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/scribble.py

@@ -0,0 +1,74 @@
+from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT, nms
+import comfy.model_management as model_management
+import cv2
+
+class Scribble_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(resolution=INPUT.RESOLUTION())
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Line Extractors"
+
+    def execute(self, image, resolution=512, **kwargs):
+        from custom_controlnet_aux.scribble import ScribbleDetector
+
+        model = ScribbleDetector()
+        return (common_annotator_call(model, image, resolution=resolution), )
+
+class Scribble_XDoG_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            threshold=INPUT.INT(default=32, min=1, max=64),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Line Extractors"
+
+    def execute(self, image, threshold=32, resolution=512, **kwargs):
+        from custom_controlnet_aux.scribble import ScribbleXDog_Detector
+
+        model = ScribbleXDog_Detector()
+        return (common_annotator_call(model, image, resolution=resolution, thr_a=threshold), )
+
+class Scribble_PiDiNet_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            safe=(["enable", "disable"],),
+            resolution=INPUT.RESOLUTION()
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors/Line Extractors"
+
+    def execute(self, image, safe="enable", resolution=512):
+        def model(img, **kwargs):
+            from custom_controlnet_aux.pidi import PidiNetDetector
+            pidinet = PidiNetDetector.from_pretrained().to(model_management.get_torch_device())
+            result = pidinet(img, scribble=True, **kwargs)
+            result = nms(result, 127, 3.0)
+            result = cv2.GaussianBlur(result, (0, 0), 3.0)
+            result[result > 4] = 255
+            result[result < 255] = 0
+            return result
+        return (common_annotator_call(model, image, resolution=resolution, safe=safe=="enable"),)
+
+NODE_CLASS_MAPPINGS = {
+    "ScribblePreprocessor": Scribble_Preprocessor,
+    "Scribble_XDoG_Preprocessor": Scribble_XDoG_Preprocessor,
+    "Scribble_PiDiNet_Preprocessor": Scribble_PiDiNet_Preprocessor
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "ScribblePreprocessor": "Scribble Lines",
+    "Scribble_XDoG_Preprocessor": "Scribble XDoG Lines",
+    "Scribble_PiDiNet_Preprocessor": "Scribble PiDiNet Lines"
+}