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from ..utils import common_annotator_call, annotator_ckpts_path, HF_MODEL_NAME, DWPOSE_MODEL_NAME, create_node_input_types |
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import comfy.model_management as model_management |
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class OpenPose_Preprocessor: |
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@classmethod |
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def INPUT_TYPES(s): |
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return create_node_input_types( |
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detect_hand = (["enable", "disable"], {"default": "enable"}), |
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detect_body = (["enable", "disable"], {"default": "enable"}), |
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detect_face = (["enable", "disable"], {"default": "enable"}) |
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) |
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RETURN_TYPES = ("IMAGE",) |
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FUNCTION = "estimate_pose" |
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CATEGORY = "ControlNet Preprocessors/Faces and Poses" |
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def estimate_pose(self, image, detect_hand, detect_body, detect_face, resolution=512, **kwargs): |
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from controlnet_aux.open_pose import OpenposeDetector |
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detect_hand = detect_hand == "enable" |
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detect_body = detect_body == "enable" |
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detect_face = detect_face == "enable" |
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self.openpose_json = None |
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model = OpenposeDetector.from_pretrained(HF_MODEL_NAME, cache_dir=annotator_ckpts_path).to(model_management.get_torch_device()) |
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def cb(image, **kwargs): |
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result = model(image, **kwargs) |
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self.openpose_json = result[1] |
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return result[0] |
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out = common_annotator_call(cb, image, include_hand=detect_hand, include_face=detect_face, include_body=detect_body, image_and_json=True, resolution=resolution) |
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del model |
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return { |
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'ui': { "openpose_json": [self.openpose_json] }, |
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"result": (out, ) |
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} |
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NODE_CLASS_MAPPINGS = { |
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"OpenposePreprocessor": OpenPose_Preprocessor, |
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} |
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NODE_DISPLAY_NAME_MAPPINGS = { |
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"OpenposePreprocessor": "OpenPose Pose Recognition", |
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} |
