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>
31 lines
1.2 KiB
Python
31 lines
1.2 KiB
Python
from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs
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import comfy.model_management as model_management
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class DensePose_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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model=INPUT.COMBO(["densepose_r50_fpn_dl.torchscript", "densepose_r101_fpn_dl.torchscript"]),
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cmap=INPUT.COMBO(["Viridis (MagicAnimate)", "Parula (CivitAI)"]),
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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/Faces and Poses Estimators"
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def execute(self, image, model="densepose_r50_fpn_dl.torchscript", cmap="Viridis (MagicAnimate)", resolution=512):
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from custom_controlnet_aux.densepose import DenseposeDetector
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model = DenseposeDetector \
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.from_pretrained(filename=model) \
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.to(model_management.get_torch_device())
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return (common_annotator_call(model, image, cmap="viridis" if "Viridis" in cmap else "parula", resolution=resolution), )
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NODE_CLASS_MAPPINGS = {
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"DensePosePreprocessor": DensePose_Preprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DensePosePreprocessor": "DensePose Estimator"
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} |