Delete files

asdff/__init__.py

@@ -1,9 +0,0 @@
-from .__version__ import __version__
-from .sd import AdPipeline
-from .yolo import yolo_detector
-
-__all__ = [
-    "AdPipeline",
-    "yolo_detector",
-    "__version__",
-]

asdff/__version__.py

@@ -1 +0,0 @@
-__version__ = "0.1.0.dev1"

asdff/sd.py

@@ -1,122 +0,0 @@
-from __future__ import annotations
-
-from functools import cached_property
-from typing import Any, Callable, Iterable, List, Optional
-
-from diffusers import StableDiffusionInpaintPipeline, StableDiffusionPipeline
-from diffusers.utils import logging
-from PIL import Image
-
-from asdff.utils import (
-    ADOutput,
-    bbox_padding,
-    composite,
-    mask_dilate,
-    mask_gaussian_blur,
-)
-from asdff.yolo import yolo_detector
-
-logger = logging.get_logger("diffusers")
-
-
-DetectorType = Callable[[Image.Image], Optional[List[Image.Image]]]
-
-
-def ordinal(n: int) -> str:
-    d = {1: "st", 2: "nd", 3: "rd"}
-    return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th"))
-
-
-class AdPipeline(StableDiffusionPipeline):
-    @cached_property
-    def inpaine_pipeline(self):
-        return StableDiffusionInpaintPipeline(
-            vae=self.vae,
-            text_encoder=self.text_encoder,
-            tokenizer=self.tokenizer,
-            unet=self.unet,
-            scheduler=self.scheduler,
-            safety_checker=self.safety_checker,
-            feature_extractor=self.feature_extractor,
-            requires_safety_checker=self.config.requires_safety_checker,
-        )
-
-    def __call__(  # noqa: C901
-        self,
-        common: dict[str, Any] | None = None,
-        txt2img_only: dict[str, Any] | None = None,
-        inpaint_only: dict[str, Any] | None = None,
-        detectors: DetectorType | Iterable[DetectorType] | None = None,
-        mask_dilation: int = 4,
-        mask_blur: int = 4,
-        mask_padding: int = 32,
-    ):
-        if common is None:
-            common = {}
-        if txt2img_only is None:
-            txt2img_only = {}
-        if inpaint_only is None:
-            inpaint_only = {}
-        inpaint_only.setdefault("strength", 0.4)
-
-        if detectors is None:
-            detectors = [self.default_detector]
-        elif callable(detectors):
-            detectors = [detectors]
-
-        txt2img_output = super().__call__(**common, **txt2img_only, output_type="pil")
-        txt2img_images: list[Image.Image] = txt2img_output[0]
-
-        init_images = []
-        final_images = []
-
-        for i, init_image in enumerate(txt2img_images):
-            init_images.append(init_image.copy())
-            final_image = None
-
-            for j, detector in enumerate(detectors):
-                masks = detector(init_image)
-                if masks is None:
-                    logger.info(
-                        f"No object detected on {ordinal(i + 1)} image with {ordinal(j + 1)} detector."
-                    )
-                    continue
-
-                for k, mask in enumerate(masks):
-                    mask = mask.convert("L")
-                    mask = mask_dilate(mask, mask_dilation)
-                    bbox = mask.getbbox()
-                    if bbox is None:
-                        logger.info(f"No object in {ordinal(k + 1)} mask.")
-                        continue
-                    mask = mask_gaussian_blur(mask, mask_blur)
-                    bbox_padded = bbox_padding(bbox, init_image.size, mask_padding)
-
-                    crop_image = init_image.crop(bbox_padded)
-                    crop_mask = mask.crop(bbox_padded)
-
-                    inpaint_output = self.inpaine_pipeline(
-                        **common,
-                        **inpaint_only,
-                        image=crop_image,
-                        mask_image=crop_mask,
-                        num_images_per_prompt=1,
-                        output_type="pil",
-                    )
-                    inpaint_image: Image.Image = inpaint_output[0][0]
-                    final_image = composite(
-                        init=init_image,
-                        mask=mask,
-                        gen=inpaint_image,
-                        bbox_padded=bbox_padded,
-                    )
-                    init_image = final_image
-
-            if final_image is not None:
-                final_images.append(final_image)
-
-        return ADOutput(images=final_images, init_images=init_images)
-
-    @property
-    def default_detector(self) -> Callable[..., list[Image.Image] | None]:
-        return yolo_detector

asdff/utils.py

@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-from dataclasses import dataclass
-
-import cv2
-import numpy as np
-from diffusers.utils import BaseOutput
-from PIL import Image, ImageFilter, ImageOps
-
-
-@dataclass
-class ADOutput(BaseOutput):
-    images: list[Image.Image]
-    init_images: list[Image.Image]
-
-
-def mask_dilate(image: Image.Image, value: int = 4) -> Image.Image:
-    if value <= 0:
-        return image
-
-    arr = np.array(image)
-    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (value, value))
-    dilated = cv2.dilate(arr, kernel, iterations=1)
-    return Image.fromarray(dilated)
-
-
-def mask_gaussian_blur(image: Image.Image, value: int = 4) -> Image.Image:
-    if value <= 0:
-        return image
-
-    blur = ImageFilter.GaussianBlur(value)
-    return image.filter(blur)
-
-
-def bbox_padding(
-    bbox: tuple[int, int, int, int], image_size: tuple[int, int], value: int = 32
-) -> tuple[int, int, int, int]:
-    if value <= 0:
-        return bbox
-
-    arr = np.array(bbox).reshape(2, 2)
-    arr[0] -= value
-    arr[1] += value
-    arr = np.clip(arr, (0, 0), image_size)
-    return tuple(arr.flatten())
-
-
-def composite(
-    init: Image.Image,
-    mask: Image.Image,
-    gen: Image.Image,
-    bbox_padded: tuple[int, int, int, int],
-) -> Image.Image:
-    img_masked = Image.new("RGBa", init.size)
-    img_masked.paste(
-        init.convert("RGBA").convert("RGBa"),
-        mask=ImageOps.invert(mask),
-    )
-    img_masked = img_masked.convert("RGBA")
-
-    size = (
-        bbox_padded[2] - bbox_padded[0],
-        bbox_padded[3] - bbox_padded[1],
-    )
-    resized = gen.resize(size)
-
-    output = Image.new("RGBA", init.size)
-    output.paste(resized, bbox_padded)
-    output.alpha_composite(img_masked)
-    return output.convert("RGB")

asdff/yolo.py

@@ -1,73 +0,0 @@
-from __future__ import annotations
-
-import numpy as np
-import torch
-from huggingface_hub import hf_hub_download
-from PIL import Image, ImageDraw
-from torchvision.transforms.functional import to_pil_image
-from ultralytics import YOLO
-
-
-def create_mask_from_bbox(
-    bboxes: np.ndarray, shape: tuple[int, int]
-) -> list[Image.Image]:
-    """
-    Parameters
-    ----------
-        bboxes: list[list[float]]
-            list of [x1, y1, x2, y2]
-            bounding boxes
-        shape: tuple[int, int]
-            shape of the image (width, height)
-
-    Returns
-    -------
-        masks: list[Image.Image]
-        A list of masks
-
-    """
-    masks = []
-    for bbox in bboxes:
-        mask = Image.new("L", shape, "black")
-        mask_draw = ImageDraw.Draw(mask)
-        mask_draw.rectangle(bbox, fill="white")
-        masks.append(mask)
-    return masks
-
-
-def mask_to_pil(masks: torch.Tensor, shape: tuple[int, int]) -> list[Image.Image]:
-    """
-    Parameters
-    ----------
-    masks: torch.Tensor, dtype=torch.float32, shape=(N, H, W).
-        The device can be CUDA, but `to_pil_image` takes care of that.
-
-    shape: tuple[int, int]
-        (width, height) of the original image
-
-    Returns
-    -------
-    images: list[Image.Image]
-    """
-    n = masks.shape[0]
-    return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]
-
-
-def yolo_detector(
-    image: Image.Image, model_path: str | None = None, confidence: float = 0.3
-) -> list[Image.Image] | None:
-    if not model_path:
-        model_path = hf_hub_download("Bingsu/adetailer", "face_yolov8n.pt")
-    model = YOLO(model_path)
-    pred = model(image, conf=confidence)
-
-    bboxes = pred[0].boxes.xyxy.cpu().numpy()
-    if bboxes.size == 0:
-        return None
-
-    if pred[0].masks is None:
-        masks = create_mask_from_bbox(bboxes, image.size)
-    else:
-        masks = mask_to_pil(pred[0].masks.data, image.size)
-
-    return masks

pipeline.py

@@ -1 +0,0 @@
-from asdff import AdPipeline  # noqa: F401