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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>
39 lines
1.5 KiB
Python
39 lines
1.5 KiB
Python
from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, run_script
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import comfy.model_management as model_management
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import os, sys
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import subprocess, threading
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def install_deps():
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try:
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import mediapipe
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except ImportError:
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run_script([sys.executable, '-s', '-m', 'pip', 'install', 'mediapipe'])
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run_script([sys.executable, '-s', '-m', 'pip', 'install', '--upgrade', 'protobuf'])
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class Media_Pipe_Face_Mesh_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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max_faces=INPUT.INT(default=10, min=1, max=50), #Which image has more than 50 detectable faces?
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min_confidence=INPUT.FLOAT(default=0.5, min=0.1),
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resolution=INPUT.RESOLUTION()
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "detect"
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CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators"
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def detect(self, image, max_faces=10, min_confidence=0.5, resolution=512):
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#Ref: https://github.com/Fannovel16/comfy_controlnet_preprocessors/issues/70#issuecomment-1677967369
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install_deps()
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from custom_controlnet_aux.mediapipe_face import MediapipeFaceDetector
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return (common_annotator_call(MediapipeFaceDetector(), image, max_faces=max_faces, min_confidence=min_confidence, resolution=resolution), )
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NODE_CLASS_MAPPINGS = {
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"MediaPipe-FaceMeshPreprocessor": Media_Pipe_Face_Mesh_Preprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"MediaPipe-FaceMeshPreprocessor": "MediaPipe Face Mesh"
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} |