refactor(nodes): declare loras widget as LORAS input type on lora nodes

py/nodes/create_hook_lora.py

@@ -39,6 +39,7 @@ class CreateHookLoraLM:
                         ),
                     },
                 ),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -52,7 +53,7 @@ class CreateHookLoraLM:
     RETURN_NAMES = ("HOOKS", "trigger_words", "active_loras")
     FUNCTION = "create_hook"
 
-    def create_hook(self, text: str, **kwargs):
+    def create_hook(self, text: str, loras, **kwargs):
         """Create a HookGroup from the selected LoRAs, chained with prev_hooks.
 
         Each active LoRA from the widget is loaded and wrapped in a WeightHook
@@ -73,7 +74,7 @@ class CreateHookLoraLM:
         all_trigger_words: list[str] = []
         active_loras: list[tuple[str, float, float]] = []
 
-        for lora in get_loras_list(kwargs):
+        for lora in get_loras_list({"loras": loras}):
             if not lora.get("active", False):
                 continue
 

py/nodes/lora_loader.py

@@ -49,9 +49,9 @@ def _collect_stack_entries(lora_stack):
     return entries
 
 
-def _collect_widget_entries(kwargs):
+def _collect_widget_entries(loras):
     entries = []
-    for lora in get_loras_list(kwargs):
+    for lora in get_loras_list({"loras": loras}):
         if not lora.get("active", False):
             continue
         lora_name = apply_lora_syntax_format(lora["name"])
@@ -139,6 +139,7 @@ class LoraLoaderLM:
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -152,12 +153,12 @@ class LoraLoaderLM:
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras"
 
-    def load_loras(self, model, text, **kwargs):
-        """Loads multiple LoRAs based on the kwargs input and lora_stack."""
+    def load_loras(self, model, text, loras, **kwargs):
+        """Loads multiple LoRAs based on the widget input and lora_stack."""
         del text
         clip = kwargs.get("clip", None)
         lora_entries = _collect_stack_entries(kwargs.get("lora_stack", None))
-        lora_entries.extend(_collect_widget_entries(kwargs))
+        lora_entries.extend(_collect_widget_entries(loras))
 
         nunchaku_model_kind = detect_nunchaku_model_kind(model)
         if nunchaku_model_kind == "flux":

py/nodes/lora_stacker.py

@@ -18,6 +18,7 @@ class LoraStackerLM:
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -31,8 +32,8 @@ class LoraStackerLM:
     RETURN_NAMES = ("LORA_STACK", "trigger_words", "active_loras")
     FUNCTION = "stack_loras"
     
-    def stack_loras(self, text, **kwargs):
-        """Stacks multiple LoRAs based on the kwargs input without loading them."""
+    def stack_loras(self, text, loras, **kwargs):
+        """Stacks multiple LoRAs based on the widget input without loading them."""
         stack = []
         active_loras = []
         all_trigger_words = []
@@ -47,8 +48,8 @@ class LoraStackerLM:
                 _, trigger_words = get_lora_info(lora_name)
                 all_trigger_words.extend(trigger_words)
         
-        # Process loras from kwargs with support for both old and new formats
-        loras_list = get_loras_list(kwargs)
+        # Process loras from the widget with support for both old and new formats
+        loras_list = get_loras_list({"loras": loras})
         for lora in loras_list:
             if not lora.get('active', False):
                 continue

py/nodes/wanvideo_lora_select.py

@@ -31,6 +31,7 @@ class WanVideoLoraSelectLM:
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
+                "loras": ("LORAS", {}),
             },
             "optional": FlexibleOptionalInputType(any_type),
         }
@@ -44,7 +45,7 @@ class WanVideoLoraSelectLM:
     RETURN_NAMES = ("lora", "trigger_words", "active_loras")
     FUNCTION = "process_loras"
     
-    def process_loras(self, text, low_mem_load=False, merge_loras=True, **kwargs):
+    def process_loras(self, text, loras, low_mem_load=False, merge_loras=True, **kwargs):
         loras_list = []
         all_trigger_words = []
         active_loras = []
@@ -62,8 +63,8 @@ class WanVideoLoraSelectLM:
         selected_blocks = blocks.get("selected_blocks", {})
         layer_filter = blocks.get("layer_filter", "")
         
-        # Process loras from kwargs with support for both old and new formats
-        loras_from_widget = get_loras_list(kwargs)
+        # Process loras from the widget with support for both old and new formats
+        loras_from_widget = get_loras_list({"loras": loras})
         for lora in loras_from_widget:
             if not lora.get('active', False):
                 continue