├── .gitignore ├── .pre-commit-config.yaml ├── LICENSE ├── README.md ├── Taskfile.yml ├── asdff ├── __init__.py ├── __version__.py ├── base.py ├── sd.py ├── utils.py └── yolo.py ├── pdm.lock ├── pipelines ├── adsd_pipeline.py ├── adsdcn_pipeline.py └── template.txt ├── pyproject.toml ├── tests └── test_pipeline.py └── upload.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Created by https://www.toptal.com/developers/gitignore/api/python 2 | # Edit at https://www.toptal.com/developers/gitignore?templates=python 3 | 4 | ### Python ### 5 | # Byte-compiled / optimized / DLL files 6 | __pycache__/ 7 | *.py[cod] 8 | *$py.class 9 | 10 | # C extensions 11 | *.so 12 | 13 | # Distribution / packaging 14 | .Python 15 | build/ 16 | develop-eggs/ 17 | dist/ 18 | downloads/ 19 | eggs/ 20 | .eggs/ 21 | lib/ 22 | lib64/ 23 | parts/ 24 | sdist/ 25 | var/ 26 | wheels/ 27 | share/python-wheels/ 28 | *.egg-info/ 29 | .installed.cfg 30 | *.egg 31 | MANIFEST 32 | 33 | # PyInstaller 34 | # Usually these files are written by a python script from a template 35 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 36 | *.manifest 37 | *.spec 38 | 39 | # Installer logs 40 | pip-log.txt 41 | pip-delete-this-directory.txt 42 | 43 | # Unit test / coverage reports 44 | htmlcov/ 45 | .tox/ 46 | .nox/ 47 | .coverage 48 | .coverage.* 49 | .cache 50 | nosetests.xml 51 | coverage.xml 52 | *.cover 53 | *.py,cover 54 | .hypothesis/ 55 | .pytest_cache/ 56 | cover/ 57 | 58 | # Translations 59 | *.mo 60 | *.pot 61 | 62 | # Django stuff: 63 | *.log 64 | local_settings.py 65 | db.sqlite3 66 | db.sqlite3-journal 67 | 68 | # Flask stuff: 69 | instance/ 70 | .webassets-cache 71 | 72 | # Scrapy stuff: 73 | .scrapy 74 | 75 | # Sphinx documentation 76 | docs/_build/ 77 | 78 | # PyBuilder 79 | .pybuilder/ 80 | target/ 81 | 82 | # Jupyter Notebook 83 | .ipynb_checkpoints 84 | 85 | # IPython 86 | profile_default/ 87 | ipython_config.py 88 | 89 | # pyenv 90 | # For a library or package, you might want to ignore these files since the code is 91 | # intended to run in multiple environments; otherwise, check them in: 92 | # .python-version 93 | 94 | # pipenv 95 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 96 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 97 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 98 | # install all needed dependencies. 99 | #Pipfile.lock 100 | 101 | # poetry 102 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 103 | # This is especially recommended for binary packages to ensure reproducibility, and is more 104 | # commonly ignored for libraries. 105 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 106 | #poetry.lock 107 | 108 | # pdm 109 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 110 | #pdm.lock 111 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 112 | # in version control. 113 | # https://pdm.fming.dev/#use-with-ide 114 | .pdm.toml 115 | .pdm-python 116 | .pdm-build/ 117 | 118 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 119 | __pypackages__/ 120 | 121 | # Celery stuff 122 | celerybeat-schedule 123 | celerybeat.pid 124 | 125 | # SageMath parsed files 126 | *.sage.py 127 | 128 | # Environments 129 | .env 130 | .venv 131 | env/ 132 | venv/ 133 | ENV/ 134 | env.bak/ 135 | venv.bak/ 136 | 137 | # Spyder project settings 138 | .spyderproject 139 | .spyproject 140 | 141 | # Rope project settings 142 | .ropeproject 143 | 144 | # mkdocs documentation 145 | /site 146 | 147 | # mypy 148 | .mypy_cache/ 149 | .dmypy.json 150 | dmypy.json 151 | 152 | # Pyre type checker 153 | .pyre/ 154 | 155 | # pytype static type analyzer 156 | .pytype/ 157 | 158 | # Cython debug symbols 159 | cython_debug/ 160 | 161 | # PyCharm 162 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 163 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 164 | # and can be added to the global gitignore or merged into this file. 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If not, see . 647 | 648 | Also add information on how to contact you by electronic and paper mail. 649 | 650 | If your software can interact with users remotely through a computer 651 | network, you should also make sure that it provides a way for users to 652 | get its source. For example, if your program is a web application, its 653 | interface could display a "Source" link that leads users to an archive 654 | of the code. There are many ways you could offer source, and different 655 | solutions will be better for different programs; see section 13 for the 656 | specific requirements. 657 | 658 | You should also get your employer (if you work as a programmer) or school, 659 | if any, to sign a "copyright disclaimer" for the program, if necessary. 660 | For more information on this, and how to apply and follow the GNU AGPL, see 661 | . 662 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # asdff 2 | 3 | Adetailer Stable Diffusion diFFusers pipeline 4 | 5 | ## 예시 6 | 7 | ### from pip install 8 | 9 | ``` 10 | pip install asdff 11 | ``` 12 | 13 | ```py 14 | import torch 15 | from asdff import AdPipeline 16 | 17 | pipe = AdPipeline.from_pretrained("stablediffusionapi/counterfeit-v30", torch_dtype=torch.float16) 18 | pipe.safety_checker = None 19 | pipe.to("cuda") 20 | 21 | common = {"prompt": "masterpiece, best quality, 1girl", "num_inference_steps": 28} 22 | result = pipe(common=common) 23 | 24 | images = result[0] 25 | ``` 26 | 27 | ### from custom pipeline 28 | 29 | ultralytics 설치 필요 30 | 31 | ``` 32 | pip install ultralytics 33 | ``` 34 | 35 | ```py 36 | import torch 37 | from diffusers import DiffusionPipeline 38 | 39 | pipe = DiffusionPipeline.from_pretrained( 40 | "stablediffusionapi/counterfeit-v30", 41 | torch_dtype=torch.float16, 42 | custom_pipeline="Bingsu/adsd_pipeline" 43 | ) 44 | pipe.safety_checker = None 45 | pipe.to("cuda") 46 | 47 | common = {"prompt": "masterpiece, best quality, 1girl", "num_inference_steps": 28} 48 | result = pipe(common=common) 49 | 50 | images = result[0] 51 | ``` 52 | 53 | ### 그 외 54 | 55 | 스케줄러를 변경하고, 입력 이미지를 제공하는 예시 56 | 57 | ```py 58 | import torch 59 | from asdff import AdPipeline 60 | from diffusers import DPMSolverMultistepScheduler 61 | from diffusers.utils import load_image 62 | 63 | pipe = AdPipeline.from_pretrained("stablediffusionapi/counterfeit-v30", torch_dtype=torch.float16) 64 | pipe.safety_checker = None 65 | pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) 66 | pipe = pipe.to("cuda") 67 | 68 | common = {"prompt": "masterpiece, best quality, 1girl", "num_inference_steps": 20} 69 | images = load_image("https://i.imgur.com/8TX2AX6.png") 70 | result = pipe(common=common, images=[images]) 71 | ``` 72 | 73 | 74 | ## arguments 75 | 76 | - `common: Mapping[str, Any] | None` 77 | 78 | txt2img와 inpaint에서 공통적으로 사용할 인자들 79 | 80 | - `txt2img_only: Mapping[str, Any] | None` 81 | 82 | txt2img에서만 사용할 인자. common과 겹치는 인자는 덮어씁니다. 83 | 84 | [StableDiffusionPipeline.__call__](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img#diffusers.StableDiffusionPipeline.__call__) 85 | 86 | - `inpaint_only: Mapping[str, Any] | None` 87 | 88 | inpaint에서만 사용할 인자. common과 겹치는 인자는 덮어씁니다. 89 | 90 | `strength: 0.4`가 기본값으로 적용됩니다. 91 | 92 | [StableDiffusionInpaintPipeline.__call__](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint#diffusers.StableDiffusionInpaintPipeline.__call__) 93 | 94 | - `images: Image | Iterable[Image] | None` 95 | 96 | inpaint를 수행할 이미지들. 주어지면 txt2img의 결과를 대체하기 때문에 `txt2img_only`는 무시됩니다. 97 | 98 | - `detectors: DetectorType | Iterable[DetectorType] | None` 99 | 100 | `DetectorType: Callable[[Image.Image], Optional[List[Image.Image]]]` 101 | 102 | pil Image를 입력으로 받아 마스크 이미지의 리스트(마스크), 또는 None을 반환하는 Callable. 103 | 104 | 그런 Callable 하나, Callable의 리스트 또는 None 105 | 106 | `None`일경우, `default_detector`가 사용됩니다. 107 | 108 | ```py 109 | from asdff import AdPipeline 110 | 111 | pipe = AdPipeline.from_pretrained(...) 112 | pipe.default_detector 113 | >>> 'list[Image.Image] | None'> 114 | ``` 115 | 116 | 사용 예시 117 | 118 | ```py 119 | from functools import partial 120 | 121 | import torch 122 | from asdff import AdPipeline, yolo_detector 123 | from huggingface_hub import hf_hub_download 124 | 125 | pipe = AdPipeline.from_pretrained("stablediffusionapi/counterfeit-v30", torch_dtype=torch.float16) 126 | pipe.safety_checker = None 127 | pipe.to("cuda") 128 | 129 | person_model_path = hf_hub_download("Bingsu/adetailer", "person_yolov8s-seg.pt") 130 | person_detector = partial(yolo_detector, model_path=person_model_path) 131 | common = {"prompt": "masterpiece, best quality, 1girl", "num_inference_steps": 28} 132 | result = pipe(common=common, detectors=[person_detector, pipe.default_detector]) 133 | result 134 | ``` 135 | 136 | - `mask_dilation: int, default = 4` 137 | 138 | 마스크 감지 후, cv2.dilate 함수를 적용해 마스크 영역을 키우는 데, 이 때 적용할 커널의 크기. 139 | 140 | - `mask_blur: int, default = 4` 141 | 142 | dilation 후 적용할 가우시안 블러의 커널 크기. 143 | 144 | - `mask_padding: int, default = 32` 145 | 146 | dilation 적용 후 이 값만큼 bbox의 가로세로 영역을 더해서 이미지를 자른 뒤, inpaint를 시도하게 됩니다. 147 | -------------------------------------------------------------------------------- /Taskfile.yml: -------------------------------------------------------------------------------- 1 | # https://taskfile.dev 2 | 3 | version: "3" 4 | 5 | tasks: 6 | lint: 7 | cmds: 8 | - pre-commit run -a 9 | 10 | test: 11 | cmds: 12 | - pytest 13 | 14 | huggingface: 15 | cmds: 16 | - python upload.py 17 | 18 | publish: 19 | cmds: 20 | - task: huggingface 21 | - pdm publish 22 | -------------------------------------------------------------------------------- /asdff/__init__.py: -------------------------------------------------------------------------------- 1 | from .__version__ import __version__ 2 | from .sd import AdCnPipeline, AdPipeline 3 | from .yolo import yolo_detector 4 | 5 | __all__ = [ 6 | "AdPipeline", 7 | "AdCnPipeline", 8 | "yolo_detector", 9 | "__version__", 10 | ] 11 | -------------------------------------------------------------------------------- /asdff/__version__.py: -------------------------------------------------------------------------------- 1 | __version__ = "0.2.1" 2 | -------------------------------------------------------------------------------- /asdff/base.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | import inspect 4 | from abc import ABC, abstractmethod 5 | from typing import Any, Callable, Iterable, List, Mapping, Optional 6 | 7 | from diffusers.utils import logging 8 | from PIL import Image 9 | 10 | from asdff.utils import ( 11 | ADOutput, 12 | bbox_padding, 13 | composite, 14 | mask_dilate, 15 | mask_gaussian_blur, 16 | ) 17 | from asdff.yolo import yolo_detector 18 | 19 | logger = logging.get_logger("diffusers") 20 | 21 | 22 | DetectorType = Callable[[Image.Image], Optional[List[Image.Image]]] 23 | 24 | 25 | def ordinal(n: int) -> str: 26 | d = {1: "st", 2: "nd", 3: "rd"} 27 | return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th")) 28 | 29 | 30 | class AdPipelineBase(ABC): 31 | @property 32 | @abstractmethod 33 | def inpaint_pipeline(self) -> Callable: 34 | raise NotImplementedError 35 | 36 | @property 37 | @abstractmethod 38 | def txt2img_class(self) -> type: 39 | raise NotImplementedError 40 | 41 | def __call__( # noqa: C901 42 | self, 43 | common: Mapping[str, Any] | None = None, 44 | txt2img_only: Mapping[str, Any] | None = None, 45 | inpaint_only: Mapping[str, Any] | None = None, 46 | images: Image.Image | Iterable[Image.Image] | None = None, 47 | detectors: DetectorType | Iterable[DetectorType] | None = None, 48 | mask_dilation: int = 4, 49 | mask_blur: int = 4, 50 | mask_padding: int = 32, 51 | ): 52 | if common is None: 53 | common = {} 54 | if txt2img_only is None: 55 | txt2img_only = {} 56 | if inpaint_only is None: 57 | inpaint_only = {} 58 | if "strength" not in inpaint_only: 59 | inpaint_only = {**inpaint_only, "strength": 0.4} 60 | 61 | if detectors is None: 62 | detectors = [self.default_detector] 63 | elif not isinstance(detectors, Iterable): 64 | detectors = [detectors] 65 | 66 | if images is None: 67 | txt2img_output = self.process_txt2img(common, txt2img_only) 68 | txt2img_images = txt2img_output[0] 69 | else: 70 | if txt2img_only: 71 | msg = "Both `images` and `txt2img_only` are specified. if `images` is specified, `txt2img_only` is ignored." 72 | logger.warning(msg) 73 | 74 | txt2img_images = [images] if not isinstance(images, Iterable) else images 75 | 76 | init_images = [] 77 | final_images = [] 78 | 79 | for i, init_image in enumerate(txt2img_images): 80 | init_images.append(init_image.copy()) 81 | final_image = None 82 | 83 | for j, detector in enumerate(detectors): 84 | masks = detector(init_image) 85 | if masks is None: 86 | logger.info( 87 | f"No object detected on {ordinal(i + 1)} image with {ordinal(j + 1)} detector." 88 | ) 89 | continue 90 | 91 | for k, mask in enumerate(masks): 92 | mask = mask.convert("L") 93 | mask = mask_dilate(mask, mask_dilation) 94 | bbox = mask.getbbox() 95 | if bbox is None: 96 | logger.info(f"No object in {ordinal(k + 1)} mask.") 97 | continue 98 | mask = mask_gaussian_blur(mask, mask_blur) 99 | bbox_padded = bbox_padding(bbox, init_image.size, mask_padding) 100 | 101 | inpaint_output = self.process_inpainting( 102 | common, 103 | inpaint_only, 104 | init_image, 105 | mask, 106 | bbox_padded, 107 | ) 108 | inpaint_image = inpaint_output[0][0] 109 | 110 | final_image = composite( 111 | init_image, 112 | mask, 113 | inpaint_image, 114 | bbox_padded, 115 | ) 116 | init_image = final_image 117 | 118 | if final_image is not None: 119 | final_images.append(final_image) 120 | 121 | return ADOutput(images=final_images, init_images=init_images) 122 | 123 | @property 124 | def default_detector(self) -> Callable[..., list[Image.Image] | None]: 125 | return yolo_detector 126 | 127 | def _get_txt2img_args( 128 | self, common: Mapping[str, Any], txt2img_only: Mapping[str, Any] 129 | ): 130 | return {**common, **txt2img_only, "output_type": "pil"} 131 | 132 | def _get_inpaint_args( 133 | self, common: Mapping[str, Any], inpaint_only: Mapping[str, Any] 134 | ): 135 | common = dict(common) 136 | sig = inspect.signature(self.inpaint_pipeline) 137 | if ( 138 | "control_image" in sig.parameters 139 | and "control_image" not in common 140 | and "image" in common 141 | ): 142 | common["control_image"] = common.pop("image") 143 | return { 144 | **common, 145 | **inpaint_only, 146 | "num_images_per_prompt": 1, 147 | "output_type": "pil", 148 | } 149 | 150 | def process_txt2img( 151 | self, common: Mapping[str, Any], txt2img_only: Mapping[str, Any] 152 | ): 153 | txt2img_args = self._get_txt2img_args(common, txt2img_only) 154 | return self.txt2img_class.__call__(self, **txt2img_args) 155 | 156 | def process_inpainting( 157 | self, 158 | common: Mapping[str, Any], 159 | inpaint_only: Mapping[str, Any], 160 | init_image: Image.Image, 161 | mask: Image.Image, 162 | bbox_padded: tuple[int, int, int, int], 163 | ): 164 | crop_image = init_image.crop(bbox_padded) 165 | crop_mask = mask.crop(bbox_padded) 166 | inpaint_args = self._get_inpaint_args(common, inpaint_only) 167 | inpaint_args["image"] = crop_image 168 | inpaint_args["mask_image"] = crop_mask 169 | 170 | if "control_image" in inpaint_args: 171 | inpaint_args["control_image"] = inpaint_args["control_image"].resize( 172 | crop_image.size 173 | ) 174 | return self.inpaint_pipeline(**inpaint_args) 175 | -------------------------------------------------------------------------------- /asdff/sd.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from functools import cached_property 4 | 5 | from diffusers import ( 6 | StableDiffusionControlNetInpaintPipeline, 7 | StableDiffusionControlNetPipeline, 8 | StableDiffusionInpaintPipeline, 9 | StableDiffusionPipeline, 10 | ) 11 | 12 | from asdff.base import AdPipelineBase 13 | 14 | 15 | class AdPipeline(AdPipelineBase, StableDiffusionPipeline): 16 | @cached_property 17 | def inpaint_pipeline(self): 18 | return StableDiffusionInpaintPipeline( 19 | vae=self.vae, 20 | text_encoder=self.text_encoder, 21 | tokenizer=self.tokenizer, 22 | unet=self.unet, 23 | scheduler=self.scheduler, 24 | safety_checker=self.safety_checker, 25 | feature_extractor=self.feature_extractor, 26 | requires_safety_checker=self.config.requires_safety_checker, 27 | ) 28 | 29 | @property 30 | def txt2img_class(self): 31 | return StableDiffusionPipeline 32 | 33 | 34 | class AdCnPipeline(AdPipelineBase, StableDiffusionControlNetPipeline): 35 | @cached_property 36 | def inpaint_pipeline(self): 37 | return StableDiffusionControlNetInpaintPipeline( 38 | vae=self.vae, 39 | text_encoder=self.text_encoder, 40 | tokenizer=self.tokenizer, 41 | unet=self.unet, 42 | controlnet=self.controlnet, 43 | scheduler=self.scheduler, 44 | safety_checker=self.safety_checker, 45 | feature_extractor=self.feature_extractor, 46 | requires_safety_checker=self.config.requires_safety_checker, 47 | ) 48 | 49 | @property 50 | def txt2img_class(self): 51 | return StableDiffusionControlNetPipeline 52 | -------------------------------------------------------------------------------- /asdff/utils.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from dataclasses import dataclass 4 | 5 | import cv2 6 | import numpy as np 7 | from diffusers.utils import BaseOutput 8 | from PIL import Image, ImageFilter, ImageOps 9 | 10 | 11 | @dataclass 12 | class ADOutput(BaseOutput): 13 | images: list[Image.Image] 14 | init_images: list[Image.Image] 15 | 16 | 17 | def mask_dilate(image: Image.Image, value: int = 4) -> Image.Image: 18 | if value <= 0: 19 | return image 20 | 21 | arr = np.array(image) 22 | kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value)) 23 | dilated = cv2.dilate(arr, kernel, iterations=1) 24 | return Image.fromarray(dilated) 25 | 26 | 27 | def mask_gaussian_blur(image: Image.Image, value: int = 4) -> Image.Image: 28 | if value <= 0: 29 | return image 30 | 31 | blur = ImageFilter.GaussianBlur(value) 32 | return image.filter(blur) 33 | 34 | 35 | def bbox_padding( 36 | bbox: tuple[int, int, int, int], image_size: tuple[int, int], value: int = 32 37 | ) -> tuple[int, int, int, int]: 38 | if value <= 0: 39 | return bbox 40 | 41 | arr = np.array(bbox).reshape(2, 2) 42 | arr[0] -= value 43 | arr[1] += value 44 | arr = np.clip(arr, (0, 0), image_size) 45 | return tuple(arr.flatten()) 46 | 47 | 48 | def composite( 49 | init: Image.Image, 50 | mask: Image.Image, 51 | gen: Image.Image, 52 | bbox_padded: tuple[int, int, int, int], 53 | ) -> Image.Image: 54 | img_masked = Image.new("RGBa", init.size) 55 | img_masked.paste( 56 | init.convert("RGBA").convert("RGBa"), 57 | mask=ImageOps.invert(mask), 58 | ) 59 | img_masked = img_masked.convert("RGBA") 60 | 61 | size = ( 62 | bbox_padded[2] - bbox_padded[0], 63 | bbox_padded[3] - bbox_padded[1], 64 | ) 65 | resized = gen.resize(size) 66 | 67 | output = Image.new("RGBA", init.size) 68 | output.paste(resized, bbox_padded) 69 | output.alpha_composite(img_masked) 70 | return output.convert("RGB") 71 | -------------------------------------------------------------------------------- /asdff/yolo.py: -------------------------------------------------------------------------------- 1 | from __future__ import annotations 2 | 3 | from pathlib import Path 4 | 5 | import numpy as np 6 | import torch 7 | from huggingface_hub import hf_hub_download 8 | from PIL import Image, ImageDraw 9 | from torchvision.transforms.functional import to_pil_image 10 | 11 | try: 12 | from ultralytics import YOLO 13 | except ModuleNotFoundError: 14 | print("Please install ultralytics using `pip install ultralytics`") 15 | raise 16 | 17 | 18 | def create_mask_from_bbox( 19 | bboxes: np.ndarray, shape: tuple[int, int] 20 | ) -> list[Image.Image]: 21 | """ 22 | Parameters 23 | ---------- 24 | bboxes: list[list[float]] 25 | list of [x1, y1, x2, y2] 26 | bounding boxes 27 | shape: tuple[int, int] 28 | shape of the image (width, height) 29 | 30 | Returns 31 | ------- 32 | masks: list[Image.Image] 33 | A list of masks 34 | 35 | """ 36 | masks = [] 37 | for bbox in bboxes: 38 | mask = Image.new("L", shape, "black") 39 | mask_draw = ImageDraw.Draw(mask) 40 | mask_draw.rectangle(bbox, fill="white") 41 | masks.append(mask) 42 | return masks 43 | 44 | 45 | def mask_to_pil(masks: torch.Tensor, shape: tuple[int, int]) -> list[Image.Image]: 46 | """ 47 | Parameters 48 | ---------- 49 | masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W). 50 | The device can be CUDA, but `to_pil_image` takes care of that. 51 | 52 | shape: tuple[int, int] 53 | (width, height) of the original image 54 | 55 | Returns 56 | ------- 57 | images: list[Image.Image] 58 | """ 59 | n = masks.shape[0] 60 | return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)] 61 | 62 | 63 | def yolo_detector( 64 | image: Image.Image, model_path: str | Path | None = None, confidence: float = 0.3 65 | ) -> list[Image.Image] | None: 66 | if not model_path: 67 | model_path = hf_hub_download("Bingsu/adetailer", "face_yolov8n.pt") 68 | model = YOLO(model_path) 69 | pred = model(image, conf=confidence) 70 | 71 | bboxes = pred[0].boxes.xyxy.cpu().numpy() 72 | if bboxes.size == 0: 73 | return None 74 | 75 | if pred[0].masks is None: 76 | masks = create_mask_from_bbox(bboxes, image.size) 77 | else: 78 | masks = mask_to_pil(pred[0].masks.data, image.size) 79 | 80 | return masks 81 | -------------------------------------------------------------------------------- /pipelines/adsd_pipeline.py: -------------------------------------------------------------------------------- 1 | from asdff import AdPipeline # noqa: F401 2 | -------------------------------------------------------------------------------- /pipelines/adsdcn_pipeline.py: -------------------------------------------------------------------------------- 1 | from asdff import AdCnPipeline # noqa: F401 2 | -------------------------------------------------------------------------------- /pipelines/template.txt: -------------------------------------------------------------------------------- 1 | --- 2 | license: agpl-3.0 3 | tags: 4 | - pytorch 5 | - diffusers 6 | --- 7 | 8 | # Custom Pipeline for Auto Inpainting 9 | 10 | ```py 11 | import torch 12 | from diffusers import DiffusionPipeline 13 | 14 | pipe = DiffusionPipeline.from_pretrained( 15 | "$hf_id", 16 | torch_dtype=torch.float16, 17 | custom_pipeline="$repo_id" 18 | ) 19 | pipe.safety_checker = None 20 | pipe.to("cuda") 21 | 22 | common = {"prompt": "masterpiece, best quality, 1girl", "num_inference_steps": 28} 23 | result = pipe(common=common) 24 | 25 | images = result[0] 26 | ``` 27 | 28 | github: https://github.com/Bing-su/asdff 29 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "asdff" 3 | description = "custom pipeline for auto inpainting" 4 | authors = [{ name = "Bingsu", email = "ks2515@naver.com" }] 5 | dependencies = [ 6 | "diffusers[torch]>=0.19.0", 7 | "transformers>=4.25.1", 8 | "ultralytics", 9 | ] 10 | requires-python = ">=3.8" 11 | readme = "README.md" 12 | license = { text = "AGPL-3.0" } 13 | dynamic = ["version"] 14 | 15 | [project.urls] 16 | repository = "https://github.com/Bing-su/asdff" 17 | 18 | [build-system] 19 | requires = ["pdm-backend"] 20 | build-backend = "pdm.backend" 21 | 22 | [tool.pdm.dev-dependencies] 23 | dev = ["black", "ruff", "pre-commit", "ipykernel", "ipywidgets"] 24 | test = ["pytest>=7.4.0"] 25 | 26 | [tool.pdm.version] 27 | source = "file" 28 | path = "asdff/__version__.py" 29 | 30 | [tool.ruff] 31 | select = [ 32 | "A", 33 | "B", 34 | "C4", 35 | "C90", 36 | "E", 37 | "EM", 38 | "F", 39 | "FA", 40 | "FURB", 41 | "I001", 42 | "ISC", 43 | "N", 44 | "PERF", 45 | "PIE", 46 | "PT", 47 | "PTH", 48 | "RET", 49 | "RUF", 50 | "SIM", 51 | "T20", 52 | "UP", 53 | "W", 54 | ] 55 | ignore = ["B008", "B905", "E501"] 56 | unfixable = ["F401"] 57 | -------------------------------------------------------------------------------- /tests/test_pipeline.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from diffusers import ControlNetModel, DiffusionPipeline, DPMSolverMultistepScheduler 3 | from diffusers.utils import load_image 4 | from PIL import Image 5 | 6 | from asdff import AdCnPipeline, AdPipeline 7 | 8 | common = { 9 | "prompt": "masterpiece, best quality, 1girl", 10 | "num_inference_steps": 10, 11 | } 12 | inpaint = { 13 | "prompt": "masterpiece, best quality, 1girl, red_eyes", 14 | "num_inference_steps": 10, 15 | } 16 | counterfeit = "stablediffusionapi/counterfeit-v30" 17 | 18 | 19 | class Base: 20 | def test_pipeline(self): 21 | result = self.pipe(common=common, inpaint_only=inpaint) 22 | images = result[0] 23 | init_images = result[1] 24 | 25 | assert len(images) == 1 26 | assert len(init_images) == 1 27 | assert isinstance(images[0], Image.Image) 28 | assert isinstance(init_images[0], Image.Image) 29 | assert images[0].mode == "RGB" 30 | assert init_images[0].mode == "RGB" 31 | 32 | 33 | class TestAdPipeline(Base): 34 | pipe = AdPipeline.from_pretrained(counterfeit, torch_dtype=torch.float16) 35 | pipe.safety_checker = None 36 | pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) 37 | pipe.to("cuda") 38 | 39 | 40 | class TestDiffusersPipeline(Base): 41 | pipe = DiffusionPipeline.from_pretrained( 42 | counterfeit, 43 | torch_dtype=torch.float16, 44 | custom_pipeline="Bingsu/adsd_pipeline", 45 | ) 46 | pipe.safety_checker = None 47 | pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) 48 | pipe.to("cuda") 49 | 50 | 51 | class TestCnPipeline: 52 | controlnet = ControlNetModel.from_pretrained( 53 | "lllyasviel/sd-controlnet-openpose", torch_dtype=torch.float16 54 | ) 55 | pipe = AdCnPipeline.from_pretrained( 56 | counterfeit, 57 | controlnet=controlnet, 58 | torch_dtype=torch.float16, 59 | ) 60 | pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) 61 | pipe.safety_checker = None 62 | pipe.to("cuda") 63 | 64 | def test_cn_pipeline(self): 65 | image = load_image( 66 | "https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare/control_images/converted/control_human_openpose.png" 67 | ) 68 | common2 = common.copy() 69 | common2["image"] = image 70 | 71 | result = self.pipe(common=common2, inpaint_only=inpaint) 72 | images = result[0] 73 | init_images = result[1] 74 | 75 | assert len(images) == 1 76 | assert len(init_images) == 1 77 | assert isinstance(images[0], Image.Image) 78 | assert isinstance(init_images[0], Image.Image) 79 | assert images[0].mode == "RGB" 80 | assert init_images[0].mode == "RGB" 81 | -------------------------------------------------------------------------------- /upload.py: -------------------------------------------------------------------------------- 1 | from collections import defaultdict 2 | from contextlib import suppress 3 | from pathlib import Path 4 | from string import Template 5 | from tempfile import TemporaryDirectory 6 | 7 | from huggingface_hub import CommitOperationAdd, CommitOperationDelete, HfApi 8 | 9 | from asdff import __version__ 10 | 11 | root = Path(__file__).parent 12 | 13 | with root.joinpath("pipelines/template.txt").open("r", encoding="utf-8") as text: 14 | readme_template = Template(text.read()) 15 | 16 | api = HfApi() 17 | pipeline_py = "pipeline.py" 18 | py_files = list(root.joinpath("asdff").rglob("*.py")) 19 | 20 | m = defaultdict(lambda: "stablediffusionapi/counterfeit-v30") 21 | 22 | with TemporaryDirectory() as tmp: 23 | for pipeline in root.joinpath("pipelines").glob("*.py"): 24 | repo_id = f"Bingsu/{pipeline.stem}" 25 | hf_id = m[pipeline.stem] 26 | text = readme_template.substitute(hf_id=hf_id, repo_id=repo_id) 27 | readme = Path(tmp, "README.md") 28 | readme.write_text(text, encoding="utf-8") 29 | 30 | api.create_repo(repo_id, repo_type="model", exist_ok=True) 31 | 32 | opr = [ 33 | CommitOperationDelete("asdff", is_folder=True), 34 | CommitOperationDelete(pipeline_py), 35 | ] 36 | with suppress(Exception): 37 | api.create_commit(repo_id, opr, commit_message="Delete files") 38 | 39 | opr = [ 40 | CommitOperationAdd(file.relative_to(root).as_posix(), file) 41 | for file in py_files 42 | ] 43 | opr.append(CommitOperationAdd("README.md", readme)) 44 | opr.append(CommitOperationAdd(pipeline_py, pipeline)) 45 | api.create_commit(repo_id, opr, commit_message=f"Upload files: v{__version__}") 46 | --------------------------------------------------------------------------------