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/midas.py

@@ -0,0 +1,59 @@
+from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
+import comfy.model_management as model_management
+import numpy as np
+
+class MIDAS_Normal_Map_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0),
+            bg_threshold=INPUT.FLOAT(default=0.1),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
+
+    def execute(self, image, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs):
+        from custom_controlnet_aux.midas import MidasDetector
+
+        model = MidasDetector.from_pretrained().to(model_management.get_torch_device())
+        #Dirty hack :))
+        cb = lambda image, **kargs: model(image, **kargs)[1]
+        out = common_annotator_call(cb, image, resolution=resolution, a=a, bg_th=bg_threshold, depth_and_normal=True)
+        del model
+        return (out, )
+
+class MIDAS_Depth_Map_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            a=INPUT.FLOAT(default=np.pi * 2.0, min=0.0, max=np.pi * 5.0),
+            bg_threshold=INPUT.FLOAT(default=0.1),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
+
+    def execute(self, image, a=np.pi * 2.0, bg_threshold=0.1, resolution=512, **kwargs):
+        from custom_controlnet_aux.midas import MidasDetector
+
+        # Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py
+        model = MidasDetector.from_pretrained().to(model_management.get_torch_device())
+        out = common_annotator_call(model, image, resolution=resolution, a=a, bg_th=bg_threshold)
+        del model
+        return (out, )
+
+NODE_CLASS_MAPPINGS = {
+    "MiDaS-NormalMapPreprocessor": MIDAS_Normal_Map_Preprocessor,
+    "MiDaS-DepthMapPreprocessor": MIDAS_Depth_Map_Preprocessor
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "MiDaS-NormalMapPreprocessor": "MiDaS Normal Map",
+    "MiDaS-DepthMapPreprocessor": "MiDaS Depth Map"
+}