+ self.model_name2 = "ScuNET PSNR"
23
+ self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
24
+ self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
25
+ self.user_path = dirname
26
+ super().__init__()
27
+ model_paths = self.find_models(ext_filter=[".pth"])
28
+ scalers = []
29
+ add_model2 = True
30
+ for file in model_paths:
31
+ if "http" in file:
32
+ name = self.model_name
33
+ else:
34
+ name = modelloader.friendly_name(file)
35
+ if name == self.model_name2 or file == self.model_url2:
36
+ add_model2 = False
37
+ try:
38
+ scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
39
+ scalers.append(scaler_data)
40
+ except Exception:
41
+ errors.report(f"Error loading ScuNET model: {file}", exc_info=True)
42
+ if add_model2:
43
+ scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
44
+ scalers.append(scaler_data2)
45
+ self.scalers = scalers
46
+
47
+ @staticmethod
48
+ @torch.no_grad()
49
+ def tiled_inference(img, model):
50
+ # test the image tile by tile
51
+ h, w = img.shape[2:]
52
+ tile = opts.SCUNET_tile
53
+ tile_overlap = opts.SCUNET_tile_overlap
54
+ if tile == 0:
55
+ return model(img)
56
+
57
+ device = devices.get_device_for('scunet')
58
+ assert tile % 8 == 0, "tile size should be a multiple of window_size"
59
+ sf = 1
60
+
61
+ stride = tile - tile_overlap
62
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
63
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
64
+ E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
65
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device)
66
+
67
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
68
+ for h_idx in h_idx_list:
69
+
70
+ for w_idx in w_idx_list:
71
+
72
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
73
+
74
+ out_patch = model(in_patch)
75
+ out_patch_mask = torch.ones_like(out_patch)
76
+
77
+ E[
78
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
79
+ ].add_(out_patch)
80
+ W[
81
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
82
+ ].add_(out_patch_mask)
83
+ pbar.update(1)
84
+ output = E.div_(W)
85
+
86
+ return output
87
+
88
+ def do_upscale(self, img: PIL.Image.Image, selected_file):
89
+
90
+ torch.cuda.empty_cache()
91
+
92
+ model = self.load_model(selected_file)
93
+ if model is None:
94
+ print(f"ScuNET: Unable to load model from {selected_file}", file=sys.stderr)
95
+ return img
96
+
97
+ device = devices.get_device_for('scunet')
98
+ tile = opts.SCUNET_tile
99
+ h, w = img.height, img.width
100
+ np_img = np.array(img)
101
+ np_img = np_img[:, :, ::-1] # RGB to BGR
102
+ np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
103
+ torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
104
+
105
+ if tile > h or tile > w:
106
+ _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
107
+ _img[:, :, :h, :w] = torch_img # pad image
108
+ torch_img = _img
109
+
110
+ torch_output = self.tiled_inference(torch_img, model).squeeze(0)
111
+ torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
112
+ np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
113
+ del torch_img, torch_output
114
+ torch.cuda.empty_cache()
115
+
116
+ output = np_output.transpose((1, 2, 0)) # CHW to HWC
117
+ output = output[:, :, ::-1] # BGR to RGB
118
+ return PIL.Image.fromarray((output * 255).astype(np.uint8))
119
+
120
+ def load_model(self, path: str):
121
+ device = devices.get_device_for('scunet')
122
+ if "http" in path:
123
+ filename = load_file_from_url(url=self.model_url, model_dir=self.model_download_path, file_name="%s.pth" % self.name, progress=True)
124
+ else:
125
+ filename = path
126
+ if not os.path.exists(os.path.join(self.model_path, filename)) or filename is None:
127
+ print(f"ScuNET: Unable to load model from {filename}", file=sys.stderr)
128
+ return None
129
+
130
+ model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
131
+ model.load_state_dict(torch.load(filename), strict=True)
132
+ model.eval()
133
+ for _, v in model.named_parameters():
134
+ v.requires_grad = False
135
+ model = model.to(device)
136
+
137
+ return model
138
+
139
+
140
+ def on_ui_settings():
141
+ import gradio as gr
142
+ from modules import shared
143
+
144
+ shared.opts.add_option("SCUNET_tile", shared.OptionInfo(256, "Tile size for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}, section=('upscaling', "Upscaling")).info("0 = no tiling"))
145
+ shared.opts.add_option("SCUNET_tile_overlap", shared.OptionInfo(8, "Tile overlap for SCUNET upscalers.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}, section=('upscaling', "Upscaling")).info("Low values = visible seam"))
146
+
147
+
148
+ script_callbacks.on_ui_settings(on_ui_settings)
extensions-builtin/ScuNET/scunet_model_arch.py ADDED
@@ -0,0 +1,268 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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':
65
+ x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
66
+
67
+ x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
68
+ h_windows = x.size(1)
69
+ w_windows = x.size(2)
70
+ # square validation
71
+ # assert h_windows == w_windows
72
+
73
+ x = rearrange(x, 'b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c', p1=self.window_size, p2=self.window_size)
74
+ qkv = self.embedding_layer(x)
75
+ q, k, v = rearrange(qkv, 'b nw np (threeh c) -> threeh b nw np c', c=self.head_dim).chunk(3, dim=0)
76
+ sim = torch.einsum('hbwpc,hbwqc->hbwpq', q, k) * self.scale
77
+ # Adding learnable relative embedding
78
+ sim = sim + rearrange(self.relative_embedding(), 'h p q -> h 1 1 p q')
79
+ # Using Attn Mask to distinguish different subwindows.
80
+ if self.type != 'W':
81
+ attn_mask = self.generate_mask(h_windows, w_windows, self.window_size, shift=self.window_size // 2)
82
+ sim = sim.masked_fill_(attn_mask, float("-inf"))
83
+
84
+ probs = nn.functional.softmax(sim, dim=-1)
85
+ output = torch.einsum('hbwij,hbwjc->hbwic', probs, v)
86
+ output = rearrange(output, 'h b w p c -> b w p (h c)')
87
+ output = self.linear(output)
88
+ output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
89
+
90
+ if self.type != 'W':
91
+ output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
92
+
93
+ return output
94
+
95
+ def relative_embedding(self):
96
+ cord = torch.tensor(np.array([[i, j] for i in range(self.window_size) for j in range(self.window_size)]))
97
+ relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
98
+ # negative is allowed
99
+ return self.relative_position_params[:, relation[:, :, 0].long(), relation[:, :, 1].long()]
100
+
101
+
102
+ class Block(nn.Module):
103
+ def __init__(self, input_dim, output_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
104
+ """ SwinTransformer Block
105
+ """
106
+ super(Block, self).__init__()
107
+ self.input_dim = input_dim
108
+ self.output_dim = output_dim
109
+ assert type in ['W', 'SW']
110
+ self.type = type
111
+ if input_resolution <= window_size:
112
+ self.type = 'W'
113
+
114
+ self.ln1 = nn.LayerNorm(input_dim)
115
+ self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
116
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
117
+ self.ln2 = nn.LayerNorm(input_dim)
118
+ self.mlp = nn.Sequential(
119
+ nn.Linear(input_dim, 4 * input_dim),
120
+ nn.GELU(),
121
+ nn.Linear(4 * input_dim, output_dim),
122
+ )
123
+
124
+ def forward(self, x):
125
+ x = x + self.drop_path(self.msa(self.ln1(x)))
126
+ x = x + self.drop_path(self.mlp(self.ln2(x)))
127
+ return x
128
+
129
+
130
+ class ConvTransBlock(nn.Module):
131
+ def __init__(self, conv_dim, trans_dim, head_dim, window_size, drop_path, type='W', input_resolution=None):
132
+ """ SwinTransformer and Conv Block
133
+ """
134
+ super(ConvTransBlock, self).__init__()
135
+ self.conv_dim = conv_dim
136
+ self.trans_dim = trans_dim
137
+ self.head_dim = head_dim
138
+ self.window_size = window_size
139
+ self.drop_path = drop_path
140
+ self.type = type
141
+ self.input_resolution = input_resolution
142
+
143
+ assert self.type in ['W', 'SW']
144
+ if self.input_resolution <= self.window_size:
145
+ self.type = 'W'
146
+
147
+ self.trans_block = Block(self.trans_dim, self.trans_dim, self.head_dim, self.window_size, self.drop_path,
148
+ self.type, self.input_resolution)
149
+ self.conv1_1 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
150
+ self.conv1_2 = nn.Conv2d(self.conv_dim + self.trans_dim, self.conv_dim + self.trans_dim, 1, 1, 0, bias=True)
151
+
152
+ self.conv_block = nn.Sequential(
153
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
154
+ nn.ReLU(True),
155
+ nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False)
156
+ )
157
+
158
+ def forward(self, x):
159
+ conv_x, trans_x = torch.split(self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1)
160
+ conv_x = self.conv_block(conv_x) + conv_x
161
+ trans_x = Rearrange('b c h w -> b h w c')(trans_x)
162
+ trans_x = self.trans_block(trans_x)
163
+ trans_x = Rearrange('b h w c -> b c h w')(trans_x)
164
+ res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
165
+ x = x + res
166
+
167
+ return x
168
+
169
+
170
+ class SCUNet(nn.Module):
171
+ # def __init__(self, in_nc=3, config=[2, 2, 2, 2, 2, 2, 2], dim=64, drop_path_rate=0.0, input_resolution=256):
172
+ def __init__(self, in_nc=3, config=None, dim=64, drop_path_rate=0.0, input_resolution=256):
173
+ super(SCUNet, self).__init__()
174
+ if config is None:
175
+ config = [2, 2, 2, 2, 2, 2, 2]
176
+ self.config = config
177
+ self.dim = dim
178
+ self.head_dim = 32
179
+ self.window_size = 8
180
+
181
+ # drop path rate for each layer
182
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
183
+
184
+ self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
185
+
186
+ begin = 0
187
+ self.m_down1 = [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
188
+ 'W' if not i % 2 else 'SW', input_resolution)
189
+ for i in range(config[0])] + \
190
+ [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
191
+
192
+ begin += config[0]
193
+ self.m_down2 = [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
194
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
195
+ for i in range(config[1])] + \
196
+ [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
197
+
198
+ begin += config[1]
199
+ self.m_down3 = [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
200
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
201
+ for i in range(config[2])] + \
202
+ [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
203
+
204
+ begin += config[2]
205
+ self.m_body = [ConvTransBlock(4 * dim, 4 * dim, self.head_dim, self.window_size, dpr[i + begin],
206
+ 'W' if not i % 2 else 'SW', input_resolution // 8)
207
+ for i in range(config[3])]
208
+
209
+ begin += config[3]
210
+ self.m_up3 = [nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False), ] + \
211
+ [ConvTransBlock(2 * dim, 2 * dim, self.head_dim, self.window_size, dpr[i + begin],
212
+ 'W' if not i % 2 else 'SW', input_resolution // 4)
213
+ for i in range(config[4])]
214
+
215
+ begin += config[4]
216
+ self.m_up2 = [nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False), ] + \
217
+ [ConvTransBlock(dim, dim, self.head_dim, self.window_size, dpr[i + begin],
218
+ 'W' if not i % 2 else 'SW', input_resolution // 2)
219
+ for i in range(config[5])]
220
+
221
+ begin += config[5]
222
+ self.m_up1 = [nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False), ] + \
223
+ [ConvTransBlock(dim // 2, dim // 2, self.head_dim, self.window_size, dpr[i + begin],
224
+ 'W' if not i % 2 else 'SW', input_resolution)
225
+ for i in range(config[6])]
226
+
227
+ self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
228
+
229
+ self.m_head = nn.Sequential(*self.m_head)
230
+ self.m_down1 = nn.Sequential(*self.m_down1)
231
+ self.m_down2 = nn.Sequential(*self.m_down2)
232
+ self.m_down3 = nn.Sequential(*self.m_down3)
233
+ self.m_body = nn.Sequential(*self.m_body)
234
+ self.m_up3 = nn.Sequential(*self.m_up3)
235
+ self.m_up2 = nn.Sequential(*self.m_up2)
236
+ self.m_up1 = nn.Sequential(*self.m_up1)
237
+ self.m_tail = nn.Sequential(*self.m_tail)
238
+ # self.apply(self._init_weights)
239
+
240
+ def forward(self, x0):
241
+
242
+ h, w = x0.size()[-2:]
243
+ paddingBottom = int(np.ceil(h / 64) * 64 - h)
244
+ paddingRight = int(np.ceil(w / 64) * 64 - w)
245
+ x0 = nn.ReplicationPad2d((0, paddingRight, 0, paddingBottom))(x0)
246
+
247
+ x1 = self.m_head(x0)
248
+ x2 = self.m_down1(x1)
249
+ x3 = self.m_down2(x2)
250
+ x4 = self.m_down3(x3)
251
+ x = self.m_body(x4)
252
+ x = self.m_up3(x + x4)
253
+ x = self.m_up2(x + x3)
254
+ x = self.m_up1(x + x2)
255
+ x = self.m_tail(x + x1)
256
+
257
+ x = x[..., :h, :w]
258
+
259
+ return x
260
+
261
+ def _init_weights(self, m):
262
+ if isinstance(m, nn.Linear):
263
+ trunc_normal_(m.weight, std=.02)
264
+ if m.bias is not None:
265
+ nn.init.constant_(m.bias, 0)
266
+ elif isinstance(m, nn.LayerNorm):
267
+ nn.init.constant_(m.bias, 0)
268
+ 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 +1,177 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ import numpy as np
4
+ import torch
5
+ from PIL import Image
6
+ from basicsr.utils.download_util import load_file_from_url
7
+ from tqdm import tqdm
8
+
9
+ from modules import modelloader, devices, script_callbacks, shared
10
+ from modules.shared import opts, state
11
+ from swinir_model_arch import SwinIR as net
12
+ from swinir_model_arch_v2 import Swin2SR as net2
13
+ from modules.upscaler import Upscaler, UpscalerData
14
+
15
+
16
+ device_swinir = devices.get_device_for('swinir')
17
+
18
+
19
+ class UpscalerSwinIR(Upscaler):
20
+ def __init__(self, dirname):
21
+ self.name = "SwinIR"
22
+ self.model_url = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0" \
23
+ "/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR" \
24
+ "-L_x4_GAN.pth "
25
+ self.model_name = "SwinIR 4x"
26
+ self.user_path = dirname
27
+ super().__init__()
28
+ scalers = []
29
+ model_files = self.find_models(ext_filter=[".pt", ".pth"])
30
+ for model in model_files:
31
+ if "http" in model:
32
+ name = self.model_name
33
+ else:
34
+ name = modelloader.friendly_name(model)
35
+ model_data = UpscalerData(name, model, self)
36
+ scalers.append(model_data)
37
+ self.scalers = scalers
38
+
39
+ def do_upscale(self, img, model_file):
40
+ model = self.load_model(model_file)
41
+ if model is None:
42
+ return img
43
+ model = model.to(device_swinir, dtype=devices.dtype)
44
+ img = upscale(img, model)
45
+ try:
46
+ torch.cuda.empty_cache()
47
+ except Exception:
48
+ pass
49
+ return img
50
+
51
+ def load_model(self, path, scale=4):
52
+ if "http" in path:
53
+ dl_name = "%s%s" % (self.model_name.replace(" ", "_"), ".pth")
54
+ filename = load_file_from_url(url=path, model_dir=self.model_download_path, file_name=dl_name, progress=True)
55
+ else:
56
+ filename = path
57
+ if filename is None or not os.path.exists(filename):
58
+ return None
59
+ if filename.endswith(".v2.pth"):
60
+ model = net2(
61
+ upscale=scale,
62
+ in_chans=3,
63
+ img_size=64,
64
+ window_size=8,
65
+ img_range=1.0,
66
+ depths=[6, 6, 6, 6, 6, 6],
67
+ embed_dim=180,
68
+ num_heads=[6, 6, 6, 6, 6, 6],
69
+ mlp_ratio=2,
70
+ upsampler="nearest+conv",
71
+ resi_connection="1conv",
72
+ )
73
+ params = None
74
+ else:
75
+ model = net(
76
+ upscale=scale,
77
+ in_chans=3,
78
+ img_size=64,
79
+ window_size=8,
80
+ img_range=1.0,
81
+ depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
82
+ embed_dim=240,
83
+ num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
84
+ mlp_ratio=2,
85
+ upsampler="nearest+conv",
86
+ resi_connection="3conv",
87
+ )
88
+ params = "params_ema"
89
+
90
+ pretrained_model = torch.load(filename)
91
+ if params is not None:
92
+ model.load_state_dict(pretrained_model[params], strict=True)
93
+ else:
94
+ model.load_state_dict(pretrained_model, strict=True)
95
+ return model
96
+
97
+
98
+ def upscale(
99
+ img,
100
+ model,
101
+ tile=None,
102
+ tile_overlap=None,
103
+ window_size=8,
104
+ scale=4,
105
+ ):
106
+ tile = tile or opts.SWIN_tile
107
+ tile_overlap = tile_overlap or opts.SWIN_tile_overlap
108
+
109
+
110
+ img = np.array(img)
111
+ img = img[:, :, ::-1]
112
+ img = np.moveaxis(img, 2, 0) / 255
113
+ img = torch.from_numpy(img).float()
114
+ img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
115
+ with torch.no_grad(), devices.autocast():
116
+ _, _, h_old, w_old = img.size()
117
+ h_pad = (h_old // window_size + 1) * window_size - h_old
118
+ w_pad = (w_old // window_size + 1) * window_size - w_old
119
+ img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
120
+ img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
121
+ output = inference(img, model, tile, tile_overlap, window_size, scale)
122
+ output = output[..., : h_old * scale, : w_old * scale]
123
+ output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
124
+ if output.ndim == 3:
125
+ output = np.transpose(
126
+ output[[2, 1, 0], :, :], (1, 2, 0)
127
+ ) # CHW-RGB to HCW-BGR
128
+ output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
129
+ return Image.fromarray(output, "RGB")
130
+
131
+
132
+ def inference(img, model, tile, tile_overlap, window_size, scale):
133
+ # test the image tile by tile
134
+ b, c, h, w = img.size()
135
+ tile = min(tile, h, w)
136
+ assert tile % window_size == 0, "tile size should be a multiple of window_size"
137
+ sf = scale
138
+
139
+ stride = tile - tile_overlap
140
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
141
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
142
+ E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
143
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
144
+
145
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
146
+ for h_idx in h_idx_list:
147
+ if state.interrupted or state.skipped:
148
+ break
149
+
150
+ for w_idx in w_idx_list:
151
+ if state.interrupted or state.skipped:
152
+ break
153
+
154
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
155
+ out_patch = model(in_patch)
156
+ out_patch_mask = torch.ones_like(out_patch)
157
+
158
+ E[
159
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
160
+ ].add_(out_patch)
161
+ W[
162
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
163
+ ].add_(out_patch_mask)
164
+ pbar.update(1)
165
+ output = E.div_(W)
166
+
167
+ return output
168
+
169
+
170
+ def on_ui_settings():
171
+ import gradio as gr
172
+
173
+ 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")))
174
+ 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")))
175
+
176
+
177
+ 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 layer in 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 layer in 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/canvas-zoom-and-pan/javascript/zoom.js ADDED
@@ -0,0 +1,748 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ onUiLoaded(async() => {
2
+ const elementIDs = {
3
+ img2imgTabs: "#mode_img2img .tab-nav",
4
+ inpaint: "#img2maskimg",
5
+ inpaintSketch: "#inpaint_sketch",
6
+ rangeGroup: "#img2img_column_size",
7
+ sketch: "#img2img_sketch"
8
+ };
9
+ const tabNameToElementId = {
10
+ "Inpaint sketch": elementIDs.inpaintSketch,
11
+ "Inpaint": elementIDs.inpaint,
12
+ "Sketch": elementIDs.sketch
13
+ };
14
+
15
+ // Helper functions
16
+ // Get active tab
17
+ function getActiveTab(elements, all = false) {
18
+ const tabs = elements.img2imgTabs.querySelectorAll("button");
19
+
20
+ if (all) return tabs;
21
+
22
+ for (let tab of tabs) {
23
+ if (tab.classList.contains("selected")) {
24
+ return tab;
25
+ }
26
+ }
27
+ }
28
+
29
+ // Get tab ID
30
+ function getTabId(elements) {
31
+ const activeTab = getActiveTab(elements);
32
+ return tabNameToElementId[activeTab.innerText];
33
+ }
34
+
35
+ // Wait until opts loaded
36
+ async function waitForOpts() {
37
+ for (;;) {
38
+ if (window.opts && Object.keys(window.opts).length) {
39
+ return window.opts;
40
+ }
41
+ await new Promise(resolve => setTimeout(resolve, 100));
42
+ }
43
+ }
44
+
45
+ // Function for defining the "Ctrl", "Shift" and "Alt" keys
46
+ function isModifierKey(event, key) {
47
+ switch (key) {
48
+ case "Ctrl":
49
+ return event.ctrlKey;
50
+ case "Shift":
51
+ return event.shiftKey;
52
+ case "Alt":
53
+ return event.altKey;
54
+ default:
55
+ return false;
56
+ }
57
+ }
58
+
59
+ // Check if hotkey is valid
60
+ function isValidHotkey(value) {
61
+ const specialKeys = ["Ctrl", "Alt", "Shift", "Disable"];
62
+ return (
63
+ (typeof value === "string" &&
64
+ value.length === 1 &&
65
+ /[a-z]/i.test(value)) ||
66
+ specialKeys.includes(value)
67
+ );
68
+ }
69
+
70
+ // Normalize hotkey
71
+ function normalizeHotkey(hotkey) {
72
+ return hotkey.length === 1 ? "Key" + hotkey.toUpperCase() : hotkey;
73
+ }
74
+
75
+ // Format hotkey for display
76
+ function formatHotkeyForDisplay(hotkey) {
77
+ return hotkey.startsWith("Key") ? hotkey.slice(3) : hotkey;
78
+ }
79
+
80
+ // Create hotkey configuration with the provided options
81
+ function createHotkeyConfig(defaultHotkeysConfig, hotkeysConfigOpts) {
82
+ const result = {}; // Resulting hotkey configuration
83
+ const usedKeys = new Set(); // Set of used hotkeys
84
+
85
+ // Iterate through defaultHotkeysConfig keys
86
+ for (const key in defaultHotkeysConfig) {
87
+ const userValue = hotkeysConfigOpts[key]; // User-provided hotkey value
88
+ const defaultValue = defaultHotkeysConfig[key]; // Default hotkey value
89
+
90
+ // Apply appropriate value for undefined, boolean, or object userValue
91
+ if (
92
+ userValue === undefined ||
93
+ typeof userValue === "boolean" ||
94
+ typeof userValue === "object" ||
95
+ userValue === "disable"
96
+ ) {
97
+ result[key] =
98
+ userValue === undefined ? defaultValue : userValue;
99
+ } else if (isValidHotkey(userValue)) {
100
+ const normalizedUserValue = normalizeHotkey(userValue);
101
+
102
+ // Check for conflicting hotkeys
103
+ if (!usedKeys.has(normalizedUserValue)) {
104
+ usedKeys.add(normalizedUserValue);
105
+ result[key] = normalizedUserValue;
106
+ } else {
107
+ console.error(
108
+ `Hotkey: ${formatHotkeyForDisplay(
109
+ userValue
110
+ )} for ${key} is repeated and conflicts with another hotkey. The default hotkey is used: ${formatHotkeyForDisplay(
111
+ defaultValue
112
+ )}`
113
+ );
114
+ result[key] = defaultValue;
115
+ }
116
+ } else {
117
+ console.error(
118
+ `Hotkey: ${formatHotkeyForDisplay(
119
+ userValue
120
+ )} for ${key} is not valid. The default hotkey is used: ${formatHotkeyForDisplay(
121
+ defaultValue
122
+ )}`
123
+ );
124
+ result[key] = defaultValue;
125
+ }
126
+ }
127
+
128
+ return result;
129
+ }
130
+
131
+ // Disables functions in the config object based on the provided list of function names
132
+ function disableFunctions(config, disabledFunctions) {
133
+ // Bind the hasOwnProperty method to the functionMap object to avoid errors
134
+ const hasOwnProperty =
135
+ Object.prototype.hasOwnProperty.bind(functionMap);
136
+
137
+ // Loop through the disabledFunctions array and disable the corresponding functions in the config object
138
+ disabledFunctions.forEach(funcName => {
139
+ if (hasOwnProperty(funcName)) {
140
+ const key = functionMap[funcName];
141
+ config[key] = "disable";
142
+ }
143
+ });
144
+
145
+ // Return the updated config object
146
+ return config;
147
+ }
148
+
149
+ /**
150
+ * The restoreImgRedMask function displays a red mask around an image to indicate the aspect ratio.
151
+ * If the image display property is set to 'none', the mask breaks. To fix this, the function
152
+ * temporarily sets the display property to 'block' and then hides the mask again after 300 milliseconds
153
+ * to avoid breaking the canvas. Additionally, the function adjusts the mask to work correctly on
154
+ * very long images.
155
+ */
156
+ function restoreImgRedMask(elements) {
157
+ const mainTabId = getTabId(elements);
158
+
159
+ if (!mainTabId) return;
160
+
161
+ const mainTab = gradioApp().querySelector(mainTabId);
162
+ const img = mainTab.querySelector("img");
163
+ const imageARPreview = gradioApp().querySelector("#imageARPreview");
164
+
165
+ if (!img || !imageARPreview) return;
166
+
167
+ imageARPreview.style.transform = "";
168
+ if (parseFloat(mainTab.style.width) > 865) {
169
+ const transformString = mainTab.style.transform;
170
+ const scaleMatch = transformString.match(
171
+ /scale\(([-+]?[0-9]*\.?[0-9]+)\)/
172
+ );
173
+ let zoom = 1; // default zoom
174
+
175
+ if (scaleMatch && scaleMatch[1]) {
176
+ zoom = Number(scaleMatch[1]);
177
+ }
178
+
179
+ imageARPreview.style.transformOrigin = "0 0";
180
+ imageARPreview.style.transform = `scale(${zoom})`;
181
+ }
182
+
183
+ if (img.style.display !== "none") return;
184
+
185
+ img.style.display = "block";
186
+
187
+ setTimeout(() => {
188
+ img.style.display = "none";
189
+ }, 400);
190
+ }
191
+
192
+ const hotkeysConfigOpts = await waitForOpts();
193
+
194
+ // Default config
195
+ const defaultHotkeysConfig = {
196
+ canvas_hotkey_zoom: "Alt",
197
+ canvas_hotkey_adjust: "Ctrl",
198
+ canvas_hotkey_reset: "KeyR",
199
+ canvas_hotkey_fullscreen: "KeyS",
200
+ canvas_hotkey_move: "KeyF",
201
+ canvas_hotkey_overlap: "KeyO",
202
+ canvas_disabled_functions: [],
203
+ canvas_show_tooltip: true
204
+ };
205
+
206
+ const functionMap = {
207
+ "Zoom": "canvas_hotkey_zoom",
208
+ "Adjust brush size": "canvas_hotkey_adjust",
209
+ "Moving canvas": "canvas_hotkey_move",
210
+ "Fullscreen": "canvas_hotkey_fullscreen",
211
+ "Reset Zoom": "canvas_hotkey_reset",
212
+ "Overlap": "canvas_hotkey_overlap"
213
+ };
214
+
215
+ // Loading the configuration from opts
216
+ const preHotkeysConfig = createHotkeyConfig(
217
+ defaultHotkeysConfig,
218
+ hotkeysConfigOpts
219
+ );
220
+
221
+ // Disable functions that are not needed by the user
222
+ const hotkeysConfig = disableFunctions(
223
+ preHotkeysConfig,
224
+ preHotkeysConfig.canvas_disabled_functions
225
+ );
226
+
227
+ let isMoving = false;
228
+ let mouseX, mouseY;
229
+ let activeElement;
230
+
231
+ const elements = Object.fromEntries(
232
+ Object.keys(elementIDs).map(id => [
233
+ id,
234
+ gradioApp().querySelector(elementIDs[id])
235
+ ])
236
+ );
237
+ const elemData = {};
238
+
239
+ // Apply functionality to the range inputs. Restore redmask and correct for long images.
240
+ const rangeInputs = elements.rangeGroup ?
241
+ Array.from(elements.rangeGroup.querySelectorAll("input")) :
242
+ [
243
+ gradioApp().querySelector("#img2img_width input[type='range']"),
244
+ gradioApp().querySelector("#img2img_height input[type='range']")
245
+ ];
246
+
247
+ for (const input of rangeInputs) {
248
+ input?.addEventListener("input", () => restoreImgRedMask(elements));
249
+ }
250
+
251
+ function applyZoomAndPan(elemId) {
252
+ const targetElement = gradioApp().querySelector(elemId);
253
+
254
+ if (!targetElement) {
255
+ console.log("Element not found");
256
+ return;
257
+ }
258
+
259
+ targetElement.style.transformOrigin = "0 0";
260
+
261
+ elemData[elemId] = {
262
+ zoom: 1,
263
+ panX: 0,
264
+ panY: 0
265
+ };
266
+ let fullScreenMode = false;
267
+
268
+ // Create tooltip
269
+ function createTooltip() {
270
+ const toolTipElemnt =
271
+ targetElement.querySelector(".image-container");
272
+ const tooltip = document.createElement("div");
273
+ tooltip.className = "canvas-tooltip";
274
+
275
+ // Creating an item of information
276
+ const info = document.createElement("i");
277
+ info.className = "canvas-tooltip-info";
278
+ info.textContent = "";
279
+
280
+ // Create a container for the contents of the tooltip
281
+ const tooltipContent = document.createElement("div");
282
+ tooltipContent.className = "canvas-tooltip-content";
283
+
284
+ // Define an array with hotkey information and their actions
285
+ const hotkeysInfo = [
286
+ {
287
+ configKey: "canvas_hotkey_zoom",
288
+ action: "Zoom canvas",
289
+ keySuffix: " + wheel"
290
+ },
291
+ {
292
+ configKey: "canvas_hotkey_adjust",
293
+ action: "Adjust brush size",
294
+ keySuffix: " + wheel"
295
+ },
296
+ {configKey: "canvas_hotkey_reset", action: "Reset zoom"},
297
+ {
298
+ configKey: "canvas_hotkey_fullscreen",
299
+ action: "Fullscreen mode"
300
+ },
301
+ {configKey: "canvas_hotkey_move", action: "Move canvas"},
302
+ {configKey: "canvas_hotkey_overlap", action: "Overlap"}
303
+ ];
304
+
305
+ // Create hotkeys array with disabled property based on the config values
306
+ const hotkeys = hotkeysInfo.map(info => {
307
+ const configValue = hotkeysConfig[info.configKey];
308
+ const key = info.keySuffix ?
309
+ `${configValue}${info.keySuffix}` :
310
+ configValue.charAt(configValue.length - 1);
311
+ return {
312
+ key,
313
+ action: info.action,
314
+ disabled: configValue === "disable"
315
+ };
316
+ });
317
+
318
+ for (const hotkey of hotkeys) {
319
+ if (hotkey.disabled) {
320
+ continue;
321
+ }
322
+
323
+ const p = document.createElement("p");
324
+ p.innerHTML = `<b>${hotkey.key}</b> - ${hotkey.action}`;
325
+ tooltipContent.appendChild(p);
326
+ }
327
+
328
+ // Add information and content elements to the tooltip element
329
+ tooltip.appendChild(info);
330
+ tooltip.appendChild(tooltipContent);
331
+
332
+ // Add a hint element to the target element
333
+ toolTipElemnt.appendChild(tooltip);
334
+ }
335
+
336
+ //Show tool tip if setting enable
337
+ if (hotkeysConfig.canvas_show_tooltip) {
338
+ createTooltip();
339
+ }
340
+
341
+ // In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui.
342
+ function fixCanvas() {
343
+ const activeTab = getActiveTab(elements).textContent.trim();
344
+
345
+ if (activeTab !== "img2img") {
346
+ const img = targetElement.querySelector(`${elemId} img`);
347
+
348
+ if (img && img.style.display !== "none") {
349
+ img.style.display = "none";
350
+ img.style.visibility = "hidden";
351
+ }
352
+ }
353
+ }
354
+
355
+ // Reset the zoom level and pan position of the target element to their initial values
356
+ function resetZoom() {
357
+ elemData[elemId] = {
358
+ zoomLevel: 1,
359
+ panX: 0,
360
+ panY: 0
361
+ };
362
+
363
+ fixCanvas();
364
+ targetElement.style.transform = `scale(${elemData[elemId].zoomLevel}) translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px)`;
365
+
366
+ const canvas = gradioApp().querySelector(
367
+ `${elemId} canvas[key="interface"]`
368
+ );
369
+
370
+ toggleOverlap("off");
371
+ fullScreenMode = false;
372
+
373
+ if (
374
+ canvas &&
375
+ parseFloat(canvas.style.width) > 865 &&
376
+ parseFloat(targetElement.style.width) > 865
377
+ ) {
378
+ fitToElement();
379
+ return;
380
+ }
381
+
382
+ targetElement.style.width = "";
383
+ if (canvas) {
384
+ targetElement.style.height = canvas.style.height;
385
+ }
386
+ }
387
+
388
+ // Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
389
+ function toggleOverlap(forced = "") {
390
+ const zIndex1 = "0";
391
+ const zIndex2 = "998";
392
+
393
+ targetElement.style.zIndex =
394
+ targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1;
395
+
396
+ if (forced === "off") {
397
+ targetElement.style.zIndex = zIndex1;
398
+ } else if (forced === "on") {
399
+ targetElement.style.zIndex = zIndex2;
400
+ }
401
+ }
402
+
403
+ // Adjust the brush size based on the deltaY value from a mouse wheel event
404
+ function adjustBrushSize(
405
+ elemId,
406
+ deltaY,
407
+ withoutValue = false,
408
+ percentage = 5
409
+ ) {
410
+ const input =
411
+ gradioApp().querySelector(
412
+ `${elemId} input[aria-label='Brush radius']`
413
+ ) ||
414
+ gradioApp().querySelector(
415
+ `${elemId} button[aria-label="Use brush"]`
416
+ );
417
+
418
+ if (input) {
419
+ input.click();
420
+ if (!withoutValue) {
421
+ const maxValue =
422
+ parseFloat(input.getAttribute("max")) || 100;
423
+ const changeAmount = maxValue * (percentage / 100);
424
+ const newValue =
425
+ parseFloat(input.value) +
426
+ (deltaY > 0 ? -changeAmount : changeAmount);
427
+ input.value = Math.min(Math.max(newValue, 0), maxValue);
428
+ input.dispatchEvent(new Event("change"));
429
+ }
430
+ }
431
+ }
432
+
433
+ // Reset zoom when uploading a new image
434
+ const fileInput = gradioApp().querySelector(
435
+ `${elemId} input[type="file"][accept="image/*"].svelte-116rqfv`
436
+ );
437
+ fileInput.addEventListener("click", resetZoom);
438
+
439
+ // Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
440
+ function updateZoom(newZoomLevel, mouseX, mouseY) {
441
+ newZoomLevel = Math.max(0.5, Math.min(newZoomLevel, 15));
442
+
443
+ elemData[elemId].panX +=
444
+ mouseX - (mouseX * newZoomLevel) / elemData[elemId].zoomLevel;
445
+ elemData[elemId].panY +=
446
+ mouseY - (mouseY * newZoomLevel) / elemData[elemId].zoomLevel;
447
+
448
+ targetElement.style.transformOrigin = "0 0";
449
+ targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${newZoomLevel})`;
450
+
451
+ toggleOverlap("on");
452
+ return newZoomLevel;
453
+ }
454
+
455
+ // Change the zoom level based on user interaction
456
+ function changeZoomLevel(operation, e) {
457
+ if (isModifierKey(e, hotkeysConfig.canvas_hotkey_zoom)) {
458
+ e.preventDefault();
459
+
460
+ let zoomPosX, zoomPosY;
461
+ let delta = 0.2;
462
+ if (elemData[elemId].zoomLevel > 7) {
463
+ delta = 0.9;
464
+ } else if (elemData[elemId].zoomLevel > 2) {
465
+ delta = 0.6;
466
+ }
467
+
468
+ zoomPosX = e.clientX;
469
+ zoomPosY = e.clientY;
470
+
471
+ fullScreenMode = false;
472
+ elemData[elemId].zoomLevel = updateZoom(
473
+ elemData[elemId].zoomLevel +
474
+ (operation === "+" ? delta : -delta),
475
+ zoomPosX - targetElement.getBoundingClientRect().left,
476
+ zoomPosY - targetElement.getBoundingClientRect().top
477
+ );
478
+ }
479
+ }
480
+
481
+ /**
482
+ * This function fits the target element to the screen by calculating
483
+ * the required scale and offsets. It also updates the global variables
484
+ * zoomLevel, panX, and panY to reflect the new state.
485
+ */
486
+
487
+ function fitToElement() {
488
+ //Reset Zoom
489
+ targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
490
+
491
+ // Get element and screen dimensions
492
+ const elementWidth = targetElement.offsetWidth;
493
+ const elementHeight = targetElement.offsetHeight;
494
+ const parentElement = targetElement.parentElement;
495
+ const screenWidth = parentElement.clientWidth;
496
+ const screenHeight = parentElement.clientHeight;
497
+
498
+ // Get element's coordinates relative to the parent element
499
+ const elementRect = targetElement.getBoundingClientRect();
500
+ const parentRect = parentElement.getBoundingClientRect();
501
+ const elementX = elementRect.x - parentRect.x;
502
+
503
+ // Calculate scale and offsets
504
+ const scaleX = screenWidth / elementWidth;
505
+ const scaleY = screenHeight / elementHeight;
506
+ const scale = Math.min(scaleX, scaleY);
507
+
508
+ const transformOrigin =
509
+ window.getComputedStyle(targetElement).transformOrigin;
510
+ const [originX, originY] = transformOrigin.split(" ");
511
+ const originXValue = parseFloat(originX);
512
+ const originYValue = parseFloat(originY);
513
+
514
+ const offsetX =
515
+ (screenWidth - elementWidth * scale) / 2 -
516
+ originXValue * (1 - scale);
517
+ const offsetY =
518
+ (screenHeight - elementHeight * scale) / 2.5 -
519
+ originYValue * (1 - scale);
520
+
521
+ // Apply scale and offsets to the element
522
+ targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
523
+
524
+ // Update global variables
525
+ elemData[elemId].zoomLevel = scale;
526
+ elemData[elemId].panX = offsetX;
527
+ elemData[elemId].panY = offsetY;
528
+
529
+ fullScreenMode = false;
530
+ toggleOverlap("off");
531
+ }
532
+
533
+ /**
534
+ * This function fits the target element to the screen by calculating
535
+ * the required scale and offsets. It also updates the global variables
536
+ * zoomLevel, panX, and panY to reflect the new state.
537
+ */
538
+
539
+ // Fullscreen mode
540
+ function fitToScreen() {
541
+ const canvas = gradioApp().querySelector(
542
+ `${elemId} canvas[key="interface"]`
543
+ );
544
+
545
+ if (!canvas) return;
546
+
547
+ if (canvas.offsetWidth > 862) {
548
+ targetElement.style.width = canvas.offsetWidth + "px";
549
+ }
550
+
551
+ if (fullScreenMode) {
552
+ resetZoom();
553
+ fullScreenMode = false;
554
+ return;
555
+ }
556
+
557
+ //Reset Zoom
558
+ targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
559
+
560
+ // Get scrollbar width to right-align the image
561
+ const scrollbarWidth =
562
+ window.innerWidth - document.documentElement.clientWidth;
563
+
564
+ // Get element and screen dimensions
565
+ const elementWidth = targetElement.offsetWidth;
566
+ const elementHeight = targetElement.offsetHeight;
567
+ const screenWidth = window.innerWidth - scrollbarWidth;
568
+ const screenHeight = window.innerHeight;
569
+
570
+ // Get element's coordinates relative to the page
571
+ const elementRect = targetElement.getBoundingClientRect();
572
+ const elementY = elementRect.y;
573
+ const elementX = elementRect.x;
574
+
575
+ // Calculate scale and offsets
576
+ const scaleX = screenWidth / elementWidth;
577
+ const scaleY = screenHeight / elementHeight;
578
+ const scale = Math.min(scaleX, scaleY);
579
+
580
+ // Get the current transformOrigin
581
+ const computedStyle = window.getComputedStyle(targetElement);
582
+ const transformOrigin = computedStyle.transformOrigin;
583
+ const [originX, originY] = transformOrigin.split(" ");
584
+ const originXValue = parseFloat(originX);
585
+ const originYValue = parseFloat(originY);
586
+
587
+ // Calculate offsets with respect to the transformOrigin
588
+ const offsetX =
589
+ (screenWidth - elementWidth * scale) / 2 -
590
+ elementX -
591
+ originXValue * (1 - scale);
592
+ const offsetY =
593
+ (screenHeight - elementHeight * scale) / 2 -
594
+ elementY -
595
+ originYValue * (1 - scale);
596
+
597
+ // Apply scale and offsets to the element
598
+ targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
599
+
600
+ // Update global variables
601
+ elemData[elemId].zoomLevel = scale;
602
+ elemData[elemId].panX = offsetX;
603
+ elemData[elemId].panY = offsetY;
604
+
605
+ fullScreenMode = true;
606
+ toggleOverlap("on");
607
+ }
608
+
609
+ // Handle keydown events
610
+ function handleKeyDown(event) {
611
+ const hotkeyActions = {
612
+ [hotkeysConfig.canvas_hotkey_reset]: resetZoom,
613
+ [hotkeysConfig.canvas_hotkey_overlap]: toggleOverlap,
614
+ [hotkeysConfig.canvas_hotkey_fullscreen]: fitToScreen
615
+ };
616
+
617
+ const action = hotkeyActions[event.code];
618
+ if (action) {
619
+ event.preventDefault();
620
+ action(event);
621
+ }
622
+
623
+ if (
624
+ isModifierKey(event, hotkeysConfig.canvas_hotkey_zoom) ||
625
+ isModifierKey(event, hotkeysConfig.canvas_hotkey_adjust)
626
+ ) {
627
+ event.preventDefault();
628
+ }
629
+ }
630
+
631
+ // Get Mouse position
632
+ function getMousePosition(e) {
633
+ mouseX = e.offsetX;
634
+ mouseY = e.offsetY;
635
+ }
636
+
637
+ targetElement.addEventListener("mousemove", getMousePosition);
638
+
639
+ // Handle events only inside the targetElement
640
+ let isKeyDownHandlerAttached = false;
641
+
642
+ function handleMouseMove() {
643
+ if (!isKeyDownHandlerAttached) {
644
+ document.addEventListener("keydown", handleKeyDown);
645
+ isKeyDownHandlerAttached = true;
646
+
647
+ activeElement = elemId;
648
+ }
649
+ }
650
+
651
+ function handleMouseLeave() {
652
+ if (isKeyDownHandlerAttached) {
653
+ document.removeEventListener("keydown", handleKeyDown);
654
+ isKeyDownHandlerAttached = false;
655
+
656
+ activeElement = null;
657
+ }
658
+ }
659
+
660
+ // Add mouse event handlers
661
+ targetElement.addEventListener("mousemove", handleMouseMove);
662
+ targetElement.addEventListener("mouseleave", handleMouseLeave);
663
+
664
+ // Reset zoom when click on another tab
665
+ elements.img2imgTabs.addEventListener("click", resetZoom);
666
+ elements.img2imgTabs.addEventListener("click", () => {
667
+ // targetElement.style.width = "";
668
+ if (parseInt(targetElement.style.width) > 865) {
669
+ setTimeout(fitToElement, 0);
670
+ }
671
+ });
672
+
673
+ targetElement.addEventListener("wheel", e => {
674
+ // change zoom level
675
+ const operation = e.deltaY > 0 ? "-" : "+";
676
+ changeZoomLevel(operation, e);
677
+
678
+ // Handle brush size adjustment with ctrl key pressed
679
+ if (isModifierKey(e, hotkeysConfig.canvas_hotkey_adjust)) {
680
+ e.preventDefault();
681
+
682
+ // Increase or decrease brush size based on scroll direction
683
+ adjustBrushSize(elemId, e.deltaY);
684
+ }
685
+ });
686
+
687
+ // Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
688
+ function handleMoveKeyDown(e) {
689
+ if (e.code === hotkeysConfig.canvas_hotkey_move) {
690
+ if (!e.ctrlKey && !e.metaKey && isKeyDownHandlerAttached) {
691
+ e.preventDefault();
692
+ document.activeElement.blur();
693
+ isMoving = true;
694
+ }
695
+ }
696
+ }
697
+
698
+ function handleMoveKeyUp(e) {
699
+ if (e.code === hotkeysConfig.canvas_hotkey_move) {
700
+ isMoving = false;
701
+ }
702
+ }
703
+
704
+ document.addEventListener("keydown", handleMoveKeyDown);
705
+ document.addEventListener("keyup", handleMoveKeyUp);
706
+
707
+ // Detect zoom level and update the pan speed.
708
+ function updatePanPosition(movementX, movementY) {
709
+ let panSpeed = 2;
710
+
711
+ if (elemData[elemId].zoomLevel > 8) {
712
+ panSpeed = 3.5;
713
+ }
714
+
715
+ elemData[elemId].panX += movementX * panSpeed;
716
+ elemData[elemId].panY += movementY * panSpeed;
717
+
718
+ // Delayed redraw of an element
719
+ requestAnimationFrame(() => {
720
+ targetElement.style.transform = `translate(${elemData[elemId].panX}px, ${elemData[elemId].panY}px) scale(${elemData[elemId].zoomLevel})`;
721
+ toggleOverlap("on");
722
+ });
723
+ }
724
+
725
+ function handleMoveByKey(e) {
726
+ if (isMoving && elemId === activeElement) {
727
+ updatePanPosition(e.movementX, e.movementY);
728
+ targetElement.style.pointerEvents = "none";
729
+ } else {
730
+ targetElement.style.pointerEvents = "auto";
731
+ }
732
+ }
733
+
734
+ // Prevents sticking to the mouse
735
+ window.onblur = function() {
736
+ isMoving = false;
737
+ };
738
+
739
+ gradioApp().addEventListener("mousemove", handleMoveByKey);
740
+ }
741
+
742
+ applyZoomAndPan(elementIDs.sketch);
743
+ applyZoomAndPan(elementIDs.inpaint);
744
+ applyZoomAndPan(elementIDs.inpaintSketch);
745
+
746
+ // Make the function global so that other extensions can take advantage of this solution
747
+ window.applyZoomAndPan = applyZoomAndPan;
748
+ });
extensions-builtin/canvas-zoom-and-pan/scripts/hotkey_config.py ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from modules import shared
3
+
4
+ shared.options_templates.update(shared.options_section(('canvas_hotkey', "Canvas Hotkeys"), {
5
+ "canvas_hotkey_zoom": shared.OptionInfo("Alt", "Zoom canvas", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
6
+ "canvas_hotkey_adjust": shared.OptionInfo("Ctrl", "Adjust brush size", gr.Radio, {"choices": ["Shift","Ctrl", "Alt"]}).info("If you choose 'Shift' you cannot scroll horizontally, 'Alt' can cause a little trouble in firefox"),
7
+ "canvas_hotkey_move": shared.OptionInfo("F", "Moving the canvas").info("To work correctly in firefox, turn off 'Automatically search the page text when typing' in the browser settings"),
8
+ "canvas_hotkey_fullscreen": shared.OptionInfo("S", "Fullscreen Mode, maximizes the picture so that it fits into the screen and stretches it to its full width "),
9
+ "canvas_hotkey_reset": shared.OptionInfo("R", "Reset zoom and canvas positon"),
10
+ "canvas_hotkey_overlap": shared.OptionInfo("O", "Toggle overlap").info("Technical button, neededs for testing"),
11
+ "canvas_show_tooltip": shared.OptionInfo(True, "Enable tooltip on the canvas"),
12
+ "canvas_disabled_functions": shared.OptionInfo(["Overlap"], "Disable function that you don't use", gr.CheckboxGroup, {"choices": ["Zoom","Adjust brush size", "Moving canvas","Fullscreen","Reset Zoom","Overlap"]}),
13
+ }))
extensions-builtin/canvas-zoom-and-pan/style.css ADDED
@@ -0,0 +1,63 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ .canvas-tooltip-info {
2
+ position: absolute;
3
+ top: 10px;
4
+ left: 10px;
5
+ cursor: help;
6
+ background-color: rgba(0, 0, 0, 0.3);
7
+ width: 20px;
8
+ height: 20px;
9
+ border-radius: 50%;
10
+ display: flex;
11
+ align-items: center;
12
+ justify-content: center;
13
+ flex-direction: column;
14
+
15
+ z-index: 100;
16
+ }
17
+
18
+ .canvas-tooltip-info::after {
19
+ content: '';
20
+ display: block;
21
+ width: 2px;
22
+ height: 7px;
23
+ background-color: white;
24
+ margin-top: 2px;
25
+ }
26
+
27
+ .canvas-tooltip-info::before {
28
+ content: '';
29
+ display: block;
30
+ width: 2px;
31
+ height: 2px;
32
+ background-color: white;
33
+ }
34
+
35
+ .canvas-tooltip-content {
36
+ display: none;
37
+ background-color: #f9f9f9;
38
+ color: #333;
39
+ border: 1px solid #ddd;
40
+ padding: 15px;
41
+ position: absolute;
42
+ top: 40px;
43
+ left: 10px;
44
+ width: 250px;
45
+ font-size: 16px;
46
+ opacity: 0;
47
+ border-radius: 8px;
48
+ box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2);
49
+
50
+ z-index: 100;
51
+ }
52
+
53
+ .canvas-tooltip:hover .canvas-tooltip-content {
54
+ display: block;
55
+ animation: fadeIn 0.5s;
56
+ opacity: 1;
57
+ }
58
+
59
+ @keyframes fadeIn {
60
+ from {opacity: 0;}
61
+ to {opacity: 1;}
62
+ }
63
+
extensions-builtin/extra-options-section/scripts/extra_options_section.py ADDED
@@ -0,0 +1,48 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import gradio as gr
2
+ from modules import scripts, shared, ui_components, ui_settings
3
+ from modules.ui_components import FormColumn
4
+
5
+
6
+ class ExtraOptionsSection(scripts.Script):
7
+ section = "extra_options"
8
+
9
+ def __init__(self):
10
+ self.comps = None
11
+ self.setting_names = None
12
+
13
+ def title(self):
14
+ return "Extra options"
15
+
16
+ def show(self, is_img2img):
17
+ return scripts.AlwaysVisible
18
+
19
+ def ui(self, is_img2img):
20
+ self.comps = []
21
+ self.setting_names = []
22
+
23
+ with gr.Blocks() as interface:
24
+ with gr.Accordion("Options", open=False) if shared.opts.extra_options_accordion and shared.opts.extra_options else gr.Group(), gr.Row():
25
+ for setting_name in shared.opts.extra_options:
26
+ with FormColumn():
27
+ comp = ui_settings.create_setting_component(setting_name)
28
+
29
+ self.comps.append(comp)
30
+ self.setting_names.append(setting_name)
31
+
32
+ def get_settings_values():
33
+ return [ui_settings.get_value_for_setting(key) for key in self.setting_names]
34
+
35
+ interface.load(fn=get_settings_values, inputs=[], outputs=self.comps, queue=False, show_progress=False)
36
+
37
+ return self.comps
38
+
39
+ def before_process(self, p, *args):
40
+ for name, value in zip(self.setting_names, args):
41
+ if name not in p.override_settings:
42
+ p.override_settings[name] = value
43
+
44
+
45
+ shared.options_templates.update(shared.options_section(('ui', "User interface"), {
46
+ "extra_options": shared.OptionInfo([], "Options in main UI", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that also appear in txt2img/img2img interfaces").needs_restart(),
47
+ "extra_options_accordion": shared.OptionInfo(False, "Place options in main UI into an accordion")
48
+ }))
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
+ });
html/card-no-preview.png ADDED
html/extra-networks-card.html ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <div class='card' style={style} onclick={card_clicked} {sort_keys}>
2
+ {background_image}
3
+ {metadata_button}
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{search_only}'>{search_term}</span>
10
+ </div>
11
+ <span class='name'>{name}</span>
12
+ <span class='description'>{description}</span>
13
+ </div>
14
+ </div>
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 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ <div>
2
+ <a href="{api_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="showProfile('./internal/profile-startup'); return false;">Startup profile</a>
9
+  • 
10
+ <a href="/" onclick="javascript:gradioApp().getElementById('settings_restart_gradio').click(); return false">Reload UI</a>
11
+ </div>
12
+ <br />
13
+ <div class="versions">
14
+ {versions}
15
+ </div>
html/image-update.svg ADDED
html/licenses.html ADDED
@@ -0,0 +1,690 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ in the Software without restriction, including without limitation the rights
118
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
119
+ copies of the Software, and to permit persons to whom the Software is
120
+ furnished to do so, subject to the following conditions:
121
+
122
+ The above copyright notice and this permission notice shall be included in all
123
+ copies or substantial portions of the Software.
124
+
125
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
126
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
127
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
128
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
129
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
130
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
131
+ SOFTWARE.
132
+ </pre>
133
+
134
+ <h2><a href="https://github.com/Hafiidz/latent-diffusion/blob/main/LICENSE">LDSR</a></h2>
135
+ <small>Code added by contirubtors, most likely copied from this repository.</small>
136
+ <pre>
137
+ MIT License
138
+
139
+ Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich
140
+
141
+ Permission is hereby granted, free of charge, to any person obtaining a copy
142
+ of this software and associated documentation files (the "Software"), to deal
143
+ in the Software without restriction, including without limitation the rights
144
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
145
+ copies of the Software, and to permit persons to whom the Software is
146
+ furnished to do so, subject to the following conditions:
147
+
148
+ The above copyright notice and this permission notice shall be included in all
149
+ copies or substantial portions of the Software.
150
+
151
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
152
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
153
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
154
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
155
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
156
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
157
+ SOFTWARE.
158
+ </pre>
159
+
160
+ <h2><a href="https://github.com/pharmapsychotic/clip-interrogator/blob/main/LICENSE">CLIP Interrogator</a></h2>
161
+ <small>Some small amounts of code borrowed and reworked.</small>
162
+ <pre>
163
+ MIT License
164
+
165
+ Copyright (c) 2022 pharmapsychotic
166
+
167
+ Permission is hereby granted, free of charge, to any person obtaining a copy
168
+ of this software and associated documentation files (the "Software"), to deal
169
+ in the Software without restriction, including without limitation the rights
170
+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
171
+ copies of the Software, and to permit persons to whom the Software is
172
+ furnished to do so, subject to the following conditions:
173
+
174
+ The above copyright notice and this permission notice shall be included in all
175
+ copies or substantial portions of the Software.
176
+
177
+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
178
+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
179
+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
180
+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
181
+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
182
+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
183
+ SOFTWARE.
184
+ </pre>
185
+
186
+ <h2><a href="https://github.com/JingyunLiang/SwinIR/blob/main/LICENSE">SwinIR</a></h2>
187
+ <small>Code added by contributors, most likely copied from this repository.</small>
188
+
189
+ <pre>
190
+ Apache License
191
+ Version 2.0, January 2004
192
+ http://www.apache.org/licenses/
193
+
194
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
195
+
196
+ 1. Definitions.
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396
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+
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+ <h2><a href="https://github.com/huggingface/diffusers/blob/c7da8fd23359a22d0df2741688b5b4f33c26df21/LICENSE">Scaled Dot Product Attention</a></h2>
421
+ <small>Some small amounts of code borrowed and reworked.</small>
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@@ -0,0 +1,113 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ let currentWidth = null;
3
+ let currentHeight = null;
4
+ let arFrameTimeout = setTimeout(function() {}, 0);
5
+
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+ 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
+
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+ var targetElement = null;
22
+
23
+ var tabIndex = get_tab_index('mode_img2img');
24
+ if (tabIndex == 0) { // img2img
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+ targetElement = gradioApp().querySelector('#img2img_image div[data-testid=image] img');
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+ } else if (tabIndex == 1) { //Sketch
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+ targetElement = gradioApp().querySelector('#img2img_sketch div[data-testid=image] img');
28
+ } else if (tabIndex == 2) { // Inpaint
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+ targetElement = gradioApp().querySelector('#img2maskimg div[data-testid=image] img');
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+ } else if (tabIndex == 3) { // Inpaint sketch
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+ targetElement = gradioApp().querySelector('#inpaint_sketch div[data-testid=image] img');
32
+ }
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+
34
+
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+ if (targetElement) {
36
+
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+ var arPreviewRect = gradioApp().querySelector('#imageARPreview');
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+ if (!arPreviewRect) {
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+ arPreviewRect = document.createElement('div');
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+ arPreviewRect.id = "imageARPreview";
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+ gradioApp().appendChild(arPreviewRect);
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+ }
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+
44
+
45
+
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+ 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
+
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+ var cleintRectTop = (viewportOffset.top + window.scrollY);
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+ var cleintRectLeft = (viewportOffset.left + window.scrollX);
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+ var cleintRectCentreY = cleintRectTop + (targetElement.clientHeight / 2);
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+ 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
+
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+ var arRectTop = cleintRectCentreY - (arscaledy / 2);
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+ var arRectLeft = cleintRectCentreX - (arscaledx / 2);
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+ var arRectWidth = arscaledx;
65
+ var arRectHeight = arscaledy;
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+
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+ arPreviewRect.style.top = arRectTop + 'px';
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+ arPreviewRect.style.left = arRectLeft + 'px';
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+ 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
+ onAfterUiUpdate(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) {
100
+ dimensionChange(e, is_width, is_height);
101
+ });
102
+ e.classList.add('scrollwatch');
103
+ }
104
+ if (is_width) {
105
+ currentWidth = e.value * 1.0;
106
+ }
107
+ if (is_height) {
108
+ currentHeight = e.value * 1.0;
109
+ }
110
+ });
111
+ }
112
+ }
113
+ });