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/power_lora_loader.py

@@ -0,0 +1,101 @@
+import folder_paths
+
+from typing import Union
+
+from nodes import LoraLoader
+from .constants import get_category, get_name
+from .power_prompt_utils import get_lora_by_filename
+from .utils import FlexibleOptionalInputType, any_type
+from .server.utils_info import get_model_info_file_data
+from .log import log_node_warn
+
+NODE_NAME = get_name('Power Lora Loader')
+
+
+class RgthreePowerLoraLoader:
+  """ The Power Lora Loader is a powerful, flexible node to add multiple loras to a model/clip."""
+
+  NAME = NODE_NAME
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+      },
+      # Since we will pass any number of loras in from the UI, this needs to always allow an
+      "optional": FlexibleOptionalInputType(type=any_type, data={
+        "model": ("MODEL",),
+        "clip": ("CLIP",),
+      }),
+      "hidden": {},
+    }
+
+  RETURN_TYPES = ("MODEL", "CLIP")
+  RETURN_NAMES = ("MODEL", "CLIP")
+  FUNCTION = "load_loras"
+
+  def load_loras(self, model=None, clip=None, **kwargs):
+    """Loops over the provided loras in kwargs and applies valid ones."""
+    for key, value in kwargs.items():
+      key = key.upper()
+      if key.startswith('LORA_') and 'on' in value and 'lora' in value and 'strength' in value:
+        strength_model = value['strength']
+        # If we just passed one strength value, then use it for both, if we passed a strengthTwo
+        # as well, then our `strength` will be for the model, and `strengthTwo` for clip.
+        strength_clip = value['strengthTwo'] if 'strengthTwo' in value else None
+        if clip is None:
+          if strength_clip is not None and strength_clip != 0:
+            log_node_warn(NODE_NAME, 'Recieved clip strength eventhough no clip supplied!')
+          strength_clip = 0
+        else:
+          strength_clip = strength_clip if strength_clip is not None else strength_model
+        if value['on'] and (strength_model != 0 or strength_clip != 0):
+          lora = get_lora_by_filename(value['lora'], log_node=self.NAME)
+          if model is not None and lora is not None:
+            model, clip = LoraLoader().load_lora(model, clip, lora, strength_model, strength_clip)
+
+    return (model, clip)
+
+  @classmethod
+  def get_enabled_loras_from_prompt_node(cls,
+                                         prompt_node: dict) -> list[dict[str, Union[str, float]]]:
+    """Gets enabled loras of a node within a server prompt."""
+    result = []
+    for name, lora in prompt_node['inputs'].items():
+      if name.startswith('lora_') and lora['on']:
+        lora_file = get_lora_by_filename(lora['lora'], log_node=cls.NAME)
+        if lora_file is not None:  # Add the same safety check
+          lora_dict = {
+            'name': lora['lora'],
+            'strength': lora['strength'],
+            'path': folder_paths.get_full_path("loras", lora_file)
+          }
+          if 'strengthTwo' in lora:
+            lora_dict['strength_clip'] = lora['strengthTwo']
+          result.append(lora_dict)
+    return result
+
+  @classmethod
+  def get_enabled_triggers_from_prompt_node(cls, prompt_node: dict, max_each: int = 1):
+    """Gets trigger words up to the max for enabled loras of a node within a server prompt."""
+    loras = [l['name'] for l in cls.get_enabled_loras_from_prompt_node(prompt_node)]
+    trained_words = []
+    for lora in loras:
+      info = get_model_info_file_data(lora, 'loras', default={})
+      if not info or not info.keys():
+        log_node_warn(
+          NODE_NAME,
+          f'No info found for lora {lora} when grabbing triggers. Have you generated an info file'
+          ' from the Power Lora Loader "Show Info" dialog?'
+        )
+        continue
+      if 'trainedWords' not in info or not info['trainedWords']:
+        log_node_warn(
+          NODE_NAME,
+          f'No trained words for lora {lora} when grabbing triggers. Have you fetched data from'
+          'civitai or manually added words?'
+        )
+        continue
+      trained_words += [w for wi in info['trainedWords'][:max_each] if (wi and (w := wi['word']))]
+    return trained_words