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/diffusion_edge.py

@@ -0,0 +1,41 @@
+from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, run_script
+import comfy.model_management as model_management
+import sys
+
+def install_deps():
+    try:
+        import sklearn
+    except:
+        run_script([sys.executable, '-s', '-m', 'pip', 'install', 'scikit-learn'])
+
+class DiffusionEdge_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            environment=INPUT.COMBO(["indoor", "urban", "natrual"]),
+            patch_batch_size=INPUT.INT(default=4, min=1, max=16),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Line Extractors"
+
+    def execute(self, image, environment="indoor", patch_batch_size=4, resolution=512, **kwargs):
+        install_deps()
+        from custom_controlnet_aux.diffusion_edge import DiffusionEdgeDetector
+
+        model = DiffusionEdgeDetector \
+            .from_pretrained(filename = f"diffusion_edge_{environment}.pt") \
+            .to(model_management.get_torch_device())
+        out = common_annotator_call(model, image, resolution=resolution, patch_batch_size=patch_batch_size)
+        del model
+        return (out, )
+
+NODE_CLASS_MAPPINGS = {
+    "DiffusionEdge_Preprocessor": DiffusionEdge_Preprocessor,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DiffusionEdge_Preprocessor": "Diffusion Edge (batch size ↑ => speed ↑, VRAM ↑)",
+}