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controlnet_aux/.gitignore

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+# Initially taken from Github's Python gitignore file
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# tests and logs
+tests/fixtures/cached_*_text.txt
+logs/
+lightning_logs/
+lang_code_data/
+tests/outputs
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# vscode
+.vs
+.vscode
+
+# Pycharm
+.idea
+
+# TF code
+tensorflow_code
+
+# Models
+proc_data
+
+# examples
+runs
+/runs_old
+/wandb
+/examples/runs
+/examples/**/*.args
+/examples/rag/sweep
+
+# data
+/data
+serialization_dir
+
+# emacs
+*.*~
+debug.env
+
+# vim
+.*.swp
+
+#ctags
+tags
+
+# pre-commit
+.pre-commit*
+
+# .lock
+*.lock
+
+# DS_Store (MacOS)
+.DS_Store
+# RL pipelines may produce mp4 outputs
+*.mp4
+
+# dependencies
+/transformers
+
+# ruff
+.ruff_cache
+
+wandb
+

controlnet_aux/LICENSE.txt

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controlnet_aux/README.md

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+# ControlNet auxiliary models
+
+This is a PyPi installable package of [lllyasviel's ControlNet Annotators](https://github.com/lllyasviel/ControlNet/tree/main/annotator)
+
+The code is copy-pasted from the respective folders in <https://github.com/lllyasviel/ControlNet/tree/main/annotator> and connected to [the 🤗 Hub](https://huggingface.co/lllyasviel/Annotators).
+
+All credit & copyright goes to <https://github.com/lllyasviel> .
+
+## Install
+
+```
+pip install -U controlnet-aux
+```
+
+To support DWPose which is dependent on MMDetection, MMCV and MMPose
+
+```
+pip install -U openmim
+mim install mmengine
+mim install "mmcv>=2.0.1"
+mim install "mmdet>=3.1.0"
+mim install "mmpose>=1.1.0"
+```
+
+## Usage
+
+You can use the processor class, which can load each of the auxiliary models with the following code
+
+```python
+import requests
+from PIL import Image
+from io import BytesIO
+
+from controlnet_aux.processor import Processor
+
+# load image
+url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"
+
+response = requests.get(url)
+img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))
+
+# load processor from processor_id
+# options are:
+# ["canny", "depth_leres", "depth_leres++", "depth_midas", "depth_zoe", "lineart_anime",
+#  "lineart_coarse", "lineart_realistic", "mediapipe_face", "mlsd", "normal_bae", "normal_midas",
+#  "openpose", "openpose_face", "openpose_faceonly", "openpose_full", "openpose_hand",
+#  "scribble_hed, "scribble_pidinet", "shuffle", "softedge_hed", "softedge_hedsafe",
+#  "softedge_pidinet", "softedge_pidsafe", "dwpose"]
+processor_id = 'scribble_hed'
+processor = Processor(processor_id)
+
+processed_image = processor(img, to_pil=True)
+```
+
+Each model can be loaded individually by importing and instantiating them as follows
+
+```python
+from PIL import Image
+import requests
+from io import BytesIO
+from controlnet_aux import HEDdetector, MidasDetector, MLSDdetector, OpenposeDetector, PidiNetDetector, NormalBaeDetector, LineartDetector, LineartAnimeDetector, CannyDetector, ContentShuffleDetector, ZoeDetector, MediapipeFaceDetector, SamDetector, LeresDetector, DWposeDetector
+
+# load image
+url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"
+
+response = requests.get(url)
+img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))
+
+# load checkpoints
+hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
+midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
+mlsd = MLSDdetector.from_pretrained("lllyasviel/Annotators")
+open_pose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
+pidi = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
+normal_bae = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
+lineart = LineartDetector.from_pretrained("lllyasviel/Annotators")
+lineart_anime = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
+zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
+sam = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
+mobile_sam = SamDetector.from_pretrained("dhkim2810/MobileSAM", model_type="vit_t", filename="mobile_sam.pt")
+leres = LeresDetector.from_pretrained("lllyasviel/Annotators")
+teed = TEEDdetector.from_pretrained("fal-ai/teed", filename="5_model.pth")
+anyline = AnylineDetector.from_pretrained(
+    "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
+)
+
+# specify configs, ckpts and device, or it will be downloaded automatically and use cpu by default
+# det_config: ./src/controlnet_aux/dwpose/yolox_config/yolox_l_8xb8-300e_coco.py
+# det_ckpt: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
+# pose_config: ./src/controlnet_aux/dwpose/dwpose_config/dwpose-l_384x288.py
+# pose_ckpt: https://huggingface.co/wanghaofan/dw-ll_ucoco_384/resolve/main/dw-ll_ucoco_384.pth
+import torch
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+dwpose = DWposeDetector(det_config=det_config, det_ckpt=det_ckpt, pose_config=pose_config, pose_ckpt=pose_ckpt, device=device)
+
+# instantiate
+canny = CannyDetector()
+content = ContentShuffleDetector()
+face_detector = MediapipeFaceDetector()
+lineart_standard = LineartStandardDetector()
+
+
+# process
+processed_image_hed = hed(img)
+processed_image_midas = midas(img)
+processed_image_mlsd = mlsd(img)
+processed_image_open_pose = open_pose(img, hand_and_face=True)
+processed_image_pidi = pidi(img, safe=True)
+processed_image_normal_bae = normal_bae(img)
+processed_image_lineart = lineart(img, coarse=True)
+processed_image_lineart_anime = lineart_anime(img)
+processed_image_zoe = zoe(img)
+processed_image_sam = sam(img)
+processed_image_leres = leres(img)
+processed_image_teed = teed(img, detect_resolution=1024)
+processed_image_anyline = anyline(img, detect_resolution=1280)
+
+processed_image_canny = canny(img)
+processed_image_content = content(img)
+processed_image_mediapipe_face = face_detector(img)
+processed_image_dwpose = dwpose(img)
+processed_image_lineart_standard = lineart_standard(img, detect_resolution=1024)
+```
+
+### Image resolution
+
+In order to maintain the image aspect ratio, `detect_resolution`, `image_resolution` and images sizes need to be using multiple of `64`.

controlnet_aux/setup.py

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+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Simple check list from AllenNLP repo: https://github.com/allenai/allennlp/blob/main/setup.py
+
+To create the package for pypi.
+
+1. Run `make pre-release` (or `make pre-patch` for a patch release) then run `make fix-copies` to fix the index of the
+   documentation.
+
+   If releasing on a special branch, copy the updated README.md on the main branch for your the commit you will make
+   for the post-release and run `make fix-copies` on the main branch as well.
+
+2. Run Tests for Amazon Sagemaker. The documentation is located in `./tests/sagemaker/README.md`, otherwise @philschmid.
+
+3. Unpin specific versions from setup.py that use a git install.
+
+4. Checkout the release branch (v<RELEASE>-release, for example v4.19-release), and commit these changes with the
+   message: "Release: <RELEASE>" and push.
+
+5. Wait for the tests on main to be completed and be green (otherwise revert and fix bugs)
+
+6. Add a tag in git to mark the release: "git tag v<RELEASE> -m 'Adds tag v<RELEASE> for pypi' "
+   Push the tag to git: git push --tags origin v<RELEASE>-release
+
+7. Build both the sources and the wheel. Do not change anything in setup.py between
+   creating the wheel and the source distribution (obviously).
+
+   For the wheel, run: "python setup.py bdist_wheel" in the top level directory.
+   (this will build a wheel for the python version you use to build it).
+
+   For the sources, run: "python setup.py sdist"
+   You should now have a /dist directory with both .whl and .tar.gz source versions.
+
+8. Check that everything looks correct by uploading the package to the pypi test server:
+
+   twine upload dist/* -r pypitest
+   (pypi suggest using twine as other methods upload files via plaintext.)
+   You may have to specify the repository url, use the following command then:
+   twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/
+
+   Check that you can install it in a virtualenv by running:
+   pip install -i https://testpypi.python.org/pypi diffusers
+
+   Check you can run the following commands:
+   python -c "from diffusers import pipeline; classifier = pipeline('text-classification'); print(classifier('What a nice release'))"
+   python -c "from diffusers import *"
+
+9. Upload the final version to actual pypi:
+   twine upload dist/* -r pypi
+
+10. Copy the release notes from RELEASE.md to the tag in github once everything is looking hunky-dory.
+
+11. Run `make post-release` (or, for a patch release, `make post-patch`). If you were on a branch for the release,
+    you need to go back to main before executing this.
+"""
+
+import os
+import re
+from distutils.core import Command
+
+from setuptools import find_packages, setup
+
+# IMPORTANT:
+# 1. all dependencies should be listed here with their version requirements if any
+# 2. once modified, run: `make deps_table_update` to update src/diffusers/dependency_versions_table.py
+_deps = [
+    "Pillow",
+    "torch",
+    "numpy",
+    "filelock",
+    "importlib_metadata",
+    "opencv-python-headless",
+    "scipy",
+    "huggingface_hub",
+    "einops",
+    "timm<=0.6.7",
+    "torchvision",
+    "scikit-image",
+]
+
+# this is a lookup table with items like:
+#
+# tokenizers: "huggingface-hub==0.8.0"
+# packaging: "packaging"
+#
+# some of the values are versioned whereas others aren't.
+deps = {
+    b: a for a, b in (re.findall(r"^(([^!=<>~]+)(?:[!=<>~].*)?$)", x)[0] for x in _deps)
+}
+
+# since we save this data in src/diffusers/dependency_versions_table.py it can be easily accessed from
+# anywhere. If you need to quickly access the data from this table in a shell, you can do so easily with:
+#
+# python -c 'import sys; from diffusers.dependency_versions_table import deps; \
+# print(" ".join([ deps[x] for x in sys.argv[1:]]))' tokenizers datasets
+#
+# Just pass the desired package names to that script as it's shown with 2 packages above.
+#
+# If diffusers is not yet installed and the work is done from the cloned repo remember to add `PYTHONPATH=src` to the script above
+#
+# You can then feed this for example to `pip`:
+#
+# pip install -U $(python -c 'import sys; from diffusers.dependency_versions_table import deps; \
+# print(" ".join([ deps[x] for x in sys.argv[1:]]))' tokenizers datasets)
+#
+
+
+def deps_list(*pkgs):
+    return [deps[pkg] for pkg in pkgs]
+
+
+class DepsTableUpdateCommand(Command):
+    """
+    A custom distutils command that updates the dependency table.
+    usage: python setup.py deps_table_update
+    """
+
+    description = "build runtime dependency table"
+    user_options = [
+        # format: (long option, short option, description).
+        (
+            "dep-table-update",
+            None,
+            "updates src/diffusers/dependency_versions_table.py",
+        ),
+    ]
+
+    def initialize_options(self):
+        pass
+
+    def finalize_options(self):
+        pass
+
+    def run(self):
+        entries = "\n".join([f'    "{k}": "{v}",' for k, v in deps.items()])
+        content = [
+            "# THIS FILE HAS BEEN AUTOGENERATED. To update:",
+            "# 1. modify the `_deps` dict in setup.py",
+            "# 2. run `make deps_table_update``",
+            "deps = {",
+            entries,
+            "}",
+            "",
+        ]
+        target = "src/controlnet_aux/dependency_versions_table.py"
+        print(f"updating {target}")
+        with open(target, "w", encoding="utf-8", newline="\n") as f:
+            f.write("\n".join(content))
+
+
+extras = {}
+
+install_requires = [
+    deps["torch"],
+    deps["importlib_metadata"],
+    deps["huggingface_hub"],
+    deps["scipy"],
+    deps["opencv-python-headless"],
+    deps["filelock"],
+    deps["numpy"],
+    deps["Pillow"],
+    deps["einops"],
+    deps["torchvision"],
+    deps["timm"],
+    deps["scikit-image"],
+]
+
+setup(
+    name="controlnet_aux",
+    version="0.0.9",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    description="Auxillary models for controlnet",
+    long_description=open("README.md", "r", encoding="utf-8").read(),
+    long_description_content_type="text/markdown",
+    keywords="deep learning",
+    license="Apache",
+    author="The HuggingFace team",
+    author_email="[email protected]",
+    url="https://github.com/patrickvonplaten/controlnet_aux",
+    package_dir={"": "src"},
+    packages=find_packages("src"),
+    include_package_data=True,
+    python_requires=">=3.7.0",
+    install_requires=install_requires,
+    extras_require=extras,
+    classifiers=[
+        "Development Status :: 5 - Production/Stable",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: Apache Software License",
+        "Operating System :: OS Independent",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+    ],
+    cmdclass={"deps_table_update": DepsTableUpdateCommand},
+    package_data={'controlnet_aux' : ['zoe/zoedepth/models/zoedepth/*.json', 'zoe/zoedepth/models/zoedepth_nk/*.json']}
+)
+
+# Release checklist
+# 1. Change the version in __init__.py and setup.py.
+# 2. Commit these changes with the message: "Release: Release"
+# 3. Add a tag in git to mark the release: "git tag RELEASE -m 'Adds tag RELEASE for pypi' "
+#    Push the tag to git: git push --tags origin main
+# 4. Run the following commands in the top-level directory:
+#      python setup.py bdist_wheel
+#      python setup.py sdist
+# 5. Upload the package to the pypi test server first:
+#      twine upload dist/* -r pypitest
+#      twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/
+# 6. Check that you can install it in a virtualenv by running:
+#      pip install -i https://testpypi.python.org/pypi diffusers
+#      diffusers env
+#      diffusers test
+# 7. Upload the final version to actual pypi:
+#      twine upload dist/* -r pypi
+# 8. Add release notes to the tag in github once everything is looking hunky-dory.
+# 9. Update the version in __init__.py, setup.py to the new version "-dev" and push to master

controlnet_aux/src/controlnet_aux.egg-info/PKG-INFO

@@ -0,0 +1,163 @@
+Metadata-Version: 2.1
+Name: controlnet_aux
+Version: 0.0.9
+Summary: Auxillary models for controlnet
+Home-page: https://github.com/patrickvonplaten/controlnet_aux
+Author: The HuggingFace team
+Author-email: [email protected]
+License: Apache
+Keywords: deep learning
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
+Requires-Python: >=3.7.0
+Description-Content-Type: text/markdown
+License-File: LICENSE.txt
+Requires-Dist: torch
+Requires-Dist: importlib_metadata
+Requires-Dist: huggingface_hub
+Requires-Dist: scipy
+Requires-Dist: opencv-python-headless
+Requires-Dist: filelock
+Requires-Dist: numpy
+Requires-Dist: Pillow
+Requires-Dist: einops
+Requires-Dist: torchvision
+Requires-Dist: timm<=0.6.7
+Requires-Dist: scikit-image
+
+# ControlNet auxiliary models
+
+This is a PyPi installable package of [lllyasviel's ControlNet Annotators](https://github.com/lllyasviel/ControlNet/tree/main/annotator)
+
+The code is copy-pasted from the respective folders in <https://github.com/lllyasviel/ControlNet/tree/main/annotator> and connected to [the 🤗 Hub](https://huggingface.co/lllyasviel/Annotators).
+
+All credit & copyright goes to <https://github.com/lllyasviel> .
+
+## Install
+
+```
+pip install -U controlnet-aux
+```
+
+To support DWPose which is dependent on MMDetection, MMCV and MMPose
+
+```
+pip install -U openmim
+mim install mmengine
+mim install "mmcv>=2.0.1"
+mim install "mmdet>=3.1.0"
+mim install "mmpose>=1.1.0"
+```
+
+## Usage
+
+You can use the processor class, which can load each of the auxiliary models with the following code
+
+```python
+import requests
+from PIL import Image
+from io import BytesIO
+
+from controlnet_aux.processor import Processor
+
+# load image
+url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"
+
+response = requests.get(url)
+img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))
+
+# load processor from processor_id
+# options are:
+# ["canny", "depth_leres", "depth_leres++", "depth_midas", "depth_zoe", "lineart_anime",
+#  "lineart_coarse", "lineart_realistic", "mediapipe_face", "mlsd", "normal_bae", "normal_midas",
+#  "openpose", "openpose_face", "openpose_faceonly", "openpose_full", "openpose_hand",
+#  "scribble_hed, "scribble_pidinet", "shuffle", "softedge_hed", "softedge_hedsafe",
+#  "softedge_pidinet", "softedge_pidsafe", "dwpose"]
+processor_id = 'scribble_hed'
+processor = Processor(processor_id)
+
+processed_image = processor(img, to_pil=True)
+```
+
+Each model can be loaded individually by importing and instantiating them as follows
+
+```python
+from PIL import Image
+import requests
+from io import BytesIO
+from controlnet_aux import HEDdetector, MidasDetector, MLSDdetector, OpenposeDetector, PidiNetDetector, NormalBaeDetector, LineartDetector, LineartAnimeDetector, CannyDetector, ContentShuffleDetector, ZoeDetector, MediapipeFaceDetector, SamDetector, LeresDetector, DWposeDetector
+
+# load image
+url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"
+
+response = requests.get(url)
+img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))
+
+# load checkpoints
+hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
+midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
+mlsd = MLSDdetector.from_pretrained("lllyasviel/Annotators")
+open_pose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
+pidi = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
+normal_bae = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
+lineart = LineartDetector.from_pretrained("lllyasviel/Annotators")
+lineart_anime = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
+zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
+sam = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
+mobile_sam = SamDetector.from_pretrained("dhkim2810/MobileSAM", model_type="vit_t", filename="mobile_sam.pt")
+leres = LeresDetector.from_pretrained("lllyasviel/Annotators")
+teed = TEEDdetector.from_pretrained("fal-ai/teed", filename="5_model.pth")
+anyline = AnylineDetector.from_pretrained(
+    "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
+)
+
+# specify configs, ckpts and device, or it will be downloaded automatically and use cpu by default
+# det_config: ./src/controlnet_aux/dwpose/yolox_config/yolox_l_8xb8-300e_coco.py
+# det_ckpt: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
+# pose_config: ./src/controlnet_aux/dwpose/dwpose_config/dwpose-l_384x288.py
+# pose_ckpt: https://huggingface.co/wanghaofan/dw-ll_ucoco_384/resolve/main/dw-ll_ucoco_384.pth
+import torch
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+dwpose = DWposeDetector(det_config=det_config, det_ckpt=det_ckpt, pose_config=pose_config, pose_ckpt=pose_ckpt, device=device)
+
+# instantiate
+canny = CannyDetector()
+content = ContentShuffleDetector()
+face_detector = MediapipeFaceDetector()
+lineart_standard = LineartStandardDetector()
+
+
+# process
+processed_image_hed = hed(img)
+processed_image_midas = midas(img)
+processed_image_mlsd = mlsd(img)
+processed_image_open_pose = open_pose(img, hand_and_face=True)
+processed_image_pidi = pidi(img, safe=True)
+processed_image_normal_bae = normal_bae(img)
+processed_image_lineart = lineart(img, coarse=True)
+processed_image_lineart_anime = lineart_anime(img)
+processed_image_zoe = zoe(img)
+processed_image_sam = sam(img)
+processed_image_leres = leres(img)
+processed_image_teed = teed(img, detect_resolution=1024)
+processed_image_anyline = anyline(img, detect_resolution=1280)
+
+processed_image_canny = canny(img)
+processed_image_content = content(img)
+processed_image_mediapipe_face = face_detector(img)
+processed_image_dwpose = dwpose(img)
+processed_image_lineart_standard = lineart_standard(img, detect_resolution=1024)
+```
+
+### Image resolution
+
+In order to maintain the image aspect ratio, `detect_resolution`, `image_resolution` and images sizes need to be using multiple of `64`.

controlnet_aux/src/controlnet_aux.egg-info/SOURCES.txt

@@ -0,0 +1,166 @@
+LICENSE.txt
+README.md
+setup.py
+src/controlnet_aux/__init__.py
+src/controlnet_aux/processor.py
+src/controlnet_aux/util.py
+src/controlnet_aux.egg-info/PKG-INFO
+src/controlnet_aux.egg-info/SOURCES.txt
+src/controlnet_aux.egg-info/dependency_links.txt
+src/controlnet_aux.egg-info/requires.txt
+src/controlnet_aux.egg-info/top_level.txt
+src/controlnet_aux/anyline/__init__.py
+src/controlnet_aux/canny/__init__.py
+src/controlnet_aux/dwpose/__init__.py
+src/controlnet_aux/dwpose/util.py
+src/controlnet_aux/dwpose/wholebody.py
+src/controlnet_aux/dwpose/dwpose_config/__init__.py
+src/controlnet_aux/dwpose/dwpose_config/dwpose-l_384x288.py
+src/controlnet_aux/dwpose/yolox_config/__init__.py
+src/controlnet_aux/dwpose/yolox_config/yolox_l_8xb8-300e_coco.py
+src/controlnet_aux/hed/__init__.py
+src/controlnet_aux/leres/__init__.py
+src/controlnet_aux/leres/leres/Resnet.py
+src/controlnet_aux/leres/leres/Resnext_torch.py
+src/controlnet_aux/leres/leres/__init__.py
+src/controlnet_aux/leres/leres/depthmap.py
+src/controlnet_aux/leres/leres/multi_depth_model_woauxi.py
+src/controlnet_aux/leres/leres/net_tools.py
+src/controlnet_aux/leres/leres/network_auxi.py
+src/controlnet_aux/leres/pix2pix/__init__.py
+src/controlnet_aux/leres/pix2pix/models/__init__.py
+src/controlnet_aux/leres/pix2pix/models/base_model.py
+src/controlnet_aux/leres/pix2pix/models/base_model_hg.py
+src/controlnet_aux/leres/pix2pix/models/networks.py
+src/controlnet_aux/leres/pix2pix/models/pix2pix4depth_model.py
+src/controlnet_aux/leres/pix2pix/options/__init__.py
+src/controlnet_aux/leres/pix2pix/options/base_options.py
+src/controlnet_aux/leres/pix2pix/options/test_options.py
+src/controlnet_aux/leres/pix2pix/util/__init__.py
+src/controlnet_aux/leres/pix2pix/util/util.py
+src/controlnet_aux/lineart/__init__.py
+src/controlnet_aux/lineart_anime/__init__.py
+src/controlnet_aux/lineart_standard/__init__.py
+src/controlnet_aux/mediapipe_face/__init__.py
+src/controlnet_aux/mediapipe_face/mediapipe_face_common.py
+src/controlnet_aux/midas/__init__.py
+src/controlnet_aux/midas/api.py
+src/controlnet_aux/midas/utils.py
+src/controlnet_aux/midas/midas/__init__.py
+src/controlnet_aux/midas/midas/base_model.py
+src/controlnet_aux/midas/midas/blocks.py
+src/controlnet_aux/midas/midas/dpt_depth.py
+src/controlnet_aux/midas/midas/midas_net.py
+src/controlnet_aux/midas/midas/midas_net_custom.py
+src/controlnet_aux/midas/midas/transforms.py
+src/controlnet_aux/midas/midas/vit.py
+src/controlnet_aux/mlsd/__init__.py
+src/controlnet_aux/mlsd/utils.py
+src/controlnet_aux/mlsd/models/__init__.py
+src/controlnet_aux/mlsd/models/mbv2_mlsd_large.py
+src/controlnet_aux/mlsd/models/mbv2_mlsd_tiny.py
+src/controlnet_aux/normalbae/__init__.py
+src/controlnet_aux/normalbae/nets/NNET.py
+src/controlnet_aux/normalbae/nets/__init__.py
+src/controlnet_aux/normalbae/nets/baseline.py
+src/controlnet_aux/normalbae/nets/submodules/__init__.py
+src/controlnet_aux/normalbae/nets/submodules/decoder.py
+src/controlnet_aux/normalbae/nets/submodules/encoder.py
+src/controlnet_aux/normalbae/nets/submodules/submodules.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/__init__.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/caffe2_benchmark.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/caffe2_validate.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/hubconf.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/onnx_export.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/onnx_optimize.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/onnx_to_caffe.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/onnx_validate.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/setup.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/utils.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/validate.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/__init__.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/config.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/conv2d_layers.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/efficientnet_builder.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/gen_efficientnet.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/helpers.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/mobilenetv3.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/model_factory.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/version.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/__init__.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/activations.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/activations_jit.py
+src/controlnet_aux/normalbae/nets/submodules/efficientnet_repo/geffnet/activations/activations_me.py
+src/controlnet_aux/open_pose/__init__.py
+src/controlnet_aux/open_pose/body.py
+src/controlnet_aux/open_pose/face.py
+src/controlnet_aux/open_pose/hand.py
+src/controlnet_aux/open_pose/model.py
+src/controlnet_aux/open_pose/util.py
+src/controlnet_aux/pidi/__init__.py
+src/controlnet_aux/pidi/model.py
+src/controlnet_aux/segment_anything/__init__.py
+src/controlnet_aux/segment_anything/automatic_mask_generator.py
+src/controlnet_aux/segment_anything/build_sam.py
+src/controlnet_aux/segment_anything/predictor.py
+src/controlnet_aux/segment_anything/modeling/__init__.py
+src/controlnet_aux/segment_anything/modeling/common.py
+src/controlnet_aux/segment_anything/modeling/image_encoder.py
+src/controlnet_aux/segment_anything/modeling/mask_decoder.py
+src/controlnet_aux/segment_anything/modeling/prompt_encoder.py
+src/controlnet_aux/segment_anything/modeling/sam.py
+src/controlnet_aux/segment_anything/modeling/tiny_vit_sam.py
+src/controlnet_aux/segment_anything/modeling/transformer.py
+src/controlnet_aux/segment_anything/utils/__init__.py
+src/controlnet_aux/segment_anything/utils/amg.py
+src/controlnet_aux/segment_anything/utils/onnx.py
+src/controlnet_aux/segment_anything/utils/transforms.py
+src/controlnet_aux/shuffle/__init__.py
+src/controlnet_aux/teed/Fsmish.py
+src/controlnet_aux/teed/Xsmish.py
+src/controlnet_aux/teed/__init__.py
+src/controlnet_aux/teed/ted.py
+src/controlnet_aux/zoe/__init__.py
+src/controlnet_aux/zoe/zoedepth/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/builder.py
+src/controlnet_aux/zoe/zoedepth/models/depth_model.py
+src/controlnet_aux/zoe/zoedepth/models/model_io.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/hubconf.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/base_model.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/blocks.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/dpt_depth.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/midas_net.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/midas_net_custom.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/model_loader.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/transforms.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/beit.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/levit.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/next_vit.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin2.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/swin_common.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/utils.py
+src/controlnet_aux/zoe/zoedepth/models/base_models/midas_repo/midas/backbones/vit.py
+src/controlnet_aux/zoe/zoedepth/models/layers/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/layers/attractor.py
+src/controlnet_aux/zoe/zoedepth/models/layers/dist_layers.py
+src/controlnet_aux/zoe/zoedepth/models/layers/localbins_layers.py
+src/controlnet_aux/zoe/zoedepth/models/layers/patch_transformer.py
+src/controlnet_aux/zoe/zoedepth/models/zoedepth/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/zoedepth/config_zoedepth.json
+src/controlnet_aux/zoe/zoedepth/models/zoedepth/config_zoedepth_kitti.json
+src/controlnet_aux/zoe/zoedepth/models/zoedepth/zoedepth_v1.py
+src/controlnet_aux/zoe/zoedepth/models/zoedepth_nk/__init__.py
+src/controlnet_aux/zoe/zoedepth/models/zoedepth_nk/config_zoedepth_nk.json
+src/controlnet_aux/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py
+src/controlnet_aux/zoe/zoedepth/utils/__init__.py
+src/controlnet_aux/zoe/zoedepth/utils/arg_utils.py
+src/controlnet_aux/zoe/zoedepth/utils/config.py
+src/controlnet_aux/zoe/zoedepth/utils/easydict/__init__.py
+tests/test_controlnet_aux.py

controlnet_aux/src/controlnet_aux.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

controlnet_aux/src/controlnet_aux.egg-info/requires.txt

@@ -0,0 +1,12 @@
+torch
+importlib_metadata
+huggingface_hub
+scipy
+opencv-python-headless
+filelock
+numpy
+Pillow
+einops
+torchvision
+timm<=0.6.7
+scikit-image

controlnet_aux/src/controlnet_aux.egg-info/top_level.txt

@@ -0,0 +1 @@
+controlnet_aux

controlnet_aux/src/controlnet_aux/__init__.py

@@ -0,0 +1,5 @@
+__version__ = "0.0.9"
+
+from .canny import CannyDetector
+from .open_pose import OpenposeDetector
+

controlnet_aux/src/controlnet_aux/canny/__init__.py

@@ -0,0 +1,36 @@
+import warnings
+import cv2
+import numpy as np
+from PIL import Image
+from ..util import HWC3, resize_image
+
+class CannyDetector:
+    def __call__(self, input_image=None, low_threshold=100, high_threshold=200, detect_resolution=512, image_resolution=512, output_type=None, **kwargs):
+        if "img" in kwargs:
+            warnings.warn("img is deprecated, please use `input_image=...` instead.", DeprecationWarning)
+            input_image = kwargs.pop("img")
+        
+        if input_image is None:
+            raise ValueError("input_image must be defined.")
+
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+            output_type = output_type or "pil"
+        else:
+            output_type = output_type or "np"
+        
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+
+        detected_map = cv2.Canny(input_image, low_threshold, high_threshold)
+        detected_map = HWC3(detected_map)      
+         
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+        
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+            
+        return detected_map

controlnet_aux/src/controlnet_aux/open_pose/LICENSE

@@ -0,0 +1,108 @@
+OPENPOSE: MULTIPERSON KEYPOINT DETECTION
+SOFTWARE LICENSE AGREEMENT
+ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+
+BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+
+This is a license agreement ("Agreement") between your academic institution or non-profit organization or self (called "Licensee" or "You" in this Agreement) and Carnegie Mellon University (called "Licensor" in this Agreement).  All rights not specifically granted to you in this Agreement are reserved for Licensor. 
+
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+
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+
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+************************************************************************
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+
+This project incorporates material from the project(s) listed below (collectively, "Third Party Code").  This Third Party Code is licensed to you under their original license terms set forth below.  We reserves all other rights not expressly granted, whether by implication, estoppel or otherwise.
+ 
+1.	Caffe, version 1.0.0, (https://github.com/BVLC/caffe/)
+
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+
+All contributions by the University of California:
+Copyright (c) 2014-2017 The Regents of the University of California (Regents)
+All rights reserved.
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+Copyright (c) 2014-2017, the respective contributors
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+their specific copyright on a particular contribution, they should indicate
+their copyright solely in the commit message of the change when it is
+committed.
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+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met: 
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer. 
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution. 
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+license and copyright terms herein.
+
+************END OF THIRD-PARTY SOFTWARE NOTICES AND INFORMATION**********

controlnet_aux/src/controlnet_aux/open_pose/__init__.py

@@ -0,0 +1,234 @@
+# Openpose
+# Original from CMU https://github.com/CMU-Perceptual-Computing-Lab/openpose
+# 2nd Edited by https://github.com/Hzzone/pytorch-openpose
+# 3rd Edited by ControlNet
+# 4th Edited by ControlNet (added face and correct hands)
+# 5th Edited by ControlNet (Improved JSON serialization/deserialization, and lots of bug fixs)
+# This preprocessor is licensed by CMU for non-commercial use only.
+
+
+import os
+
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import json
+import warnings
+from typing import Callable, List, NamedTuple, Tuple, Union
+
+import cv2
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from ..util import HWC3, resize_image
+from . import util
+from .body import Body, BodyResult, Keypoint
+from .face import Face
+from .hand import Hand
+
+HandResult = List[Keypoint]
+FaceResult = List[Keypoint]
+
+class PoseResult(NamedTuple):
+    body: BodyResult
+    left_hand: Union[HandResult, None]
+    right_hand: Union[HandResult, None]
+    face: Union[FaceResult, None]
+
+def draw_poses(poses: List[PoseResult], H, W, draw_body=True, draw_hand=True, draw_face=True):
+    """
+    Draw the detected poses on an empty canvas.
+
+    Args:
+        poses (List[PoseResult]): A list of PoseResult objects containing the detected poses.
+        H (int): The height of the canvas.
+        W (int): The width of the canvas.
+        draw_body (bool, optional): Whether to draw body keypoints. Defaults to True.
+        draw_hand (bool, optional): Whether to draw hand keypoints. Defaults to True.
+        draw_face (bool, optional): Whether to draw face keypoints. Defaults to True.
+
+    Returns:
+        numpy.ndarray: A 3D numpy array representing the canvas with the drawn poses.
+    """
+    canvas = np.zeros(shape=(H, W, 3), dtype=np.uint8)
+
+    for pose in poses:
+        if draw_body:
+            canvas = util.draw_bodypose(canvas, pose.body.keypoints)
+
+        if draw_hand:
+            canvas = util.draw_handpose(canvas, pose.left_hand)
+            canvas = util.draw_handpose(canvas, pose.right_hand)
+
+        if draw_face:
+            canvas = util.draw_facepose(canvas, pose.face)
+
+    return canvas
+    
+    
+class OpenposeDetector:
+    """
+    A class for detecting human poses in images using the Openpose model.
+
+    Attributes:
+        model_dir (str): Path to the directory where the pose models are stored.
+    """
+    def __init__(self, body_estimation, hand_estimation=None, face_estimation=None):
+        self.body_estimation = body_estimation
+        self.hand_estimation = hand_estimation
+        self.face_estimation = face_estimation
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_or_path, filename=None, hand_filename=None, face_filename=None, cache_dir=None, local_files_only=False):
+
+        if pretrained_model_or_path == "lllyasviel/ControlNet":
+            filename = filename or "annotator/ckpts/body_pose_model.pth"
+            hand_filename = hand_filename or "annotator/ckpts/hand_pose_model.pth"
+            face_filename = face_filename or "facenet.pth"
+
+            face_pretrained_model_or_path = "lllyasviel/Annotators"
+        else:
+            filename = filename or "body_pose_model.pth"
+            hand_filename = hand_filename or "hand_pose_model.pth"
+            face_filename = face_filename or "facenet.pth"
+
+            face_pretrained_model_or_path = pretrained_model_or_path
+
+        if os.path.isdir(pretrained_model_or_path):
+            body_model_path = os.path.join(pretrained_model_or_path, filename)
+            hand_model_path = os.path.join(pretrained_model_or_path, hand_filename)
+            face_model_path = os.path.join(face_pretrained_model_or_path, face_filename)
+        else:
+            body_model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
+            hand_model_path = hf_hub_download(pretrained_model_or_path, hand_filename, cache_dir=cache_dir, local_files_only=local_files_only)
+            face_model_path = hf_hub_download(face_pretrained_model_or_path, face_filename, cache_dir=cache_dir, local_files_only=local_files_only)
+
+        body_estimation = Body(body_model_path)
+        hand_estimation = Hand(hand_model_path)
+        face_estimation = Face(face_model_path)
+
+        return cls(body_estimation, hand_estimation, face_estimation)
+
+    def to(self, device):
+        self.body_estimation.to(device)
+        self.hand_estimation.to(device)
+        self.face_estimation.to(device)
+        return self
+
+    def detect_hands(self, body: BodyResult, oriImg) -> Tuple[Union[HandResult, None], Union[HandResult, None]]:
+        left_hand = None
+        right_hand = None
+        H, W, _ = oriImg.shape
+        for x, y, w, is_left in util.handDetect(body, oriImg):
+            peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :]).astype(np.float32)
+            if peaks.ndim == 2 and peaks.shape[1] == 2:
+                peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
+                peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
+                
+                hand_result = [
+                    Keypoint(x=peak[0], y=peak[1])
+                    for peak in peaks
+                ]
+
+                if is_left:
+                    left_hand = hand_result
+                else:
+                    right_hand = hand_result
+
+        return left_hand, right_hand
+
+    def detect_face(self, body: BodyResult, oriImg) -> Union[FaceResult, None]:
+        face = util.faceDetect(body, oriImg)
+        if face is None:
+            return None
+        
+        x, y, w = face
+        H, W, _ = oriImg.shape
+        heatmaps = self.face_estimation(oriImg[y:y+w, x:x+w, :])
+        peaks = self.face_estimation.compute_peaks_from_heatmaps(heatmaps).astype(np.float32)
+        if peaks.ndim == 2 and peaks.shape[1] == 2:
+            peaks[:, 0] = np.where(peaks[:, 0] < 1e-6, -1, peaks[:, 0] + x) / float(W)
+            peaks[:, 1] = np.where(peaks[:, 1] < 1e-6, -1, peaks[:, 1] + y) / float(H)
+            return [
+                Keypoint(x=peak[0], y=peak[1])
+                for peak in peaks
+            ]
+        
+        return None
+
+    def detect_poses(self, oriImg, include_hand=False, include_face=False) -> List[PoseResult]:
+        """
+        Detect poses in the given image.
+            Args:
+                oriImg (numpy.ndarray): The input image for pose detection.
+                include_hand (bool, optional): Whether to include hand detection. Defaults to False.
+                include_face (bool, optional): Whether to include face detection. Defaults to False.
+
+        Returns:
+            List[PoseResult]: A list of PoseResult objects containing the detected poses.
+        """
+        oriImg = oriImg[:, :, ::-1].copy()
+        H, W, C = oriImg.shape
+        with torch.no_grad():
+            candidate, subset = self.body_estimation(oriImg)
+            bodies = self.body_estimation.format_body_result(candidate, subset)
+
+            results = []
+            for body in bodies:
+                left_hand, right_hand, face = (None,) * 3
+                if include_hand:
+                    left_hand, right_hand = self.detect_hands(body, oriImg)
+                if include_face:
+                    face = self.detect_face(body, oriImg)
+                
+                results.append(PoseResult(BodyResult(
+                    keypoints=[
+                        Keypoint(
+                            x=keypoint.x / float(W),
+                            y=keypoint.y / float(H)
+                        ) if keypoint is not None else None
+                        for keypoint in body.keypoints
+                    ], 
+                    total_score=body.total_score,
+                    total_parts=body.total_parts
+                ), left_hand, right_hand, face))
+            
+            return results
+        
+    def __call__(self, input_image, detect_resolution=512, image_resolution=512, include_body=True, include_hand=False, include_face=False, hand_and_face=None, output_type="pil", **kwargs):
+        if hand_and_face is not None:
+            warnings.warn("hand_and_face is deprecated. Use include_hand and include_face instead.", DeprecationWarning)
+            include_hand = hand_and_face
+            include_face = hand_and_face
+
+        if "return_pil" in kwargs:
+            warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
+            output_type = "pil" if kwargs["return_pil"] else "np"
+        if type(output_type) is bool:
+            warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
+            if output_type:
+                output_type = "pil"
+
+        if not isinstance(input_image, np.ndarray):
+            input_image = np.array(input_image, dtype=np.uint8)
+
+        input_image = HWC3(input_image)
+        input_image = resize_image(input_image, detect_resolution)
+        H, W, C = input_image.shape
+        
+        poses = self.detect_poses(input_image, include_hand, include_face)
+        canvas = draw_poses(poses, H, W, draw_body=include_body, draw_hand=include_hand, draw_face=include_face) 
+
+        detected_map = canvas
+        detected_map = HWC3(detected_map)
+        
+        img = resize_image(input_image, image_resolution)
+        H, W, C = img.shape
+
+        detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
+
+        if output_type == "pil":
+            detected_map = Image.fromarray(detected_map)
+
+        return detected_map

controlnet_aux/src/controlnet_aux/open_pose/body.py

@@ -0,0 +1,260 @@
+import math
+from typing import List, NamedTuple, Union
+
+import cv2
+import numpy as np
+import torch
+from scipy.ndimage.filters import gaussian_filter
+
+from . import util
+from .model import bodypose_model
+
+
+class Keypoint(NamedTuple):
+    x: float
+    y: float
+    score: float = 1.0
+    id: int = -1
+
+
+class BodyResult(NamedTuple):
+    # Note: Using `Union` instead of `|` operator as the ladder is a Python
+    # 3.10 feature.
+    # Annotator code should be Python 3.8 Compatible, as controlnet repo uses
+    # Python 3.8 environment.
+    # https://github.com/lllyasviel/ControlNet/blob/d3284fcd0972c510635a4f5abe2eeb71dc0de524/environment.yaml#L6
+    keypoints: List[Union[Keypoint, None]]
+    total_score: float
+    total_parts: int
+
+
+class Body(object):
+    def __init__(self, model_path):
+        self.model = bodypose_model()
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, oriImg):
+        device = next(iter(self.model.parameters())).device
+        # scale_search = [0.5, 1.0, 1.5, 2.0]
+        scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre1 = 0.1
+        thre2 = 0.05
+        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
+        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
+        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = util.smart_resize_k(oriImg, fx=scale, fy=scale)
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            data = data.to(device)
+            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
+            with torch.no_grad():
+                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
+            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
+            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = np.transpose(np.squeeze(Mconv7_stage6_L2), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = util.smart_resize(heatmap, (oriImg.shape[0], oriImg.shape[1]))
+
+            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
+            paf = np.transpose(np.squeeze(Mconv7_stage6_L1), (1, 2, 0))  # output 0 is PAFs
+            paf = util.smart_resize_k(paf, fx=stride, fy=stride)
+            paf = paf[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            paf = util.smart_resize(paf, (oriImg.shape[0], oriImg.shape[1]))
+
+            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
+            paf_avg += + paf / len(multiplier)
+
+        all_peaks = []
+        peak_counter = 0
+
+        for part in range(18):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+
+            map_left = np.zeros(one_heatmap.shape)
+            map_left[1:, :] = one_heatmap[:-1, :]
+            map_right = np.zeros(one_heatmap.shape)
+            map_right[:-1, :] = one_heatmap[1:, :]
+            map_up = np.zeros(one_heatmap.shape)
+            map_up[:, 1:] = one_heatmap[:, :-1]
+            map_down = np.zeros(one_heatmap.shape)
+            map_down[:, :-1] = one_heatmap[:, 1:]
+
+            peaks_binary = np.logical_and.reduce(
+                (one_heatmap >= map_left, one_heatmap >= map_right, one_heatmap >= map_up, one_heatmap >= map_down, one_heatmap > thre1))
+            peaks = list(zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0]))  # note reverse
+            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
+            peak_id = range(peak_counter, peak_counter + len(peaks))
+            peaks_with_score_and_id = [peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))]
+
+            all_peaks.append(peaks_with_score_and_id)
+            peak_counter += len(peaks)
+
+        # find connection in the specified sequence, center 29 is in the position 15
+        limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
+                   [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
+                   [1, 16], [16, 18], [3, 17], [6, 18]]
+        # the middle joints heatmap correpondence
+        mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
+                  [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
+                  [55, 56], [37, 38], [45, 46]]
+
+        connection_all = []
+        special_k = []
+        mid_num = 10
+
+        for k in range(len(mapIdx)):
+            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
+            candA = all_peaks[limbSeq[k][0] - 1]
+            candB = all_peaks[limbSeq[k][1] - 1]
+            nA = len(candA)
+            nB = len(candB)
+            indexA, indexB = limbSeq[k]
+            if (nA != 0 and nB != 0):
+                connection_candidate = []
+                for i in range(nA):
+                    for j in range(nB):
+                        vec = np.subtract(candB[j][:2], candA[i][:2])
+                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
+                        norm = max(0.001, norm)
+                        vec = np.divide(vec, norm)
+
+                        startend = list(zip(np.linspace(candA[i][0], candB[j][0], num=mid_num), \
+                                            np.linspace(candA[i][1], candB[j][1], num=mid_num)))
+
+                        vec_x = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 0] \
+                                          for I in range(len(startend))])
+                        vec_y = np.array([score_mid[int(round(startend[I][1])), int(round(startend[I][0])), 1] \
+                                          for I in range(len(startend))])
+
+                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(vec_y, vec[1])
+                        score_with_dist_prior = sum(score_midpts) / len(score_midpts) + min(
+                            0.5 * oriImg.shape[0] / norm - 1, 0)
+                        criterion1 = len(np.nonzero(score_midpts > thre2)[0]) > 0.8 * len(score_midpts)
+                        criterion2 = score_with_dist_prior > 0
+                        if criterion1 and criterion2:
+                            connection_candidate.append(
+                                [i, j, score_with_dist_prior, score_with_dist_prior + candA[i][2] + candB[j][2]])
+
+                connection_candidate = sorted(connection_candidate, key=lambda x: x[2], reverse=True)
+                connection = np.zeros((0, 5))
+                for c in range(len(connection_candidate)):
+                    i, j, s = connection_candidate[c][0:3]
+                    if (i not in connection[:, 3] and j not in connection[:, 4]):
+                        connection = np.vstack([connection, [candA[i][3], candB[j][3], s, i, j]])
+                        if (len(connection) >= min(nA, nB)):
+                            break
+
+                connection_all.append(connection)
+            else:
+                special_k.append(k)
+                connection_all.append([])
+
+        # last number in each row is the total parts number of that person
+        # the second last number in each row is the score of the overall configuration
+        subset = -1 * np.ones((0, 20))
+        candidate = np.array([item for sublist in all_peaks for item in sublist])
+
+        for k in range(len(mapIdx)):
+            if k not in special_k:
+                partAs = connection_all[k][:, 0]
+                partBs = connection_all[k][:, 1]
+                indexA, indexB = np.array(limbSeq[k]) - 1
+
+                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
+                    found = 0
+                    subset_idx = [-1, -1]
+                    for j in range(len(subset)):  # 1:size(subset,1):
+                        if subset[j][indexA] == partAs[i] or subset[j][indexB] == partBs[i]:
+                            subset_idx[found] = j
+                            found += 1
+
+                    if found == 1:
+                        j = subset_idx[0]
+                        if subset[j][indexB] != partBs[i]:
+                            subset[j][indexB] = partBs[i]
+                            subset[j][-1] += 1
+                            subset[j][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+                    elif found == 2:  # if found 2 and disjoint, merge them
+                        j1, j2 = subset_idx
+                        membership = ((subset[j1] >= 0).astype(int) + (subset[j2] >= 0).astype(int))[:-2]
+                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
+                            subset[j1][:-2] += (subset[j2][:-2] + 1)
+                            subset[j1][-2:] += subset[j2][-2:]
+                            subset[j1][-2] += connection_all[k][i][2]
+                            subset = np.delete(subset, j2, 0)
+                        else:  # as like found == 1
+                            subset[j1][indexB] = partBs[i]
+                            subset[j1][-1] += 1
+                            subset[j1][-2] += candidate[partBs[i].astype(int), 2] + connection_all[k][i][2]
+
+                    # if find no partA in the subset, create a new subset
+                    elif not found and k < 17:
+                        row = -1 * np.ones(20)
+                        row[indexA] = partAs[i]
+                        row[indexB] = partBs[i]
+                        row[-1] = 2
+                        row[-2] = sum(candidate[connection_all[k][i, :2].astype(int), 2]) + connection_all[k][i][2]
+                        subset = np.vstack([subset, row])
+        # delete some rows of subset which has few parts occur
+        deleteIdx = []
+        for i in range(len(subset)):
+            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
+                deleteIdx.append(i)
+        subset = np.delete(subset, deleteIdx, axis=0)
+
+        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
+        # candidate: x, y, score, id
+        return candidate, subset
+    
+    @staticmethod
+    def format_body_result(candidate: np.ndarray, subset: np.ndarray) -> List[BodyResult]:
+        """
+        Format the body results from the candidate and subset arrays into a list of BodyResult objects.
+        
+        Args:
+            candidate (np.ndarray): An array of candidates containing the x, y coordinates, score, and id
+                for each body part.
+            subset (np.ndarray): An array of subsets containing indices to the candidate array for each
+                person detected. The last two columns of each row hold the total score and total parts
+                of the person.
+
+        Returns:
+            List[BodyResult]: A list of BodyResult objects, where each object represents a person with
+                detected keypoints, total score, and total parts.
+        """
+        return [
+            BodyResult(
+                keypoints=[
+                    Keypoint(
+                        x=candidate[candidate_index][0],
+                        y=candidate[candidate_index][1],
+                        score=candidate[candidate_index][2],
+                        id=candidate[candidate_index][3]
+                    ) if candidate_index != -1 else None
+                    for candidate_index in person[:18].astype(int)
+                ],
+                total_score=person[18],
+                total_parts=person[19]
+            )
+            for person in subset
+        ]

controlnet_aux/src/controlnet_aux/open_pose/face.py

@@ -0,0 +1,364 @@
+import logging
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.nn import Conv2d, MaxPool2d, Module, ReLU, init
+from torchvision.transforms import ToPILImage, ToTensor
+
+from . import util
+
+
+class FaceNet(Module):
+    """Model the cascading heatmaps. """
+    def __init__(self):
+        super(FaceNet, self).__init__()
+        # cnn to make feature map
+        self.relu = ReLU()
+        self.max_pooling_2d = MaxPool2d(kernel_size=2, stride=2)
+        self.conv1_1 = Conv2d(in_channels=3, out_channels=64,
+                              kernel_size=3, stride=1, padding=1)
+        self.conv1_2 = Conv2d(
+            in_channels=64, out_channels=64, kernel_size=3, stride=1,
+            padding=1)
+        self.conv2_1 = Conv2d(
+            in_channels=64, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+        self.conv2_2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_1 = Conv2d(
+            in_channels=128, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_2 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_3 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv3_4 = Conv2d(
+            in_channels=256, out_channels=256, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_1 = Conv2d(
+            in_channels=256, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_2 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_3 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv4_4 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_1 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_2 = Conv2d(
+            in_channels=512, out_channels=512, kernel_size=3, stride=1,
+            padding=1)
+        self.conv5_3_CPM = Conv2d(
+            in_channels=512, out_channels=128, kernel_size=3, stride=1,
+            padding=1)
+
+        # stage1
+        self.conv6_1_CPM = Conv2d(
+            in_channels=128, out_channels=512, kernel_size=1, stride=1,
+            padding=0)
+        self.conv6_2_CPM = Conv2d(
+            in_channels=512, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage2
+        self.Mconv1_stage2 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage2 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage2 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage3
+        self.Mconv1_stage3 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage3 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage3 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage4
+        self.Mconv1_stage4 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage4 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage4 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage5
+        self.Mconv1_stage5 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage5 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage5 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        # stage6
+        self.Mconv1_stage6 = Conv2d(
+            in_channels=199, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv2_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv3_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv4_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv5_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=7, stride=1,
+            padding=3)
+        self.Mconv6_stage6 = Conv2d(
+            in_channels=128, out_channels=128, kernel_size=1, stride=1,
+            padding=0)
+        self.Mconv7_stage6 = Conv2d(
+            in_channels=128, out_channels=71, kernel_size=1, stride=1,
+            padding=0)
+
+        for m in self.modules():
+            if isinstance(m, Conv2d):
+                init.constant_(m.bias, 0)
+
+    def forward(self, x):
+        """Return a list of heatmaps."""
+        heatmaps = []
+
+        h = self.relu(self.conv1_1(x))
+        h = self.relu(self.conv1_2(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv2_1(h))
+        h = self.relu(self.conv2_2(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv3_1(h))
+        h = self.relu(self.conv3_2(h))
+        h = self.relu(self.conv3_3(h))
+        h = self.relu(self.conv3_4(h))
+        h = self.max_pooling_2d(h)
+        h = self.relu(self.conv4_1(h))
+        h = self.relu(self.conv4_2(h))
+        h = self.relu(self.conv4_3(h))
+        h = self.relu(self.conv4_4(h))
+        h = self.relu(self.conv5_1(h))
+        h = self.relu(self.conv5_2(h))
+        h = self.relu(self.conv5_3_CPM(h))
+        feature_map = h
+
+        # stage1
+        h = self.relu(self.conv6_1_CPM(h))
+        h = self.conv6_2_CPM(h)
+        heatmaps.append(h)
+
+        # stage2
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage2(h))
+        h = self.relu(self.Mconv2_stage2(h))
+        h = self.relu(self.Mconv3_stage2(h))
+        h = self.relu(self.Mconv4_stage2(h))
+        h = self.relu(self.Mconv5_stage2(h))
+        h = self.relu(self.Mconv6_stage2(h))
+        h = self.Mconv7_stage2(h)
+        heatmaps.append(h)
+
+        # stage3
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage3(h))
+        h = self.relu(self.Mconv2_stage3(h))
+        h = self.relu(self.Mconv3_stage3(h))
+        h = self.relu(self.Mconv4_stage3(h))
+        h = self.relu(self.Mconv5_stage3(h))
+        h = self.relu(self.Mconv6_stage3(h))
+        h = self.Mconv7_stage3(h)
+        heatmaps.append(h)
+
+        # stage4
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage4(h))
+        h = self.relu(self.Mconv2_stage4(h))
+        h = self.relu(self.Mconv3_stage4(h))
+        h = self.relu(self.Mconv4_stage4(h))
+        h = self.relu(self.Mconv5_stage4(h))
+        h = self.relu(self.Mconv6_stage4(h))
+        h = self.Mconv7_stage4(h)
+        heatmaps.append(h)
+
+        # stage5
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage5(h))
+        h = self.relu(self.Mconv2_stage5(h))
+        h = self.relu(self.Mconv3_stage5(h))
+        h = self.relu(self.Mconv4_stage5(h))
+        h = self.relu(self.Mconv5_stage5(h))
+        h = self.relu(self.Mconv6_stage5(h))
+        h = self.Mconv7_stage5(h)
+        heatmaps.append(h)
+
+        # stage6
+        h = torch.cat([h, feature_map], dim=1)  # channel concat
+        h = self.relu(self.Mconv1_stage6(h))
+        h = self.relu(self.Mconv2_stage6(h))
+        h = self.relu(self.Mconv3_stage6(h))
+        h = self.relu(self.Mconv4_stage6(h))
+        h = self.relu(self.Mconv5_stage6(h))
+        h = self.relu(self.Mconv6_stage6(h))
+        h = self.Mconv7_stage6(h)
+        heatmaps.append(h)
+
+        return heatmaps
+
+
+LOG = logging.getLogger(__name__)
+TOTEN = ToTensor()
+TOPIL = ToPILImage()
+
+
+params = {
+    'gaussian_sigma': 2.5,
+    'inference_img_size': 736,  # 368, 736, 1312
+    'heatmap_peak_thresh': 0.1,
+    'crop_scale': 1.5,
+    'line_indices': [
+        [0, 1], [1, 2], [2, 3], [3, 4], [4, 5], [5, 6],
+        [6, 7], [7, 8], [8, 9], [9, 10], [10, 11], [11, 12], [12, 13],
+        [13, 14], [14, 15], [15, 16],
+        [17, 18], [18, 19], [19, 20], [20, 21],
+        [22, 23], [23, 24], [24, 25], [25, 26],
+        [27, 28], [28, 29], [29, 30],
+        [31, 32], [32, 33], [33, 34], [34, 35],
+        [36, 37], [37, 38], [38, 39], [39, 40], [40, 41], [41, 36],
+        [42, 43], [43, 44], [44, 45], [45, 46], [46, 47], [47, 42],
+        [48, 49], [49, 50], [50, 51], [51, 52], [52, 53], [53, 54],
+        [54, 55], [55, 56], [56, 57], [57, 58], [58, 59], [59, 48],
+        [60, 61], [61, 62], [62, 63], [63, 64], [64, 65], [65, 66],
+        [66, 67], [67, 60]
+    ],
+}
+
+
+class Face(object):
+    """
+    The OpenPose face landmark detector model.
+
+    Args:
+        inference_size: set the size of the inference image size, suggested:
+            368, 736, 1312, default 736
+        gaussian_sigma: blur the heatmaps, default 2.5
+        heatmap_peak_thresh: return landmark if over threshold, default 0.1
+
+    """
+    def __init__(self, face_model_path,
+                 inference_size=None,
+                 gaussian_sigma=None,
+                 heatmap_peak_thresh=None):
+        self.inference_size = inference_size or params["inference_img_size"]
+        self.sigma = gaussian_sigma or params['gaussian_sigma']
+        self.threshold = heatmap_peak_thresh or params["heatmap_peak_thresh"]
+        self.model = FaceNet()
+        self.model.load_state_dict(torch.load(face_model_path))
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, face_img):
+        device = next(iter(self.model.parameters())).device
+        H, W, C = face_img.shape
+
+        w_size = 384
+        x_data = torch.from_numpy(util.smart_resize(face_img, (w_size, w_size))).permute([2, 0, 1]) / 256.0 - 0.5
+
+        x_data = x_data.to(device)
+
+        with torch.no_grad():
+            hs = self.model(x_data[None, ...])
+            heatmaps = F.interpolate(
+                hs[-1],
+                (H, W),
+                mode='bilinear', align_corners=True).cpu().numpy()[0]
+        return heatmaps
+
+    def compute_peaks_from_heatmaps(self, heatmaps):
+        all_peaks = []
+        for part in range(heatmaps.shape[0]):
+            map_ori = heatmaps[part].copy()
+            binary = np.ascontiguousarray(map_ori > 0.05, dtype=np.uint8)
+
+            if np.sum(binary) == 0:
+                continue
+
+            positions = np.where(binary > 0.5)
+            intensities = map_ori[positions]
+            mi = np.argmax(intensities)
+            y, x = positions[0][mi], positions[1][mi]
+            all_peaks.append([x, y])
+
+        return np.array(all_peaks)

controlnet_aux/src/controlnet_aux/open_pose/hand.py

@@ -0,0 +1,90 @@
+import cv2
+import numpy as np
+import torch
+from scipy.ndimage.filters import gaussian_filter
+from skimage.measure import label
+
+from . import util
+from .model import handpose_model
+
+
+class Hand(object):
+    def __init__(self, model_path):
+        self.model = handpose_model()
+        model_dict = util.transfer(self.model, torch.load(model_path))
+        self.model.load_state_dict(model_dict)
+        self.model.eval()
+
+    def to(self, device):
+        self.model.to(device)
+        return self
+
+    def __call__(self, oriImgRaw):
+        device = next(iter(self.model.parameters())).device
+        scale_search = [0.5, 1.0, 1.5, 2.0]
+        # scale_search = [0.5]
+        boxsize = 368
+        stride = 8
+        padValue = 128
+        thre = 0.05
+        multiplier = [x * boxsize for x in scale_search]
+
+        wsize = 128
+        heatmap_avg = np.zeros((wsize, wsize, 22))
+
+        Hr, Wr, Cr = oriImgRaw.shape
+
+        oriImg = cv2.GaussianBlur(oriImgRaw, (0, 0), 0.8)
+
+        for m in range(len(multiplier)):
+            scale = multiplier[m]
+            imageToTest = util.smart_resize(oriImg, (scale, scale))
+
+            imageToTest_padded, pad = util.padRightDownCorner(imageToTest, stride, padValue)
+            im = np.transpose(np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)) / 256 - 0.5
+            im = np.ascontiguousarray(im)
+
+            data = torch.from_numpy(im).float()
+            data = data.to(device)
+
+            with torch.no_grad():
+                output = self.model(data).cpu().numpy()
+
+            # extract outputs, resize, and remove padding
+            heatmap = np.transpose(np.squeeze(output), (1, 2, 0))  # output 1 is heatmaps
+            heatmap = util.smart_resize_k(heatmap, fx=stride, fy=stride)
+            heatmap = heatmap[:imageToTest_padded.shape[0] - pad[2], :imageToTest_padded.shape[1] - pad[3], :]
+            heatmap = util.smart_resize(heatmap, (wsize, wsize))
+
+            heatmap_avg += heatmap / len(multiplier)
+
+        all_peaks = []
+        for part in range(21):
+            map_ori = heatmap_avg[:, :, part]
+            one_heatmap = gaussian_filter(map_ori, sigma=3)
+            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
+
+            if np.sum(binary) == 0:
+                all_peaks.append([0, 0])
+                continue
+            label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
+            max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
+            label_img[label_img != max_index] = 0
+            map_ori[label_img == 0] = 0
+
+            y, x = util.npmax(map_ori)
+            y = int(float(y) * float(Hr) / float(wsize))
+            x = int(float(x) * float(Wr) / float(wsize))
+            all_peaks.append([x, y])
+        return np.array(all_peaks)
+
+if __name__ == "__main__":
+    hand_estimation = Hand('../model/hand_pose_model.pth')
+
+    # test_image = '../images/hand.jpg'
+    test_image = '../images/hand.jpg'
+    oriImg = cv2.imread(test_image)  # B,G,R order
+    peaks = hand_estimation(oriImg)
+    canvas = util.draw_handpose(oriImg, peaks, True)
+    cv2.imshow('', canvas)
+    cv2.waitKey(0)

controlnet_aux/src/controlnet_aux/open_pose/model.py

@@ -0,0 +1,217 @@
+import torch
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+
+def make_layers(block, no_relu_layers):
+    layers = []
+    for layer_name, v in block.items():
+        if 'pool' in layer_name:
+            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
+                                    padding=v[2])
+            layers.append((layer_name, layer))
+        else:
+            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
+                               kernel_size=v[2], stride=v[3],
+                               padding=v[4])
+            layers.append((layer_name, conv2d))
+            if layer_name not in no_relu_layers:
+                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))
+
+    return nn.Sequential(OrderedDict(layers))
+
+class bodypose_model(nn.Module):
+    def __init__(self):
+        super(bodypose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
+                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
+                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
+                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
+        blocks = {}
+        block0 = OrderedDict([
+                      ('conv1_1', [3, 64, 3, 1, 1]),
+                      ('conv1_2', [64, 64, 3, 1, 1]),
+                      ('pool1_stage1', [2, 2, 0]),
+                      ('conv2_1', [64, 128, 3, 1, 1]),
+                      ('conv2_2', [128, 128, 3, 1, 1]),
+                      ('pool2_stage1', [2, 2, 0]),
+                      ('conv3_1', [128, 256, 3, 1, 1]),
+                      ('conv3_2', [256, 256, 3, 1, 1]),
+                      ('conv3_3', [256, 256, 3, 1, 1]),
+                      ('conv3_4', [256, 256, 3, 1, 1]),
+                      ('pool3_stage1', [2, 2, 0]),
+                      ('conv4_1', [256, 512, 3, 1, 1]),
+                      ('conv4_2', [512, 512, 3, 1, 1]),
+                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
+                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
+                  ])
+
+
+        # Stage 1
+        block1_1 = OrderedDict([
+                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
+                    ])
+
+        block1_2 = OrderedDict([
+                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
+                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
+                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
+                    ])
+        blocks['block1_1'] = block1_1
+        blocks['block1_2'] = block1_2
+
+        self.model0 = make_layers(block0, no_relu_layers)
+
+        # Stages 2 - 6
+        for i in range(2, 7):
+            blocks['block%d_1' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
+                ])
+
+            blocks['block%d_2' % i] = OrderedDict([
+                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_1 = blocks['block1_1']
+        self.model2_1 = blocks['block2_1']
+        self.model3_1 = blocks['block3_1']
+        self.model4_1 = blocks['block4_1']
+        self.model5_1 = blocks['block5_1']
+        self.model6_1 = blocks['block6_1']
+
+        self.model1_2 = blocks['block1_2']
+        self.model2_2 = blocks['block2_2']
+        self.model3_2 = blocks['block3_2']
+        self.model4_2 = blocks['block4_2']
+        self.model5_2 = blocks['block5_2']
+        self.model6_2 = blocks['block6_2']
+
+
+    def forward(self, x):
+
+        out1 = self.model0(x)
+
+        out1_1 = self.model1_1(out1)
+        out1_2 = self.model1_2(out1)
+        out2 = torch.cat([out1_1, out1_2, out1], 1)
+
+        out2_1 = self.model2_1(out2)
+        out2_2 = self.model2_2(out2)
+        out3 = torch.cat([out2_1, out2_2, out1], 1)
+
+        out3_1 = self.model3_1(out3)
+        out3_2 = self.model3_2(out3)
+        out4 = torch.cat([out3_1, out3_2, out1], 1)
+
+        out4_1 = self.model4_1(out4)
+        out4_2 = self.model4_2(out4)
+        out5 = torch.cat([out4_1, out4_2, out1], 1)
+
+        out5_1 = self.model5_1(out5)
+        out5_2 = self.model5_2(out5)
+        out6 = torch.cat([out5_1, out5_2, out1], 1)
+
+        out6_1 = self.model6_1(out6)
+        out6_2 = self.model6_2(out6)
+
+        return out6_1, out6_2
+
+class handpose_model(nn.Module):
+    def __init__(self):
+        super(handpose_model, self).__init__()
+
+        # these layers have no relu layer
+        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
+                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
+        # stage 1
+        block1_0 = OrderedDict([
+                ('conv1_1', [3, 64, 3, 1, 1]),
+                ('conv1_2', [64, 64, 3, 1, 1]),
+                ('pool1_stage1', [2, 2, 0]),
+                ('conv2_1', [64, 128, 3, 1, 1]),
+                ('conv2_2', [128, 128, 3, 1, 1]),
+                ('pool2_stage1', [2, 2, 0]),
+                ('conv3_1', [128, 256, 3, 1, 1]),
+                ('conv3_2', [256, 256, 3, 1, 1]),
+                ('conv3_3', [256, 256, 3, 1, 1]),
+                ('conv3_4', [256, 256, 3, 1, 1]),
+                ('pool3_stage1', [2, 2, 0]),
+                ('conv4_1', [256, 512, 3, 1, 1]),
+                ('conv4_2', [512, 512, 3, 1, 1]),
+                ('conv4_3', [512, 512, 3, 1, 1]),
+                ('conv4_4', [512, 512, 3, 1, 1]),
+                ('conv5_1', [512, 512, 3, 1, 1]),
+                ('conv5_2', [512, 512, 3, 1, 1]),
+                ('conv5_3_CPM', [512, 128, 3, 1, 1])
+            ])
+
+        block1_1 = OrderedDict([
+            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
+            ('conv6_2_CPM', [512, 22, 1, 1, 0])
+        ])
+
+        blocks = {}
+        blocks['block1_0'] = block1_0
+        blocks['block1_1'] = block1_1
+
+        # stage 2-6
+        for i in range(2, 7):
+            blocks['block%d' % i] = OrderedDict([
+                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
+                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
+                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
+                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
+                ])
+
+        for k in blocks.keys():
+            blocks[k] = make_layers(blocks[k], no_relu_layers)
+
+        self.model1_0 = blocks['block1_0']
+        self.model1_1 = blocks['block1_1']
+        self.model2 = blocks['block2']
+        self.model3 = blocks['block3']
+        self.model4 = blocks['block4']
+        self.model5 = blocks['block5']
+        self.model6 = blocks['block6']
+
+    def forward(self, x):
+        out1_0 = self.model1_0(x)
+        out1_1 = self.model1_1(out1_0)
+        concat_stage2 = torch.cat([out1_1, out1_0], 1)
+        out_stage2 = self.model2(concat_stage2)
+        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
+        out_stage3 = self.model3(concat_stage3)
+        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
+        out_stage4 = self.model4(concat_stage4)
+        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
+        out_stage5 = self.model5(concat_stage5)
+        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
+        out_stage6 = self.model6(concat_stage6)
+        return out_stage6

controlnet_aux/src/controlnet_aux/open_pose/util.py

@@ -0,0 +1,383 @@
+import math
+import numpy as np
+import cv2
+from typing import List, Tuple, Union
+
+from .body import BodyResult, Keypoint
+
+eps = 0.01
+
+
+def smart_resize(x, s):
+    Ht, Wt = s
+    if x.ndim == 2:
+        Ho, Wo = x.shape
+        Co = 1
+    else:
+        Ho, Wo, Co = x.shape
+    if Co == 3 or Co == 1:
+        k = float(Ht + Wt) / float(Ho + Wo)
+        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
+    else:
+        return np.stack([smart_resize(x[:, :, i], s) for i in range(Co)], axis=2)
+
+
+def smart_resize_k(x, fx, fy):
+    if x.ndim == 2:
+        Ho, Wo = x.shape
+        Co = 1
+    else:
+        Ho, Wo, Co = x.shape
+    Ht, Wt = Ho * fy, Wo * fx
+    if Co == 3 or Co == 1:
+        k = float(Ht + Wt) / float(Ho + Wo)
+        return cv2.resize(x, (int(Wt), int(Ht)), interpolation=cv2.INTER_AREA if k < 1 else cv2.INTER_LANCZOS4)
+    else:
+        return np.stack([smart_resize_k(x[:, :, i], fx, fy) for i in range(Co)], axis=2)
+
+
+def padRightDownCorner(img, stride, padValue):
+    h = img.shape[0]
+    w = img.shape[1]
+
+    pad = 4 * [None]
+    pad[0] = 0 # up
+    pad[1] = 0 # left
+    pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
+    pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right
+
+    img_padded = img
+    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
+    img_padded = np.concatenate((pad_up, img_padded), axis=0)
+    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
+    img_padded = np.concatenate((pad_left, img_padded), axis=1)
+    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
+    img_padded = np.concatenate((img_padded, pad_down), axis=0)
+    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
+    img_padded = np.concatenate((img_padded, pad_right), axis=1)
+
+    return img_padded, pad
+
+
+def transfer(model, model_weights):
+    transfered_model_weights = {}
+    for weights_name in model.state_dict().keys():
+        transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
+    return transfered_model_weights
+
+
+def draw_bodypose(canvas: np.ndarray, keypoints: List[Keypoint]) -> np.ndarray:
+    """
+    Draw keypoints and limbs representing body pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the body pose.
+        keypoints (List[Keypoint]): A list of Keypoint objects representing the body keypoints to be drawn.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn body pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    H, W, C = canvas.shape
+    stickwidth = 4
+
+    limbSeq = [
+        [2, 3], [2, 6], [3, 4], [4, 5], 
+        [6, 7], [7, 8], [2, 9], [9, 10], 
+        [10, 11], [2, 12], [12, 13], [13, 14], 
+        [2, 1], [1, 15], [15, 17], [1, 16], 
+        [16, 18],
+    ]
+
+    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
+              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
+              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
+
+    for (k1_index, k2_index), color in zip(limbSeq, colors):
+        keypoint1 = keypoints[k1_index - 1]
+        keypoint2 = keypoints[k2_index - 1]
+
+        if keypoint1 is None or keypoint2 is None:
+            continue
+
+        Y = np.array([keypoint1.x, keypoint2.x]) * float(W)
+        X = np.array([keypoint1.y, keypoint2.y]) * float(H)
+        mX = np.mean(X)
+        mY = np.mean(Y)
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+        angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
+        cv2.fillConvexPoly(canvas, polygon, [int(float(c) * 0.6) for c in color])
+
+    for keypoint, color in zip(keypoints, colors):
+        if keypoint is None:
+            continue
+
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        cv2.circle(canvas, (int(x), int(y)), 4, color, thickness=-1)
+
+    return canvas
+
+
+def draw_handpose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
+    import matplotlib
+    """
+    Draw keypoints and connections representing hand pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the hand pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the hand keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn hand pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """
+    if not keypoints:
+        return canvas
+    
+    H, W, C = canvas.shape
+
+    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
+             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
+
+    for ie, (e1, e2) in enumerate(edges):
+        k1 = keypoints[e1]
+        k2 = keypoints[e2]
+        if k1 is None or k2 is None:
+            continue
+        
+        x1 = int(k1.x * W)
+        y1 = int(k1.y * H)
+        x2 = int(k2.x * W)
+        y2 = int(k2.y * H)
+        if x1 > eps and y1 > eps and x2 > eps and y2 > eps:
+            cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie / float(len(edges)), 1.0, 1.0]) * 255, thickness=2)
+
+    for keypoint in keypoints:
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
+    return canvas
+
+
+def draw_facepose(canvas: np.ndarray, keypoints: Union[List[Keypoint], None]) -> np.ndarray:
+    """
+    Draw keypoints representing face pose on a given canvas.
+
+    Args:
+        canvas (np.ndarray): A 3D numpy array representing the canvas (image) on which to draw the face pose.
+        keypoints (List[Keypoint]| None): A list of Keypoint objects representing the face keypoints to be drawn
+                                          or None if no keypoints are present.
+
+    Returns:
+        np.ndarray: A 3D numpy array representing the modified canvas with the drawn face pose.
+
+    Note:
+        The function expects the x and y coordinates of the keypoints to be normalized between 0 and 1.
+    """    
+    if not keypoints:
+        return canvas
+    
+    H, W, C = canvas.shape
+    for keypoint in keypoints:
+        x, y = keypoint.x, keypoint.y
+        x = int(x * W)
+        y = int(y * H)
+        if x > eps and y > eps:
+            cv2.circle(canvas, (x, y), 3, (255, 255, 255), thickness=-1)
+    return canvas
+
+
+# detect hand according to body pose keypoints
+# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
+def handDetect(body: BodyResult, oriImg) -> List[Tuple[int, int, int, bool]]:
+    """
+    Detect hands in the input body pose keypoints and calculate the bounding box for each hand.
+
+    Args:
+        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
+        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
+
+    Returns:
+        List[Tuple[int, int, int, bool]]: A list of tuples, each containing the coordinates (x, y) of the top-left
+                                          corner of the bounding box, the width (height) of the bounding box, and
+                                          a boolean flag indicating whether the hand is a left hand (True) or a
+                                          right hand (False).
+
+    Notes:
+        - The width and height of the bounding boxes are equal since the network requires squared input.
+        - The minimum bounding box size is 20 pixels.
+    """
+    ratioWristElbow = 0.33
+    detect_result = []
+    image_height, image_width = oriImg.shape[0:2]
+    
+    keypoints = body.keypoints
+    # right hand: wrist 4, elbow 3, shoulder 2
+    # left hand: wrist 7, elbow 6, shoulder 5
+    left_shoulder = keypoints[5]
+    left_elbow = keypoints[6]
+    left_wrist = keypoints[7]
+    right_shoulder = keypoints[2]
+    right_elbow = keypoints[3]
+    right_wrist = keypoints[4]
+
+    # if any of three not detected
+    has_left = all(keypoint is not None for keypoint in (left_shoulder, left_elbow, left_wrist))
+    has_right = all(keypoint is not None for keypoint in (right_shoulder, right_elbow, right_wrist))
+    if not (has_left or has_right):
+        return []
+    
+    hands = []
+    #left hand
+    if has_left:
+        hands.append([
+            left_shoulder.x, left_shoulder.y,
+            left_elbow.x, left_elbow.y,
+            left_wrist.x, left_wrist.y,
+            True
+        ])
+    # right hand
+    if has_right:
+        hands.append([
+            right_shoulder.x, right_shoulder.y,
+            right_elbow.x, right_elbow.y,
+            right_wrist.x, right_wrist.y,
+            False
+        ])
+
+    for x1, y1, x2, y2, x3, y3, is_left in hands:
+        # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
+        # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
+        # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
+        # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
+        # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
+        # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
+        x = x3 + ratioWristElbow * (x3 - x2)
+        y = y3 + ratioWristElbow * (y3 - y2)
+        distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
+        distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
+        width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
+        # x-y refers to the center --> offset to topLeft point
+        # handRectangle.x -= handRectangle.width / 2.f;
+        # handRectangle.y -= handRectangle.height / 2.f;
+        x -= width / 2
+        y -= width / 2  # width = height
+        # overflow the image
+        if x < 0: x = 0
+        if y < 0: y = 0
+        width1 = width
+        width2 = width
+        if x + width > image_width: width1 = image_width - x
+        if y + width > image_height: width2 = image_height - y
+        width = min(width1, width2)
+        # the max hand box value is 20 pixels
+        if width >= 20:
+            detect_result.append((int(x), int(y), int(width), is_left))
+
+    '''
+    return value: [[x, y, w, True if left hand else False]].
+    width=height since the network require squared input.
+    x, y is the coordinate of top left 
+    '''
+    return detect_result
+
+
+# Written by Lvmin
+def faceDetect(body: BodyResult, oriImg) -> Union[Tuple[int, int, int], None]:
+    """
+    Detect the face in the input body pose keypoints and calculate the bounding box for the face.
+
+    Args:
+        body (BodyResult): A BodyResult object containing the detected body pose keypoints.
+        oriImg (numpy.ndarray): A 3D numpy array representing the original input image.
+
+    Returns:
+        Tuple[int, int, int] | None: A tuple containing the coordinates (x, y) of the top-left corner of the
+                                   bounding box and the width (height) of the bounding box, or None if the
+                                   face is not detected or the bounding box width is less than 20 pixels.
+
+    Notes:
+        - The width and height of the bounding box are equal.
+        - The minimum bounding box size is 20 pixels.
+    """
+    # left right eye ear 14 15 16 17
+    image_height, image_width = oriImg.shape[0:2]
+    
+    keypoints = body.keypoints
+    head = keypoints[0]
+    left_eye = keypoints[14]
+    right_eye = keypoints[15]
+    left_ear = keypoints[16]
+    right_ear = keypoints[17]
+    
+    if head is None or all(keypoint is None for keypoint in (left_eye, right_eye, left_ear, right_ear)):
+        return None
+
+    width = 0.0
+    x0, y0 = head.x, head.y
+
+    if left_eye is not None:
+        x1, y1 = left_eye.x, left_eye.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 3.0)
+
+    if right_eye is not None:
+        x1, y1 = right_eye.x, right_eye.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 3.0)
+
+    if left_ear is not None:
+        x1, y1 = left_ear.x, left_ear.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 1.5)
+
+    if right_ear is not None:
+        x1, y1 = right_ear.x, right_ear.y
+        d = max(abs(x0 - x1), abs(y0 - y1))
+        width = max(width, d * 1.5)
+
+    x, y = x0, y0
+
+    x -= width
+    y -= width
+
+    if x < 0:
+        x = 0
+
+    if y < 0:
+        y = 0
+
+    width1 = width * 2
+    width2 = width * 2
+
+    if x + width > image_width:
+        width1 = image_width - x
+
+    if y + width > image_height:
+        width2 = image_height - y
+
+    width = min(width1, width2)
+
+    if width >= 20:
+        return int(x), int(y), int(width)
+    else:
+        return None
+
+
+# get max index of 2d array
+def npmax(array):
+    arrayindex = array.argmax(1)
+    arrayvalue = array.max(1)
+    i = arrayvalue.argmax()
+    j = arrayindex[i]
+    return i, j

controlnet_aux/src/controlnet_aux/util.py

@@ -0,0 +1,146 @@
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+
+annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
+
+
+def HWC3(x):
+    assert x.dtype == np.uint8
+    if x.ndim == 2:
+        x = x[:, :, None]
+    assert x.ndim == 3
+    H, W, C = x.shape
+    assert C == 1 or C == 3 or C == 4
+    if C == 3:
+        return x
+    if C == 1:
+        return np.concatenate([x, x, x], axis=2)
+    if C == 4:
+        color = x[:, :, 0:3].astype(np.float32)
+        alpha = x[:, :, 3:4].astype(np.float32) / 255.0
+        y = color * alpha + 255.0 * (1.0 - alpha)
+        y = y.clip(0, 255).astype(np.uint8)
+        return y
+
+
+def make_noise_disk(H, W, C, F):
+    noise = np.random.uniform(low=0, high=1, size=((H // F) + 2, (W // F) + 2, C))
+    noise = cv2.resize(noise, (W + 2 * F, H + 2 * F), interpolation=cv2.INTER_CUBIC)
+    noise = noise[F: F + H, F: F + W]
+    noise -= np.min(noise)
+    noise /= np.max(noise)
+    if C == 1:
+        noise = noise[:, :, None]
+    return noise
+
+
+def nms(x, t, s):
+    x = cv2.GaussianBlur(x.astype(np.float32), (0, 0), s)
+
+    f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
+    f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
+    f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
+    f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
+
+    y = np.zeros_like(x)
+
+    for f in [f1, f2, f3, f4]:
+        np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
+
+    z = np.zeros_like(y, dtype=np.uint8)
+    z[y > t] = 255
+    return z
+
+def min_max_norm(x):
+    x -= np.min(x)
+    x /= np.maximum(np.max(x), 1e-5)
+    return x
+
+
+def safe_step(x, step=2):
+    y = x.astype(np.float32) * float(step + 1)
+    y = y.astype(np.int32).astype(np.float32) / float(step)
+    return y
+
+
+def img2mask(img, H, W, low=10, high=90):
+    assert img.ndim == 3 or img.ndim == 2
+    assert img.dtype == np.uint8
+
+    if img.ndim == 3:
+        y = img[:, :, random.randrange(0, img.shape[2])]
+    else:
+        y = img
+
+    y = cv2.resize(y, (W, H), interpolation=cv2.INTER_CUBIC)
+
+    if random.uniform(0, 1) < 0.5:
+        y = 255 - y
+
+    return y < np.percentile(y, random.randrange(low, high))
+
+
+def resize_image(input_image, resolution):
+    H, W, C = input_image.shape
+    H = float(H)
+    W = float(W)
+    k = float(resolution) / min(H, W)
+    H *= k
+    W *= k
+    H = int(np.round(H / 64.0)) * 64
+    W = int(np.round(W / 64.0)) * 64
+    img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
+    return img
+
+
+def torch_gc():
+    if torch.cuda.is_available():
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
+
+
+def ade_palette():
+    """ADE20K palette that maps each class to RGB values."""
+    return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
+            [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
+            [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
+            [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
+            [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
+            [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
+            [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
+            [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
+            [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
+            [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
+            [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
+            [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
+            [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
+            [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
+            [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
+            [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
+            [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
+            [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
+            [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
+            [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
+            [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
+            [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
+            [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
+            [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
+            [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
+            [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
+            [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
+            [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
+            [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
+            [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
+            [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
+            [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
+            [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
+            [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
+            [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
+            [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
+            [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
+            [102, 255, 0], [92, 0, 255]]
+

controlnet_aux/tests/test_controlnet_aux.py

@@ -0,0 +1,126 @@
+import os
+import shutil
+from io import BytesIO
+
+import numpy as np
+import pytest
+import requests
+from PIL import Image
+
+from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
+                            LeresDetector, LineartAnimeDetector,
+                            LineartDetector, MediapipeFaceDetector,
+                            MidasDetector, MLSDdetector, NormalBaeDetector,
+                            OpenposeDetector, PidiNetDetector, SamDetector,
+                            ZoeDetector, DWposeDetector)
+
+OUTPUT_DIR = "tests/outputs"
+
+def output(name, img):
+    img.save(os.path.join(OUTPUT_DIR, "{:s}.png".format(name)))
+
+def common(name, processor, img):
+    output(name, processor(img))
+    output(name + "_pil_np", Image.fromarray(processor(img, output_type="np")))
+    output(name + "_np_np", Image.fromarray(processor(np.array(img, dtype=np.uint8), output_type="np")))
+    output(name + "_np_pil", processor(np.array(img, dtype=np.uint8), output_type="pil"))
+    output(name + "_scaled", processor(img, detect_resolution=640, image_resolution=768))
+
+def return_pil(name, processor, img):
+    output(name + "_pil_false", Image.fromarray(processor(img, return_pil=False)))
+    output(name + "_pil_true", processor(img, return_pil=True))
+
+@pytest.fixture(scope="module")
+def img():
+    if os.path.exists(OUTPUT_DIR):
+        shutil.rmtree(OUTPUT_DIR)
+    os.mkdir(OUTPUT_DIR)
+    url = "https://huggingface.co/lllyasviel/sd-controlnet-openpose/resolve/main/images/pose.png"
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content)).convert("RGB").resize((512, 512))
+    return img
+
+def test_canny(img):
+    canny = CannyDetector()
+    common("canny", canny, img)
+    output("canny_img", canny(img=img))
+
+def test_hed(img):
+    hed = HEDdetector.from_pretrained("lllyasviel/Annotators")
+    common("hed", hed, img)
+    return_pil("hed", hed, img)
+    output("hed_safe", hed(img, safe=True))
+    output("hed_scribble", hed(img, scribble=True))
+
+def test_leres(img):
+    leres = LeresDetector.from_pretrained("lllyasviel/Annotators")
+    common("leres", leres, img)
+    output("leres_boost", leres(img, boost=True))
+
+def test_lineart(img):
+    lineart = LineartDetector.from_pretrained("lllyasviel/Annotators")
+    common("lineart", lineart, img)
+    return_pil("lineart", lineart, img)
+    output("lineart_coarse", lineart(img, coarse=True))
+
+def test_lineart_anime(img):
+    lineart_anime = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
+    common("lineart_anime", lineart_anime, img)
+    return_pil("lineart_anime", lineart_anime, img)
+
+def test_mediapipe_face(img):
+    mediapipe = MediapipeFaceDetector()
+    common("mediapipe", mediapipe, img)
+    output("mediapipe_image", mediapipe(image=img))
+
+def test_midas(img):
+    midas = MidasDetector.from_pretrained("lllyasviel/Annotators")
+    common("midas", midas, img)
+    output("midas_normal", midas(img, depth_and_normal=True)[1])
+
+def test_mlsd(img):
+    mlsd = MLSDdetector.from_pretrained("lllyasviel/Annotators")
+    common("mlsd", mlsd, img)
+    return_pil("mlsd", mlsd, img)
+
+def test_normalbae(img):
+    normal_bae = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
+    common("normal_bae", normal_bae, img)
+    return_pil("normal_bae", normal_bae, img)
+
+def test_openpose(img):
+    openpose = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
+    common("openpose", openpose, img)
+    return_pil("openpose", openpose, img)
+    output("openpose_hand_and_face_false", openpose(img, hand_and_face=False))
+    output("openpose_hand_and_face_true", openpose(img, hand_and_face=True))
+    output("openpose_face", openpose(img, include_body=True, include_hand=False, include_face=True))
+    output("openpose_faceonly", openpose(img, include_body=False, include_hand=False, include_face=True))
+    output("openpose_full", openpose(img, include_body=True, include_hand=True, include_face=True))
+    output("openpose_hand", openpose(img, include_body=True, include_hand=True, include_face=False))
+
+def test_pidi(img):
+    pidi = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
+    common("pidi", pidi, img)
+    return_pil("pidi", pidi, img)
+    output("pidi_safe", pidi(img, safe=True))
+    output("pidi_scribble", pidi(img, scribble=True))
+
+def test_sam(img):
+    sam = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
+    common("sam", sam, img)
+    output("sam_image", sam(image=img))
+
+def test_shuffle(img):
+    shuffle = ContentShuffleDetector()
+    common("shuffle", shuffle, img)
+    return_pil("shuffle", shuffle, img)
+
+def test_zoe(img):
+    zoe = ZoeDetector.from_pretrained("lllyasviel/Annotators")
+    common("zoe", zoe, img)
+
+def test_dwpose(img):
+    dwpose = DWposeDetector()
+    common("dwpose", dwpose, img)
+    return_pil("dwpose", dwpose, img)