Add custom nodes, Civitai loras (LFS), and vast.ai setup script

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>

custom_nodes/comfyui_controlnet_aux/node_wrappers/metric3d.py

@@ -0,0 +1,62 @@
+import os
+# Disable NPU device initialization and problematic MMCV ops to prevent RuntimeError
+os.environ['NPU_DEVICE_COUNT'] = '0'
+os.environ['MMCV_WITH_OPS'] = '0'
+
+from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, MAX_RESOLUTION
+import comfy.model_management as model_management
+
+class Metric3D_Depth_Map_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            backbone=INPUT.COMBO(["vit-small", "vit-large", "vit-giant2"]),
+            fx=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
+            fy=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
+
+    def execute(self, image, backbone="vit-small", fx=1000, fy=1000, resolution=512):
+        from custom_controlnet_aux.metric3d import Metric3DDetector
+        model = Metric3DDetector.from_pretrained(filename=f"metric_depth_{backbone.replace('-', '_')}_800k.pth").to(model_management.get_torch_device())
+        cb = lambda image, **kwargs: model(image, **kwargs)[0]
+        out = common_annotator_call(cb, image, resolution=resolution, fx=fx, fy=fy, depth_and_normal=True)
+        del model
+        return (out, )
+
+class Metric3D_Normal_Map_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            backbone=INPUT.COMBO(["vit-small", "vit-large", "vit-giant2"]),
+            fx=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
+            fy=INPUT.INT(default=1000, min=1, max=MAX_RESOLUTION),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
+
+    def execute(self, image, backbone="vit-small", fx=1000, fy=1000, resolution=512):
+        from custom_controlnet_aux.metric3d import Metric3DDetector
+        model = Metric3DDetector.from_pretrained(filename=f"metric_depth_{backbone.replace('-', '_')}_800k.pth").to(model_management.get_torch_device())
+        cb = lambda image, **kwargs: model(image, **kwargs)[1]
+        out = common_annotator_call(cb, image, resolution=resolution, fx=fx, fy=fy, depth_and_normal=True)
+        del model
+        return (out, )
+
+NODE_CLASS_MAPPINGS = {
+    "Metric3D-DepthMapPreprocessor": Metric3D_Depth_Map_Preprocessor,
+    "Metric3D-NormalMapPreprocessor": Metric3D_Normal_Map_Preprocessor
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Metric3D-DepthMapPreprocessor": "Metric3D Depth Map",
+    "Metric3D-NormalMapPreprocessor": "Metric3D Normal Map"
+}