themahani commited on
Commit
36b6cbc
1 Parent(s): ccbc426

Upload folder using huggingface_hub

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. .eslintignore +4 -0
  2. .eslintrc.js +98 -0
  3. .git-blame-ignore-revs +2 -0
  4. .github/ISSUE_TEMPLATE/bug_report.yml +105 -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 +81 -0
  10. .github/workflows/warns_merge_master.yml +19 -0
  11. .gitignore +44 -0
  12. .pylintrc +3 -0
  13. CHANGELOG.md +1085 -0
  14. CITATION.cff +7 -0
  15. CODEOWNERS +12 -0
  16. LICENSE.txt +663 -0
  17. README.md +211 -12
  18. _typos.toml +5 -0
  19. configs/alt-diffusion-inference.yaml +72 -0
  20. configs/alt-diffusion-m18-inference.yaml +73 -0
  21. configs/instruct-pix2pix.yaml +98 -0
  22. configs/sd3-inference.yaml +5 -0
  23. configs/sd_xl_inpaint.yaml +98 -0
  24. configs/v1-inference.yaml +70 -0
  25. configs/v1-inpainting-inference.yaml +70 -0
  26. environment-wsl2.yaml +11 -0
  27. extensions-builtin/LDSR/ldsr_model_arch.py +250 -0
  28. extensions-builtin/LDSR/preload.py +6 -0
  29. extensions-builtin/LDSR/scripts/ldsr_model.py +68 -0
  30. extensions-builtin/LDSR/sd_hijack_autoencoder.py +293 -0
  31. extensions-builtin/LDSR/sd_hijack_ddpm_v1.py +1443 -0
  32. extensions-builtin/LDSR/vqvae_quantize.py +147 -0
  33. extensions-builtin/Lora/extra_networks_lora.py +62 -0
  34. extensions-builtin/Lora/lora.py +9 -0
  35. extensions-builtin/Lora/lora_logger.py +33 -0
  36. extensions-builtin/Lora/lora_patches.py +31 -0
  37. extensions-builtin/Lora/lyco_helpers.py +68 -0
  38. extensions-builtin/Lora/network.py +228 -0
  39. extensions-builtin/Lora/network_full.py +27 -0
  40. extensions-builtin/Lora/network_glora.py +33 -0
  41. extensions-builtin/Lora/network_hada.py +55 -0
  42. extensions-builtin/Lora/network_ia3.py +30 -0
  43. extensions-builtin/Lora/network_lokr.py +64 -0
  44. extensions-builtin/Lora/network_lora.py +94 -0
  45. extensions-builtin/Lora/network_norm.py +28 -0
  46. extensions-builtin/Lora/network_oft.py +118 -0
  47. extensions-builtin/Lora/networks.py +737 -0
  48. extensions-builtin/Lora/preload.py +8 -0
  49. extensions-builtin/Lora/scripts/lora_script.py +102 -0
  50. extensions-builtin/Lora/ui_edit_user_metadata.py +226 -0
.eslintignore ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ extensions
2
+ extensions-disabled
3
+ repositories
4
+ venv
.eslintrc.js ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ onEdit: "readonly",
78
+ //extraNetworks.js
79
+ requestGet: "readonly",
80
+ popup: "readonly",
81
+ // profilerVisualization.js
82
+ createVisualizationTable: "readonly",
83
+ // from python
84
+ localization: "readonly",
85
+ // progrssbar.js
86
+ randomId: "readonly",
87
+ requestProgress: "readonly",
88
+ // imageviewer.js
89
+ modalPrevImage: "readonly",
90
+ modalNextImage: "readonly",
91
+ // localStorage.js
92
+ localSet: "readonly",
93
+ localGet: "readonly",
94
+ localRemove: "readonly",
95
+ // resizeHandle.js
96
+ setupResizeHandle: "writable"
97
+ }
98
+ };
.git-blame-ignore-revs ADDED
@@ -0,0 +1,2 @@
 
 
 
1
+ # Apply ESlint
2
+ 9c54b78d9dde5601e916f308d9a9d6953ec39430
.github/ISSUE_TEMPLATE/bug_report.yml ADDED
@@ -0,0 +1,105 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: Bug Report
2
+ description: You think something is broken in the UI
3
+ title: "[Bug]: "
4
+ labels: ["bug-report"]
5
+
6
+ body:
7
+ - type: markdown
8
+ attributes:
9
+ value: |
10
+ > The title of the bug report should be short and descriptive.
11
+ > Use relevant keywords for searchability.
12
+ > Do not leave it blank, but also do not put an entire error log in it.
13
+ - type: checkboxes
14
+ attributes:
15
+ label: Checklist
16
+ description: |
17
+ Please perform basic debugging to see if extensions or configuration is the cause of the issue.
18
+ Basic debug procedure
19
+  1. Disable all third-party extensions - check if extension is the cause
20
+  2. Update extensions and webui - sometimes things just need to be updated
21
+  3. Backup and remove your config.json and ui-config.json - check if the issue is caused by bad configuration
22
+  4. Delete venv with third-party extensions disabled - sometimes extensions might cause wrong libraries to be installed
23
+  5. Try a fresh installation webui in a different directory - see if a clean installation solves the issue
24
+ Before making a issue report please, check that the issue hasn't been reported recently.
25
+ options:
26
+ - label: The issue exists after disabling all extensions
27
+ - label: The issue exists on a clean installation of webui
28
+ - label: The issue is caused by an extension, but I believe it is caused by a bug in the webui
29
+ - label: The issue exists in the current version of the webui
30
+ - label: The issue has not been reported before recently
31
+ - label: The issue has been reported before but has not been fixed yet
32
+ - type: markdown
33
+ attributes:
34
+ value: |
35
+ > Please fill this form with as much information as possible. Don't forget to "Upload Sysinfo" and "What browsers" and provide screenshots if possible
36
+ - type: textarea
37
+ id: what-did
38
+ attributes:
39
+ label: What happened?
40
+ description: Tell us what happened in a very clear and simple way
41
+ placeholder: |
42
+ txt2img is not working as intended.
43
+ validations:
44
+ required: true
45
+ - type: textarea
46
+ id: steps
47
+ attributes:
48
+ label: Steps to reproduce the problem
49
+ description: Please provide us with precise step by step instructions on how to reproduce the bug
50
+ placeholder: |
51
+ 1. Go to ...
52
+ 2. Press ...
53
+ 3. ...
54
+ validations:
55
+ required: true
56
+ - type: textarea
57
+ id: what-should
58
+ attributes:
59
+ label: What should have happened?
60
+ description: Tell us what you think the normal behavior should be
61
+ placeholder: |
62
+ WebUI should ...
63
+ validations:
64
+ required: true
65
+ - type: dropdown
66
+ id: browsers
67
+ attributes:
68
+ label: What browsers do you use to access the UI ?
69
+ multiple: true
70
+ options:
71
+ - Mozilla Firefox
72
+ - Google Chrome
73
+ - Brave
74
+ - Apple Safari
75
+ - Microsoft Edge
76
+ - Android
77
+ - iOS
78
+ - Other
79
+ - type: textarea
80
+ id: sysinfo
81
+ attributes:
82
+ label: Sysinfo
83
+ 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.
84
+ placeholder: |
85
+ 1. Go to WebUI Settings -> Sysinfo -> Download system info.
86
+ If WebUI fails to launch, use --dump-sysinfo commandline argument to generate the file
87
+ 2. Upload the Sysinfo as a attached file, Do NOT paste it in as plain text.
88
+ validations:
89
+ required: true
90
+ - type: textarea
91
+ id: logs
92
+ attributes:
93
+ label: Console logs
94
+ description: Please provide **full** cmd/terminal logs from the moment you started UI to the end of it, after the bug occurred. If it's very long, provide a link to pastebin or similar service.
95
+ render: Shell
96
+ validations:
97
+ required: true
98
+ - type: textarea
99
+ id: misc
100
+ attributes:
101
+ label: Additional information
102
+ description: |
103
+ Please provide us with any relevant additional info or context.
104
+ Examples:
105
+  I have updated my GPU driver recently.
.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@v4
15
+ - uses: actions/setup-python@v5
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==0.3.3
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@v4
33
+ - name: Install Node.js
34
+ uses: actions/setup-node@v4
35
+ with:
36
+ node-version: 18
37
+ - run: npm i --ci
38
+ - run: npm run lint
.github/workflows/run_tests.yaml ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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@v4
15
+ - name: Set up Python 3.10
16
+ uses: actions/setup-python@v5
17
+ with:
18
+ python-version: 3.10.6
19
+ cache: pip
20
+ cache-dependency-path: |
21
+ **/requirements*txt
22
+ launch.py
23
+ - name: Cache models
24
+ id: cache-models
25
+ uses: actions/cache@v4
26
+ with:
27
+ path: models
28
+ key: "2023-12-30"
29
+ - name: Install test dependencies
30
+ run: pip install wait-for-it -r requirements-test.txt
31
+ env:
32
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
33
+ PIP_PROGRESS_BAR: "off"
34
+ - name: Setup environment
35
+ run: python launch.py --skip-torch-cuda-test --exit
36
+ env:
37
+ PIP_DISABLE_PIP_VERSION_CHECK: "1"
38
+ PIP_PROGRESS_BAR: "off"
39
+ TORCH_INDEX_URL: https://download.pytorch.org/whl/cpu
40
+ WEBUI_LAUNCH_LIVE_OUTPUT: "1"
41
+ PYTHONUNBUFFERED: "1"
42
+ - name: Print installed packages
43
+ run: pip freeze
44
+ - name: Start test server
45
+ run: >
46
+ python -m coverage run
47
+ --data-file=.coverage.server
48
+ launch.py
49
+ --skip-prepare-environment
50
+ --skip-torch-cuda-test
51
+ --test-server
52
+ --do-not-download-clip
53
+ --no-half
54
+ --disable-opt-split-attention
55
+ --use-cpu all
56
+ --api-server-stop
57
+ 2>&1 | tee output.txt &
58
+ - name: Run tests
59
+ run: |
60
+ wait-for-it --service 127.0.0.1:7860 -t 20
61
+ python -m pytest -vv --junitxml=test/results.xml --cov . --cov-report=xml --verify-base-url test
62
+ - name: Kill test server
63
+ if: always()
64
+ run: curl -vv -XPOST http://127.0.0.1:7860/sdapi/v1/server-stop && sleep 10
65
+ - name: Show coverage
66
+ run: |
67
+ python -m coverage combine .coverage*
68
+ python -m coverage report -i
69
+ python -m coverage html -i
70
+ - name: Upload main app output
71
+ uses: actions/upload-artifact@v4
72
+ if: always()
73
+ with:
74
+ name: output
75
+ path: output.txt
76
+ - name: Upload coverage HTML
77
+ uses: actions/upload-artifact@v4
78
+ if: always()
79
+ with:
80
+ name: htmlcov
81
+ 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 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ __pycache__
2
+ *.ckpt
3
+ *.safetensors
4
+ *.pth
5
+ .DS_Store
6
+ /ESRGAN/*
7
+ /SwinIR/*
8
+ /repositories
9
+ /venv
10
+ /tmp
11
+ /model.ckpt
12
+ /models/**/*
13
+ /GFPGANv1.3.pth
14
+ /gfpgan/weights/*.pth
15
+ /ui-config.json
16
+ /outputs
17
+ /config.json
18
+ /log
19
+ /webui.settings.bat
20
+ /embeddings
21
+ /styles.csv
22
+ /params.txt
23
+ /styles.csv.bak
24
+ /webui-user.bat
25
+ /webui-user.sh
26
+ /interrogate
27
+ /user.css
28
+ /.idea
29
+ notification.mp3
30
+ /SwinIR
31
+ /textual_inversion
32
+ .vscode
33
+ /extensions
34
+ /test/stdout.txt
35
+ /test/stderr.txt
36
+ /cache.json*
37
+ /config_states/
38
+ /node_modules
39
+ /package-lock.json
40
+ /.coverage*
41
+ /test/test_outputs
42
+ /cache
43
+ trace.json
44
+ /sysinfo-????-??-??-??-??.json
.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 +1,1085 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ## 1.10.1
2
+
3
+ ### Bug Fixes:
4
+ * fix image upscale on cpu ([#16275](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16275))
5
+
6
+
7
+ ## 1.10.0
8
+
9
+ ### Features:
10
+ * A lot of performance improvements (see below in Performance section)
11
+ * Stable Diffusion 3 support ([#16030](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16030), [#16164](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16164), [#16212](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16212))
12
+ * Recommended Euler sampler; DDIM and other timestamp samplers currently not supported
13
+ * T5 text model is disabled by default, enable it in settings
14
+ * New schedulers:
15
+ * Align Your Steps ([#15751](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15751))
16
+ * KL Optimal ([#15608](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608))
17
+ * Normal ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
18
+ * DDIM ([#16149](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16149))
19
+ * Simple ([#16142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16142))
20
+ * Beta ([#16235](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16235))
21
+ * New sampler: DDIM CFG++ ([#16035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16035))
22
+
23
+ ### Minor:
24
+ * Option to skip CFG on early steps ([#15607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15607))
25
+ * Add --models-dir option ([#15742](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15742))
26
+ * Allow mobile users to open context menu by using two fingers press ([#15682](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15682))
27
+ * Infotext: add Lora name as TI hashes for bundled Textual Inversion ([#15679](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15679))
28
+ * Check model's hash after downloading it to prevent corruped downloads ([#15602](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15602))
29
+ * More extension tag filtering options ([#15627](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15627))
30
+ * When saving AVIF, use JPEG's quality setting ([#15610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15610))
31
+ * Add filename pattern: `[basename]` ([#15978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15978))
32
+ * Add option to enable clip skip for clip L on SDXL ([#15992](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15992))
33
+ * Option to prevent screen sleep during generation ([#16001](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16001))
34
+ * ToggleLivePriview button in image viewer ([#16065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16065))
35
+ * Remove ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
36
+ * option to disable save button log.csv ([#16242](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16242))
37
+
38
+ ### Extensions and API:
39
+ * Add process_before_every_sampling hook ([#15984](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15984))
40
+ * Return HTTP 400 instead of 404 on invalid sampler error ([#16140](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16140))
41
+
42
+ ### Performance:
43
+ * [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803))
44
+ * [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804))
45
+ * [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806))
46
+ * [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816))
47
+ * [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820))
48
+ * [Performance] LDM optimization patches ([#15824](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15824))
49
+ * [Performance] Keep sigmas on CPU ([#15823](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15823))
50
+ * Check for nans in unet only once, after all steps have been completed
51
+ * Added pption to run torch profiler for image generation
52
+
53
+ ### Bug Fixes:
54
+ * Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958))
55
+ * feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943))
56
+ * Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893))
57
+ * Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600))
58
+ * Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656))
59
+ * more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681))
60
+ * Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831))
61
+ * XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832))
62
+ * fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815))
63
+ * fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797))
64
+ * update pickle safe filenames
65
+ * use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757))
66
+ * When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750))
67
+ * Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738))
68
+ * use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705))
69
+ * fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664))
70
+ * use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680))
71
+ * Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657))
72
+ * Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654))
73
+ * QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632))
74
+ * replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968))
75
+ * Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976))
76
+ * multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988))
77
+ * fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118))
78
+ * Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151))
79
+ * Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116))
80
+ * Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092))
81
+ * stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085))
82
+ * Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079))
83
+ * fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062))
84
+ * Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054))
85
+ * fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
86
+ * fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239))
87
+ * [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202))
88
+ * fix OSError: cannot write mode P as JPEG ([#16194](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16194))
89
+
90
+ ### Other:
91
+ * fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907))
92
+ * ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864))
93
+ * Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851))
94
+ * XYZ override rework ([#15836](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15836))
95
+ * scroll extensions table on overflow ([#15830](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15830))
96
+ * img2img batch upload method ([#15817](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15817))
97
+ * chore: sync v1.8.0 packages according to changelog ([#15783](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15783))
98
+ * Add AVIF MIME type support to mimetype definitions ([#15739](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15739))
99
+ * Update imageviewer.js ([#15730](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15730))
100
+ * no-referrer ([#15641](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15641))
101
+ * .gitignore trace.json ([#15980](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15980))
102
+ * Bump spandrel to 0.3.4 ([#16144](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16144))
103
+ * Defunct --max-batch-count ([#16119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16119))
104
+ * docs: update bug_report.yml ([#16102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16102))
105
+ * Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088), [#16169](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16169), [#16192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16192))
106
+ * Update torch for ARM Macs to 2.3.1 ([#16059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16059))
107
+ * remove deprecated setting dont_fix_second_order_samplers_schedule ([#16061](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16061))
108
+ * chore: fix typos ([#16060](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16060))
109
+ * shlex.join launch args in console log ([#16170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16170))
110
+ * activate venv .bat ([#16231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16231))
111
+ * add ids to the resize tabs in img2img ([#16218](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16218))
112
+ * update installation guide linux ([#16178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16178))
113
+ * Robust sysinfo ([#16173](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16173))
114
+ * do not send image size on paste inpaint ([#16180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16180))
115
+ * Fix noisy DS_Store files for MacOS ([#16166](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16166))
116
+
117
+
118
+ ## 1.9.4
119
+
120
+ ### Bug Fixes:
121
+ * pin setuptools version to fix the startup error ([#15882](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15882))
122
+
123
+ ## 1.9.3
124
+
125
+ ### Bug Fixes:
126
+ * fix get_crop_region_v2 ([#15594](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15594))
127
+
128
+ ## 1.9.2
129
+
130
+ ### Extensions and API:
131
+ * restore 1.8.0-style naming of scripts
132
+
133
+ ## 1.9.1
134
+
135
+ ### Minor:
136
+ * Add avif support ([#15582](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15582))
137
+ * Add filename patterns: `[sampler_scheduler]` and `[scheduler]` ([#15581](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15581))
138
+
139
+ ### Extensions and API:
140
+ * undo adding scripts to sys.modules
141
+ * Add schedulers API endpoint ([#15577](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15577))
142
+ * Remove API upscaling factor limits ([#15560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15560))
143
+
144
+ ### Bug Fixes:
145
+ * Fix images do not match / Coordinate 'right' is less than 'left' ([#15534](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15534))
146
+ * fix: remove_callbacks_for_function should also remove from the ordered map ([#15533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15533))
147
+ * fix x1 upscalers ([#15555](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15555))
148
+ * Fix cls.__module__ value in extension script ([#15532](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15532))
149
+ * fix typo in function call (eror -> error) ([#15531](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15531))
150
+
151
+ ### Other:
152
+ * Hide 'No Image data blocks found.' message ([#15567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15567))
153
+ * Allow webui.sh to be runnable from arbitrary directories containing a .git file ([#15561](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15561))
154
+ * Compatibility with Debian 11, Fedora 34+ and openSUSE 15.4+ ([#15544](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15544))
155
+ * numpy DeprecationWarning product -> prod ([#15547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15547))
156
+ * get_crop_region_v2 ([#15583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15583), [#15587](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15587))
157
+
158
+
159
+ ## 1.9.0
160
+
161
+ ### Features:
162
+ * Make refiner switchover based on model timesteps instead of sampling steps ([#14978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14978))
163
+ * add an option to have old-style directory view instead of tree view; stylistic changes for extra network sorting/search controls
164
+ * add UI for reordering callbacks, support for specifying callback order in extension metadata ([#15205](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15205))
165
+ * Sgm uniform scheduler for SDXL-Lightning models ([#15325](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15325))
166
+ * Scheduler selection in main UI ([#15333](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15333), [#15361](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15361), [#15394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15394))
167
+
168
+ ### Minor:
169
+ * "open images directory" button now opens the actual dir ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947))
170
+ * Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973))
171
+ * make extra network card description plaintext by default, with an option to re-enable HTML as it was
172
+ * resize handle for extra networks ([#15041](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15041))
173
+ * cmd args: `--unix-filenames-sanitization` and `--filenames-max-length` ([#15031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15031))
174
+ * show extra networks parameters in HTML table rather than raw JSON ([#15131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15131))
175
+ * Add DoRA (weight-decompose) support for LoRA/LoHa/LoKr ([#15160](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15160), [#15283](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15283))
176
+ * Add '--no-prompt-history' cmd args for disable last generation prompt history ([#15189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15189))
177
+ * update preview on Replace Preview ([#15201](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15201))
178
+ * only fetch updates for extensions' active git branches ([#15233](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15233))
179
+ * put upscale postprocessing UI into an accordion ([#15223](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15223))
180
+ * Support dragdrop for URLs to read infotext ([#15262](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15262))
181
+ * use diskcache library for caching ([#15287](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15287), [#15299](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15299))
182
+ * Allow PNG-RGBA for Extras Tab ([#15334](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15334))
183
+ * Support cover images embedded in safetensors metadata ([#15319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15319))
184
+ * faster interrupt when using NN upscale ([#15380](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15380))
185
+ * Extras upscaler: an input field to limit maximul side length for the output image ([#15293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15293), [#15415](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15415), [#15417](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15417), [#15425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15425))
186
+ * add an option to hide postprocessing options in Extras tab
187
+
188
+ ### Extensions and API:
189
+ * ResizeHandleRow - allow overriden column scale parametr ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
190
+ * call script_callbacks.ui_settings_callback earlier; fix extra-options-section built-in extension killing the ui if using a setting that doesn't exist
191
+ * make it possible to use zoom.js outside webui context ([#15286](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15286), [#15288](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15288))
192
+ * allow variants for extension name in metadata.ini ([#15290](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15290))
193
+ * make reloading UI scripts optional when doing Reload UI, and off by default
194
+ * put request: gr.Request at start of img2img function similar to txt2img
195
+ * open_folder as util ([#15442](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15442))
196
+ * make it possible to import extensions' script files as `import scripts.<filename>` ([#15423](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15423))
197
+
198
+ ### Performance:
199
+ * performance optimization for extra networks HTML pages
200
+ * optimization for extra networks filtering
201
+ * optimization for extra networks sorting
202
+
203
+ ### Bug Fixes:
204
+ * prevent escape button causing an interrupt when no generation has been made yet
205
+ * [bug] avoid doble upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
206
+ * possible fix for reload button not appearing in some cases for extra networks.
207
+ * fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
208
+ * Fix resize-handle visability for vertical layout (mobile) ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010))
209
+ * register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012))
210
+ * Protect alphas_cumprod during refiner switchover ([#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979))
211
+ * Fix EXIF orientation in API image loading ([#15062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15062))
212
+ * Only override emphasis if actually used in prompt ([#15141](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15141))
213
+ * Fix emphasis infotext missing from `params.txt` ([#15142](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15142))
214
+ * fix extract_style_text_from_prompt #15132 ([#15135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15135))
215
+ * Fix Soft Inpaint for AnimateDiff ([#15148](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15148))
216
+ * edit-attention: deselect surrounding whitespace ([#15178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15178))
217
+ * chore: fix font not loaded ([#15183](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15183))
218
+ * use natural sort in extra networks when ordering by path
219
+ * Fix built-in lora system bugs caused by torch.nn.MultiheadAttention ([#15190](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15190))
220
+ * Avoid error from None in get_learned_conditioning ([#15191](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15191))
221
+ * Add entry to MassFileLister after writing metadata ([#15199](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15199))
222
+ * fix issue with Styles when Hires prompt is used ([#15269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15269), [#15276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15276))
223
+ * Strip comments from hires fix prompt ([#15263](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15263))
224
+ * Make imageviewer event listeners browser consistent ([#15261](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15261))
225
+ * Fix AttributeError in OFT when trying to get MultiheadAttention weight ([#15260](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15260))
226
+ * Add missing .mean() back ([#15239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15239))
227
+ * fix "Restore progress" button ([#15221](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15221))
228
+ * fix ui-config for InputAccordion [custom_script_source] ([#15231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15231))
229
+ * handle 0 wheel deltaY ([#15268](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15268))
230
+ * prevent alt menu for firefox ([#15267](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15267))
231
+ * fix: fix syntax errors ([#15179](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15179))
232
+ * restore outputs path ([#15307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15307))
233
+ * Escape btn_copy_path filename ([#15316](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15316))
234
+ * Fix extra networks buttons when filename contains an apostrophe ([#15331](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15331))
235
+ * escape brackets in lora random prompt generator ([#15343](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15343))
236
+ * fix: Python version check for PyTorch installation compatibility ([#15390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15390))
237
+ * fix typo in call_queue.py ([#15386](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15386))
238
+ * fix: when find already_loaded model, remove loaded by array index ([#15382](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15382))
239
+ * minor bug fix of sd model memory management ([#15350](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15350))
240
+ * Fix CodeFormer weight ([#15414](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15414))
241
+ * Fix: Remove script callbacks in ordered_callbacks_map ([#15428](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15428))
242
+ * fix limited file write (thanks, Sylwia)
243
+ * Fix extra-single-image API not doing upscale failed ([#15465](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15465))
244
+ * error handling paste_field callables ([#15470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15470))
245
+
246
+ ### Hardware:
247
+ * Add training support and change lspci for Ascend NPU ([#14981](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14981))
248
+ * Update to ROCm5.7 and PyTorch ([#14820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14820))
249
+ * Better workaround for Navi1, removing --pre for Navi3 ([#15224](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15224))
250
+ * Ascend NPU wiki page ([#15228](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15228))
251
+
252
+ ### Other:
253
+ * Update comment for Pad prompt/negative prompt v0 to add a warning about truncation, make it override the v1 implementation
254
+ * support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002))
255
+ * Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
256
+ * Use `absolute` path for normalized filepath ([#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035))
257
+ * resizeHandle handle double tap ([#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
258
+ * --dat-models-path cmd flag ([#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
259
+ * Add a direct link to the binary release ([#15059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15059))
260
+ * upscaler_utils: Reduce logging ([#15084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15084))
261
+ * Fix various typos with crate-ci/typos ([#15116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15116))
262
+ * fix_jpeg_live_preview ([#15102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15102))
263
+ * [alternative fix] can't load webui if selected wrong extra option in ui ([#15121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15121))
264
+ * Error handling for unsupported transparency ([#14958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14958))
265
+ * Add model description to searched terms ([#15198](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15198))
266
+ * bump action version ([#15272](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15272))
267
+ * PEP 604 annotations ([#15259](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15259))
268
+ * Automatically Set the Scale by value when user selects an Upscale Model ([#15244](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15244))
269
+ * move postprocessing-for-training into builtin extensions ([#15222](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15222))
270
+ * type hinting in shared.py ([#15211](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15211))
271
+ * update ruff to 0.3.3
272
+ * Update pytorch lightning utilities ([#15310](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15310))
273
+ * Add Size as an XYZ Grid option ([#15354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15354))
274
+ * Use HF_ENDPOINT variable for HuggingFace domain with default ([#15443](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15443))
275
+ * re-add update_file_entry ([#15446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15446))
276
+ * create_infotext allow index and callable, re-work Hires prompt infotext ([#15460](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15460))
277
+ * update restricted_opts to include more options for --hide-ui-dir-config ([#15492](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15492))
278
+
279
+
280
+ ## 1.8.0
281
+
282
+ ### Features:
283
+ * Update torch to version 2.1.2
284
+ * Soft Inpainting ([#14208](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14208))
285
+ * FP8 support ([#14031](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14031), [#14327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14327))
286
+ * Support for SDXL-Inpaint Model ([#14390](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14390))
287
+ * Use Spandrel for upscaling and face restoration architectures ([#14425](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14425), [#14467](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14467), [#14473](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14473), [#14474](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14474), [#14477](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14477), [#14476](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14476), [#14484](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14484), [#14500](https://github.com/AUTOMATIC1111/stable-difusion-webui/pull/14500), [#14501](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14501), [#14504](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14504), [#14524](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14524), [#14809](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14809))
288
+ * Automatic backwards version compatibility (when loading infotexts from old images with program version specified, will add compatibility settings)
289
+ * Implement zero terminal SNR noise schedule option (**[SEED BREAKING CHANGE](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Seed-breaking-changes#180-dev-170-225-2024-01-01---zero-terminal-snr-noise-schedule-option)**, [#14145](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14145), [#14979](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14979))
290
+ * Add a [✨] button to run hires fix on selected image in the gallery (with help from [#14598](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14598), [#14626](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14626), [#14728](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14728))
291
+ * [Separate assets repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui-assets); serve fonts locally rather than from google's servers
292
+ * Official LCM Sampler Support ([#14583](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14583))
293
+ * Add support for DAT upscaler models ([#14690](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14690), [#15039](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15039))
294
+ * Extra Networks Tree View ([#14588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14588), [#14900](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14900))
295
+ * NPU Support ([#14801](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14801))
296
+ * Prompt comments support
297
+
298
+ ### Minor:
299
+ * Allow pasting in WIDTHxHEIGHT strings into the width/height fields ([#14296](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14296))
300
+ * add option: Live preview in full page image viewer ([#14230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14230), [#14307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14307))
301
+ * Add keyboard shortcuts for generate/skip/interrupt ([#14269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14269))
302
+ * Better TCMALLOC support on different platforms ([#14227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14227), [#14883](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14883), [#14910](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14910))
303
+ * Lora not found warning ([#14464](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14464))
304
+ * Adding negative prompts to Loras in extra networks ([#14475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14475))
305
+ * xyz_grid: allow varying the seed along an axis separate from axis options ([#12180](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12180))
306
+ * option to convert VAE to bfloat16 (implementation of [#9295](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9295))
307
+ * Better IPEX support ([#14229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14229), [#14353](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14353), [#14559](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14559), [#14562](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14562), [#14597](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14597))
308
+ * Option to interrupt after current generation rather than immediately ([#13653](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13653), [#14659](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14659))
309
+ * Fullscreen Preview control fading/disable ([#14291](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14291))
310
+ * Finer settings freezing control ([#13789](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13789))
311
+ * Increase Upscaler Limits ([#14589](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14589))
312
+ * Adjust brush size with hotkeys ([#14638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14638))
313
+ * Add checkpoint info to csv log file when saving images ([#14663](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14663))
314
+ * Make more columns resizable ([#14740](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14740), [#14884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14884))
315
+ * Add an option to not overlay original image for inpainting for #14727
316
+ * Add Pad conds v0 option to support same generation with DDIM as before 1.6.0
317
+ * Add "Interrupting..." placeholder.
318
+ * Button for refresh extensions list ([#14857](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14857))
319
+ * Add an option to disable normalization after calculating emphasis. ([#14874](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14874))
320
+ * When counting tokens, also include enabled styles (can be disabled in settings to revert to previous behavior)
321
+ * Configuration for the [📂] button for image gallery ([#14947](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14947))
322
+ * Support inference with LyCORIS BOFT networks ([#14871](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14871), [#14973](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14973))
323
+ * support resizable columns for touch (tablets) ([#15002](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15002))
324
+
325
+ ### Extensions and API:
326
+ * Removed packages from requirements: basicsr, gfpgan, realesrgan; as well as their dependencies: absl-py, addict, beautifulsoup4, future, gdown, grpcio, importlib-metadata, lmdb, lpips, Markdown, platformdirs, PySocks, soupsieve, tb-nightly, tensorboard-data-server, tomli, Werkzeug, yapf, zipp, soupsieve
327
+ * Enable task ids for API ([#14314](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14314))
328
+ * add override_settings support for infotext API
329
+ * rename generation_parameters_copypaste module to infotext_utils
330
+ * prevent crash due to Script __init__ exception ([#14407](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14407))
331
+ * Bump numpy to 1.26.2 ([#14471](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14471))
332
+ * Add utility to inspect a model's dtype/device ([#14478](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14478))
333
+ * Implement general forward method for all method in built-in lora ext ([#14547](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14547))
334
+ * Execute model_loaded_callback after moving to target device ([#14563](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14563))
335
+ * Add self to CFGDenoiserParams ([#14573](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14573))
336
+ * Allow TLS with API only mode (--nowebui) ([#14593](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14593))
337
+ * New callback: postprocess_image_after_composite ([#14657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14657))
338
+ * modules/api/api.py: add api endpoint to refresh embeddings list ([#14715](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14715))
339
+ * set_named_arg ([#14773](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14773))
340
+ * add before_token_counter callback and use it for prompt comments
341
+ * ResizeHandleRow - allow overridden column scale parameter ([#15004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15004))
342
+
343
+ ### Performance:
344
+ * Massive performance improvement for extra networks directories with a huge number of files in them in an attempt to tackle #14507 ([#14528](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14528))
345
+ * Reduce unnecessary re-indexing extra networks directory ([#14512](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14512))
346
+ * Avoid unnecessary `isfile`/`exists` calls ([#14527](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14527))
347
+
348
+ ### Bug Fixes:
349
+ * fix multiple bugs related to styles multi-file support ([#14203](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14203), [#14276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14276), [#14707](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14707))
350
+ * Lora fixes ([#14300](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14300), [#14237](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14237), [#14546](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14546), [#14726](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14726))
351
+ * Re-add setting lost as part of e294e46 ([#14266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14266))
352
+ * fix extras caption BLIP ([#14330](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14330))
353
+ * include infotext into saved init image for img2img ([#14452](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14452))
354
+ * xyz grid handle axis_type is None ([#14394](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14394))
355
+ * Update Added (Fixed) IPV6 Functionality When there is No Webui Argument Passed webui.py ([#14354](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14354))
356
+ * fix API thread safe issues of txt2img and img2img ([#14421](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14421))
357
+ * handle selectable script_index is None ([#14487](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14487))
358
+ * handle config.json failed to load ([#14525](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14525), [#14767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14767))
359
+ * paste infotext cast int as float ([#14523](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14523))
360
+ * Ensure GRADIO_ANALYTICS_ENABLED is set early enough ([#14537](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14537))
361
+ * Fix logging configuration again ([#14538](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14538))
362
+ * Handle CondFunc exception when resolving attributes ([#14560](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14560))
363
+ * Fix extras big batch crashes ([#14699](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14699))
364
+ * Fix using wrong model caused by alias ([#14655](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14655))
365
+ * Add # to the invalid_filename_chars list ([#14640](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14640))
366
+ * Fix extension check for requirements ([#14639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14639))
367
+ * Fix tab indexes are reset after restart UI ([#14637](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14637))
368
+ * Fix nested manual cast ([#14689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14689))
369
+ * Keep postprocessing upscale selected tab after restart ([#14702](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14702))
370
+ * XYZ grid: filter out blank vals when axis is int or float type (like int axis seed) ([#14754](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14754))
371
+ * fix CLIP Interrogator topN regex ([#14775](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14775))
372
+ * Fix dtype error in MHA layer/change dtype checking mechanism for manual cast ([#14791](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14791))
373
+ * catch load style.csv error ([#14814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14814))
374
+ * fix error when editing extra networks card
375
+ * fix extra networks metadata failing to work properly when you create the .json file with metadata for the first time.
376
+ * util.walk_files extensions case insensitive ([#14879](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14879))
377
+ * if extensions page not loaded, prevent apply ([#14873](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14873))
378
+ * call the right function for token counter in img2img
379
+ * Fix the bugs that search/reload will disappear when using other ExtraNetworks extensions ([#14939](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14939))
380
+ * Gracefully handle mtime read exception from cache ([#14933](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14933))
381
+ * Only trigger interrupt on `Esc` when interrupt button visible ([#14932](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14932))
382
+ * Disable prompt token counters option actually disables token counting rather than just hiding results.
383
+ * avoid double upscaling in inpaint ([#14966](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14966))
384
+ * Fix #14591 using translated content to do categories mapping ([#14995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14995))
385
+ * fix: the `split_threshold` parameter does not work when running Split oversized images ([#15006](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15006))
386
+ * Fix resize-handle for mobile ([#15010](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15010), [#15065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15065))
387
+
388
+ ### Other:
389
+ * Assign id for "extra_options". Replace numeric field with slider. ([#14270](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14270))
390
+ * change state dict comparison to ref compare ([#14216](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14216))
391
+ * Bump torch-rocm to 5.6/5.7 ([#14293](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14293))
392
+ * Base output path off data path ([#14446](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14446))
393
+ * reorder training preprocessing modules in extras tab ([#14367](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14367))
394
+ * Remove `cleanup_models` code ([#14472](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14472))
395
+ * only rewrite ui-config when there is change ([#14352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14352))
396
+ * Fix lint issue from 501993eb ([#14495](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14495))
397
+ * Update README.md ([#14548](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14548))
398
+ * hires button, fix seeds ()
399
+ * Logging: set formatter correctly for fallback logger too ([#14618](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14618))
400
+ * Read generation info from infotexts rather than json for internal needs (save, extract seed from generated pic) ([#14645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14645))
401
+ * improve get_crop_region ([#14709](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14709))
402
+ * Bump safetensors' version to 0.4.2 ([#14782](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14782))
403
+ * add tooltip create_submit_box ([#14803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14803))
404
+ * extensions tab table row hover highlight ([#14885](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14885))
405
+ * Always add timestamp to displayed image ([#14890](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14890))
406
+ * Added core.filemode=false so doesn't track changes in file permission… ([#14930](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14930))
407
+ * Normalize command-line argument paths ([#14934](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14934), [#15035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15035))
408
+ * Use original App Title in progress bar ([#14916](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14916))
409
+ * register_tmp_file also for mtime ([#15012](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15012))
410
+
411
+ ## 1.7.0
412
+
413
+ ### Features:
414
+ * settings tab rework: add search field, add categories, split UI settings page into many
415
+ * add altdiffusion-m18 support ([#13364](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13364))
416
+ * support inference with LyCORIS GLora networks ([#13610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13610))
417
+ * add lora-embedding bundle system ([#13568](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13568))
418
+ * option to move prompt from top row into generation parameters
419
+ * add support for SSD-1B ([#13865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13865))
420
+ * support inference with OFT networks ([#13692](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13692))
421
+ * script metadata and DAG sorting mechanism ([#13944](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13944))
422
+ * support HyperTile optimization ([#13948](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13948))
423
+ * add support for SD 2.1 Turbo ([#14170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14170))
424
+ * remove Train->Preprocessing tab and put all its functionality into Extras tab
425
+ * initial IPEX support for Intel Arc GPU ([#14171](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14171))
426
+
427
+ ### Minor:
428
+ * allow reading model hash from images in img2img batch mode ([#12767](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12767))
429
+ * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
430
+ * extra field for lora metadata viewer: `ss_output_name` ([#12838](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12838))
431
+ * add action in settings page to calculate all SD checkpoint hashes ([#12909](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12909))
432
+ * add button to copy prompt to style editor ([#12975](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12975))
433
+ * add --skip-load-model-at-start option ([#13253](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13253))
434
+ * write infotext to gif images
435
+ * read infotext from gif images ([#13068](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13068))
436
+ * allow configuring the initial state of InputAccordion in ui-config.json ([#13189](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13189))
437
+ * allow editing whitespace delimiters for ctrl+up/ctrl+down prompt editing ([#13444](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13444))
438
+ * prevent accidentally closing popup dialogs ([#13480](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13480))
439
+ * added option to play notification sound or not ([#13631](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13631))
440
+ * show the preview image in the full screen image viewer if available ([#13459](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13459))
441
+ * support for webui.settings.bat ([#13638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13638))
442
+ * add an option to not print stack traces on ctrl+c
443
+ * start/restart generation by Ctrl (Alt) + Enter ([#13644](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13644))
444
+ * update prompts_from_file script to allow concatenating entries with the general prompt ([#13733](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13733))
445
+ * added a visible checkbox to input accordion
446
+ * added an option to hide all txt2img/img2img parameters in an accordion ([#13826](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13826))
447
+ * added 'Path' sorting option for Extra network cards ([#13968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13968))
448
+ * enable prompt hotkeys in style editor ([#13931](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13931))
449
+ * option to show batch img2img results in UI ([#14009](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14009))
450
+ * infotext updates: add option to disregard certain infotext fields, add option to not include VAE in infotext, add explanation to infotext settings page, move some options to infotext settings page
451
+ * add FP32 fallback support on sd_vae_approx ([#14046](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14046))
452
+ * support XYZ scripts / split hires path from unet ([#14126](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14126))
453
+ * allow use of multiple styles csv files ([#14125](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14125))
454
+ * make extra network card description plaintext by default, with an option (Treat card description as HTML) to re-enable HTML as it was (originally by [#13241](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13241))
455
+
456
+ ### Extensions and API:
457
+ * update gradio to 3.41.2
458
+ * support installed extensions list api ([#12774](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12774))
459
+ * update pnginfo API to return dict with parsed values
460
+ * add noisy latent to `ExtraNoiseParams` for callback ([#12856](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12856))
461
+ * show extension datetime in UTC ([#12864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12864), [#12865](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12865), [#13281](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13281))
462
+ * add an option to choose how to combine hires fix and refiner
463
+ * include program version in info response. ([#13135](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13135))
464
+ * sd_unet support for SDXL
465
+ * patch DDPM.register_betas so that users can put given_betas in model yaml ([#13276](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13276))
466
+ * xyz_grid: add prepare ([#13266](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13266))
467
+ * allow multiple localization files with same language in extensions ([#13077](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13077))
468
+ * add onEdit function for js and rework token-counter.js to use it
469
+ * fix the key error exception when processing override_settings keys ([#13567](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13567))
470
+ * ability for extensions to return custom data via api in response.images ([#13463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13463))
471
+ * call state.jobnext() before postproces*() ([#13762](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13762))
472
+ * add option to set notification sound volume ([#13884](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13884))
473
+ * update Ruff to 0.1.6 ([#14059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14059))
474
+ * add Block component creation callback ([#14119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14119))
475
+ * catch uncaught exception with ui creation scripts ([#14120](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14120))
476
+ * use extension name for determining an extension is installed in the index ([#14063](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14063))
477
+ * update is_installed() from launch_utils.py to fix reinstalling already installed packages ([#14192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14192))
478
+
479
+ ### Bug Fixes:
480
+ * fix pix2pix producing bad results
481
+ * fix defaults settings page breaking when any of main UI tabs are hidden
482
+ * fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
483
+ * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
484
+ * prevent duplicate resize handler ([#12795](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12795))
485
+ * small typo: vae resolve bug ([#12797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12797))
486
+ * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12792))
487
+ * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12780))
488
+ * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
489
+ * hide --gradio-auth and --api-auth values from /internal/sysinfo report
490
+ * add missing infotext for RNG in options ([#12819](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12819))
491
+ * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
492
+ * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
493
+ * don't print blank stdout in extension installers ([#12833](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12833), [#12855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12855))
494
+ * get progressbar to display correctly in extensions tab
495
+ * keep order in list of checkpoints when loading model that doesn't have a checksum
496
+ * fix inpainting models in txt2img creating black pictures
497
+ * fix generation params regex ([#12876](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12876))
498
+ * fix batch img2img output dir with script ([#12926](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12926))
499
+ * fix #13080 - Hypernetwork/TI preview generation ([#13084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13084))
500
+ * fix bug with sigma min/max overrides. ([#12995](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12995))
501
+ * more accurate check for enabling cuDNN benchmark on 16XX cards ([#12924](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12924))
502
+ * don't use multicond parser for negative prompt counter ([#13118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13118))
503
+ * fix data-sort-name containing spaces ([#13412](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13412))
504
+ * update card on correct tab when editing metadata ([#13411](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13411))
505
+ * fix viewing/editing metadata when filename contains an apostrophe ([#13395](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13395))
506
+ * fix: --sd_model in "Prompts from file or textbox" script is not working ([#13302](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13302))
507
+ * better Support for Portable Git ([#13231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13231))
508
+ * fix issues when webui_dir is not work_dir ([#13210](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13210))
509
+ * fix: lora-bias-backup don't reset cache ([#13178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13178))
510
+ * account for customizable extra network separators whyen removing extra network text from the prompt ([#12877](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12877))
511
+ * re fix batch img2img output dir with script ([#13170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13170))
512
+ * fix `--ckpt-dir` path separator and option use `short name` for checkpoint dropdown ([#13139](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13139))
513
+ * consolidated allowed preview formats, Fix extra network `.gif` not woking as preview ([#13121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13121))
514
+ * fix venv_dir=- environment variable not working as expected on linux ([#13469](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13469))
515
+ * repair unload sd checkpoint button
516
+ * edit-attention fixes ([#13533](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13533))
517
+ * fix bug when using --gfpgan-models-path ([#13718](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13718))
518
+ * properly apply sort order for extra network cards when selected from dropdown
519
+ * fixes generation restart not working for some users when 'Ctrl+Enter' is pressed ([#13962](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13962))
520
+ * thread safe extra network list_items ([#13014](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13014))
521
+ * fix not able to exit metadata popup when pop up is too big ([#14156](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14156))
522
+ * fix auto focal point crop for opencv >= 4.8 ([#14121](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14121))
523
+ * make 'use-cpu all' actually apply to 'all' ([#14131](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14131))
524
+ * extras tab batch: actually use original filename
525
+ * make webui not crash when running with --disable-all-extensions option
526
+
527
+ ### Other:
528
+ * non-local condition ([#12814](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12814))
529
+ * fix minor typos ([#12827](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12827))
530
+ * remove xformers Python version check ([#12842](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12842))
531
+ * style: file-metadata word-break ([#12837](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12837))
532
+ * revert SGM noise multiplier change for img2img because it breaks hires fix
533
+ * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
534
+ * [RC 1.6.0 - zoom is partly hidden] Update style.css ([#12839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12839))
535
+ * chore: change extension time format ([#12851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12851))
536
+ * WEBUI.SH - Use torch 2.1.0 release candidate for Navi 3 ([#12929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12929))
537
+ * add Fallback at images.read_info_from_image if exif data was invalid ([#13028](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13028))
538
+ * update cmd arg description ([#12986](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12986))
539
+ * fix: update shared.opts.data when add_option ([#12957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12957), [#13213](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13213))
540
+ * restore missing tooltips ([#12976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12976))
541
+ * use default dropdown padding on mobile ([#12880](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12880))
542
+ * put enable console prompts option into settings from commandline args ([#13119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13119))
543
+ * fix some deprecated types ([#12846](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12846))
544
+ * bump to torchsde==0.2.6 ([#13418](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13418))
545
+ * update dragdrop.js ([#13372](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13372))
546
+ * use orderdict as lru cache:opt/bug ([#13313](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13313))
547
+ * XYZ if not include sub grids do not save sub grid ([#13282](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13282))
548
+ * initialize state.time_start befroe state.job_count ([#13229](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13229))
549
+ * fix fieldname regex ([#13458](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13458))
550
+ * change denoising_strength default to None. ([#13466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13466))
551
+ * fix regression ([#13475](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13475))
552
+ * fix IndexError ([#13630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13630))
553
+ * fix: checkpoints_loaded:{checkpoint:state_dict}, model.load_state_dict issue in dict value empty ([#13535](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13535))
554
+ * update bug_report.yml ([#12991](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12991))
555
+ * requirements_versions httpx==0.24.1 ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839))
556
+ * fix parenthesis auto selection ([#13829](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13829))
557
+ * fix #13796 ([#13797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13797))
558
+ * corrected a typo in `modules/cmd_args.py` ([#13855](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13855))
559
+ * feat: fix randn found element of type float at pos 2 ([#14004](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14004))
560
+ * adds tqdm handler to logging_config.py for progress bar integration ([#13996](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13996))
561
+ * hotfix: call shared.state.end() after postprocessing done ([#13977](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13977))
562
+ * fix dependency address patch 1 ([#13929](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13929))
563
+ * save sysinfo as .json ([#14035](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14035))
564
+ * move exception_records related methods to errors.py ([#14084](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14084))
565
+ * compatibility ([#13936](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13936))
566
+ * json.dump(ensure_ascii=False) ([#14108](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14108))
567
+ * dir buttons start with / so only the correct dir will be shown and no… ([#13957](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13957))
568
+ * alternate implementation for unet forward replacement that does not depend on hijack being applied
569
+ * re-add `keyedit_delimiters_whitespace` setting lost as part of commit e294e46 ([#14178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14178))
570
+ * fix `save_samples` being checked early when saving masked composite ([#14177](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14177))
571
+ * slight optimization for mask and mask_composite ([#14181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14181))
572
+ * add import_hook hack to work around basicsr/torchvision incompatibility ([#14186](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/14186))
573
+
574
+ ## 1.6.1
575
+
576
+ ### Bug Fixes:
577
+ * fix an error causing the webui to fail to start ([#13839](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/13839))
578
+
579
+ ## 1.6.0
580
+
581
+ ### Features:
582
+ * refiner support [#12371](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12371)
583
+ * add NV option for Random number generator source setting, which allows to generate same pictures on CPU/AMD/Mac as on NVidia videocards
584
+ * add style editor dialog
585
+ * hires fix: add an option to use a different checkpoint for second pass ([#12181](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12181))
586
+ * option to keep multiple loaded models in memory ([#12227](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12227))
587
+ * 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))
588
+ * rework DDIM, PLMS, UniPC to use CFG denoiser same as in k-diffusion samplers:
589
+ * makes all of them work with img2img
590
+ * makes prompt composition possible (AND)
591
+ * makes them available for SDXL
592
+ * always show extra networks tabs in the UI ([#11808](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/11808))
593
+ * 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))
594
+ * textual inversion inference support for SDXL
595
+ * extra networks UI: show metadata for SD checkpoints
596
+ * checkpoint merger: add metadata support
597
+ * 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))
598
+ * VAE: allow selecting own VAE for each checkpoint (in user metadata editor)
599
+ * VAE: add selected VAE to infotext
600
+ * 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))
601
+ * 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))
602
+ * 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
603
+ * 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))
604
+ * add `--medvram-sdxl` flag that only enables `--medvram` for SDXL models
605
+ * 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))
606
+
607
+ ### Minor:
608
+ * 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))
609
+ * postprocessing/extras: RAM savings ([#12479](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12479))
610
+ * XYZ: in the axis labels, remove pathnames from model filenames
611
+ * XYZ: support hires sampler ([#12298](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12298))
612
+ * XYZ: new option: use text inputs instead of dropdowns ([#12491](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12491))
613
+ * add gradio version warning
614
+ * sort list of VAE checkpoints ([#12297](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12297))
615
+ * 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))
616
+ * move some settings to their own section: img2img, VAE
617
+ * add checkbox to show/hide dirs for extra networks
618
+ * Add TAESD(or more) options for all the VAE encode/decode operation ([#12311](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12311))
619
+ * 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))
620
+ * 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))
621
+ * update README.md with correct instructions for Linux installation ([#12352](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12352))
622
+ * option to not save incomplete images, on by default ([#12338](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12338))
623
+ * enable cond cache by default
624
+ * git autofix for repos that are corrupted ([#12230](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12230))
625
+ * allow to open images in new browser tab by middle mouse button ([#12379](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12379))
626
+ * automatically open webui in browser when running "locally" ([#12254](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12254))
627
+ * put commonly used samplers on top, make DPM++ 2M Karras the default choice
628
+ * 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))
629
+ * option to cache Lora networks in memory
630
+ * rework hires fix UI to use accordion
631
+ * face restoration and tiling moved to settings - use "Options in main UI" setting if you want them back
632
+ * change quicksettings items to have variable width
633
+ * Lora: add Norm module, add support for bias ([#12503](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12503))
634
+ * Lora: output warnings in UI rather than fail for unfitting loras; switch to logging for error output in console
635
+ * support search and display of hashes for all extra network items ([#12510](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12510))
636
+ * add extra noise param for img2img operations ([#12564](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12564))
637
+ * support for Lora with bias ([#12584](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12584))
638
+ * make interrupt quicker ([#12634](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12634))
639
+ * configurable gallery height ([#12648](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12648))
640
+ * make results column sticky ([#12645](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12645))
641
+ * more hash filename patterns ([#12639](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12639))
642
+ * make image viewer actually fit the whole page ([#12635](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12635))
643
+ * make progress bar work independently from live preview display which results in it being updated a lot more often
644
+ * forbid Full live preview method for medvram and add a setting to undo the forbidding
645
+ * make it possible to localize tooltips and placeholders
646
+ * add option to align with sgm repo's sampling implementation ([#12818](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12818))
647
+ * Restore faces and Tiling generation parameters have been moved to settings out of main UI
648
+ * if you want to put them back into main UI, use `Options in main UI` setting on the UI page.
649
+
650
+ ### Extensions and API:
651
+ * gradio 3.41.2
652
+ * also bump versions for packages: transformers, GitPython, accelerate, scikit-image, timm, tomesd
653
+ * support tooltip kwarg for gradio elements: gr.Textbox(label='hello', tooltip='world')
654
+ * properly clear the total console progressbar when using txt2img and img2img from API
655
+ * add cmd_arg --disable-extra-extensions and --disable-all-extensions ([#12294](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12294))
656
+ * shared.py and webui.py split into many files
657
+ * add --loglevel commandline argument for logging
658
+ * add a custom UI element that combines accordion and checkbox
659
+ * avoid importing gradio in tests because it spams warnings
660
+ * put infotext label for setting into OptionInfo definition rather than in a separate list
661
+ * make `StableDiffusionProcessingImg2Img.mask_blur` a property, make more inline with PIL `GaussianBlur` ([#12470](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12470))
662
+ * option to make scripts UI without gr.Group
663
+ * add a way for scripts to register a callback for before/after just a single component's creation
664
+ * use dataclass for StableDiffusionProcessing
665
+ * store patches for Lora in a specialized module instead of inside torch
666
+ * 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))
667
+ * add extra noise callback ([#12616](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12616))
668
+ * dump current stack traces when exiting with SIGINT
669
+ * add type annotations for extra fields of shared.sd_model
670
+
671
+ ### Bug Fixes:
672
+ * Don't crash if out of local storage quota for javascriot localStorage
673
+ * XYZ plot do not fail if an exception occurs
674
+ * fix missing TI hash in infotext if generation uses both negative and positive TI ([#12269](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12269))
675
+ * localization fixes ([#12307](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12307))
676
+ * fix sdxl model invalid configuration after the hijack
677
+ * correctly toggle extras checkbox for infotext paste ([#12304](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12304))
678
+ * open raw sysinfo link in new page ([#12318](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12318))
679
+ * prompt parser: Account for empty field in alternating words syntax ([#12319](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12319))
680
+ * add tab and carriage return to invalid filename chars ([#12327](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12327))
681
+ * fix api only Lora not working ([#12387](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12387))
682
+ * fix options in main UI misbehaving when there's just one element
683
+ * make it possible to use a sampler from infotext even if it's hidden in the dropdown
684
+ * fix styles missing from the prompt in infotext when making a grid of batch of multiplie images
685
+ * prevent bogus progress output in console when calculating hires fix dimensions
686
+ * fix --use-textbox-seed
687
+ * fix broken `Lora/Networks: use old method` option ([#12466](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12466))
688
+ * properly return `None` for VAE hash when using `--no-hashing` ([#12463](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12463))
689
+ * MPS/macOS fixes and optimizations ([#12526](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12526))
690
+ * add second_order to samplers that mistakenly didn't have it
691
+ * when refreshing cards in extra networks UI, do not discard user's custom resolution
692
+ * 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))
693
+ * fix inpaint upload for alpha masks ([#12588](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12588))
694
+ * fix exception when image sizes are not integers ([#12586](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12586))
695
+ * fix incorrect TAESD Latent scale ([#12596](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12596))
696
+ * auto add data-dir to gradio-allowed-path ([#12603](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12603))
697
+ * fix exception if extensuions dir is missing ([#12607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12607))
698
+ * fix issues with api model-refresh and vae-refresh ([#12638](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12638))
699
+ * fix img2img background color for transparent images option not being used ([#12633](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12633))
700
+ * attempt to resolve NaN issue with unstable VAEs in fp32 mk2 ([#12630](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12630))
701
+ * implement missing undo hijack for SDXL
702
+ * fix xyz swap axes ([#12684](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12684))
703
+ * fix errors in backup/restore tab if any of config files are broken ([#12689](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12689))
704
+ * fix SD VAE switch error after model reuse ([#12685](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12685))
705
+ * fix trying to create images too large for the chosen format ([#12667](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12667))
706
+ * create Gradio temp directory if necessary ([#12717](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12717))
707
+ * prevent possible cache loss if exiting as it's being written by using an atomic operation to replace the cache with the new version
708
+ * set devices.dtype_unet correctly
709
+ * run RealESRGAN on GPU for non-CUDA devices ([#12737](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
710
+ * prevent extra network buttons being obscured by description for very small card sizes ([#12745](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12745))
711
+ * fix error that causes some extra networks to be disabled if both <lora:> and <lyco:> are present in the prompt
712
+ * fix defaults settings page breaking when any of main UI tabs are hidden
713
+ * fix incorrect save/display of new values in Defaults page in settings
714
+ * fix for Reload UI function: if you reload UI on one tab, other opened tabs will no longer stop working
715
+ * 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))
716
+ * hide broken image crop tool ([#12792](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
717
+ * don't show hidden samplers in dropdown for XYZ script ([#12780](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12737))
718
+ * fix style editing dialog breaking if it's opened in both img2img and txt2img tabs
719
+ * fix a bug allowing users to bypass gradio and API authentication (reported by vysecurity)
720
+ * fix notification not playing when built-in webui tab is inactive ([#12834](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12834))
721
+ * honor `--skip-install` for extension installers ([#12832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12832))
722
+ * 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))
723
+ * do not change quicksettings dropdown option when value returned is `None` ([#12854](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/12854))
724
+ * get progressbar to display correctly in extensions tab
725
+
726
+
727
+ ## 1.5.2
728
+
729
+ ### Bug Fixes:
730
+ * fix memory leak when generation fails
731
+ * update doggettx cross attention optimization to not use an unreasonable amount of memory in some edge cases -- suggestion by MorkTheOrk
732
+
733
+
734
+ ## 1.5.1
735
+
736
+ ### Minor:
737
+ * support parsing text encoder blocks in some new LoRAs
738
+ * delete scale checker script due to user demand
739
+
740
+ ### Extensions and API:
741
+ * add postprocess_batch_list script callback
742
+
743
+ ### Bug Fixes:
744
+ * fix TI training for SD1
745
+ * fix reload altclip model error
746
+ * prepend the pythonpath instead of overriding it
747
+ * fix typo in SD_WEBUI_RESTARTING
748
+ * if txt2img/img2img raises an exception, finally call state.end()
749
+ * fix composable diffusion weight parsing
750
+ * restyle Startup profile for black users
751
+ * fix webui not launching with --nowebui
752
+ * catch exception for non git extensions
753
+ * fix some options missing from /sdapi/v1/options
754
+ * fix for extension update status always saying "unknown"
755
+ * fix display of extra network cards that have `<>` in the name
756
+ * update lora extension to work with python 3.8
757
+
758
+
759
+ ## 1.5.0
760
+
761
+ ### Features:
762
+ * SD XL support
763
+ * user metadata system for custom networks
764
+ * extended Lora metadata editor: set activation text, default weight, view tags, training info
765
+ * Lora extension rework to include other types of networks (all that were previously handled by LyCORIS extension)
766
+ * show github stars for extensions
767
+ * img2img batch mode can read extra stuff from png info
768
+ * img2img batch works with subdirectories
769
+ * hotkeys to move prompt elements: alt+left/right
770
+ * restyle time taken/VRAM display
771
+ * add textual inversion hashes to infotext
772
+ * optimization: cache git extension repo information
773
+ * move generate button next to the generated picture for mobile clients
774
+ * hide cards for networks of incompatible Stable Diffusion version in Lora extra networks interface
775
+ * skip installing packages with pip if they all are already installed - startup speedup of about 2 seconds
776
+
777
+ ### Minor:
778
+ * checkbox to check/uncheck all extensions in the Installed tab
779
+ * add gradio user to infotext and to filename patterns
780
+ * allow gif for extra network previews
781
+ * add options to change colors in grid
782
+ * use natural sort for items in extra networks
783
+ * Mac: use empty_cache() from torch 2 to clear VRAM
784
+ * added automatic support for installing the right libraries for Navi3 (AMD)
785
+ * add option SWIN_torch_compile to accelerate SwinIR upscale
786
+ * suppress printing TI embedding info at start to console by default
787
+ * speedup extra networks listing
788
+ * added `[none]` filename token.
789
+ * removed thumbs extra networks view mode (use settings tab to change width/height/scale to get thumbs)
790
+ * add always_discard_next_to_last_sigma option to XYZ plot
791
+ * automatically switch to 32-bit float VAE if the generated picture has NaNs without the need for `--no-half-vae` commandline flag.
792
+
793
+ ### Extensions and API:
794
+ * api endpoints: /sdapi/v1/server-kill, /sdapi/v1/server-restart, /sdapi/v1/server-stop
795
+ * allow Script to have custom metaclass
796
+ * add model exists status check /sdapi/v1/options
797
+ * rename --add-stop-route to --api-server-stop
798
+ * add `before_hr` script callback
799
+ * add callback `after_extra_networks_activate`
800
+ * disable rich exception output in console for API by default, use WEBUI_RICH_EXCEPTIONS env var to enable
801
+ * return http 404 when thumb file not found
802
+ * allow replacing extensions index with environment variable
803
+
804
+ ### Bug Fixes:
805
+ * fix for catch errors when retrieving extension index #11290
806
+ * fix very slow loading speed of .safetensors files when reading from network drives
807
+ * API cache cleanup
808
+ * fix UnicodeEncodeError when writing to file CLIP Interrogator batch mode
809
+ * fix warning of 'has_mps' deprecated from PyTorch
810
+ * fix problem with extra network saving images as previews losing generation info
811
+ * fix throwing exception when trying to resize image with I;16 mode
812
+ * fix for #11534: canvas zoom and pan extension hijacking shortcut keys
813
+ * fixed launch script to be runnable from any directory
814
+ * don't add "Seed Resize: -1x-1" to API image metadata
815
+ * correctly remove end parenthesis with ctrl+up/down
816
+ * fixing --subpath on newer gradio version
817
+ * fix: check fill size none zero when resize (fixes #11425)
818
+ * use submit and blur for quick settings textbox
819
+ * save img2img batch with images.save_image()
820
+ * prevent running preload.py for disabled extensions
821
+ * fix: previously, model name was added together with directory name to infotext and to [model_name] filename pattern; directory name is now not included
822
+
823
+
824
+ ## 1.4.1
825
+
826
+ ### Bug Fixes:
827
+ * add queue lock for refresh-checkpoints
828
+
829
+ ## 1.4.0
830
+
831
+ ### Features:
832
+ * zoom controls for inpainting
833
+ * run basic torch calculation at startup in parallel to reduce the performance impact of first generation
834
+ * option to pad prompt/neg prompt to be same length
835
+ * remove taming_transformers dependency
836
+ * custom k-diffusion scheduler settings
837
+ * add an option to show selected settings in main txt2img/img2img UI
838
+ * sysinfo tab in settings
839
+ * infer styles from prompts when pasting params into the UI
840
+ * an option to control the behavior of the above
841
+
842
+ ### Minor:
843
+ * bump Gradio to 3.32.0
844
+ * bump xformers to 0.0.20
845
+ * Add option to disable token counters
846
+ * tooltip fixes & optimizations
847
+ * make it possible to configure filename for the zip download
848
+ * `[vae_filename]` pattern for filenames
849
+ * Revert discarding penultimate sigma for DPM-Solver++(2M) SDE
850
+ * change UI reorder setting to multiselect
851
+ * read version info form CHANGELOG.md if git version info is not available
852
+ * link footer API to Wiki when API is not active
853
+ * persistent conds cache (opt-in optimization)
854
+
855
+ ### Extensions:
856
+ * After installing extensions, webui properly restarts the process rather than reloads the UI
857
+ * Added VAE listing to web API. Via: /sdapi/v1/sd-vae
858
+ * custom unet support
859
+ * Add onAfterUiUpdate callback
860
+ * refactor EmbeddingDatabase.register_embedding() to allow unregistering
861
+ * add before_process callback for scripts
862
+ * add ability for alwayson scripts to specify section and let user reorder those sections
863
+
864
+ ### Bug Fixes:
865
+ * Fix dragging text to prompt
866
+ * fix incorrect quoting for infotext values with colon in them
867
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
868
+ * Fix s_min_uncond default type int
869
+ * Fix for #10643 (Inpainting mask sometimes not working)
870
+ * fix bad styling for thumbs view in extra networks #10639
871
+ * fix for empty list of optimizations #10605
872
+ * small fixes to prepare_tcmalloc for Debian/Ubuntu compatibility
873
+ * fix --ui-debug-mode exit
874
+ * patch GitPython to not use leaky persistent processes
875
+ * fix duplicate Cross attention optimization after UI reload
876
+ * torch.cuda.is_available() check for SdOptimizationXformers
877
+ * fix hires fix using wrong conds in second pass if using Loras.
878
+ * handle exception when parsing generation parameters from png info
879
+ * fix upcast attention dtype error
880
+ * forcing Torch Version to 1.13.1 for RX 5000 series GPUs
881
+ * split mask blur into X and Y components, patch Outpainting MK2 accordingly
882
+ * don't die when a LoRA is a broken symlink
883
+ * allow activation of Generate Forever during generation
884
+
885
+
886
+ ## 1.3.2
887
+
888
+ ### Bug Fixes:
889
+ * fix files served out of tmp directory even if they are saved to disk
890
+ * fix postprocessing overwriting parameters
891
+
892
+ ## 1.3.1
893
+
894
+ ### Features:
895
+ * revert default cross attention optimization to Doggettx
896
+
897
+ ### Bug Fixes:
898
+ * fix bug: LoRA don't apply on dropdown list sd_lora
899
+ * fix png info always added even if setting is not enabled
900
+ * fix some fields not applying in xyz plot
901
+ * fix "hires. fix" prompt sharing same labels with txt2img_prompt
902
+ * fix lora hashes not being added properly to infotex if there is only one lora
903
+ * fix --use-cpu failing to work properly at startup
904
+ * make --disable-opt-split-attention command line option work again
905
+
906
+ ## 1.3.0
907
+
908
+ ### Features:
909
+ * add UI to edit defaults
910
+ * token merging (via dbolya/tomesd)
911
+ * settings tab rework: add a lot of additional explanations and links
912
+ * load extensions' Git metadata in parallel to loading the main program to save a ton of time during startup
913
+ * update extensions table: show branch, show date in separate column, and show version from tags if available
914
+ * TAESD - another option for cheap live previews
915
+ * allow choosing sampler and prompts for second pass of hires fix - hidden by default, enabled in settings
916
+ * calculate hashes for Lora
917
+ * add lora hashes to infotext
918
+ * when pasting infotext, use infotext's lora hashes to find local loras for `<lora:xxx:1>` entries whose hashes match loras the user has
919
+ * select cross attention optimization from UI
920
+
921
+ ### Minor:
922
+ * bump Gradio to 3.31.0
923
+ * bump PyTorch to 2.0.1 for macOS and Linux AMD
924
+ * allow setting defaults for elements in extensions' tabs
925
+ * allow selecting file type for live previews
926
+ * show "Loading..." for extra networks when displaying for the first time
927
+ * suppress ENSD infotext for samplers that don't use it
928
+ * clientside optimizations
929
+ * add options to show/hide hidden files and dirs in extra networks, and to not list models/files in hidden directories
930
+ * allow whitespace in styles.csv
931
+ * add option to reorder tabs
932
+ * move some functionality (swap resolution and set seed to -1) to client
933
+ * option to specify editor height for img2img
934
+ * button to copy image resolution into img2img width/height sliders
935
+ * switch from pyngrok to ngrok-py
936
+ * lazy-load images in extra networks UI
937
+ * set "Navigate image viewer with gamepad" option to false by default, by request
938
+ * change upscalers to download models into user-specified directory (from commandline args) rather than the default models/<...>
939
+ * allow hiding buttons in ui-config.json
940
+
941
+ ### Extensions:
942
+ * add /sdapi/v1/script-info api
943
+ * use Ruff to lint Python code
944
+ * use ESlint to lint Javascript code
945
+ * add/modify CFG callbacks for Self-Attention Guidance extension
946
+ * add command and endpoint for graceful server stopping
947
+ * add some locals (prompts/seeds/etc) from processing function into the Processing class as fields
948
+ * 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)
949
+ * add /sdapi/v1/refresh-loras api checkpoint post request
950
+ * tests overhaul
951
+
952
+ ### Bug Fixes:
953
+ * fix an issue preventing the program from starting if the user specifies a bad Gradio theme
954
+ * fix broken prompts from file script
955
+ * fix symlink scanning for extra networks
956
+ * fix --data-dir ignored when launching via webui-user.bat COMMANDLINE_ARGS
957
+ * allow web UI to be ran fully offline
958
+ * fix inability to run with --freeze-settings
959
+ * fix inability to merge checkpoint without adding metadata
960
+ * fix extra networks' save preview image not adding infotext for jpeg/webm
961
+ * remove blinking effect from text in hires fix and scale resolution preview
962
+ * make links to `http://<...>.git` extensions work in the extension tab
963
+ * fix bug with webui hanging at startup due to hanging git process
964
+
965
+
966
+ ## 1.2.1
967
+
968
+ ### Features:
969
+ * add an option to always refer to LoRA by filenames
970
+
971
+ ### Bug Fixes:
972
+ * never refer to LoRA by an alias if multiple LoRAs have same alias or the alias is called none
973
+ * fix upscalers disappearing after the user reloads UI
974
+ * allow bf16 in safe unpickler (resolves problems with loading some LoRAs)
975
+ * allow web UI to be ran fully offline
976
+ * fix localizations not working
977
+ * fix error for LoRAs: `'LatentDiffusion' object has no attribute 'lora_layer_mapping'`
978
+
979
+ ## 1.2.0
980
+
981
+ ### Features:
982
+ * do not wait for Stable Diffusion model to load at startup
983
+ * add filename patterns: `[denoising]`
984
+ * directory hiding for extra networks: dirs starting with `.` will hide their cards on extra network tabs unless specifically searched for
985
+ * LoRA: for the `<...>` text in prompt, use name of LoRA that is in the metadata of the file, if present, instead of filename (both can be used to activate LoRA)
986
+ * LoRA: read infotext params from kohya-ss's extension parameters if they are present and if his extension is not active
987
+ * LoRA: fix some LoRAs not working (ones that have 3x3 convolution layer)
988
+ * LoRA: add an option to use old method of applying LoRAs (producing same results as with kohya-ss)
989
+ * add version to infotext, footer and console output when starting
990
+ * add links to wiki for filename pattern settings
991
+ * add extended info for quicksettings setting and use multiselect input instead of a text field
992
+
993
+ ### Minor:
994
+ * bump Gradio to 3.29.0
995
+ * bump PyTorch to 2.0.1
996
+ * `--subpath` option for gradio for use with reverse proxy
997
+ * Linux/macOS: use existing virtualenv if already active (the VIRTUAL_ENV environment variable)
998
+ * do not apply localizations if there are none (possible frontend optimization)
999
+ * add extra `None` option for VAE in XYZ plot
1000
+ * print error to console when batch processing in img2img fails
1001
+ * create HTML for extra network pages only on demand
1002
+ * allow directories starting with `.` to still list their models for LoRA, checkpoints, etc
1003
+ * put infotext options into their own category in settings tab
1004
+ * do not show licenses page when user selects Show all pages in settings
1005
+
1006
+ ### Extensions:
1007
+ * tooltip localization support
1008
+ * add API method to get LoRA models with prompt
1009
+
1010
+ ### Bug Fixes:
1011
+ * re-add `/docs` endpoint
1012
+ * fix gamepad navigation
1013
+ * make the lightbox fullscreen image function properly
1014
+ * fix squished thumbnails in extras tab
1015
+ * keep "search" filter for extra networks when user refreshes the tab (previously it showed everything after you refreshed)
1016
+ * fix webui showing the same image if you configure the generation to always save results into same file
1017
+ * fix bug with upscalers not working properly
1018
+ * fix MPS on PyTorch 2.0.1, Intel Macs
1019
+ * make it so that custom context menu from contextMenu.js only disappears after user's click, ignoring non-user click events
1020
+ * prevent Reload UI button/link from reloading the page when it's not yet ready
1021
+ * fix prompts from file script failing to read contents from a drag/drop file
1022
+
1023
+
1024
+ ## 1.1.1
1025
+ ### Bug Fixes:
1026
+ * fix an error that prevents running webui on PyTorch<2.0 without --disable-safe-unpickle
1027
+
1028
+ ## 1.1.0
1029
+ ### Features:
1030
+ * switch to PyTorch 2.0.0 (except for AMD GPUs)
1031
+ * visual improvements to custom code scripts
1032
+ * add filename patterns: `[clip_skip]`, `[hasprompt<>]`, `[batch_number]`, `[generation_number]`
1033
+ * add support for saving init images in img2img, and record their hashes in infotext for reproducibility
1034
+ * automatically select current word when adjusting weight with ctrl+up/down
1035
+ * add dropdowns for X/Y/Z plot
1036
+ * add setting: Stable Diffusion/Random number generator source: makes it possible to make images generated from a given manual seed consistent across different GPUs
1037
+ * support Gradio's theme API
1038
+ * use TCMalloc on Linux by default; possible fix for memory leaks
1039
+ * add optimization option to remove negative conditioning at low sigma values #9177
1040
+ * embed model merge metadata in .safetensors file
1041
+ * extension settings backup/restore feature #9169
1042
+ * add "resize by" and "resize to" tabs to img2img
1043
+ * add option "keep original size" to textual inversion images preprocess
1044
+ * image viewer scrolling via analog stick
1045
+ * button to restore the progress from session lost / tab reload
1046
+
1047
+ ### Minor:
1048
+ * bump Gradio to 3.28.1
1049
+ * change "scale to" to sliders in Extras tab
1050
+ * add labels to tool buttons to make it possible to hide them
1051
+ * add tiled inference support for ScuNET
1052
+ * add branch support for extension installation
1053
+ * change Linux installation script to install into current directory rather than `/home/username`
1054
+ * sort textual inversion embeddings by name (case-insensitive)
1055
+ * allow styles.csv to be symlinked or mounted in docker
1056
+ * remove the "do not add watermark to images" option
1057
+ * make selected tab configurable with UI config
1058
+ * make the extra networks UI fixed height and scrollable
1059
+ * add `disable_tls_verify` arg for use with self-signed certs
1060
+
1061
+ ### Extensions:
1062
+ * add reload callback
1063
+ * add `is_hr_pass` field for processing
1064
+
1065
+ ### Bug Fixes:
1066
+ * fix broken batch image processing on 'Extras/Batch Process' tab
1067
+ * add "None" option to extra networks dropdowns
1068
+ * fix FileExistsError for CLIP Interrogator
1069
+ * fix /sdapi/v1/txt2img endpoint not working on Linux #9319
1070
+ * fix disappearing live previews and progressbar during slow tasks
1071
+ * fix fullscreen image view not working properly in some cases
1072
+ * prevent alwayson_scripts args param resizing script_arg list when they are inserted in it
1073
+ * fix prompt schedule for second order samplers
1074
+ * fix image mask/composite for weird resolutions #9628
1075
+ * use correct images for previews when using AND (see #9491)
1076
+ * one broken image in img2img batch won't stop all processing
1077
+ * fix image orientation bug in train/preprocess
1078
+ * fix Ngrok recreating tunnels every reload
1079
+ * fix `--realesrgan-models-path` and `--ldsr-models-path` not working
1080
+ * fix `--skip-install` not working
1081
+ * use SAMPLE file format in Outpainting Mk2 & Poorman
1082
+ * do not fail all LoRAs if some have failed to load when making a picture
1083
+
1084
+ ## 1.0.0
1085
+ * 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 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ GNU AFFERO GENERAL PUBLIC LICENSE
2
+ Version 3, 19 November 2007
3
+
4
+ Copyright (c) 2023 AUTOMATIC1111
5
+
6
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
7
+ Everyone is permitted to copy and distribute verbatim copies
8
+ of this license document, but changing it is not allowed.
9
+
10
+ Preamble
11
+
12
+ The GNU Affero General Public License is a free, copyleft license for
13
+ software and other kinds of works, specifically designed to ensure
14
+ cooperation with the community in the case of network server software.
15
+
16
+ The licenses for most software and other practical works are designed
17
+ to take away your freedom to share and change the works. By contrast,
18
+ our General Public Licenses are intended to guarantee your freedom to
19
+ share and change all versions of a program--to make sure it remains free
20
+ software for all its users.
21
+
22
+ When we speak of free software, we are referring to freedom, not
23
+ price. Our General Public Licenses are designed to make sure that you
24
+ have the freedom to distribute copies of free software (and charge for
25
+ them if you wish), that you receive source code or can get it if you
26
+ want it, that you can change the software or use pieces of it in new
27
+ free programs, and that you know you can do these things.
28
+
29
+ Developers that use our General Public Licenses protect your rights
30
+ with two steps: (1) assert copyright on the software, and (2) offer
31
+ you this License which gives you legal permission to copy, distribute
32
+ and/or modify the software.
33
+
34
+ A secondary benefit of defending all users' freedom is that
35
+ improvements made in alternate versions of the program, if they
36
+ receive widespread use, become available for other developers to
37
+ incorporate. Many developers of free software are heartened and
38
+ encouraged by the resulting cooperation. However, in the case of
39
+ software used on network servers, this result may fail to come about.
40
+ The GNU General Public License permits making a modified version and
41
+ letting the public access it on a server without ever releasing its
42
+ source code to the public.
43
+
44
+ The GNU Affero General Public License is designed specifically to
45
+ ensure that, in such cases, the modified source code becomes available
46
+ to the community. It requires the operator of a network server to
47
+ provide the source code of the modified version running there to the
48
+ users of that server. Therefore, public use of a modified version, on
49
+ a publicly accessible server, gives the public access to the source
50
+ code of the modified version.
51
+
52
+ An older license, called the Affero General Public License and
53
+ published by Affero, was designed to accomplish similar goals. This is
54
+ a different license, not a version of the Affero GPL, but Affero has
55
+ released a new version of the Affero GPL which permits relicensing under
56
+ this license.
57
+
58
+ The precise terms and conditions for copying, distribution and
59
+ modification follow.
60
+
61
+ TERMS AND CONDITIONS
62
+
63
+ 0. Definitions.
64
+
65
+ "This License" refers to version 3 of the GNU Affero General Public License.
66
+
67
+ "Copyright" also means copyright-like laws that apply to other kinds of
68
+ works, such as semiconductor masks.
69
+
70
+ "The Program" refers to any copyrightable work licensed under this
71
+ License. Each licensee is addressed as "you". "Licensees" and
72
+ "recipients" may be individuals or organizations.
73
+
74
+ To "modify" a work means to copy from or adapt all or part of the work
75
+ in a fashion requiring copyright permission, other than the making of an
76
+ exact copy. The resulting work is called a "modified version" of the
77
+ earlier work or a work "based on" the earlier work.
78
+
79
+ A "covered work" means either the unmodified Program or a work based
80
+ on the Program.
81
+
82
+ To "propagate" a work means to do anything with it that, without
83
+ permission, would make you directly or secondarily liable for
84
+ infringement under applicable copyright law, except executing it on a
85
+ computer or modifying a private copy. Propagation includes copying,
86
+ distribution (with or without modification), making available to the
87
+ public, and in some countries other activities as well.
88
+
89
+ To "convey" a work means any kind of propagation that enables other
90
+ parties to make or receive copies. Mere interaction with a user through
91
+ a computer network, with no transfer of a copy, is not conveying.
92
+
93
+ An interactive user interface displays "Appropriate Legal Notices"
94
+ to the extent that it includes a convenient and prominently visible
95
+ feature that (1) displays an appropriate copyright notice, and (2)
96
+ tells the user that there is no warranty for the work (except to the
97
+ extent that warranties are provided), that licensees may convey the
98
+ work under this License, and how to view a copy of this License. If
99
+ the interface presents a list of user commands or options, such as a
100
+ menu, a prominent item in the list meets this criterion.
101
+
102
+ 1. Source Code.
103
+
104
+ The "source code" for a work means the preferred form of the work
105
+ for making modifications to it. "Object code" means any non-source
106
+ form of a work.
107
+
108
+ A "Standard Interface" means an interface that either is an official
109
+ standard defined by a recognized standards body, or, in the case of
110
+ interfaces specified for a particular programming language, one that
111
+ is widely used among developers working in that language.
112
+
113
+ The "System Libraries" of an executable work include anything, other
114
+ than the work as a whole, that (a) is included in the normal form of
115
+ packaging a Major Component, but which is not part of that Major
116
+ Component, and (b) serves only to enable use of the work with that
117
+ Major Component, or to implement a Standard Interface for which an
118
+ implementation is available to the public in source code form. A
119
+ "Major Component", in this context, means a major essential component
120
+ (kernel, window system, and so on) of the specific operating system
121
+ (if any) on which the executable work runs, or a compiler used to
122
+ produce the work, or an object code interpreter used to run it.
123
+
124
+ The "Corresponding Source" for a work in object code form means all
125
+ the source code needed to generate, install, and (for an executable
126
+ work) run the object code and to modify the work, including scripts to
127
+ control those activities. However, it does not include the work's
128
+ System Libraries, or general-purpose tools or generally available free
129
+ programs which are used unmodified in performing those activities but
130
+ which are not part of the work. For example, Corresponding Source
131
+ includes interface definition files associated with source files for
132
+ the work, and the source code for shared libraries and dynamically
133
+ linked subprograms that the work is specifically designed to require,
134
+ such as by intimate data communication or control flow between those
135
+ subprograms and other parts of the work.
136
+
137
+ The Corresponding Source need not include anything that users
138
+ can regenerate automatically from other parts of the Corresponding
139
+ Source.
140
+
141
+ The Corresponding Source for a work in source code form is that
142
+ same work.
143
+
144
+ 2. Basic Permissions.
145
+
146
+ All rights granted under this License are granted for the term of
147
+ copyright on the Program, and are irrevocable provided the stated
148
+ conditions are met. This License explicitly affirms your unlimited
149
+ permission to run the unmodified Program. The output from running a
150
+ covered work is covered by this License only if the output, given its
151
+ content, constitutes a covered work. This License acknowledges your
152
+ rights of fair use or other equivalent, as provided by copyright law.
153
+
154
+ You may make, run and propagate covered works that you do not
155
+ convey, without conditions so long as your license otherwise remains
156
+ in force. You may convey covered works to others for the sole purpose
157
+ of having them make modifications exclusively for you, or provide you
158
+ with facilities for running those works, provided that you comply with
159
+ the terms of this License in conveying all material for which you do
160
+ not control copyright. Those thus making or running the covered works
161
+ for you must do so exclusively on your behalf, under your direction
162
+ and control, on terms that prohibit them from making any copies of
163
+ your copyrighted material outside their relationship with you.
164
+
165
+ Conveying under any other circumstances is permitted solely under
166
+ the conditions stated below. Sublicensing is not allowed; section 10
167
+ makes it unnecessary.
168
+
169
+ 3. Protecting Users' Legal Rights From Anti-Circumvention Law.
170
+
171
+ No covered work shall be deemed part of an effective technological
172
+ measure under any applicable law fulfilling obligations under article
173
+ 11 of the WIPO copyright treaty adopted on 20 December 1996, or
174
+ similar laws prohibiting or restricting circumvention of such
175
+ measures.
176
+
177
+ When you convey a covered work, you waive any legal power to forbid
178
+ circumvention of technological measures to the extent such circumvention
179
+ is effected by exercising rights under this License with respect to
180
+ the covered work, and you disclaim any intention to limit operation or
181
+ modification of the work as a means of enforcing, against the work's
182
+ users, your or third parties' legal rights to forbid circumvention of
183
+ technological measures.
184
+
185
+ 4. Conveying Verbatim Copies.
186
+
187
+ You may convey verbatim copies of the Program's source code as you
188
+ receive it, in any medium, provided that you conspicuously and
189
+ appropriately publish on each copy an appropriate copyright notice;
190
+ keep intact all notices stating that this License and any
191
+ non-permissive terms added in accord with section 7 apply to the code;
192
+ keep intact all notices of the absence of any warranty; and give all
193
+ recipients a copy of this License along with the Program.
194
+
195
+ You may charge any price or no price for each copy that you convey,
196
+ and you may offer support or warranty protection for a fee.
197
+
198
+ 5. Conveying Modified Source Versions.
199
+
200
+ You may convey a work based on the Program, or the modifications to
201
+ produce it from the Program, in the form of source code under the
202
+ terms of section 4, provided that you also meet all of these conditions:
203
+
204
+ a) The work must carry prominent notices stating that you modified
205
+ it, and giving a relevant date.
206
+
207
+ b) The work must carry prominent notices stating that it is
208
+ released under this License and any conditions added under section
209
+ 7. This requirement modifies the requirement in section 4 to
210
+ "keep intact all notices".
211
+
212
+ c) You must license the entire work, as a whole, under this
213
+ License to anyone who comes into possession of a copy. This
214
+ License will therefore apply, along with any applicable section 7
215
+ additional terms, to the whole of the work, and all its parts,
216
+ regardless of how they are packaged. This License gives no
217
+ permission to license the work in any other way, but it does not
218
+ invalidate such permission if you have separately received it.
219
+
220
+ d) If the work has interactive user interfaces, each must display
221
+ Appropriate Legal Notices; however, if the Program has interactive
222
+ interfaces that do not display Appropriate Legal Notices, your
223
+ work need not make them do so.
224
+
225
+ A compilation of a covered work with other separate and independent
226
+ works, which are not by their nature extensions of the covered work,
227
+ and which are not combined with it such as to form a larger program,
228
+ in or on a volume of a storage or distribution medium, is called an
229
+ "aggregate" if the compilation and its resulting copyright are not
230
+ used to limit the access or legal rights of the compilation's users
231
+ beyond what the individual works permit. Inclusion of a covered work
232
+ in an aggregate does not cause this License to apply to the other
233
+ parts of the aggregate.
234
+
235
+ 6. Conveying Non-Source Forms.
236
+
237
+ You may convey a covered work in object code form under the terms
238
+ of sections 4 and 5, provided that you also convey the
239
+ machine-readable Corresponding Source under the terms of this License,
240
+ in one of these ways:
241
+
242
+ a) Convey the object code in, or embodied in, a physical product
243
+ (including a physical distribution medium), accompanied by the
244
+ Corresponding Source fixed on a durable physical medium
245
+ customarily used for software interchange.
246
+
247
+ b) Convey the object code in, or embodied in, a physical product
248
+ (including a physical distribution medium), accompanied by a
249
+ written offer, valid for at least three years and valid for as
250
+ long as you offer spare parts or customer support for that product
251
+ model, to give anyone who possesses the object code either (1) a
252
+ copy of the Corresponding Source for all the software in the
253
+ product that is covered by this License, on a durable physical
254
+ medium customarily used for software interchange, for a price no
255
+ more than your reasonable cost of physically performing this
256
+ conveying of source, or (2) access to copy the
257
+ Corresponding Source from a network server at no charge.
258
+
259
+ c) Convey individual copies of the object code with a copy of the
260
+ written offer to provide the Corresponding Source. This
261
+ alternative is allowed only occasionally and noncommercially, and
262
+ only if you received the object code with such an offer, in accord
263
+ with subsection 6b.
264
+
265
+ d) Convey the object code by offering access from a designated
266
+ place (gratis or for a charge), and offer equivalent access to the
267
+ Corresponding Source in the same way through the same place at no
268
+ further charge. You need not require recipients to copy the
269
+ Corresponding Source along with the object code. If the place to
270
+ copy the object code is a network server, the Corresponding Source
271
+ may be on a different server (operated by you or a third party)
272
+ that supports equivalent copying facilities, provided you maintain
273
+ clear directions next to the object code saying where to find the
274
+ Corresponding Source. Regardless of what server hosts the
275
+ Corresponding Source, you remain obligated to ensure that it is
276
+ available for as long as needed to satisfy these requirements.
277
+
278
+ e) Convey the object code using peer-to-peer transmission, provided
279
+ you inform other peers where the object code and Corresponding
280
+ Source of the work are being offered to the general public at no
281
+ charge under subsection 6d.
282
+
283
+ A separable portion of the object code, whose source code is excluded
284
+ from the Corresponding Source as a System Library, need not be
285
+ included in conveying the object code work.
286
+
287
+ A "User Product" is either (1) a "consumer product", which means any
288
+ tangible personal property which is normally used for personal, family,
289
+ or household purposes, or (2) anything designed or sold for incorporation
290
+ into a dwelling. In determining whether a product is a consumer product,
291
+ doubtful cases shall be resolved in favor of coverage. For a particular
292
+ product received by a particular user, "normally used" refers to a
293
+ typical or common use of that class of product, regardless of the status
294
+ of the particular user or of the way in which the particular user
295
+ actually uses, or expects or is expected to use, the product. A product
296
+ is a consumer product regardless of whether the product has substantial
297
+ commercial, industrial or non-consumer uses, unless such uses represent
298
+ the only significant mode of use of the product.
299
+
300
+ "Installation Information" for a User Product means any methods,
301
+ procedures, authorization keys, or other information required to install
302
+ and execute modified versions of a covered work in that User Product from
303
+ a modified version of its Corresponding Source. The information must
304
+ suffice to ensure that the continued functioning of the modified object
305
+ code is in no case prevented or interfered with solely because
306
+ modification has been made.
307
+
308
+ If you convey an object code work under this section in, or with, or
309
+ specifically for use in, a User Product, and the conveying occurs as
310
+ part of a transaction in which the right of possession and use of the
311
+ User Product is transferred to the recipient in perpetuity or for a
312
+ fixed term (regardless of how the transaction is characterized), the
313
+ Corresponding Source conveyed under this section must be accompanied
314
+ by the Installation Information. But this requirement does not apply
315
+ if neither you nor any third party retains the ability to install
316
+ modified object code on the User Product (for example, the work has
317
+ been installed in ROM).
318
+
319
+ The requirement to provide Installation Information does not include a
320
+ requirement to continue to provide support service, warranty, or updates
321
+ for a work that has been modified or installed by the recipient, or for
322
+ the User Product in which it has been modified or installed. Access to a
323
+ network may be denied when the modification itself materially and
324
+ adversely affects the operation of the network or violates the rules and
325
+ protocols for communication across the network.
326
+
327
+ Corresponding Source conveyed, and Installation Information provided,
328
+ in accord with this section must be in a format that is publicly
329
+ documented (and with an implementation available to the public in
330
+ source code form), and must require no special password or key for
331
+ unpacking, reading or copying.
332
+
333
+ 7. Additional Terms.
334
+
335
+ "Additional permissions" are terms that supplement the terms of this
336
+ License by making exceptions from one or more of its conditions.
337
+ Additional permissions that are applicable to the entire Program shall
338
+ be treated as though they were included in this License, to the extent
339
+ that they are valid under applicable law. If additional permissions
340
+ apply only to part of the Program, that part may be used separately
341
+ under those permissions, but the entire Program remains governed by
342
+ this License without regard to the additional permissions.
343
+
344
+ When you convey a copy of a covered work, you may at your option
345
+ remove any additional permissions from that copy, or from any part of
346
+ it. (Additional permissions may be written to require their own
347
+ removal in certain cases when you modify the work.) You may place
348
+ additional permissions on material, added by you to a covered work,
349
+ for which you have or can give appropriate copyright permission.
350
+
351
+ Notwithstanding any other provision of this License, for material you
352
+ add to a covered work, you may (if authorized by the copyright holders of
353
+ that material) supplement the terms of this License with terms:
354
+
355
+ a) Disclaiming warranty or limiting liability differently from the
356
+ terms of sections 15 and 16 of this License; or
357
+
358
+ b) Requiring preservation of specified reasonable legal notices or
359
+ author attributions in that material or in the Appropriate Legal
360
+ Notices displayed by works containing it; or
361
+
362
+ c) Prohibiting misrepresentation of the origin of that material, or
363
+ requiring that modified versions of such material be marked in
364
+ reasonable ways as different from the original version; or
365
+
366
+ d) Limiting the use for publicity purposes of names of licensors or
367
+ authors of the material; or
368
+
369
+ e) Declining to grant rights under trademark law for use of some
370
+ trade names, trademarks, or service marks; or
371
+
372
+ f) Requiring indemnification of licensors and authors of that
373
+ material by anyone who conveys the material (or modified versions of
374
+ it) with contractual assumptions of liability to the recipient, for
375
+ any liability that these contractual assumptions directly impose on
376
+ those licensors and authors.
377
+
378
+ All other non-permissive additional terms are considered "further
379
+ restrictions" within the meaning of section 10. If the Program as you
380
+ received it, or any part of it, contains a notice stating that it is
381
+ governed by this License along with a term that is a further
382
+ restriction, you may remove that term. If a license document contains
383
+ a further restriction but permits relicensing or conveying under this
384
+ License, you may add to a covered work material governed by the terms
385
+ of that license document, provided that the further restriction does
386
+ not survive such relicensing or conveying.
387
+
388
+ If you add terms to a covered work in accord with this section, you
389
+ must place, in the relevant source files, a statement of the
390
+ additional terms that apply to those files, or a notice indicating
391
+ where to find the applicable terms.
392
+
393
+ Additional terms, permissive or non-permissive, may be stated in the
394
+ form of a separately written license, or stated as exceptions;
395
+ the above requirements apply either way.
396
+
397
+ 8. Termination.
398
+
399
+ You may not propagate or modify a covered work except as expressly
400
+ provided under this License. Any attempt otherwise to propagate or
401
+ modify it is void, and will automatically terminate your rights under
402
+ this License (including any patent licenses granted under the third
403
+ paragraph of section 11).
404
+
405
+ However, if you cease all violation of this License, then your
406
+ license from a particular copyright holder is reinstated (a)
407
+ provisionally, unless and until the copyright holder explicitly and
408
+ finally terminates your license, and (b) permanently, if the copyright
409
+ holder fails to notify you of the violation by some reasonable means
410
+ prior to 60 days after the cessation.
411
+
412
+ Moreover, your license from a particular copyright holder is
413
+ reinstated permanently if the copyright holder notifies you of the
414
+ violation by some reasonable means, this is the first time you have
415
+ received notice of violation of this License (for any work) from that
416
+ copyright holder, and you cure the violation prior to 30 days after
417
+ your receipt of the notice.
418
+
419
+ Termination of your rights under this section does not terminate the
420
+ licenses of parties who have received copies or rights from you under
421
+ this License. If your rights have been terminated and not permanently
422
+ reinstated, you do not qualify to receive new licenses for the same
423
+ material under section 10.
424
+
425
+ 9. Acceptance Not Required for Having Copies.
426
+
427
+ You are not required to accept this License in order to receive or
428
+ run a copy of the Program. Ancillary propagation of a covered work
429
+ occurring solely as a consequence of using peer-to-peer transmission
430
+ to receive a copy likewise does not require acceptance. However,
431
+ nothing other than this License grants you permission to propagate or
432
+ modify any covered work. These actions infringe copyright if you do
433
+ not accept this License. Therefore, by modifying or propagating a
434
+ covered work, you indicate your acceptance of this License to do so.
435
+
436
+ 10. Automatic Licensing of Downstream Recipients.
437
+
438
+ Each time you convey a covered work, the recipient automatically
439
+ receives a license from the original licensors, to run, modify and
440
+ propagate that work, subject to this License. You are not responsible
441
+ for enforcing compliance by third parties with this License.
442
+
443
+ An "entity transaction" is a transaction transferring control of an
444
+ organization, or substantially all assets of one, or subdividing an
445
+ organization, or merging organizations. If propagation of a covered
446
+ work results from an entity transaction, each party to that
447
+ transaction who receives a copy of the work also receives whatever
448
+ licenses to the work the party's predecessor in interest had or could
449
+ give under the previous paragraph, plus a right to possession of the
450
+ Corresponding Source of the work from the predecessor in interest, if
451
+ the predecessor has it or can get it with reasonable efforts.
452
+
453
+ You may not impose any further restrictions on the exercise of the
454
+ rights granted or affirmed under this License. For example, you may
455
+ not impose a license fee, royalty, or other charge for exercise of
456
+ rights granted under this License, and you may not initiate litigation
457
+ (including a cross-claim or counterclaim in a lawsuit) alleging that
458
+ any patent claim is infringed by making, using, selling, offering for
459
+ sale, or importing the Program or any portion of it.
460
+
461
+ 11. Patents.
462
+
463
+ A "contributor" is a copyright holder who authorizes use under this
464
+ License of the Program or a work on which the Program is based. The
465
+ work thus licensed is called the contributor's "contributor version".
466
+
467
+ A contributor's "essential patent claims" are all patent claims
468
+ owned or controlled by the contributor, whether already acquired or
469
+ hereafter acquired, that would be infringed by some manner, permitted
470
+ by this License, of making, using, or selling its contributor version,
471
+ but do not include claims that would be infringed only as a
472
+ consequence of further modification of the contributor version. For
473
+ purposes of this definition, "control" includes the right to grant
474
+ patent sublicenses in a manner consistent with the requirements of
475
+ this License.
476
+
477
+ Each contributor grants you a non-exclusive, worldwide, royalty-free
478
+ patent license under the contributor's essential patent claims, to
479
+ make, use, sell, offer for sale, import and otherwise run, modify and
480
+ propagate the contents of its contributor version.
481
+
482
+ In the following three paragraphs, a "patent license" is any express
483
+ agreement or commitment, however denominated, not to enforce a patent
484
+ (such as an express permission to practice a patent or covenant not to
485
+ sue for patent infringement). To "grant" such a patent license to a
486
+ party means to make such an agreement or commitment not to enforce a
487
+ patent against the party.
488
+
489
+ If you convey a covered work, knowingly relying on a patent license,
490
+ and the Corresponding Source of the work is not available for anyone
491
+ to copy, free of charge and under the terms of this License, through a
492
+ publicly available network server or other readily accessible means,
493
+ then you must either (1) cause the Corresponding Source to be so
494
+ available, or (2) arrange to deprive yourself of the benefit of the
495
+ patent license for this particular work, or (3) arrange, in a manner
496
+ consistent with the requirements of this License, to extend the patent
497
+ license to downstream recipients. "Knowingly relying" means you have
498
+ actual knowledge that, but for the patent license, your conveying the
499
+ covered work in a country, or your recipient's use of the covered work
500
+ in a country, would infringe one or more identifiable patents in that
501
+ country that you have reason to believe are valid.
502
+
503
+ If, pursuant to or in connection with a single transaction or
504
+ arrangement, you convey, or propagate by procuring conveyance of, a
505
+ covered work, and grant a patent license to some of the parties
506
+ receiving the covered work authorizing them to use, propagate, modify
507
+ or convey a specific copy of the covered work, then the patent license
508
+ you grant is automatically extended to all recipients of the covered
509
+ work and works based on it.
510
+
511
+ A patent license is "discriminatory" if it does not include within
512
+ the scope of its coverage, prohibits the exercise of, or is
513
+ conditioned on the non-exercise of one or more of the rights that are
514
+ specifically granted under this License. You may not convey a covered
515
+ work if you are a party to an arrangement with a third party that is
516
+ in the business of distributing software, under which you make payment
517
+ to the third party based on the extent of your activity of conveying
518
+ the work, and under which the third party grants, to any of the
519
+ parties who would receive the covered work from you, a discriminatory
520
+ patent license (a) in connection with copies of the covered work
521
+ conveyed by you (or copies made from those copies), or (b) primarily
522
+ for and in connection with specific products or compilations that
523
+ contain the covered work, unless you entered into that arrangement,
524
+ or that patent license was granted, prior to 28 March 2007.
525
+
526
+ Nothing in this License shall be construed as excluding or limiting
527
+ any implied license or other defenses to infringement that may
528
+ otherwise be available to you under applicable patent law.
529
+
530
+ 12. No Surrender of Others' Freedom.
531
+
532
+ If conditions are imposed on you (whether by court order, agreement or
533
+ otherwise) that contradict the conditions of this License, they do not
534
+ excuse you from the conditions of this License. If you cannot convey a
535
+ covered work so as to satisfy simultaneously your obligations under this
536
+ License and any other pertinent obligations, then as a consequence you may
537
+ not convey it at all. For example, if you agree to terms that obligate you
538
+ to collect a royalty for further conveying from those to whom you convey
539
+ the Program, the only way you could satisfy both those terms and this
540
+ License would be to refrain entirely from conveying the Program.
541
+
542
+ 13. Remote Network Interaction; Use with the GNU General Public License.
543
+
544
+ Notwithstanding any other provision of this License, if you modify the
545
+ Program, your modified version must prominently offer all users
546
+ interacting with it remotely through a computer network (if your version
547
+ supports such interaction) an opportunity to receive the Corresponding
548
+ Source of your version by providing access to the Corresponding Source
549
+ from a network server at no charge, through some standard or customary
550
+ means of facilitating copying of software. This Corresponding Source
551
+ shall include the Corresponding Source for any work covered by version 3
552
+ of the GNU General Public License that is incorporated pursuant to the
553
+ following paragraph.
554
+
555
+ Notwithstanding any other provision of this License, you have
556
+ permission to link or combine any covered work with a work licensed
557
+ under version 3 of the GNU General Public License into a single
558
+ combined work, and to convey the resulting work. The terms of this
559
+ License will continue to apply to the part which is the covered work,
560
+ but the work with which it is combined will remain governed by version
561
+ 3 of the GNU General Public License.
562
+
563
+ 14. Revised Versions of this License.
564
+
565
+ The Free Software Foundation may publish revised and/or new versions of
566
+ the GNU Affero General Public License from time to time. Such new versions
567
+ will be similar in spirit to the present version, but may differ in detail to
568
+ address new problems or concerns.
569
+
570
+ Each version is given a distinguishing version number. If the
571
+ Program specifies that a certain numbered version of the GNU Affero General
572
+ Public License "or any later version" applies to it, you have the
573
+ option of following the terms and conditions either of that numbered
574
+ version or of any later version published by the Free Software
575
+ Foundation. If the Program does not specify a version number of the
576
+ GNU Affero General Public License, you may choose any version ever published
577
+ by the Free Software Foundation.
578
+
579
+ If the Program specifies that a proxy can decide which future
580
+ versions of the GNU Affero General Public License can be used, that proxy's
581
+ public statement of acceptance of a version permanently authorizes you
582
+ to choose that version for the Program.
583
+
584
+ Later license versions may give you additional or different
585
+ permissions. However, no additional obligations are imposed on any
586
+ author or copyright holder as a result of your choosing to follow a
587
+ later version.
588
+
589
+ 15. Disclaimer of Warranty.
590
+
591
+ THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592
+ APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593
+ HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594
+ OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595
+ THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596
+ PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597
+ IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598
+ ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599
+
600
+ 16. Limitation of Liability.
601
+
602
+ IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603
+ WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604
+ THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605
+ GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606
+ USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607
+ DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608
+ PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609
+ EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610
+ SUCH DAMAGES.
611
+
612
+ 17. Interpretation of Sections 15 and 16.
613
+
614
+ If the disclaimer of warranty and limitation of liability provided
615
+ above cannot be given local legal effect according to their terms,
616
+ reviewing courts shall apply local law that most closely approximates
617
+ an absolute waiver of all civil liability in connection with the
618
+ Program, unless a warranty or assumption of liability accompanies a
619
+ copy of the Program in return for a fee.
620
+
621
+ END OF TERMS AND CONDITIONS
622
+
623
+ How to Apply These Terms to Your New Programs
624
+
625
+ If you develop a new program, and you want it to be of the greatest
626
+ possible use to the public, the best way to achieve this is to make it
627
+ free software which everyone can redistribute and change under these terms.
628
+
629
+ To do so, attach the following notices to the program. It is safest
630
+ to attach them to the start of each source file to most effectively
631
+ state the exclusion of warranty; and each file should have at least
632
+ the "copyright" line and a pointer to where the full notice is found.
633
+
634
+ <one line to give the program's name and a brief idea of what it does.>
635
+ Copyright (C) <year> <name of author>
636
+
637
+ This program is free software: you can redistribute it and/or modify
638
+ it under the terms of the GNU Affero General Public License as published by
639
+ the Free Software Foundation, either version 3 of the License, or
640
+ (at your option) any later version.
641
+
642
+ This program is distributed in the hope that it will be useful,
643
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
644
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
645
+ GNU Affero General Public License for more details.
646
+
647
+ You should have received a copy of the GNU Affero General Public License
648
+ along with this program. If not, see <https://www.gnu.org/licenses/>.
649
+
650
+ Also add information on how to contact you by electronic and paper mail.
651
+
652
+ If your software can interact with users remotely through a computer
653
+ network, you should also make sure that it provides a way for users to
654
+ get its source. For example, if your program is a web application, its
655
+ interface could display a "Source" link that leads users to an archive
656
+ of the code. There are many ways you could offer source, and different
657
+ solutions will be better for different programs; see section 13 for the
658
+ specific requirements.
659
+
660
+ You should also get your employer (if you work as a programmer) or school,
661
+ if any, to sign a "copyright disclaimer" for the program, if necessary.
662
+ For more information on this, and how to apply and follow the GNU AGPL, see
663
+ <https://www.gnu.org/licenses/>.
README.md CHANGED
@@ -1,12 +1,211 @@
1
- ---
2
- title: Ellas SD
3
- emoji: 💻
4
- colorFrom: purple
5
- colorTo: green
6
- sdk: gradio
7
- sdk_version: 4.44.1
8
- app_file: app.py
9
- pinned: false
10
- ---
11
-
12
- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ title: Ellas_SD
3
+ app_file: ./venv/bin/gradio
4
+ sdk: gradio
5
+ sdk_version: 3.41.2
6
+ ---
7
+ # Stable Diffusion web UI
8
+ A web interface for Stable Diffusion, implemented using Gradio library.
9
+
10
+ ![](screenshot.png)
11
+
12
+ ## Features
13
+ [Detailed feature showcase with images](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features):
14
+ - Original txt2img and img2img modes
15
+ - One click install and run script (but you still must install python and git)
16
+ - Outpainting
17
+ - Inpainting
18
+ - Color Sketch
19
+ - Prompt Matrix
20
+ - Stable Diffusion Upscale
21
+ - Attention, specify parts of text that the model should pay more attention to
22
+ - a man in a `((tuxedo))` - will pay more attention to tuxedo
23
+ - a man in a `(tuxedo:1.21)` - alternative syntax
24
+ - 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)
25
+ - Loopback, run img2img processing multiple times
26
+ - X/Y/Z plot, a way to draw a 3 dimensional plot of images with different parameters
27
+ - Textual Inversion
28
+ - have as many embeddings as you want and use any names you like for them
29
+ - use multiple embeddings with different numbers of vectors per token
30
+ - works with half precision floating point numbers
31
+ - train embeddings on 8GB (also reports of 6GB working)
32
+ - Extras tab with:
33
+ - GFPGAN, neural network that fixes faces
34
+ - CodeFormer, face restoration tool as an alternative to GFPGAN
35
+ - RealESRGAN, neural network upscaler
36
+ - ESRGAN, neural network upscaler with a lot of third party models
37
+ - SwinIR and Swin2SR ([see here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/2092)), neural network upscalers
38
+ - LDSR, Latent diffusion super resolution upscaling
39
+ - Resizing aspect ratio options
40
+ - Sampling method selection
41
+ - Adjust sampler eta values (noise multiplier)
42
+ - More advanced noise setting options
43
+ - Interrupt processing at any time
44
+ - 4GB video card support (also reports of 2GB working)
45
+ - Correct seeds for batches
46
+ - Live prompt token length validation
47
+ - Generation parameters
48
+ - parameters you used to generate images are saved with that image
49
+ - in PNG chunks for PNG, in EXIF for JPEG
50
+ - can drag the image to PNG info tab to restore generation parameters and automatically copy them into UI
51
+ - can be disabled in settings
52
+ - drag and drop an image/text-parameters to promptbox
53
+ - Read Generation Parameters Button, loads parameters in promptbox to UI
54
+ - Settings page
55
+ - Running arbitrary python code from UI (must run with `--allow-code` to enable)
56
+ - Mouseover hints for most UI elements
57
+ - Possible to change defaults/mix/max/step values for UI elements via text config
58
+ - Tiling support, a checkbox to create images that can be tiled like textures
59
+ - Progress bar and live image generation preview
60
+ - Can use a separate neural network to produce previews with almost none VRAM or compute requirement
61
+ - Negative prompt, an extra text field that allows you to list what you don't want to see in generated image
62
+ - Styles, a way to save part of prompt and easily apply them via dropdown later
63
+ - Variations, a way to generate same image but with tiny differences
64
+ - Seed resizing, a way to generate same image but at slightly different resolution
65
+ - CLIP interrogator, a button that tries to guess prompt from an image
66
+ - Prompt Editing, a way to change prompt mid-generation, say to start making a watermelon and switch to anime girl midway
67
+ - Batch Processing, process a group of files using img2img
68
+ - Img2img Alternative, reverse Euler method of cross attention control
69
+ - Highres Fix, a convenience option to produce high resolution pictures in one click without usual distortions
70
+ - Reloading checkpoints on the fly
71
+ - Checkpoint Merger, a tab that allows you to merge up to 3 checkpoints into one
72
+ - [Custom scripts](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts) with many extensions from community
73
+ - [Composable-Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/), a way to use multiple prompts at once
74
+ - separate prompts using uppercase `AND`
75
+ - also supports weights for prompts: `a cat :1.2 AND a dog AND a penguin :2.2`
76
+ - No token limit for prompts (original stable diffusion lets you use up to 75 tokens)
77
+ - DeepDanbooru integration, creates danbooru style tags for anime prompts
78
+ - [xformers](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Xformers), major speed increase for select cards: (add `--xformers` to commandline args)
79
+ - via extension: [History tab](https://github.com/yfszzx/stable-diffusion-webui-images-browser): view, direct and delete images conveniently within the UI
80
+ - Generate forever option
81
+ - Training tab
82
+ - hypernetworks and embeddings options
83
+ - Preprocessing images: cropping, mirroring, autotagging using BLIP or deepdanbooru (for anime)
84
+ - Clip skip
85
+ - Hypernetworks
86
+ - Loras (same as Hypernetworks but more pretty)
87
+ - A separate UI where you can choose, with preview, which embeddings, hypernetworks or Loras to add to your prompt
88
+ - Can select to load a different VAE from settings screen
89
+ - Estimated completion time in progress bar
90
+ - API
91
+ - Support for dedicated [inpainting model](https://github.com/runwayml/stable-diffusion#inpainting-with-stable-diffusion) by RunwayML
92
+ - 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))
93
+ - [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
94
+ - [Alt-Diffusion](https://arxiv.org/abs/2211.06679) support - see [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#alt-diffusion) for instructions
95
+ - Now without any bad letters!
96
+ - Load checkpoints in safetensors format
97
+ - Eased resolution restriction: generated image's dimensions must be a multiple of 8 rather than 64
98
+ - Now with a license!
99
+ - Reorder elements in the UI from settings screen
100
+ - [Segmind Stable Diffusion](https://huggingface.co/segmind/SSD-1B) support
101
+
102
+ ## Installation and Running
103
+ Make sure the required [dependencies](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Dependencies) are met and follow the instructions available for:
104
+ - [NVidia](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs) (recommended)
105
+ - [AMD](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs) GPUs.
106
+ - [Intel CPUs, Intel GPUs (both integrated and discrete)](https://github.com/openvinotoolkit/stable-diffusion-webui/wiki/Installation-on-Intel-Silicon) (external wiki page)
107
+ - [Ascend NPUs](https://github.com/wangshuai09/stable-diffusion-webui/wiki/Install-and-run-on-Ascend-NPUs) (external wiki page)
108
+
109
+ Alternatively, use online services (like Google Colab):
110
+
111
+ - [List of Online Services](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Online-Services)
112
+
113
+ ### Installation on Windows 10/11 with NVidia-GPUs using release package
114
+ 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 its contents.
115
+ 2. Run `update.bat`.
116
+ 3. Run `run.bat`.
117
+ > For more details see [Install-and-Run-on-NVidia-GPUs](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-NVidia-GPUs)
118
+
119
+ ### Automatic Installation on Windows
120
+ 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".
121
+ 2. Install [git](https://git-scm.com/download/win).
122
+ 3. Download the stable-diffusion-webui repository, for example by running `git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git`.
123
+ 4. Run `webui-user.bat` from Windows Explorer as normal, non-administrator, user.
124
+
125
+ ### Automatic Installation on Linux
126
+ 1. Install the dependencies:
127
+ ```bash
128
+ # Debian-based:
129
+ sudo apt install wget git python3 python3-venv libgl1 libglib2.0-0
130
+ # Red Hat-based:
131
+ sudo dnf install wget git python3 gperftools-libs libglvnd-glx
132
+ # openSUSE-based:
133
+ sudo zypper install wget git python3 libtcmalloc4 libglvnd
134
+ # Arch-based:
135
+ sudo pacman -S wget git python3
136
+ ```
137
+ If your system is very new, you need to install python3.11 or python3.10:
138
+ ```bash
139
+ # Ubuntu 24.04
140
+ sudo add-apt-repository ppa:deadsnakes/ppa
141
+ sudo apt update
142
+ sudo apt install python3.11
143
+
144
+ # Manjaro/Arch
145
+ sudo pacman -S yay
146
+ yay -S python311 # do not confuse with python3.11 package
147
+
148
+ # Only for 3.11
149
+ # Then set up env variable in launch script
150
+ export python_cmd="python3.11"
151
+ # or in webui-user.sh
152
+ python_cmd="python3.11"
153
+ ```
154
+ 2. Navigate to the directory you would like the webui to be installed and execute the following command:
155
+ ```bash
156
+ wget -q https://raw.githubusercontent.com/AUTOMATIC1111/stable-diffusion-webui/master/webui.sh
157
+ ```
158
+ Or just clone the repo wherever you want:
159
+ ```bash
160
+ git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui
161
+ ```
162
+
163
+ 3. Run `webui.sh`.
164
+ 4. Check `webui-user.sh` for options.
165
+ ### Installation on Apple Silicon
166
+
167
+ Find the instructions [here](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Installation-on-Apple-Silicon).
168
+
169
+ ## Contributing
170
+ Here's how to add code to this repo: [Contributing](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Contributing)
171
+
172
+ ## Documentation
173
+
174
+ The documentation was moved from this README over to the project's [wiki](https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki).
175
+
176
+ 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).
177
+
178
+ ## Credits
179
+ Licenses for borrowed code can be found in `Settings -> Licenses` screen, and also in `html/licenses.html` file.
180
+
181
+ - Stable Diffusion - https://github.com/Stability-AI/stablediffusion, https://github.com/CompVis/taming-transformers, https://github.com/mcmonkey4eva/sd3-ref
182
+ - k-diffusion - https://github.com/crowsonkb/k-diffusion.git
183
+ - Spandrel - https://github.com/chaiNNer-org/spandrel implementing
184
+ - GFPGAN - https://github.com/TencentARC/GFPGAN.git
185
+ - CodeFormer - https://github.com/sczhou/CodeFormer
186
+ - ESRGAN - https://github.com/xinntao/ESRGAN
187
+ - SwinIR - https://github.com/JingyunLiang/SwinIR
188
+ - Swin2SR - https://github.com/mv-lab/swin2sr
189
+ - LDSR - https://github.com/Hafiidz/latent-diffusion
190
+ - MiDaS - https://github.com/isl-org/MiDaS
191
+ - Ideas for optimizations - https://github.com/basujindal/stable-diffusion
192
+ - Cross Attention layer optimization - Doggettx - https://github.com/Doggettx/stable-diffusion, original idea for prompt editing.
193
+ - Cross Attention layer optimization - InvokeAI, lstein - https://github.com/invoke-ai/InvokeAI (originally http://github.com/lstein/stable-diffusion)
194
+ - 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)
195
+ - Textual Inversion - Rinon Gal - https://github.com/rinongal/textual_inversion (we're not using his code, but we are using his ideas).
196
+ - Idea for SD upscale - https://github.com/jquesnelle/txt2imghd
197
+ - Noise generation for outpainting mk2 - https://github.com/parlance-zz/g-diffuser-bot
198
+ - CLIP interrogator idea and borrowing some code - https://github.com/pharmapsychotic/clip-interrogator
199
+ - Idea for Composable Diffusion - https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
200
+ - xformers - https://github.com/facebookresearch/xformers
201
+ - DeepDanbooru - interrogator for anime diffusers https://github.com/KichangKim/DeepDanbooru
202
+ - 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)
203
+ - Instruct pix2pix - Tim Brooks (star), Aleksander Holynski (star), Alexei A. Efros (no star) - https://github.com/timothybrooks/instruct-pix2pix
204
+ - Security advice - RyotaK
205
+ - UniPC sampler - Wenliang Zhao - https://github.com/wl-zhao/UniPC
206
+ - TAESD - Ollin Boer Bohan - https://github.com/madebyollin/taesd
207
+ - LyCORIS - KohakuBlueleaf
208
+ - Restart sampling - lambertae - https://github.com/Newbeeer/diffusion_restart_sampling
209
+ - Hypertile - tfernd - https://github.com/tfernd/HyperTile
210
+ - Initial Gradio script - posted on 4chan by an Anonymous user. Thank you Anonymous user.
211
+ - (You)
_typos.toml ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ [default.extend-words]
2
+ # Part of "RGBa" (Pillow's pre-multiplied alpha RGB mode)
3
+ Ba = "Ba"
4
+ # HSA is something AMD uses for their GPUs
5
+ HSA = "HSA"
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: False
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/alt-diffusion-m18-inference.yaml ADDED
@@ -0,0 +1,73 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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_head_channels: 64
40
+ use_spatial_transformer: True
41
+ use_linear_in_transformer: True
42
+ transformer_depth: 1
43
+ context_dim: 1024
44
+ use_checkpoint: False
45
+ legacy: False
46
+
47
+ first_stage_config:
48
+ target: ldm.models.autoencoder.AutoencoderKL
49
+ params:
50
+ embed_dim: 4
51
+ monitor: val/rec_loss
52
+ ddconfig:
53
+ double_z: true
54
+ z_channels: 4
55
+ resolution: 256
56
+ in_channels: 3
57
+ out_ch: 3
58
+ ch: 128
59
+ ch_mult:
60
+ - 1
61
+ - 2
62
+ - 4
63
+ - 4
64
+ num_res_blocks: 2
65
+ attn_resolutions: []
66
+ dropout: 0.0
67
+ lossconfig:
68
+ target: torch.nn.Identity
69
+
70
+ cond_stage_config:
71
+ target: modules.xlmr_m18.BertSeriesModelWithTransformation
72
+ params:
73
+ 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: False
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/sd3-inference.yaml ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ model:
2
+ target: modules.models.sd3.sd3_model.SD3Inferencer
3
+ params:
4
+ shift: 3
5
+ state_dict: null
configs/sd_xl_inpaint.yaml ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ model:
2
+ target: sgm.models.diffusion.DiffusionEngine
3
+ params:
4
+ scale_factor: 0.13025
5
+ disable_first_stage_autocast: True
6
+
7
+ denoiser_config:
8
+ target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser
9
+ params:
10
+ num_idx: 1000
11
+
12
+ weighting_config:
13
+ target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
14
+ scaling_config:
15
+ target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling
16
+ discretization_config:
17
+ target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization
18
+
19
+ network_config:
20
+ target: sgm.modules.diffusionmodules.openaimodel.UNetModel
21
+ params:
22
+ adm_in_channels: 2816
23
+ num_classes: sequential
24
+ use_checkpoint: False
25
+ in_channels: 9
26
+ out_channels: 4
27
+ model_channels: 320
28
+ attention_resolutions: [4, 2]
29
+ num_res_blocks: 2
30
+ channel_mult: [1, 2, 4]
31
+ num_head_channels: 64
32
+ use_spatial_transformer: True
33
+ use_linear_in_transformer: True
34
+ transformer_depth: [1, 2, 10] # note: the first is unused (due to attn_res starting at 2) 32, 16, 8 --> 64, 32, 16
35
+ context_dim: 2048
36
+ spatial_transformer_attn_type: softmax-xformers
37
+ legacy: False
38
+
39
+ conditioner_config:
40
+ target: sgm.modules.GeneralConditioner
41
+ params:
42
+ emb_models:
43
+ # crossattn cond
44
+ - is_trainable: False
45
+ input_key: txt
46
+ target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
47
+ params:
48
+ layer: hidden
49
+ layer_idx: 11
50
+ # crossattn and vector cond
51
+ - is_trainable: False
52
+ input_key: txt
53
+ target: sgm.modules.encoders.modules.FrozenOpenCLIPEmbedder2
54
+ params:
55
+ arch: ViT-bigG-14
56
+ version: laion2b_s39b_b160k
57
+ freeze: True
58
+ layer: penultimate
59
+ always_return_pooled: True
60
+ legacy: False
61
+ # vector cond
62
+ - is_trainable: False
63
+ input_key: original_size_as_tuple
64
+ target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
65
+ params:
66
+ outdim: 256 # multiplied by two
67
+ # vector cond
68
+ - is_trainable: False
69
+ input_key: crop_coords_top_left
70
+ target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
71
+ params:
72
+ outdim: 256 # multiplied by two
73
+ # vector cond
74
+ - is_trainable: False
75
+ input_key: target_size_as_tuple
76
+ target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
77
+ params:
78
+ outdim: 256 # multiplied by two
79
+
80
+ first_stage_config:
81
+ target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
82
+ params:
83
+ embed_dim: 4
84
+ monitor: val/rec_loss
85
+ ddconfig:
86
+ attn_type: vanilla-xformers
87
+ double_z: true
88
+ z_channels: 4
89
+ resolution: 256
90
+ in_channels: 3
91
+ out_ch: 3
92
+ ch: 128
93
+ ch_mult: [1, 2, 4, 4]
94
+ num_res_blocks: 2
95
+ attn_resolutions: []
96
+ dropout: 0.0
97
+ lossconfig:
98
+ target: torch.nn.Identity
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: False
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: False
44
+ legacy: False
45
+
46
+ first_stage_config:
47
+ target: ldm.models.autoencoder.AutoencoderKL
48
+ params:
49
+ embed_dim: 4
50
+ monitor: val/rec_loss
51
+ ddconfig:
52
+ double_z: true
53
+ z_channels: 4
54
+ resolution: 256
55
+ in_channels: 3
56
+ out_ch: 3
57
+ ch: 128
58
+ ch_mult:
59
+ - 1
60
+ - 2
61
+ - 4
62
+ - 4
63
+ num_res_blocks: 2
64
+ attn_resolutions: []
65
+ dropout: 0.0
66
+ lossconfig:
67
+ target: torch.nn.Identity
68
+
69
+ cond_stage_config:
70
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
environment-wsl2.yaml ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ name: automatic
2
+ channels:
3
+ - pytorch
4
+ - defaults
5
+ dependencies:
6
+ - python=3.10
7
+ - pip=23.0
8
+ - cudatoolkit=11.8
9
+ - pytorch=2.0
10
+ - torchvision=0.15
11
+ - numpy=1.23
extensions-builtin/LDSR/ldsr_model_arch.py ADDED
@@ -0,0 +1,250 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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"Parameterization {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
+ with 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 expected 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, 'BoundingBoxRescaling 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 positions 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 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ remove_symbols = str.maketrans('', '', ":,")
13
+
14
+ def activate(self, p, params_list):
15
+ additional = shared.opts.sd_lora
16
+
17
+ self.errors.clear()
18
+
19
+ if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
20
+ p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
21
+ params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
22
+
23
+ names = []
24
+ te_multipliers = []
25
+ unet_multipliers = []
26
+ dyn_dims = []
27
+ for params in params_list:
28
+ assert params.items
29
+
30
+ names.append(params.positional[0])
31
+
32
+ te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0
33
+ te_multiplier = float(params.named.get("te", te_multiplier))
34
+
35
+ unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier
36
+ unet_multiplier = float(params.named.get("unet", unet_multiplier))
37
+
38
+ dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
39
+ dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
40
+
41
+ te_multipliers.append(te_multiplier)
42
+ unet_multipliers.append(unet_multiplier)
43
+ dyn_dims.append(dyn_dim)
44
+
45
+ networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
46
+
47
+ if shared.opts.lora_add_hashes_to_infotext:
48
+ if not getattr(p, "is_hr_pass", False) or not hasattr(p, "lora_hashes"):
49
+ p.lora_hashes = {}
50
+
51
+ for item in networks.loaded_networks:
52
+ if item.network_on_disk.shorthash and item.mentioned_name:
53
+ p.lora_hashes[item.mentioned_name.translate(self.remove_symbols)] = item.network_on_disk.shorthash
54
+
55
+ if p.lora_hashes:
56
+ p.extra_generation_params["Lora hashes"] = ', '.join(f'{k}: {v}' for k, v in p.lora_hashes.items())
57
+
58
+ def deactivate(self, p):
59
+ if self.errors:
60
+ p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
61
+
62
+ 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_logger.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import sys
2
+ import copy
3
+ import logging
4
+
5
+
6
+ class ColoredFormatter(logging.Formatter):
7
+ COLORS = {
8
+ "DEBUG": "\033[0;36m", # CYAN
9
+ "INFO": "\033[0;32m", # GREEN
10
+ "WARNING": "\033[0;33m", # YELLOW
11
+ "ERROR": "\033[0;31m", # RED
12
+ "CRITICAL": "\033[0;37;41m", # WHITE ON RED
13
+ "RESET": "\033[0m", # RESET COLOR
14
+ }
15
+
16
+ def format(self, record):
17
+ colored_record = copy.copy(record)
18
+ levelname = colored_record.levelname
19
+ seq = self.COLORS.get(levelname, self.COLORS["RESET"])
20
+ colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
21
+ return super().format(colored_record)
22
+
23
+
24
+ logger = logging.getLogger("lora")
25
+ logger.propagate = False
26
+
27
+
28
+ if not logger.handlers:
29
+ handler = logging.StreamHandler(sys.stdout)
30
+ handler.setFormatter(
31
+ ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s")
32
+ )
33
+ logger.addHandler(handler)
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 +1,68 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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)
22
+
23
+
24
+ # copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
25
+ def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
26
+ '''
27
+ return a tuple of two value of input dimension decomposed by the number closest to factor
28
+ second value is higher or equal than first value.
29
+
30
+ In LoRA with Kroneckor Product, first value is a value for weight scale.
31
+ secon value is a value for weight.
32
+
33
+ Because of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
34
+
35
+ examples)
36
+ factor
37
+ -1 2 4 8 16 ...
38
+ 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127 127 -> 1, 127
39
+ 128 -> 8, 16 128 -> 2, 64 128 -> 4, 32 128 -> 8, 16 128 -> 8, 16
40
+ 250 -> 10, 25 250 -> 2, 125 250 -> 2, 125 250 -> 5, 50 250 -> 10, 25
41
+ 360 -> 8, 45 360 -> 2, 180 360 -> 4, 90 360 -> 8, 45 360 -> 12, 30
42
+ 512 -> 16, 32 512 -> 2, 256 512 -> 4, 128 512 -> 8, 64 512 -> 16, 32
43
+ 1024 -> 32, 32 1024 -> 2, 512 1024 -> 4, 256 1024 -> 8, 128 1024 -> 16, 64
44
+ '''
45
+
46
+ if factor > 0 and (dimension % factor) == 0:
47
+ m = factor
48
+ n = dimension // factor
49
+ if m > n:
50
+ n, m = m, n
51
+ return m, n
52
+ if factor < 0:
53
+ factor = dimension
54
+ m, n = 1, dimension
55
+ length = m + n
56
+ while m<n:
57
+ new_m = m + 1
58
+ while dimension%new_m != 0:
59
+ new_m += 1
60
+ new_n = dimension // new_m
61
+ if new_m + new_n > length or new_m>factor:
62
+ break
63
+ else:
64
+ m, n = new_m, new_n
65
+ if m > n:
66
+ n, m = m, n
67
+ return m, n
68
+
extensions-builtin/Lora/network.py ADDED
@@ -0,0 +1,228 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __future__ import annotations
2
+ import os
3
+ from collections import namedtuple
4
+ import enum
5
+
6
+ import torch.nn as nn
7
+ import torch.nn.functional as F
8
+
9
+ from modules import sd_models, cache, errors, hashes, shared
10
+ import modules.models.sd3.mmdit
11
+
12
+ NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
13
+
14
+ metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
15
+
16
+
17
+ class SdVersion(enum.Enum):
18
+ Unknown = 1
19
+ SD1 = 2
20
+ SD2 = 3
21
+ SDXL = 4
22
+
23
+
24
+ class NetworkOnDisk:
25
+ def __init__(self, name, filename):
26
+ self.name = name
27
+ self.filename = filename
28
+ self.metadata = {}
29
+ self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
30
+
31
+ def read_metadata():
32
+ metadata = sd_models.read_metadata_from_safetensors(filename)
33
+
34
+ return metadata
35
+
36
+ if self.is_safetensors:
37
+ try:
38
+ self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
39
+ except Exception as e:
40
+ errors.display(e, f"reading lora {filename}")
41
+
42
+ if self.metadata:
43
+ m = {}
44
+ for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
45
+ m[k] = v
46
+
47
+ self.metadata = m
48
+
49
+ self.alias = self.metadata.get('ss_output_name', self.name)
50
+
51
+ self.hash = None
52
+ self.shorthash = None
53
+ self.set_hash(
54
+ self.metadata.get('sshs_model_hash') or
55
+ hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
56
+ ''
57
+ )
58
+
59
+ self.sd_version = self.detect_version()
60
+
61
+ def detect_version(self):
62
+ if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
63
+ return SdVersion.SDXL
64
+ elif str(self.metadata.get('ss_v2', "")) == "True":
65
+ return SdVersion.SD2
66
+ elif len(self.metadata):
67
+ return SdVersion.SD1
68
+
69
+ return SdVersion.Unknown
70
+
71
+ def set_hash(self, v):
72
+ self.hash = v
73
+ self.shorthash = self.hash[0:12]
74
+
75
+ if self.shorthash:
76
+ import networks
77
+ networks.available_network_hash_lookup[self.shorthash] = self
78
+
79
+ def read_hash(self):
80
+ if not self.hash:
81
+ self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
82
+
83
+ def get_alias(self):
84
+ import networks
85
+ if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
86
+ return self.name
87
+ else:
88
+ return self.alias
89
+
90
+
91
+ class Network: # LoraModule
92
+ def __init__(self, name, network_on_disk: NetworkOnDisk):
93
+ self.name = name
94
+ self.network_on_disk = network_on_disk
95
+ self.te_multiplier = 1.0
96
+ self.unet_multiplier = 1.0
97
+ self.dyn_dim = None
98
+ self.modules = {}
99
+ self.bundle_embeddings = {}
100
+ self.mtime = None
101
+
102
+ self.mentioned_name = None
103
+ """the text that was used to add the network to prompt - can be either name or an alias"""
104
+
105
+
106
+ class ModuleType:
107
+ def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
108
+ return None
109
+
110
+
111
+ class NetworkModule:
112
+ def __init__(self, net: Network, weights: NetworkWeights):
113
+ self.network = net
114
+ self.network_key = weights.network_key
115
+ self.sd_key = weights.sd_key
116
+ self.sd_module = weights.sd_module
117
+
118
+ if isinstance(self.sd_module, modules.models.sd3.mmdit.QkvLinear):
119
+ s = self.sd_module.weight.shape
120
+ self.shape = (s[0] // 3, s[1])
121
+ elif hasattr(self.sd_module, 'weight'):
122
+ self.shape = self.sd_module.weight.shape
123
+ elif isinstance(self.sd_module, nn.MultiheadAttention):
124
+ # For now, only self-attn use Pytorch's MHA
125
+ # So assume all qkvo proj have same shape
126
+ self.shape = self.sd_module.out_proj.weight.shape
127
+ else:
128
+ self.shape = None
129
+
130
+ self.ops = None
131
+ self.extra_kwargs = {}
132
+ if isinstance(self.sd_module, nn.Conv2d):
133
+ self.ops = F.conv2d
134
+ self.extra_kwargs = {
135
+ 'stride': self.sd_module.stride,
136
+ 'padding': self.sd_module.padding
137
+ }
138
+ elif isinstance(self.sd_module, nn.Linear):
139
+ self.ops = F.linear
140
+ elif isinstance(self.sd_module, nn.LayerNorm):
141
+ self.ops = F.layer_norm
142
+ self.extra_kwargs = {
143
+ 'normalized_shape': self.sd_module.normalized_shape,
144
+ 'eps': self.sd_module.eps
145
+ }
146
+ elif isinstance(self.sd_module, nn.GroupNorm):
147
+ self.ops = F.group_norm
148
+ self.extra_kwargs = {
149
+ 'num_groups': self.sd_module.num_groups,
150
+ 'eps': self.sd_module.eps
151
+ }
152
+
153
+ self.dim = None
154
+ self.bias = weights.w.get("bias")
155
+ self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
156
+ self.scale = weights.w["scale"].item() if "scale" in weights.w else None
157
+
158
+ self.dora_scale = weights.w.get("dora_scale", None)
159
+ self.dora_norm_dims = len(self.shape) - 1
160
+
161
+ def multiplier(self):
162
+ if 'transformer' in self.sd_key[:20]:
163
+ return self.network.te_multiplier
164
+ else:
165
+ return self.network.unet_multiplier
166
+
167
+ def calc_scale(self):
168
+ if self.scale is not None:
169
+ return self.scale
170
+ if self.dim is not None and self.alpha is not None:
171
+ return self.alpha / self.dim
172
+
173
+ return 1.0
174
+
175
+ def apply_weight_decompose(self, updown, orig_weight):
176
+ # Match the device/dtype
177
+ orig_weight = orig_weight.to(updown.dtype)
178
+ dora_scale = self.dora_scale.to(device=orig_weight.device, dtype=updown.dtype)
179
+ updown = updown.to(orig_weight.device)
180
+
181
+ merged_scale1 = updown + orig_weight
182
+ merged_scale1_norm = (
183
+ merged_scale1.transpose(0, 1)
184
+ .reshape(merged_scale1.shape[1], -1)
185
+ .norm(dim=1, keepdim=True)
186
+ .reshape(merged_scale1.shape[1], *[1] * self.dora_norm_dims)
187
+ .transpose(0, 1)
188
+ )
189
+
190
+ dora_merged = (
191
+ merged_scale1 * (dora_scale / merged_scale1_norm)
192
+ )
193
+ final_updown = dora_merged - orig_weight
194
+ return final_updown
195
+
196
+ def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
197
+ if self.bias is not None:
198
+ updown = updown.reshape(self.bias.shape)
199
+ updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
200
+ updown = updown.reshape(output_shape)
201
+
202
+ if len(output_shape) == 4:
203
+ updown = updown.reshape(output_shape)
204
+
205
+ if orig_weight.size().numel() == updown.size().numel():
206
+ updown = updown.reshape(orig_weight.shape)
207
+
208
+ if ex_bias is not None:
209
+ ex_bias = ex_bias * self.multiplier()
210
+
211
+ updown = updown * self.calc_scale()
212
+
213
+ if self.dora_scale is not None:
214
+ updown = self.apply_weight_decompose(updown, orig_weight)
215
+
216
+ return updown * self.multiplier(), ex_bias
217
+
218
+ def calc_updown(self, target):
219
+ raise NotImplementedError()
220
+
221
+ def forward(self, x, y):
222
+ """A general forward implementation for all modules"""
223
+ if self.ops is None:
224
+ raise NotImplementedError()
225
+ else:
226
+ updown, ex_bias = self.calc_updown(self.sd_module.weight)
227
+ return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs)
228
+
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)
22
+ if self.ex_bias is not None:
23
+ ex_bias = self.ex_bias.to(orig_weight.device)
24
+ else:
25
+ ex_bias = None
26
+
27
+ return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
extensions-builtin/Lora/network_glora.py ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import network
3
+
4
+ class ModuleTypeGLora(network.ModuleType):
5
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
6
+ if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):
7
+ return NetworkModuleGLora(net, weights)
8
+
9
+ return None
10
+
11
+ # adapted from https://github.com/KohakuBlueleaf/LyCORIS
12
+ class NetworkModuleGLora(network.NetworkModule):
13
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
14
+ super().__init__(net, weights)
15
+
16
+ if hasattr(self.sd_module, 'weight'):
17
+ self.shape = self.sd_module.weight.shape
18
+
19
+ self.w1a = weights.w["a1.weight"]
20
+ self.w1b = weights.w["b1.weight"]
21
+ self.w2a = weights.w["a2.weight"]
22
+ self.w2b = weights.w["b2.weight"]
23
+
24
+ def calc_updown(self, orig_weight):
25
+ w1a = self.w1a.to(orig_weight.device)
26
+ w1b = self.w1b.to(orig_weight.device)
27
+ w2a = self.w2a.to(orig_weight.device)
28
+ w2b = self.w2b.to(orig_weight.device)
29
+
30
+ output_shape = [w1a.size(0), w1b.size(1)]
31
+ updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a))
32
+
33
+ return self.finalize_updown(updown, orig_weight, output_shape)
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)
31
+ w1b = self.w1b.to(orig_weight.device)
32
+ w2a = self.w2a.to(orig_weight.device)
33
+ w2b = self.w2b.to(orig_weight.device)
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)
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)
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)
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)
41
+ else:
42
+ w1a = self.w1a.to(orig_weight.device)
43
+ w1b = self.w1b.to(orig_weight.device)
44
+ w1 = w1a @ w1b
45
+
46
+ if self.w2 is not None:
47
+ w2 = self.w2.to(orig_weight.device)
48
+ elif self.t2 is None:
49
+ w2a = self.w2a.to(orig_weight.device)
50
+ w2b = self.w2b.to(orig_weight.device)
51
+ w2 = w2a @ w2b
52
+ else:
53
+ t2 = self.t2.to(orig_weight.device)
54
+ w2a = self.w2a.to(orig_weight.device)
55
+ w2b = self.w2b.to(orig_weight.device)
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 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+
3
+ import lyco_helpers
4
+ import modules.models.sd3.mmdit
5
+ import network
6
+ from modules import devices
7
+
8
+
9
+ class ModuleTypeLora(network.ModuleType):
10
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
11
+ if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
12
+ return NetworkModuleLora(net, weights)
13
+
14
+ if all(x in weights.w for x in ["lora_A.weight", "lora_B.weight"]):
15
+ w = weights.w.copy()
16
+ weights.w.clear()
17
+ weights.w.update({"lora_up.weight": w["lora_B.weight"], "lora_down.weight": w["lora_A.weight"]})
18
+
19
+ return NetworkModuleLora(net, weights)
20
+
21
+ return None
22
+
23
+
24
+ class NetworkModuleLora(network.NetworkModule):
25
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
26
+ super().__init__(net, weights)
27
+
28
+ self.up_model = self.create_module(weights.w, "lora_up.weight")
29
+ self.down_model = self.create_module(weights.w, "lora_down.weight")
30
+ self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True)
31
+
32
+ self.dim = weights.w["lora_down.weight"].shape[0]
33
+
34
+ def create_module(self, weights, key, none_ok=False):
35
+ weight = weights.get(key)
36
+
37
+ if weight is None and none_ok:
38
+ return None
39
+
40
+ is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear]
41
+ is_conv = type(self.sd_module) in [torch.nn.Conv2d]
42
+
43
+ if is_linear:
44
+ weight = weight.reshape(weight.shape[0], -1)
45
+ module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
46
+ elif is_conv and key == "lora_down.weight" or key == "dyn_up":
47
+ if len(weight.shape) == 2:
48
+ weight = weight.reshape(weight.shape[0], -1, 1, 1)
49
+
50
+ if weight.shape[2] != 1 or weight.shape[3] != 1:
51
+ 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)
52
+ else:
53
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
54
+ elif is_conv and key == "lora_mid.weight":
55
+ 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)
56
+ elif is_conv and key == "lora_up.weight" or key == "dyn_down":
57
+ module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
58
+ else:
59
+ raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
60
+
61
+ with torch.no_grad():
62
+ if weight.shape != module.weight.shape:
63
+ weight = weight.reshape(module.weight.shape)
64
+ module.weight.copy_(weight)
65
+
66
+ module.to(device=devices.cpu, dtype=devices.dtype)
67
+ module.weight.requires_grad_(False)
68
+
69
+ return module
70
+
71
+ def calc_updown(self, orig_weight):
72
+ up = self.up_model.weight.to(orig_weight.device)
73
+ down = self.down_model.weight.to(orig_weight.device)
74
+
75
+ output_shape = [up.size(0), down.size(1)]
76
+ if self.mid_model is not None:
77
+ # cp-decomposition
78
+ mid = self.mid_model.weight.to(orig_weight.device)
79
+ updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
80
+ output_shape += mid.shape[2:]
81
+ else:
82
+ if len(down.shape) == 4:
83
+ output_shape += down.shape[2:]
84
+ updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim)
85
+
86
+ return self.finalize_updown(updown, orig_weight, output_shape)
87
+
88
+ def forward(self, x, y):
89
+ self.up_model.to(device=devices.device)
90
+ self.down_model.to(device=devices.device)
91
+
92
+ return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
93
+
94
+
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)
22
+
23
+ if self.b_norm is not None:
24
+ ex_bias = self.b_norm.to(orig_weight.device)
25
+ else:
26
+ ex_bias = None
27
+
28
+ return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)
extensions-builtin/Lora/network_oft.py ADDED
@@ -0,0 +1,118 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ import network
3
+ from einops import rearrange
4
+
5
+
6
+ class ModuleTypeOFT(network.ModuleType):
7
+ def create_module(self, net: network.Network, weights: network.NetworkWeights):
8
+ if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
9
+ return NetworkModuleOFT(net, weights)
10
+
11
+ return None
12
+
13
+ # Supports both kohya-ss' implementation of COFT https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
14
+ # and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
15
+ class NetworkModuleOFT(network.NetworkModule):
16
+ def __init__(self, net: network.Network, weights: network.NetworkWeights):
17
+
18
+ super().__init__(net, weights)
19
+
20
+ self.lin_module = None
21
+ self.org_module: list[torch.Module] = [self.sd_module]
22
+
23
+ self.scale = 1.0
24
+ self.is_R = False
25
+ self.is_boft = False
26
+
27
+ # kohya-ss/New LyCORIS OFT/BOFT
28
+ if "oft_blocks" in weights.w.keys():
29
+ self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
30
+ self.alpha = weights.w.get("alpha", None) # alpha is constraint
31
+ self.dim = self.oft_blocks.shape[0] # lora dim
32
+ # Old LyCORIS OFT
33
+ elif "oft_diag" in weights.w.keys():
34
+ self.is_R = True
35
+ self.oft_blocks = weights.w["oft_diag"]
36
+ # self.alpha is unused
37
+ self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
38
+
39
+ is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
40
+ is_conv = type(self.sd_module) in [torch.nn.Conv2d]
41
+ is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
42
+
43
+ if is_linear:
44
+ self.out_dim = self.sd_module.out_features
45
+ elif is_conv:
46
+ self.out_dim = self.sd_module.out_channels
47
+ elif is_other_linear:
48
+ self.out_dim = self.sd_module.embed_dim
49
+
50
+ # LyCORIS BOFT
51
+ if self.oft_blocks.dim() == 4:
52
+ self.is_boft = True
53
+ self.rescale = weights.w.get('rescale', None)
54
+ if self.rescale is not None and not is_other_linear:
55
+ self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1))
56
+
57
+ self.num_blocks = self.dim
58
+ self.block_size = self.out_dim // self.dim
59
+ self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim
60
+ if self.is_R:
61
+ self.constraint = None
62
+ self.block_size = self.dim
63
+ self.num_blocks = self.out_dim // self.dim
64
+ elif self.is_boft:
65
+ self.boft_m = self.oft_blocks.shape[0]
66
+ self.num_blocks = self.oft_blocks.shape[1]
67
+ self.block_size = self.oft_blocks.shape[2]
68
+ self.boft_b = self.block_size
69
+
70
+ def calc_updown(self, orig_weight):
71
+ oft_blocks = self.oft_blocks.to(orig_weight.device)
72
+ eye = torch.eye(self.block_size, device=oft_blocks.device)
73
+
74
+ if not self.is_R:
75
+ block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix
76
+ if self.constraint != 0:
77
+ norm_Q = torch.norm(block_Q.flatten())
78
+ new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device))
79
+ block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
80
+ oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
81
+
82
+ R = oft_blocks.to(orig_weight.device)
83
+
84
+ if not self.is_boft:
85
+ # This errors out for MultiheadAttention, might need to be handled up-stream
86
+ merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
87
+ merged_weight = torch.einsum(
88
+ 'k n m, k n ... -> k m ...',
89
+ R,
90
+ merged_weight
91
+ )
92
+ merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
93
+ else:
94
+ # TODO: determine correct value for scale
95
+ scale = 1.0
96
+ m = self.boft_m
97
+ b = self.boft_b
98
+ r_b = b // 2
99
+ inp = orig_weight
100
+ for i in range(m):
101
+ bi = R[i] # b_num, b_size, b_size
102
+ if i == 0:
103
+ # Apply multiplier/scale and rescale into first weight
104
+ bi = bi * scale + (1 - scale) * eye
105
+ inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b)
106
+ inp = rearrange(inp, "(d b) ... -> d b ...", b=b)
107
+ inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp)
108
+ inp = rearrange(inp, "d b ... -> (d b) ...")
109
+ inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b)
110
+ merged_weight = inp
111
+
112
+ # Rescale mechanism
113
+ if self.rescale is not None:
114
+ merged_weight = self.rescale.to(merged_weight) * merged_weight
115
+
116
+ updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
117
+ output_shape = orig_weight.shape
118
+ return self.finalize_updown(updown, orig_weight, output_shape)
extensions-builtin/Lora/networks.py ADDED
@@ -0,0 +1,737 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __future__ import annotations
2
+ import gradio as gr
3
+ import logging
4
+ import os
5
+ import re
6
+
7
+ import lora_patches
8
+ import network
9
+ import network_lora
10
+ import network_glora
11
+ import network_hada
12
+ import network_ia3
13
+ import network_lokr
14
+ import network_full
15
+ import network_norm
16
+ import network_oft
17
+
18
+ import torch
19
+ from typing import Union
20
+
21
+ from modules import shared, devices, sd_models, errors, scripts, sd_hijack
22
+ import modules.textual_inversion.textual_inversion as textual_inversion
23
+ import modules.models.sd3.mmdit
24
+
25
+ from lora_logger import logger
26
+
27
+ module_types = [
28
+ network_lora.ModuleTypeLora(),
29
+ network_hada.ModuleTypeHada(),
30
+ network_ia3.ModuleTypeIa3(),
31
+ network_lokr.ModuleTypeLokr(),
32
+ network_full.ModuleTypeFull(),
33
+ network_norm.ModuleTypeNorm(),
34
+ network_glora.ModuleTypeGLora(),
35
+ network_oft.ModuleTypeOFT(),
36
+ ]
37
+
38
+
39
+ re_digits = re.compile(r"\d+")
40
+ re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
41
+ re_compiled = {}
42
+
43
+ suffix_conversion = {
44
+ "attentions": {},
45
+ "resnets": {
46
+ "conv1": "in_layers_2",
47
+ "conv2": "out_layers_3",
48
+ "norm1": "in_layers_0",
49
+ "norm2": "out_layers_0",
50
+ "time_emb_proj": "emb_layers_1",
51
+ "conv_shortcut": "skip_connection",
52
+ }
53
+ }
54
+
55
+
56
+ def convert_diffusers_name_to_compvis(key, is_sd2):
57
+ def match(match_list, regex_text):
58
+ regex = re_compiled.get(regex_text)
59
+ if regex is None:
60
+ regex = re.compile(regex_text)
61
+ re_compiled[regex_text] = regex
62
+
63
+ r = re.match(regex, key)
64
+ if not r:
65
+ return False
66
+
67
+ match_list.clear()
68
+ match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
69
+ return True
70
+
71
+ m = []
72
+
73
+ if match(m, r"lora_unet_conv_in(.*)"):
74
+ return f'diffusion_model_input_blocks_0_0{m[0]}'
75
+
76
+ if match(m, r"lora_unet_conv_out(.*)"):
77
+ return f'diffusion_model_out_2{m[0]}'
78
+
79
+ if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
80
+ return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
81
+
82
+ if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
83
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
84
+ return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
85
+
86
+ if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
87
+ suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
88
+ return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
89
+
90
+ if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
91
+ suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
92
+ return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
93
+
94
+ if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
95
+ return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
96
+
97
+ if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
98
+ return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
99
+
100
+ if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
101
+ if is_sd2:
102
+ if 'mlp_fc1' in m[1]:
103
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
104
+ elif 'mlp_fc2' in m[1]:
105
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
106
+ else:
107
+ return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
108
+
109
+ return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
110
+
111
+ if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
112
+ if 'mlp_fc1' in m[1]:
113
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
114
+ elif 'mlp_fc2' in m[1]:
115
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
116
+ else:
117
+ return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
118
+
119
+ return key
120
+
121
+
122
+ def assign_network_names_to_compvis_modules(sd_model):
123
+ network_layer_mapping = {}
124
+
125
+ if shared.sd_model.is_sdxl:
126
+ for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
127
+ if not hasattr(embedder, 'wrapped'):
128
+ continue
129
+
130
+ for name, module in embedder.wrapped.named_modules():
131
+ network_name = f'{i}_{name.replace(".", "_")}'
132
+ network_layer_mapping[network_name] = module
133
+ module.network_layer_name = network_name
134
+ else:
135
+ cond_stage_model = getattr(shared.sd_model.cond_stage_model, 'wrapped', shared.sd_model.cond_stage_model)
136
+
137
+ for name, module in cond_stage_model.named_modules():
138
+ network_name = name.replace(".", "_")
139
+ network_layer_mapping[network_name] = module
140
+ module.network_layer_name = network_name
141
+
142
+ for name, module in shared.sd_model.model.named_modules():
143
+ network_name = name.replace(".", "_")
144
+ network_layer_mapping[network_name] = module
145
+ module.network_layer_name = network_name
146
+
147
+ sd_model.network_layer_mapping = network_layer_mapping
148
+
149
+
150
+ class BundledTIHash(str):
151
+ def __init__(self, hash_str):
152
+ self.hash = hash_str
153
+
154
+ def __str__(self):
155
+ return self.hash if shared.opts.lora_bundled_ti_to_infotext else ''
156
+
157
+
158
+ def load_network(name, network_on_disk):
159
+ net = network.Network(name, network_on_disk)
160
+ net.mtime = os.path.getmtime(network_on_disk.filename)
161
+
162
+ sd = sd_models.read_state_dict(network_on_disk.filename)
163
+
164
+ # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
165
+ if not hasattr(shared.sd_model, 'network_layer_mapping'):
166
+ assign_network_names_to_compvis_modules(shared.sd_model)
167
+
168
+ keys_failed_to_match = {}
169
+ is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
170
+ if hasattr(shared.sd_model, 'diffusers_weight_map'):
171
+ diffusers_weight_map = shared.sd_model.diffusers_weight_map
172
+ elif hasattr(shared.sd_model, 'diffusers_weight_mapping'):
173
+ diffusers_weight_map = {}
174
+ for k, v in shared.sd_model.diffusers_weight_mapping():
175
+ diffusers_weight_map[k] = v
176
+ shared.sd_model.diffusers_weight_map = diffusers_weight_map
177
+ else:
178
+ diffusers_weight_map = None
179
+
180
+ matched_networks = {}
181
+ bundle_embeddings = {}
182
+
183
+ for key_network, weight in sd.items():
184
+
185
+ if diffusers_weight_map:
186
+ key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2)
187
+ network_part = network_name + '.' + network_weight
188
+ else:
189
+ key_network_without_network_parts, _, network_part = key_network.partition(".")
190
+
191
+ if key_network_without_network_parts == "bundle_emb":
192
+ emb_name, vec_name = network_part.split(".", 1)
193
+ emb_dict = bundle_embeddings.get(emb_name, {})
194
+ if vec_name.split('.')[0] == 'string_to_param':
195
+ _, k2 = vec_name.split('.', 1)
196
+ emb_dict['string_to_param'] = {k2: weight}
197
+ else:
198
+ emb_dict[vec_name] = weight
199
+ bundle_embeddings[emb_name] = emb_dict
200
+
201
+ if diffusers_weight_map:
202
+ key = diffusers_weight_map.get(key_network_without_network_parts, key_network_without_network_parts)
203
+ else:
204
+ key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
205
+
206
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
207
+
208
+ if sd_module is None:
209
+ m = re_x_proj.match(key)
210
+ if m:
211
+ sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)
212
+
213
+ # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
214
+ if sd_module is None and "lora_unet" in key_network_without_network_parts:
215
+ key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
216
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
217
+ elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
218
+ key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
219
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
220
+
221
+ # some SD1 Loras also have correct compvis keys
222
+ if sd_module is None:
223
+ key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
224
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
225
+
226
+ # kohya_ss OFT module
227
+ elif sd_module is None and "oft_unet" in key_network_without_network_parts:
228
+ key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
229
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
230
+
231
+ # KohakuBlueLeaf OFT module
232
+ if sd_module is None and "oft_diag" in key:
233
+ key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
234
+ key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
235
+ sd_module = shared.sd_model.network_layer_mapping.get(key, None)
236
+
237
+ if sd_module is None:
238
+ keys_failed_to_match[key_network] = key
239
+ continue
240
+
241
+ if key not in matched_networks:
242
+ matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)
243
+
244
+ matched_networks[key].w[network_part] = weight
245
+
246
+ for key, weights in matched_networks.items():
247
+ net_module = None
248
+ for nettype in module_types:
249
+ net_module = nettype.create_module(net, weights)
250
+ if net_module is not None:
251
+ break
252
+
253
+ if net_module is None:
254
+ 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)}")
255
+
256
+ net.modules[key] = net_module
257
+
258
+ embeddings = {}
259
+ for emb_name, data in bundle_embeddings.items():
260
+ embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
261
+ embedding.loaded = None
262
+ embedding.shorthash = BundledTIHash(name)
263
+ embeddings[emb_name] = embedding
264
+
265
+ net.bundle_embeddings = embeddings
266
+
267
+ if keys_failed_to_match:
268
+ logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
269
+
270
+ return net
271
+
272
+
273
+ def purge_networks_from_memory():
274
+ while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
275
+ name = next(iter(networks_in_memory))
276
+ networks_in_memory.pop(name, None)
277
+
278
+ devices.torch_gc()
279
+
280
+
281
+ def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
282
+ emb_db = sd_hijack.model_hijack.embedding_db
283
+ already_loaded = {}
284
+
285
+ for net in loaded_networks:
286
+ if net.name in names:
287
+ already_loaded[net.name] = net
288
+ for emb_name, embedding in net.bundle_embeddings.items():
289
+ if embedding.loaded:
290
+ emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)
291
+
292
+ loaded_networks.clear()
293
+
294
+ unavailable_networks = []
295
+ for name in names:
296
+ if name.lower() in forbidden_network_aliases and available_networks.get(name) is None:
297
+ unavailable_networks.append(name)
298
+ elif available_network_aliases.get(name) is None:
299
+ unavailable_networks.append(name)
300
+
301
+ if unavailable_networks:
302
+ update_available_networks_by_names(unavailable_networks)
303
+
304
+ networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
305
+ if any(x is None for x in networks_on_disk):
306
+ list_available_networks()
307
+
308
+ networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
309
+
310
+ failed_to_load_networks = []
311
+
312
+ for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
313
+ net = already_loaded.get(name, None)
314
+
315
+ if network_on_disk is not None:
316
+ if net is None:
317
+ net = networks_in_memory.get(name)
318
+
319
+ if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
320
+ try:
321
+ net = load_network(name, network_on_disk)
322
+
323
+ networks_in_memory.pop(name, None)
324
+ networks_in_memory[name] = net
325
+ except Exception as e:
326
+ errors.display(e, f"loading network {network_on_disk.filename}")
327
+ continue
328
+
329
+ net.mentioned_name = name
330
+
331
+ network_on_disk.read_hash()
332
+
333
+ if net is None:
334
+ failed_to_load_networks.append(name)
335
+ logging.info(f"Couldn't find network with name {name}")
336
+ continue
337
+
338
+ net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
339
+ net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
340
+ net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
341
+ loaded_networks.append(net)
342
+
343
+ for emb_name, embedding in net.bundle_embeddings.items():
344
+ if embedding.loaded is None and emb_name in emb_db.word_embeddings:
345
+ logger.warning(
346
+ f'Skip bundle embedding: "{emb_name}"'
347
+ ' as it was already loaded from embeddings folder'
348
+ )
349
+ continue
350
+
351
+ embedding.loaded = False
352
+ if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
353
+ embedding.loaded = True
354
+ emb_db.register_embedding(embedding, shared.sd_model)
355
+ else:
356
+ emb_db.skipped_embeddings[name] = embedding
357
+
358
+ if failed_to_load_networks:
359
+ lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
360
+ sd_hijack.model_hijack.comments.append(lora_not_found_message)
361
+ if shared.opts.lora_not_found_warning_console:
362
+ print(f'\n{lora_not_found_message}\n')
363
+ if shared.opts.lora_not_found_gradio_warning:
364
+ gr.Warning(lora_not_found_message)
365
+
366
+ purge_networks_from_memory()
367
+
368
+
369
+ def allowed_layer_without_weight(layer):
370
+ if isinstance(layer, torch.nn.LayerNorm) and not layer.elementwise_affine:
371
+ return True
372
+
373
+ return False
374
+
375
+
376
+ def store_weights_backup(weight):
377
+ if weight is None:
378
+ return None
379
+
380
+ return weight.to(devices.cpu, copy=True)
381
+
382
+
383
+ def restore_weights_backup(obj, field, weight):
384
+ if weight is None:
385
+ setattr(obj, field, None)
386
+ return
387
+
388
+ getattr(obj, field).copy_(weight)
389
+
390
+
391
+ def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
392
+ weights_backup = getattr(self, "network_weights_backup", None)
393
+ bias_backup = getattr(self, "network_bias_backup", None)
394
+
395
+ if weights_backup is None and bias_backup is None:
396
+ return
397
+
398
+ if weights_backup is not None:
399
+ if isinstance(self, torch.nn.MultiheadAttention):
400
+ restore_weights_backup(self, 'in_proj_weight', weights_backup[0])
401
+ restore_weights_backup(self.out_proj, 'weight', weights_backup[1])
402
+ else:
403
+ restore_weights_backup(self, 'weight', weights_backup)
404
+
405
+ if isinstance(self, torch.nn.MultiheadAttention):
406
+ restore_weights_backup(self.out_proj, 'bias', bias_backup)
407
+ else:
408
+ restore_weights_backup(self, 'bias', bias_backup)
409
+
410
+
411
+ def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
412
+ """
413
+ Applies the currently selected set of networks to the weights of torch layer self.
414
+ If weights already have this particular set of networks applied, does nothing.
415
+ If not, restores original weights from backup and alters weights according to networks.
416
+ """
417
+
418
+ network_layer_name = getattr(self, 'network_layer_name', None)
419
+ if network_layer_name is None:
420
+ return
421
+
422
+ current_names = getattr(self, "network_current_names", ())
423
+ wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
424
+
425
+ weights_backup = getattr(self, "network_weights_backup", None)
426
+ if weights_backup is None and wanted_names != ():
427
+ if current_names != () and not allowed_layer_without_weight(self):
428
+ raise RuntimeError(f"{network_layer_name} - no backup weights found and current weights are not unchanged")
429
+
430
+ if isinstance(self, torch.nn.MultiheadAttention):
431
+ weights_backup = (store_weights_backup(self.in_proj_weight), store_weights_backup(self.out_proj.weight))
432
+ else:
433
+ weights_backup = store_weights_backup(self.weight)
434
+
435
+ self.network_weights_backup = weights_backup
436
+
437
+ bias_backup = getattr(self, "network_bias_backup", None)
438
+ if bias_backup is None and wanted_names != ():
439
+ if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
440
+ bias_backup = store_weights_backup(self.out_proj.bias)
441
+ elif getattr(self, 'bias', None) is not None:
442
+ bias_backup = store_weights_backup(self.bias)
443
+ else:
444
+ bias_backup = None
445
+
446
+ # Unlike weight which always has value, some modules don't have bias.
447
+ # Only report if bias is not None and current bias are not unchanged.
448
+ if bias_backup is not None and current_names != ():
449
+ raise RuntimeError("no backup bias found and current bias are not unchanged")
450
+
451
+ self.network_bias_backup = bias_backup
452
+
453
+ if current_names != wanted_names:
454
+ network_restore_weights_from_backup(self)
455
+
456
+ for net in loaded_networks:
457
+ module = net.modules.get(network_layer_name, None)
458
+ if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear):
459
+ try:
460
+ with torch.no_grad():
461
+ if getattr(self, 'fp16_weight', None) is None:
462
+ weight = self.weight
463
+ bias = self.bias
464
+ else:
465
+ weight = self.fp16_weight.clone().to(self.weight.device)
466
+ bias = getattr(self, 'fp16_bias', None)
467
+ if bias is not None:
468
+ bias = bias.clone().to(self.bias.device)
469
+ updown, ex_bias = module.calc_updown(weight)
470
+
471
+ if len(weight.shape) == 4 and weight.shape[1] == 9:
472
+ # inpainting model. zero pad updown to make channel[1] 4 to 9
473
+ updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
474
+
475
+ self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
476
+ if ex_bias is not None and hasattr(self, 'bias'):
477
+ if self.bias is None:
478
+ self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
479
+ else:
480
+ self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
481
+ except RuntimeError as e:
482
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
483
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
484
+
485
+ continue
486
+
487
+ module_q = net.modules.get(network_layer_name + "_q_proj", None)
488
+ module_k = net.modules.get(network_layer_name + "_k_proj", None)
489
+ module_v = net.modules.get(network_layer_name + "_v_proj", None)
490
+ module_out = net.modules.get(network_layer_name + "_out_proj", None)
491
+
492
+ if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
493
+ try:
494
+ with torch.no_grad():
495
+ # Send "real" orig_weight into MHA's lora module
496
+ qw, kw, vw = self.in_proj_weight.chunk(3, 0)
497
+ updown_q, _ = module_q.calc_updown(qw)
498
+ updown_k, _ = module_k.calc_updown(kw)
499
+ updown_v, _ = module_v.calc_updown(vw)
500
+ del qw, kw, vw
501
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
502
+ updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
503
+
504
+ self.in_proj_weight += updown_qkv
505
+ self.out_proj.weight += updown_out
506
+ if ex_bias is not None:
507
+ if self.out_proj.bias is None:
508
+ self.out_proj.bias = torch.nn.Parameter(ex_bias)
509
+ else:
510
+ self.out_proj.bias += ex_bias
511
+
512
+ except RuntimeError as e:
513
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
514
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
515
+
516
+ continue
517
+
518
+ if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v:
519
+ try:
520
+ with torch.no_grad():
521
+ # Send "real" orig_weight into MHA's lora module
522
+ qw, kw, vw = self.weight.chunk(3, 0)
523
+ updown_q, _ = module_q.calc_updown(qw)
524
+ updown_k, _ = module_k.calc_updown(kw)
525
+ updown_v, _ = module_v.calc_updown(vw)
526
+ del qw, kw, vw
527
+ updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
528
+ self.weight += updown_qkv
529
+
530
+ except RuntimeError as e:
531
+ logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
532
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
533
+
534
+ continue
535
+
536
+ if module is None:
537
+ continue
538
+
539
+ logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
540
+ extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
541
+
542
+ self.network_current_names = wanted_names
543
+
544
+
545
+ def network_forward(org_module, input, original_forward):
546
+ """
547
+ Old way of applying Lora by executing operations during layer's forward.
548
+ Stacking many loras this way results in big performance degradation.
549
+ """
550
+
551
+ if len(loaded_networks) == 0:
552
+ return original_forward(org_module, input)
553
+
554
+ input = devices.cond_cast_unet(input)
555
+
556
+ network_restore_weights_from_backup(org_module)
557
+ network_reset_cached_weight(org_module)
558
+
559
+ y = original_forward(org_module, input)
560
+
561
+ network_layer_name = getattr(org_module, 'network_layer_name', None)
562
+ for lora in loaded_networks:
563
+ module = lora.modules.get(network_layer_name, None)
564
+ if module is None:
565
+ continue
566
+
567
+ y = module.forward(input, y)
568
+
569
+ return y
570
+
571
+
572
+ def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
573
+ self.network_current_names = ()
574
+ self.network_weights_backup = None
575
+ self.network_bias_backup = None
576
+
577
+
578
+ def network_Linear_forward(self, input):
579
+ if shared.opts.lora_functional:
580
+ return network_forward(self, input, originals.Linear_forward)
581
+
582
+ network_apply_weights(self)
583
+
584
+ return originals.Linear_forward(self, input)
585
+
586
+
587
+ def network_Linear_load_state_dict(self, *args, **kwargs):
588
+ network_reset_cached_weight(self)
589
+
590
+ return originals.Linear_load_state_dict(self, *args, **kwargs)
591
+
592
+
593
+ def network_Conv2d_forward(self, input):
594
+ if shared.opts.lora_functional:
595
+ return network_forward(self, input, originals.Conv2d_forward)
596
+
597
+ network_apply_weights(self)
598
+
599
+ return originals.Conv2d_forward(self, input)
600
+
601
+
602
+ def network_Conv2d_load_state_dict(self, *args, **kwargs):
603
+ network_reset_cached_weight(self)
604
+
605
+ return originals.Conv2d_load_state_dict(self, *args, **kwargs)
606
+
607
+
608
+ def network_GroupNorm_forward(self, input):
609
+ if shared.opts.lora_functional:
610
+ return network_forward(self, input, originals.GroupNorm_forward)
611
+
612
+ network_apply_weights(self)
613
+
614
+ return originals.GroupNorm_forward(self, input)
615
+
616
+
617
+ def network_GroupNorm_load_state_dict(self, *args, **kwargs):
618
+ network_reset_cached_weight(self)
619
+
620
+ return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
621
+
622
+
623
+ def network_LayerNorm_forward(self, input):
624
+ if shared.opts.lora_functional:
625
+ return network_forward(self, input, originals.LayerNorm_forward)
626
+
627
+ network_apply_weights(self)
628
+
629
+ return originals.LayerNorm_forward(self, input)
630
+
631
+
632
+ def network_LayerNorm_load_state_dict(self, *args, **kwargs):
633
+ network_reset_cached_weight(self)
634
+
635
+ return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
636
+
637
+
638
+ def network_MultiheadAttention_forward(self, *args, **kwargs):
639
+ network_apply_weights(self)
640
+
641
+ return originals.MultiheadAttention_forward(self, *args, **kwargs)
642
+
643
+
644
+ def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
645
+ network_reset_cached_weight(self)
646
+
647
+ return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
648
+
649
+
650
+ def process_network_files(names: list[str] | None = None):
651
+ candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
652
+ candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
653
+ for filename in candidates:
654
+ if os.path.isdir(filename):
655
+ continue
656
+ name = os.path.splitext(os.path.basename(filename))[0]
657
+ # if names is provided, only load networks with names in the list
658
+ if names and name not in names:
659
+ continue
660
+ try:
661
+ entry = network.NetworkOnDisk(name, filename)
662
+ except OSError: # should catch FileNotFoundError and PermissionError etc.
663
+ errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
664
+ continue
665
+
666
+ available_networks[name] = entry
667
+
668
+ if entry.alias in available_network_aliases:
669
+ forbidden_network_aliases[entry.alias.lower()] = 1
670
+
671
+ available_network_aliases[name] = entry
672
+ available_network_aliases[entry.alias] = entry
673
+
674
+
675
+ def update_available_networks_by_names(names: list[str]):
676
+ process_network_files(names)
677
+
678
+
679
+ def list_available_networks():
680
+ available_networks.clear()
681
+ available_network_aliases.clear()
682
+ forbidden_network_aliases.clear()
683
+ available_network_hash_lookup.clear()
684
+ forbidden_network_aliases.update({"none": 1, "Addams": 1})
685
+
686
+ os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
687
+
688
+ process_network_files()
689
+
690
+
691
+ re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
692
+
693
+
694
+ def infotext_pasted(infotext, params):
695
+ if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
696
+ return # if the other extension is active, it will handle those fields, no need to do anything
697
+
698
+ added = []
699
+
700
+ for k in params:
701
+ if not k.startswith("AddNet Model "):
702
+ continue
703
+
704
+ num = k[13:]
705
+
706
+ if params.get("AddNet Module " + num) != "LoRA":
707
+ continue
708
+
709
+ name = params.get("AddNet Model " + num)
710
+ if name is None:
711
+ continue
712
+
713
+ m = re_network_name.match(name)
714
+ if m:
715
+ name = m.group(1)
716
+
717
+ multiplier = params.get("AddNet Weight A " + num, "1.0")
718
+
719
+ added.append(f"<lora:{name}:{multiplier}>")
720
+
721
+ if added:
722
+ params["Prompt"] += "\n" + "".join(added)
723
+
724
+
725
+ originals: lora_patches.LoraPatches = None
726
+
727
+ extra_network_lora = None
728
+
729
+ available_networks = {}
730
+ available_network_aliases = {}
731
+ loaded_networks = []
732
+ loaded_bundle_embeddings = {}
733
+ networks_in_memory = {}
734
+ available_network_hash_lookup = {}
735
+ forbidden_network_aliases = {}
736
+
737
+ list_available_networks()
extensions-builtin/Lora/preload.py ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ from modules import paths
3
+ from modules.paths_internal import normalized_filepath
4
+
5
+
6
+ def preload(parser):
7
+ parser.add_argument("--lora-dir", type=normalized_filepath, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
8
+ parser.add_argument("--lyco-dir-backcompat", type=normalized_filepath, 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 +1,102 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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_bundled_ti_to_infotext": shared.OptionInfo(True, "Add Lora name as TI hashes for bundled Textual Inversion").info('"Add Textual Inversion hashes to infotext" needs to be enabled'),
40
+ "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"),
41
+ "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
42
+ "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
43
+ "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"),
44
+ "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"),
45
+ }))
46
+
47
+
48
+ shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
49
+ "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"),
50
+ }))
51
+
52
+
53
+ def create_lora_json(obj: network.NetworkOnDisk):
54
+ return {
55
+ "name": obj.name,
56
+ "alias": obj.alias,
57
+ "path": obj.filename,
58
+ "metadata": obj.metadata,
59
+ }
60
+
61
+
62
+ def api_networks(_: gr.Blocks, app: FastAPI):
63
+ @app.get("/sdapi/v1/loras")
64
+ async def get_loras():
65
+ return [create_lora_json(obj) for obj in networks.available_networks.values()]
66
+
67
+ @app.post("/sdapi/v1/refresh-loras")
68
+ async def refresh_loras():
69
+ return networks.list_available_networks()
70
+
71
+
72
+ script_callbacks.on_app_started(api_networks)
73
+
74
+ re_lora = re.compile("<lora:([^:]+):")
75
+
76
+
77
+ def infotext_pasted(infotext, d):
78
+ hashes = d.get("Lora hashes")
79
+ if not hashes:
80
+ return
81
+
82
+ hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
83
+ hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
84
+
85
+ def network_replacement(m):
86
+ alias = m.group(1)
87
+ shorthash = hashes.get(alias)
88
+ if shorthash is None:
89
+ return m.group(0)
90
+
91
+ network_on_disk = networks.available_network_hash_lookup.get(shorthash)
92
+ if network_on_disk is None:
93
+ return m.group(0)
94
+
95
+ return f'<lora:{network_on_disk.get_alias()}:'
96
+
97
+ d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
98
+
99
+
100
+ script_callbacks.on_infotext_pasted(infotext_pasted)
101
+
102
+ shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)
extensions-builtin/Lora/ui_edit_user_metadata.py ADDED
@@ -0,0 +1,226 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ ss_tag_frequency = metadata.get("ss_tag_frequency", {})
25
+ if ss_tag_frequency is not None and hasattr(ss_tag_frequency, 'items'):
26
+ for _, tags_dict in ss_tag_frequency.items():
27
+ for tag, tag_count in tags_dict.items():
28
+ tag = tag.strip()
29
+ tags[tag] = tags.get(tag, 0) + int(tag_count)
30
+
31
+ if tags and is_non_comma_tagset(tags):
32
+ new_tags = {}
33
+
34
+ for text, text_count in tags.items():
35
+ for word in re.findall(re_word, text):
36
+ if len(word) < 3:
37
+ continue
38
+
39
+ new_tags[word] = new_tags.get(word, 0) + text_count
40
+
41
+ tags = new_tags
42
+
43
+ ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True)
44
+
45
+ return [(tag, tags[tag]) for tag in ordered_tags]
46
+
47
+
48
+ class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
49
+ def __init__(self, ui, tabname, page):
50
+ super().__init__(ui, tabname, page)
51
+
52
+ self.select_sd_version = None
53
+
54
+ self.taginfo = None
55
+ self.edit_activation_text = None
56
+ self.slider_preferred_weight = None
57
+ self.edit_notes = None
58
+
59
+ def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes):
60
+ user_metadata = self.get_user_metadata(name)
61
+ user_metadata["description"] = desc
62
+ user_metadata["sd version"] = sd_version
63
+ user_metadata["activation text"] = activation_text
64
+ user_metadata["preferred weight"] = preferred_weight
65
+ user_metadata["negative text"] = negative_text
66
+ user_metadata["notes"] = notes
67
+
68
+ self.write_user_metadata(name, user_metadata)
69
+
70
+ def get_metadata_table(self, name):
71
+ table = super().get_metadata_table(name)
72
+ item = self.page.items.get(name, {})
73
+ metadata = item.get("metadata") or {}
74
+
75
+ keys = {
76
+ 'ss_output_name': "Output name:",
77
+ 'ss_sd_model_name': "Model:",
78
+ 'ss_clip_skip': "Clip skip:",
79
+ 'ss_network_module': "Kohya module:",
80
+ }
81
+
82
+ for key, label in keys.items():
83
+ value = metadata.get(key, None)
84
+ if value is not None and str(value) != "None":
85
+ table.append((label, html.escape(value)))
86
+
87
+ ss_training_started_at = metadata.get('ss_training_started_at')
88
+ if ss_training_started_at:
89
+ table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M')))
90
+
91
+ ss_bucket_info = metadata.get("ss_bucket_info")
92
+ if ss_bucket_info and "buckets" in ss_bucket_info:
93
+ resolutions = {}
94
+ for _, bucket in ss_bucket_info["buckets"].items():
95
+ resolution = bucket["resolution"]
96
+ resolution = f'{resolution[1]}x{resolution[0]}'
97
+
98
+ resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"])
99
+
100
+ resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True)
101
+ resolutions_text = html.escape(", ".join(resolutions_list[0:4]))
102
+ if len(resolutions) > 4:
103
+ resolutions_text += ", ..."
104
+ resolutions_text = f"<span title='{html.escape(', '.join(resolutions_list))}'>{resolutions_text}</span>"
105
+
106
+ table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text))
107
+
108
+ image_count = 0
109
+ for _, params in metadata.get("ss_dataset_dirs", {}).items():
110
+ image_count += int(params.get("img_count", 0))
111
+
112
+ if image_count:
113
+ table.append(("Dataset size:", image_count))
114
+
115
+ return table
116
+
117
+ def put_values_into_components(self, name):
118
+ user_metadata = self.get_user_metadata(name)
119
+ values = super().put_values_into_components(name)
120
+
121
+ item = self.page.items.get(name, {})
122
+ metadata = item.get("metadata") or {}
123
+
124
+ tags = build_tags(metadata)
125
+ gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]]
126
+
127
+ return [
128
+ *values[0:5],
129
+ item.get("sd_version", "Unknown"),
130
+ gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
131
+ user_metadata.get('activation text', ''),
132
+ float(user_metadata.get('preferred weight', 0.0)),
133
+ user_metadata.get('negative text', ''),
134
+ gr.update(visible=True if tags else False),
135
+ gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
136
+ ]
137
+
138
+ def generate_random_prompt(self, name):
139
+ item = self.page.items.get(name, {})
140
+ metadata = item.get("metadata") or {}
141
+ tags = build_tags(metadata)
142
+
143
+ return self.generate_random_prompt_from_tags(tags)
144
+
145
+ def generate_random_prompt_from_tags(self, tags):
146
+ max_count = None
147
+ res = []
148
+ for tag, count in tags:
149
+ if not max_count:
150
+ max_count = count
151
+
152
+ v = random.random() * max_count
153
+ if count > v:
154
+ for x in "({[]})":
155
+ tag = tag.replace(x, '\\' + x)
156
+ res.append(tag)
157
+
158
+ return ", ".join(sorted(res))
159
+
160
+ def create_extra_default_items_in_left_column(self):
161
+
162
+ # this would be a lot better as gr.Radio but I can't make it work
163
+ self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
164
+
165
+ def create_editor(self):
166
+ self.create_default_editor_elems()
167
+
168
+ self.taginfo = gr.HighlightedText(label="Training dataset tags")
169
+ self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
170
+ self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
171
+ self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts")
172
+ with gr.Row() as row_random_prompt:
173
+ with gr.Column(scale=8):
174
+ random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
175
+
176
+ with gr.Column(scale=1, min_width=120):
177
+ generate_random_prompt = gr.Button('Generate', size="lg", scale=1)
178
+
179
+ self.edit_notes = gr.TextArea(label='Notes', lines=4)
180
+
181
+ generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False)
182
+
183
+ def select_tag(activation_text, evt: gr.SelectData):
184
+ tag = evt.value[0]
185
+
186
+ words = re.split(re_comma, activation_text)
187
+ if tag in words:
188
+ words = [x for x in words if x != tag and x.strip()]
189
+ return ", ".join(words)
190
+
191
+ return activation_text + ", " + tag if activation_text else tag
192
+
193
+ self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False)
194
+
195
+ self.create_default_buttons()
196
+
197
+ viewed_components = [
198
+ self.edit_name,
199
+ self.edit_description,
200
+ self.html_filedata,
201
+ self.html_preview,
202
+ self.edit_notes,
203
+ self.select_sd_version,
204
+ self.taginfo,
205
+ self.edit_activation_text,
206
+ self.slider_preferred_weight,
207
+ self.edit_negative_text,
208
+ row_random_prompt,
209
+ random_prompt,
210
+ ]
211
+
212
+ self.button_edit\
213
+ .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
214
+ .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
215
+
216
+ edited_components = [
217
+ self.edit_description,
218
+ self.select_sd_version,
219
+ self.edit_activation_text,
220
+ self.slider_preferred_weight,
221
+ self.edit_negative_text,
222
+ self.edit_notes,
223
+ ]
224
+
225
+
226
+ self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)