Allow for empty lora (no loras option) in Lora Pool

py/nodes/lora_cycler.py

@@ -100,11 +100,16 @@ class LoraCyclerLM:
         current_lora = lora_list[clamped_index - 1]
 
         # Build LORA_STACK with single LoRA
-        lora_path, _ = get_lora_info(current_lora["file_name"])
+        if current_lora["file_name"] == "None":
+            lora_path = None
+        else:
+            lora_path, _ = get_lora_info(current_lora["file_name"])
+
         if not lora_path:
-            logger.warning(
-                f"[LoraCyclerLM] Could not find path for LoRA: {current_lora['file_name']}"
-            )
+            if current_lora["file_name"] != "None":
+                logger.warning(
+                    f"[LoraCyclerLM] Could not find path for LoRA: {current_lora['file_name']}"
+                )
             lora_stack = []
         else:
             # Normalize path separators

py/nodes/lora_loader.py

@@ -53,6 +53,8 @@ class LoraLoaderLM:
         # First process lora_stack if available
         if lora_stack:
             for lora_path, model_strength, clip_strength in lora_stack:
+                if lora_path == "None" or not lora_path:
+                    continue
                 # Extract lora name and convert to absolute path
                 # lora_stack stores relative paths, but load_torch_file needs absolute paths
                 lora_name = extract_lora_name(lora_path)
@@ -78,7 +80,7 @@ class LoraLoaderLM:
         # Then process loras from kwargs with support for both old and new formats
         loras_list = get_loras_list(kwargs)
         for lora in loras_list:
-            if not lora.get('active', False):
+            if not lora.get('active', False) or lora.get('name') == "None":
                 continue
                 
             lora_name = lora['name']
@@ -197,6 +199,8 @@ class LoraTextLoaderLM:
         # First process lora_stack if available
         if lora_stack:
             for lora_path, model_strength, clip_strength in lora_stack:
+                if lora_path == "None" or not lora_path:
+                    continue
                 # Extract lora name and convert to absolute path
                 # lora_stack stores relative paths, but load_torch_file needs absolute paths
                 lora_name = extract_lora_name(lora_path)
@@ -223,6 +227,8 @@ class LoraTextLoaderLM:
         parsed_loras = self.parse_lora_syntax(lora_syntax)
         for lora in parsed_loras:
             lora_name = lora['name']
+            if lora_name == "None":
+                continue
             model_strength = lora['model_strength']
             clip_strength = lora['clip_strength']
             

py/nodes/lora_pool.py

@@ -82,6 +82,7 @@ class LoraPoolLM:
                 "folders": {"include": [], "exclude": []},
                 "favoritesOnly": False,
                 "license": {"noCreditRequired": False, "allowSelling": False},
+                "includeEmptyLora": False,
             },
             "preview": {"matchCount": 0, "lastUpdated": 0},
         }

py/nodes/lora_randomizer.py

@@ -120,7 +120,7 @@ class LoraRandomizerLM:
         """
         lora_stack = []
         for lora in loras:
-            if not lora.get("active", False):
+            if not lora.get("active", False) or lora.get("name") == "None":
                 continue
 
             # Get file path

py/nodes/lora_stacker.py

@@ -38,6 +38,8 @@ class LoraStackerLM:
             stack.extend(lora_stack)
             # Get trigger words from existing stack entries
             for lora_path, _, _ in lora_stack:
+                if lora_path == "None" or not lora_path:
+                    continue
                 lora_name = extract_lora_name(lora_path)
                 _, trigger_words = get_lora_info(lora_name)
                 all_trigger_words.extend(trigger_words)
@@ -45,7 +47,7 @@ class LoraStackerLM:
         # Process loras from kwargs with support for both old and new formats
         loras_list = get_loras_list(kwargs)
         for lora in loras_list:
-            if not lora.get('active', False):
+            if not lora.get('active', False) or lora.get('name') == "None":
                 continue
                 
             lora_name = lora['name']

py/routes/lora_routes.py

@@ -97,6 +97,10 @@ class LoraRoutes(BaseModelRoutes):
                 h.lower() for h in request.query["lora_hashes"].split(",")
             ]
 
+        include_empty_lora = request.query.get("include_empty_lora")
+        if include_empty_lora is not None:
+            params["include_empty_lora"] = include_empty_lora.lower() == "true"
+
         return params
 
     def _validate_civitai_model_type(self, model_type: str) -> bool:

py/services/lora_service.py

@@ -62,6 +62,17 @@ class LoraService(BaseModelService):
         if first_letter:
             data = self._filter_by_first_letter(data, first_letter)
 
+        if kwargs.get("include_empty_lora"):
+            data.append({
+                "file_name": "None",
+                "model_name": "None",
+                "file_path": "",
+                "folder": "",
+                "base_model": "",
+                "tags": [],
+                "civitai": {},
+            })
+
         return data
 
     def _filter_by_first_letter(self, data: List[Dict], letter: str) -> List[Dict]:
@@ -403,7 +414,7 @@ class LoraService(BaseModelService):
         """
         from .model_query import FilterCriteria
 
-        filter_section = pool_config
+        filter_section = pool_config.get("filters", pool_config)
 
         # Extract filter parameters
         selected_base_models = filter_section.get("baseModels", [])
@@ -416,6 +427,7 @@ class LoraService(BaseModelService):
         license_dict = filter_section.get("license", {})
         no_credit_required = license_dict.get("noCreditRequired", False)
         allow_selling = license_dict.get("allowSelling", False)
+        include_empty_lora = filter_section.get("includeEmptyLora", False)
 
         # Build tag filters dict
         tag_filters = {}
@@ -485,6 +497,18 @@ class LoraService(BaseModelService):
                 if bool(lora.get("license_flags", 127) & (1 << 1))
             ]
 
+        if include_empty_lora:
+            
+            available_loras.append({
+                "file_name": "None",
+                "model_name": "None",
+                "file_path": "",
+                "folder": "",
+                "base_model": "",
+                "tags": [],
+                "civitai": {},
+            })
+
         return available_loras
 
     async def get_cycler_list(