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import torch
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import numpy as np
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import comfy.model_management as model_management
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import comfy.utils
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from ..utils import common_annotator_call, MAX_RESOLUTION
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def get_intensity_mask(image_array, lower_bound, upper_bound):
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mask = image_array[:, :, 0]
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mask = np.where((mask >= lower_bound) & (mask <= upper_bound), mask, 0)
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mask = np.expand_dims(mask, 2).repeat(3, axis=2)
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return mask
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def combine_layers(base_layer, top_layer):
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mask = top_layer.astype(bool)
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temp = 1 - (1 - top_layer) * (1 - base_layer)
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result = base_layer * (~mask) + temp * mask
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return result
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class AnyLinePreprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image": ("IMAGE",),
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"merge_with_lineart": (["lineart_standard", "lineart_realisitic", "lineart_anime", "manga_line"], {"default": "lineart_standard"}),
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"resolution": ("INT", {"default": 1280, "min": 512, "max": MAX_RESOLUTION, "step": 8})
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},
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"optional": {
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"lineart_lower_bound": ("FLOAT", {"default": 0, "min": 0, "max": 1, "step": 0.01}),
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"lineart_upper_bound": ("FLOAT", {"default": 1, "min": 0, "max": 1, "step": 0.01}),
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"object_min_size": ("INT", {"default": 36, "min": 1, "max": MAX_RESOLUTION}),
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"object_connectivity": ("INT", {"default": 1, "min": 1, "max": MAX_RESOLUTION}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("image",)
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FUNCTION = "get_anyline"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def __init__(self):
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self.device = model_management.get_torch_device()
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def get_anyline(self, image, merge_with_lineart, resolution, lineart_lower_bound=0, lineart_upper_bound=1, object_min_size=36, object_connectivity=1):
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from controlnet_aux.teed import TEDDetector
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from skimage import morphology
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pbar = comfy.utils.ProgressBar(3)
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mteed_model = TEDDetector.from_pretrained("TheMistoAI/MistoLine", "MTEED.pth", subfolder="Anyline").to(self.device)
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mteed_result = common_annotator_call(mteed_model, image, resolution=resolution, show_pbar=False)
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mteed_result = mteed_result.numpy()
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del mteed_model
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pbar.update(1)
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if merge_with_lineart == "lineart_standard":
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from controlnet_aux.lineart_standard import LineartStandardDetector
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lineart_standard_detector = LineartStandardDetector()
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lineart_result = common_annotator_call(lineart_standard_detector, image, guassian_sigma=2, intensity_threshold=3, resolution=resolution, show_pbar=False).numpy()
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del lineart_standard_detector
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else:
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from controlnet_aux.lineart import LineartDetector
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from controlnet_aux.lineart_anime import LineartAnimeDetector
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from controlnet_aux.manga_line import LineartMangaDetector
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lineart_detector = dict(lineart_realisitic=LineartDetector, lineart_anime=LineartAnimeDetector, manga_line=LineartMangaDetector)[merge_with_lineart]
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lineart_detector = lineart_detector.from_pretrained().to(self.device)
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lineart_result = common_annotator_call(lineart_detector, image, resolution=resolution, show_pbar=False).numpy()
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del lineart_detector
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pbar.update(1)
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final_result = []
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for i in range(len(image)):
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_lineart_result = get_intensity_mask(lineart_result[i], lower_bound=lineart_lower_bound, upper_bound=lineart_upper_bound)
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_cleaned = morphology.remove_small_objects(_lineart_result.astype(bool), min_size=object_min_size, connectivity=object_connectivity)
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_lineart_result = _lineart_result * _cleaned
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_mteed_result = mteed_result[i]
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final_result.append(torch.from_numpy(combine_layers(_mteed_result, _lineart_result)))
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pbar.update(1)
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return (torch.stack(final_result),)
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NODE_CLASS_MAPPINGS = {
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"AnyLineArtPreprocessor_aux": AnyLinePreprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"AnyLineArtPreprocessor_aux": "AnyLine Lineart"
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}
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