checkpoint

py/nodes/lora_loader.py

@@ -8,7 +8,7 @@ from .utils import FlexibleOptionalInputType, any_type
 
 class LoraManagerLoader:
     NAME = "Lora Loader (LoraManager)"
-    CATEGORY = "loaders"
+    CATEGORY = "Lora Manager/loaders"
 
     @classmethod
     def INPUT_TYPES(cls):
@@ -23,7 +23,10 @@ class LoraManagerLoader:
                     "placeholder": "LoRA syntax input: <lora:name:strength>"
                 }),
             },
-            "optional": FlexibleOptionalInputType(any_type),
+            "optional": {
+                **FlexibleOptionalInputType(any_type),
+                "lora_stack": ("LORA_STACK", {"default": None}),
+            }
         }
 
     RETURN_TYPES = ("MODEL", "CLIP", IO.STRING)
@@ -49,11 +52,32 @@ class LoraManagerLoader:
                             return relative_path, trigger_words
         return lora_name, []  # Fallback if not found
 
-    def load_loras(self, model, clip, text, **kwargs):
-        """Loads multiple LoRAs based on the kwargs input."""
+    def extract_lora_name(self, lora_path):
+        """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
+        # Get the basename without extension
+        basename = os.path.basename(lora_path)
+        return os.path.splitext(basename)[0]
+    
+    def load_loras(self, model, clip, text, lora_stack=None, **kwargs):
+        print("load_loras kwargs: ", kwargs)
+        """Loads multiple LoRAs based on the kwargs input and lora_stack."""
         loaded_loras = []
         all_trigger_words = []
         
+        # First process lora_stack if available
+        if lora_stack:
+            for lora_path, model_strength, clip_strength in lora_stack:
+                # Apply the LoRA using the provided path and strengths
+                model, clip = LoraLoader().load_lora(model, clip, lora_path, model_strength, clip_strength)
+                
+                # Extract lora name for trigger words lookup
+                lora_name = self.extract_lora_name(lora_path)
+                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                
+                all_trigger_words.extend(trigger_words)
+                loaded_loras.append(f"{lora_name}: {model_strength}")
+        
+        # Then process loras from kwargs
         if 'loras' in kwargs:
             for lora in kwargs['loras']:
                 if not lora.get('active', False):

py/nodes/lora_stacker.py

@@ -0,0 +1,94 @@
+from comfy.comfy_types import IO # type: ignore
+from ..services.lora_scanner import LoraScanner
+from ..config import config
+import asyncio
+import os
+from .utils import FlexibleOptionalInputType, any_type
+
+class LoraStacker:
+    NAME = "Lora Stacker (LoraManager)"
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "text": (IO.STRING, {
+                    "multiline": True, 
+                    "dynamicPrompts": True, 
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
+                    "placeholder": "LoRA syntax input: <lora:name:strength>"
+                }),
+            },
+            "optional": {
+                **FlexibleOptionalInputType(any_type),
+                "lora_stack": ("LORA_STACK", {"default": None}),
+            }
+        }
+
+    RETURN_TYPES = ("LORA_STACK", IO.STRING)
+    RETURN_NAMES = ("LORA_STACK", "trigger_words")
+    FUNCTION = "stack_loras"
+
+    async def get_lora_info(self, lora_name):
+        """Get the lora path and trigger words from cache"""
+        scanner = await LoraScanner.get_instance()
+        cache = await scanner.get_cached_data()
+        
+        for item in cache.raw_data:
+            if item.get('file_name') == lora_name:
+                file_path = item.get('file_path')
+                if file_path:
+                    for root in config.loras_roots:
+                        root = root.replace(os.sep, '/')
+                        if file_path.startswith(root):
+                            relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
+                            # Get trigger words from civitai metadata
+                            civitai = item.get('civitai', {})
+                            trigger_words = civitai.get('trainedWords', []) if civitai else []
+                            return relative_path, trigger_words
+        return lora_name, []  # Fallback if not found
+
+    def extract_lora_name(self, lora_path):
+        """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
+        # Get the basename without extension
+        basename = os.path.basename(lora_path)
+        return os.path.splitext(basename)[0]
+    
+    def stack_loras(self, text, lora_stack=None, **kwargs):
+        print("stack_loras kwargs: ", kwargs)
+        """Stacks multiple LoRAs based on the kwargs input without loading them."""
+        stack = []
+        all_trigger_words = []
+        
+        # Process existing lora_stack if available
+        if lora_stack:
+            stack.extend(lora_stack)
+            # Get trigger words from existing stack entries
+            for lora_path, _, _ in lora_stack:
+                lora_name = self.extract_lora_name(lora_path)
+                _, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                all_trigger_words.extend(trigger_words)
+        
+        if 'loras' in kwargs:
+            for lora in kwargs['loras']:
+                if not lora.get('active', False):
+                    continue
+                    
+                lora_name = lora['name']
+                model_strength = float(lora['strength'])
+                clip_strength = model_strength  # Using same strength for both as in the original loader
+                
+                # Get lora path and trigger words
+                lora_path, trigger_words = asyncio.run(self.get_lora_info(lora_name))
+                
+                # Add to stack without loading
+                stack.append((lora_path, model_strength, clip_strength))
+                
+                # Add trigger words to collection
+                all_trigger_words.extend(trigger_words)
+        
+        # use ',, ' to separate trigger words for group mode
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+
+        return (stack, trigger_words_text)

py/nodes/trigger_word_toggle.py

@@ -4,7 +4,7 @@ from .utils import FlexibleOptionalInputType, any_type
 
 class TriggerWordToggle:
     NAME = "TriggerWord Toggle (LoraManager)"
-    CATEGORY = "lora manager"
+    CATEGORY = "Lora Manager/utils"
     DESCRIPTION = "Toggle trigger words on/off"
     
     @classmethod
@@ -13,10 +13,7 @@ class TriggerWordToggle:
             "required": {
                 "group_mode": ("BOOLEAN", {"default": True}),
             },
-            "optional": {
-                **FlexibleOptionalInputType(any_type),
-                "trigger_words": ("STRING", {"default": "", "defaultInput": True}),
-            },
+            "optional": FlexibleOptionalInputType(any_type),
             "hidden": {
                 "id": "UNIQUE_ID",  # 会被 ComfyUI 自动替换为唯一ID
             },
@@ -26,7 +23,9 @@ class TriggerWordToggle:
     RETURN_NAMES = ("filtered_trigger_words",)
     FUNCTION = "process_trigger_words"
 
-    def process_trigger_words(self, id, trigger_words="", **kwargs):
+    def process_trigger_words(self, id, **kwargs):
+        print("trigger_words ", kwargs)
+        trigger_words = kwargs.get("trigger_words", "")
         # Send trigger words to frontend
         PromptServer.instance.send_sync("trigger_word_update", {
             "id": id,