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Running
on
Zero
| # code based in https://github.com/TheMistoAI/ComfyUI-Anyline/blob/main/anyline.py | |
| import os | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from einops import rearrange | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from skimage import morphology | |
| from ..teed.ted import TED | |
| from ..util import HWC3, resize_image, safe_step | |
| class AnylineDetector: | |
| def __init__(self, model): | |
| self.model = model | |
| def from_pretrained(cls, pretrained_model_or_path, filename=None, subfolder=None): | |
| if os.path.isdir(pretrained_model_or_path): | |
| model_path = os.path.join(pretrained_model_or_path, filename) | |
| else: | |
| model_path = hf_hub_download( | |
| pretrained_model_or_path, filename, subfolder=subfolder | |
| ) | |
| model = TED() | |
| model.load_state_dict(torch.load(model_path, map_location="cpu")) | |
| return cls(model) | |
| def to(self, device): | |
| self.model.to(device) | |
| return self | |
| def __call__( | |
| self, | |
| input_image, | |
| detect_resolution=1280, | |
| guassian_sigma=2.0, | |
| intensity_threshold=3, | |
| output_type="pil", | |
| ): | |
| device = next(iter(self.model.parameters())).device | |
| 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" | |
| original_height, original_width, _ = input_image.shape | |
| input_image = HWC3(input_image) | |
| input_image = resize_image(input_image, detect_resolution) | |
| assert input_image.ndim == 3 | |
| height, width, _ = input_image.shape | |
| with torch.no_grad(): | |
| image_teed = torch.from_numpy(input_image.copy()).float().to(device) | |
| image_teed = rearrange(image_teed, "h w c -> 1 c h w") | |
| edges = self.model(image_teed) | |
| edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges] | |
| edges = [ | |
| cv2.resize(e, (width, height), interpolation=cv2.INTER_LINEAR) | |
| for e in edges | |
| ] | |
| edges = np.stack(edges, axis=2) | |
| edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64))) | |
| edge = safe_step(edge, 2) | |
| edge = (edge * 255.0).clip(0, 255).astype(np.uint8) | |
| mteed_result = edge | |
| mteed_result = HWC3(mteed_result) | |
| x = input_image.astype(np.float32) | |
| g = cv2.GaussianBlur(x, (0, 0), guassian_sigma) | |
| intensity = np.min(g - x, axis=2).clip(0, 255) | |
| intensity /= max(16, np.median(intensity[intensity > intensity_threshold])) | |
| intensity *= 127 | |
| lineart_result = intensity.clip(0, 255).astype(np.uint8) | |
| lineart_result = HWC3(lineart_result) | |
| lineart_result = self.get_intensity_mask( | |
| lineart_result, lower_bound=0, upper_bound=255 | |
| ) | |
| cleaned = morphology.remove_small_objects( | |
| lineart_result.astype(bool), min_size=36, connectivity=1 | |
| ) | |
| lineart_result = lineart_result * cleaned | |
| final_result = self.combine_layers(mteed_result, lineart_result) | |
| final_result = cv2.resize( | |
| final_result, | |
| (original_width, original_height), | |
| interpolation=cv2.INTER_LINEAR, | |
| ) | |
| if output_type == "pil": | |
| final_result = Image.fromarray(final_result) | |
| return final_result | |
| def get_intensity_mask(self, image_array, lower_bound, upper_bound): | |
| mask = image_array[:, :, 0] | |
| mask = np.where((mask >= lower_bound) & (mask <= upper_bound), mask, 0) | |
| mask = np.expand_dims(mask, 2).repeat(3, axis=2) | |
| return mask | |
| def combine_layers(self, base_layer, top_layer): | |
| mask = top_layer.astype(bool) | |
| temp = 1 - (1 - top_layer) * (1 - base_layer) | |
| result = base_layer * (~mask) + temp * mask | |
| return result | |