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/openpose.py

@@ -0,0 +1,48 @@
+from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT
+import comfy.model_management as model_management
+import json
+
+class OpenPose_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            detect_hand=INPUT.COMBO(["enable", "disable"]),
+            detect_body=INPUT.COMBO(["enable", "disable"]),
+            detect_face=INPUT.COMBO(["enable", "disable"]),
+            resolution=INPUT.RESOLUTION(),
+            scale_stick_for_xinsr_cn=INPUT.COMBO(["disable", "enable"])
+        )
+        
+    RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT")
+    FUNCTION = "estimate_pose"
+
+    CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators"
+
+    def estimate_pose(self, image, detect_hand="enable", detect_body="enable", detect_face="enable", scale_stick_for_xinsr_cn="disable", resolution=512, **kwargs):
+        from custom_controlnet_aux.open_pose import OpenposeDetector
+
+        detect_hand = detect_hand == "enable"
+        detect_body = detect_body == "enable"
+        detect_face = detect_face == "enable"
+        scale_stick_for_xinsr_cn = scale_stick_for_xinsr_cn == "enable"
+
+        model = OpenposeDetector.from_pretrained().to(model_management.get_torch_device())        
+        self.openpose_dicts = []
+        def func(image, **kwargs):
+            pose_img, openpose_dict = model(image, **kwargs)
+            self.openpose_dicts.append(openpose_dict)
+            return pose_img
+        
+        out = common_annotator_call(func, image, include_hand=detect_hand, include_face=detect_face, include_body=detect_body, image_and_json=True, xinsr_stick_scaling=scale_stick_for_xinsr_cn, resolution=resolution)
+        del model
+        return {
+            'ui': { "openpose_json": [json.dumps(self.openpose_dicts, indent=4)] },
+            "result": (out, self.openpose_dicts)
+        }
+
+NODE_CLASS_MAPPINGS = {
+    "OpenposePreprocessor": OpenPose_Preprocessor,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "OpenposePreprocessor": "OpenPose Pose",
+}