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Runtime error
| from controlnet_aux import ( | |
| CannyDetector, | |
| ContentShuffleDetector, | |
| HEDdetector, | |
| LineartAnimeDetector, | |
| LineartDetector, | |
| MediapipeFaceDetector, | |
| MidasDetector, | |
| MLSDdetector, | |
| NormalBaeDetector, | |
| OpenposeDetector, | |
| PidiNetDetector, | |
| SamDetector, | |
| ) | |
| import numpy as np | |
| import cv2 | |
| def pad64(x): | |
| return int(np.ceil(float(x) / 64.0) * 64 - x) | |
| 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 safer_memory(x): | |
| return np.ascontiguousarray(x.copy()).copy() | |
| def resize_image_with_pad(input_image, resolution, skip_hwc3=False): | |
| if skip_hwc3: | |
| img = input_image | |
| else: | |
| img = HWC3(input_image) | |
| H_raw, W_raw, _ = img.shape | |
| k = float(resolution) / float(min(H_raw, W_raw)) | |
| interpolation = cv2.INTER_CUBIC if k > 1 else cv2.INTER_AREA | |
| H_target = int(np.round(float(H_raw) * k)) | |
| W_target = int(np.round(float(W_raw) * k)) | |
| img = cv2.resize(img, (W_target, H_target), interpolation=interpolation) | |
| H_pad, W_pad = pad64(H_target), pad64(W_target) | |
| img_padded = np.pad(img, [[0, H_pad], [0, W_pad], [0, 0]], mode='edge') | |
| def remove_pad(x): | |
| return safer_memory(x[:H_target, :W_target]) | |
| return safer_memory(img_padded), remove_pad | |
| def scribble_xdog(img, res=512, thr_a=32, **kwargs): | |
| img, remove_pad = resize_image_with_pad(img, res) | |
| g1 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 0.5) | |
| g2 = cv2.GaussianBlur(img.astype(np.float32), (0, 0), 5.0) | |
| dog = (255 - np.min(g2 - g1, axis=2)).clip(0, 255).astype(np.uint8) | |
| result = np.zeros_like(img, dtype=np.uint8) | |
| result[2 * (255 - dog) > thr_a] = 255 | |
| return remove_pad(result), True | |
| def none_preprocces(image_path:str): | |
| return Image.open(image_path) | |
| PREPROCCES_DICT = { | |
| "Hed": HEDdetector.from_pretrained("lllyasviel/Annotators"), | |
| "Midas": MidasDetector.from_pretrained("lllyasviel/Annotators"), | |
| "MLSD": MLSDdetector.from_pretrained("lllyasviel/Annotators"), | |
| "Openpose": OpenposeDetector.from_pretrained("lllyasviel/Annotators"), | |
| "PidiNet": PidiNetDetector.from_pretrained("lllyasviel/Annotators"), | |
| "NormalBae": NormalBaeDetector.from_pretrained("lllyasviel/Annotators"), | |
| "Lineart": LineartDetector.from_pretrained("lllyasviel/Annotators"), | |
| "LineartAnime": LineartAnimeDetector.from_pretrained( | |
| "lllyasviel/Annotators" | |
| ), | |
| "Canny": CannyDetector(), | |
| "ContentShuffle": ContentShuffleDetector(), | |
| "MediapipeFace": MediapipeFaceDetector(), | |
| "ScribbleXDOG": scribble_xdog, | |
| "None": none_preprocces | |
| } | |