jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, nms
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import comfy.model_management as model_management
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import cv2
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class Scribble_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return define_preprocessor_inputs(resolution=INPUT.RESOLUTION())
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def execute(self, image, resolution=512, **kwargs):
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from custom_controlnet_aux.scribble import ScribbleDetector
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model = ScribbleDetector()
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return (common_annotator_call(model, image, resolution=resolution), )
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class Scribble_XDoG_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return define_preprocessor_inputs(
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threshold=INPUT.INT(default=32, min=1, max=64),
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resolution=INPUT.RESOLUTION()
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def execute(self, image, threshold=32, resolution=512, **kwargs):
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from custom_controlnet_aux.scribble import ScribbleXDog_Detector
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model = ScribbleXDog_Detector()
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return (common_annotator_call(model, image, resolution=resolution, thr_a=threshold), )
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class Scribble_PiDiNet_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return define_preprocessor_inputs(
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safe=(["enable", "disable"],),
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resolution=INPUT.RESOLUTION()
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Line Extractors"
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def execute(self, image, safe="enable", resolution=512):
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def model(img, **kwargs):
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from custom_controlnet_aux.pidi import PidiNetDetector
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pidinet = PidiNetDetector.from_pretrained().to(model_management.get_torch_device())
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result = pidinet(img, scribble=True, **kwargs)
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result = nms(result, 127, 3.0)
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result = cv2.GaussianBlur(result, (0, 0), 3.0)
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result[result > 4] = 255
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result[result < 255] = 0
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return result
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return (common_annotator_call(model, image, resolution=resolution, safe=safe=="enable"),)
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NODE_CLASS_MAPPINGS = {
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"ScribblePreprocessor": Scribble_Preprocessor,
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"Scribble_XDoG_Preprocessor": Scribble_XDoG_Preprocessor,
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"Scribble_PiDiNet_Preprocessor": Scribble_PiDiNet_Preprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"ScribblePreprocessor": "Scribble Lines",
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"Scribble_XDoG_Preprocessor": "Scribble XDoG Lines",
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"Scribble_PiDiNet_Preprocessor": "Scribble PiDiNet Lines"
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}
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