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/rgthree-comfy/py/lora_stack.py

@@ -0,0 +1,46 @@
+from .constants import get_category, get_name
+from nodes import LoraLoader
+import folder_paths
+
+
+class RgthreeLoraLoaderStack:
+
+    NAME = get_name('Lora Loader Stack')
+    CATEGORY = get_category()
+
+    @classmethod
+    def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "clip": ("CLIP", ),
+
+                "lora_01": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_01":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+
+                "lora_02": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_02":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+
+                "lora_03": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_03":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+
+                "lora_04": (['None'] + folder_paths.get_filename_list("loras"), ),
+                "strength_04":("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP")
+    FUNCTION = "load_lora"
+
+    def load_lora(self, model, clip, lora_01, strength_01, lora_02, strength_02, lora_03, strength_03, lora_04, strength_04):
+        if lora_01 != "None" and strength_01 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_01, strength_01, strength_01)
+        if lora_02 != "None" and strength_02 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_02, strength_02, strength_02)
+        if lora_03 != "None" and strength_03 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_03, strength_03, strength_03)
+        if lora_04 != "None" and strength_04 != 0:
+            model, clip = LoraLoader().load_lora(model, clip, lora_04, strength_04, strength_04)
+
+        return (model, clip)
+