koenverhagen commited on
Commit
677b082
1 Parent(s): ab2d075

Delete extensions/sd-webui-controlnet

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. extensions/sd-webui-controlnet/.github/ISSUE_TEMPLATE/bug_report.yml +0 -91
  2. extensions/sd-webui-controlnet/.github/ISSUE_TEMPLATE/config.yml +0 -1
  3. extensions/sd-webui-controlnet/.github/workflows/tests.yml +0 -37
  4. extensions/sd-webui-controlnet/.gitignore +0 -179
  5. extensions/sd-webui-controlnet/LICENSE +0 -674
  6. extensions/sd-webui-controlnet/README.md +0 -242
  7. extensions/sd-webui-controlnet/__pycache__/preload.cpython-310.pyc +0 -0
  8. extensions/sd-webui-controlnet/annotator/__pycache__/annotator_path.cpython-310.pyc +0 -0
  9. extensions/sd-webui-controlnet/annotator/__pycache__/util.cpython-310.pyc +0 -0
  10. extensions/sd-webui-controlnet/annotator/annotator_path.py +0 -22
  11. extensions/sd-webui-controlnet/annotator/binary/__init__.py +0 -14
  12. extensions/sd-webui-controlnet/annotator/canny/__init__.py +0 -5
  13. extensions/sd-webui-controlnet/annotator/canny/__pycache__/__init__.cpython-310.pyc +0 -0
  14. extensions/sd-webui-controlnet/annotator/clipvision/__init__.py +0 -127
  15. extensions/sd-webui-controlnet/annotator/clipvision/clip_vision_h_uc.data +0 -0
  16. extensions/sd-webui-controlnet/annotator/color/__init__.py +0 -20
  17. extensions/sd-webui-controlnet/annotator/downloads/leres/latest_net_G.pth +0 -3
  18. extensions/sd-webui-controlnet/annotator/downloads/leres/res101.pth +0 -3
  19. extensions/sd-webui-controlnet/annotator/downloads/midas/dpt_hybrid-midas-501f0c75.pt +0 -3
  20. extensions/sd-webui-controlnet/annotator/downloads/oneformer/150_16_swin_l_oneformer_coco_100ep.pth +0 -3
  21. extensions/sd-webui-controlnet/annotator/downloads/oneformer/250_16_swin_l_oneformer_ade20k_160k.pth +0 -3
  22. extensions/sd-webui-controlnet/annotator/downloads/uniformer/upernet_global_small.pth +0 -3
  23. extensions/sd-webui-controlnet/annotator/hed/__init__.py +0 -98
  24. extensions/sd-webui-controlnet/annotator/keypose/__init__.py +0 -212
  25. extensions/sd-webui-controlnet/annotator/keypose/faster_rcnn_r50_fpn_coco.py +0 -182
  26. extensions/sd-webui-controlnet/annotator/keypose/hrnet_w48_coco_256x192.py +0 -169
  27. extensions/sd-webui-controlnet/annotator/lama/__init__.py +0 -58
  28. extensions/sd-webui-controlnet/annotator/lama/config.yaml +0 -157
  29. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/__init__.py +0 -0
  30. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/__init__.py +0 -0
  31. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/data/__init__.py +0 -0
  32. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/data/masks.py +0 -332
  33. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/__init__.py +0 -0
  34. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/adversarial.py +0 -177
  35. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/constants.py +0 -152
  36. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/distance_weighting.py +0 -126
  37. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/feature_matching.py +0 -33
  38. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/perceptual.py +0 -113
  39. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/segmentation.py +0 -43
  40. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/style_loss.py +0 -155
  41. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/__init__.py +0 -31
  42. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/base.py +0 -80
  43. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/depthwise_sep_conv.py +0 -17
  44. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/fake_fakes.py +0 -47
  45. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/ffc.py +0 -485
  46. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/multidilated_conv.py +0 -98
  47. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/multiscale.py +0 -244
  48. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/pix2pixhd.py +0 -669
  49. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/spatial_transform.py +0 -49
  50. extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/squeeze_excitation.py +0 -20
extensions/sd-webui-controlnet/.github/ISSUE_TEMPLATE/bug_report.yml DELETED
@@ -1,91 +0,0 @@
1
- name: Bug Report
2
- description: Create a report
3
- title: "[Bug]: "
4
- labels: ["bug-report"]
5
-
6
- body:
7
- - type: checkboxes
8
- attributes:
9
- label: Is there an existing issue for this?
10
- description: Please search to see if an issue already exists for the bug you encountered, and that it hasn't been fixed in a recent build/commit.
11
- options:
12
- - label: I have searched the existing issues and checked the recent builds/commits of both this extension and the webui
13
- required: true
14
- - type: markdown
15
- attributes:
16
- value: |
17
- *Please fill this form with as much information as possible, don't forget to fill "What OS..." and "What browsers" and *provide screenshots if possible**
18
- - type: textarea
19
- id: what-did
20
- attributes:
21
- label: What happened?
22
- description: Tell us what happened in a very clear and simple way
23
- validations:
24
- required: true
25
- - type: textarea
26
- id: steps
27
- attributes:
28
- label: Steps to reproduce the problem
29
- description: Please provide us with precise step by step information on how to reproduce the bug
30
- value: |
31
- 1. Go to ....
32
- 2. Press ....
33
- 3. ...
34
- validations:
35
- required: true
36
- - type: textarea
37
- id: what-should
38
- attributes:
39
- label: What should have happened?
40
- description: Tell what you think the normal behavior should be
41
- validations:
42
- required: true
43
- - type: textarea
44
- id: commits
45
- attributes:
46
- label: Commit where the problem happens
47
- description: Which commit of the extension are you running on? Please include the commit of both the extension and the webui (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
48
- value: |
49
- webui:
50
- controlnet:
51
- validations:
52
- required: true
53
- - type: dropdown
54
- id: browsers
55
- attributes:
56
- label: What browsers do you use to access the UI ?
57
- multiple: true
58
- options:
59
- - Mozilla Firefox
60
- - Google Chrome
61
- - Brave
62
- - Apple Safari
63
- - Microsoft Edge
64
- - type: textarea
65
- id: cmdargs
66
- attributes:
67
- label: Command Line Arguments
68
- description: Are you using any launching parameters/command line arguments (modified webui-user .bat/.sh) ? If yes, please write them below. Write "No" otherwise.
69
- render: Shell
70
- validations:
71
- required: true
72
- - type: textarea
73
- id: extensions
74
- attributes:
75
- label: List of enabled extensions
76
- description: Please provide a full list of enabled extensions or screenshots of your "Extensions" tab.
77
- validations:
78
- required: true
79
- - type: textarea
80
- id: logs
81
- attributes:
82
- label: Console logs
83
- description: Please provide full cmd/terminal logs from the moment you started UI to the end of it, after your bug happened. If it's very long, provide a link to pastebin or similar service.
84
- render: Shell
85
- validations:
86
- required: true
87
- - type: textarea
88
- id: misc
89
- attributes:
90
- label: Additional information
91
- description: Please provide us with any relevant additional info or context.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/.github/ISSUE_TEMPLATE/config.yml DELETED
@@ -1 +0,0 @@
1
- blank_issues_enabled: true
 
 
extensions/sd-webui-controlnet/.github/workflows/tests.yml DELETED
@@ -1,37 +0,0 @@
1
- name: Run basic features tests on CPU
2
-
3
- on:
4
- - push
5
- - pull_request
6
-
7
- jobs:
8
- build:
9
- runs-on: ubuntu-latest
10
- steps:
11
- - name: Checkout Code
12
- uses: actions/checkout@v3
13
- with:
14
- repository: 'AUTOMATIC1111/stable-diffusion-webui'
15
- path: 'stable-diffusion-webui'
16
- ref: '5ab7f213bec2f816f9c5644becb32eb72c8ffb89'
17
-
18
- - name: Checkout Code
19
- uses: actions/checkout@v3
20
- with:
21
- repository: 'Mikubill/sd-webui-controlnet'
22
- path: 'stable-diffusion-webui/extensions/sd-webui-controlnet'
23
-
24
- - name: Set up Python 3.10
25
- uses: actions/setup-python@v4
26
- with:
27
- python-version: 3.10.6
28
- cache: pip
29
- cache-dependency-path: |
30
- **/requirements*txt
31
- stable-diffusion-webui/requirements*txt
32
-
33
- - run: |
34
- pip install torch torchvision
35
- curl -Lo stable-diffusion-webui/extensions/sd-webui-controlnet/models/control_canny-fp16.safetensors https://huggingface.co/webui/ControlNet-modules-safetensors/resolve/main/control_canny-fp16.safetensors
36
- cd stable-diffusion-webui && python launch.py --no-half --disable-opt-split-attention --use-cpu all --skip-torch-cuda-test --api --tests ./extensions/sd-webui-controlnet/tests
37
- rm -fr stable-diffusion-webui/extensions/sd-webui-controlnet/models/control_canny-fp16.safetensors
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/.gitignore DELETED
@@ -1,179 +0,0 @@
1
- # Byte-compiled / optimized / DLL files
2
- __pycache__/
3
- *.py[cod]
4
- *$py.class
5
-
6
- # C extensions
7
- *.so
8
-
9
- # Distribution / packaging
10
- .Python
11
- build/
12
- develop-eggs/
13
- dist/
14
- downloads/
15
- eggs/
16
- .eggs/
17
- lib/
18
- lib64/
19
- parts/
20
- sdist/
21
- var/
22
- wheels/
23
- share/python-wheels/
24
- *.egg-info/
25
- .installed.cfg
26
- *.egg
27
- MANIFEST
28
-
29
- # PyInstaller
30
- # Usually these files are written by a python script from a template
31
- # before PyInstaller builds the exe, so as to inject date/other infos into it.
32
- *.manifest
33
- *.spec
34
-
35
- # Installer logs
36
- pip-log.txt
37
- pip-delete-this-directory.txt
38
-
39
- # Unit test / coverage reports
40
- htmlcov/
41
- .tox/
42
- .nox/
43
- .coverage
44
- .coverage.*
45
- .cache
46
- nosetests.xml
47
- coverage.xml
48
- *.cover
49
- *.py,cover
50
- .hypothesis/
51
- .pytest_cache/
52
- cover/
53
-
54
- # Translations
55
- *.mo
56
- *.pot
57
-
58
- # Django stuff:
59
- *.log
60
- local_settings.py
61
- db.sqlite3
62
- db.sqlite3-journal
63
-
64
- # Flask stuff:
65
- instance/
66
- .webassets-cache
67
-
68
- # Scrapy stuff:
69
- .scrapy
70
-
71
- # Sphinx documentation
72
- docs/_build/
73
-
74
- # PyBuilder
75
- .pybuilder/
76
- target/
77
-
78
- # Jupyter Notebook
79
- .ipynb_checkpoints
80
-
81
- # IPython
82
- profile_default/
83
- ipython_config.py
84
-
85
- # pyenv
86
- # For a library or package, you might want to ignore these files since the code is
87
- # intended to run in multiple environments; otherwise, check them in:
88
- # .python-version
89
-
90
- # pipenv
91
- # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
92
- # However, in case of collaboration, if having platform-specific dependencies or dependencies
93
- # having no cross-platform support, pipenv may install dependencies that don't work, or not
94
- # install all needed dependencies.
95
- #Pipfile.lock
96
-
97
- # poetry
98
- # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
99
- # This is especially recommended for binary packages to ensure reproducibility, and is more
100
- # commonly ignored for libraries.
101
- # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102
- #poetry.lock
103
-
104
- # pdm
105
- # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
106
- #pdm.lock
107
- # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108
- # in version control.
109
- # https://pdm.fming.dev/#use-with-ide
110
- .pdm.toml
111
-
112
- # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
113
- __pypackages__/
114
-
115
- # Celery stuff
116
- celerybeat-schedule
117
- celerybeat.pid
118
-
119
- # SageMath parsed files
120
- *.sage.py
121
-
122
- # Environments
123
- .env
124
- .venv
125
- env/
126
- venv/
127
- ENV/
128
- env.bak/
129
- venv.bak/
130
-
131
- # Spyder project settings
132
- .spyderproject
133
- .spyproject
134
-
135
- # Rope project settings
136
- .ropeproject
137
-
138
- # mkdocs documentation
139
- /site
140
-
141
- # mypy
142
- .mypy_cache/
143
- .dmypy.json
144
- dmypy.json
145
-
146
- # Pyre type checker
147
- .pyre/
148
-
149
- # pytype static type analyzer
150
- .pytype/
151
-
152
- # Cython debug symbols
153
- cython_debug/
154
-
155
- # PyCharm
156
- # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157
- # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158
- # and can be added to the global gitignore or merged into this file. For a more nuclear
159
- # option (not recommended) you can uncomment the following to ignore the entire idea folder.
160
- #.idea
161
- *.pt
162
- *.pth
163
- *.ckpt
164
- *.bin
165
- *.safetensors
166
-
167
- # Editor setting metadata
168
- .idea/
169
- .vscode/
170
- detected_maps/
171
- annotator/downloads/
172
-
173
- # test results and expectations
174
- web_tests/results/
175
- web_tests/expectations/
176
- *_diff.png
177
-
178
- # Presets
179
- presets/
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/LICENSE DELETED
@@ -1,674 +0,0 @@
1
- GNU GENERAL PUBLIC LICENSE
2
- Version 3, 29 June 2007
3
-
4
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
5
- Everyone is permitted to copy and distribute verbatim copies
6
- of this license document, but changing it is not allowed.
7
-
8
- Preamble
9
-
10
- The GNU General Public License is a free, copyleft license for
11
- software and other kinds of works.
12
-
13
- The licenses for most software and other practical works are designed
14
- to take away your freedom to share and change the works. By contrast,
15
- the GNU General Public License is intended to guarantee your freedom to
16
- share and change all versions of a program--to make sure it remains free
17
- software for all its users. We, the Free Software Foundation, use the
18
- GNU General Public License for most of our software; it applies also to
19
- any other work released this way by its authors. You can apply it to
20
- your programs, too.
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
- To protect your rights, we need to prevent others from denying you
30
- these rights or asking you to surrender the rights. Therefore, you have
31
- certain responsibilities if you distribute copies of the software, or if
32
- you modify it: responsibilities to respect the freedom of others.
33
-
34
- For example, if you distribute copies of such a program, whether
35
- gratis or for a fee, you must pass on to the recipients the same
36
- freedoms that you received. You must make sure that they, too, receive
37
- or can get the source code. And you must show them these terms so they
38
- know their rights.
39
-
40
- Developers that use the GNU GPL protect your rights with two steps:
41
- (1) assert copyright on the software, and (2) offer you this License
42
- giving you legal permission to copy, distribute and/or modify it.
43
-
44
- For the developers' and authors' protection, the GPL clearly explains
45
- that there is no warranty for this free software. For both users' and
46
- authors' sake, the GPL requires that modified versions be marked as
47
- changed, so that their problems will not be attributed erroneously to
48
- authors of previous versions.
49
-
50
- Some devices are designed to deny users access to install or run
51
- modified versions of the software inside them, although the manufacturer
52
- can do so. This is fundamentally incompatible with the aim of
53
- protecting users' freedom to change the software. The systematic
54
- pattern of such abuse occurs in the area of products for individuals to
55
- use, which is precisely where it is most unacceptable. Therefore, we
56
- have designed this version of the GPL to prohibit the practice for those
57
- products. If such problems arise substantially in other domains, we
58
- stand ready to extend this provision to those domains in future versions
59
- of the GPL, as needed to protect the freedom of users.
60
-
61
- Finally, every program is threatened constantly by software patents.
62
- States should not allow patents to restrict development and use of
63
- software on general-purpose computers, but in those that do, we wish to
64
- avoid the special danger that patents applied to a free program could
65
- make it effectively proprietary. To prevent this, the GPL assures that
66
- patents cannot be used to render the program non-free.
67
-
68
- The precise terms and conditions for copying, distribution and
69
- modification follow.
70
-
71
- TERMS AND CONDITIONS
72
-
73
- 0. Definitions.
74
-
75
- "This License" refers to version 3 of the GNU General Public License.
76
-
77
- "Copyright" also means copyright-like laws that apply to other kinds of
78
- works, such as semiconductor masks.
79
-
80
- "The Program" refers to any copyrightable work licensed under this
81
- License. Each licensee is addressed as "you". "Licensees" and
82
- "recipients" may be individuals or organizations.
83
-
84
- To "modify" a work means to copy from or adapt all or part of the work
85
- in a fashion requiring copyright permission, other than the making of an
86
- exact copy. The resulting work is called a "modified version" of the
87
- earlier work or a work "based on" the earlier work.
88
-
89
- A "covered work" means either the unmodified Program or a work based
90
- on the Program.
91
-
92
- To "propagate" a work means to do anything with it that, without
93
- permission, would make you directly or secondarily liable for
94
- infringement under applicable copyright law, except executing it on a
95
- computer or modifying a private copy. Propagation includes copying,
96
- distribution (with or without modification), making available to the
97
- public, and in some countries other activities as well.
98
-
99
- To "convey" a work means any kind of propagation that enables other
100
- parties to make or receive copies. Mere interaction with a user through
101
- a computer network, with no transfer of a copy, is not conveying.
102
-
103
- An interactive user interface displays "Appropriate Legal Notices"
104
- to the extent that it includes a convenient and prominently visible
105
- feature that (1) displays an appropriate copyright notice, and (2)
106
- tells the user that there is no warranty for the work (except to the
107
- extent that warranties are provided), that licensees may convey the
108
- work under this License, and how to view a copy of this License. If
109
- the interface presents a list of user commands or options, such as a
110
- menu, a prominent item in the list meets this criterion.
111
-
112
- 1. Source Code.
113
-
114
- The "source code" for a work means the preferred form of the work
115
- for making modifications to it. "Object code" means any non-source
116
- form of a work.
117
-
118
- A "Standard Interface" means an interface that either is an official
119
- standard defined by a recognized standards body, or, in the case of
120
- interfaces specified for a particular programming language, one that
121
- is widely used among developers working in that language.
122
-
123
- The "System Libraries" of an executable work include anything, other
124
- than the work as a whole, that (a) is included in the normal form of
125
- packaging a Major Component, but which is not part of that Major
126
- Component, and (b) serves only to enable use of the work with that
127
- Major Component, or to implement a Standard Interface for which an
128
- implementation is available to the public in source code form. A
129
- "Major Component", in this context, means a major essential component
130
- (kernel, window system, and so on) of the specific operating system
131
- (if any) on which the executable work runs, or a compiler used to
132
- produce the work, or an object code interpreter used to run it.
133
-
134
- The "Corresponding Source" for a work in object code form means all
135
- the source code needed to generate, install, and (for an executable
136
- work) run the object code and to modify the work, including scripts to
137
- control those activities. However, it does not include the work's
138
- System Libraries, or general-purpose tools or generally available free
139
- programs which are used unmodified in performing those activities but
140
- which are not part of the work. For example, Corresponding Source
141
- includes interface definition files associated with source files for
142
- the work, and the source code for shared libraries and dynamically
143
- linked subprograms that the work is specifically designed to require,
144
- such as by intimate data communication or control flow between those
145
- subprograms and other parts of the work.
146
-
147
- The Corresponding Source need not include anything that users
148
- can regenerate automatically from other parts of the Corresponding
149
- Source.
150
-
151
- The Corresponding Source for a work in source code form is that
152
- same work.
153
-
154
- 2. Basic Permissions.
155
-
156
- All rights granted under this License are granted for the term of
157
- copyright on the Program, and are irrevocable provided the stated
158
- conditions are met. This License explicitly affirms your unlimited
159
- permission to run the unmodified Program. The output from running a
160
- covered work is covered by this License only if the output, given its
161
- content, constitutes a covered work. This License acknowledges your
162
- rights of fair use or other equivalent, as provided by copyright law.
163
-
164
- You may make, run and propagate covered works that you do not
165
- convey, without conditions so long as your license otherwise remains
166
- in force. You may convey covered works to others for the sole purpose
167
- of having them make modifications exclusively for you, or provide you
168
- with facilities for running those works, provided that you comply with
169
- the terms of this License in conveying all material for which you do
170
- not control copyright. Those thus making or running the covered works
171
- for you must do so exclusively on your behalf, under your direction
172
- and control, on terms that prohibit them from making any copies of
173
- your copyrighted material outside their relationship with you.
174
-
175
- Conveying under any other circumstances is permitted solely under
176
- the conditions stated below. Sublicensing is not allowed; section 10
177
- makes it unnecessary.
178
-
179
- 3. Protecting Users' Legal Rights From Anti-Circumvention Law.
180
-
181
- No covered work shall be deemed part of an effective technological
182
- measure under any applicable law fulfilling obligations under article
183
- 11 of the WIPO copyright treaty adopted on 20 December 1996, or
184
- similar laws prohibiting or restricting circumvention of such
185
- measures.
186
-
187
- When you convey a covered work, you waive any legal power to forbid
188
- circumvention of technological measures to the extent such circumvention
189
- is effected by exercising rights under this License with respect to
190
- the covered work, and you disclaim any intention to limit operation or
191
- modification of the work as a means of enforcing, against the work's
192
- users, your or third parties' legal rights to forbid circumvention of
193
- technological measures.
194
-
195
- 4. Conveying Verbatim Copies.
196
-
197
- You may convey verbatim copies of the Program's source code as you
198
- receive it, in any medium, provided that you conspicuously and
199
- appropriately publish on each copy an appropriate copyright notice;
200
- keep intact all notices stating that this License and any
201
- non-permissive terms added in accord with section 7 apply to the code;
202
- keep intact all notices of the absence of any warranty; and give all
203
- recipients a copy of this License along with the Program.
204
-
205
- You may charge any price or no price for each copy that you convey,
206
- and you may offer support or warranty protection for a fee.
207
-
208
- 5. Conveying Modified Source Versions.
209
-
210
- You may convey a work based on the Program, or the modifications to
211
- produce it from the Program, in the form of source code under the
212
- terms of section 4, provided that you also meet all of these conditions:
213
-
214
- a) The work must carry prominent notices stating that you modified
215
- it, and giving a relevant date.
216
-
217
- b) The work must carry prominent notices stating that it is
218
- released under this License and any conditions added under section
219
- 7. This requirement modifies the requirement in section 4 to
220
- "keep intact all notices".
221
-
222
- c) You must license the entire work, as a whole, under this
223
- License to anyone who comes into possession of a copy. This
224
- License will therefore apply, along with any applicable section 7
225
- additional terms, to the whole of the work, and all its parts,
226
- regardless of how they are packaged. This License gives no
227
- permission to license the work in any other way, but it does not
228
- invalidate such permission if you have separately received it.
229
-
230
- d) If the work has interactive user interfaces, each must display
231
- Appropriate Legal Notices; however, if the Program has interactive
232
- interfaces that do not display Appropriate Legal Notices, your
233
- work need not make them do so.
234
-
235
- A compilation of a covered work with other separate and independent
236
- works, which are not by their nature extensions of the covered work,
237
- and which are not combined with it such as to form a larger program,
238
- in or on a volume of a storage or distribution medium, is called an
239
- "aggregate" if the compilation and its resulting copyright are not
240
- used to limit the access or legal rights of the compilation's users
241
- beyond what the individual works permit. Inclusion of a covered work
242
- in an aggregate does not cause this License to apply to the other
243
- parts of the aggregate.
244
-
245
- 6. Conveying Non-Source Forms.
246
-
247
- You may convey a covered work in object code form under the terms
248
- of sections 4 and 5, provided that you also convey the
249
- machine-readable Corresponding Source under the terms of this License,
250
- in one of these ways:
251
-
252
- a) Convey the object code in, or embodied in, a physical product
253
- (including a physical distribution medium), accompanied by the
254
- Corresponding Source fixed on a durable physical medium
255
- customarily used for software interchange.
256
-
257
- b) Convey the object code in, or embodied in, a physical product
258
- (including a physical distribution medium), accompanied by a
259
- written offer, valid for at least three years and valid for as
260
- long as you offer spare parts or customer support for that product
261
- model, to give anyone who possesses the object code either (1) a
262
- copy of the Corresponding Source for all the software in the
263
- product that is covered by this License, on a durable physical
264
- medium customarily used for software interchange, for a price no
265
- more than your reasonable cost of physically performing this
266
- conveying of source, or (2) access to copy the
267
- Corresponding Source from a network server at no charge.
268
-
269
- c) Convey individual copies of the object code with a copy of the
270
- written offer to provide the Corresponding Source. This
271
- alternative is allowed only occasionally and noncommercially, and
272
- only if you received the object code with such an offer, in accord
273
- with subsection 6b.
274
-
275
- d) Convey the object code by offering access from a designated
276
- place (gratis or for a charge), and offer equivalent access to the
277
- Corresponding Source in the same way through the same place at no
278
- further charge. You need not require recipients to copy the
279
- Corresponding Source along with the object code. If the place to
280
- copy the object code is a network server, the Corresponding Source
281
- may be on a different server (operated by you or a third party)
282
- that supports equivalent copying facilities, provided you maintain
283
- clear directions next to the object code saying where to find the
284
- Corresponding Source. Regardless of what server hosts the
285
- Corresponding Source, you remain obligated to ensure that it is
286
- available for as long as needed to satisfy these requirements.
287
-
288
- e) Convey the object code using peer-to-peer transmission, provided
289
- you inform other peers where the object code and Corresponding
290
- Source of the work are being offered to the general public at no
291
- charge under subsection 6d.
292
-
293
- A separable portion of the object code, whose source code is excluded
294
- from the Corresponding Source as a System Library, need not be
295
- included in conveying the object code work.
296
-
297
- A "User Product" is either (1) a "consumer product", which means any
298
- tangible personal property which is normally used for personal, family,
299
- or household purposes, or (2) anything designed or sold for incorporation
300
- into a dwelling. In determining whether a product is a consumer product,
301
- doubtful cases shall be resolved in favor of coverage. For a particular
302
- product received by a particular user, "normally used" refers to a
303
- typical or common use of that class of product, regardless of the status
304
- of the particular user or of the way in which the particular user
305
- actually uses, or expects or is expected to use, the product. A product
306
- is a consumer product regardless of whether the product has substantial
307
- commercial, industrial or non-consumer uses, unless such uses represent
308
- the only significant mode of use of the product.
309
-
310
- "Installation Information" for a User Product means any methods,
311
- procedures, authorization keys, or other information required to install
312
- and execute modified versions of a covered work in that User Product from
313
- a modified version of its Corresponding Source. The information must
314
- suffice to ensure that the continued functioning of the modified object
315
- code is in no case prevented or interfered with solely because
316
- modification has been made.
317
-
318
- If you convey an object code work under this section in, or with, or
319
- specifically for use in, a User Product, and the conveying occurs as
320
- part of a transaction in which the right of possession and use of the
321
- User Product is transferred to the recipient in perpetuity or for a
322
- fixed term (regardless of how the transaction is characterized), the
323
- Corresponding Source conveyed under this section must be accompanied
324
- by the Installation Information. But this requirement does not apply
325
- if neither you nor any third party retains the ability to install
326
- modified object code on the User Product (for example, the work has
327
- been installed in ROM).
328
-
329
- The requirement to provide Installation Information does not include a
330
- requirement to continue to provide support service, warranty, or updates
331
- for a work that has been modified or installed by the recipient, or for
332
- the User Product in which it has been modified or installed. Access to a
333
- network may be denied when the modification itself materially and
334
- adversely affects the operation of the network or violates the rules and
335
- protocols for communication across the network.
336
-
337
- Corresponding Source conveyed, and Installation Information provided,
338
- in accord with this section must be in a format that is publicly
339
- documented (and with an implementation available to the public in
340
- source code form), and must require no special password or key for
341
- unpacking, reading or copying.
342
-
343
- 7. Additional Terms.
344
-
345
- "Additional permissions" are terms that supplement the terms of this
346
- License by making exceptions from one or more of its conditions.
347
- Additional permissions that are applicable to the entire Program shall
348
- be treated as though they were included in this License, to the extent
349
- that they are valid under applicable law. If additional permissions
350
- apply only to part of the Program, that part may be used separately
351
- under those permissions, but the entire Program remains governed by
352
- this License without regard to the additional permissions.
353
-
354
- When you convey a copy of a covered work, you may at your option
355
- remove any additional permissions from that copy, or from any part of
356
- it. (Additional permissions may be written to require their own
357
- removal in certain cases when you modify the work.) You may place
358
- additional permissions on material, added by you to a covered work,
359
- for which you have or can give appropriate copyright permission.
360
-
361
- Notwithstanding any other provision of this License, for material you
362
- add to a covered work, you may (if authorized by the copyright holders of
363
- that material) supplement the terms of this License with terms:
364
-
365
- a) Disclaiming warranty or limiting liability differently from the
366
- terms of sections 15 and 16 of this License; or
367
-
368
- b) Requiring preservation of specified reasonable legal notices or
369
- author attributions in that material or in the Appropriate Legal
370
- Notices displayed by works containing it; or
371
-
372
- c) Prohibiting misrepresentation of the origin of that material, or
373
- requiring that modified versions of such material be marked in
374
- reasonable ways as different from the original version; or
375
-
376
- d) Limiting the use for publicity purposes of names of licensors or
377
- authors of the material; or
378
-
379
- e) Declining to grant rights under trademark law for use of some
380
- trade names, trademarks, or service marks; or
381
-
382
- f) Requiring indemnification of licensors and authors of that
383
- material by anyone who conveys the material (or modified versions of
384
- it) with contractual assumptions of liability to the recipient, for
385
- any liability that these contractual assumptions directly impose on
386
- those licensors and authors.
387
-
388
- All other non-permissive additional terms are considered "further
389
- restrictions" within the meaning of section 10. If the Program as you
390
- received it, or any part of it, contains a notice stating that it is
391
- governed by this License along with a term that is a further
392
- restriction, you may remove that term. If a license document contains
393
- a further restriction but permits relicensing or conveying under this
394
- License, you may add to a covered work material governed by the terms
395
- of that license document, provided that the further restriction does
396
- not survive such relicensing or conveying.
397
-
398
- If you add terms to a covered work in accord with this section, you
399
- must place, in the relevant source files, a statement of the
400
- additional terms that apply to those files, or a notice indicating
401
- where to find the applicable terms.
402
-
403
- Additional terms, permissive or non-permissive, may be stated in the
404
- form of a separately written license, or stated as exceptions;
405
- the above requirements apply either way.
406
-
407
- 8. Termination.
408
-
409
- You may not propagate or modify a covered work except as expressly
410
- provided under this License. Any attempt otherwise to propagate or
411
- modify it is void, and will automatically terminate your rights under
412
- this License (including any patent licenses granted under the third
413
- paragraph of section 11).
414
-
415
- However, if you cease all violation of this License, then your
416
- license from a particular copyright holder is reinstated (a)
417
- provisionally, unless and until the copyright holder explicitly and
418
- finally terminates your license, and (b) permanently, if the copyright
419
- holder fails to notify you of the violation by some reasonable means
420
- prior to 60 days after the cessation.
421
-
422
- Moreover, your license from a particular copyright holder is
423
- reinstated permanently if the copyright holder notifies you of the
424
- violation by some reasonable means, this is the first time you have
425
- received notice of violation of this License (for any work) from that
426
- copyright holder, and you cure the violation prior to 30 days after
427
- your receipt of the notice.
428
-
429
- Termination of your rights under this section does not terminate the
430
- licenses of parties who have received copies or rights from you under
431
- this License. If your rights have been terminated and not permanently
432
- reinstated, you do not qualify to receive new licenses for the same
433
- material under section 10.
434
-
435
- 9. Acceptance Not Required for Having Copies.
436
-
437
- You are not required to accept this License in order to receive or
438
- run a copy of the Program. Ancillary propagation of a covered work
439
- occurring solely as a consequence of using peer-to-peer transmission
440
- to receive a copy likewise does not require acceptance. However,
441
- nothing other than this License grants you permission to propagate or
442
- modify any covered work. These actions infringe copyright if you do
443
- not accept this License. Therefore, by modifying or propagating a
444
- covered work, you indicate your acceptance of this License to do so.
445
-
446
- 10. Automatic Licensing of Downstream Recipients.
447
-
448
- Each time you convey a covered work, the recipient automatically
449
- receives a license from the original licensors, to run, modify and
450
- propagate that work, subject to this License. You are not responsible
451
- for enforcing compliance by third parties with this License.
452
-
453
- An "entity transaction" is a transaction transferring control of an
454
- organization, or substantially all assets of one, or subdividing an
455
- organization, or merging organizations. If propagation of a covered
456
- work results from an entity transaction, each party to that
457
- transaction who receives a copy of the work also receives whatever
458
- licenses to the work the party's predecessor in interest had or could
459
- give under the previous paragraph, plus a right to possession of the
460
- Corresponding Source of the work from the predecessor in interest, if
461
- the predecessor has it or can get it with reasonable efforts.
462
-
463
- You may not impose any further restrictions on the exercise of the
464
- rights granted or affirmed under this License. For example, you may
465
- not impose a license fee, royalty, or other charge for exercise of
466
- rights granted under this License, and you may not initiate litigation
467
- (including a cross-claim or counterclaim in a lawsuit) alleging that
468
- any patent claim is infringed by making, using, selling, offering for
469
- sale, or importing the Program or any portion of it.
470
-
471
- 11. Patents.
472
-
473
- A "contributor" is a copyright holder who authorizes use under this
474
- License of the Program or a work on which the Program is based. The
475
- work thus licensed is called the contributor's "contributor version".
476
-
477
- A contributor's "essential patent claims" are all patent claims
478
- owned or controlled by the contributor, whether already acquired or
479
- hereafter acquired, that would be infringed by some manner, permitted
480
- by this License, of making, using, or selling its contributor version,
481
- but do not include claims that would be infringed only as a
482
- consequence of further modification of the contributor version. For
483
- purposes of this definition, "control" includes the right to grant
484
- patent sublicenses in a manner consistent with the requirements of
485
- this License.
486
-
487
- Each contributor grants you a non-exclusive, worldwide, royalty-free
488
- patent license under the contributor's essential patent claims, to
489
- make, use, sell, offer for sale, import and otherwise run, modify and
490
- propagate the contents of its contributor version.
491
-
492
- In the following three paragraphs, a "patent license" is any express
493
- agreement or commitment, however denominated, not to enforce a patent
494
- (such as an express permission to practice a patent or covenant not to
495
- sue for patent infringement). To "grant" such a patent license to a
496
- party means to make such an agreement or commitment not to enforce a
497
- patent against the party.
498
-
499
- If you convey a covered work, knowingly relying on a patent license,
500
- and the Corresponding Source of the work is not available for anyone
501
- to copy, free of charge and under the terms of this License, through a
502
- publicly available network server or other readily accessible means,
503
- then you must either (1) cause the Corresponding Source to be so
504
- available, or (2) arrange to deprive yourself of the benefit of the
505
- patent license for this particular work, or (3) arrange, in a manner
506
- consistent with the requirements of this License, to extend the patent
507
- license to downstream recipients. "Knowingly relying" means you have
508
- actual knowledge that, but for the patent license, your conveying the
509
- covered work in a country, or your recipient's use of the covered work
510
- in a country, would infringe one or more identifiable patents in that
511
- country that you have reason to believe are valid.
512
-
513
- If, pursuant to or in connection with a single transaction or
514
- arrangement, you convey, or propagate by procuring conveyance of, a
515
- covered work, and grant a patent license to some of the parties
516
- receiving the covered work authorizing them to use, propagate, modify
517
- or convey a specific copy of the covered work, then the patent license
518
- you grant is automatically extended to all recipients of the covered
519
- work and works based on it.
520
-
521
- A patent license is "discriminatory" if it does not include within
522
- the scope of its coverage, prohibits the exercise of, or is
523
- conditioned on the non-exercise of one or more of the rights that are
524
- specifically granted under this License. You may not convey a covered
525
- work if you are a party to an arrangement with a third party that is
526
- in the business of distributing software, under which you make payment
527
- to the third party based on the extent of your activity of conveying
528
- the work, and under which the third party grants, to any of the
529
- parties who would receive the covered work from you, a discriminatory
530
- patent license (a) in connection with copies of the covered work
531
- conveyed by you (or copies made from those copies), or (b) primarily
532
- for and in connection with specific products or compilations that
533
- contain the covered work, unless you entered into that arrangement,
534
- or that patent license was granted, prior to 28 March 2007.
535
-
536
- Nothing in this License shall be construed as excluding or limiting
537
- any implied license or other defenses to infringement that may
538
- otherwise be available to you under applicable patent law.
539
-
540
- 12. No Surrender of Others' Freedom.
541
-
542
- If conditions are imposed on you (whether by court order, agreement or
543
- otherwise) that contradict the conditions of this License, they do not
544
- excuse you from the conditions of this License. If you cannot convey a
545
- covered work so as to satisfy simultaneously your obligations under this
546
- License and any other pertinent obligations, then as a consequence you may
547
- not convey it at all. For example, if you agree to terms that obligate you
548
- to collect a royalty for further conveying from those to whom you convey
549
- the Program, the only way you could satisfy both those terms and this
550
- License would be to refrain entirely from conveying the Program.
551
-
552
- 13. Use with the GNU Affero General Public License.
553
-
554
- Notwithstanding any other provision of this License, you have
555
- permission to link or combine any covered work with a work licensed
556
- under version 3 of the GNU Affero General Public License into a single
557
- combined work, and to convey the resulting work. The terms of this
558
- License will continue to apply to the part which is the covered work,
559
- but the special requirements of the GNU Affero General Public License,
560
- section 13, concerning interaction through a network will apply to the
561
- combination as such.
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 General Public License from time to time. Such new versions will
567
- 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 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 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 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 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 General Public License for more details.
646
-
647
- You should have received a copy of the GNU 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 the program does terminal interaction, make it output a short
653
- notice like this when it starts in an interactive mode:
654
-
655
- <program> Copyright (C) <year> <name of author>
656
- This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657
- This is free software, and you are welcome to redistribute it
658
- under certain conditions; type `show c' for details.
659
-
660
- The hypothetical commands `show w' and `show c' should show the appropriate
661
- parts of the General Public License. Of course, your program's commands
662
- might be different; for a GUI interface, you would use an "about box".
663
-
664
- You should also get your employer (if you work as a programmer) or school,
665
- if any, to sign a "copyright disclaimer" for the program, if necessary.
666
- For more information on this, and how to apply and follow the GNU GPL, see
667
- <https://www.gnu.org/licenses/>.
668
-
669
- The GNU General Public License does not permit incorporating your program
670
- into proprietary programs. If your program is a subroutine library, you
671
- may consider it more useful to permit linking proprietary applications with
672
- the library. If this is what you want to do, use the GNU Lesser General
673
- Public License instead of this License. But first, please read
674
- <https://www.gnu.org/licenses/why-not-lgpl.html>.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/README.md DELETED
@@ -1,242 +0,0 @@
1
- # ControlNet for Stable Diffusion WebUI
2
-
3
- The WebUI extension for ControlNet and other injection-based SD controls.
4
-
5
- ![image](https://github.com/Mikubill/sd-webui-controlnet/assets/19834515/00787fd1-1bc5-4b90-9a23-9683f8458b85)
6
-
7
- This extension is for AUTOMATIC1111's [Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui), allows the Web UI to add [ControlNet](https://github.com/lllyasviel/ControlNet) to the original Stable Diffusion model to generate images. The addition is on-the-fly, the merging is not required.
8
-
9
- # Installation
10
-
11
- 1. Open "Extensions" tab.
12
- 2. Open "Install from URL" tab in the tab.
13
- 3. Enter `https://github.com/Mikubill/sd-webui-controlnet.git` to "URL for extension's git repository".
14
- 4. Press "Install" button.
15
- 5. Wait for 5 seconds, and you will see the message "Installed into stable-diffusion-webui\extensions\sd-webui-controlnet. Use Installed tab to restart".
16
- 6. Go to "Installed" tab, click "Check for updates", and then click "Apply and restart UI". (The next time you can also use these buttons to update ControlNet.)
17
- 7. Completely restart A1111 webui including your terminal. (If you do not know what is a "terminal", you can reboot your computer to achieve the same effect.)
18
- 8. Download models (see below).
19
- 9. After you put models in the correct folder, you may need to refresh to see the models. The refresh button is right to your "Model" dropdown.
20
-
21
- # Download Models
22
-
23
- Right now all the 14 models of ControlNet 1.1 are in the beta test.
24
-
25
- Download the models from ControlNet 1.1: https://huggingface.co/lllyasviel/ControlNet-v1-1/tree/main
26
-
27
- You need to download model files ending with ".pth" .
28
-
29
- Put models in your "stable-diffusion-webui\extensions\sd-webui-controlnet\models". You only need to download "pth" files.
30
-
31
- Do not right-click the filenames in HuggingFace website to download. Some users right-clicked those HuggingFace HTML websites and saved those HTML pages as PTH/YAML files. They are not downloading correct files. Instead, please click the small download arrow “↓” icon in HuggingFace to download.
32
-
33
- # Download Models for SDXL
34
-
35
- See instructions [here](https://github.com/Mikubill/sd-webui-controlnet/discussions/2039).
36
-
37
- # Features in ControlNet 1.1
38
-
39
- ### Perfect Support for All ControlNet 1.0/1.1 and T2I Adapter Models.
40
-
41
- Now we have perfect support all available models and preprocessors, including perfect support for T2I style adapter and ControlNet 1.1 Shuffle. (Make sure that your YAML file names and model file names are same, see also YAML files in "stable-diffusion-webui\extensions\sd-webui-controlnet\models".)
42
-
43
- ### Perfect Support for A1111 High-Res. Fix
44
-
45
- Now if you turn on High-Res Fix in A1111, each controlnet will output two different control images: a small one and a large one. The small one is for your basic generating, and the big one is for your High-Res Fix generating. The two control images are computed by a smart algorithm called "super high-quality control image resampling". This is turned on by default, and you do not need to change any setting.
46
-
47
- ### Perfect Support for All A1111 Img2Img or Inpaint Settings and All Mask Types
48
-
49
- Now ControlNet is extensively tested with A1111's different types of masks, including "Inpaint masked"/"Inpaint not masked", and "Whole picture"/"Only masked", and "Only masked padding"&"Mask blur". The resizing perfectly matches A1111's "Just resize"/"Crop and resize"/"Resize and fill". This means you can use ControlNet in nearly everywhere in your A1111 UI without difficulty!
50
-
51
- ### The New "Pixel-Perfect" Mode
52
-
53
- Now if you turn on pixel-perfect mode, you do not need to set preprocessor (annotator) resolutions manually. The ControlNet will automatically compute the best annotator resolution for you so that each pixel perfectly matches Stable Diffusion.
54
-
55
- ### User-Friendly GUI and Preprocessor Preview
56
-
57
- We reorganized some previously confusing UI like "canvas width/height for new canvas" and it is in the 📝 button now. Now the preview GUI is controlled by the "allow preview" option and the trigger button 💥. The preview image size is better than before, and you do not need to scroll up and down - your a1111 GUI will not be messed up anymore!
58
-
59
- ### Support for Almost All Upscaling Scripts
60
-
61
- Now ControlNet 1.1 can support almost all Upscaling/Tile methods. ControlNet 1.1 support the script "Ultimate SD upscale" and almost all other tile-based extensions. Please do not confuse ["Ultimate SD upscale"](https://github.com/Coyote-A/ultimate-upscale-for-automatic1111) with "SD upscale" - they are different scripts. Note that the most recommended upscaling method is ["Tiled VAE/Diffusion"](https://github.com/pkuliyi2015/multidiffusion-upscaler-for-automatic1111) but we test as many methods/extensions as possible. Note that "SD upscale" is supported since 1.1.117, and if you use it, you need to leave all ControlNet images as blank (We do not recommend "SD upscale" since it is somewhat buggy and cannot be maintained - use the "Ultimate SD upscale" instead).
62
-
63
- ### More Control Modes (previously called Guess Mode)
64
-
65
- We have fixed many bugs in previous 1.0’s Guess Mode and now it is called Control Mode
66
-
67
- ![image](https://user-images.githubusercontent.com/19834515/236641759-6c44ddf6-c7ad-4bda-92be-e90a52911d75.png)
68
-
69
- Now you can control which aspect is more important (your prompt or your ControlNet):
70
-
71
- * "Balanced": ControlNet on both sides of CFG scale, same as turning off "Guess Mode" in ControlNet 1.0
72
-
73
- * "My prompt is more important": ControlNet on both sides of CFG scale, with progressively reduced SD U-Net injections (layer_weight*=0.825**I, where 0<=I <13, and the 13 means ControlNet injected SD 13 times). In this way, you can make sure that your prompts are perfectly displayed in your generated images.
74
-
75
- * "ControlNet is more important": ControlNet only on the Conditional Side of CFG scale (the cond in A1111's batch-cond-uncond). This means the ControlNet will be X times stronger if your cfg-scale is X. For example, if your cfg-scale is 7, then ControlNet is 7 times stronger. Note that here the X times stronger is different from "Control Weights" since your weights are not modified. This "stronger" effect usually has less artifact and give ControlNet more room to guess what is missing from your prompts (and in the previous 1.0, it is called "Guess Mode").
76
-
77
- <table width="100%">
78
- <tr>
79
- <td width="25%" style="text-align: center">Input (depth+canny+hed)</td>
80
- <td width="25%" style="text-align: center">"Balanced"</td>
81
- <td width="25%" style="text-align: center">"My prompt is more important"</td>
82
- <td width="25%" style="text-align: center">"ControlNet is more important"</td>
83
- </tr>
84
- <tr>
85
- <td width="25%" style="text-align: center"><img src="samples/cm1.png"></td>
86
- <td width="25%" style="text-align: center"><img src="samples/cm2.png"></td>
87
- <td width="25%" style="text-align: center"><img src="samples/cm3.png"></td>
88
- <td width="25%" style="text-align: center"><img src="samples/cm4.png"></td>
89
- </tr>
90
- </table>
91
-
92
- ### Reference-Only Control
93
-
94
- Now we have a `reference-only` preprocessor that does not require any control models. It can guide the diffusion directly using images as references.
95
-
96
- (Prompt "a dog running on grassland, best quality, ...")
97
-
98
- ![image](samples/ref.png)
99
-
100
- This method is similar to inpaint-based reference but it does not make your image disordered.
101
-
102
- Many professional A1111 users know a trick to diffuse image with references by inpaint. For example, if you have a 512x512 image of a dog, and want to generate another 512x512 image with the same dog, some users will connect the 512x512 dog image and a 512x512 blank image into a 1024x512 image, send to inpaint, and mask out the blank 512x512 part to diffuse a dog with similar appearance. However, that method is usually not very satisfying since images are connected and many distortions will appear.
103
-
104
- This `reference-only` ControlNet can directly link the attention layers of your SD to any independent images, so that your SD will read arbitary images for reference. You need at least ControlNet 1.1.153 to use it.
105
-
106
- To use, just select `reference-only` as preprocessor and put an image. Your SD will just use the image as reference.
107
-
108
- *Note that this method is as "non-opinioned" as possible. It only contains very basic connection codes, without any personal preferences, to connect the attention layers with your reference images. However, even if we tried best to not include any opinioned codes, we still need to write some subjective implementations to deal with weighting, cfg-scale, etc - tech report is on the way.*
109
-
110
- More examples [here](https://github.com/Mikubill/sd-webui-controlnet/discussions/1236).
111
-
112
- # Technical Documents
113
-
114
- See also the documents of ControlNet 1.1:
115
-
116
- https://github.com/lllyasviel/ControlNet-v1-1-nightly#model-specification
117
-
118
- # Default Setting
119
-
120
- This is my setting. If you run into any problem, you can use this setting as a sanity check
121
-
122
- ![image](https://user-images.githubusercontent.com/19834515/235620638-17937171-8ac1-45bc-a3cb-3aebf605b4ef.png)
123
-
124
- # Use Previous Models
125
-
126
- ### Use ControlNet 1.0 Models
127
-
128
- https://huggingface.co/lllyasviel/ControlNet/tree/main/models
129
-
130
- You can still use all previous models in the previous ControlNet 1.0. Now, the previous "depth" is now called "depth_midas", the previous "normal" is called "normal_midas", the previous "hed" is called "softedge_hed". And starting from 1.1, all line maps, edge maps, lineart maps, boundary maps will have black background and white lines.
131
-
132
- ### Use T2I-Adapter Models
133
-
134
- (From TencentARC/T2I-Adapter)
135
-
136
- To use T2I-Adapter models:
137
-
138
- 1. Download files from https://huggingface.co/TencentARC/T2I-Adapter/tree/main/models
139
- 2. Put them in "stable-diffusion-webui\extensions\sd-webui-controlnet\models".
140
- 3. Make sure that the file names of pth files and yaml files are consistent.
141
-
142
- *Note that "CoAdapter" is not implemented yet.*
143
-
144
- # Gallery
145
-
146
- The below results are from ControlNet 1.0.
147
-
148
- | Source | Input | Output |
149
- |:-------------------------:|:-------------------------:|:-------------------------:|
150
- | (no preprocessor) | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/bal-source.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/bal-gen.png?raw=true"> |
151
- | (no preprocessor) | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/dog_rel.jpg?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/dog_rel.png?raw=true"> |
152
- |<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/mahiro_input.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/mahiro_canny.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/mahiro-out.png?raw=true"> |
153
- |<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/evt_source.jpg?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/evt_hed.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/evt_gen.png?raw=true"> |
154
- |<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/an-source.jpg?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/an-pose.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/an-gen.png?raw=true"> |
155
- |<img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/sk-b-src.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/sk-b-dep.png?raw=true"> | <img width="256" alt="" src="https://github.com/Mikubill/sd-webui-controlnet/blob/main/samples/sk-b-out.png?raw=true"> |
156
-
157
- The below examples are from T2I-Adapter.
158
-
159
- From `t2iadapter_color_sd14v1.pth` :
160
-
161
- | Source | Input | Output |
162
- |:-------------------------:|:-------------------------:|:-------------------------:|
163
- | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947416-ec9e52a4-a1d0-48d8-bb81-736bf636145e.jpeg"> | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947435-1164e7d8-d857-42f9-ab10-2d4a4b25f33a.png"> | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947557-5520d5f8-88b4-474d-a576-5c9cd3acac3a.png"> |
164
-
165
- From `t2iadapter_style_sd14v1.pth` :
166
-
167
- | Source | Input | Output |
168
- |:-------------------------:|:-------------------------:|:-------------------------:|
169
- | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222947416-ec9e52a4-a1d0-48d8-bb81-736bf636145e.jpeg"> | (clip, non-image) | <img width="256" alt="" src="https://user-images.githubusercontent.com/31246794/222965711-7b884c9e-7095-45cb-a91c-e50d296ba3a2.png"> |
170
-
171
- # Minimum Requirements
172
-
173
- * (Windows) (NVIDIA: Ampere) 4gb - with `--xformers` enabled, and `Low VRAM` mode ticked in the UI, goes up to 768x832
174
-
175
- # Multi-ControlNet
176
-
177
- This option allows multiple ControlNet inputs for a single generation. To enable this option, change `Multi ControlNet: Max models amount (requires restart)` in the settings. Note that you will need to restart the WebUI for changes to take effect.
178
-
179
- <table width="100%">
180
- <tr>
181
- <td width="25%" style="text-align: center">Source A</td>
182
- <td width="25%" style="text-align: center">Source B</td>
183
- <td width="25%" style="text-align: center">Output</td>
184
- </tr>
185
- <tr>
186
- <td width="25%" style="text-align: center"><img src="https://user-images.githubusercontent.com/31246794/220448620-cd3ede92-8d3f-43d5-b771-32dd8417618f.png"></td>
187
- <td width="25%" style="text-align: center"><img src="https://user-images.githubusercontent.com/31246794/220448619-beed9bdb-f6bb-41c2-a7df-aa3ef1f653c5.png"></td>
188
- <td width="25%" style="text-align: center"><img src="https://user-images.githubusercontent.com/31246794/220448613-c99a9e04-0450-40fd-bc73-a9122cefaa2c.png"></td>
189
- </tr>
190
- </table>
191
-
192
- # Control Weight/Start/End
193
-
194
- Weight is the weight of the controlnet "influence". It's analogous to prompt attention/emphasis. E.g. (myprompt: 1.2). Technically, it's the factor by which to multiply the ControlNet outputs before merging them with original SD Unet.
195
-
196
- Guidance Start/End is the percentage of total steps the controlnet applies (guidance strength = guidance end). It's analogous to prompt editing/shifting. E.g. \[myprompt::0.8\] (It applies from the beginning until 80% of total steps)
197
-
198
- # Batch Mode
199
-
200
- Put any unit into batch mode to activate batch mode for all units. Specify a batch directory for each unit, or use the new textbox in the img2img batch tab as a fallback. Although the textbox is located in the img2img batch tab, you can use it to generate images in the txt2img tab as well.
201
-
202
- Note that this feature is only available in the gradio user interface. Call the APIs as many times as you want for custom batch scheduling.
203
-
204
- # API and Script Access
205
-
206
- This extension can accept txt2img or img2img tasks via API or external extension call. Note that you may need to enable `Allow other scripts to control this extension` in settings for external calls.
207
-
208
- To use the API: start WebUI with argument `--api` and go to `http://webui-address/docs` for documents or checkout [examples](https://github.com/Mikubill/sd-webui-controlnet/blob/main/example/api_txt2img.ipynb).
209
-
210
- To use external call: Checkout [Wiki](https://github.com/Mikubill/sd-webui-controlnet/wiki/API)
211
-
212
- # Command Line Arguments
213
-
214
- This extension adds these command line arguments to the webui:
215
-
216
- ```
217
- --controlnet-dir <path to directory with controlnet models> ADD a controlnet models directory
218
- --controlnet-annotator-models-path <path to directory with annotator model directories> SET the directory for annotator models
219
- --no-half-controlnet load controlnet models in full precision
220
- --controlnet-preprocessor-cache-size Cache size for controlnet preprocessor results
221
- --controlnet-loglevel Log level for the controlnet extension
222
- ```
223
-
224
- # MacOS Support
225
-
226
- Tested with pytorch nightly: https://github.com/Mikubill/sd-webui-controlnet/pull/143#issuecomment-1435058285
227
-
228
- To use this extension with mps and normal pytorch, currently you may need to start WebUI with `--no-half`.
229
-
230
- # Archive of Deprecated Versions
231
-
232
- The previous version (sd-webui-controlnet 1.0) is archived in
233
-
234
- https://github.com/lllyasviel/webui-controlnet-v1-archived
235
-
236
- Using this version is not a temporary stop of updates. You will stop all updates forever.
237
-
238
- Please consider this version if you work with professional studios that requires 100% reproducing of all previous results pixel by pixel.
239
-
240
- # Thanks
241
-
242
- This implementation is inspired by kohya-ss/sd-webui-additional-networks
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/__pycache__/preload.cpython-310.pyc DELETED
Binary file (1.03 kB)
 
extensions/sd-webui-controlnet/annotator/__pycache__/annotator_path.cpython-310.pyc DELETED
Binary file (741 Bytes)
 
extensions/sd-webui-controlnet/annotator/__pycache__/util.cpython-310.pyc DELETED
Binary file (2.19 kB)
 
extensions/sd-webui-controlnet/annotator/annotator_path.py DELETED
@@ -1,22 +0,0 @@
1
- import os
2
- from modules import shared
3
-
4
- models_path = shared.opts.data.get('control_net_modules_path', None)
5
- if not models_path:
6
- models_path = getattr(shared.cmd_opts, 'controlnet_annotator_models_path', None)
7
- if not models_path:
8
- models_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'downloads')
9
-
10
- if not os.path.isabs(models_path):
11
- models_path = os.path.join(shared.data_path, models_path)
12
-
13
- clip_vision_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision')
14
- # clip vision is always inside controlnet "extensions\sd-webui-controlnet"
15
- # and any problem can be solved by removing controlnet and reinstall
16
-
17
- models_path = os.path.realpath(models_path)
18
- os.makedirs(models_path, exist_ok=True)
19
- print(f'ControlNet preprocessor location: {models_path}')
20
- # Make sure that the default location is inside controlnet "extensions\sd-webui-controlnet"
21
- # so that any problem can be solved by removing controlnet and reinstall
22
- # if users do not change configs on their own (otherwise users will know what is wrong)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/binary/__init__.py DELETED
@@ -1,14 +0,0 @@
1
- import cv2
2
-
3
-
4
- def apply_binary(img, bin_threshold):
5
- img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
6
-
7
- if bin_threshold == 0 or bin_threshold == 255:
8
- # Otsu's threshold
9
- otsu_threshold, img_bin = cv2.threshold(img_gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
10
- print("Otsu threshold:", otsu_threshold)
11
- else:
12
- _, img_bin = cv2.threshold(img_gray, bin_threshold, 255, cv2.THRESH_BINARY_INV)
13
-
14
- return cv2.cvtColor(img_bin, cv2.COLOR_GRAY2RGB)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/canny/__init__.py DELETED
@@ -1,5 +0,0 @@
1
- import cv2
2
-
3
-
4
- def apply_canny(img, low_threshold, high_threshold):
5
- return cv2.Canny(img, low_threshold, high_threshold)
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/canny/__pycache__/__init__.cpython-310.pyc DELETED
Binary file (389 Bytes)
 
extensions/sd-webui-controlnet/annotator/clipvision/__init__.py DELETED
@@ -1,127 +0,0 @@
1
- import os
2
- import torch
3
-
4
- from modules import devices
5
- from modules.modelloader import load_file_from_url
6
- from annotator.annotator_path import models_path
7
- from transformers import CLIPVisionModelWithProjection, CLIPVisionConfig, CLIPImageProcessor
8
-
9
-
10
- config_clip_g = {
11
- "attention_dropout": 0.0,
12
- "dropout": 0.0,
13
- "hidden_act": "gelu",
14
- "hidden_size": 1664,
15
- "image_size": 224,
16
- "initializer_factor": 1.0,
17
- "initializer_range": 0.02,
18
- "intermediate_size": 8192,
19
- "layer_norm_eps": 1e-05,
20
- "model_type": "clip_vision_model",
21
- "num_attention_heads": 16,
22
- "num_channels": 3,
23
- "num_hidden_layers": 48,
24
- "patch_size": 14,
25
- "projection_dim": 1280,
26
- "torch_dtype": "float32"
27
- }
28
-
29
- config_clip_h = {
30
- "attention_dropout": 0.0,
31
- "dropout": 0.0,
32
- "hidden_act": "gelu",
33
- "hidden_size": 1280,
34
- "image_size": 224,
35
- "initializer_factor": 1.0,
36
- "initializer_range": 0.02,
37
- "intermediate_size": 5120,
38
- "layer_norm_eps": 1e-05,
39
- "model_type": "clip_vision_model",
40
- "num_attention_heads": 16,
41
- "num_channels": 3,
42
- "num_hidden_layers": 32,
43
- "patch_size": 14,
44
- "projection_dim": 1024,
45
- "torch_dtype": "float32"
46
- }
47
-
48
- config_clip_vitl = {
49
- "attention_dropout": 0.0,
50
- "dropout": 0.0,
51
- "hidden_act": "quick_gelu",
52
- "hidden_size": 1024,
53
- "image_size": 224,
54
- "initializer_factor": 1.0,
55
- "initializer_range": 0.02,
56
- "intermediate_size": 4096,
57
- "layer_norm_eps": 1e-05,
58
- "model_type": "clip_vision_model",
59
- "num_attention_heads": 16,
60
- "num_channels": 3,
61
- "num_hidden_layers": 24,
62
- "patch_size": 14,
63
- "projection_dim": 768,
64
- "torch_dtype": "float32"
65
- }
66
-
67
- configs = {
68
- 'clip_g': config_clip_g,
69
- 'clip_h': config_clip_h,
70
- 'clip_vitl': config_clip_vitl,
71
- }
72
-
73
- downloads = {
74
- 'clip_vitl': 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/pytorch_model.bin',
75
- 'clip_g': 'https://huggingface.co/lllyasviel/Annotators/resolve/main/clip_g.pth',
76
- 'clip_h': 'https://huggingface.co/h94/IP-Adapter/resolve/main/models/image_encoder/pytorch_model.bin'
77
- }
78
-
79
-
80
- clip_vision_h_uc = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'clip_vision_h_uc.data')
81
- clip_vision_h_uc = torch.load(clip_vision_h_uc)['uc']
82
-
83
-
84
- class ClipVisionDetector:
85
- def __init__(self, config):
86
- assert config in downloads
87
- self.download_link = downloads[config]
88
- self.model_path = os.path.join(models_path, 'clip_vision')
89
- self.file_name = config + '.pth'
90
- self.config = configs[config]
91
- self.device = devices.get_device_for("controlnet")
92
- os.makedirs(self.model_path, exist_ok=True)
93
- file_path = os.path.join(self.model_path, self.file_name)
94
- if not os.path.exists(file_path):
95
- load_file_from_url(url=self.download_link, model_dir=self.model_path, file_name=self.file_name)
96
- config = CLIPVisionConfig(**self.config)
97
- self.model = CLIPVisionModelWithProjection(config)
98
- self.processor = CLIPImageProcessor(crop_size=224,
99
- do_center_crop=True,
100
- do_convert_rgb=True,
101
- do_normalize=True,
102
- do_resize=True,
103
- image_mean=[0.48145466, 0.4578275, 0.40821073],
104
- image_std=[0.26862954, 0.26130258, 0.27577711],
105
- resample=3,
106
- size=224)
107
-
108
- sd = torch.load(file_path, map_location=torch.device('cpu'))
109
- self.model.load_state_dict(sd, strict=False)
110
- del sd
111
-
112
- self.model.eval()
113
- self.model.cpu()
114
-
115
- def unload_model(self):
116
- if self.model is not None:
117
- self.model.to('meta')
118
-
119
- def __call__(self, input_image):
120
- with torch.no_grad():
121
- clip_vision_model = self.model.cpu()
122
- feat = self.processor(images=input_image, return_tensors="pt")
123
- feat['pixel_values'] = feat['pixel_values'].cpu()
124
- result = clip_vision_model(**feat, output_hidden_states=True)
125
- result['hidden_states'] = [v.to(devices.get_device_for("controlnet")) for v in result['hidden_states']]
126
- result = {k: v.to(devices.get_device_for("controlnet")) if isinstance(v, torch.Tensor) else v for k, v in result.items()}
127
- return result
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/clipvision/clip_vision_h_uc.data DELETED
Binary file (659 kB)
 
extensions/sd-webui-controlnet/annotator/color/__init__.py DELETED
@@ -1,20 +0,0 @@
1
- import cv2
2
-
3
- def cv2_resize_shortest_edge(image, size):
4
- h, w = image.shape[:2]
5
- if h < w:
6
- new_h = size
7
- new_w = int(round(w / h * size))
8
- else:
9
- new_w = size
10
- new_h = int(round(h / w * size))
11
- resized_image = cv2.resize(image, (new_w, new_h), interpolation=cv2.INTER_AREA)
12
- return resized_image
13
-
14
- def apply_color(img, res=512):
15
- img = cv2_resize_shortest_edge(img, res)
16
- h, w = img.shape[:2]
17
-
18
- input_img_color = cv2.resize(img, (w//64, h//64), interpolation=cv2.INTER_CUBIC)
19
- input_img_color = cv2.resize(input_img_color, (w, h), interpolation=cv2.INTER_NEAREST)
20
- return input_img_color
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/downloads/leres/latest_net_G.pth DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:50ec735d74ed6499562d898f41b49343e521808b8dae589aa3c2f5c9ac9f7462
3
- size 318268048
 
 
 
 
extensions/sd-webui-controlnet/annotator/downloads/leres/res101.pth DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:1d696b2ef3e8336b057d0c15bc82d2fecef821bfebe5ef9d7671a5ec5dde520b
3
- size 530760553
 
 
 
 
extensions/sd-webui-controlnet/annotator/downloads/midas/dpt_hybrid-midas-501f0c75.pt DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:501f0c75b3bca7daec6b3682c5054c09b366765aef6fa3a09d03a5cb4b230853
3
- size 492757791
 
 
 
 
extensions/sd-webui-controlnet/annotator/downloads/oneformer/150_16_swin_l_oneformer_coco_100ep.pth DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:0684dfc39720c772b81d43639c3ae1896b5c15aa9ee9a76f4c593b19dfa33855
3
- size 949602739
 
 
 
 
extensions/sd-webui-controlnet/annotator/downloads/oneformer/250_16_swin_l_oneformer_ade20k_160k.pth DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:f7ac095c28ddea4715e854a587eaee24327c624cbbdb17095bc9903c51930b16
3
- size 949729587
 
 
 
 
extensions/sd-webui-controlnet/annotator/downloads/uniformer/upernet_global_small.pth DELETED
@@ -1,3 +0,0 @@
1
- version https://git-lfs.github.com/spec/v1
2
- oid sha256:bebfa1264c10381e389d8065056baaadbdadee8ddc6e36770d1ec339dc84d970
3
- size 206313115
 
 
 
 
extensions/sd-webui-controlnet/annotator/hed/__init__.py DELETED
@@ -1,98 +0,0 @@
1
- # This is an improved version and model of HED edge detection with Apache License, Version 2.0.
2
- # Please use this implementation in your products
3
- # This implementation may produce slightly different results from Saining Xie's official implementations,
4
- # but it generates smoother edges and is more suitable for ControlNet as well as other image-to-image translations.
5
- # Different from official models and other implementations, this is an RGB-input model (rather than BGR)
6
- # and in this way it works better for gradio's RGB protocol
7
-
8
- import os
9
- import cv2
10
- import torch
11
- import numpy as np
12
-
13
- from einops import rearrange
14
- import os
15
- from modules import devices
16
- from annotator.annotator_path import models_path
17
- from annotator.util import safe_step, nms
18
-
19
-
20
- class DoubleConvBlock(torch.nn.Module):
21
- def __init__(self, input_channel, output_channel, layer_number):
22
- super().__init__()
23
- self.convs = torch.nn.Sequential()
24
- self.convs.append(torch.nn.Conv2d(in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
25
- for i in range(1, layer_number):
26
- self.convs.append(torch.nn.Conv2d(in_channels=output_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1))
27
- self.projection = torch.nn.Conv2d(in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0)
28
-
29
- def __call__(self, x, down_sampling=False):
30
- h = x
31
- if down_sampling:
32
- h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2))
33
- for conv in self.convs:
34
- h = conv(h)
35
- h = torch.nn.functional.relu(h)
36
- return h, self.projection(h)
37
-
38
-
39
- class ControlNetHED_Apache2(torch.nn.Module):
40
- def __init__(self):
41
- super().__init__()
42
- self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1)))
43
- self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2)
44
- self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2)
45
- self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3)
46
- self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3)
47
- self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3)
48
-
49
- def __call__(self, x):
50
- h = x - self.norm
51
- h, projection1 = self.block1(h)
52
- h, projection2 = self.block2(h, down_sampling=True)
53
- h, projection3 = self.block3(h, down_sampling=True)
54
- h, projection4 = self.block4(h, down_sampling=True)
55
- h, projection5 = self.block5(h, down_sampling=True)
56
- return projection1, projection2, projection3, projection4, projection5
57
-
58
-
59
- netNetwork = None
60
- remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetHED.pth"
61
- modeldir = os.path.join(models_path, "hed")
62
- old_modeldir = os.path.dirname(os.path.realpath(__file__))
63
-
64
-
65
- def apply_hed(input_image, is_safe=False):
66
- global netNetwork
67
- if netNetwork is None:
68
- modelpath = os.path.join(modeldir, "ControlNetHED.pth")
69
- old_modelpath = os.path.join(old_modeldir, "ControlNetHED.pth")
70
- if os.path.exists(old_modelpath):
71
- modelpath = old_modelpath
72
- elif not os.path.exists(modelpath):
73
- from basicsr.utils.download_util import load_file_from_url
74
- load_file_from_url(remote_model_path, model_dir=modeldir)
75
- netNetwork = ControlNetHED_Apache2().to(devices.get_device_for("controlnet"))
76
- netNetwork.load_state_dict(torch.load(modelpath, map_location='cpu'))
77
- netNetwork.to(devices.get_device_for("controlnet")).float().eval()
78
-
79
- assert input_image.ndim == 3
80
- H, W, C = input_image.shape
81
- with torch.no_grad():
82
- image_hed = torch.from_numpy(input_image.copy()).float().to(devices.get_device_for("controlnet"))
83
- image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
84
- edges = netNetwork(image_hed)
85
- edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
86
- edges = [cv2.resize(e, (W, H), interpolation=cv2.INTER_LINEAR) for e in edges]
87
- edges = np.stack(edges, axis=2)
88
- edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
89
- if is_safe:
90
- edge = safe_step(edge)
91
- edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
92
- return edge
93
-
94
-
95
- def unload_hed_model():
96
- global netNetwork
97
- if netNetwork is not None:
98
- netNetwork.cpu()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/keypose/__init__.py DELETED
@@ -1,212 +0,0 @@
1
- import numpy as np
2
- import cv2
3
- import torch
4
-
5
- import os
6
- from modules import devices
7
- from annotator.annotator_path import models_path
8
-
9
- import mmcv
10
- from mmdet.apis import inference_detector, init_detector
11
- from mmpose.apis import inference_top_down_pose_model
12
- from mmpose.apis import init_pose_model, process_mmdet_results, vis_pose_result
13
-
14
-
15
- def preprocessing(image, device):
16
- # Resize
17
- scale = 640 / max(image.shape[:2])
18
- image = cv2.resize(image, dsize=None, fx=scale, fy=scale)
19
- raw_image = image.astype(np.uint8)
20
-
21
- # Subtract mean values
22
- image = image.astype(np.float32)
23
- image -= np.array(
24
- [
25
- float(104.008),
26
- float(116.669),
27
- float(122.675),
28
- ]
29
- )
30
-
31
- # Convert to torch.Tensor and add "batch" axis
32
- image = torch.from_numpy(image.transpose(2, 0, 1)).float().unsqueeze(0)
33
- image = image.to(device)
34
-
35
- return image, raw_image
36
-
37
-
38
- def imshow_keypoints(img,
39
- pose_result,
40
- skeleton=None,
41
- kpt_score_thr=0.1,
42
- pose_kpt_color=None,
43
- pose_link_color=None,
44
- radius=4,
45
- thickness=1):
46
- """Draw keypoints and links on an image.
47
- Args:
48
- img (ndarry): The image to draw poses on.
49
- pose_result (list[kpts]): The poses to draw. Each element kpts is
50
- a set of K keypoints as an Kx3 numpy.ndarray, where each
51
- keypoint is represented as x, y, score.
52
- kpt_score_thr (float, optional): Minimum score of keypoints
53
- to be shown. Default: 0.3.
54
- pose_kpt_color (np.array[Nx3]`): Color of N keypoints. If None,
55
- the keypoint will not be drawn.
56
- pose_link_color (np.array[Mx3]): Color of M links. If None, the
57
- links will not be drawn.
58
- thickness (int): Thickness of lines.
59
- """
60
-
61
- img_h, img_w, _ = img.shape
62
- img = np.zeros(img.shape)
63
-
64
- for idx, kpts in enumerate(pose_result):
65
- if idx > 1:
66
- continue
67
- kpts = kpts['keypoints']
68
- # print(kpts)
69
- kpts = np.array(kpts, copy=False)
70
-
71
- # draw each point on image
72
- if pose_kpt_color is not None:
73
- assert len(pose_kpt_color) == len(kpts)
74
-
75
- for kid, kpt in enumerate(kpts):
76
- x_coord, y_coord, kpt_score = int(kpt[0]), int(kpt[1]), kpt[2]
77
-
78
- if kpt_score < kpt_score_thr or pose_kpt_color[kid] is None:
79
- # skip the point that should not be drawn
80
- continue
81
-
82
- color = tuple(int(c) for c in pose_kpt_color[kid])
83
- cv2.circle(img, (int(x_coord), int(y_coord)),
84
- radius, color, -1)
85
-
86
- # draw links
87
- if skeleton is not None and pose_link_color is not None:
88
- assert len(pose_link_color) == len(skeleton)
89
-
90
- for sk_id, sk in enumerate(skeleton):
91
- pos1 = (int(kpts[sk[0], 0]), int(kpts[sk[0], 1]))
92
- pos2 = (int(kpts[sk[1], 0]), int(kpts[sk[1], 1]))
93
-
94
- if (pos1[0] <= 0 or pos1[0] >= img_w or pos1[1] <= 0 or pos1[1] >= img_h or pos2[0] <= 0
95
- or pos2[0] >= img_w or pos2[1] <= 0 or pos2[1] >= img_h or kpts[sk[0], 2] < kpt_score_thr
96
- or kpts[sk[1], 2] < kpt_score_thr or pose_link_color[sk_id] is None):
97
- # skip the link that should not be drawn
98
- continue
99
- color = tuple(int(c) for c in pose_link_color[sk_id])
100
- cv2.line(img, pos1, pos2, color, thickness=thickness)
101
-
102
- return img
103
-
104
-
105
- human_det, pose_model = None, None
106
- det_model_path = "https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth"
107
- pose_model_path = "https://download.openmmlab.com/mmpose/top_down/hrnet/hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth"
108
-
109
- modeldir = os.path.join(models_path, "keypose")
110
- old_modeldir = os.path.dirname(os.path.realpath(__file__))
111
-
112
- det_config = 'faster_rcnn_r50_fpn_coco.py'
113
- pose_config = 'hrnet_w48_coco_256x192.py'
114
-
115
- det_checkpoint = 'faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
116
- pose_checkpoint = 'hrnet_w48_coco_256x192-b9e0b3ab_20200708.pth'
117
- det_cat_id = 1
118
- bbox_thr = 0.2
119
-
120
- skeleton = [
121
- [15, 13], [13, 11], [16, 14], [14, 12], [11, 12], [5, 11], [6, 12], [5, 6], [5, 7], [6, 8],
122
- [7, 9], [8, 10],
123
- [1, 2], [0, 1], [0, 2], [1, 3], [2, 4], [3, 5], [4, 6]
124
- ]
125
-
126
- pose_kpt_color = [
127
- [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255],
128
- [0, 255, 0],
129
- [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0], [0, 255, 0],
130
- [255, 128, 0],
131
- [0, 255, 0], [255, 128, 0], [0, 255, 0], [255, 128, 0]
132
- ]
133
-
134
- pose_link_color = [
135
- [0, 255, 0], [0, 255, 0], [255, 128, 0], [255, 128, 0],
136
- [51, 153, 255], [51, 153, 255], [51, 153, 255], [51, 153, 255], [0, 255, 0],
137
- [255, 128, 0],
138
- [0, 255, 0], [255, 128, 0], [51, 153, 255], [51, 153, 255], [51, 153, 255],
139
- [51, 153, 255],
140
- [51, 153, 255], [51, 153, 255], [51, 153, 255]
141
- ]
142
-
143
- def find_download_model(checkpoint, remote_path):
144
- modelpath = os.path.join(modeldir, checkpoint)
145
- old_modelpath = os.path.join(old_modeldir, checkpoint)
146
-
147
- if os.path.exists(old_modelpath):
148
- modelpath = old_modelpath
149
- elif not os.path.exists(modelpath):
150
- from basicsr.utils.download_util import load_file_from_url
151
- load_file_from_url(remote_path, model_dir=modeldir)
152
-
153
- return modelpath
154
-
155
- def apply_keypose(input_image):
156
- global human_det, pose_model
157
- if netNetwork is None:
158
- det_model_local = find_download_model(det_checkpoint, det_model_path)
159
- hrnet_model_local = find_download_model(pose_checkpoint, pose_model_path)
160
- det_config_mmcv = mmcv.Config.fromfile(det_config)
161
- pose_config_mmcv = mmcv.Config.fromfile(pose_config)
162
- human_det = init_detector(det_config_mmcv, det_model_local, device=devices.get_device_for("controlnet"))
163
- pose_model = init_pose_model(pose_config_mmcv, hrnet_model_local, device=devices.get_device_for("controlnet"))
164
-
165
- assert input_image.ndim == 3
166
- input_image = input_image.copy()
167
- with torch.no_grad():
168
- image = torch.from_numpy(input_image).float().to(devices.get_device_for("controlnet"))
169
- image = image / 255.0
170
- mmdet_results = inference_detector(human_det, image)
171
-
172
- # keep the person class bounding boxes.
173
- person_results = process_mmdet_results(mmdet_results, det_cat_id)
174
-
175
- return_heatmap = False
176
- dataset = pose_model.cfg.data['test']['type']
177
-
178
- # e.g. use ('backbone', ) to return backbone feature
179
- output_layer_names = None
180
- pose_results, _ = inference_top_down_pose_model(
181
- pose_model,
182
- image,
183
- person_results,
184
- bbox_thr=bbox_thr,
185
- format='xyxy',
186
- dataset=dataset,
187
- dataset_info=None,
188
- return_heatmap=return_heatmap,
189
- outputs=output_layer_names
190
- )
191
-
192
- im_keypose_out = imshow_keypoints(
193
- image,
194
- pose_results,
195
- skeleton=skeleton,
196
- pose_kpt_color=pose_kpt_color,
197
- pose_link_color=pose_link_color,
198
- radius=2,
199
- thickness=2
200
- )
201
- im_keypose_out = im_keypose_out.astype(np.uint8)
202
-
203
- # image_hed = rearrange(image_hed, 'h w c -> 1 c h w')
204
- # edge = netNetwork(image_hed)[0]
205
- # edge = (edge.cpu().numpy() * 255.0).clip(0, 255).astype(np.uint8)
206
- return im_keypose_out
207
-
208
-
209
- def unload_hed_model():
210
- global netNetwork
211
- if netNetwork is not None:
212
- netNetwork.cpu()
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/keypose/faster_rcnn_r50_fpn_coco.py DELETED
@@ -1,182 +0,0 @@
1
- checkpoint_config = dict(interval=1)
2
- # yapf:disable
3
- log_config = dict(
4
- interval=50,
5
- hooks=[
6
- dict(type='TextLoggerHook'),
7
- # dict(type='TensorboardLoggerHook')
8
- ])
9
- # yapf:enable
10
- dist_params = dict(backend='nccl')
11
- log_level = 'INFO'
12
- load_from = None
13
- resume_from = None
14
- workflow = [('train', 1)]
15
- # optimizer
16
- optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
17
- optimizer_config = dict(grad_clip=None)
18
- # learning policy
19
- lr_config = dict(
20
- policy='step',
21
- warmup='linear',
22
- warmup_iters=500,
23
- warmup_ratio=0.001,
24
- step=[8, 11])
25
- total_epochs = 12
26
-
27
- model = dict(
28
- type='FasterRCNN',
29
- pretrained='torchvision://resnet50',
30
- backbone=dict(
31
- type='ResNet',
32
- depth=50,
33
- num_stages=4,
34
- out_indices=(0, 1, 2, 3),
35
- frozen_stages=1,
36
- norm_cfg=dict(type='BN', requires_grad=True),
37
- norm_eval=True,
38
- style='pytorch'),
39
- neck=dict(
40
- type='FPN',
41
- in_channels=[256, 512, 1024, 2048],
42
- out_channels=256,
43
- num_outs=5),
44
- rpn_head=dict(
45
- type='RPNHead',
46
- in_channels=256,
47
- feat_channels=256,
48
- anchor_generator=dict(
49
- type='AnchorGenerator',
50
- scales=[8],
51
- ratios=[0.5, 1.0, 2.0],
52
- strides=[4, 8, 16, 32, 64]),
53
- bbox_coder=dict(
54
- type='DeltaXYWHBBoxCoder',
55
- target_means=[.0, .0, .0, .0],
56
- target_stds=[1.0, 1.0, 1.0, 1.0]),
57
- loss_cls=dict(
58
- type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
59
- loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
60
- roi_head=dict(
61
- type='StandardRoIHead',
62
- bbox_roi_extractor=dict(
63
- type='SingleRoIExtractor',
64
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
65
- out_channels=256,
66
- featmap_strides=[4, 8, 16, 32]),
67
- bbox_head=dict(
68
- type='Shared2FCBBoxHead',
69
- in_channels=256,
70
- fc_out_channels=1024,
71
- roi_feat_size=7,
72
- num_classes=80,
73
- bbox_coder=dict(
74
- type='DeltaXYWHBBoxCoder',
75
- target_means=[0., 0., 0., 0.],
76
- target_stds=[0.1, 0.1, 0.2, 0.2]),
77
- reg_class_agnostic=False,
78
- loss_cls=dict(
79
- type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
80
- loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
81
- # model training and testing settings
82
- train_cfg=dict(
83
- rpn=dict(
84
- assigner=dict(
85
- type='MaxIoUAssigner',
86
- pos_iou_thr=0.7,
87
- neg_iou_thr=0.3,
88
- min_pos_iou=0.3,
89
- match_low_quality=True,
90
- ignore_iof_thr=-1),
91
- sampler=dict(
92
- type='RandomSampler',
93
- num=256,
94
- pos_fraction=0.5,
95
- neg_pos_ub=-1,
96
- add_gt_as_proposals=False),
97
- allowed_border=-1,
98
- pos_weight=-1,
99
- debug=False),
100
- rpn_proposal=dict(
101
- nms_pre=2000,
102
- max_per_img=1000,
103
- nms=dict(type='nms', iou_threshold=0.7),
104
- min_bbox_size=0),
105
- rcnn=dict(
106
- assigner=dict(
107
- type='MaxIoUAssigner',
108
- pos_iou_thr=0.5,
109
- neg_iou_thr=0.5,
110
- min_pos_iou=0.5,
111
- match_low_quality=False,
112
- ignore_iof_thr=-1),
113
- sampler=dict(
114
- type='RandomSampler',
115
- num=512,
116
- pos_fraction=0.25,
117
- neg_pos_ub=-1,
118
- add_gt_as_proposals=True),
119
- pos_weight=-1,
120
- debug=False)),
121
- test_cfg=dict(
122
- rpn=dict(
123
- nms_pre=1000,
124
- max_per_img=1000,
125
- nms=dict(type='nms', iou_threshold=0.7),
126
- min_bbox_size=0),
127
- rcnn=dict(
128
- score_thr=0.05,
129
- nms=dict(type='nms', iou_threshold=0.5),
130
- max_per_img=100)
131
- # soft-nms is also supported for rcnn testing
132
- # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
133
- ))
134
-
135
- dataset_type = 'CocoDataset'
136
- data_root = 'data/coco'
137
- img_norm_cfg = dict(
138
- mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
139
- train_pipeline = [
140
- dict(type='LoadImageFromFile'),
141
- dict(type='LoadAnnotations', with_bbox=True),
142
- dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
143
- dict(type='RandomFlip', flip_ratio=0.5),
144
- dict(type='Normalize', **img_norm_cfg),
145
- dict(type='Pad', size_divisor=32),
146
- dict(type='DefaultFormatBundle'),
147
- dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
148
- ]
149
- test_pipeline = [
150
- dict(type='LoadImageFromFile'),
151
- dict(
152
- type='MultiScaleFlipAug',
153
- img_scale=(1333, 800),
154
- flip=False,
155
- transforms=[
156
- dict(type='Resize', keep_ratio=True),
157
- dict(type='RandomFlip'),
158
- dict(type='Normalize', **img_norm_cfg),
159
- dict(type='Pad', size_divisor=32),
160
- dict(type='DefaultFormatBundle'),
161
- dict(type='Collect', keys=['img']),
162
- ])
163
- ]
164
- data = dict(
165
- samples_per_gpu=2,
166
- workers_per_gpu=2,
167
- train=dict(
168
- type=dataset_type,
169
- ann_file=f'{data_root}/annotations/instances_train2017.json',
170
- img_prefix=f'{data_root}/train2017/',
171
- pipeline=train_pipeline),
172
- val=dict(
173
- type=dataset_type,
174
- ann_file=f'{data_root}/annotations/instances_val2017.json',
175
- img_prefix=f'{data_root}/val2017/',
176
- pipeline=test_pipeline),
177
- test=dict(
178
- type=dataset_type,
179
- ann_file=f'{data_root}/annotations/instances_val2017.json',
180
- img_prefix=f'{data_root}/val2017/',
181
- pipeline=test_pipeline))
182
- evaluation = dict(interval=1, metric='bbox')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/keypose/hrnet_w48_coco_256x192.py DELETED
@@ -1,169 +0,0 @@
1
- # _base_ = [
2
- # '../../../../_base_/default_runtime.py',
3
- # '../../../../_base_/datasets/coco.py'
4
- # ]
5
- evaluation = dict(interval=10, metric='mAP', save_best='AP')
6
-
7
- optimizer = dict(
8
- type='Adam',
9
- lr=5e-4,
10
- )
11
- optimizer_config = dict(grad_clip=None)
12
- # learning policy
13
- lr_config = dict(
14
- policy='step',
15
- warmup='linear',
16
- warmup_iters=500,
17
- warmup_ratio=0.001,
18
- step=[170, 200])
19
- total_epochs = 210
20
- channel_cfg = dict(
21
- num_output_channels=17,
22
- dataset_joints=17,
23
- dataset_channel=[
24
- [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],
25
- ],
26
- inference_channel=[
27
- 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16
28
- ])
29
-
30
- # model settings
31
- model = dict(
32
- type='TopDown',
33
- pretrained='https://download.openmmlab.com/mmpose/'
34
- 'pretrain_models/hrnet_w48-8ef0771d.pth',
35
- backbone=dict(
36
- type='HRNet',
37
- in_channels=3,
38
- extra=dict(
39
- stage1=dict(
40
- num_modules=1,
41
- num_branches=1,
42
- block='BOTTLENECK',
43
- num_blocks=(4, ),
44
- num_channels=(64, )),
45
- stage2=dict(
46
- num_modules=1,
47
- num_branches=2,
48
- block='BASIC',
49
- num_blocks=(4, 4),
50
- num_channels=(48, 96)),
51
- stage3=dict(
52
- num_modules=4,
53
- num_branches=3,
54
- block='BASIC',
55
- num_blocks=(4, 4, 4),
56
- num_channels=(48, 96, 192)),
57
- stage4=dict(
58
- num_modules=3,
59
- num_branches=4,
60
- block='BASIC',
61
- num_blocks=(4, 4, 4, 4),
62
- num_channels=(48, 96, 192, 384))),
63
- ),
64
- keypoint_head=dict(
65
- type='TopdownHeatmapSimpleHead',
66
- in_channels=48,
67
- out_channels=channel_cfg['num_output_channels'],
68
- num_deconv_layers=0,
69
- extra=dict(final_conv_kernel=1, ),
70
- loss_keypoint=dict(type='JointsMSELoss', use_target_weight=True)),
71
- train_cfg=dict(),
72
- test_cfg=dict(
73
- flip_test=True,
74
- post_process='default',
75
- shift_heatmap=True,
76
- modulate_kernel=11))
77
-
78
- data_cfg = dict(
79
- image_size=[192, 256],
80
- heatmap_size=[48, 64],
81
- num_output_channels=channel_cfg['num_output_channels'],
82
- num_joints=channel_cfg['dataset_joints'],
83
- dataset_channel=channel_cfg['dataset_channel'],
84
- inference_channel=channel_cfg['inference_channel'],
85
- soft_nms=False,
86
- nms_thr=1.0,
87
- oks_thr=0.9,
88
- vis_thr=0.2,
89
- use_gt_bbox=False,
90
- det_bbox_thr=0.0,
91
- bbox_file='data/coco/person_detection_results/'
92
- 'COCO_val2017_detections_AP_H_56_person.json',
93
- )
94
-
95
- train_pipeline = [
96
- dict(type='LoadImageFromFile'),
97
- dict(type='TopDownGetBboxCenterScale', padding=1.25),
98
- dict(type='TopDownRandomShiftBboxCenter', shift_factor=0.16, prob=0.3),
99
- dict(type='TopDownRandomFlip', flip_prob=0.5),
100
- dict(
101
- type='TopDownHalfBodyTransform',
102
- num_joints_half_body=8,
103
- prob_half_body=0.3),
104
- dict(
105
- type='TopDownGetRandomScaleRotation', rot_factor=40, scale_factor=0.5),
106
- dict(type='TopDownAffine'),
107
- dict(type='ToTensor'),
108
- dict(
109
- type='NormalizeTensor',
110
- mean=[0.485, 0.456, 0.406],
111
- std=[0.229, 0.224, 0.225]),
112
- dict(type='TopDownGenerateTarget', sigma=2),
113
- dict(
114
- type='Collect',
115
- keys=['img', 'target', 'target_weight'],
116
- meta_keys=[
117
- 'image_file', 'joints_3d', 'joints_3d_visible', 'center', 'scale',
118
- 'rotation', 'bbox_score', 'flip_pairs'
119
- ]),
120
- ]
121
-
122
- val_pipeline = [
123
- dict(type='LoadImageFromFile'),
124
- dict(type='TopDownGetBboxCenterScale', padding=1.25),
125
- dict(type='TopDownAffine'),
126
- dict(type='ToTensor'),
127
- dict(
128
- type='NormalizeTensor',
129
- mean=[0.485, 0.456, 0.406],
130
- std=[0.229, 0.224, 0.225]),
131
- dict(
132
- type='Collect',
133
- keys=['img'],
134
- meta_keys=[
135
- 'image_file', 'center', 'scale', 'rotation', 'bbox_score',
136
- 'flip_pairs'
137
- ]),
138
- ]
139
-
140
- test_pipeline = val_pipeline
141
-
142
- data_root = 'data/coco'
143
- data = dict(
144
- samples_per_gpu=32,
145
- workers_per_gpu=2,
146
- val_dataloader=dict(samples_per_gpu=32),
147
- test_dataloader=dict(samples_per_gpu=32),
148
- train=dict(
149
- type='TopDownCocoDataset',
150
- ann_file=f'{data_root}/annotations/person_keypoints_train2017.json',
151
- img_prefix=f'{data_root}/train2017/',
152
- data_cfg=data_cfg,
153
- pipeline=train_pipeline,
154
- dataset_info={{_base_.dataset_info}}),
155
- val=dict(
156
- type='TopDownCocoDataset',
157
- ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
158
- img_prefix=f'{data_root}/val2017/',
159
- data_cfg=data_cfg,
160
- pipeline=val_pipeline,
161
- dataset_info={{_base_.dataset_info}}),
162
- test=dict(
163
- type='TopDownCocoDataset',
164
- ann_file=f'{data_root}/annotations/person_keypoints_val2017.json',
165
- img_prefix=f'{data_root}/val2017/',
166
- data_cfg=data_cfg,
167
- pipeline=test_pipeline,
168
- dataset_info={{_base_.dataset_info}}),
169
- )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/__init__.py DELETED
@@ -1,58 +0,0 @@
1
- # https://github.com/advimman/lama
2
-
3
- import yaml
4
- import torch
5
- from omegaconf import OmegaConf
6
- import numpy as np
7
-
8
- from einops import rearrange
9
- import os
10
- from modules import devices
11
- from annotator.annotator_path import models_path
12
- from annotator.lama.saicinpainting.training.trainers import load_checkpoint
13
-
14
-
15
- class LamaInpainting:
16
- model_dir = os.path.join(models_path, "lama")
17
-
18
- def __init__(self):
19
- self.model = None
20
- self.device = devices.get_device_for("controlnet")
21
-
22
- def load_model(self):
23
- remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetLama.pth"
24
- modelpath = os.path.join(self.model_dir, "ControlNetLama.pth")
25
- if not os.path.exists(modelpath):
26
- from basicsr.utils.download_util import load_file_from_url
27
- load_file_from_url(remote_model_path, model_dir=self.model_dir)
28
- config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'config.yaml')
29
- cfg = yaml.safe_load(open(config_path, 'rt'))
30
- cfg = OmegaConf.create(cfg)
31
- cfg.training_model.predict_only = True
32
- cfg.visualizer.kind = 'noop'
33
- self.model = load_checkpoint(cfg, os.path.abspath(modelpath), strict=False, map_location='cpu')
34
- self.model = self.model.to(self.device)
35
- self.model.eval()
36
-
37
- def unload_model(self):
38
- if self.model is not None:
39
- self.model.cpu()
40
-
41
- def __call__(self, input_image):
42
- if self.model is None:
43
- self.load_model()
44
- self.model.to(self.device)
45
- color = np.ascontiguousarray(input_image[:, :, 0:3]).astype(np.float32) / 255.0
46
- mask = np.ascontiguousarray(input_image[:, :, 3:4]).astype(np.float32) / 255.0
47
- with torch.no_grad():
48
- color = torch.from_numpy(color).float().to(self.device)
49
- mask = torch.from_numpy(mask).float().to(self.device)
50
- mask = (mask > 0.5).float()
51
- color = color * (1 - mask)
52
- image_feed = torch.cat([color, mask], dim=2)
53
- image_feed = rearrange(image_feed, 'h w c -> 1 c h w')
54
- result = self.model(image_feed)[0]
55
- result = rearrange(result, 'c h w -> h w c')
56
- result = result * mask + color * (1 - mask)
57
- result *= 255.0
58
- return result.detach().cpu().numpy().clip(0, 255).astype(np.uint8)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/config.yaml DELETED
@@ -1,157 +0,0 @@
1
- run_title: b18_ffc075_batch8x15
2
- training_model:
3
- kind: default
4
- visualize_each_iters: 1000
5
- concat_mask: true
6
- store_discr_outputs_for_vis: true
7
- losses:
8
- l1:
9
- weight_missing: 0
10
- weight_known: 10
11
- perceptual:
12
- weight: 0
13
- adversarial:
14
- kind: r1
15
- weight: 10
16
- gp_coef: 0.001
17
- mask_as_fake_target: true
18
- allow_scale_mask: true
19
- feature_matching:
20
- weight: 100
21
- resnet_pl:
22
- weight: 30
23
- weights_path: ${env:TORCH_HOME}
24
-
25
- optimizers:
26
- generator:
27
- kind: adam
28
- lr: 0.001
29
- discriminator:
30
- kind: adam
31
- lr: 0.0001
32
- visualizer:
33
- key_order:
34
- - image
35
- - predicted_image
36
- - discr_output_fake
37
- - discr_output_real
38
- - inpainted
39
- rescale_keys:
40
- - discr_output_fake
41
- - discr_output_real
42
- kind: directory
43
- outdir: /group-volume/User-Driven-Content-Generation/r.suvorov/inpainting/experiments/r.suvorov_2021-04-30_14-41-12_train_simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15/samples
44
- location:
45
- data_root_dir: /group-volume/User-Driven-Content-Generation/datasets/inpainting_data_root_large
46
- out_root_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/experiments
47
- tb_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/tb_logs
48
- data:
49
- batch_size: 15
50
- val_batch_size: 2
51
- num_workers: 3
52
- train:
53
- indir: ${location.data_root_dir}/train
54
- out_size: 256
55
- mask_gen_kwargs:
56
- irregular_proba: 1
57
- irregular_kwargs:
58
- max_angle: 4
59
- max_len: 200
60
- max_width: 100
61
- max_times: 5
62
- min_times: 1
63
- box_proba: 1
64
- box_kwargs:
65
- margin: 10
66
- bbox_min_size: 30
67
- bbox_max_size: 150
68
- max_times: 3
69
- min_times: 1
70
- segm_proba: 0
71
- segm_kwargs:
72
- confidence_threshold: 0.5
73
- max_object_area: 0.5
74
- min_mask_area: 0.07
75
- downsample_levels: 6
76
- num_variants_per_mask: 1
77
- rigidness_mode: 1
78
- max_foreground_coverage: 0.3
79
- max_foreground_intersection: 0.7
80
- max_mask_intersection: 0.1
81
- max_hidden_area: 0.1
82
- max_scale_change: 0.25
83
- horizontal_flip: true
84
- max_vertical_shift: 0.2
85
- position_shuffle: true
86
- transform_variant: distortions
87
- dataloader_kwargs:
88
- batch_size: ${data.batch_size}
89
- shuffle: true
90
- num_workers: ${data.num_workers}
91
- val:
92
- indir: ${location.data_root_dir}/val
93
- img_suffix: .png
94
- dataloader_kwargs:
95
- batch_size: ${data.val_batch_size}
96
- shuffle: false
97
- num_workers: ${data.num_workers}
98
- visual_test:
99
- indir: ${location.data_root_dir}/korean_test
100
- img_suffix: _input.png
101
- pad_out_to_modulo: 32
102
- dataloader_kwargs:
103
- batch_size: 1
104
- shuffle: false
105
- num_workers: ${data.num_workers}
106
- generator:
107
- kind: ffc_resnet
108
- input_nc: 4
109
- output_nc: 3
110
- ngf: 64
111
- n_downsampling: 3
112
- n_blocks: 18
113
- add_out_act: sigmoid
114
- init_conv_kwargs:
115
- ratio_gin: 0
116
- ratio_gout: 0
117
- enable_lfu: false
118
- downsample_conv_kwargs:
119
- ratio_gin: ${generator.init_conv_kwargs.ratio_gout}
120
- ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin}
121
- enable_lfu: false
122
- resnet_conv_kwargs:
123
- ratio_gin: 0.75
124
- ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin}
125
- enable_lfu: false
126
- discriminator:
127
- kind: pix2pixhd_nlayer
128
- input_nc: 3
129
- ndf: 64
130
- n_layers: 4
131
- evaluator:
132
- kind: default
133
- inpainted_key: inpainted
134
- integral_kind: ssim_fid100_f1
135
- trainer:
136
- kwargs:
137
- gpus: -1
138
- accelerator: ddp
139
- max_epochs: 200
140
- gradient_clip_val: 1
141
- log_gpu_memory: None
142
- limit_train_batches: 25000
143
- val_check_interval: ${trainer.kwargs.limit_train_batches}
144
- log_every_n_steps: 1000
145
- precision: 32
146
- terminate_on_nan: false
147
- check_val_every_n_epoch: 1
148
- num_sanity_val_steps: 8
149
- limit_val_batches: 1000
150
- replace_sampler_ddp: false
151
- checkpoint_kwargs:
152
- verbose: true
153
- save_top_k: 5
154
- save_last: true
155
- period: 1
156
- monitor: val_ssim_fid100_f1_total_mean
157
- mode: max
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/__init__.py DELETED
File without changes
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/__init__.py DELETED
File without changes
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/data/__init__.py DELETED
File without changes
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/data/masks.py DELETED
@@ -1,332 +0,0 @@
1
- import math
2
- import random
3
- import hashlib
4
- import logging
5
- from enum import Enum
6
-
7
- import cv2
8
- import numpy as np
9
-
10
- # from annotator.lama.saicinpainting.evaluation.masks.mask import SegmentationMask
11
- from annotator.lama.saicinpainting.utils import LinearRamp
12
-
13
- LOGGER = logging.getLogger(__name__)
14
-
15
-
16
- class DrawMethod(Enum):
17
- LINE = 'line'
18
- CIRCLE = 'circle'
19
- SQUARE = 'square'
20
-
21
-
22
- def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10,
23
- draw_method=DrawMethod.LINE):
24
- draw_method = DrawMethod(draw_method)
25
-
26
- height, width = shape
27
- mask = np.zeros((height, width), np.float32)
28
- times = np.random.randint(min_times, max_times + 1)
29
- for i in range(times):
30
- start_x = np.random.randint(width)
31
- start_y = np.random.randint(height)
32
- for j in range(1 + np.random.randint(5)):
33
- angle = 0.01 + np.random.randint(max_angle)
34
- if i % 2 == 0:
35
- angle = 2 * 3.1415926 - angle
36
- length = 10 + np.random.randint(max_len)
37
- brush_w = 5 + np.random.randint(max_width)
38
- end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width)
39
- end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height)
40
- if draw_method == DrawMethod.LINE:
41
- cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w)
42
- elif draw_method == DrawMethod.CIRCLE:
43
- cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1)
44
- elif draw_method == DrawMethod.SQUARE:
45
- radius = brush_w // 2
46
- mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1
47
- start_x, start_y = end_x, end_y
48
- return mask[None, ...]
49
-
50
-
51
- class RandomIrregularMaskGenerator:
52
- def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None,
53
- draw_method=DrawMethod.LINE):
54
- self.max_angle = max_angle
55
- self.max_len = max_len
56
- self.max_width = max_width
57
- self.min_times = min_times
58
- self.max_times = max_times
59
- self.draw_method = draw_method
60
- self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
61
-
62
- def __call__(self, img, iter_i=None, raw_image=None):
63
- coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
64
- cur_max_len = int(max(1, self.max_len * coef))
65
- cur_max_width = int(max(1, self.max_width * coef))
66
- cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef)
67
- return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len,
68
- max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times,
69
- draw_method=self.draw_method)
70
-
71
-
72
- def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3):
73
- height, width = shape
74
- mask = np.zeros((height, width), np.float32)
75
- bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2)
76
- times = np.random.randint(min_times, max_times + 1)
77
- for i in range(times):
78
- box_width = np.random.randint(bbox_min_size, bbox_max_size)
79
- box_height = np.random.randint(bbox_min_size, bbox_max_size)
80
- start_x = np.random.randint(margin, width - margin - box_width + 1)
81
- start_y = np.random.randint(margin, height - margin - box_height + 1)
82
- mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1
83
- return mask[None, ...]
84
-
85
-
86
- class RandomRectangleMaskGenerator:
87
- def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None):
88
- self.margin = margin
89
- self.bbox_min_size = bbox_min_size
90
- self.bbox_max_size = bbox_max_size
91
- self.min_times = min_times
92
- self.max_times = max_times
93
- self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
94
-
95
- def __call__(self, img, iter_i=None, raw_image=None):
96
- coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
97
- cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef)
98
- cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef)
99
- return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size,
100
- bbox_max_size=cur_bbox_max_size, min_times=self.min_times,
101
- max_times=cur_max_times)
102
-
103
-
104
- class RandomSegmentationMaskGenerator:
105
- def __init__(self, **kwargs):
106
- self.impl = None # will be instantiated in first call (effectively in subprocess)
107
- self.kwargs = kwargs
108
-
109
- def __call__(self, img, iter_i=None, raw_image=None):
110
- if self.impl is None:
111
- self.impl = SegmentationMask(**self.kwargs)
112
-
113
- masks = self.impl.get_masks(np.transpose(img, (1, 2, 0)))
114
- masks = [m for m in masks if len(np.unique(m)) > 1]
115
- return np.random.choice(masks)
116
-
117
-
118
- def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3):
119
- height, width = shape
120
- mask = np.zeros((height, width), np.float32)
121
- step_x = np.random.randint(min_step, max_step + 1)
122
- width_x = np.random.randint(min_width, min(step_x, max_width + 1))
123
- offset_x = np.random.randint(0, step_x)
124
-
125
- step_y = np.random.randint(min_step, max_step + 1)
126
- width_y = np.random.randint(min_width, min(step_y, max_width + 1))
127
- offset_y = np.random.randint(0, step_y)
128
-
129
- for dy in range(width_y):
130
- mask[offset_y + dy::step_y] = 1
131
- for dx in range(width_x):
132
- mask[:, offset_x + dx::step_x] = 1
133
- return mask[None, ...]
134
-
135
-
136
- class RandomSuperresMaskGenerator:
137
- def __init__(self, **kwargs):
138
- self.kwargs = kwargs
139
-
140
- def __call__(self, img, iter_i=None):
141
- return make_random_superres_mask(img.shape[1:], **self.kwargs)
142
-
143
-
144
- class DumbAreaMaskGenerator:
145
- min_ratio = 0.1
146
- max_ratio = 0.35
147
- default_ratio = 0.225
148
-
149
- def __init__(self, is_training):
150
- #Parameters:
151
- # is_training(bool): If true - random rectangular mask, if false - central square mask
152
- self.is_training = is_training
153
-
154
- def _random_vector(self, dimension):
155
- if self.is_training:
156
- lower_limit = math.sqrt(self.min_ratio)
157
- upper_limit = math.sqrt(self.max_ratio)
158
- mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension)
159
- u = random.randint(0, dimension-mask_side-1)
160
- v = u+mask_side
161
- else:
162
- margin = (math.sqrt(self.default_ratio) / 2) * dimension
163
- u = round(dimension/2 - margin)
164
- v = round(dimension/2 + margin)
165
- return u, v
166
-
167
- def __call__(self, img, iter_i=None, raw_image=None):
168
- c, height, width = img.shape
169
- mask = np.zeros((height, width), np.float32)
170
- x1, x2 = self._random_vector(width)
171
- y1, y2 = self._random_vector(height)
172
- mask[x1:x2, y1:y2] = 1
173
- return mask[None, ...]
174
-
175
-
176
- class OutpaintingMaskGenerator:
177
- def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5,
178
- right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False):
179
- """
180
- is_fixed_randomness - get identical paddings for the same image if args are the same
181
- """
182
- self.min_padding_percent = min_padding_percent
183
- self.max_padding_percent = max_padding_percent
184
- self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob]
185
- self.is_fixed_randomness = is_fixed_randomness
186
-
187
- assert self.min_padding_percent <= self.max_padding_percent
188
- assert self.max_padding_percent > 0
189
- assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]"
190
- assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}"
191
- assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}"
192
- if len([x for x in self.probs if x > 0]) == 1:
193
- LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side")
194
-
195
- def apply_padding(self, mask, coord):
196
- mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h),
197
- int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1
198
- return mask
199
-
200
- def get_padding(self, size):
201
- n1 = int(self.min_padding_percent*size)
202
- n2 = int(self.max_padding_percent*size)
203
- return self.rnd.randint(n1, n2) / size
204
-
205
- @staticmethod
206
- def _img2rs(img):
207
- arr = np.ascontiguousarray(img.astype(np.uint8))
208
- str_hash = hashlib.sha1(arr).hexdigest()
209
- res = hash(str_hash)%(2**32)
210
- return res
211
-
212
- def __call__(self, img, iter_i=None, raw_image=None):
213
- c, self.img_h, self.img_w = img.shape
214
- mask = np.zeros((self.img_h, self.img_w), np.float32)
215
- at_least_one_mask_applied = False
216
-
217
- if self.is_fixed_randomness:
218
- assert raw_image is not None, f"Cant calculate hash on raw_image=None"
219
- rs = self._img2rs(raw_image)
220
- self.rnd = np.random.RandomState(rs)
221
- else:
222
- self.rnd = np.random
223
-
224
- coords = [[
225
- (0,0),
226
- (1,self.get_padding(size=self.img_h))
227
- ],
228
- [
229
- (0,0),
230
- (self.get_padding(size=self.img_w),1)
231
- ],
232
- [
233
- (0,1-self.get_padding(size=self.img_h)),
234
- (1,1)
235
- ],
236
- [
237
- (1-self.get_padding(size=self.img_w),0),
238
- (1,1)
239
- ]]
240
-
241
- for pp, coord in zip(self.probs, coords):
242
- if self.rnd.random() < pp:
243
- at_least_one_mask_applied = True
244
- mask = self.apply_padding(mask=mask, coord=coord)
245
-
246
- if not at_least_one_mask_applied:
247
- idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs))
248
- mask = self.apply_padding(mask=mask, coord=coords[idx])
249
- return mask[None, ...]
250
-
251
-
252
- class MixedMaskGenerator:
253
- def __init__(self, irregular_proba=1/3, irregular_kwargs=None,
254
- box_proba=1/3, box_kwargs=None,
255
- segm_proba=1/3, segm_kwargs=None,
256
- squares_proba=0, squares_kwargs=None,
257
- superres_proba=0, superres_kwargs=None,
258
- outpainting_proba=0, outpainting_kwargs=None,
259
- invert_proba=0):
260
- self.probas = []
261
- self.gens = []
262
-
263
- if irregular_proba > 0:
264
- self.probas.append(irregular_proba)
265
- if irregular_kwargs is None:
266
- irregular_kwargs = {}
267
- else:
268
- irregular_kwargs = dict(irregular_kwargs)
269
- irregular_kwargs['draw_method'] = DrawMethod.LINE
270
- self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs))
271
-
272
- if box_proba > 0:
273
- self.probas.append(box_proba)
274
- if box_kwargs is None:
275
- box_kwargs = {}
276
- self.gens.append(RandomRectangleMaskGenerator(**box_kwargs))
277
-
278
- if segm_proba > 0:
279
- self.probas.append(segm_proba)
280
- if segm_kwargs is None:
281
- segm_kwargs = {}
282
- self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs))
283
-
284
- if squares_proba > 0:
285
- self.probas.append(squares_proba)
286
- if squares_kwargs is None:
287
- squares_kwargs = {}
288
- else:
289
- squares_kwargs = dict(squares_kwargs)
290
- squares_kwargs['draw_method'] = DrawMethod.SQUARE
291
- self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs))
292
-
293
- if superres_proba > 0:
294
- self.probas.append(superres_proba)
295
- if superres_kwargs is None:
296
- superres_kwargs = {}
297
- self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs))
298
-
299
- if outpainting_proba > 0:
300
- self.probas.append(outpainting_proba)
301
- if outpainting_kwargs is None:
302
- outpainting_kwargs = {}
303
- self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs))
304
-
305
- self.probas = np.array(self.probas, dtype='float32')
306
- self.probas /= self.probas.sum()
307
- self.invert_proba = invert_proba
308
-
309
- def __call__(self, img, iter_i=None, raw_image=None):
310
- kind = np.random.choice(len(self.probas), p=self.probas)
311
- gen = self.gens[kind]
312
- result = gen(img, iter_i=iter_i, raw_image=raw_image)
313
- if self.invert_proba > 0 and random.random() < self.invert_proba:
314
- result = 1 - result
315
- return result
316
-
317
-
318
- def get_mask_generator(kind, kwargs):
319
- if kind is None:
320
- kind = "mixed"
321
- if kwargs is None:
322
- kwargs = {}
323
-
324
- if kind == "mixed":
325
- cl = MixedMaskGenerator
326
- elif kind == "outpainting":
327
- cl = OutpaintingMaskGenerator
328
- elif kind == "dumb":
329
- cl = DumbAreaMaskGenerator
330
- else:
331
- raise NotImplementedError(f"No such generator kind = {kind}")
332
- return cl(**kwargs)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/__init__.py DELETED
File without changes
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/adversarial.py DELETED
@@ -1,177 +0,0 @@
1
- from typing import Tuple, Dict, Optional
2
-
3
- import torch
4
- import torch.nn as nn
5
- import torch.nn.functional as F
6
-
7
-
8
- class BaseAdversarialLoss:
9
- def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
10
- generator: nn.Module, discriminator: nn.Module):
11
- """
12
- Prepare for generator step
13
- :param real_batch: Tensor, a batch of real samples
14
- :param fake_batch: Tensor, a batch of samples produced by generator
15
- :param generator:
16
- :param discriminator:
17
- :return: None
18
- """
19
-
20
- def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
21
- generator: nn.Module, discriminator: nn.Module):
22
- """
23
- Prepare for discriminator step
24
- :param real_batch: Tensor, a batch of real samples
25
- :param fake_batch: Tensor, a batch of samples produced by generator
26
- :param generator:
27
- :param discriminator:
28
- :return: None
29
- """
30
-
31
- def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
32
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
33
- mask: Optional[torch.Tensor] = None) \
34
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
35
- """
36
- Calculate generator loss
37
- :param real_batch: Tensor, a batch of real samples
38
- :param fake_batch: Tensor, a batch of samples produced by generator
39
- :param discr_real_pred: Tensor, discriminator output for real_batch
40
- :param discr_fake_pred: Tensor, discriminator output for fake_batch
41
- :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
42
- :return: total generator loss along with some values that might be interesting to log
43
- """
44
- raise NotImplemented()
45
-
46
- def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
47
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
48
- mask: Optional[torch.Tensor] = None) \
49
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
50
- """
51
- Calculate discriminator loss and call .backward() on it
52
- :param real_batch: Tensor, a batch of real samples
53
- :param fake_batch: Tensor, a batch of samples produced by generator
54
- :param discr_real_pred: Tensor, discriminator output for real_batch
55
- :param discr_fake_pred: Tensor, discriminator output for fake_batch
56
- :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
57
- :return: total discriminator loss along with some values that might be interesting to log
58
- """
59
- raise NotImplemented()
60
-
61
- def interpolate_mask(self, mask, shape):
62
- assert mask is not None
63
- assert self.allow_scale_mask or shape == mask.shape[-2:]
64
- if shape != mask.shape[-2:] and self.allow_scale_mask:
65
- if self.mask_scale_mode == 'maxpool':
66
- mask = F.adaptive_max_pool2d(mask, shape)
67
- else:
68
- mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)
69
- return mask
70
-
71
- def make_r1_gp(discr_real_pred, real_batch):
72
- if torch.is_grad_enabled():
73
- grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]
74
- grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()
75
- else:
76
- grad_penalty = 0
77
- real_batch.requires_grad = False
78
-
79
- return grad_penalty
80
-
81
- class NonSaturatingWithR1(BaseAdversarialLoss):
82
- def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,
83
- mask_scale_mode='nearest', extra_mask_weight_for_gen=0,
84
- use_unmasked_for_gen=True, use_unmasked_for_discr=True):
85
- self.gp_coef = gp_coef
86
- self.weight = weight
87
- # use for discr => use for gen;
88
- # otherwise we teach only the discr to pay attention to very small difference
89
- assert use_unmasked_for_gen or (not use_unmasked_for_discr)
90
- # mask as target => use unmasked for discr:
91
- # if we don't care about unmasked regions at all
92
- # then it doesn't matter if the value of mask_as_fake_target is true or false
93
- assert use_unmasked_for_discr or (not mask_as_fake_target)
94
- self.use_unmasked_for_gen = use_unmasked_for_gen
95
- self.use_unmasked_for_discr = use_unmasked_for_discr
96
- self.mask_as_fake_target = mask_as_fake_target
97
- self.allow_scale_mask = allow_scale_mask
98
- self.mask_scale_mode = mask_scale_mode
99
- self.extra_mask_weight_for_gen = extra_mask_weight_for_gen
100
-
101
- def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
102
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
103
- mask=None) \
104
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
105
- fake_loss = F.softplus(-discr_fake_pred)
106
- if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \
107
- not self.use_unmasked_for_gen: # == if masked region should be treated differently
108
- mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
109
- if not self.use_unmasked_for_gen:
110
- fake_loss = fake_loss * mask
111
- else:
112
- pixel_weights = 1 + mask * self.extra_mask_weight_for_gen
113
- fake_loss = fake_loss * pixel_weights
114
-
115
- return fake_loss.mean() * self.weight, dict()
116
-
117
- def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
118
- generator: nn.Module, discriminator: nn.Module):
119
- real_batch.requires_grad = True
120
-
121
- def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
122
- discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
123
- mask=None) \
124
- -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
125
-
126
- real_loss = F.softplus(-discr_real_pred)
127
- grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef
128
- fake_loss = F.softplus(discr_fake_pred)
129
-
130
- if not self.use_unmasked_for_discr or self.mask_as_fake_target:
131
- # == if masked region should be treated differently
132
- mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
133
- # use_unmasked_for_discr=False only makes sense for fakes;
134
- # for reals there is no difference beetween two regions
135
- fake_loss = fake_loss * mask
136
- if self.mask_as_fake_target:
137
- fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)
138
-
139
- sum_discr_loss = real_loss + grad_penalty + fake_loss
140
- metrics = dict(discr_real_out=discr_real_pred.mean(),
141
- discr_fake_out=discr_fake_pred.mean(),
142
- discr_real_gp=grad_penalty)
143
- return sum_discr_loss.mean(), metrics
144
-
145
- class BCELoss(BaseAdversarialLoss):
146
- def __init__(self, weight):
147
- self.weight = weight
148
- self.bce_loss = nn.BCEWithLogitsLoss()
149
-
150
- def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
151
- real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)
152
- fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight
153
- return fake_loss, dict()
154
-
155
- def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
156
- generator: nn.Module, discriminator: nn.Module):
157
- real_batch.requires_grad = True
158
-
159
- def discriminator_loss(self,
160
- mask: torch.Tensor,
161
- discr_real_pred: torch.Tensor,
162
- discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
163
-
164
- real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)
165
- sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) + self.bce_loss(discr_fake_pred, mask)) / 2
166
- metrics = dict(discr_real_out=discr_real_pred.mean(),
167
- discr_fake_out=discr_fake_pred.mean(),
168
- discr_real_gp=0)
169
- return sum_discr_loss, metrics
170
-
171
-
172
- def make_discrim_loss(kind, **kwargs):
173
- if kind == 'r1':
174
- return NonSaturatingWithR1(**kwargs)
175
- elif kind == 'bce':
176
- return BCELoss(**kwargs)
177
- raise ValueError(f'Unknown adversarial loss kind {kind}')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/constants.py DELETED
@@ -1,152 +0,0 @@
1
- weights = {"ade20k":
2
- [6.34517766497462,
3
- 9.328358208955224,
4
- 11.389521640091116,
5
- 16.10305958132045,
6
- 20.833333333333332,
7
- 22.22222222222222,
8
- 25.125628140703515,
9
- 43.29004329004329,
10
- 50.5050505050505,
11
- 54.6448087431694,
12
- 55.24861878453038,
13
- 60.24096385542168,
14
- 62.5,
15
- 66.2251655629139,
16
- 84.74576271186442,
17
- 90.90909090909092,
18
- 91.74311926605505,
19
- 96.15384615384616,
20
- 96.15384615384616,
21
- 97.08737864077669,
22
- 102.04081632653062,
23
- 135.13513513513513,
24
- 149.2537313432836,
25
- 153.84615384615384,
26
- 163.93442622950818,
27
- 166.66666666666666,
28
- 188.67924528301887,
29
- 192.30769230769232,
30
- 217.3913043478261,
31
- 227.27272727272725,
32
- 227.27272727272725,
33
- 227.27272727272725,
34
- 303.03030303030306,
35
- 322.5806451612903,
36
- 333.3333333333333,
37
- 370.3703703703703,
38
- 384.61538461538464,
39
- 416.6666666666667,
40
- 416.6666666666667,
41
- 434.7826086956522,
42
- 434.7826086956522,
43
- 454.5454545454545,
44
- 454.5454545454545,
45
- 500.0,
46
- 526.3157894736842,
47
- 526.3157894736842,
48
- 555.5555555555555,
49
- 555.5555555555555,
50
- 555.5555555555555,
51
- 555.5555555555555,
52
- 555.5555555555555,
53
- 555.5555555555555,
54
- 555.5555555555555,
55
- 588.2352941176471,
56
- 588.2352941176471,
57
- 588.2352941176471,
58
- 588.2352941176471,
59
- 588.2352941176471,
60
- 666.6666666666666,
61
- 666.6666666666666,
62
- 666.6666666666666,
63
- 666.6666666666666,
64
- 714.2857142857143,
65
- 714.2857142857143,
66
- 714.2857142857143,
67
- 714.2857142857143,
68
- 714.2857142857143,
69
- 769.2307692307693,
70
- 769.2307692307693,
71
- 769.2307692307693,
72
- 833.3333333333334,
73
- 833.3333333333334,
74
- 833.3333333333334,
75
- 833.3333333333334,
76
- 909.090909090909,
77
- 1000.0,
78
- 1111.111111111111,
79
- 1111.111111111111,
80
- 1111.111111111111,
81
- 1111.111111111111,
82
- 1111.111111111111,
83
- 1250.0,
84
- 1250.0,
85
- 1250.0,
86
- 1250.0,
87
- 1250.0,
88
- 1428.5714285714287,
89
- 1428.5714285714287,
90
- 1428.5714285714287,
91
- 1428.5714285714287,
92
- 1428.5714285714287,
93
- 1428.5714285714287,
94
- 1428.5714285714287,
95
- 1666.6666666666667,
96
- 1666.6666666666667,
97
- 1666.6666666666667,
98
- 1666.6666666666667,
99
- 1666.6666666666667,
100
- 1666.6666666666667,
101
- 1666.6666666666667,
102
- 1666.6666666666667,
103
- 1666.6666666666667,
104
- 1666.6666666666667,
105
- 1666.6666666666667,
106
- 2000.0,
107
- 2000.0,
108
- 2000.0,
109
- 2000.0,
110
- 2000.0,
111
- 2000.0,
112
- 2000.0,
113
- 2000.0,
114
- 2000.0,
115
- 2000.0,
116
- 2000.0,
117
- 2000.0,
118
- 2000.0,
119
- 2000.0,
120
- 2000.0,
121
- 2000.0,
122
- 2000.0,
123
- 2500.0,
124
- 2500.0,
125
- 2500.0,
126
- 2500.0,
127
- 2500.0,
128
- 2500.0,
129
- 2500.0,
130
- 2500.0,
131
- 2500.0,
132
- 2500.0,
133
- 2500.0,
134
- 2500.0,
135
- 2500.0,
136
- 3333.3333333333335,
137
- 3333.3333333333335,
138
- 3333.3333333333335,
139
- 3333.3333333333335,
140
- 3333.3333333333335,
141
- 3333.3333333333335,
142
- 3333.3333333333335,
143
- 3333.3333333333335,
144
- 3333.3333333333335,
145
- 3333.3333333333335,
146
- 3333.3333333333335,
147
- 3333.3333333333335,
148
- 3333.3333333333335,
149
- 5000.0,
150
- 5000.0,
151
- 5000.0]
152
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/distance_weighting.py DELETED
@@ -1,126 +0,0 @@
1
- import torch
2
- import torch.nn as nn
3
- import torch.nn.functional as F
4
- import torchvision
5
-
6
- from annotator.lama.saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN
7
-
8
-
9
- def dummy_distance_weighter(real_img, pred_img, mask):
10
- return mask
11
-
12
-
13
- def get_gauss_kernel(kernel_size, width_factor=1):
14
- coords = torch.stack(torch.meshgrid(torch.arange(kernel_size),
15
- torch.arange(kernel_size)),
16
- dim=0).float()
17
- diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor)
18
- diff /= diff.sum()
19
- return diff
20
-
21
-
22
- class BlurMask(nn.Module):
23
- def __init__(self, kernel_size=5, width_factor=1):
24
- super().__init__()
25
- self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False)
26
- self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor))
27
-
28
- def forward(self, real_img, pred_img, mask):
29
- with torch.no_grad():
30
- result = self.filter(mask) * mask
31
- return result
32
-
33
-
34
- class EmulatedEDTMask(nn.Module):
35
- def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1):
36
- super().__init__()
37
- self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate',
38
- bias=False)
39
- self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float))
40
- self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False)
41
- self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor))
42
-
43
- def forward(self, real_img, pred_img, mask):
44
- with torch.no_grad():
45
- known_mask = 1 - mask
46
- dilated_known_mask = (self.dilate_filter(known_mask) > 1).float()
47
- result = self.blur_filter(1 - dilated_known_mask) * mask
48
- return result
49
-
50
-
51
- class PropagatePerceptualSim(nn.Module):
52
- def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3):
53
- super().__init__()
54
- vgg = torchvision.models.vgg19(pretrained=True).features
55
- vgg_avg_pooling = []
56
-
57
- for weights in vgg.parameters():
58
- weights.requires_grad = False
59
-
60
- cur_level_i = 0
61
- for module in vgg.modules():
62
- if module.__class__.__name__ == 'Sequential':
63
- continue
64
- elif module.__class__.__name__ == 'MaxPool2d':
65
- vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
66
- else:
67
- vgg_avg_pooling.append(module)
68
- if module.__class__.__name__ == 'ReLU':
69
- cur_level_i += 1
70
- if cur_level_i == level:
71
- break
72
-
73
- self.features = nn.Sequential(*vgg_avg_pooling)
74
-
75
- self.max_iters = max_iters
76
- self.temperature = temperature
77
- self.do_erode = erode_mask_size > 0
78
- if self.do_erode:
79
- self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False)
80
- self.erode_mask.weight.data.fill_(1)
81
-
82
- def forward(self, real_img, pred_img, mask):
83
- with torch.no_grad():
84
- real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img)
85
- real_feats = self.features(real_img)
86
-
87
- vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True)
88
- / self.temperature)
89
- horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True)
90
- / self.temperature)
91
-
92
- mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False)
93
- if self.do_erode:
94
- mask_scaled = (self.erode_mask(mask_scaled) > 1).float()
95
-
96
- cur_knowness = 1 - mask_scaled
97
-
98
- for iter_i in range(self.max_iters):
99
- new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate')
100
- new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate')
101
-
102
- new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate')
103
- new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate')
104
-
105
- new_knowness = torch.stack([new_top_knowness, new_bottom_knowness,
106
- new_left_knowness, new_right_knowness],
107
- dim=0).max(0).values
108
-
109
- cur_knowness = torch.max(cur_knowness, new_knowness)
110
-
111
- cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear')
112
- result = torch.min(mask, 1 - cur_knowness)
113
-
114
- return result
115
-
116
-
117
- def make_mask_distance_weighter(kind='none', **kwargs):
118
- if kind == 'none':
119
- return dummy_distance_weighter
120
- if kind == 'blur':
121
- return BlurMask(**kwargs)
122
- if kind == 'edt':
123
- return EmulatedEDTMask(**kwargs)
124
- if kind == 'pps':
125
- return PropagatePerceptualSim(**kwargs)
126
- raise ValueError(f'Unknown mask distance weighter kind {kind}')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/feature_matching.py DELETED
@@ -1,33 +0,0 @@
1
- from typing import List
2
-
3
- import torch
4
- import torch.nn.functional as F
5
-
6
-
7
- def masked_l2_loss(pred, target, mask, weight_known, weight_missing):
8
- per_pixel_l2 = F.mse_loss(pred, target, reduction='none')
9
- pixel_weights = mask * weight_missing + (1 - mask) * weight_known
10
- return (pixel_weights * per_pixel_l2).mean()
11
-
12
-
13
- def masked_l1_loss(pred, target, mask, weight_known, weight_missing):
14
- per_pixel_l1 = F.l1_loss(pred, target, reduction='none')
15
- pixel_weights = mask * weight_missing + (1 - mask) * weight_known
16
- return (pixel_weights * per_pixel_l1).mean()
17
-
18
-
19
- def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):
20
- if mask is None:
21
- res = torch.stack([F.mse_loss(fake_feat, target_feat)
22
- for fake_feat, target_feat in zip(fake_features, target_features)]).mean()
23
- else:
24
- res = 0
25
- norm = 0
26
- for fake_feat, target_feat in zip(fake_features, target_features):
27
- cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)
28
- error_weights = 1 - cur_mask
29
- cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()
30
- res = res + cur_val
31
- norm += 1
32
- res = res / norm
33
- return res
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/perceptual.py DELETED
@@ -1,113 +0,0 @@
1
- import torch
2
- import torch.nn as nn
3
- import torch.nn.functional as F
4
- import torchvision
5
-
6
- # from models.ade20k import ModelBuilder
7
- from annotator.lama.saicinpainting.utils import check_and_warn_input_range
8
-
9
-
10
- IMAGENET_MEAN = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None]
11
- IMAGENET_STD = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None]
12
-
13
-
14
- class PerceptualLoss(nn.Module):
15
- def __init__(self, normalize_inputs=True):
16
- super(PerceptualLoss, self).__init__()
17
-
18
- self.normalize_inputs = normalize_inputs
19
- self.mean_ = IMAGENET_MEAN
20
- self.std_ = IMAGENET_STD
21
-
22
- vgg = torchvision.models.vgg19(pretrained=True).features
23
- vgg_avg_pooling = []
24
-
25
- for weights in vgg.parameters():
26
- weights.requires_grad = False
27
-
28
- for module in vgg.modules():
29
- if module.__class__.__name__ == 'Sequential':
30
- continue
31
- elif module.__class__.__name__ == 'MaxPool2d':
32
- vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
33
- else:
34
- vgg_avg_pooling.append(module)
35
-
36
- self.vgg = nn.Sequential(*vgg_avg_pooling)
37
-
38
- def do_normalize_inputs(self, x):
39
- return (x - self.mean_.to(x.device)) / self.std_.to(x.device)
40
-
41
- def partial_losses(self, input, target, mask=None):
42
- check_and_warn_input_range(target, 0, 1, 'PerceptualLoss target in partial_losses')
43
-
44
- # we expect input and target to be in [0, 1] range
45
- losses = []
46
-
47
- if self.normalize_inputs:
48
- features_input = self.do_normalize_inputs(input)
49
- features_target = self.do_normalize_inputs(target)
50
- else:
51
- features_input = input
52
- features_target = target
53
-
54
- for layer in self.vgg[:30]:
55
-
56
- features_input = layer(features_input)
57
- features_target = layer(features_target)
58
-
59
- if layer.__class__.__name__ == 'ReLU':
60
- loss = F.mse_loss(features_input, features_target, reduction='none')
61
-
62
- if mask is not None:
63
- cur_mask = F.interpolate(mask, size=features_input.shape[-2:],
64
- mode='bilinear', align_corners=False)
65
- loss = loss * (1 - cur_mask)
66
-
67
- loss = loss.mean(dim=tuple(range(1, len(loss.shape))))
68
- losses.append(loss)
69
-
70
- return losses
71
-
72
- def forward(self, input, target, mask=None):
73
- losses = self.partial_losses(input, target, mask=mask)
74
- return torch.stack(losses).sum(dim=0)
75
-
76
- def get_global_features(self, input):
77
- check_and_warn_input_range(input, 0, 1, 'PerceptualLoss input in get_global_features')
78
-
79
- if self.normalize_inputs:
80
- features_input = self.do_normalize_inputs(input)
81
- else:
82
- features_input = input
83
-
84
- features_input = self.vgg(features_input)
85
- return features_input
86
-
87
-
88
- class ResNetPL(nn.Module):
89
- def __init__(self, weight=1,
90
- weights_path=None, arch_encoder='resnet50dilated', segmentation=True):
91
- super().__init__()
92
- self.impl = ModelBuilder.get_encoder(weights_path=weights_path,
93
- arch_encoder=arch_encoder,
94
- arch_decoder='ppm_deepsup',
95
- fc_dim=2048,
96
- segmentation=segmentation)
97
- self.impl.eval()
98
- for w in self.impl.parameters():
99
- w.requires_grad_(False)
100
-
101
- self.weight = weight
102
-
103
- def forward(self, pred, target):
104
- pred = (pred - IMAGENET_MEAN.to(pred)) / IMAGENET_STD.to(pred)
105
- target = (target - IMAGENET_MEAN.to(target)) / IMAGENET_STD.to(target)
106
-
107
- pred_feats = self.impl(pred, return_feature_maps=True)
108
- target_feats = self.impl(target, return_feature_maps=True)
109
-
110
- result = torch.stack([F.mse_loss(cur_pred, cur_target)
111
- for cur_pred, cur_target
112
- in zip(pred_feats, target_feats)]).sum() * self.weight
113
- return result
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/segmentation.py DELETED
@@ -1,43 +0,0 @@
1
- import torch
2
- import torch.nn as nn
3
- import torch.nn.functional as F
4
-
5
- from .constants import weights as constant_weights
6
-
7
-
8
- class CrossEntropy2d(nn.Module):
9
- def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs):
10
- """
11
- weight (Tensor, optional): a manual rescaling weight given to each class.
12
- If given, has to be a Tensor of size "nclasses"
13
- """
14
- super(CrossEntropy2d, self).__init__()
15
- self.reduction = reduction
16
- self.ignore_label = ignore_label
17
- self.weights = weights
18
- if self.weights is not None:
19
- device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
20
- self.weights = torch.FloatTensor(constant_weights[weights]).to(device)
21
-
22
- def forward(self, predict, target):
23
- """
24
- Args:
25
- predict:(n, c, h, w)
26
- target:(n, 1, h, w)
27
- """
28
- target = target.long()
29
- assert not target.requires_grad
30
- assert predict.dim() == 4, "{0}".format(predict.size())
31
- assert target.dim() == 4, "{0}".format(target.size())
32
- assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0))
33
- assert target.size(1) == 1, "{0}".format(target.size(1))
34
- assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2))
35
- assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3))
36
- target = target.squeeze(1)
37
- n, c, h, w = predict.size()
38
- target_mask = (target >= 0) * (target != self.ignore_label)
39
- target = target[target_mask]
40
- predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
41
- predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
42
- loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction)
43
- return loss
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/losses/style_loss.py DELETED
@@ -1,155 +0,0 @@
1
- import torch
2
- import torch.nn as nn
3
- import torchvision.models as models
4
-
5
-
6
- class PerceptualLoss(nn.Module):
7
- r"""
8
- Perceptual loss, VGG-based
9
- https://arxiv.org/abs/1603.08155
10
- https://github.com/dxyang/StyleTransfer/blob/master/utils.py
11
- """
12
-
13
- def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):
14
- super(PerceptualLoss, self).__init__()
15
- self.add_module('vgg', VGG19())
16
- self.criterion = torch.nn.L1Loss()
17
- self.weights = weights
18
-
19
- def __call__(self, x, y):
20
- # Compute features
21
- x_vgg, y_vgg = self.vgg(x), self.vgg(y)
22
-
23
- content_loss = 0.0
24
- content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1'])
25
- content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1'])
26
- content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1'])
27
- content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1'])
28
- content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1'])
29
-
30
-
31
- return content_loss
32
-
33
-
34
- class VGG19(torch.nn.Module):
35
- def __init__(self):
36
- super(VGG19, self).__init__()
37
- features = models.vgg19(pretrained=True).features
38
- self.relu1_1 = torch.nn.Sequential()
39
- self.relu1_2 = torch.nn.Sequential()
40
-
41
- self.relu2_1 = torch.nn.Sequential()
42
- self.relu2_2 = torch.nn.Sequential()
43
-
44
- self.relu3_1 = torch.nn.Sequential()
45
- self.relu3_2 = torch.nn.Sequential()
46
- self.relu3_3 = torch.nn.Sequential()
47
- self.relu3_4 = torch.nn.Sequential()
48
-
49
- self.relu4_1 = torch.nn.Sequential()
50
- self.relu4_2 = torch.nn.Sequential()
51
- self.relu4_3 = torch.nn.Sequential()
52
- self.relu4_4 = torch.nn.Sequential()
53
-
54
- self.relu5_1 = torch.nn.Sequential()
55
- self.relu5_2 = torch.nn.Sequential()
56
- self.relu5_3 = torch.nn.Sequential()
57
- self.relu5_4 = torch.nn.Sequential()
58
-
59
- for x in range(2):
60
- self.relu1_1.add_module(str(x), features[x])
61
-
62
- for x in range(2, 4):
63
- self.relu1_2.add_module(str(x), features[x])
64
-
65
- for x in range(4, 7):
66
- self.relu2_1.add_module(str(x), features[x])
67
-
68
- for x in range(7, 9):
69
- self.relu2_2.add_module(str(x), features[x])
70
-
71
- for x in range(9, 12):
72
- self.relu3_1.add_module(str(x), features[x])
73
-
74
- for x in range(12, 14):
75
- self.relu3_2.add_module(str(x), features[x])
76
-
77
- for x in range(14, 16):
78
- self.relu3_2.add_module(str(x), features[x])
79
-
80
- for x in range(16, 18):
81
- self.relu3_4.add_module(str(x), features[x])
82
-
83
- for x in range(18, 21):
84
- self.relu4_1.add_module(str(x), features[x])
85
-
86
- for x in range(21, 23):
87
- self.relu4_2.add_module(str(x), features[x])
88
-
89
- for x in range(23, 25):
90
- self.relu4_3.add_module(str(x), features[x])
91
-
92
- for x in range(25, 27):
93
- self.relu4_4.add_module(str(x), features[x])
94
-
95
- for x in range(27, 30):
96
- self.relu5_1.add_module(str(x), features[x])
97
-
98
- for x in range(30, 32):
99
- self.relu5_2.add_module(str(x), features[x])
100
-
101
- for x in range(32, 34):
102
- self.relu5_3.add_module(str(x), features[x])
103
-
104
- for x in range(34, 36):
105
- self.relu5_4.add_module(str(x), features[x])
106
-
107
- # don't need the gradients, just want the features
108
- for param in self.parameters():
109
- param.requires_grad = False
110
-
111
- def forward(self, x):
112
- relu1_1 = self.relu1_1(x)
113
- relu1_2 = self.relu1_2(relu1_1)
114
-
115
- relu2_1 = self.relu2_1(relu1_2)
116
- relu2_2 = self.relu2_2(relu2_1)
117
-
118
- relu3_1 = self.relu3_1(relu2_2)
119
- relu3_2 = self.relu3_2(relu3_1)
120
- relu3_3 = self.relu3_3(relu3_2)
121
- relu3_4 = self.relu3_4(relu3_3)
122
-
123
- relu4_1 = self.relu4_1(relu3_4)
124
- relu4_2 = self.relu4_2(relu4_1)
125
- relu4_3 = self.relu4_3(relu4_2)
126
- relu4_4 = self.relu4_4(relu4_3)
127
-
128
- relu5_1 = self.relu5_1(relu4_4)
129
- relu5_2 = self.relu5_2(relu5_1)
130
- relu5_3 = self.relu5_3(relu5_2)
131
- relu5_4 = self.relu5_4(relu5_3)
132
-
133
- out = {
134
- 'relu1_1': relu1_1,
135
- 'relu1_2': relu1_2,
136
-
137
- 'relu2_1': relu2_1,
138
- 'relu2_2': relu2_2,
139
-
140
- 'relu3_1': relu3_1,
141
- 'relu3_2': relu3_2,
142
- 'relu3_3': relu3_3,
143
- 'relu3_4': relu3_4,
144
-
145
- 'relu4_1': relu4_1,
146
- 'relu4_2': relu4_2,
147
- 'relu4_3': relu4_3,
148
- 'relu4_4': relu4_4,
149
-
150
- 'relu5_1': relu5_1,
151
- 'relu5_2': relu5_2,
152
- 'relu5_3': relu5_3,
153
- 'relu5_4': relu5_4,
154
- }
155
- return out
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/__init__.py DELETED
@@ -1,31 +0,0 @@
1
- import logging
2
-
3
- from annotator.lama.saicinpainting.training.modules.ffc import FFCResNetGenerator
4
- from annotator.lama.saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \
5
- NLayerDiscriminator, MultidilatedNLayerDiscriminator
6
-
7
- def make_generator(config, kind, **kwargs):
8
- logging.info(f'Make generator {kind}')
9
-
10
- if kind == 'pix2pixhd_multidilated':
11
- return MultiDilatedGlobalGenerator(**kwargs)
12
-
13
- if kind == 'pix2pixhd_global':
14
- return GlobalGenerator(**kwargs)
15
-
16
- if kind == 'ffc_resnet':
17
- return FFCResNetGenerator(**kwargs)
18
-
19
- raise ValueError(f'Unknown generator kind {kind}')
20
-
21
-
22
- def make_discriminator(kind, **kwargs):
23
- logging.info(f'Make discriminator {kind}')
24
-
25
- if kind == 'pix2pixhd_nlayer_multidilated':
26
- return MultidilatedNLayerDiscriminator(**kwargs)
27
-
28
- if kind == 'pix2pixhd_nlayer':
29
- return NLayerDiscriminator(**kwargs)
30
-
31
- raise ValueError(f'Unknown discriminator kind {kind}')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/base.py DELETED
@@ -1,80 +0,0 @@
1
- import abc
2
- from typing import Tuple, List
3
-
4
- import torch
5
- import torch.nn as nn
6
-
7
- from annotator.lama.saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
8
- from annotator.lama.saicinpainting.training.modules.multidilated_conv import MultidilatedConv
9
-
10
-
11
- class BaseDiscriminator(nn.Module):
12
- @abc.abstractmethod
13
- def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
14
- """
15
- Predict scores and get intermediate activations. Useful for feature matching loss
16
- :return tuple (scores, list of intermediate activations)
17
- """
18
- raise NotImplemented()
19
-
20
-
21
- def get_conv_block_ctor(kind='default'):
22
- if not isinstance(kind, str):
23
- return kind
24
- if kind == 'default':
25
- return nn.Conv2d
26
- if kind == 'depthwise':
27
- return DepthWiseSeperableConv
28
- if kind == 'multidilated':
29
- return MultidilatedConv
30
- raise ValueError(f'Unknown convolutional block kind {kind}')
31
-
32
-
33
- def get_norm_layer(kind='bn'):
34
- if not isinstance(kind, str):
35
- return kind
36
- if kind == 'bn':
37
- return nn.BatchNorm2d
38
- if kind == 'in':
39
- return nn.InstanceNorm2d
40
- raise ValueError(f'Unknown norm block kind {kind}')
41
-
42
-
43
- def get_activation(kind='tanh'):
44
- if kind == 'tanh':
45
- return nn.Tanh()
46
- if kind == 'sigmoid':
47
- return nn.Sigmoid()
48
- if kind is False:
49
- return nn.Identity()
50
- raise ValueError(f'Unknown activation kind {kind}')
51
-
52
-
53
- class SimpleMultiStepGenerator(nn.Module):
54
- def __init__(self, steps: List[nn.Module]):
55
- super().__init__()
56
- self.steps = nn.ModuleList(steps)
57
-
58
- def forward(self, x):
59
- cur_in = x
60
- outs = []
61
- for step in self.steps:
62
- cur_out = step(cur_in)
63
- outs.append(cur_out)
64
- cur_in = torch.cat((cur_in, cur_out), dim=1)
65
- return torch.cat(outs[::-1], dim=1)
66
-
67
- def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features):
68
- if kind == 'convtranspose':
69
- return [nn.ConvTranspose2d(min(max_features, ngf * mult),
70
- min(max_features, int(ngf * mult / 2)),
71
- kernel_size=3, stride=2, padding=1, output_padding=1),
72
- norm_layer(min(max_features, int(ngf * mult / 2))), activation]
73
- elif kind == 'bilinear':
74
- return [nn.Upsample(scale_factor=2, mode='bilinear'),
75
- DepthWiseSeperableConv(min(max_features, ngf * mult),
76
- min(max_features, int(ngf * mult / 2)),
77
- kernel_size=3, stride=1, padding=1),
78
- norm_layer(min(max_features, int(ngf * mult / 2))), activation]
79
- else:
80
- raise Exception(f"Invalid deconv kind: {kind}")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/depthwise_sep_conv.py DELETED
@@ -1,17 +0,0 @@
1
- import torch
2
- import torch.nn as nn
3
-
4
- class DepthWiseSeperableConv(nn.Module):
5
- def __init__(self, in_dim, out_dim, *args, **kwargs):
6
- super().__init__()
7
- if 'groups' in kwargs:
8
- # ignoring groups for Depthwise Sep Conv
9
- del kwargs['groups']
10
-
11
- self.depthwise = nn.Conv2d(in_dim, in_dim, *args, groups=in_dim, **kwargs)
12
- self.pointwise = nn.Conv2d(in_dim, out_dim, kernel_size=1)
13
-
14
- def forward(self, x):
15
- out = self.depthwise(x)
16
- out = self.pointwise(out)
17
- return out
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/fake_fakes.py DELETED
@@ -1,47 +0,0 @@
1
- import torch
2
- from kornia import SamplePadding
3
- from kornia.augmentation import RandomAffine, CenterCrop
4
-
5
-
6
- class FakeFakesGenerator:
7
- def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):
8
- self.grad_aug = RandomAffine(degrees=360,
9
- translate=0.2,
10
- padding_mode=SamplePadding.REFLECTION,
11
- keepdim=False,
12
- p=1)
13
- self.img_aug = RandomAffine(degrees=img_aug_degree,
14
- translate=img_aug_translate,
15
- padding_mode=SamplePadding.REFLECTION,
16
- keepdim=True,
17
- p=1)
18
- self.aug_proba = aug_proba
19
-
20
- def __call__(self, input_images, masks):
21
- blend_masks = self._fill_masks_with_gradient(masks)
22
- blend_target = self._make_blend_target(input_images)
23
- result = input_images * (1 - blend_masks) + blend_target * blend_masks
24
- return result, blend_masks
25
-
26
- def _make_blend_target(self, input_images):
27
- batch_size = input_images.shape[0]
28
- permuted = input_images[torch.randperm(batch_size)]
29
- augmented = self.img_aug(input_images)
30
- is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()
31
- result = augmented * is_aug + permuted * (1 - is_aug)
32
- return result
33
-
34
- def _fill_masks_with_gradient(self, masks):
35
- batch_size, _, height, width = masks.shape
36
- grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \
37
- .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)
38
- grad = self.grad_aug(grad)
39
- grad = CenterCrop((height, width))(grad)
40
- grad *= masks
41
-
42
- grad_for_min = grad + (1 - masks) * 10
43
- grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]
44
- grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6
45
- grad.clamp_(min=0, max=1)
46
-
47
- return grad
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/ffc.py DELETED
@@ -1,485 +0,0 @@
1
- # Fast Fourier Convolution NeurIPS 2020
2
- # original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
3
- # paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
4
-
5
- import numpy as np
6
- import torch
7
- import torch.nn as nn
8
- import torch.nn.functional as F
9
-
10
- from annotator.lama.saicinpainting.training.modules.base import get_activation, BaseDiscriminator
11
- from annotator.lama.saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper
12
- from annotator.lama.saicinpainting.training.modules.squeeze_excitation import SELayer
13
- from annotator.lama.saicinpainting.utils import get_shape
14
-
15
-
16
- class FFCSE_block(nn.Module):
17
-
18
- def __init__(self, channels, ratio_g):
19
- super(FFCSE_block, self).__init__()
20
- in_cg = int(channels * ratio_g)
21
- in_cl = channels - in_cg
22
- r = 16
23
-
24
- self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
25
- self.conv1 = nn.Conv2d(channels, channels // r,
26
- kernel_size=1, bias=True)
27
- self.relu1 = nn.ReLU(inplace=True)
28
- self.conv_a2l = None if in_cl == 0 else nn.Conv2d(
29
- channels // r, in_cl, kernel_size=1, bias=True)
30
- self.conv_a2g = None if in_cg == 0 else nn.Conv2d(
31
- channels // r, in_cg, kernel_size=1, bias=True)
32
- self.sigmoid = nn.Sigmoid()
33
-
34
- def forward(self, x):
35
- x = x if type(x) is tuple else (x, 0)
36
- id_l, id_g = x
37
-
38
- x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)
39
- x = self.avgpool(x)
40
- x = self.relu1(self.conv1(x))
41
-
42
- x_l = 0 if self.conv_a2l is None else id_l * \
43
- self.sigmoid(self.conv_a2l(x))
44
- x_g = 0 if self.conv_a2g is None else id_g * \
45
- self.sigmoid(self.conv_a2g(x))
46
- return x_l, x_g
47
-
48
-
49
- class FourierUnit(nn.Module):
50
-
51
- def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
52
- spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
53
- # bn_layer not used
54
- super(FourierUnit, self).__init__()
55
- self.groups = groups
56
-
57
- self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
58
- out_channels=out_channels * 2,
59
- kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
60
- self.bn = torch.nn.BatchNorm2d(out_channels * 2)
61
- self.relu = torch.nn.ReLU(inplace=True)
62
-
63
- # squeeze and excitation block
64
- self.use_se = use_se
65
- if use_se:
66
- if se_kwargs is None:
67
- se_kwargs = {}
68
- self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
69
-
70
- self.spatial_scale_factor = spatial_scale_factor
71
- self.spatial_scale_mode = spatial_scale_mode
72
- self.spectral_pos_encoding = spectral_pos_encoding
73
- self.ffc3d = ffc3d
74
- self.fft_norm = fft_norm
75
-
76
- def forward(self, x):
77
- batch = x.shape[0]
78
-
79
- if self.spatial_scale_factor is not None:
80
- orig_size = x.shape[-2:]
81
- x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)
82
-
83
- r_size = x.size()
84
- # (batch, c, h, w/2+1, 2)
85
- fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
86
- ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
87
- ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
88
- ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
89
- ffted = ffted.view((batch, -1,) + ffted.size()[3:])
90
-
91
- if self.spectral_pos_encoding:
92
- height, width = ffted.shape[-2:]
93
- coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(ffted)
94
- coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
95
- ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
96
-
97
- if self.use_se:
98
- ffted = self.se(ffted)
99
-
100
- ffted = self.conv_layer(ffted) # (batch, c*2, h, w/2+1)
101
- ffted = self.relu(self.bn(ffted))
102
-
103
- ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
104
- 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
105
- ffted = torch.complex(ffted[..., 0], ffted[..., 1])
106
-
107
- ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
108
- output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
109
-
110
- if self.spatial_scale_factor is not None:
111
- output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
112
-
113
- return output
114
-
115
-
116
- class SeparableFourierUnit(nn.Module):
117
-
118
- def __init__(self, in_channels, out_channels, groups=1, kernel_size=3):
119
- # bn_layer not used
120
- super(SeparableFourierUnit, self).__init__()
121
- self.groups = groups
122
- row_out_channels = out_channels // 2
123
- col_out_channels = out_channels - row_out_channels
124
- self.row_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
125
- out_channels=row_out_channels * 2,
126
- kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed
127
- stride=1, padding=(kernel_size // 2, 0),
128
- padding_mode='reflect',
129
- groups=self.groups, bias=False)
130
- self.col_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
131
- out_channels=col_out_channels * 2,
132
- kernel_size=(kernel_size, 1), # kernel size is always like this, but the data will be transposed
133
- stride=1, padding=(kernel_size // 2, 0),
134
- padding_mode='reflect',
135
- groups=self.groups, bias=False)
136
- self.row_bn = torch.nn.BatchNorm2d(row_out_channels * 2)
137
- self.col_bn = torch.nn.BatchNorm2d(col_out_channels * 2)
138
- self.relu = torch.nn.ReLU(inplace=True)
139
-
140
- def process_branch(self, x, conv, bn):
141
- batch = x.shape[0]
142
-
143
- r_size = x.size()
144
- # (batch, c, h, w/2+1, 2)
145
- ffted = torch.fft.rfft(x, norm="ortho")
146
- ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
147
- ffted = ffted.permute(0, 1, 4, 2, 3).contiguous() # (batch, c, 2, h, w/2+1)
148
- ffted = ffted.view((batch, -1,) + ffted.size()[3:])
149
-
150
- ffted = self.relu(bn(conv(ffted)))
151
-
152
- ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
153
- 0, 1, 3, 4, 2).contiguous() # (batch,c, t, h, w/2+1, 2)
154
- ffted = torch.complex(ffted[..., 0], ffted[..., 1])
155
-
156
- output = torch.fft.irfft(ffted, s=x.shape[-1:], norm="ortho")
157
- return output
158
-
159
-
160
- def forward(self, x):
161
- rowwise = self.process_branch(x, self.row_conv, self.row_bn)
162
- colwise = self.process_branch(x.permute(0, 1, 3, 2), self.col_conv, self.col_bn).permute(0, 1, 3, 2)
163
- out = torch.cat((rowwise, colwise), dim=1)
164
- return out
165
-
166
-
167
- class SpectralTransform(nn.Module):
168
-
169
- def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, separable_fu=False, **fu_kwargs):
170
- # bn_layer not used
171
- super(SpectralTransform, self).__init__()
172
- self.enable_lfu = enable_lfu
173
- if stride == 2:
174
- self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
175
- else:
176
- self.downsample = nn.Identity()
177
-
178
- self.stride = stride
179
- self.conv1 = nn.Sequential(
180
- nn.Conv2d(in_channels, out_channels //
181
- 2, kernel_size=1, groups=groups, bias=False),
182
- nn.BatchNorm2d(out_channels // 2),
183
- nn.ReLU(inplace=True)
184
- )
185
- fu_class = SeparableFourierUnit if separable_fu else FourierUnit
186
- self.fu = fu_class(
187
- out_channels // 2, out_channels // 2, groups, **fu_kwargs)
188
- if self.enable_lfu:
189
- self.lfu = fu_class(
190
- out_channels // 2, out_channels // 2, groups)
191
- self.conv2 = torch.nn.Conv2d(
192
- out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)
193
-
194
- def forward(self, x):
195
-
196
- x = self.downsample(x)
197
- x = self.conv1(x)
198
- output = self.fu(x)
199
-
200
- if self.enable_lfu:
201
- n, c, h, w = x.shape
202
- split_no = 2
203
- split_s = h // split_no
204
- xs = torch.cat(torch.split(
205
- x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
206
- xs = torch.cat(torch.split(xs, split_s, dim=-1),
207
- dim=1).contiguous()
208
- xs = self.lfu(xs)
209
- xs = xs.repeat(1, 1, split_no, split_no).contiguous()
210
- else:
211
- xs = 0
212
-
213
- output = self.conv2(x + output + xs)
214
-
215
- return output
216
-
217
-
218
- class FFC(nn.Module):
219
-
220
- def __init__(self, in_channels, out_channels, kernel_size,
221
- ratio_gin, ratio_gout, stride=1, padding=0,
222
- dilation=1, groups=1, bias=False, enable_lfu=True,
223
- padding_type='reflect', gated=False, **spectral_kwargs):
224
- super(FFC, self).__init__()
225
-
226
- assert stride == 1 or stride == 2, "Stride should be 1 or 2."
227
- self.stride = stride
228
-
229
- in_cg = int(in_channels * ratio_gin)
230
- in_cl = in_channels - in_cg
231
- out_cg = int(out_channels * ratio_gout)
232
- out_cl = out_channels - out_cg
233
- #groups_g = 1 if groups == 1 else int(groups * ratio_gout)
234
- #groups_l = 1 if groups == 1 else groups - groups_g
235
-
236
- self.ratio_gin = ratio_gin
237
- self.ratio_gout = ratio_gout
238
- self.global_in_num = in_cg
239
-
240
- module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
241
- self.convl2l = module(in_cl, out_cl, kernel_size,
242
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
243
- module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
244
- self.convl2g = module(in_cl, out_cg, kernel_size,
245
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
246
- module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
247
- self.convg2l = module(in_cg, out_cl, kernel_size,
248
- stride, padding, dilation, groups, bias, padding_mode=padding_type)
249
- module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
250
- self.convg2g = module(
251
- in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)
252
-
253
- self.gated = gated
254
- module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
255
- self.gate = module(in_channels, 2, 1)
256
-
257
- def forward(self, x):
258
- x_l, x_g = x if type(x) is tuple else (x, 0)
259
- out_xl, out_xg = 0, 0
260
-
261
- if self.gated:
262
- total_input_parts = [x_l]
263
- if torch.is_tensor(x_g):
264
- total_input_parts.append(x_g)
265
- total_input = torch.cat(total_input_parts, dim=1)
266
-
267
- gates = torch.sigmoid(self.gate(total_input))
268
- g2l_gate, l2g_gate = gates.chunk(2, dim=1)
269
- else:
270
- g2l_gate, l2g_gate = 1, 1
271
-
272
- if self.ratio_gout != 1:
273
- out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
274
- if self.ratio_gout != 0:
275
- out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
276
-
277
- return out_xl, out_xg
278
-
279
-
280
- class FFC_BN_ACT(nn.Module):
281
-
282
- def __init__(self, in_channels, out_channels,
283
- kernel_size, ratio_gin, ratio_gout,
284
- stride=1, padding=0, dilation=1, groups=1, bias=False,
285
- norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity,
286
- padding_type='reflect',
287
- enable_lfu=True, **kwargs):
288
- super(FFC_BN_ACT, self).__init__()
289
- self.ffc = FFC(in_channels, out_channels, kernel_size,
290
- ratio_gin, ratio_gout, stride, padding, dilation,
291
- groups, bias, enable_lfu, padding_type=padding_type, **kwargs)
292
- lnorm = nn.Identity if ratio_gout == 1 else norm_layer
293
- gnorm = nn.Identity if ratio_gout == 0 else norm_layer
294
- global_channels = int(out_channels * ratio_gout)
295
- self.bn_l = lnorm(out_channels - global_channels)
296
- self.bn_g = gnorm(global_channels)
297
-
298
- lact = nn.Identity if ratio_gout == 1 else activation_layer
299
- gact = nn.Identity if ratio_gout == 0 else activation_layer
300
- self.act_l = lact(inplace=True)
301
- self.act_g = gact(inplace=True)
302
-
303
- def forward(self, x):
304
- x_l, x_g = self.ffc(x)
305
- x_l = self.act_l(self.bn_l(x_l))
306
- x_g = self.act_g(self.bn_g(x_g))
307
- return x_l, x_g
308
-
309
-
310
- class FFCResnetBlock(nn.Module):
311
- def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1,
312
- spatial_transform_kwargs=None, inline=False, **conv_kwargs):
313
- super().__init__()
314
- self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
315
- norm_layer=norm_layer,
316
- activation_layer=activation_layer,
317
- padding_type=padding_type,
318
- **conv_kwargs)
319
- self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
320
- norm_layer=norm_layer,
321
- activation_layer=activation_layer,
322
- padding_type=padding_type,
323
- **conv_kwargs)
324
- if spatial_transform_kwargs is not None:
325
- self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs)
326
- self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs)
327
- self.inline = inline
328
-
329
- def forward(self, x):
330
- if self.inline:
331
- x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
332
- else:
333
- x_l, x_g = x if type(x) is tuple else (x, 0)
334
-
335
- id_l, id_g = x_l, x_g
336
-
337
- x_l, x_g = self.conv1((x_l, x_g))
338
- x_l, x_g = self.conv2((x_l, x_g))
339
-
340
- x_l, x_g = id_l + x_l, id_g + x_g
341
- out = x_l, x_g
342
- if self.inline:
343
- out = torch.cat(out, dim=1)
344
- return out
345
-
346
-
347
- class ConcatTupleLayer(nn.Module):
348
- def forward(self, x):
349
- assert isinstance(x, tuple)
350
- x_l, x_g = x
351
- assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
352
- if not torch.is_tensor(x_g):
353
- return x_l
354
- return torch.cat(x, dim=1)
355
-
356
-
357
- class FFCResNetGenerator(nn.Module):
358
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
359
- padding_type='reflect', activation_layer=nn.ReLU,
360
- up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True),
361
- init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={},
362
- spatial_transform_layers=None, spatial_transform_kwargs={},
363
- add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}):
364
- assert (n_blocks >= 0)
365
- super().__init__()
366
-
367
- model = [nn.ReflectionPad2d(3),
368
- FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer,
369
- activation_layer=activation_layer, **init_conv_kwargs)]
370
-
371
- ### downsample
372
- for i in range(n_downsampling):
373
- mult = 2 ** i
374
- if i == n_downsampling - 1:
375
- cur_conv_kwargs = dict(downsample_conv_kwargs)
376
- cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0)
377
- else:
378
- cur_conv_kwargs = downsample_conv_kwargs
379
- model += [FFC_BN_ACT(min(max_features, ngf * mult),
380
- min(max_features, ngf * mult * 2),
381
- kernel_size=3, stride=2, padding=1,
382
- norm_layer=norm_layer,
383
- activation_layer=activation_layer,
384
- **cur_conv_kwargs)]
385
-
386
- mult = 2 ** n_downsampling
387
- feats_num_bottleneck = min(max_features, ngf * mult)
388
-
389
- ### resnet blocks
390
- for i in range(n_blocks):
391
- cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer,
392
- norm_layer=norm_layer, **resnet_conv_kwargs)
393
- if spatial_transform_layers is not None and i in spatial_transform_layers:
394
- cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs)
395
- model += [cur_resblock]
396
-
397
- model += [ConcatTupleLayer()]
398
-
399
- ### upsample
400
- for i in range(n_downsampling):
401
- mult = 2 ** (n_downsampling - i)
402
- model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
403
- min(max_features, int(ngf * mult / 2)),
404
- kernel_size=3, stride=2, padding=1, output_padding=1),
405
- up_norm_layer(min(max_features, int(ngf * mult / 2))),
406
- up_activation]
407
-
408
- if out_ffc:
409
- model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer,
410
- norm_layer=norm_layer, inline=True, **out_ffc_kwargs)]
411
-
412
- model += [nn.ReflectionPad2d(3),
413
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
414
- if add_out_act:
415
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
416
- self.model = nn.Sequential(*model)
417
-
418
- def forward(self, input):
419
- return self.model(input)
420
-
421
-
422
- class FFCNLayerDiscriminator(BaseDiscriminator):
423
- def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512,
424
- init_conv_kwargs={}, conv_kwargs={}):
425
- super().__init__()
426
- self.n_layers = n_layers
427
-
428
- def _act_ctor(inplace=True):
429
- return nn.LeakyReLU(negative_slope=0.2, inplace=inplace)
430
-
431
- kw = 3
432
- padw = int(np.ceil((kw-1.0)/2))
433
- sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer,
434
- activation_layer=_act_ctor, **init_conv_kwargs)]]
435
-
436
- nf = ndf
437
- for n in range(1, n_layers):
438
- nf_prev = nf
439
- nf = min(nf * 2, max_features)
440
-
441
- cur_model = [
442
- FFC_BN_ACT(nf_prev, nf,
443
- kernel_size=kw, stride=2, padding=padw,
444
- norm_layer=norm_layer,
445
- activation_layer=_act_ctor,
446
- **conv_kwargs)
447
- ]
448
- sequence.append(cur_model)
449
-
450
- nf_prev = nf
451
- nf = min(nf * 2, 512)
452
-
453
- cur_model = [
454
- FFC_BN_ACT(nf_prev, nf,
455
- kernel_size=kw, stride=1, padding=padw,
456
- norm_layer=norm_layer,
457
- activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs),
458
- **conv_kwargs),
459
- ConcatTupleLayer()
460
- ]
461
- sequence.append(cur_model)
462
-
463
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
464
-
465
- for n in range(len(sequence)):
466
- setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
467
-
468
- def get_all_activations(self, x):
469
- res = [x]
470
- for n in range(self.n_layers + 2):
471
- model = getattr(self, 'model' + str(n))
472
- res.append(model(res[-1]))
473
- return res[1:]
474
-
475
- def forward(self, x):
476
- act = self.get_all_activations(x)
477
- feats = []
478
- for out in act[:-1]:
479
- if isinstance(out, tuple):
480
- if torch.is_tensor(out[1]):
481
- out = torch.cat(out, dim=1)
482
- else:
483
- out = out[0]
484
- feats.append(out)
485
- return act[-1], feats
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/multidilated_conv.py DELETED
@@ -1,98 +0,0 @@
1
- import torch
2
- import torch.nn as nn
3
- import random
4
- from annotator.lama.saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
5
-
6
- class MultidilatedConv(nn.Module):
7
- def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True,
8
- shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs):
9
- super().__init__()
10
- convs = []
11
- self.equal_dim = equal_dim
12
- assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode
13
- if comb_mode in ('cat_out', 'cat_both'):
14
- self.cat_out = True
15
- if equal_dim:
16
- assert out_dim % dilation_num == 0
17
- out_dims = [out_dim // dilation_num] * dilation_num
18
- self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], [])
19
- else:
20
- out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
21
- out_dims.append(out_dim - sum(out_dims))
22
- index = []
23
- starts = [0] + out_dims[:-1]
24
- lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)]
25
- for i in range(out_dims[-1]):
26
- for j in range(dilation_num):
27
- index += list(range(starts[j], starts[j] + lengths[j]))
28
- starts[j] += lengths[j]
29
- self.index = index
30
- assert(len(index) == out_dim)
31
- self.out_dims = out_dims
32
- else:
33
- self.cat_out = False
34
- self.out_dims = [out_dim] * dilation_num
35
-
36
- if comb_mode in ('cat_in', 'cat_both'):
37
- if equal_dim:
38
- assert in_dim % dilation_num == 0
39
- in_dims = [in_dim // dilation_num] * dilation_num
40
- else:
41
- in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
42
- in_dims.append(in_dim - sum(in_dims))
43
- self.in_dims = in_dims
44
- self.cat_in = True
45
- else:
46
- self.cat_in = False
47
- self.in_dims = [in_dim] * dilation_num
48
-
49
- conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d
50
- dilation = min_dilation
51
- for i in range(dilation_num):
52
- if isinstance(padding, int):
53
- cur_padding = padding * dilation
54
- else:
55
- cur_padding = padding[i]
56
- convs.append(conv_type(
57
- self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs
58
- ))
59
- if i > 0 and shared_weights:
60
- convs[-1].weight = convs[0].weight
61
- convs[-1].bias = convs[0].bias
62
- dilation *= 2
63
- self.convs = nn.ModuleList(convs)
64
-
65
- self.shuffle_in_channels = shuffle_in_channels
66
- if self.shuffle_in_channels:
67
- # shuffle list as shuffling of tensors is nondeterministic
68
- in_channels_permute = list(range(in_dim))
69
- random.shuffle(in_channels_permute)
70
- # save as buffer so it is saved and loaded with checkpoint
71
- self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute))
72
-
73
- def forward(self, x):
74
- if self.shuffle_in_channels:
75
- x = x[:, self.in_channels_permute]
76
-
77
- outs = []
78
- if self.cat_in:
79
- if self.equal_dim:
80
- x = x.chunk(len(self.convs), dim=1)
81
- else:
82
- new_x = []
83
- start = 0
84
- for dim in self.in_dims:
85
- new_x.append(x[:, start:start+dim])
86
- start += dim
87
- x = new_x
88
- for i, conv in enumerate(self.convs):
89
- if self.cat_in:
90
- input = x[i]
91
- else:
92
- input = x
93
- outs.append(conv(input))
94
- if self.cat_out:
95
- out = torch.cat(outs, dim=1)[:, self.index]
96
- else:
97
- out = sum(outs)
98
- return out
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/multiscale.py DELETED
@@ -1,244 +0,0 @@
1
- from typing import List, Tuple, Union, Optional
2
-
3
- import torch
4
- import torch.nn as nn
5
- import torch.nn.functional as F
6
-
7
- from annotator.lama.saicinpainting.training.modules.base import get_conv_block_ctor, get_activation
8
- from annotator.lama.saicinpainting.training.modules.pix2pixhd import ResnetBlock
9
-
10
-
11
- class ResNetHead(nn.Module):
12
- def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
13
- padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)):
14
- assert (n_blocks >= 0)
15
- super(ResNetHead, self).__init__()
16
-
17
- conv_layer = get_conv_block_ctor(conv_kind)
18
-
19
- model = [nn.ReflectionPad2d(3),
20
- conv_layer(input_nc, ngf, kernel_size=7, padding=0),
21
- norm_layer(ngf),
22
- activation]
23
-
24
- ### downsample
25
- for i in range(n_downsampling):
26
- mult = 2 ** i
27
- model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
28
- norm_layer(ngf * mult * 2),
29
- activation]
30
-
31
- mult = 2 ** n_downsampling
32
-
33
- ### resnet blocks
34
- for i in range(n_blocks):
35
- model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
36
- conv_kind=conv_kind)]
37
-
38
- self.model = nn.Sequential(*model)
39
-
40
- def forward(self, input):
41
- return self.model(input)
42
-
43
-
44
- class ResNetTail(nn.Module):
45
- def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
46
- padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
47
- up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
48
- add_in_proj=None):
49
- assert (n_blocks >= 0)
50
- super(ResNetTail, self).__init__()
51
-
52
- mult = 2 ** n_downsampling
53
-
54
- model = []
55
-
56
- if add_in_proj is not None:
57
- model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1))
58
-
59
- ### resnet blocks
60
- for i in range(n_blocks):
61
- model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
62
- conv_kind=conv_kind)]
63
-
64
- ### upsample
65
- for i in range(n_downsampling):
66
- mult = 2 ** (n_downsampling - i)
67
- model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
68
- output_padding=1),
69
- up_norm_layer(int(ngf * mult / 2)),
70
- up_activation]
71
- self.model = nn.Sequential(*model)
72
-
73
- out_layers = []
74
- for _ in range(out_extra_layers_n):
75
- out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0),
76
- up_norm_layer(ngf),
77
- up_activation]
78
- out_layers += [nn.ReflectionPad2d(3),
79
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
80
-
81
- if add_out_act:
82
- out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act))
83
-
84
- self.out_proj = nn.Sequential(*out_layers)
85
-
86
- def forward(self, input, return_last_act=False):
87
- features = self.model(input)
88
- out = self.out_proj(features)
89
- if return_last_act:
90
- return out, features
91
- else:
92
- return out
93
-
94
-
95
- class MultiscaleResNet(nn.Module):
96
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3,
97
- norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
98
- up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
99
- out_cumulative=False, return_only_hr=False):
100
- super().__init__()
101
-
102
- self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling,
103
- n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type,
104
- conv_kind=conv_kind, activation=activation)
105
- for i in range(n_scales)])
106
- tail_in_feats = ngf * (2 ** n_downsampling) + ngf
107
- self.tails = nn.ModuleList([ResNetTail(output_nc,
108
- ngf=ngf, n_downsampling=n_downsampling,
109
- n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type,
110
- conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer,
111
- up_activation=up_activation, add_out_act=add_out_act,
112
- out_extra_layers_n=out_extra_layers_n,
113
- add_in_proj=None if (i == n_scales - 1) else tail_in_feats)
114
- for i in range(n_scales)])
115
-
116
- self.out_cumulative = out_cumulative
117
- self.return_only_hr = return_only_hr
118
-
119
- @property
120
- def num_scales(self):
121
- return len(self.heads)
122
-
123
- def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
124
- -> Union[torch.Tensor, List[torch.Tensor]]:
125
- """
126
- :param ms_inputs: List of inputs of different resolutions from HR to LR
127
- :param smallest_scales_num: int or None, number of smallest scales to take at input
128
- :return: Depending on return_only_hr:
129
- True: Only the most HR output
130
- False: List of outputs of different resolutions from HR to LR
131
- """
132
- if smallest_scales_num is None:
133
- assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num)
134
- smallest_scales_num = len(self.heads)
135
- else:
136
- assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num)
137
-
138
- cur_heads = self.heads[-smallest_scales_num:]
139
- ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)]
140
-
141
- all_outputs = []
142
- prev_tail_features = None
143
- for i in range(len(ms_features)):
144
- scale_i = -i - 1
145
-
146
- cur_tail_input = ms_features[-i - 1]
147
- if prev_tail_features is not None:
148
- if prev_tail_features.shape != cur_tail_input.shape:
149
- prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:],
150
- mode='bilinear', align_corners=False)
151
- cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1)
152
-
153
- cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True)
154
-
155
- prev_tail_features = cur_tail_feats
156
- all_outputs.append(cur_out)
157
-
158
- if self.out_cumulative:
159
- all_outputs_cum = [all_outputs[0]]
160
- for i in range(1, len(ms_features)):
161
- cur_out = all_outputs[i]
162
- cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:],
163
- mode='bilinear', align_corners=False)
164
- all_outputs_cum.append(cur_out_cum)
165
- all_outputs = all_outputs_cum
166
-
167
- if self.return_only_hr:
168
- return all_outputs[-1]
169
- else:
170
- return all_outputs[::-1]
171
-
172
-
173
- class MultiscaleDiscriminatorSimple(nn.Module):
174
- def __init__(self, ms_impl):
175
- super().__init__()
176
- self.ms_impl = nn.ModuleList(ms_impl)
177
-
178
- @property
179
- def num_scales(self):
180
- return len(self.ms_impl)
181
-
182
- def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
183
- -> List[Tuple[torch.Tensor, List[torch.Tensor]]]:
184
- """
185
- :param ms_inputs: List of inputs of different resolutions from HR to LR
186
- :param smallest_scales_num: int or None, number of smallest scales to take at input
187
- :return: List of pairs (prediction, features) for different resolutions from HR to LR
188
- """
189
- if smallest_scales_num is None:
190
- assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
191
- smallest_scales_num = len(self.heads)
192
- else:
193
- assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \
194
- (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
195
-
196
- return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)]
197
-
198
-
199
- class SingleToMultiScaleInputMixin:
200
- def forward(self, x: torch.Tensor) -> List:
201
- orig_height, orig_width = x.shape[2:]
202
- factors = [2 ** i for i in range(self.num_scales)]
203
- ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False)
204
- for f in factors]
205
- return super().forward(ms_inputs)
206
-
207
-
208
- class GeneratorMultiToSingleOutputMixin:
209
- def forward(self, x):
210
- return super().forward(x)[0]
211
-
212
-
213
- class DiscriminatorMultiToSingleOutputMixin:
214
- def forward(self, x):
215
- out_feat_tuples = super().forward(x)
216
- return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist]
217
-
218
-
219
- class DiscriminatorMultiToSingleOutputStackedMixin:
220
- def __init__(self, *args, return_feats_only_levels=None, **kwargs):
221
- super().__init__(*args, **kwargs)
222
- self.return_feats_only_levels = return_feats_only_levels
223
-
224
- def forward(self, x):
225
- out_feat_tuples = super().forward(x)
226
- outs = [out for out, _ in out_feat_tuples]
227
- scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:],
228
- mode='bilinear', align_corners=False)
229
- for cur_out in outs[1:]]
230
- out = torch.cat(scaled_outs, dim=1)
231
- if self.return_feats_only_levels is not None:
232
- feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels]
233
- else:
234
- feat_lists = [flist for _, flist in out_feat_tuples]
235
- feats = [f for flist in feat_lists for f in flist]
236
- return out, feats
237
-
238
-
239
- class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple):
240
- pass
241
-
242
-
243
- class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet):
244
- pass
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/pix2pixhd.py DELETED
@@ -1,669 +0,0 @@
1
- # original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py
2
- import collections
3
- from functools import partial
4
- import functools
5
- import logging
6
- from collections import defaultdict
7
-
8
- import numpy as np
9
- import torch.nn as nn
10
-
11
- from annotator.lama.saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation
12
- from annotator.lama.saicinpainting.training.modules.ffc import FFCResnetBlock
13
- from annotator.lama.saicinpainting.training.modules.multidilated_conv import MultidilatedConv
14
-
15
- class DotDict(defaultdict):
16
- # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary
17
- """dot.notation access to dictionary attributes"""
18
- __getattr__ = defaultdict.get
19
- __setattr__ = defaultdict.__setitem__
20
- __delattr__ = defaultdict.__delitem__
21
-
22
- class Identity(nn.Module):
23
- def __init__(self):
24
- super().__init__()
25
-
26
- def forward(self, x):
27
- return x
28
-
29
-
30
- class ResnetBlock(nn.Module):
31
- def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
32
- dilation=1, in_dim=None, groups=1, second_dilation=None):
33
- super(ResnetBlock, self).__init__()
34
- self.in_dim = in_dim
35
- self.dim = dim
36
- if second_dilation is None:
37
- second_dilation = dilation
38
- self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
39
- conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
40
- second_dilation=second_dilation)
41
-
42
- if self.in_dim is not None:
43
- self.input_conv = nn.Conv2d(in_dim, dim, 1)
44
-
45
- self.out_channnels = dim
46
-
47
- def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
48
- dilation=1, in_dim=None, groups=1, second_dilation=1):
49
- conv_layer = get_conv_block_ctor(conv_kind)
50
-
51
- conv_block = []
52
- p = 0
53
- if padding_type == 'reflect':
54
- conv_block += [nn.ReflectionPad2d(dilation)]
55
- elif padding_type == 'replicate':
56
- conv_block += [nn.ReplicationPad2d(dilation)]
57
- elif padding_type == 'zero':
58
- p = dilation
59
- else:
60
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
61
-
62
- if in_dim is None:
63
- in_dim = dim
64
-
65
- conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation),
66
- norm_layer(dim),
67
- activation]
68
- if use_dropout:
69
- conv_block += [nn.Dropout(0.5)]
70
-
71
- p = 0
72
- if padding_type == 'reflect':
73
- conv_block += [nn.ReflectionPad2d(second_dilation)]
74
- elif padding_type == 'replicate':
75
- conv_block += [nn.ReplicationPad2d(second_dilation)]
76
- elif padding_type == 'zero':
77
- p = second_dilation
78
- else:
79
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
80
- conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups),
81
- norm_layer(dim)]
82
-
83
- return nn.Sequential(*conv_block)
84
-
85
- def forward(self, x):
86
- x_before = x
87
- if self.in_dim is not None:
88
- x = self.input_conv(x)
89
- out = x + self.conv_block(x_before)
90
- return out
91
-
92
- class ResnetBlock5x5(nn.Module):
93
- def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
94
- dilation=1, in_dim=None, groups=1, second_dilation=None):
95
- super(ResnetBlock5x5, self).__init__()
96
- self.in_dim = in_dim
97
- self.dim = dim
98
- if second_dilation is None:
99
- second_dilation = dilation
100
- self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
101
- conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
102
- second_dilation=second_dilation)
103
-
104
- if self.in_dim is not None:
105
- self.input_conv = nn.Conv2d(in_dim, dim, 1)
106
-
107
- self.out_channnels = dim
108
-
109
- def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
110
- dilation=1, in_dim=None, groups=1, second_dilation=1):
111
- conv_layer = get_conv_block_ctor(conv_kind)
112
-
113
- conv_block = []
114
- p = 0
115
- if padding_type == 'reflect':
116
- conv_block += [nn.ReflectionPad2d(dilation * 2)]
117
- elif padding_type == 'replicate':
118
- conv_block += [nn.ReplicationPad2d(dilation * 2)]
119
- elif padding_type == 'zero':
120
- p = dilation * 2
121
- else:
122
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
123
-
124
- if in_dim is None:
125
- in_dim = dim
126
-
127
- conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation),
128
- norm_layer(dim),
129
- activation]
130
- if use_dropout:
131
- conv_block += [nn.Dropout(0.5)]
132
-
133
- p = 0
134
- if padding_type == 'reflect':
135
- conv_block += [nn.ReflectionPad2d(second_dilation * 2)]
136
- elif padding_type == 'replicate':
137
- conv_block += [nn.ReplicationPad2d(second_dilation * 2)]
138
- elif padding_type == 'zero':
139
- p = second_dilation * 2
140
- else:
141
- raise NotImplementedError('padding [%s] is not implemented' % padding_type)
142
- conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups),
143
- norm_layer(dim)]
144
-
145
- return nn.Sequential(*conv_block)
146
-
147
- def forward(self, x):
148
- x_before = x
149
- if self.in_dim is not None:
150
- x = self.input_conv(x)
151
- out = x + self.conv_block(x_before)
152
- return out
153
-
154
-
155
- class MultidilatedResnetBlock(nn.Module):
156
- def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False):
157
- super().__init__()
158
- self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout)
159
-
160
- def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1):
161
- conv_block = []
162
- conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
163
- norm_layer(dim),
164
- activation]
165
- if use_dropout:
166
- conv_block += [nn.Dropout(0.5)]
167
-
168
- conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
169
- norm_layer(dim)]
170
-
171
- return nn.Sequential(*conv_block)
172
-
173
- def forward(self, x):
174
- out = x + self.conv_block(x)
175
- return out
176
-
177
-
178
- class MultiDilatedGlobalGenerator(nn.Module):
179
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
180
- n_blocks=3, norm_layer=nn.BatchNorm2d,
181
- padding_type='reflect', conv_kind='default',
182
- deconv_kind='convtranspose', activation=nn.ReLU(True),
183
- up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
184
- add_out_act=True, max_features=1024, multidilation_kwargs={},
185
- ffc_positions=None, ffc_kwargs={}):
186
- assert (n_blocks >= 0)
187
- super().__init__()
188
-
189
- conv_layer = get_conv_block_ctor(conv_kind)
190
- resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs)
191
- norm_layer = get_norm_layer(norm_layer)
192
- if affine is not None:
193
- norm_layer = partial(norm_layer, affine=affine)
194
- up_norm_layer = get_norm_layer(up_norm_layer)
195
- if affine is not None:
196
- up_norm_layer = partial(up_norm_layer, affine=affine)
197
-
198
- model = [nn.ReflectionPad2d(3),
199
- conv_layer(input_nc, ngf, kernel_size=7, padding=0),
200
- norm_layer(ngf),
201
- activation]
202
-
203
- identity = Identity()
204
- ### downsample
205
- for i in range(n_downsampling):
206
- mult = 2 ** i
207
-
208
- model += [conv_layer(min(max_features, ngf * mult),
209
- min(max_features, ngf * mult * 2),
210
- kernel_size=3, stride=2, padding=1),
211
- norm_layer(min(max_features, ngf * mult * 2)),
212
- activation]
213
-
214
- mult = 2 ** n_downsampling
215
- feats_num_bottleneck = min(max_features, ngf * mult)
216
-
217
- ### resnet blocks
218
- for i in range(n_blocks):
219
- if ffc_positions is not None and i in ffc_positions:
220
- model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
221
- inline=True, **ffc_kwargs)]
222
- model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
223
- conv_layer=resnet_conv_layer, activation=activation,
224
- norm_layer=norm_layer)]
225
-
226
- ### upsample
227
- for i in range(n_downsampling):
228
- mult = 2 ** (n_downsampling - i)
229
- model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
230
- model += [nn.ReflectionPad2d(3),
231
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
232
- if add_out_act:
233
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
234
- self.model = nn.Sequential(*model)
235
-
236
- def forward(self, input):
237
- return self.model(input)
238
-
239
- class ConfigGlobalGenerator(nn.Module):
240
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
241
- n_blocks=3, norm_layer=nn.BatchNorm2d,
242
- padding_type='reflect', conv_kind='default',
243
- deconv_kind='convtranspose', activation=nn.ReLU(True),
244
- up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
245
- add_out_act=True, max_features=1024,
246
- manual_block_spec=[],
247
- resnet_block_kind='multidilatedresnetblock',
248
- resnet_conv_kind='multidilated',
249
- resnet_dilation=1,
250
- multidilation_kwargs={}):
251
- assert (n_blocks >= 0)
252
- super().__init__()
253
-
254
- conv_layer = get_conv_block_ctor(conv_kind)
255
- resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs)
256
- norm_layer = get_norm_layer(norm_layer)
257
- if affine is not None:
258
- norm_layer = partial(norm_layer, affine=affine)
259
- up_norm_layer = get_norm_layer(up_norm_layer)
260
- if affine is not None:
261
- up_norm_layer = partial(up_norm_layer, affine=affine)
262
-
263
- model = [nn.ReflectionPad2d(3),
264
- conv_layer(input_nc, ngf, kernel_size=7, padding=0),
265
- norm_layer(ngf),
266
- activation]
267
-
268
- identity = Identity()
269
-
270
- ### downsample
271
- for i in range(n_downsampling):
272
- mult = 2 ** i
273
- model += [conv_layer(min(max_features, ngf * mult),
274
- min(max_features, ngf * mult * 2),
275
- kernel_size=3, stride=2, padding=1),
276
- norm_layer(min(max_features, ngf * mult * 2)),
277
- activation]
278
-
279
- mult = 2 ** n_downsampling
280
- feats_num_bottleneck = min(max_features, ngf * mult)
281
-
282
- if len(manual_block_spec) == 0:
283
- manual_block_spec = [
284
- DotDict(lambda : None, {
285
- 'n_blocks': n_blocks,
286
- 'use_default': True})
287
- ]
288
-
289
- ### resnet blocks
290
- for block_spec in manual_block_spec:
291
- def make_and_add_blocks(model, block_spec):
292
- block_spec = DotDict(lambda : None, block_spec)
293
- if not block_spec.use_default:
294
- resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs)
295
- resnet_conv_kind = block_spec.resnet_conv_kind
296
- resnet_block_kind = block_spec.resnet_block_kind
297
- if block_spec.resnet_dilation is not None:
298
- resnet_dilation = block_spec.resnet_dilation
299
- for i in range(block_spec.n_blocks):
300
- if resnet_block_kind == "multidilatedresnetblock":
301
- model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
302
- conv_layer=resnet_conv_layer, activation=activation,
303
- norm_layer=norm_layer)]
304
- if resnet_block_kind == "resnetblock":
305
- model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
306
- conv_kind=resnet_conv_kind)]
307
- if resnet_block_kind == "resnetblock5x5":
308
- model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
309
- conv_kind=resnet_conv_kind)]
310
- if resnet_block_kind == "resnetblockdwdil":
311
- model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
312
- conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)]
313
- make_and_add_blocks(model, block_spec)
314
-
315
- ### upsample
316
- for i in range(n_downsampling):
317
- mult = 2 ** (n_downsampling - i)
318
- model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
319
- model += [nn.ReflectionPad2d(3),
320
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
321
- if add_out_act:
322
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
323
- self.model = nn.Sequential(*model)
324
-
325
- def forward(self, input):
326
- return self.model(input)
327
-
328
-
329
- def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs):
330
- blocks = []
331
- for i in range(dilated_blocks_n):
332
- if dilation_block_kind == 'simple':
333
- blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1)))
334
- elif dilation_block_kind == 'multi':
335
- blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs))
336
- else:
337
- raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"')
338
- return blocks
339
-
340
-
341
- class GlobalGenerator(nn.Module):
342
- def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
343
- padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
344
- up_norm_layer=nn.BatchNorm2d, affine=None,
345
- up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0,
346
- dilated_blocks_n_middle=0,
347
- add_out_act=True,
348
- max_features=1024, is_resblock_depthwise=False,
349
- ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None,
350
- dilation_block_kind='simple', multidilation_kwargs={}):
351
- assert (n_blocks >= 0)
352
- super().__init__()
353
-
354
- conv_layer = get_conv_block_ctor(conv_kind)
355
- norm_layer = get_norm_layer(norm_layer)
356
- if affine is not None:
357
- norm_layer = partial(norm_layer, affine=affine)
358
- up_norm_layer = get_norm_layer(up_norm_layer)
359
- if affine is not None:
360
- up_norm_layer = partial(up_norm_layer, affine=affine)
361
-
362
- if ffc_positions is not None:
363
- ffc_positions = collections.Counter(ffc_positions)
364
-
365
- model = [nn.ReflectionPad2d(3),
366
- conv_layer(input_nc, ngf, kernel_size=7, padding=0),
367
- norm_layer(ngf),
368
- activation]
369
-
370
- identity = Identity()
371
- ### downsample
372
- for i in range(n_downsampling):
373
- mult = 2 ** i
374
-
375
- model += [conv_layer(min(max_features, ngf * mult),
376
- min(max_features, ngf * mult * 2),
377
- kernel_size=3, stride=2, padding=1),
378
- norm_layer(min(max_features, ngf * mult * 2)),
379
- activation]
380
-
381
- mult = 2 ** n_downsampling
382
- feats_num_bottleneck = min(max_features, ngf * mult)
383
-
384
- dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type,
385
- activation=activation, norm_layer=norm_layer)
386
- if dilation_block_kind == 'simple':
387
- dilated_block_kwargs['conv_kind'] = conv_kind
388
- elif dilation_block_kind == 'multi':
389
- dilated_block_kwargs['conv_layer'] = functools.partial(
390
- get_conv_block_ctor('multidilated'), **multidilation_kwargs)
391
-
392
- # dilated blocks at the start of the bottleneck sausage
393
- if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0:
394
- model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs)
395
-
396
- # resnet blocks
397
- for i in range(n_blocks):
398
- # dilated blocks at the middle of the bottleneck sausage
399
- if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0:
400
- model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs)
401
-
402
- if ffc_positions is not None and i in ffc_positions:
403
- for _ in range(ffc_positions[i]): # same position can occur more than once
404
- model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
405
- inline=True, **ffc_kwargs)]
406
-
407
- if is_resblock_depthwise:
408
- resblock_groups = feats_num_bottleneck
409
- else:
410
- resblock_groups = 1
411
-
412
- model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation,
413
- norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups,
414
- dilation=dilation, second_dilation=second_dilation)]
415
-
416
-
417
- # dilated blocks at the end of the bottleneck sausage
418
- if dilated_blocks_n is not None and dilated_blocks_n > 0:
419
- model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs)
420
-
421
- # upsample
422
- for i in range(n_downsampling):
423
- mult = 2 ** (n_downsampling - i)
424
- model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
425
- min(max_features, int(ngf * mult / 2)),
426
- kernel_size=3, stride=2, padding=1, output_padding=1),
427
- up_norm_layer(min(max_features, int(ngf * mult / 2))),
428
- up_activation]
429
- model += [nn.ReflectionPad2d(3),
430
- nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
431
- if add_out_act:
432
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
433
- self.model = nn.Sequential(*model)
434
-
435
- def forward(self, input):
436
- return self.model(input)
437
-
438
-
439
- class GlobalGeneratorGated(GlobalGenerator):
440
- def __init__(self, *args, **kwargs):
441
- real_kwargs=dict(
442
- conv_kind='gated_bn_relu',
443
- activation=nn.Identity(),
444
- norm_layer=nn.Identity
445
- )
446
- real_kwargs.update(kwargs)
447
- super().__init__(*args, **real_kwargs)
448
-
449
-
450
- class GlobalGeneratorFromSuperChannels(nn.Module):
451
- def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True):
452
- super().__init__()
453
- self.n_downsampling = n_downsampling
454
- norm_layer = get_norm_layer(norm_layer)
455
- if type(norm_layer) == functools.partial:
456
- use_bias = (norm_layer.func == nn.InstanceNorm2d)
457
- else:
458
- use_bias = (norm_layer == nn.InstanceNorm2d)
459
-
460
- channels = self.convert_super_channels(super_channels)
461
- self.channels = channels
462
-
463
- model = [nn.ReflectionPad2d(3),
464
- nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias),
465
- norm_layer(channels[0]),
466
- nn.ReLU(True)]
467
-
468
- for i in range(n_downsampling): # add downsampling layers
469
- mult = 2 ** i
470
- model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias),
471
- norm_layer(channels[1+i]),
472
- nn.ReLU(True)]
473
-
474
- mult = 2 ** n_downsampling
475
-
476
- n_blocks1 = n_blocks // 3
477
- n_blocks2 = n_blocks1
478
- n_blocks3 = n_blocks - n_blocks1 - n_blocks2
479
-
480
- for i in range(n_blocks1):
481
- c = n_downsampling
482
- dim = channels[c]
483
- model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)]
484
-
485
- for i in range(n_blocks2):
486
- c = n_downsampling+1
487
- dim = channels[c]
488
- kwargs = {}
489
- if i == 0:
490
- kwargs = {"in_dim": channels[c-1]}
491
- model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
492
-
493
- for i in range(n_blocks3):
494
- c = n_downsampling+2
495
- dim = channels[c]
496
- kwargs = {}
497
- if i == 0:
498
- kwargs = {"in_dim": channels[c-1]}
499
- model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
500
-
501
- for i in range(n_downsampling): # add upsampling layers
502
- mult = 2 ** (n_downsampling - i)
503
- model += [nn.ConvTranspose2d(channels[n_downsampling+3+i],
504
- channels[n_downsampling+3+i+1],
505
- kernel_size=3, stride=2,
506
- padding=1, output_padding=1,
507
- bias=use_bias),
508
- norm_layer(channels[n_downsampling+3+i+1]),
509
- nn.ReLU(True)]
510
- model += [nn.ReflectionPad2d(3)]
511
- model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)]
512
-
513
- if add_out_act:
514
- model.append(get_activation('tanh' if add_out_act is True else add_out_act))
515
- self.model = nn.Sequential(*model)
516
-
517
- def convert_super_channels(self, super_channels):
518
- n_downsampling = self.n_downsampling
519
- result = []
520
- cnt = 0
521
-
522
- if n_downsampling == 2:
523
- N1 = 10
524
- elif n_downsampling == 3:
525
- N1 = 13
526
- else:
527
- raise NotImplementedError
528
-
529
- for i in range(0, N1):
530
- if i in [1,4,7,10]:
531
- channel = super_channels[cnt] * (2 ** cnt)
532
- config = {'channel': channel}
533
- result.append(channel)
534
- logging.info(f"Downsample channels {result[-1]}")
535
- cnt += 1
536
-
537
- for i in range(3):
538
- for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)):
539
- if len(super_channels) == 6:
540
- channel = super_channels[3] * 4
541
- else:
542
- channel = super_channels[i + 3] * 4
543
- config = {'channel': channel}
544
- if counter == 0:
545
- result.append(channel)
546
- logging.info(f"Bottleneck channels {result[-1]}")
547
- cnt = 2
548
-
549
- for i in range(N1+9, N1+21):
550
- if i in [22, 25,28]:
551
- cnt -= 1
552
- if len(super_channels) == 6:
553
- channel = super_channels[5 - cnt] * (2 ** cnt)
554
- else:
555
- channel = super_channels[7 - cnt] * (2 ** cnt)
556
- result.append(int(channel))
557
- logging.info(f"Upsample channels {result[-1]}")
558
- return result
559
-
560
- def forward(self, input):
561
- return self.model(input)
562
-
563
-
564
- # Defines the PatchGAN discriminator with the specified arguments.
565
- class NLayerDiscriminator(BaseDiscriminator):
566
- def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,):
567
- super().__init__()
568
- self.n_layers = n_layers
569
-
570
- kw = 4
571
- padw = int(np.ceil((kw-1.0)/2))
572
- sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
573
- nn.LeakyReLU(0.2, True)]]
574
-
575
- nf = ndf
576
- for n in range(1, n_layers):
577
- nf_prev = nf
578
- nf = min(nf * 2, 512)
579
-
580
- cur_model = []
581
- cur_model += [
582
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
583
- norm_layer(nf),
584
- nn.LeakyReLU(0.2, True)
585
- ]
586
- sequence.append(cur_model)
587
-
588
- nf_prev = nf
589
- nf = min(nf * 2, 512)
590
-
591
- cur_model = []
592
- cur_model += [
593
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
594
- norm_layer(nf),
595
- nn.LeakyReLU(0.2, True)
596
- ]
597
- sequence.append(cur_model)
598
-
599
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
600
-
601
- for n in range(len(sequence)):
602
- setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
603
-
604
- def get_all_activations(self, x):
605
- res = [x]
606
- for n in range(self.n_layers + 2):
607
- model = getattr(self, 'model' + str(n))
608
- res.append(model(res[-1]))
609
- return res[1:]
610
-
611
- def forward(self, x):
612
- act = self.get_all_activations(x)
613
- return act[-1], act[:-1]
614
-
615
-
616
- class MultidilatedNLayerDiscriminator(BaseDiscriminator):
617
- def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}):
618
- super().__init__()
619
- self.n_layers = n_layers
620
-
621
- kw = 4
622
- padw = int(np.ceil((kw-1.0)/2))
623
- sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
624
- nn.LeakyReLU(0.2, True)]]
625
-
626
- nf = ndf
627
- for n in range(1, n_layers):
628
- nf_prev = nf
629
- nf = min(nf * 2, 512)
630
-
631
- cur_model = []
632
- cur_model += [
633
- MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs),
634
- norm_layer(nf),
635
- nn.LeakyReLU(0.2, True)
636
- ]
637
- sequence.append(cur_model)
638
-
639
- nf_prev = nf
640
- nf = min(nf * 2, 512)
641
-
642
- cur_model = []
643
- cur_model += [
644
- nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
645
- norm_layer(nf),
646
- nn.LeakyReLU(0.2, True)
647
- ]
648
- sequence.append(cur_model)
649
-
650
- sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
651
-
652
- for n in range(len(sequence)):
653
- setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
654
-
655
- def get_all_activations(self, x):
656
- res = [x]
657
- for n in range(self.n_layers + 2):
658
- model = getattr(self, 'model' + str(n))
659
- res.append(model(res[-1]))
660
- return res[1:]
661
-
662
- def forward(self, x):
663
- act = self.get_all_activations(x)
664
- return act[-1], act[:-1]
665
-
666
-
667
- class NLayerDiscriminatorAsGen(NLayerDiscriminator):
668
- def forward(self, x):
669
- return super().forward(x)[0]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/spatial_transform.py DELETED
@@ -1,49 +0,0 @@
1
- import torch
2
- import torch.nn as nn
3
- import torch.nn.functional as F
4
- from kornia.geometry.transform import rotate
5
-
6
-
7
- class LearnableSpatialTransformWrapper(nn.Module):
8
- def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):
9
- super().__init__()
10
- self.impl = impl
11
- self.angle = torch.rand(1) * angle_init_range
12
- if train_angle:
13
- self.angle = nn.Parameter(self.angle, requires_grad=True)
14
- self.pad_coef = pad_coef
15
-
16
- def forward(self, x):
17
- if torch.is_tensor(x):
18
- return self.inverse_transform(self.impl(self.transform(x)), x)
19
- elif isinstance(x, tuple):
20
- x_trans = tuple(self.transform(elem) for elem in x)
21
- y_trans = self.impl(x_trans)
22
- return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x))
23
- else:
24
- raise ValueError(f'Unexpected input type {type(x)}')
25
-
26
- def transform(self, x):
27
- height, width = x.shape[2:]
28
- pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
29
- x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect')
30
- x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded))
31
- return x_padded_rotated
32
-
33
- def inverse_transform(self, y_padded_rotated, orig_x):
34
- height, width = orig_x.shape[2:]
35
- pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
36
-
37
- y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated))
38
- y_height, y_width = y_padded.shape[2:]
39
- y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]
40
- return y
41
-
42
-
43
- if __name__ == '__main__':
44
- layer = LearnableSpatialTransformWrapper(nn.Identity())
45
- x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float()
46
- y = layer(x)
47
- assert x.shape == y.shape
48
- assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1])
49
- print('all ok')
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
extensions/sd-webui-controlnet/annotator/lama/saicinpainting/training/modules/squeeze_excitation.py DELETED
@@ -1,20 +0,0 @@
1
- import torch.nn as nn
2
-
3
-
4
- class SELayer(nn.Module):
5
- def __init__(self, channel, reduction=16):
6
- super(SELayer, self).__init__()
7
- self.avg_pool = nn.AdaptiveAvgPool2d(1)
8
- self.fc = nn.Sequential(
9
- nn.Linear(channel, channel // reduction, bias=False),
10
- nn.ReLU(inplace=True),
11
- nn.Linear(channel // reduction, channel, bias=False),
12
- nn.Sigmoid()
13
- )
14
-
15
- def forward(self, x):
16
- b, c, _, _ = x.size()
17
- y = self.avg_pool(x).view(b, c)
18
- y = self.fc(y).view(b, c, 1, 1)
19
- res = x * y.expand_as(x)
20
- return res