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  1. .eslintignore +4 -0
  2. .eslintrc.js +97 -0
  3. .git-blame-ignore-revs +2 -0
  4. .github/ISSUE_TEMPLATE/bug_report.yml +74 -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 +38 -0
  9. .github/workflows/run_tests.yaml +73 -0
  10. .github/workflows/warns_merge_master.yml +19 -0
  11. .gitignore +39 -0
  12. .pylintrc +3 -0
  13. CHANGELOG.md +507 -0
  14. CITATION.cff +7 -0
  15. CODEOWNERS +12 -0
  16. LICENSE.txt +663 -0
  17. README.md +177 -0
  18. configs/alt-diffusion-inference.yaml +72 -0
  19. configs/instruct-pix2pix.yaml +98 -0
  20. configs/v1-inference.yaml +70 -0
  21. configs/v1-inpainting-inference.yaml +70 -0
  22. embeddings/Place Textual Inversion embeddings here.txt +0 -0
  23. environment-wsl2.yaml +11 -0
  24. extensions-builtin/LDSR/ldsr_model_arch.py +250 -0
  25. extensions-builtin/LDSR/preload.py +6 -0
  26. extensions-builtin/LDSR/scripts/ldsr_model.py +68 -0
  27. extensions-builtin/LDSR/sd_hijack_autoencoder.py +293 -0
  28. extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +1443 -0
  29. extensions-builtin/LDSR/vqvae_quantize.py +147 -0
  30. extensions-builtin/Lora/extra_networks_lora.py +67 -0
  31. extensions-builtin/Lora/lora.py +9 -0
  32. extensions-builtin/Lora/lora_patches.py +31 -0
  33. extensions-builtin/Lora/lyco_helpers.py +21 -0
  34. extensions-builtin/Lora/network.py +158 -0
  35. extensions-builtin/Lora/network_full.py +27 -0
  36. extensions-builtin/Lora/network_hada.py +55 -0
  37. extensions-builtin/Lora/network_ia3.py +30 -0
  38. extensions-builtin/Lora/network_lokr.py +64 -0
  39. extensions-builtin/Lora/network_lora.py +86 -0
  40. extensions-builtin/Lora/network_norm.py +28 -0
  41. extensions-builtin/Lora/networks.py +571 -0
  42. extensions-builtin/Lora/preload.py +7 -0
  43. extensions-builtin/Lora/scripts/lora_script.py +99 -0
  44. extensions-builtin/Lora/ui_edit_user_metadata.py +217 -0
  45. extensions-builtin/Lora/ui_extra_networks_lora.py +79 -0
  46. extensions-builtin/ScuNET/preload.py +6 -0
  47. extensions-builtin/ScuNET/scripts/scunet_model.py +144 -0
  48. extensions-builtin/ScuNET/scunet_model_arch.py +268 -0
  49. extensions-builtin/SwinIR/preload.py +6 -0
  50. extensions-builtin/SwinIR/scripts/swinir_model.py +192 -0
.eslintignore ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ extensions
2
+ extensions-disabled
3
+ repositories
4
+ venv
.eslintrc.js ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ // localStorage.js
91
+ localSet: "readonly",
92
+ localGet: "readonly",
93
+ localRemove: "readonly",
94
+ // resizeHandle.js
95
+ setupResizeHandle: "writable"
96
+ }
97
+ };
.git-blame-ignore-revs ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ # Apply ESlint
2
+ 9c54b78d9dde5601e916f308d9a9d6953ec39430
.github/ISSUE_TEMPLATE/bug_report.yml ADDED
@@ -0,0 +1,74 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 instructions 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 us what you think the normal behavior should be
41
+ validations:
42
+ required: true
43
+ - type: textarea
44
+ id: sysinfo
45
+ attributes:
46
+ label: Sysinfo
47
+ description: System info file, generated by WebUI. You can generate it in settings, on the Sysinfo page. Drag the file into the field to upload it. If you submit your report without including the sysinfo file, the report will be closed. If needed, review the report to make sure it includes no personal information you don't want to share. If you can't start WebUI, you can use --dump-sysinfo commandline argument to generate the file.
48
+ validations:
49
+ required: true
50
+ - type: dropdown
51
+ id: browsers
52
+ attributes:
53
+ label: What browsers do you use to access the UI ?
54
+ multiple: true
55
+ options:
56
+ - Mozilla Firefox
57
+ - Google Chrome
58
+ - Brave
59
+ - Apple Safari
60
+ - Microsoft Edge
61
+ - Other
62
+ - type: textarea
63
+ id: logs
64
+ attributes:
65
+ label: Console logs
66
+ 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.
67
+ render: Shell
68
+ validations:
69
+ required: true
70
+ - type: textarea
71
+ id: misc
72
+ attributes:
73
+ label: Additional information
74
+ 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 +1,38 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Linter
2
+
3
+ on:
4
+ - push
5
+ - pull_request
6
+
7
+ jobs:
8
+ lint-python:
9
+ name: ruff
10
+ runs-on: ubuntu-latest
11
+ if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
12
+ steps:
13
+ - name: Checkout Code
14
+ uses: actions/checkout@v3
15
+ - uses: actions/setup-python@v4
16
+ with:
17
+ python-version: 3.11
18
+ # NB: there's no cache: pip here since we're not installing anything
19
+ # from the requirements.txt file(s) in the repository; it's faster
20
+ # not to have GHA download an (at the time of writing) 4 GB cache
21
+ # of PyTorch and other dependencies.
22
+ - name: Install Ruff
23
+ run: pip install ruff==0.0.272
24
+ - name: Run Ruff
25
+ run: ruff .
26
+ lint-js:
27
+ name: eslint
28
+ runs-on: ubuntu-latest
29
+ if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
30
+ steps:
31
+ - name: Checkout Code
32
+ uses: actions/checkout@v3
33
+ - name: Install Node.js
34
+ uses: actions/setup-node@v3
35
+ with:
36
+ node-version: 18
37
+ - run: npm i --ci
38
+ - run: npm run lint
.github/workflows/run_tests.yaml ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Tests
2
+
3
+ on:
4
+ - push
5
+ - pull_request
6
+
7
+ jobs:
8
+ test:
9
+ name: tests on CPU with empty model
10
+ runs-on: ubuntu-latest
11
+ if: github.event_name != 'pull_request' || github.event.pull_request.head.repo.full_name != github.event.pull_request.base.repo.full_name
12
+ steps:
13
+ - name: Checkout Code
14
+ uses: actions/checkout@v3
15
+ - name: Set up Python 3.10
16
+ uses: actions/setup-python@v4
17
+ with:
18
+ python-version: 3.10.6
19
+ cache: pip
20
+ cache-dependency-path: |
21
+ **/requirements*txt
22
+ launch.py
23
+ - name: Install test dependencies
24
+ run: pip install wait-for-it -r requirements-test.txt
25
+ env:
26
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
27
+ PIP_PROGRESS_BAR: "off"
28
+ - name: Setup environment
29
+ run: python launch.py --skip-torch-cuda-test --exit
30
+ env:
31
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
32
+ PIP_PROGRESS_BAR: "off"
33
+ TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
34
+ WEBUI_LAUNCH_LIVE_OUTPUT: "1"
35
+ PYTHONUNBUFFERED: "1"
36
+ - name: Start test server
37
+ run: >
38
+ python -m coverage run
39
+ --data-file=.coverage.server
40
+ launch.py
41
+ --skip-prepare-environment
42
+ --skip-torch-cuda-test
43
+ --test-server
44
+ --do-not-download-clip
45
+ --no-half
46
+ --disable-opt-split-attention
47
+ --use-cpu all
48
+ --api-server-stop
49
+ 2>&1 | tee output.txt &
50
+ - name: Run tests
51
+ run: |
52
+ wait-for-it --service 127.0.0.1:7860 -t 600
53
+ python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
54
+ - name: Kill test server
55
+ if: always()
56
+ run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
57
+ - name: Show coverage
58
+ run: |
59
+ python -m coverage combine .coverage*
60
+ python -m coverage report -i
61
+ python -m coverage html -i
62
+ - name: Upload main app output
63
+ uses: actions/upload-artifact@v3
64
+ if: always()
65
+ with:
66
+ name: output
67
+ path: output.txt
68
+ - name: Upload coverage HTML
69
+ uses: actions/upload-artifact@v3
70
+ if: always()
71
+ with:
72
+ name: htmlcov
73
+ path: htmlcov
.github/workflows/warns_merge_master.yml ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Pull requests can't target master branch
2
+
3
+ "on":
4
+ pull_request:
5
+ types:
6
+ - opened
7
+ - synchronize
8
+ - reopened
9
+ branches:
10
+ - master
11
+
12
+ jobs:
13
+ check:
14
+ runs-on: ubuntu-latest
15
+ steps:
16
+ - name: Warning marge into master
17
+ run: |
18
+ echo -e "::warning::This pull request directly merge into \"master\" branch, normally development happens on \"dev\" branch."
19
+ exit 1
.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 +1,507 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ## 1.6.0
2
+
3
+ ### Features:
4
+ * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371)
5
+ * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards
6
+ * add style editor dialog
7
+ * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181))
8
+ * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227))
9
+ * new samplers: Restart, DPM++ 2M SDE Exponential, DPM++ 2M SDE Heun, DPM++ 2M SDE Heun Karras, DPM++ 2M SDE Heun Exponential, DPM++ 3M SDE, DPM++ 3M SDE Karras, DPM++ 3M SDE Exponential ([#12300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12300), [#12519](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12519), [#12542](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12542))
10
+ * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers:
11
+ * makes all of them work with img2img
12
+ * makes prompt composition posssible (AND)
13
+ * makes them available for SDXL
14
+ * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808))
15
+ * use less RAM when creating models ([#11958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11958), [#12599](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12599))
16
+ * textual inversion inference support for SDXL
17
+ * extra networks UI: show metadata for SD checkpoints
18
+ * checkpoint merger: add metadata support
19
+ * prompt editing and attention: add support for whitespace after the number ([ red : green : 0.5 ]) (seed breaking change) ([#12177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12177))
20
+ * VAE: allow selecting own VAE for each checkpoint (in user metadata editor)
21
+ * VAE: add selected VAE to infotext
22
+ * options in main UI: add own separate setting for txt2img and img2img, correctly read values from pasted infotext, add setting for column count ([#12551](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12551))
23
+ * add resize handle to txt2img and img2img tabs, allowing to change the amount of horizontable space given to generation parameters and resulting image gallery ([#12687](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12687), [#12723](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12723))
24
+ * change default behavior for batching cond/uncond -- now it's on by default, and is disabled by an UI setting (Optimizatios -> Batch cond/uncond) - if you are on lowvram/medvram and are getting OOM exceptions, you will need to enable it
25
+ * show current position in queue and make it so that requests are processed in the order of arrival ([#12707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12707))
26
+ * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models
27
+ * prompt editing timeline has separate range for first pass and hires-fix pass (seed breaking change) ([#12457](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12457))
28
+
29
+ ### Minor:
30
+ * img2img batch: RAM savings, VRAM savings, .tif, .tiff in img2img batch ([#12120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12120), [#12514](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12514), [#12515](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12515))
31
+ * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479))
32
+ * XYZ: in the axis labels, remove pathnames from model filenames
33
+ * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298))
34
+ * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491))
35
+ * add gradio version warning
36
+ * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297))
37
+ * use transparent white for mask in inpainting, along with an option to select the color ([#12326](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12326))
38
+ * move some settings to their own section: img2img, VAE
39
+ * add checkbox to show/hide dirs for extra networks
40
+ * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311))
41
+ * gradio theme cache, new gradio themes, along with explanation that the user can input his own values ([#12346](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12346), [#12355](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12355))
42
+ * sampler fixes/tweaks: s_tmax, s_churn, s_noise, s_tmax ([#12354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12354), [#12356](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12356), [#12357](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12357), [#12358](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12358), [#12375](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12375), [#12521](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12521))
43
+ * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352))
44
+ * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338))
45
+ * enable cond cache by default
46
+ * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230))
47
+ * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379))
48
+ * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254))
49
+ * put commonly used samplers on top, make DPM++ 2M Karras the default choice
50
+ * zoom and pan: option to auto-expand a wide image, improved integration ([#12413](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12413), [#12727](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12727))
51
+ * option to cache Lora networks in memory
52
+ * rework hires fix UI to use accordion
53
+ * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back
54
+ * change quicksettings items to have variable width
55
+ * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503))
56
+ * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console
57
+ * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510))
58
+ * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564))
59
+ * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584))
60
+ * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634))
61
+ * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648))
62
+ * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645))
63
+ * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639))
64
+ * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635))
65
+ * make progress bar work independently from live preview display which results in it being updated a lot more often
66
+ * forbid Full live preview method for medvram and add a setting to undo the forbidding
67
+ * make it possible to localize tooltips and placeholders
68
+ * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
69
+ * Restore faces and Tiling generation parameters have been moved to settings out of main UI
70
+ * if you want to put them back into main UI, use `Options in main UI` setting on the UI page.
71
+
72
+ ### Extensions and API:
73
+ * gradio 3.41.2
74
+ * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd
75
+ * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world')
76
+ * properly clear the total console progressbar when using txt2img and img2img from API
77
+ * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294))
78
+ * shared.py and webui.py split into many files
79
+ * add --loglevel commandline argument for logging
80
+ * add a custom UI element that combines accordion and checkbox
81
+ * avoid importing gradio in tests because it spams warnings
82
+ * put infotext label for setting into OptionInfo definition rather than in a separate list
83
+ * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470))
84
+ * option to make scripts UI without gr.Group
85
+ * add a way for scripts to register a callback for before/after just a single component's creation
86
+ * use dataclass for StableDiffusionProcessing
87
+ * store patches for Lora in a specialized module instead of inside torch
88
+ * support http/https URLs in API ([#12663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12663), [#12698](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12698))
89
+ * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616))
90
+ * dump current stack traces when exiting with SIGINT
91
+ * add type annotations for extra fields of shared.sd_model
92
+
93
+ ### Bug Fixes:
94
+ * Don't crash if out of local storage quota for javascriot localStorage
95
+ * XYZ plot do not fail if an exception occurs
96
+ * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269))
97
+ * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307))
98
+ * fix sdxl model invalid configuration after the hijack
99
+ * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304))
100
+ * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318))
101
+ * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319))
102
+ * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327))
103
+ * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387))
104
+ * fix options in main UI misbehaving when there's just one element
105
+ * make it possible to use a sampler from infotext even if it's hidden in the dropdown
106
+ * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images
107
+ * prevent bogus progress output in console when calculating hires fix dimensions
108
+ * fix --use-textbox-seed
109
+ * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466))
110
+ * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463))
111
+ * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526))
112
+ * add second_order to samplers that mistakenly didn't have it
113
+ * when refreshing cards in extra networks UI, do not discard user's custom resolution
114
+ * fix processing error that happens if batch_size is not a multiple of how many prompts/negative prompts there are ([#12509](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12509))
115
+ * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588))
116
+ * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586))
117
+ * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596))
118
+ * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603))
119
+ * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607))
120
+ * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638))
121
+ * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633))
122
+ * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630))
123
+ * implement missing undo hijack for SDXL
124
+ * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684))
125
+ * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689))
126
+ * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685))
127
+ * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667))
128
+ * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717))
129
+ * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version
130
+ * set devices.dtype_unet correctly
131
+ * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
132
+ * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745))
133
+ * fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
134
+ * fix defaults settings page breaking when any of main UI tabs are hidden
135
+ * fix incorrect save/display of new values in Defaults page in settings
136
+ * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
137
+ * fix an error that prevents VAE being reloaded after an option change if a VAE near the checkpoint exists ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
138
+ * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
139
+ * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
140
+ * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
141
+ * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity)
142
+ * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
143
+ * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
144
+ * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855))
145
+ * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
146
+ * get progressbar to display correctly in extensions tab
147
+
148
+
149
+ ## 1.5.2
150
+
151
+ ### Bug Fixes:
152
+ * fix memory leak when generation fails
153
+ * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk
154
+
155
+
156
+ ## 1.5.1
157
+
158
+ ### Minor:
159
+ * support parsing text encoder blocks in some new LoRAs
160
+ * delete scale checker script due to user demand
161
+
162
+ ### Extensions and API:
163
+ * add postprocess_batch_list script callback
164
+
165
+ ### Bug Fixes:
166
+ * fix TI training for SD1
167
+ * fix reload altclip model error
168
+ * prepend the pythonpath instead of overriding it
169
+ * fix typo in SD_WEBUI_RESTARTING
170
+ * if txt2img/img2img raises an exception, finally call state.end()
171
+ * fix composable diffusion weight parsing
172
+ * restyle Startup profile for black users
173
+ * fix webui not launching with --nowebui
174
+ * catch exception for non git extensions
175
+ * fix some options missing from /sdapi/v1/options
176
+ * fix for extension update status always saying "unknown"
177
+ * fix display of extra network cards that have `<>` in the name
178
+ * update lora extension to work with python 3.8
179
+
180
+
181
+ ## 1.5.0
182
+
183
+ ### Features:
184
+ * SD XL support
185
+ * user metadata system for custom networks
186
+ * extended Lora metadata editor: set activation text, default weight, view tags, training info
187
+ * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
188
+ * show github stars for extenstions
189
+ * img2img batch mode can read extra stuff from png info
190
+ * img2img batch works with subdirectories
191
+ * hotkeys to move prompt elements: alt+left/right
192
+ * restyle time taken/VRAM display
193
+ * add textual inversion hashes to infotext
194
+ * optimization: cache git extension repo information
195
+ * move generate button next to the generated picture for mobile clients
196
+ * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface
197
+ * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds
198
+
199
+ ### Minor:
200
+ * checkbox to check/uncheck all extensions in the Installed tab
201
+ * add gradio user to infotext and to filename patterns
202
+ * allow gif for extra network previews
203
+ * add options to change colors in grid
204
+ * use natural sort for items in extra networks
205
+ * Mac: use empty_cache() from torch 2 to clear VRAM
206
+ * added automatic support for installing the right libraries for Navi3 (AMD)
207
+ * add option SWIN_torch_compile to accelerate SwinIR upscale
208
+ * suppress printing TI embedding info at start to console by default
209
+ * speedup extra networks listing
210
+ * added `[none]` filename token.
211
+ * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)
212
+ * add always_discard_next_to_last_sigma option to XYZ plot
213
+ * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.
214
+
215
+ ### Extensions and API:
216
+ * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop
217
+ * allow Script to have custom metaclass
218
+ * add model exists status check /sdapi/v1/options
219
+ * rename --add-stop-route to --api-server-stop
220
+ * add `before_hr` script callback
221
+ * add callback `after_extra_networks_activate`
222
+ * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable
223
+ * return http 404 when thumb file not found
224
+ * allow replacing extensions index with environment variable
225
+
226
+ ### Bug Fixes:
227
+ * fix for catch errors when retrieving extension index #11290
228
+ * fix very slow loading speed of .safetensors files when reading from network drives
229
+ * API cache cleanup
230
+ * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode
231
+ * fix warning of 'has_mps' deprecated from PyTorch
232
+ * fix problem with extra network saving images as previews losing generation info
233
+ * fix throwing exception when trying to resize image with I;16 mode
234
+ * fix for #11534: canvas zoom and pan extension hijacking shortcut keys
235
+ * fixed launch script to be runnable from any directory
236
+ * don't add "Seed Resize: -1x-1" to API image metadata
237
+ * correctly remove end parenthesis with ctrl+up/down
238
+ * fixing --subpath on newer gradio version
239
+ * fix: check fill size none zero when resize (fixes #11425)
240
+ * use submit and blur for quick settings textbox
241
+ * save img2img batch with images.save_image()
242
+ * prevent running preload.py for disabled extensions
243
+ * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included
244
+
245
+
246
+ ## 1.4.1
247
+
248
+ ### Bug Fixes:
249
+ * add queue lock for refresh-checkpoints
250
+
251
+ ## 1.4.0
252
+
253
+ ### Features:
254
+ * zoom controls for inpainting
255
+ * run basic torch calculation at startup in parallel to reduce the performance impact of first generation
256
+ * option to pad prompt/neg prompt to be same length
257
+ * remove taming_transformers dependency
258
+ * custom k-diffusion scheduler settings
259
+ * add an option to show selected settings in main txt2img/img2img UI
260
+ * sysinfo tab in settings
261
+ * infer styles from prompts when pasting params into the UI
262
+ * an option to control the behavior of the above
263
+
264
+ ### Minor:
265
+ * bump Gradio to 3.32.0
266
+ * bump xformers to 0.0.20
267
+ * Add option to disable token counters
268
+ * tooltip fixes & optimizations
269
+ * make it possible to configure filename for the zip download
270
+ * `[vae_filename]` pattern for filenames
271
+ * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE
272
+ * change UI reorder setting to multiselect
273
+ * read version info form CHANGELOG.md if git version info is not available
274
+ * link footer API to Wiki when API is not active
275
+ * persistent conds cache (opt-in optimization)
276
+
277
+ ### Extensions:
278
+ * After installing extensions, webui properly restarts the process rather than reloads the UI
279
+ * Added VAE listing to web API. Via: /sdapi/v1/sd-vae
280
+ * custom unet support
281
+ * Add onAfterUiUpdate callback
282
+ * refactor EmbeddingDatabase.register_embedding() to allow unregistering
283
+ * add before_process callback for scripts
284
+ * add ability for alwayson scripts to specify section and let user reorder those sections
285
+
286
+ ### Bug Fixes:
287
+ * Fix dragging text to prompt
288
+ * fix incorrect quoting for infotext values with colon in them
289
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
290
+ * Fix s_min_uncond default type int
291
+ * Fix for #10643 (Inpainting mask sometimes not working)
292
+ * fix bad styling for thumbs view in extra networks #10639
293
+ * fix for empty list of optimizations #10605
294
+ * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility
295
+ * fix --ui-debug-mode exit
296
+ * patch GitPython to not use leaky persistent processes
297
+ * fix duplicate Cross attention optimization after UI reload
298
+ * torch.cuda.is_available() check for SdOptimizationXformers
299
+ * fix hires fix using wrong conds in second pass if using Loras.
300
+ * handle exception when parsing generation parameters from png info
301
+ * fix upcast attention dtype error
302
+ * forcing Torch Version to 1.13.1 for RX 5000 series GPUs
303
+ * split mask blur into X and Y components, patch Outpainting MK2 accordingly
304
+ * don't die when a LoRA is a broken symlink
305
+ * allow activation of Generate Forever during generation
306
+
307
+
308
+ ## 1.3.2
309
+
310
+ ### Bug Fixes:
311
+ * fix files served out of tmp directory even if they are saved to disk
312
+ * fix postprocessing overwriting parameters
313
+
314
+ ## 1.3.1
315
+
316
+ ### Features:
317
+ * revert default cross attention optimization to Doggettx
318
+
319
+ ### Bug Fixes:
320
+ * fix bug: LoRA don't apply on dropdown list sd_lora
321
+ * fix png info always added even if setting is not enabled
322
+ * fix some fields not applying in xyz plot
323
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
324
+ * fix lora hashes not being added properly to infotex if there is only one lora
325
+ * fix --use-cpu failing to work properly at startup
326
+ * make --disable-opt-split-attention command line option work again
327
+
328
+ ## 1.3.0
329
+
330
+ ### Features:
331
+ * add UI to edit defaults
332
+ * token merging (via dbolya/tomesd)
333
+ * settings tab rework: add a lot of additional explanations and links
334
+ * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup
335
+ * update extensions table: show branch, show date in separate column, and show version from tags if available
336
+ * TAESD - another option for cheap live previews
337
+ * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
338
+ * calculate hashes for Lora
339
+ * add lora hashes to infotext
340
+ * when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
341
+ * select cross attention optimization from UI
342
+
343
+ ### Minor:
344
+ * bump Gradio to 3.31.0
345
+ * bump PyTorch to 2.0.1 for macOS and Linux AMD
346
+ * allow setting defaults for elements in extensions' tabs
347
+ * allow selecting file type for live previews
348
+ * show "Loading..." for extra networks when displaying for the first time
349
+ * suppress ENSD infotext for samplers that don't use it
350
+ * clientside optimizations
351
+ * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories
352
+ * allow whitespace in styles.csv
353
+ * add option to reorder tabs
354
+ * move some functionality (swap resolution and set seed to -1) to client
355
+ * option to specify editor height for img2img
356
+ * button to copy image resolution into img2img width/height sliders
357
+ * switch from pyngrok to ngrok-py
358
+ * lazy-load images in extra networks UI
359
+ * set "Navigate image viewer with gamepad" option to false by default, by request
360
+ * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
361
+ * allow hiding buttons in ui-config.json
362
+
363
+ ### Extensions:
364
+ * add /sdapi/v1/script-info api
365
+ * use Ruff to lint Python code
366
+ * use ESlint to lint Javascript code
367
+ * add/modify CFG callbacks for Self-Attention Guidance extension
368
+ * add command and endpoint for graceful server stopping
369
+ * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
370
+ * 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)
371
+ * add /sdapi/v1/refresh-loras api checkpoint post request
372
+ * tests overhaul
373
+
374
+ ### Bug Fixes:
375
+ * fix an issue preventing the program from starting if the user specifies a bad Gradio theme
376
+ * fix broken prompts from file script
377
+ * fix symlink scanning for extra networks
378
+ * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS
379
+ * allow web UI to be ran fully offline
380
+ * fix inability to run with --freeze-settings
381
+ * fix inability to merge checkpoint without adding metadata
382
+ * fix extra networks' save preview image not adding infotext for jpeg/webm
383
+ * remove blinking effect from text in hires fix and scale resolution preview
384
+ * make links to `http://<...>.git` extensions work in the extension tab
385
+ * fix bug with webui hanging at startup due to hanging git process
386
+
387
+
388
+ ## 1.2.1
389
+
390
+ ### Features:
391
+ * add an option to always refer to LoRA by filenames
392
+
393
+ ### Bug Fixes:
394
+ * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none
395
+ * fix upscalers disappearing after the user reloads UI
396
+ * allow bf16 in safe unpickler (resolves problems with loading some LoRAs)
397
+ * allow web UI to be ran fully offline
398
+ * fix localizations not working
399
+ * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'`
400
+
401
+ ## 1.2.0
402
+
403
+ ### Features:
404
+ * do not wait for Stable Diffusion model to load at startup
405
+ * add filename patterns: `[denoising]`
406
+ * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
407
+ * 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)
408
+ * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
409
+ * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
410
+ * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
411
+ * add version to infotext, footer and console output when starting
412
+ * add links to wiki for filename pattern settings
413
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
414
+
415
+ ### Minor:
416
+ * bump Gradio to 3.29.0
417
+ * bump PyTorch to 2.0.1
418
+ * `--subpath` option for gradio for use with reverse proxy
419
+ * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
420
+ * do not apply localizations if there are none (possible frontend optimization)
421
+ * add extra `None` option for VAE in XYZ plot
422
+ * print error to console when batch processing in img2img fails
423
+ * create HTML for extra network pages only on demand
424
+ * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc
425
+ * put infotext options into their own category in settings tab
426
+ * do not show licenses page when user selects Show all pages in settings
427
+
428
+ ### Extensions:
429
+ * tooltip localization support
430
+ * add API method to get LoRA models with prompt
431
+
432
+ ### Bug Fixes:
433
+ * re-add `/docs` endpoint
434
+ * fix gamepad navigation
435
+ * make the lightbox fullscreen image function properly
436
+ * fix squished thumbnails in extras tab
437
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everthing after you refreshed)
438
+ * fix webui showing the same image if you configure the generation to always save results into same file
439
+ * fix bug with upscalers not working properly
440
+ * fix MPS on PyTorch 2.0.1, Intel Macs
441
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
442
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
443
+ * fix prompts from file script failing to read contents from a drag/drop file
444
+
445
+
446
+ ## 1.1.1
447
+ ### Bug Fixes:
448
+ * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle
449
+
450
+ ## 1.1.0
451
+ ### Features:
452
+ * switch to PyTorch 2.0.0 (except for AMD GPUs)
453
+ * visual improvements to custom code scripts
454
+ * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
455
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducability
456
+ * automatically select current word when adjusting weight with ctrl+up/down
457
+ * add dropdowns for X/Y/Z plot
458
+ * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
459
+ * support Gradio's theme API
460
+ * use TCMalloc on Linux by default; possible fix for memory leaks
461
+ * add optimization option to remove negative conditioning at low sigma values #9177
462
+ * embed model merge metadata in .safetensors file
463
+ * extension settings backup/restore feature #9169
464
+ * add "resize by" and "resize to" tabs to img2img
465
+ * add option "keep original size" to textual inversion images preprocess
466
+ * image viewer scrolling via analog stick
467
+ * button to restore the progress from session lost / tab reload
468
+
469
+ ### Minor:
470
+ * bump Gradio to 3.28.1
471
+ * change "scale to" to sliders in Extras tab
472
+ * add labels to tool buttons to make it possible to hide them
473
+ * add tiled inference support for ScuNET
474
+ * add branch support for extension installation
475
+ * change Linux installation script to install into current directory rather than `/home/username`
476
+ * sort textual inversion embeddings by name (case-insensitive)
477
+ * allow styles.csv to be symlinked or mounted in docker
478
+ * remove the "do not add watermark to images" option
479
+ * make selected tab configurable with UI config
480
+ * make the extra networks UI fixed height and scrollable
481
+ * add `disable_tls_verify` arg for use with self-signed certs
482
+
483
+ ### Extensions:
484
+ * add reload callback
485
+ * add `is_hr_pass` field for processing
486
+
487
+ ### Bug Fixes:
488
+ * fix broken batch image processing on 'Extras/Batch Process' tab
489
+ * add "None" option to extra networks dropdowns
490
+ * fix FileExistsError for CLIP Interrogator
491
+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319
492
+ * fix disappearing live previews and progressbar during slow tasks
493
+ * fix fullscreen image view not working properly in some cases
494
+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
495
+ * fix prompt schedule for second order samplers
496
+ * fix image mask/composite for weird resolutions #9628
497
+ * use correct images for previews when using AND (see #9491)
498
+ * one broken image in img2img batch won't stop all processing
499
+ * fix image orientation bug in train/preprocess
500
+ * fix Ngrok recreating tunnels every reload
501
+ * fix `--realesrgan-models-path` and `--ldsr-models-path` not working
502
+ * fix `--skip-install` not working
503
+ * use SAMPLE file format in Outpainting Mk2 & Poorman
504
+ * do not fail all LoRAs if some have failed to load when making a picture
505
+
506
+ ## 1.0.0
507
+ * everything
CITATION.cff ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ cff-version: 1.2.0
2
+ message: "If you use this software, please cite it as below."
3
+ authors:
4
+ - given-names: AUTOMATIC1111
5
+ title: "Stable Diffusion Web UI"
6
+ date-released: 2022-08-22
7
+ url: "https://github.com/AUTOMATIC1111/stable-diffusion-webui"
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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+ END OF TERMS AND CONDITIONS
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+ How to Apply These Terms to Your New Programs
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+ If you develop a new program, and you want it to be of the greatest
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+ You should also get your employer (if you work as a programmer) or school,
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README.md ADDED
@@ -0,0 +1,177 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 separate 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 dimension 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:
97
+ - [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended)
98
+ - [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
99
+ - [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page)
100
+
101
+ Alternatively, use online services (like Google Colab):
102
+
103
+ - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
104
+
105
+ ### Installation on Windows 10/11 with NVidia-GPUs using release package
106
+ 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.
107
+ 2. Run `update.bat`.
108
+ 3. Run `run.bat`.
109
+ > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
110
+
111
+ ### Automatic Installation on Windows
112
+ 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".
113
+ 2. Install [git](https://git-scm.com/download/win).
114
+ 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
115
+ 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
116
+
117
+ ### Automatic Installation on Linux
118
+ 1. Install the dependencies:
119
+ ```bash
120
+ # Debian-based:
121
+ sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
122
+ # Red Hat-based:
123
+ sudo dnf install wget git python3
124
+ # Arch-based:
125
+ sudo pacman -S wget git python3
126
+ ```
127
+ 2. Navigate to the directory you would like the webui to be installed and execute the following command:
128
+ ```bash
129
+ wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
130
+ ```
131
+ 3. Run `webui.sh`.
132
+ 4. Check `webui-user.sh` for options.
133
+ ### Installation on Apple Silicon
134
+
135
+ Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
136
+
137
+ ## Contributing
138
+ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
139
+
140
+ ## Documentation
141
+
142
+ The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
143
+
144
+ For the purposes of getting Google and other search engines to crawl the wiki, here's a link to the (not for humans) [crawlable wiki](https://github-wiki-see.page/m/AUTOMATIC1111/stable-diffusion-webui/wiki).
145
+
146
+ ## Credits
147
+ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
148
+
149
+ - Stable Diffusion - https://github.com/CompVis/stable-diffusion, https://github.com/CompVis/taming-transformers
150
+ - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
151
+ - GFPGAN - https://github.com/TencentARC/GFPGAN.git
152
+ - CodeFormer - https://github.com/sczhou/CodeFormer
153
+ - ESRGAN - https://github.com/xinntao/ESRGAN
154
+ - SwinIR - https://github.com/JingyunLiang/SwinIR
155
+ - Swin2SR - https://github.com/mv-lab/swin2sr
156
+ - LDSR - https://github.com/Hafiidz/latent-diffusion
157
+ - MiDaS - https://github.com/isl-org/MiDaS
158
+ - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
159
+ - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
160
+ - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
161
+ - 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)
162
+ - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
163
+ - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
164
+ - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
165
+ - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
166
+ - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
167
+ - xformers - https://github.com/facebookresearch/xformers
168
+ - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
169
+ - 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)
170
+ - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
171
+ - Security advice - RyotaK
172
+ - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
173
+ - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
174
+ - LyCORIS - KohakuBlueleaf
175
+ - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
176
+ - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
177
+ - (You)
configs/alt-diffusion-inference.yaml ADDED
@@ -0,0 +1,72 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ base_learning_rate: 1.0e-04
3
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
4
+ params:
5
+ linear_start: 0.00085
6
+ linear_end: 0.0120
7
+ num_timesteps_cond: 1
8
+ log_every_t: 200
9
+ timesteps: 1000
10
+ first_stage_key: "jpg"
11
+ cond_stage_key: "txt"
12
+ image_size: 64
13
+ channels: 4
14
+ cond_stage_trainable: false # Note: different from the one we trained before
15
+ conditioning_key: crossattn
16
+ monitor: val/loss_simple_ema
17
+ scale_factor: 0.18215
18
+ use_ema: False
19
+
20
+ scheduler_config: # 10000 warmup steps
21
+ target: ldm.lr_scheduler.LambdaLinearScheduler
22
+ params:
23
+ warm_up_steps: [ 10000 ]
24
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
25
+ f_start: [ 1.e-6 ]
26
+ f_max: [ 1. ]
27
+ f_min: [ 1. ]
28
+
29
+ unet_config:
30
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
31
+ params:
32
+ image_size: 32 # unused
33
+ in_channels: 4
34
+ out_channels: 4
35
+ model_channels: 320
36
+ attention_resolutions: [ 4, 2, 1 ]
37
+ num_res_blocks: 2
38
+ channel_mult: [ 1, 2, 4, 4 ]
39
+ num_heads: 8
40
+ use_spatial_transformer: True
41
+ transformer_depth: 1
42
+ context_dim: 768
43
+ use_checkpoint: True
44
+ legacy: False
45
+
46
+ first_stage_config:
47
+ target: ldm.models.autoencoder.AutoencoderKL
48
+ params:
49
+ embed_dim: 4
50
+ monitor: val/rec_loss
51
+ ddconfig:
52
+ double_z: true
53
+ z_channels: 4
54
+ resolution: 256
55
+ in_channels: 3
56
+ out_ch: 3
57
+ ch: 128
58
+ ch_mult:
59
+ - 1
60
+ - 2
61
+ - 4
62
+ - 4
63
+ num_res_blocks: 2
64
+ attn_resolutions: []
65
+ dropout: 0.0
66
+ lossconfig:
67
+ target: torch.nn.Identity
68
+
69
+ cond_stage_config:
70
+ target: modules.xlmr.BertSeriesModelWithTransformation
71
+ params:
72
+ name: "XLMR-Large"
configs/instruct-pix2pix.yaml ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # File modified by authors of InstructPix2Pix from original (https://github.com/CompVis/stable-diffusion).
2
+ # See more details in LICENSE.
3
+
4
+ model:
5
+ base_learning_rate: 1.0e-04
6
+ target: modules.models.diffusion.ddpm_edit.LatentDiffusion
7
+ params:
8
+ linear_start: 0.00085
9
+ linear_end: 0.0120
10
+ num_timesteps_cond: 1
11
+ log_every_t: 200
12
+ timesteps: 1000
13
+ first_stage_key: edited
14
+ cond_stage_key: edit
15
+ # image_size: 64
16
+ # image_size: 32
17
+ image_size: 16
18
+ channels: 4
19
+ cond_stage_trainable: false # Note: different from the one we trained before
20
+ conditioning_key: hybrid
21
+ monitor: val/loss_simple_ema
22
+ scale_factor: 0.18215
23
+ use_ema: false
24
+
25
+ scheduler_config: # 10000 warmup steps
26
+ target: ldm.lr_scheduler.LambdaLinearScheduler
27
+ params:
28
+ warm_up_steps: [ 0 ]
29
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
30
+ f_start: [ 1.e-6 ]
31
+ f_max: [ 1. ]
32
+ f_min: [ 1. ]
33
+
34
+ unet_config:
35
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
36
+ params:
37
+ image_size: 32 # unused
38
+ in_channels: 8
39
+ out_channels: 4
40
+ model_channels: 320
41
+ attention_resolutions: [ 4, 2, 1 ]
42
+ num_res_blocks: 2
43
+ channel_mult: [ 1, 2, 4, 4 ]
44
+ num_heads: 8
45
+ use_spatial_transformer: True
46
+ transformer_depth: 1
47
+ context_dim: 768
48
+ use_checkpoint: True
49
+ legacy: False
50
+
51
+ first_stage_config:
52
+ target: ldm.models.autoencoder.AutoencoderKL
53
+ params:
54
+ embed_dim: 4
55
+ monitor: val/rec_loss
56
+ ddconfig:
57
+ double_z: true
58
+ z_channels: 4
59
+ resolution: 256
60
+ in_channels: 3
61
+ out_ch: 3
62
+ ch: 128
63
+ ch_mult:
64
+ - 1
65
+ - 2
66
+ - 4
67
+ - 4
68
+ num_res_blocks: 2
69
+ attn_resolutions: []
70
+ dropout: 0.0
71
+ lossconfig:
72
+ target: torch.nn.Identity
73
+
74
+ cond_stage_config:
75
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
76
+
77
+ data:
78
+ target: main.DataModuleFromConfig
79
+ params:
80
+ batch_size: 128
81
+ num_workers: 1
82
+ wrap: false
83
+ validation:
84
+ target: edit_dataset.EditDataset
85
+ params:
86
+ path: data/clip-filtered-dataset
87
+ cache_dir: data/
88
+ cache_name: data_10k
89
+ split: val
90
+ min_text_sim: 0.2
91
+ min_image_sim: 0.75
92
+ min_direction_sim: 0.2
93
+ max_samples_per_prompt: 1
94
+ min_resize_res: 512
95
+ max_resize_res: 512
96
+ crop_res: 512
97
+ output_as_edit: False
98
+ real_input: True
configs/v1-inference.yaml ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ base_learning_rate: 1.0e-04
3
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
4
+ params:
5
+ linear_start: 0.00085
6
+ linear_end: 0.0120
7
+ num_timesteps_cond: 1
8
+ log_every_t: 200
9
+ timesteps: 1000
10
+ first_stage_key: "jpg"
11
+ cond_stage_key: "txt"
12
+ image_size: 64
13
+ channels: 4
14
+ cond_stage_trainable: false # Note: different from the one we trained before
15
+ conditioning_key: crossattn
16
+ monitor: val/loss_simple_ema
17
+ scale_factor: 0.18215
18
+ use_ema: False
19
+
20
+ scheduler_config: # 10000 warmup steps
21
+ target: ldm.lr_scheduler.LambdaLinearScheduler
22
+ params:
23
+ warm_up_steps: [ 10000 ]
24
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
25
+ f_start: [ 1.e-6 ]
26
+ f_max: [ 1. ]
27
+ f_min: [ 1. ]
28
+
29
+ unet_config:
30
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
31
+ params:
32
+ image_size: 32 # unused
33
+ in_channels: 4
34
+ out_channels: 4
35
+ model_channels: 320
36
+ attention_resolutions: [ 4, 2, 1 ]
37
+ num_res_blocks: 2
38
+ channel_mult: [ 1, 2, 4, 4 ]
39
+ num_heads: 8
40
+ use_spatial_transformer: True
41
+ transformer_depth: 1
42
+ context_dim: 768
43
+ use_checkpoint: True
44
+ legacy: False
45
+
46
+ first_stage_config:
47
+ target: ldm.models.autoencoder.AutoencoderKL
48
+ params:
49
+ embed_dim: 4
50
+ monitor: val/rec_loss
51
+ ddconfig:
52
+ double_z: true
53
+ z_channels: 4
54
+ resolution: 256
55
+ in_channels: 3
56
+ out_ch: 3
57
+ ch: 128
58
+ ch_mult:
59
+ - 1
60
+ - 2
61
+ - 4
62
+ - 4
63
+ num_res_blocks: 2
64
+ attn_resolutions: []
65
+ dropout: 0.0
66
+ lossconfig:
67
+ target: torch.nn.Identity
68
+
69
+ cond_stage_config:
70
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
configs/v1-inpainting-inference.yaml ADDED
@@ -0,0 +1,70 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ base_learning_rate: 7.5e-05
3
+ target: ldm.models.diffusion.ddpm.LatentInpaintDiffusion
4
+ params:
5
+ linear_start: 0.00085
6
+ linear_end: 0.0120
7
+ num_timesteps_cond: 1
8
+ log_every_t: 200
9
+ timesteps: 1000
10
+ first_stage_key: "jpg"
11
+ cond_stage_key: "txt"
12
+ image_size: 64
13
+ channels: 4
14
+ cond_stage_trainable: false # Note: different from the one we trained before
15
+ conditioning_key: hybrid # important
16
+ monitor: val/loss_simple_ema
17
+ scale_factor: 0.18215
18
+ finetune_keys: null
19
+
20
+ scheduler_config: # 10000 warmup steps
21
+ target: ldm.lr_scheduler.LambdaLinearScheduler
22
+ params:
23
+ warm_up_steps: [ 2500 ] # NOTE for resuming. use 10000 if starting from scratch
24
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
25
+ f_start: [ 1.e-6 ]
26
+ f_max: [ 1. ]
27
+ f_min: [ 1. ]
28
+
29
+ unet_config:
30
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
31
+ params:
32
+ image_size: 32 # unused
33
+ in_channels: 9 # 4 data + 4 downscaled image + 1 mask
34
+ out_channels: 4
35
+ model_channels: 320
36
+ attention_resolutions: [ 4, 2, 1 ]
37
+ num_res_blocks: 2
38
+ channel_mult: [ 1, 2, 4, 4 ]
39
+ num_heads: 8
40
+ use_spatial_transformer: True
41
+ transformer_depth: 1
42
+ context_dim: 768
43
+ use_checkpoint: True
44
+ legacy: False
45
+
46
+ first_stage_config:
47
+ target: ldm.models.autoencoder.AutoencoderKL
48
+ params:
49
+ embed_dim: 4
50
+ monitor: val/rec_loss
51
+ ddconfig:
52
+ double_z: true
53
+ z_channels: 4
54
+ resolution: 256
55
+ in_channels: 3
56
+ out_ch: 3
57
+ ch: 128
58
+ ch_mult:
59
+ - 1
60
+ - 2
61
+ - 4
62
+ - 4
63
+ num_res_blocks: 2
64
+ attn_resolutions: []
65
+ dropout: 0.0
66
+ lossconfig:
67
+ target: torch.nn.Identity
68
+
69
+ cond_stage_config:
70
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
embeddings/Place Textual Inversion embeddings here.txt ADDED
File without changes
environment-wsl2.yaml ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: automatic
2
+ channels:
3
+ - pytorch
4
+ - defaults
5
+ dependencies:
6
+ - python=3.10
7
+ - pip=23.0
8
+ - cudatoolkit=11.8
9
+ - pytorch=2.0
10
+ - torchvision=0.15
11
+ - numpy=1.23
extensions-builtin/LDSR/ldsr_model_arch.py ADDED
@@ -0,0 +1,250 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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, devices
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
+ devices.torch_gc()
116
+
117
+ im_og = image
118
+ width_og, height_og = im_og.size
119
+ # If we can adjust the max upscale size, then the 4 below should be our variable
120
+ down_sample_rate = target_scale / 4
121
+ wd = width_og * down_sample_rate
122
+ hd = height_og * down_sample_rate
123
+ width_downsampled_pre = int(np.ceil(wd))
124
+ height_downsampled_pre = int(np.ceil(hd))
125
+
126
+ if down_sample_rate != 1:
127
+ print(
128
+ f'Downsampling from [{width_og}, {height_og}] to [{width_downsampled_pre}, {height_downsampled_pre}]')
129
+ im_og = im_og.resize((width_downsampled_pre, height_downsampled_pre), Image.LANCZOS)
130
+ else:
131
+ print(f"Down sample rate is 1 from {target_scale} / 4 (Not downsampling)")
132
+
133
+ # pad width and height to multiples of 64, pads with the edge values of image to avoid artifacts
134
+ pad_w, pad_h = np.max(((2, 2), np.ceil(np.array(im_og.size) / 64).astype(int)), axis=0) * 64 - im_og.size
135
+ im_padded = Image.fromarray(np.pad(np.array(im_og), ((0, pad_h), (0, pad_w), (0, 0)), mode='edge'))
136
+
137
+ logs = self.run(model["model"], im_padded, diffusion_steps, eta)
138
+
139
+ sample = logs["sample"]
140
+ sample = sample.detach().cpu()
141
+ sample = torch.clamp(sample, -1., 1.)
142
+ sample = (sample + 1.) / 2. * 255
143
+ sample = sample.numpy().astype(np.uint8)
144
+ sample = np.transpose(sample, (0, 2, 3, 1))
145
+ a = Image.fromarray(sample[0])
146
+
147
+ # remove padding
148
+ a = a.crop((0, 0) + tuple(np.array(im_og.size) * 4))
149
+
150
+ del model
151
+ gc.collect()
152
+ devices.torch_gc()
153
+
154
+ return a
155
+
156
+
157
+ def get_cond(selected_path):
158
+ example = {}
159
+ up_f = 4
160
+ c = selected_path.convert('RGB')
161
+ c = torch.unsqueeze(torchvision.transforms.ToTensor()(c), 0)
162
+ c_up = torchvision.transforms.functional.resize(c, size=[up_f * c.shape[2], up_f * c.shape[3]],
163
+ antialias=True)
164
+ c_up = rearrange(c_up, '1 c h w -> 1 h w c')
165
+ c = rearrange(c, '1 c h w -> 1 h w c')
166
+ c = 2. * c - 1.
167
+
168
+ c = c.to(shared.device)
169
+ example["LR_image"] = c
170
+ example["image"] = c_up
171
+
172
+ return example
173
+
174
+
175
+ @torch.no_grad()
176
+ def convsample_ddim(model, cond, steps, shape, eta=1.0, callback=None, normals_sequence=None,
177
+ mask=None, x0=None, quantize_x0=False, temperature=1., score_corrector=None,
178
+ corrector_kwargs=None, x_t=None
179
+ ):
180
+ ddim = DDIMSampler(model)
181
+ bs = shape[0]
182
+ shape = shape[1:]
183
+ print(f"Sampling with eta = {eta}; steps: {steps}")
184
+ samples, intermediates = ddim.sample(steps, batch_size=bs, shape=shape, conditioning=cond, callback=callback,
185
+ normals_sequence=normals_sequence, quantize_x0=quantize_x0, eta=eta,
186
+ mask=mask, x0=x0, temperature=temperature, verbose=False,
187
+ score_corrector=score_corrector,
188
+ corrector_kwargs=corrector_kwargs, x_t=x_t)
189
+
190
+ return samples, intermediates
191
+
192
+
193
+ @torch.no_grad()
194
+ 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,
195
+ corrector_kwargs=None, x_T=None, ddim_use_x0_pred=False):
196
+ log = {}
197
+
198
+ z, c, x, xrec, xc = model.get_input(batch, model.first_stage_key,
199
+ return_first_stage_outputs=True,
200
+ force_c_encode=not (hasattr(model, 'split_input_params')
201
+ and model.cond_stage_key == 'coordinates_bbox'),
202
+ return_original_cond=True)
203
+
204
+ if custom_shape is not None:
205
+ z = torch.randn(custom_shape)
206
+ print(f"Generating {custom_shape[0]} samples of shape {custom_shape[1:]}")
207
+
208
+ z0 = None
209
+
210
+ log["input"] = x
211
+ log["reconstruction"] = xrec
212
+
213
+ if ismap(xc):
214
+ log["original_conditioning"] = model.to_rgb(xc)
215
+ if hasattr(model, 'cond_stage_key'):
216
+ log[model.cond_stage_key] = model.to_rgb(xc)
217
+
218
+ else:
219
+ log["original_conditioning"] = xc if xc is not None else torch.zeros_like(x)
220
+ if model.cond_stage_model:
221
+ log[model.cond_stage_key] = xc if xc is not None else torch.zeros_like(x)
222
+ if model.cond_stage_key == 'class_label':
223
+ log[model.cond_stage_key] = xc[model.cond_stage_key]
224
+
225
+ with model.ema_scope("Plotting"):
226
+ t0 = time.time()
227
+
228
+ sample, intermediates = convsample_ddim(model, c, steps=custom_steps, shape=z.shape,
229
+ eta=eta,
230
+ quantize_x0=quantize_x0, mask=None, x0=z0,
231
+ temperature=temperature, score_corrector=corrector, corrector_kwargs=corrector_kwargs,
232
+ x_t=x_T)
233
+ t1 = time.time()
234
+
235
+ if ddim_use_x0_pred:
236
+ sample = intermediates['pred_x0'][-1]
237
+
238
+ x_sample = model.decode_first_stage(sample)
239
+
240
+ try:
241
+ x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
242
+ log["sample_noquant"] = x_sample_noquant
243
+ log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
244
+ except Exception:
245
+ pass
246
+
247
+ log["sample"] = x_sample
248
+ log["time"] = t1 - t0
249
+
250
+ 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 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ from modules.modelloader import load_file_from_url
4
+ from modules.upscaler import Upscaler, UpscalerData
5
+ from ldsr_model_arch import LDSR
6
+ from modules import shared, script_callbacks, errors
7
+ import sd_hijack_autoencoder # noqa: F401
8
+ import sd_hijack_ddpm_v1 # noqa: F401
9
+
10
+
11
+ class UpscalerLDSR(Upscaler):
12
+ def __init__(self, user_path):
13
+ self.name = "LDSR"
14
+ self.user_path = user_path
15
+ self.model_url = "https://heibox.uni-heidelberg.de/f/578df07c8fc04ffbadf3/?dl=1"
16
+ self.yaml_url = "https://heibox.uni-heidelberg.de/f/31a76b13ea27482981b4/?dl=1"
17
+ super().__init__()
18
+ scaler_data = UpscalerData("LDSR", None, self)
19
+ self.scalers = [scaler_data]
20
+
21
+ def load_model(self, path: str):
22
+ # Remove incorrect project.yaml file if too big
23
+ yaml_path = os.path.join(self.model_path, "project.yaml")
24
+ old_model_path = os.path.join(self.model_path, "model.pth")
25
+ new_model_path = os.path.join(self.model_path, "model.ckpt")
26
+
27
+ local_model_paths = self.find_models(ext_filter=[".ckpt", ".safetensors"])
28
+ local_ckpt_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.ckpt")]), None)
29
+ local_safetensors_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("model.safetensors")]), None)
30
+ local_yaml_path = next(iter([local_model for local_model in local_model_paths if local_model.endswith("project.yaml")]), None)
31
+
32
+ if os.path.exists(yaml_path):
33
+ statinfo = os.stat(yaml_path)
34
+ if statinfo.st_size >= 10485760:
35
+ print("Removing invalid LDSR YAML file.")
36
+ os.remove(yaml_path)
37
+
38
+ if os.path.exists(old_model_path):
39
+ print("Renaming model from model.pth to model.ckpt")
40
+ os.rename(old_model_path, new_model_path)
41
+
42
+ if local_safetensors_path is not None and os.path.exists(local_safetensors_path):
43
+ model = local_safetensors_path
44
+ else:
45
+ model = local_ckpt_path or load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name="model.ckpt")
46
+
47
+ yaml = local_yaml_path or load_file_from_url(self.yaml_url, model_dir=self.model_download_path, file_name="project.yaml")
48
+
49
+ return LDSR(model, yaml)
50
+
51
+ def do_upscale(self, img, path):
52
+ try:
53
+ ldsr = self.load_model(path)
54
+ except Exception:
55
+ errors.report(f"Failed loading LDSR model {path}", exc_info=True)
56
+ return img
57
+ ddim_steps = shared.opts.ldsr_steps
58
+ return ldsr.super_resolution(img, ddim_steps, self.scale)
59
+
60
+
61
+ def on_ui_settings():
62
+ import gradio as gr
63
+
64
+ 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")))
65
+ shared.opts.add_option("ldsr_cached", shared.OptionInfo(False, "Cache LDSR model in memory", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")))
66
+
67
+
68
+ 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 +1,67 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from modules import extra_networks, shared
2
+ import networks
3
+
4
+
5
+ class ExtraNetworkLora(extra_networks.ExtraNetwork):
6
+ def __init__(self):
7
+ super().__init__('lora')
8
+
9
+ self.errors = {}
10
+ """mapping of network names to the number of errors the network had during operation"""
11
+
12
+ def activate(self, p, params_list):
13
+ additional = shared.opts.sd_lora
14
+
15
+ self.errors.clear()
16
+
17
+ if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
18
+ p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
19
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
20
+
21
+ names = []
22
+ te_multipliers = []
23
+ unet_multipliers = []
24
+ dyn_dims = []
25
+ for params in params_list:
26
+ assert params.items
27
+
28
+ names.append(params.positional[0])
29
+
30
+ te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0
31
+ te_multiplier = float(params.named.get("te", te_multiplier))
32
+
33
+ unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier
34
+ unet_multiplier = float(params.named.get("unet", unet_multiplier))
35
+
36
+ dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
37
+ dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
38
+
39
+ te_multipliers.append(te_multiplier)
40
+ unet_multipliers.append(unet_multiplier)
41
+ dyn_dims.append(dyn_dim)
42
+
43
+ networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
44
+
45
+ if shared.opts.lora_add_hashes_to_infotext:
46
+ network_hashes = []
47
+ for item in networks.loaded_networks:
48
+ shorthash = item.network_on_disk.shorthash
49
+ if not shorthash:
50
+ continue
51
+
52
+ alias = item.mentioned_name
53
+ if not alias:
54
+ continue
55
+
56
+ alias = alias.replace(":", "").replace(",", "")
57
+
58
+ network_hashes.append(f"{alias}: {shorthash}")
59
+
60
+ if network_hashes:
61
+ p.extra_generation_params["Lora hashes"] = ", ".join(network_hashes)
62
+
63
+ def deactivate(self, p):
64
+ if self.errors:
65
+ p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
66
+
67
+ self.errors.clear()
extensions-builtin/Lora/lora.py ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ import networks
2
+
3
+ list_available_loras = networks.list_available_networks
4
+
5
+ available_loras = networks.available_networks
6
+ available_lora_aliases = networks.available_network_aliases
7
+ available_lora_hash_lookup = networks.available_network_hash_lookup
8
+ forbidden_lora_aliases = networks.forbidden_network_aliases
9
+ loaded_loras = networks.loaded_networks
extensions-builtin/Lora/lora_patches.py ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+ import networks
4
+ from modules import patches
5
+
6
+
7
+ class LoraPatches:
8
+ def __init__(self):
9
+ self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
10
+ self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
11
+ self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
12
+ self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
13
+ self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
14
+ self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
15
+ self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward)
16
+ self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict)
17
+ self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward)
18
+ self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict)
19
+
20
+ def undo(self):
21
+ self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward')
22
+ self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict')
23
+ self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward')
24
+ self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict')
25
+ self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward')
26
+ self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict')
27
+ self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward')
28
+ self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict')
29
+ self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward')
30
+ self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict')
31
+
extensions-builtin/Lora/lyco_helpers.py ADDED
@@ -0,0 +1,21 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+
4
+ def make_weight_cp(t, wa, wb):
5
+ temp = torch.einsum('i j k l, j r -> i r k l', t, wb)
6
+ return torch.einsum('i j k l, i r -> r j k l', temp, wa)
7
+
8
+
9
+ def rebuild_conventional(up, down, shape, dyn_dim=None):
10
+ up = up.reshape(up.size(0), -1)
11
+ down = down.reshape(down.size(0), -1)
12
+ if dyn_dim is not None:
13
+ up = up[:, :dyn_dim]
14
+ down = down[:dyn_dim, :]
15
+ return (up @ down).reshape(shape)
16
+
17
+
18
+ def rebuild_cp_decomposition(up, down, mid):
19
+ up = up.reshape(up.size(0), -1)
20
+ down = down.reshape(down.size(0), -1)
21
+ return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
extensions-builtin/Lora/network.py ADDED
@@ -0,0 +1,158 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __future__ import annotations
2
+ import os
3
+ from collections import namedtuple
4
+ import enum
5
+
6
+ from modules import sd_models, cache, errors, hashes, shared
7
+
8
+ NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
9
+
10
+ metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
11
+
12
+
13
+ class SdVersion(enum.Enum):
14
+ Unknown = 1
15
+ SD1 = 2
16
+ SD2 = 3
17
+ SDXL = 4
18
+
19
+
20
+ class NetworkOnDisk:
21
+ def __init__(self, name, filename):
22
+ self.name = name
23
+ self.filename = filename
24
+ self.metadata = {}
25
+ self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
26
+
27
+ def read_metadata():
28
+ metadata = sd_models.read_metadata_from_safetensors(filename)
29
+ metadata.pop('ssmd_cover_images', None) # those are cover images, and they are too big to display in UI as text
30
+
31
+ return metadata
32
+
33
+ if self.is_safetensors:
34
+ try:
35
+ self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
36
+ except Exception as e:
37
+ errors.display(e, f"reading lora {filename}")
38
+
39
+ if self.metadata:
40
+ m = {}
41
+ for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
42
+ m[k] = v
43
+
44
+ self.metadata = m
45
+
46
+ self.alias = self.metadata.get('ss_output_name', self.name)
47
+
48
+ self.hash = None
49
+ self.shorthash = None
50
+ self.set_hash(
51
+ self.metadata.get('sshs_model_hash') or
52
+ hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
53
+ ''
54
+ )
55
+
56
+ self.sd_version = self.detect_version()
57
+
58
+ def detect_version(self):
59
+ if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
60
+ return SdVersion.SDXL
61
+ elif str(self.metadata.get('ss_v2', "")) == "True":
62
+ return SdVersion.SD2
63
+ elif len(self.metadata):
64
+ return SdVersion.SD1
65
+
66
+ return SdVersion.Unknown
67
+
68
+ def set_hash(self, v):
69
+ self.hash = v
70
+ self.shorthash = self.hash[0:12]
71
+
72
+ if self.shorthash:
73
+ import networks
74
+ networks.available_network_hash_lookup[self.shorthash] = self
75
+
76
+ def read_hash(self):
77
+ if not self.hash:
78
+ self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
79
+
80
+ def get_alias(self):
81
+ import networks
82
+ if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
83
+ return self.name
84
+ else:
85
+ return self.alias
86
+
87
+
88
+ class Network: # LoraModule
89
+ def __init__(self, name, network_on_disk: NetworkOnDisk):
90
+ self.name = name
91
+ self.network_on_disk = network_on_disk
92
+ self.te_multiplier = 1.0
93
+ self.unet_multiplier = 1.0
94
+ self.dyn_dim = None
95
+ self.modules = {}
96
+ self.mtime = None
97
+
98
+ self.mentioned_name = None
99
+ """the text that was used to add the network to prompt - can be either name or an alias"""
100
+
101
+
102
+ class ModuleType:
103
+ def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
104
+ return None
105
+
106
+
107
+ class NetworkModule:
108
+ def __init__(self, net: Network, weights: NetworkWeights):
109
+ self.network = net
110
+ self.network_key = weights.network_key
111
+ self.sd_key = weights.sd_key
112
+ self.sd_module = weights.sd_module
113
+
114
+ if hasattr(self.sd_module, 'weight'):
115
+ self.shape = self.sd_module.weight.shape
116
+
117
+ self.dim = None
118
+ self.bias = weights.w.get("bias")
119
+ self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
120
+ self.scale = weights.w["scale"].item() if "scale" in weights.w else None
121
+
122
+ def multiplier(self):
123
+ if 'transformer' in self.sd_key[:20]:
124
+ return self.network.te_multiplier
125
+ else:
126
+ return self.network.unet_multiplier
127
+
128
+ def calc_scale(self):
129
+ if self.scale is not None:
130
+ return self.scale
131
+ if self.dim is not None and self.alpha is not None:
132
+ return self.alpha / self.dim
133
+
134
+ return 1.0
135
+
136
+ def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
137
+ if self.bias is not None:
138
+ updown = updown.reshape(self.bias.shape)
139
+ updown += self.bias.to(orig_weight.device, dtype=orig_weight.dtype)
140
+ updown = updown.reshape(output_shape)
141
+
142
+ if len(output_shape) == 4:
143
+ updown = updown.reshape(output_shape)
144
+
145
+ if orig_weight.size().numel() == updown.size().numel():
146
+ updown = updown.reshape(orig_weight.shape)
147
+
148
+ if ex_bias is not None:
149
+ ex_bias = ex_bias * self.multiplier()
150
+
151
+ return updown * self.calc_scale() * self.multiplier(), ex_bias
152
+
153
+ def calc_updown(self, target):
154
+ raise NotImplementedError()
155
+
156
+ def forward(self, x, y):
157
+ raise NotImplementedError()
158
+
extensions-builtin/Lora/network_full.py ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import network
2
+
3
+
4
+ class ModuleTypeFull(network.ModuleType):
5
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
6
+ if all(x in weights.w for x in ["diff"]):
7
+ return NetworkModuleFull(net, weights)
8
+
9
+ return None
10
+
11
+
12
+ class NetworkModuleFull(network.NetworkModule):
13
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
14
+ super().__init__(net, weights)
15
+
16
+ self.weight = weights.w.get("diff")
17
+ self.ex_bias = weights.w.get("diff_b")
18
+
19
+ def calc_updown(self, orig_weight):
20
+ output_shape = self.weight.shape
21
+ updown = self.weight.to(orig_weight.device, dtype=orig_weight.dtype)
22
+ if self.ex_bias is not None:
23
+ ex_bias = self.ex_bias.to(orig_weight.device, dtype=orig_weight.dtype)
24
+ else:
25
+ ex_bias = None
26
+
27
+ return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
extensions-builtin/Lora/network_hada.py ADDED
@@ -0,0 +1,55 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import lyco_helpers
2
+ import network
3
+
4
+
5
+ class ModuleTypeHada(network.ModuleType):
6
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
7
+ if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]):
8
+ return NetworkModuleHada(net, weights)
9
+
10
+ return None
11
+
12
+
13
+ class NetworkModuleHada(network.NetworkModule):
14
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
15
+ super().__init__(net, weights)
16
+
17
+ if hasattr(self.sd_module, 'weight'):
18
+ self.shape = self.sd_module.weight.shape
19
+
20
+ self.w1a = weights.w["hada_w1_a"]
21
+ self.w1b = weights.w["hada_w1_b"]
22
+ self.dim = self.w1b.shape[0]
23
+ self.w2a = weights.w["hada_w2_a"]
24
+ self.w2b = weights.w["hada_w2_b"]
25
+
26
+ self.t1 = weights.w.get("hada_t1")
27
+ self.t2 = weights.w.get("hada_t2")
28
+
29
+ def calc_updown(self, orig_weight):
30
+ w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
31
+ w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
32
+ w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
33
+ w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
34
+
35
+ output_shape = [w1a.size(0), w1b.size(1)]
36
+
37
+ if self.t1 is not None:
38
+ output_shape = [w1a.size(1), w1b.size(1)]
39
+ t1 = self.t1.to(orig_weight.device, dtype=orig_weight.dtype)
40
+ updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
41
+ output_shape += t1.shape[2:]
42
+ else:
43
+ if len(w1b.shape) == 4:
44
+ output_shape += w1b.shape[2:]
45
+ updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
46
+
47
+ if self.t2 is not None:
48
+ t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
49
+ updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
50
+ else:
51
+ updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
52
+
53
+ updown = updown1 * updown2
54
+
55
+ return self.finalize_updown(updown, orig_weight, output_shape)
extensions-builtin/Lora/network_ia3.py ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import network
2
+
3
+
4
+ class ModuleTypeIa3(network.ModuleType):
5
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
6
+ if all(x in weights.w for x in ["weight"]):
7
+ return NetworkModuleIa3(net, weights)
8
+
9
+ return None
10
+
11
+
12
+ class NetworkModuleIa3(network.NetworkModule):
13
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
14
+ super().__init__(net, weights)
15
+
16
+ self.w = weights.w["weight"]
17
+ self.on_input = weights.w["on_input"].item()
18
+
19
+ def calc_updown(self, orig_weight):
20
+ w = self.w.to(orig_weight.device, dtype=orig_weight.dtype)
21
+
22
+ output_shape = [w.size(0), orig_weight.size(1)]
23
+ if self.on_input:
24
+ output_shape.reverse()
25
+ else:
26
+ w = w.reshape(-1, 1)
27
+
28
+ updown = orig_weight * w
29
+
30
+ return self.finalize_updown(updown, orig_weight, output_shape)
extensions-builtin/Lora/network_lokr.py ADDED
@@ -0,0 +1,64 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+ import lyco_helpers
4
+ import network
5
+
6
+
7
+ class ModuleTypeLokr(network.ModuleType):
8
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
9
+ has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w)
10
+ has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w)
11
+ if has_1 and has_2:
12
+ return NetworkModuleLokr(net, weights)
13
+
14
+ return None
15
+
16
+
17
+ def make_kron(orig_shape, w1, w2):
18
+ if len(w2.shape) == 4:
19
+ w1 = w1.unsqueeze(2).unsqueeze(2)
20
+ w2 = w2.contiguous()
21
+ return torch.kron(w1, w2).reshape(orig_shape)
22
+
23
+
24
+ class NetworkModuleLokr(network.NetworkModule):
25
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
26
+ super().__init__(net, weights)
27
+
28
+ self.w1 = weights.w.get("lokr_w1")
29
+ self.w1a = weights.w.get("lokr_w1_a")
30
+ self.w1b = weights.w.get("lokr_w1_b")
31
+ self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim
32
+ self.w2 = weights.w.get("lokr_w2")
33
+ self.w2a = weights.w.get("lokr_w2_a")
34
+ self.w2b = weights.w.get("lokr_w2_b")
35
+ self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim
36
+ self.t2 = weights.w.get("lokr_t2")
37
+
38
+ def calc_updown(self, orig_weight):
39
+ if self.w1 is not None:
40
+ w1 = self.w1.to(orig_weight.device, dtype=orig_weight.dtype)
41
+ else:
42
+ w1a = self.w1a.to(orig_weight.device, dtype=orig_weight.dtype)
43
+ w1b = self.w1b.to(orig_weight.device, dtype=orig_weight.dtype)
44
+ w1 = w1a @ w1b
45
+
46
+ if self.w2 is not None:
47
+ w2 = self.w2.to(orig_weight.device, dtype=orig_weight.dtype)
48
+ elif self.t2 is None:
49
+ w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
50
+ w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
51
+ w2 = w2a @ w2b
52
+ else:
53
+ t2 = self.t2.to(orig_weight.device, dtype=orig_weight.dtype)
54
+ w2a = self.w2a.to(orig_weight.device, dtype=orig_weight.dtype)
55
+ w2b = self.w2b.to(orig_weight.device, dtype=orig_weight.dtype)
56
+ w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
57
+
58
+ output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
59
+ if len(orig_weight.shape) == 4:
60
+ output_shape = orig_weight.shape
61
+
62
+ updown = make_kron(output_shape, w1, w2)
63
+
64
+ return self.finalize_updown(updown, orig_weight, output_shape)
extensions-builtin/Lora/network_lora.py ADDED
@@ -0,0 +1,86 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+ import lyco_helpers
4
+ import network
5
+ from modules import devices
6
+
7
+
8
+ class ModuleTypeLora(network.ModuleType):
9
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
10
+ if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
11
+ return NetworkModuleLora(net, weights)
12
+
13
+ return None
14
+
15
+
16
+ class NetworkModuleLora(network.NetworkModule):
17
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
18
+ super().__init__(net, weights)
19
+
20
+ self.up_model = self.create_module(weights.w, "lora_up.weight")
21
+ self.down_model = self.create_module(weights.w, "lora_down.weight")
22
+ self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True)
23
+
24
+ self.dim = weights.w["lora_down.weight"].shape[0]
25
+
26
+ def create_module(self, weights, key, none_ok=False):
27
+ weight = weights.get(key)
28
+
29
+ if weight is None and none_ok:
30
+ return None
31
+
32
+ is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention]
33
+ is_conv = type(self.sd_module) in [torch.nn.Conv2d]
34
+
35
+ if is_linear:
36
+ weight = weight.reshape(weight.shape[0], -1)
37
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
38
+ elif is_conv and key == "lora_down.weight" or key == "dyn_up":
39
+ if len(weight.shape) == 2:
40
+ weight = weight.reshape(weight.shape[0], -1, 1, 1)
41
+
42
+ if weight.shape[2] != 1 or weight.shape[3] != 1:
43
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
44
+ else:
45
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
46
+ elif is_conv and key == "lora_mid.weight":
47
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
48
+ elif is_conv and key == "lora_up.weight" or key == "dyn_down":
49
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
50
+ else:
51
+ raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
52
+
53
+ with torch.no_grad():
54
+ if weight.shape != module.weight.shape:
55
+ weight = weight.reshape(module.weight.shape)
56
+ module.weight.copy_(weight)
57
+
58
+ module.to(device=devices.cpu, dtype=devices.dtype)
59
+ module.weight.requires_grad_(False)
60
+
61
+ return module
62
+
63
+ def calc_updown(self, orig_weight):
64
+ up = self.up_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
65
+ down = self.down_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
66
+
67
+ output_shape = [up.size(0), down.size(1)]
68
+ if self.mid_model is not None:
69
+ # cp-decomposition
70
+ mid = self.mid_model.weight.to(orig_weight.device, dtype=orig_weight.dtype)
71
+ updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
72
+ output_shape += mid.shape[2:]
73
+ else:
74
+ if len(down.shape) == 4:
75
+ output_shape += down.shape[2:]
76
+ updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim)
77
+
78
+ return self.finalize_updown(updown, orig_weight, output_shape)
79
+
80
+ def forward(self, x, y):
81
+ self.up_model.to(device=devices.device)
82
+ self.down_model.to(device=devices.device)
83
+
84
+ return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
85
+
86
+
extensions-builtin/Lora/network_norm.py ADDED
@@ -0,0 +1,28 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import network
2
+
3
+
4
+ class ModuleTypeNorm(network.ModuleType):
5
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
6
+ if all(x in weights.w for x in ["w_norm", "b_norm"]):
7
+ return NetworkModuleNorm(net, weights)
8
+
9
+ return None
10
+
11
+
12
+ class NetworkModuleNorm(network.NetworkModule):
13
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
14
+ super().__init__(net, weights)
15
+
16
+ self.w_norm = weights.w.get("w_norm")
17
+ self.b_norm = weights.w.get("b_norm")
18
+
19
+ def calc_updown(self, orig_weight):
20
+ output_shape = self.w_norm.shape
21
+ updown = self.w_norm.to(orig_weight.device, dtype=orig_weight.dtype)
22
+
23
+ if self.b_norm is not None:
24
+ ex_bias = self.b_norm.to(orig_weight.device, dtype=orig_weight.dtype)
25
+ else:
26
+ ex_bias = None
27
+
28
+ return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
extensions-builtin/Lora/networks.py ADDED
@@ -0,0 +1,571 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ import os
3
+ import re
4
+
5
+ import lora_patches
6
+ import network
7
+ import network_lora
8
+ import network_hada
9
+ import network_ia3
10
+ import network_lokr
11
+ import network_full
12
+ import network_norm
13
+
14
+ import torch
15
+ from typing import Union
16
+
17
+ from modules import shared, devices, sd_models, errors, scripts, sd_hijack
18
+
19
+ module_types = [
20
+ network_lora.ModuleTypeLora(),
21
+ network_hada.ModuleTypeHada(),
22
+ network_ia3.ModuleTypeIa3(),
23
+ network_lokr.ModuleTypeLokr(),
24
+ network_full.ModuleTypeFull(),
25
+ network_norm.ModuleTypeNorm(),
26
+ ]
27
+
28
+
29
+ re_digits = re.compile(r"\d+")
30
+ re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
31
+ re_compiled = {}
32
+
33
+ suffix_conversion = {
34
+ "attentions": {},
35
+ "resnets": {
36
+ "conv1": "in_layers_2",
37
+ "conv2": "out_layers_3",
38
+ "norm1": "in_layers_0",
39
+ "norm2": "out_layers_0",
40
+ "time_emb_proj": "emb_layers_1",
41
+ "conv_shortcut": "skip_connection",
42
+ }
43
+ }
44
+
45
+
46
+ def convert_diffusers_name_to_compvis(key, is_sd2):
47
+ def match(match_list, regex_text):
48
+ regex = re_compiled.get(regex_text)
49
+ if regex is None:
50
+ regex = re.compile(regex_text)
51
+ re_compiled[regex_text] = regex
52
+
53
+ r = re.match(regex, key)
54
+ if not r:
55
+ return False
56
+
57
+ match_list.clear()
58
+ match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
59
+ return True
60
+
61
+ m = []
62
+
63
+ if match(m, r"lora_unet_conv_in(.*)"):
64
+ return f'diffusion_model_input_blocks_0_0{m[0]}'
65
+
66
+ if match(m, r"lora_unet_conv_out(.*)"):
67
+ return f'diffusion_model_out_2{m[0]}'
68
+
69
+ if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
70
+ return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
71
+
72
+ if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
73
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
74
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
75
+
76
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
77
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
78
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
79
+
80
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
81
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
82
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
83
+
84
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
85
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
86
+
87
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
88
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
89
+
90
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
91
+ if is_sd2:
92
+ if 'mlp_fc1' in m[1]:
93
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
94
+ elif 'mlp_fc2' in m[1]:
95
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
96
+ else:
97
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
98
+
99
+ return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
100
+
101
+ if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
102
+ if 'mlp_fc1' in m[1]:
103
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
104
+ elif 'mlp_fc2' in m[1]:
105
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
106
+ else:
107
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
108
+
109
+ return key
110
+
111
+
112
+ def assign_network_names_to_compvis_modules(sd_model):
113
+ network_layer_mapping = {}
114
+
115
+ if shared.sd_model.is_sdxl:
116
+ for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
117
+ if not hasattr(embedder, 'wrapped'):
118
+ continue
119
+
120
+ for name, module in embedder.wrapped.named_modules():
121
+ network_name = f'{i}_{name.replace(".", "_")}'
122
+ network_layer_mapping[network_name] = module
123
+ module.network_layer_name = network_name
124
+ else:
125
+ for name, module in shared.sd_model.cond_stage_model.wrapped.named_modules():
126
+ network_name = name.replace(".", "_")
127
+ network_layer_mapping[network_name] = module
128
+ module.network_layer_name = network_name
129
+
130
+ for name, module in shared.sd_model.model.named_modules():
131
+ network_name = name.replace(".", "_")
132
+ network_layer_mapping[network_name] = module
133
+ module.network_layer_name = network_name
134
+
135
+ sd_model.network_layer_mapping = network_layer_mapping
136
+
137
+
138
+ def load_network(name, network_on_disk):
139
+ net = network.Network(name, network_on_disk)
140
+ net.mtime = os.path.getmtime(network_on_disk.filename)
141
+
142
+ sd = sd_models.read_state_dict(network_on_disk.filename)
143
+
144
+ # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
145
+ if not hasattr(shared.sd_model, 'network_layer_mapping'):
146
+ assign_network_names_to_compvis_modules(shared.sd_model)
147
+
148
+ keys_failed_to_match = {}
149
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
150
+
151
+ matched_networks = {}
152
+
153
+ for key_network, weight in sd.items():
154
+ key_network_without_network_parts, network_part = key_network.split(".", 1)
155
+
156
+ key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
157
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
158
+
159
+ if sd_module is None:
160
+ m = re_x_proj.match(key)
161
+ if m:
162
+ sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)
163
+
164
+ # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
165
+ if sd_module is None and "lora_unet" in key_network_without_network_parts:
166
+ key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
167
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
168
+ elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
169
+ key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
170
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
171
+
172
+ # some SD1 Loras also have correct compvis keys
173
+ if sd_module is None:
174
+ key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
175
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
176
+
177
+ if sd_module is None:
178
+ keys_failed_to_match[key_network] = key
179
+ continue
180
+
181
+ if key not in matched_networks:
182
+ matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)
183
+
184
+ matched_networks[key].w[network_part] = weight
185
+
186
+ for key, weights in matched_networks.items():
187
+ net_module = None
188
+ for nettype in module_types:
189
+ net_module = nettype.create_module(net, weights)
190
+ if net_module is not None:
191
+ break
192
+
193
+ if net_module is None:
194
+ raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")
195
+
196
+ net.modules[key] = net_module
197
+
198
+ if keys_failed_to_match:
199
+ logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
200
+
201
+ return net
202
+
203
+
204
+ def purge_networks_from_memory():
205
+ while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
206
+ name = next(iter(networks_in_memory))
207
+ networks_in_memory.pop(name, None)
208
+
209
+ devices.torch_gc()
210
+
211
+
212
+ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
213
+ already_loaded = {}
214
+
215
+ for net in loaded_networks:
216
+ if net.name in names:
217
+ already_loaded[net.name] = net
218
+
219
+ loaded_networks.clear()
220
+
221
+ networks_on_disk = [available_network_aliases.get(name, None) for name in names]
222
+ if any(x is None for x in networks_on_disk):
223
+ list_available_networks()
224
+
225
+ networks_on_disk = [available_network_aliases.get(name, None) for name in names]
226
+
227
+ failed_to_load_networks = []
228
+
229
+ for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
230
+ net = already_loaded.get(name, None)
231
+
232
+ if network_on_disk is not None:
233
+ if net is None:
234
+ net = networks_in_memory.get(name)
235
+
236
+ if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
237
+ try:
238
+ net = load_network(name, network_on_disk)
239
+
240
+ networks_in_memory.pop(name, None)
241
+ networks_in_memory[name] = net
242
+ except Exception as e:
243
+ errors.display(e, f"loading network {network_on_disk.filename}")
244
+ continue
245
+
246
+ net.mentioned_name = name
247
+
248
+ network_on_disk.read_hash()
249
+
250
+ if net is None:
251
+ failed_to_load_networks.append(name)
252
+ logging.info(f"Couldn't find network with name {name}")
253
+ continue
254
+
255
+ net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
256
+ net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
257
+ net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
258
+ loaded_networks.append(net)
259
+
260
+ if failed_to_load_networks:
261
+ sd_hijack.model_hijack.comments.append("Networks not found: " + ", ".join(failed_to_load_networks))
262
+
263
+ purge_networks_from_memory()
264
+
265
+
266
+ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
267
+ weights_backup = getattr(self, "network_weights_backup", None)
268
+ bias_backup = getattr(self, "network_bias_backup", None)
269
+
270
+ if weights_backup is None and bias_backup is None:
271
+ return
272
+
273
+ if weights_backup is not None:
274
+ if isinstance(self, torch.nn.MultiheadAttention):
275
+ self.in_proj_weight.copy_(weights_backup[0])
276
+ self.out_proj.weight.copy_(weights_backup[1])
277
+ else:
278
+ self.weight.copy_(weights_backup)
279
+
280
+ if bias_backup is not None:
281
+ if isinstance(self, torch.nn.MultiheadAttention):
282
+ self.out_proj.bias.copy_(bias_backup)
283
+ else:
284
+ self.bias.copy_(bias_backup)
285
+ else:
286
+ if isinstance(self, torch.nn.MultiheadAttention):
287
+ self.out_proj.bias = None
288
+ else:
289
+ self.bias = None
290
+
291
+
292
+ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
293
+ """
294
+ Applies the currently selected set of networks to the weights of torch layer self.
295
+ If weights already have this particular set of networks applied, does nothing.
296
+ If not, restores orginal weights from backup and alters weights according to networks.
297
+ """
298
+
299
+ network_layer_name = getattr(self, 'network_layer_name', None)
300
+ if network_layer_name is None:
301
+ return
302
+
303
+ current_names = getattr(self, "network_current_names", ())
304
+ wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
305
+
306
+ weights_backup = getattr(self, "network_weights_backup", None)
307
+ if weights_backup is None and wanted_names != ():
308
+ if current_names != ():
309
+ raise RuntimeError("no backup weights found and current weights are not unchanged")
310
+
311
+ if isinstance(self, torch.nn.MultiheadAttention):
312
+ weights_backup = (self.in_proj_weight.to(devices.cpu, copy=True), self.out_proj.weight.to(devices.cpu, copy=True))
313
+ else:
314
+ weights_backup = self.weight.to(devices.cpu, copy=True)
315
+
316
+ self.network_weights_backup = weights_backup
317
+
318
+ bias_backup = getattr(self, "network_bias_backup", None)
319
+ if bias_backup is None:
320
+ if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
321
+ bias_backup = self.out_proj.bias.to(devices.cpu, copy=True)
322
+ elif getattr(self, 'bias', None) is not None:
323
+ bias_backup = self.bias.to(devices.cpu, copy=True)
324
+ else:
325
+ bias_backup = None
326
+ self.network_bias_backup = bias_backup
327
+
328
+ if current_names != wanted_names:
329
+ network_restore_weights_from_backup(self)
330
+
331
+ for net in loaded_networks:
332
+ module = net.modules.get(network_layer_name, None)
333
+ if module is not None and hasattr(self, 'weight'):
334
+ try:
335
+ with torch.no_grad():
336
+ updown, ex_bias = module.calc_updown(self.weight)
337
+
338
+ if len(self.weight.shape) == 4 and self.weight.shape[1] == 9:
339
+ # inpainting model. zero pad updown to make channel[1] 4 to 9
340
+ updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
341
+
342
+ self.weight += updown
343
+ if ex_bias is not None and hasattr(self, 'bias'):
344
+ if self.bias is None:
345
+ self.bias = torch.nn.Parameter(ex_bias)
346
+ else:
347
+ self.bias += ex_bias
348
+ except RuntimeError as e:
349
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
350
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
351
+
352
+ continue
353
+
354
+ module_q = net.modules.get(network_layer_name + "_q_proj", None)
355
+ module_k = net.modules.get(network_layer_name + "_k_proj", None)
356
+ module_v = net.modules.get(network_layer_name + "_v_proj", None)
357
+ module_out = net.modules.get(network_layer_name + "_out_proj", None)
358
+
359
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
360
+ try:
361
+ with torch.no_grad():
362
+ updown_q, _ = module_q.calc_updown(self.in_proj_weight)
363
+ updown_k, _ = module_k.calc_updown(self.in_proj_weight)
364
+ updown_v, _ = module_v.calc_updown(self.in_proj_weight)
365
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
366
+ updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
367
+
368
+ self.in_proj_weight += updown_qkv
369
+ self.out_proj.weight += updown_out
370
+ if ex_bias is not None:
371
+ if self.out_proj.bias is None:
372
+ self.out_proj.bias = torch.nn.Parameter(ex_bias)
373
+ else:
374
+ self.out_proj.bias += ex_bias
375
+
376
+ except RuntimeError as e:
377
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
378
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
379
+
380
+ continue
381
+
382
+ if module is None:
383
+ continue
384
+
385
+ logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
386
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
387
+
388
+ self.network_current_names = wanted_names
389
+
390
+
391
+ def network_forward(module, input, original_forward):
392
+ """
393
+ Old way of applying Lora by executing operations during layer's forward.
394
+ Stacking many loras this way results in big performance degradation.
395
+ """
396
+
397
+ if len(loaded_networks) == 0:
398
+ return original_forward(module, input)
399
+
400
+ input = devices.cond_cast_unet(input)
401
+
402
+ network_restore_weights_from_backup(module)
403
+ network_reset_cached_weight(module)
404
+
405
+ y = original_forward(module, input)
406
+
407
+ network_layer_name = getattr(module, 'network_layer_name', None)
408
+ for lora in loaded_networks:
409
+ module = lora.modules.get(network_layer_name, None)
410
+ if module is None:
411
+ continue
412
+
413
+ y = module.forward(input, y)
414
+
415
+ return y
416
+
417
+
418
+ def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
419
+ self.network_current_names = ()
420
+ self.network_weights_backup = None
421
+
422
+
423
+ def network_Linear_forward(self, input):
424
+ if shared.opts.lora_functional:
425
+ return network_forward(self, input, originals.Linear_forward)
426
+
427
+ network_apply_weights(self)
428
+
429
+ return originals.Linear_forward(self, input)
430
+
431
+
432
+ def network_Linear_load_state_dict(self, *args, **kwargs):
433
+ network_reset_cached_weight(self)
434
+
435
+ return originals.Linear_load_state_dict(self, *args, **kwargs)
436
+
437
+
438
+ def network_Conv2d_forward(self, input):
439
+ if shared.opts.lora_functional:
440
+ return network_forward(self, input, originals.Conv2d_forward)
441
+
442
+ network_apply_weights(self)
443
+
444
+ return originals.Conv2d_forward(self, input)
445
+
446
+
447
+ def network_Conv2d_load_state_dict(self, *args, **kwargs):
448
+ network_reset_cached_weight(self)
449
+
450
+ return originals.Conv2d_load_state_dict(self, *args, **kwargs)
451
+
452
+
453
+ def network_GroupNorm_forward(self, input):
454
+ if shared.opts.lora_functional:
455
+ return network_forward(self, input, originals.GroupNorm_forward)
456
+
457
+ network_apply_weights(self)
458
+
459
+ return originals.GroupNorm_forward(self, input)
460
+
461
+
462
+ def network_GroupNorm_load_state_dict(self, *args, **kwargs):
463
+ network_reset_cached_weight(self)
464
+
465
+ return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
466
+
467
+
468
+ def network_LayerNorm_forward(self, input):
469
+ if shared.opts.lora_functional:
470
+ return network_forward(self, input, originals.LayerNorm_forward)
471
+
472
+ network_apply_weights(self)
473
+
474
+ return originals.LayerNorm_forward(self, input)
475
+
476
+
477
+ def network_LayerNorm_load_state_dict(self, *args, **kwargs):
478
+ network_reset_cached_weight(self)
479
+
480
+ return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
481
+
482
+
483
+ def network_MultiheadAttention_forward(self, *args, **kwargs):
484
+ network_apply_weights(self)
485
+
486
+ return originals.MultiheadAttention_forward(self, *args, **kwargs)
487
+
488
+
489
+ def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
490
+ network_reset_cached_weight(self)
491
+
492
+ return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
493
+
494
+
495
+ def list_available_networks():
496
+ available_networks.clear()
497
+ available_network_aliases.clear()
498
+ forbidden_network_aliases.clear()
499
+ available_network_hash_lookup.clear()
500
+ forbidden_network_aliases.update({"none": 1, "Addams": 1})
501
+
502
+ os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
503
+
504
+ candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
505
+ candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
506
+ for filename in candidates:
507
+ if os.path.isdir(filename):
508
+ continue
509
+
510
+ name = os.path.splitext(os.path.basename(filename))[0]
511
+ try:
512
+ entry = network.NetworkOnDisk(name, filename)
513
+ except OSError: # should catch FileNotFoundError and PermissionError etc.
514
+ errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
515
+ continue
516
+
517
+ available_networks[name] = entry
518
+
519
+ if entry.alias in available_network_aliases:
520
+ forbidden_network_aliases[entry.alias.lower()] = 1
521
+
522
+ available_network_aliases[name] = entry
523
+ available_network_aliases[entry.alias] = entry
524
+
525
+
526
+ re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
527
+
528
+
529
+ def infotext_pasted(infotext, params):
530
+ if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
531
+ return # if the other extension is active, it will handle those fields, no need to do anything
532
+
533
+ added = []
534
+
535
+ for k in params:
536
+ if not k.startswith("AddNet Model "):
537
+ continue
538
+
539
+ num = k[13:]
540
+
541
+ if params.get("AddNet Module " + num) != "LoRA":
542
+ continue
543
+
544
+ name = params.get("AddNet Model " + num)
545
+ if name is None:
546
+ continue
547
+
548
+ m = re_network_name.match(name)
549
+ if m:
550
+ name = m.group(1)
551
+
552
+ multiplier = params.get("AddNet Weight A " + num, "1.0")
553
+
554
+ added.append(f"<lora:{name}:{multiplier}>")
555
+
556
+ if added:
557
+ params["Prompt"] += "\n" + "".join(added)
558
+
559
+
560
+ originals: lora_patches.LoraPatches = None
561
+
562
+ extra_network_lora = None
563
+
564
+ available_networks = {}
565
+ available_network_aliases = {}
566
+ loaded_networks = []
567
+ networks_in_memory = {}
568
+ available_network_hash_lookup = {}
569
+ forbidden_network_aliases = {}
570
+
571
+ list_available_networks()
extensions-builtin/Lora/preload.py ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
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'))
7
+ parser.add_argument("--lyco-dir-backcompat", type=str, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))
extensions-builtin/Lora/scripts/lora_script.py ADDED
@@ -0,0 +1,99 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import re
2
+
3
+ import gradio as gr
4
+ from fastapi import FastAPI
5
+
6
+ import network
7
+ import networks
8
+ import lora # noqa:F401
9
+ import lora_patches
10
+ import extra_networks_lora
11
+ import ui_extra_networks_lora
12
+ from modules import script_callbacks, ui_extra_networks, extra_networks, shared
13
+
14
+
15
+ def unload():
16
+ networks.originals.undo()
17
+
18
+
19
+ def before_ui():
20
+ ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
21
+
22
+ networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
23
+ extra_networks.register_extra_network(networks.extra_network_lora)
24
+ extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
25
+
26
+
27
+ networks.originals = lora_patches.LoraPatches()
28
+
29
+ script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
30
+ script_callbacks.on_script_unloaded(unload)
31
+ script_callbacks.on_before_ui(before_ui)
32
+ script_callbacks.on_infotext_pasted(networks.infotext_pasted)
33
+
34
+
35
+ shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
36
+ "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
37
+ "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
38
+ "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
39
+ "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
40
+ "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
41
+ "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
42
+ }))
43
+
44
+
45
+ shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
46
+ "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
47
+ }))
48
+
49
+
50
+ def create_lora_json(obj: network.NetworkOnDisk):
51
+ return {
52
+ "name": obj.name,
53
+ "alias": obj.alias,
54
+ "path": obj.filename,
55
+ "metadata": obj.metadata,
56
+ }
57
+
58
+
59
+ def api_networks(_: gr.Blocks, app: FastAPI):
60
+ @app.get("/sdapi/v1/loras")
61
+ async def get_loras():
62
+ return [create_lora_json(obj) for obj in networks.available_networks.values()]
63
+
64
+ @app.post("/sdapi/v1/refresh-loras")
65
+ async def refresh_loras():
66
+ return networks.list_available_networks()
67
+
68
+
69
+ script_callbacks.on_app_started(api_networks)
70
+
71
+ re_lora = re.compile("<lora:([^:]+):")
72
+
73
+
74
+ def infotext_pasted(infotext, d):
75
+ hashes = d.get("Lora hashes")
76
+ if not hashes:
77
+ return
78
+
79
+ hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
80
+ hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
81
+
82
+ def network_replacement(m):
83
+ alias = m.group(1)
84
+ shorthash = hashes.get(alias)
85
+ if shorthash is None:
86
+ return m.group(0)
87
+
88
+ network_on_disk = networks.available_network_hash_lookup.get(shorthash)
89
+ if network_on_disk is None:
90
+ return m.group(0)
91
+
92
+ return f'<lora:{network_on_disk.get_alias()}:'
93
+
94
+ d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
95
+
96
+
97
+ script_callbacks.on_infotext_pasted(infotext_pasted)
98
+
99
+ shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)
extensions-builtin/Lora/ui_edit_user_metadata.py ADDED
@@ -0,0 +1,217 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import datetime
2
+ import html
3
+ import random
4
+
5
+ import gradio as gr
6
+ import re
7
+
8
+ from modules import ui_extra_networks_user_metadata
9
+
10
+
11
+ def is_non_comma_tagset(tags):
12
+ average_tag_length = sum(len(x) for x in tags.keys()) / len(tags)
13
+
14
+ return average_tag_length >= 16
15
+
16
+
17
+ re_word = re.compile(r"[-_\w']+")
18
+ re_comma = re.compile(r" *, *")
19
+
20
+
21
+ def build_tags(metadata):
22
+ tags = {}
23
+
24
+ for _, tags_dict in metadata.get("ss_tag_frequency", {}).items():
25
+ for tag, tag_count in tags_dict.items():
26
+ tag = tag.strip()
27
+ tags[tag] = tags.get(tag, 0) + int(tag_count)
28
+
29
+ if tags and is_non_comma_tagset(tags):
30
+ new_tags = {}
31
+
32
+ for text, text_count in tags.items():
33
+ for word in re.findall(re_word, text):
34
+ if len(word) < 3:
35
+ continue
36
+
37
+ new_tags[word] = new_tags.get(word, 0) + text_count
38
+
39
+ tags = new_tags
40
+
41
+ ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True)
42
+
43
+ return [(tag, tags[tag]) for tag in ordered_tags]
44
+
45
+
46
+ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
47
+ def __init__(self, ui, tabname, page):
48
+ super().__init__(ui, tabname, page)
49
+
50
+ self.select_sd_version = None
51
+
52
+ self.taginfo = None
53
+ self.edit_activation_text = None
54
+ self.slider_preferred_weight = None
55
+ self.edit_notes = None
56
+
57
+ def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, notes):
58
+ user_metadata = self.get_user_metadata(name)
59
+ user_metadata["description"] = desc
60
+ user_metadata["sd version"] = sd_version
61
+ user_metadata["activation text"] = activation_text
62
+ user_metadata["preferred weight"] = preferred_weight
63
+ user_metadata["notes"] = notes
64
+
65
+ self.write_user_metadata(name, user_metadata)
66
+
67
+ def get_metadata_table(self, name):
68
+ table = super().get_metadata_table(name)
69
+ item = self.page.items.get(name, {})
70
+ metadata = item.get("metadata") or {}
71
+
72
+ keys = {
73
+ 'ss_output_name': "Output name:",
74
+ 'ss_sd_model_name': "Model:",
75
+ 'ss_clip_skip': "Clip skip:",
76
+ 'ss_network_module': "Kohya module:",
77
+ }
78
+
79
+ for key, label in keys.items():
80
+ value = metadata.get(key, None)
81
+ if value is not None and str(value) != "None":
82
+ table.append((label, html.escape(value)))
83
+
84
+ ss_training_started_at = metadata.get('ss_training_started_at')
85
+ if ss_training_started_at:
86
+ table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M')))
87
+
88
+ ss_bucket_info = metadata.get("ss_bucket_info")
89
+ if ss_bucket_info and "buckets" in ss_bucket_info:
90
+ resolutions = {}
91
+ for _, bucket in ss_bucket_info["buckets"].items():
92
+ resolution = bucket["resolution"]
93
+ resolution = f'{resolution[1]}x{resolution[0]}'
94
+
95
+ resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"])
96
+
97
+ resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True)
98
+ resolutions_text = html.escape(", ".join(resolutions_list[0:4]))
99
+ if len(resolutions) > 4:
100
+ resolutions_text += ", ..."
101
+ resolutions_text = f"<span title='{html.escape(', '.join(resolutions_list))}'>{resolutions_text}</span>"
102
+
103
+ table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text))
104
+
105
+ image_count = 0
106
+ for _, params in metadata.get("ss_dataset_dirs", {}).items():
107
+ image_count += int(params.get("img_count", 0))
108
+
109
+ if image_count:
110
+ table.append(("Dataset size:", image_count))
111
+
112
+ return table
113
+
114
+ def put_values_into_components(self, name):
115
+ user_metadata = self.get_user_metadata(name)
116
+ values = super().put_values_into_components(name)
117
+
118
+ item = self.page.items.get(name, {})
119
+ metadata = item.get("metadata") or {}
120
+
121
+ tags = build_tags(metadata)
122
+ gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]]
123
+
124
+ return [
125
+ *values[0:5],
126
+ item.get("sd_version", "Unknown"),
127
+ gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
128
+ user_metadata.get('activation text', ''),
129
+ float(user_metadata.get('preferred weight', 0.0)),
130
+ gr.update(visible=True if tags else False),
131
+ gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
132
+ ]
133
+
134
+ def generate_random_prompt(self, name):
135
+ item = self.page.items.get(name, {})
136
+ metadata = item.get("metadata") or {}
137
+ tags = build_tags(metadata)
138
+
139
+ return self.generate_random_prompt_from_tags(tags)
140
+
141
+ def generate_random_prompt_from_tags(self, tags):
142
+ max_count = None
143
+ res = []
144
+ for tag, count in tags:
145
+ if not max_count:
146
+ max_count = count
147
+
148
+ v = random.random() * max_count
149
+ if count > v:
150
+ res.append(tag)
151
+
152
+ return ", ".join(sorted(res))
153
+
154
+ def create_extra_default_items_in_left_column(self):
155
+
156
+ # this would be a lot better as gr.Radio but I can't make it work
157
+ self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
158
+
159
+ def create_editor(self):
160
+ self.create_default_editor_elems()
161
+
162
+ self.taginfo = gr.HighlightedText(label="Training dataset tags")
163
+ self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
164
+ self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
165
+
166
+ with gr.Row() as row_random_prompt:
167
+ with gr.Column(scale=8):
168
+ random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
169
+
170
+ with gr.Column(scale=1, min_width=120):
171
+ generate_random_prompt = gr.Button('Generate', size="lg", scale=1)
172
+
173
+ self.edit_notes = gr.TextArea(label='Notes', lines=4)
174
+
175
+ generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False)
176
+
177
+ def select_tag(activation_text, evt: gr.SelectData):
178
+ tag = evt.value[0]
179
+
180
+ words = re.split(re_comma, activation_text)
181
+ if tag in words:
182
+ words = [x for x in words if x != tag and x.strip()]
183
+ return ", ".join(words)
184
+
185
+ return activation_text + ", " + tag if activation_text else tag
186
+
187
+ self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False)
188
+
189
+ self.create_default_buttons()
190
+
191
+ viewed_components = [
192
+ self.edit_name,
193
+ self.edit_description,
194
+ self.html_filedata,
195
+ self.html_preview,
196
+ self.edit_notes,
197
+ self.select_sd_version,
198
+ self.taginfo,
199
+ self.edit_activation_text,
200
+ self.slider_preferred_weight,
201
+ row_random_prompt,
202
+ random_prompt,
203
+ ]
204
+
205
+ self.button_edit\
206
+ .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
207
+ .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
208
+
209
+ edited_components = [
210
+ self.edit_description,
211
+ self.select_sd_version,
212
+ self.edit_activation_text,
213
+ self.slider_preferred_weight,
214
+ self.edit_notes,
215
+ ]
216
+
217
+ self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)
extensions-builtin/Lora/ui_extra_networks_lora.py ADDED
@@ -0,0 +1,79 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+
3
+ import network
4
+ import networks
5
+
6
+ from modules import shared, ui_extra_networks
7
+ from modules.ui_extra_networks import quote_js
8
+ from ui_edit_user_metadata import LoraUserMetadataEditor
9
+
10
+
11
+ class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
12
+ def __init__(self):
13
+ super().__init__('Lora')
14
+
15
+ def refresh(self):
16
+ networks.list_available_networks()
17
+
18
+ def create_item(self, name, index=None, enable_filter=True):
19
+ lora_on_disk = networks.available_networks.get(name)
20
+
21
+ path, ext = os.path.splitext(lora_on_disk.filename)
22
+
23
+ alias = lora_on_disk.get_alias()
24
+
25
+ item = {
26
+ "name": name,
27
+ "filename": lora_on_disk.filename,
28
+ "shorthash": lora_on_disk.shorthash,
29
+ "preview": self.find_preview(path),
30
+ "description": self.find_description(path),
31
+ "search_term": self.search_terms_from_path(lora_on_disk.filename) + " " + (lora_on_disk.hash or ""),
32
+ "local_preview": f"{path}.{shared.opts.samples_format}",
33
+ "metadata": lora_on_disk.metadata,
34
+ "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
35
+ "sd_version": lora_on_disk.sd_version.name,
36
+ }
37
+
38
+ self.read_user_metadata(item)
39
+ activation_text = item["user_metadata"].get("activation text")
40
+ preferred_weight = item["user_metadata"].get("preferred weight", 0.0)
41
+ item["prompt"] = quote_js(f"<lora:{alias}:") + " + " + (str(preferred_weight) if preferred_weight else "opts.extra_networks_default_multiplier") + " + " + quote_js(">")
42
+
43
+ if activation_text:
44
+ item["prompt"] += " + " + quote_js(" " + activation_text)
45
+
46
+ sd_version = item["user_metadata"].get("sd version")
47
+ if sd_version in network.SdVersion.__members__:
48
+ item["sd_version"] = sd_version
49
+ sd_version = network.SdVersion[sd_version]
50
+ else:
51
+ sd_version = lora_on_disk.sd_version
52
+
53
+ if shared.opts.lora_show_all or not enable_filter:
54
+ pass
55
+ elif sd_version == network.SdVersion.Unknown:
56
+ model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
57
+ if model_version.name in shared.opts.lora_hide_unknown_for_versions:
58
+ return None
59
+ elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL:
60
+ return None
61
+ elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2:
62
+ return None
63
+ elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1:
64
+ return None
65
+
66
+ return item
67
+
68
+ def list_items(self):
69
+ for index, name in enumerate(networks.available_networks):
70
+ item = self.create_item(name, index)
71
+
72
+ if item is not None:
73
+ yield item
74
+
75
+ def allowed_directories_for_previews(self):
76
+ return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat]
77
+
78
+ def create_user_metadata_editor(self, ui, tabname):
79
+ return LoraUserMetadataEditor(ui, tabname, self)
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 +1,144 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sys
2
+
3
+ import PIL.Image
4
+ import numpy as np
5
+ import torch
6
+ from tqdm import tqdm
7
+
8
+ import modules.upscaler
9
+ from modules import devices, modelloader, script_callbacks, errors
10
+ from scunet_model_arch import SCUNet
11
+
12
+ from modules.modelloader import load_file_from_url
13
+ from modules.shared import opts
14
+
15
+
16
+ class UpscalerScuNET(modules.upscaler.Upscaler):
17
+ def __init__(self, dirname):
18
+ self.name = "ScuNET"
19
+ self.model_name = "ScuNET GAN"
20
+ self.model_name2 = "ScuNET PSNR"
21
+ self.model_url = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_gan.pth"
22
+ self.model_url2 = "https://github.com/cszn/KAIR/releases/download/v1.0/scunet_color_real_psnr.pth"
23
+ self.user_path = dirname
24
+ super().__init__()
25
+ model_paths = self.find_models(ext_filter=[".pth"])
26
+ scalers = []
27
+ add_model2 = True
28
+ for file in model_paths:
29
+ if file.startswith("http"):
30
+ name = self.model_name
31
+ else:
32
+ name = modelloader.friendly_name(file)
33
+ if name == self.model_name2 or file == self.model_url2:
34
+ add_model2 = False
35
+ try:
36
+ scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
37
+ scalers.append(scaler_data)
38
+ except Exception:
39
+ errors.report(f"Error loading ScuNET model: {file}", exc_info=True)
40
+ if add_model2:
41
+ scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
42
+ scalers.append(scaler_data2)
43
+ self.scalers = scalers
44
+
45
+ @staticmethod
46
+ @torch.no_grad()
47
+ def tiled_inference(img, model):
48
+ # test the image tile by tile
49
+ h, w = img.shape[2:]
50
+ tile = opts.SCUNET_tile
51
+ tile_overlap = opts.SCUNET_tile_overlap
52
+ if tile == 0:
53
+ return model(img)
54
+
55
+ device = devices.get_device_for('scunet')
56
+ assert tile % 8 == 0, "tile size should be a multiple of window_size"
57
+ sf = 1
58
+
59
+ stride = tile - tile_overlap
60
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
61
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
62
+ E = torch.zeros(1, 3, h * sf, w * sf, dtype=img.dtype, device=device)
63
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device)
64
+
65
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="ScuNET tiles") as pbar:
66
+ for h_idx in h_idx_list:
67
+
68
+ for w_idx in w_idx_list:
69
+
70
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
71
+
72
+ out_patch = model(in_patch)
73
+ out_patch_mask = torch.ones_like(out_patch)
74
+
75
+ E[
76
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
77
+ ].add_(out_patch)
78
+ W[
79
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
80
+ ].add_(out_patch_mask)
81
+ pbar.update(1)
82
+ output = E.div_(W)
83
+
84
+ return output
85
+
86
+ def do_upscale(self, img: PIL.Image.Image, selected_file):
87
+
88
+ devices.torch_gc()
89
+
90
+ try:
91
+ model = self.load_model(selected_file)
92
+ except Exception as e:
93
+ print(f"ScuNET: Unable to load model from {selected_file}: {e}", file=sys.stderr)
94
+ return img
95
+
96
+ device = devices.get_device_for('scunet')
97
+ tile = opts.SCUNET_tile
98
+ h, w = img.height, img.width
99
+ np_img = np.array(img)
100
+ np_img = np_img[:, :, ::-1] # RGB to BGR
101
+ np_img = np_img.transpose((2, 0, 1)) / 255 # HWC to CHW
102
+ torch_img = torch.from_numpy(np_img).float().unsqueeze(0).to(device) # type: ignore
103
+
104
+ if tile > h or tile > w:
105
+ _img = torch.zeros(1, 3, max(h, tile), max(w, tile), dtype=torch_img.dtype, device=torch_img.device)
106
+ _img[:, :, :h, :w] = torch_img # pad image
107
+ torch_img = _img
108
+
109
+ torch_output = self.tiled_inference(torch_img, model).squeeze(0)
110
+ torch_output = torch_output[:, :h * 1, :w * 1] # remove padding, if any
111
+ np_output: np.ndarray = torch_output.float().cpu().clamp_(0, 1).numpy()
112
+ del torch_img, torch_output
113
+ devices.torch_gc()
114
+
115
+ output = np_output.transpose((1, 2, 0)) # CHW to HWC
116
+ output = output[:, :, ::-1] # BGR to RGB
117
+ return PIL.Image.fromarray((output * 255).astype(np.uint8))
118
+
119
+ def load_model(self, path: str):
120
+ device = devices.get_device_for('scunet')
121
+ if path.startswith("http"):
122
+ # TODO: this doesn't use `path` at all?
123
+ filename = load_file_from_url(self.model_url, model_dir=self.model_download_path, file_name=f"{self.name}.pth")
124
+ else:
125
+ filename = path
126
+ model = SCUNet(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
127
+ model.load_state_dict(torch.load(filename), strict=True)
128
+ model.eval()
129
+ for _, v in model.named_parameters():
130
+ v.requires_grad = False
131
+ model = model.to(device)
132
+
133
+ return model
134
+
135
+
136
+ def on_ui_settings():
137
+ import gradio as gr
138
+ from modules import shared
139
+
140
+ 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"))
141
+ 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"))
142
+
143
+
144
+ 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 +1,192 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sys
2
+ import platform
3
+
4
+ import numpy as np
5
+ import torch
6
+ from PIL import Image
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
12
+ from swinir_model_arch_v2 import Swin2SR
13
+ from modules.upscaler import Upscaler, UpscalerData
14
+
15
+ SWINIR_MODEL_URL = "https://github.com/JingyunLiang/SwinIR/releases/download/v0.0/003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
16
+
17
+ device_swinir = devices.get_device_for('swinir')
18
+
19
+
20
+ class UpscalerSwinIR(Upscaler):
21
+ def __init__(self, dirname):
22
+ self._cached_model = None # keep the model when SWIN_torch_compile is on to prevent re-compile every runs
23
+ self._cached_model_config = None # to clear '_cached_model' when changing model (v1/v2) or settings
24
+ self.name = "SwinIR"
25
+ self.model_url = SWINIR_MODEL_URL
26
+ self.model_name = "SwinIR 4x"
27
+ self.user_path = dirname
28
+ super().__init__()
29
+ scalers = []
30
+ model_files = self.find_models(ext_filter=[".pt", ".pth"])
31
+ for model in model_files:
32
+ if model.startswith("http"):
33
+ name = self.model_name
34
+ else:
35
+ name = modelloader.friendly_name(model)
36
+ model_data = UpscalerData(name, model, self)
37
+ scalers.append(model_data)
38
+ self.scalers = scalers
39
+
40
+ def do_upscale(self, img, model_file):
41
+ use_compile = hasattr(opts, 'SWIN_torch_compile') and opts.SWIN_torch_compile \
42
+ and int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows"
43
+ current_config = (model_file, opts.SWIN_tile)
44
+
45
+ if use_compile and self._cached_model_config == current_config:
46
+ model = self._cached_model
47
+ else:
48
+ self._cached_model = None
49
+ try:
50
+ model = self.load_model(model_file)
51
+ except Exception as e:
52
+ print(f"Failed loading SwinIR model {model_file}: {e}", file=sys.stderr)
53
+ return img
54
+ model = model.to(device_swinir, dtype=devices.dtype)
55
+ if use_compile:
56
+ model = torch.compile(model)
57
+ self._cached_model = model
58
+ self._cached_model_config = current_config
59
+ img = upscale(img, model)
60
+ devices.torch_gc()
61
+ return img
62
+
63
+ def load_model(self, path, scale=4):
64
+ if path.startswith("http"):
65
+ filename = modelloader.load_file_from_url(
66
+ url=path,
67
+ model_dir=self.model_download_path,
68
+ file_name=f"{self.model_name.replace(' ', '_')}.pth",
69
+ )
70
+ else:
71
+ filename = path
72
+ if filename.endswith(".v2.pth"):
73
+ model = Swin2SR(
74
+ upscale=scale,
75
+ in_chans=3,
76
+ img_size=64,
77
+ window_size=8,
78
+ img_range=1.0,
79
+ depths=[6, 6, 6, 6, 6, 6],
80
+ embed_dim=180,
81
+ num_heads=[6, 6, 6, 6, 6, 6],
82
+ mlp_ratio=2,
83
+ upsampler="nearest+conv",
84
+ resi_connection="1conv",
85
+ )
86
+ params = None
87
+ else:
88
+ model = SwinIR(
89
+ upscale=scale,
90
+ in_chans=3,
91
+ img_size=64,
92
+ window_size=8,
93
+ img_range=1.0,
94
+ depths=[6, 6, 6, 6, 6, 6, 6, 6, 6],
95
+ embed_dim=240,
96
+ num_heads=[8, 8, 8, 8, 8, 8, 8, 8, 8],
97
+ mlp_ratio=2,
98
+ upsampler="nearest+conv",
99
+ resi_connection="3conv",
100
+ )
101
+ params = "params_ema"
102
+
103
+ pretrained_model = torch.load(filename)
104
+ if params is not None:
105
+ model.load_state_dict(pretrained_model[params], strict=True)
106
+ else:
107
+ model.load_state_dict(pretrained_model, strict=True)
108
+ return model
109
+
110
+
111
+ def upscale(
112
+ img,
113
+ model,
114
+ tile=None,
115
+ tile_overlap=None,
116
+ window_size=8,
117
+ scale=4,
118
+ ):
119
+ tile = tile or opts.SWIN_tile
120
+ tile_overlap = tile_overlap or opts.SWIN_tile_overlap
121
+
122
+
123
+ img = np.array(img)
124
+ img = img[:, :, ::-1]
125
+ img = np.moveaxis(img, 2, 0) / 255
126
+ img = torch.from_numpy(img).float()
127
+ img = img.unsqueeze(0).to(device_swinir, dtype=devices.dtype)
128
+ with torch.no_grad(), devices.autocast():
129
+ _, _, h_old, w_old = img.size()
130
+ h_pad = (h_old // window_size + 1) * window_size - h_old
131
+ w_pad = (w_old // window_size + 1) * window_size - w_old
132
+ img = torch.cat([img, torch.flip(img, [2])], 2)[:, :, : h_old + h_pad, :]
133
+ img = torch.cat([img, torch.flip(img, [3])], 3)[:, :, :, : w_old + w_pad]
134
+ output = inference(img, model, tile, tile_overlap, window_size, scale)
135
+ output = output[..., : h_old * scale, : w_old * scale]
136
+ output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
137
+ if output.ndim == 3:
138
+ output = np.transpose(
139
+ output[[2, 1, 0], :, :], (1, 2, 0)
140
+ ) # CHW-RGB to HCW-BGR
141
+ output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
142
+ return Image.fromarray(output, "RGB")
143
+
144
+
145
+ def inference(img, model, tile, tile_overlap, window_size, scale):
146
+ # test the image tile by tile
147
+ b, c, h, w = img.size()
148
+ tile = min(tile, h, w)
149
+ assert tile % window_size == 0, "tile size should be a multiple of window_size"
150
+ sf = scale
151
+
152
+ stride = tile - tile_overlap
153
+ h_idx_list = list(range(0, h - tile, stride)) + [h - tile]
154
+ w_idx_list = list(range(0, w - tile, stride)) + [w - tile]
155
+ E = torch.zeros(b, c, h * sf, w * sf, dtype=devices.dtype, device=device_swinir).type_as(img)
156
+ W = torch.zeros_like(E, dtype=devices.dtype, device=device_swinir)
157
+
158
+ with tqdm(total=len(h_idx_list) * len(w_idx_list), desc="SwinIR tiles") as pbar:
159
+ for h_idx in h_idx_list:
160
+ if state.interrupted or state.skipped:
161
+ break
162
+
163
+ for w_idx in w_idx_list:
164
+ if state.interrupted or state.skipped:
165
+ break
166
+
167
+ in_patch = img[..., h_idx: h_idx + tile, w_idx: w_idx + tile]
168
+ out_patch = model(in_patch)
169
+ out_patch_mask = torch.ones_like(out_patch)
170
+
171
+ E[
172
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
173
+ ].add_(out_patch)
174
+ W[
175
+ ..., h_idx * sf: (h_idx + tile) * sf, w_idx * sf: (w_idx + tile) * sf
176
+ ].add_(out_patch_mask)
177
+ pbar.update(1)
178
+ output = E.div_(W)
179
+
180
+ return output
181
+
182
+
183
+ def on_ui_settings():
184
+ import gradio as gr
185
+
186
+ 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")))
187
+ 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")))
188
+ if int(torch.__version__.split('.')[0]) >= 2 and platform.system() != "Windows": # torch.compile() require pytorch 2.0 or above, and not on Windows
189
+ shared.opts.add_option("SWIN_torch_compile", shared.OptionInfo(False, "Use torch.compile to accelerate SwinIR.", gr.Checkbox, {"interactive": True}, section=('upscaling', "Upscaling")).info("Takes longer on first run"))
190
+
191
+
192
+ script_callbacks.on_ui_settings(on_ui_settings)