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>
63 lines
2.6 KiB
Python
63 lines
2.6 KiB
Python
import os
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# Disable NPU device initialization and problematic MMCV ops to prevent RuntimeError
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os.environ['NPU_DEVICE_COUNT'] = '0'
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os.environ['MMCV_WITH_OPS'] = '0'
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from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, MAX_RESOLUTION
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import comfy.model_management as model_management
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class Metric3D_Depth_Map_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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backbone=INPUT.COMBO(["vit-small", "vit-large", "vit-giant2"]),
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fx=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
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fy=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
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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/Normal and Depth Estimators"
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def execute(self, image, backbone="vit-small", fx=1000, fy=1000, resolution=512):
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from custom_controlnet_aux.metric3d import Metric3DDetector
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model = Metric3DDetector.from_pretrained(filename=f"metric_depth_{backbone.replace('-', '_')}_800k.pth").to(model_management.get_torch_device())
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cb = lambda image, **kwargs: model(image, **kwargs)[0]
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out = common_annotator_call(cb, image, resolution=resolution, fx=fx, fy=fy, depth_and_normal=True)
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del model
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return (out, )
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class Metric3D_Normal_Map_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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backbone=INPUT.COMBO(["vit-small", "vit-large", "vit-giant2"]),
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fx=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
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fy=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
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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/Normal and Depth Estimators"
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def execute(self, image, backbone="vit-small", fx=1000, fy=1000, resolution=512):
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from custom_controlnet_aux.metric3d import Metric3DDetector
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model = Metric3DDetector.from_pretrained(filename=f"metric_depth_{backbone.replace('-', '_')}_800k.pth").to(model_management.get_torch_device())
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cb = lambda image, **kwargs: model(image, **kwargs)[1]
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out = common_annotator_call(cb, image, resolution=resolution, fx=fx, fy=fy, depth_and_normal=True)
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del model
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return (out, )
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NODE_CLASS_MAPPINGS = {
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"Metric3D-DepthMapPreprocessor": Metric3D_Depth_Map_Preprocessor,
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"Metric3D-NormalMapPreprocessor": Metric3D_Normal_Map_Preprocessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"Metric3D-DepthMapPreprocessor": "Metric3D Depth Map",
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"Metric3D-NormalMapPreprocessor": "Metric3D Normal Map"
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}
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