Spaces:
Running
on
Zero
Running
on
Zero
| import gc | |
| import numpy as np | |
| import PIL.Image | |
| import torch | |
| import torchvision | |
| from controlnet_aux import ( | |
| CannyDetector, | |
| ContentShuffleDetector, | |
| HEDdetector, | |
| LineartAnimeDetector, | |
| LineartDetector, | |
| MidasDetector, | |
| MLSDdetector, | |
| NormalBaeDetector, | |
| OpenposeDetector, | |
| PidiNetDetector, | |
| ) | |
| from controlnet_aux.util import HWC3 | |
| from cv_utils import resize_image | |
| from depth_estimator import DepthEstimator | |
| from image_segmentor import ImageSegmentor | |
| from kornia.core import Tensor | |
| from kornia.filters import canny | |
| class Canny: | |
| def __call__( | |
| self, | |
| images: np.array, | |
| low_threshold: float = 0.1, | |
| high_threshold: float = 0.2, | |
| kernel_size: tuple[int, int] | int = (5, 5), | |
| sigma: tuple[float, float] | Tensor = (1, 1), | |
| hysteresis: bool = True, | |
| eps: float = 1e-6 | |
| ) -> torch.Tensor: | |
| assert low_threshold is not None, "low_threshold must be provided" | |
| assert high_threshold is not None, "high_threshold must be provided" | |
| images = torch.from_numpy(images).permute(2, 0, 1).unsqueeze(0) / 255.0 | |
| images_tensor = canny(images, low_threshold, high_threshold, kernel_size, sigma, hysteresis, eps)[1] | |
| images_tensor = (images_tensor[0][0].numpy() * 255).astype(np.uint8) | |
| return images_tensor | |
| class Preprocessor: | |
| MODEL_ID = "lllyasviel/Annotators" | |
| def __init__(self): | |
| self.model = None | |
| self.name = "" | |
| def load(self, name: str) -> None: | |
| if name == self.name: | |
| return | |
| if name == "HED": | |
| self.model = HEDdetector.from_pretrained(self.MODEL_ID) | |
| elif name == "Midas": | |
| self.model = MidasDetector.from_pretrained(self.MODEL_ID) | |
| elif name == "MLSD": | |
| self.model = MLSDdetector.from_pretrained(self.MODEL_ID) | |
| elif name == "Openpose": | |
| self.model = OpenposeDetector.from_pretrained(self.MODEL_ID) | |
| elif name == "PidiNet": | |
| self.model = PidiNetDetector.from_pretrained(self.MODEL_ID) | |
| elif name == "NormalBae": | |
| self.model = NormalBaeDetector.from_pretrained(self.MODEL_ID) | |
| elif name == "Lineart": | |
| self.model = LineartDetector.from_pretrained(self.MODEL_ID) | |
| elif name == "LineartAnime": | |
| self.model = LineartAnimeDetector.from_pretrained(self.MODEL_ID) | |
| elif name == "Canny": | |
| self.model = Canny() | |
| elif name == "ContentShuffle": | |
| self.model = ContentShuffleDetector() | |
| elif name == "DPT": | |
| self.model = DepthEstimator() | |
| elif name == "UPerNet": | |
| self.model = ImageSegmentor() | |
| else: | |
| raise ValueError | |
| torch.cuda.empty_cache() | |
| gc.collect() | |
| self.name = name | |
| def __call__(self, image: PIL.Image.Image, **kwargs) -> PIL.Image.Image: | |
| if self.name == "Canny": | |
| if "detect_resolution" in kwargs: | |
| detect_resolution = kwargs.pop("detect_resolution") | |
| image = np.array(image) | |
| image = HWC3(image) | |
| image = resize_image(image, resolution=detect_resolution) | |
| image = self.model(image, **kwargs) | |
| return PIL.Image.fromarray(image).convert('RGB') | |
| elif self.name == "Midas": | |
| detect_resolution = kwargs.pop("detect_resolution", 512) | |
| image_resolution = kwargs.pop("image_resolution", 512) | |
| image = np.array(image) | |
| image = HWC3(image) | |
| image = resize_image(image, resolution=detect_resolution) | |
| image = self.model(image, **kwargs) | |
| image = HWC3(image) | |
| image = resize_image(image, resolution=image_resolution) | |
| return PIL.Image.fromarray(image) | |
| else: | |
| return self.model(image, **kwargs) | |