Delete multi_lora_stack_model_only.py

multi_lora_stack_model_only.py

@@ -1,115 +0,0 @@
-from nodes import LoraLoaderModelOnly
-
-class MultiLoRAStackModelOnly:
-    """A powerful node to load multiple LoRAs with individual controls (Model Only)."""
-    
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "lora_stack": ("STRING", {
-                    "multiline": True, 
-                    "default": "[]",
-                    "forceInput": False
-                }),
-            },
-            "optional": {},
-        }
-
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("MODEL",) 
-    FUNCTION = "load_loras"
-    CATEGORY = "loaders"
-
-    def load_loras(self, model, lora_stack="[]", **kwargs):
-        """Loads multiple LoRAs based on the configuration (Model Only)."""
-        import json
-        
-        # Debug output
-        print(f"\n=== MultiLoRAStackModelOnly Debug ===")
-        print(f"Received lora_stack: {lora_stack}")
-        print(f"Received kwargs: {list(kwargs.keys())}")
-        
-        # Parse lora_stack if it's a string
-        lora_configs = []
-        if isinstance(lora_stack, str):
-            try:
-                lora_configs = json.loads(lora_stack)
-                print(f"✅ Parsed {len(lora_configs)} LoRA configs from JSON")
-                for i, config in enumerate(lora_configs):
-                    print(f"  Config {i}: {config}")
-            except Exception as e:
-                print(f"❌ Failed to parse JSON: {e}")
-                lora_configs = []
-        else:
-            lora_configs = lora_stack if isinstance(lora_stack, list) else []
-        
-        # Also check kwargs for lora configurations (for backward compatibility)
-        for key, value in kwargs.items():
-            if key.upper().startswith('LORA_') and isinstance(value, dict):
-                if 'on' in value and 'lora' in value and 'strength' in value:
-                    lora_configs.append(value)
-                    print(f"➕ Added LoRA config from kwargs: {key}")
-        
-        applied_count = 0
-        
-        # Process each LoRA configuration
-        for i, config in enumerate(lora_configs):
-            print(f"\n--- Processing LoRA {i+1} ---")
-            
-            if not isinstance(config, dict):
-                print(f"❌ Skipping invalid config at index {i}: {config}")
-                continue
-                
-            # Check if this LoRA should be applied
-            if not config.get('on', False):
-                print(f"⏸️ Skipping disabled LoRA: {config.get('lora', 'Unknown')}")
-                continue
-                
-            lora_name = config.get('lora')
-            if not lora_name or lora_name == "None":
-                print(f"❌ Skipping empty/None LoRA")
-                continue
-                
-            strength_model = float(config.get('strength', 1.0))
-            
-            # Skip if strength is 0
-            if strength_model == 0:
-                print(f"⏸️ Skipping zero-strength LoRA: {lora_name}")
-                continue
-            
-            print(f"🔄 Attempting to load LoRA (Model Only):")
-            print(f"   Name: {lora_name}")
-            print(f"   Model Strength: {strength_model}")
-            
-            # Apply the LoRA using ComfyUI's model-only loader
-            try:
-                # Create a new LoraLoaderModelOnly instance
-                lora_loader = LoraLoaderModelOnly()
-                
-                # Apply the LoRA (model only)
-                model, = lora_loader.load_lora(model, lora_name, strength_model)
-                applied_count += 1
-                print(f"✅ Successfully applied LoRA (Model Only): {lora_name}")
-                
-            except Exception as e:
-                print(f"❌ Failed to load LoRA '{lora_name}': {str(e)}")
-                print(f"   Error type: {type(e).__name__}")
-                import traceback
-                print(f"   Full traceback: {traceback.format_exc()}")
-        
-        print(f"\n=== Summary ===")
-        print(f"Applied {applied_count} out of {len(lora_configs)} LoRAs (Model Only)")
-        print(f"===================\n")
-        
-        return (model,)
-
-# Register the node
-NODE_CLASS_MAPPINGS = {
-    "MultiLoRAStackModelOnly": MultiLoRAStackModelOnly
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "MultiLoRAStackModelOnly": "Multi LoRA Stack (Model Only)"
-}