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/depth_anything.py

@@ -0,0 +1,55 @@
+from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
+import comfy.model_management as model_management
+
+class Depth_Anything_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            ckpt_name=INPUT.COMBO(
+                ["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"]
+            ),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
+
+    def execute(self, image, ckpt_name="depth_anything_vitl14.pth", resolution=512, **kwargs):
+        from custom_controlnet_aux.depth_anything import DepthAnythingDetector
+
+        model = DepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
+        out = common_annotator_call(model, image, resolution=resolution)
+        del model
+        return (out, )
+
+class Zoe_Depth_Anything_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            environment=INPUT.COMBO(["indoor", "outdoor"]),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
+
+    def execute(self, image, environment="indoor", resolution=512, **kwargs):
+        from custom_controlnet_aux.zoe import ZoeDepthAnythingDetector
+        ckpt_name = "depth_anything_metric_depth_indoor.pt" if environment == "indoor" else "depth_anything_metric_depth_outdoor.pt"
+        model = ZoeDepthAnythingDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())
+        out = common_annotator_call(model, image, resolution=resolution)
+        del model
+        return (out, )
+
+NODE_CLASS_MAPPINGS = {
+    "DepthAnythingPreprocessor": Depth_Anything_Preprocessor,
+    "Zoe_DepthAnythingPreprocessor": Zoe_Depth_Anything_Preprocessor
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DepthAnythingPreprocessor": "Depth Anything",
+    "Zoe_DepthAnythingPreprocessor": "Zoe Depth Anything"
+}