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| import torch | |
| import numpy as np | |
| import comfy.model_management as model_management | |
| import comfy.utils | |
| # Requires comfyui_controlnet_aux funcsions and classes | |
| from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs | |
| def get_intensity_mask(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(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 | |
| class AnyLinePreprocessor: | |
| def INPUT_TYPES(s): | |
| return define_preprocessor_inputs( | |
| merge_with_lineart=INPUT.COMBO(["lineart_standard", "lineart_realisitic", "lineart_anime", "manga_line"], default="lineart_standard"), | |
| resolution=INPUT.RESOLUTION(default=1280, step=8), | |
| lineart_lower_bound=INPUT.FLOAT(default=0), | |
| lineart_upper_bound=INPUT.FLOAT(default=1), | |
| object_min_size=INPUT.INT(default=36, min=1), | |
| object_connectivity=INPUT.INT(default=1, min=1) | |
| ) | |
| RETURN_TYPES = ("IMAGE",) | |
| RETURN_NAMES = ("image",) | |
| FUNCTION = "get_anyline" | |
| CATEGORY = "ControlNet Preprocessors/Line Extractors" | |
| def __init__(self): | |
| self.device = model_management.get_torch_device() | |
| def get_anyline(self, image, merge_with_lineart="lineart_standard", resolution=512, lineart_lower_bound=0, lineart_upper_bound=1, object_min_size=36, object_connectivity=1): | |
| from custom_controlnet_aux.teed import TEDDetector | |
| from skimage import morphology | |
| pbar = comfy.utils.ProgressBar(3) | |
| # Process the image with MTEED model | |
| mteed_model = TEDDetector.from_pretrained("TheMistoAI/MistoLine", "MTEED.pth", subfolder="Anyline").to(self.device) | |
| mteed_result = common_annotator_call(mteed_model, image, resolution=resolution, show_pbar=False) | |
| mteed_result = mteed_result.numpy() | |
| del mteed_model | |
| pbar.update(1) | |
| # Process the image with the lineart standard preprocessor | |
| if merge_with_lineart == "lineart_standard": | |
| from custom_controlnet_aux.lineart_standard import LineartStandardDetector | |
| lineart_standard_detector = LineartStandardDetector() | |
| lineart_result = common_annotator_call(lineart_standard_detector, image, guassian_sigma=2, intensity_threshold=3, resolution=resolution, show_pbar=False).numpy() | |
| del lineart_standard_detector | |
| else: | |
| from custom_controlnet_aux.lineart import LineartDetector | |
| from custom_controlnet_aux.lineart_anime import LineartAnimeDetector | |
| from custom_controlnet_aux.manga_line import LineartMangaDetector | |
| lineart_detector = dict(lineart_realisitic=LineartDetector, lineart_anime=LineartAnimeDetector, manga_line=LineartMangaDetector)[merge_with_lineart] | |
| lineart_detector = lineart_detector.from_pretrained().to(self.device) | |
| lineart_result = common_annotator_call(lineart_detector, image, resolution=resolution, show_pbar=False).numpy() | |
| del lineart_detector | |
| pbar.update(1) | |
| final_result = [] | |
| for i in range(len(image)): | |
| _lineart_result = get_intensity_mask(lineart_result[i], lower_bound=lineart_lower_bound, upper_bound=lineart_upper_bound) | |
| _cleaned = morphology.remove_small_objects(_lineart_result.astype(bool), min_size=object_min_size, connectivity=object_connectivity) | |
| _lineart_result = _lineart_result * _cleaned | |
| _mteed_result = mteed_result[i] | |
| # Combine the results | |
| final_result.append(torch.from_numpy(combine_layers(_mteed_result, _lineart_result))) | |
| pbar.update(1) | |
| return (torch.stack(final_result),) | |
| NODE_CLASS_MAPPINGS = { | |
| "AnyLineArtPreprocessor_aux": AnyLinePreprocessor | |
| } | |
| NODE_DISPLAY_NAME_MAPPINGS = { | |
| "AnyLineArtPreprocessor_aux": "AnyLine Lineart" | |
| } | |