feat: Add configurable LoRA strength adjustment step setting

Implements issue #808 - Allow users to customize the strength
variation range for LoRA widget arrow buttons.

Changes:
- Add 'Strength Adjustment Step' setting (0.01-0.1) in settings.js
- Replace hardcoded 0.05 increments with configurable step value
- Apply to both LoRA strength and CLIP strength controls

Fixes #808

__init__.py

@@ -1,6 +1,8 @@
 try:  # pragma: no cover - import fallback for pytest collection
     from .py.lora_manager import LoraManager
     from .py.nodes.lora_loader import LoraLoaderLM, LoraTextLoaderLM
+    from .py.nodes.checkpoint_loader import CheckpointLoaderLM
+    from .py.nodes.unet_loader import UNETLoaderLM
     from .py.nodes.trigger_word_toggle import TriggerWordToggleLM
     from .py.nodes.prompt import PromptLM
     from .py.nodes.text import TextLM
@@ -27,12 +29,12 @@ except (
     PromptLM = importlib.import_module("py.nodes.prompt").PromptLM
     TextLM = importlib.import_module("py.nodes.text").TextLM
     LoraManager = importlib.import_module("py.lora_manager").LoraManager
-    LoraLoaderLM = importlib.import_module(
-        "py.nodes.lora_loader"
-    ).LoraLoaderLM
-    LoraTextLoaderLM = importlib.import_module(
-        "py.nodes.lora_loader"
-    ).LoraTextLoaderLM
+    LoraLoaderLM = importlib.import_module("py.nodes.lora_loader").LoraLoaderLM
+    LoraTextLoaderLM = importlib.import_module("py.nodes.lora_loader").LoraTextLoaderLM
+    CheckpointLoaderLM = importlib.import_module(
+        "py.nodes.checkpoint_loader"
+    ).CheckpointLoaderLM
+    UNETLoaderLM = importlib.import_module("py.nodes.unet_loader").UNETLoaderLM
     TriggerWordToggleLM = importlib.import_module(
         "py.nodes.trigger_word_toggle"
     ).TriggerWordToggleLM
@@ -49,9 +51,7 @@ except (
     LoraRandomizerLM = importlib.import_module(
         "py.nodes.lora_randomizer"
     ).LoraRandomizerLM
-    LoraCyclerLM = importlib.import_module(
-        "py.nodes.lora_cycler"
-    ).LoraCyclerLM
+    LoraCyclerLM = importlib.import_module("py.nodes.lora_cycler").LoraCyclerLM
     init_metadata_collector = importlib.import_module("py.metadata_collector").init
 
 NODE_CLASS_MAPPINGS = {
@@ -59,6 +59,8 @@ NODE_CLASS_MAPPINGS = {
     TextLM.NAME: TextLM,
     LoraLoaderLM.NAME: LoraLoaderLM,
     LoraTextLoaderLM.NAME: LoraTextLoaderLM,
+    CheckpointLoaderLM.NAME: CheckpointLoaderLM,
+    UNETLoaderLM.NAME: UNETLoaderLM,
     TriggerWordToggleLM.NAME: TriggerWordToggleLM,
     LoraStackerLM.NAME: LoraStackerLM,
     SaveImageLM.NAME: SaveImageLM,

locales/de.json

@@ -14,7 +14,8 @@
             "backToTop": "Nach oben",
             "settings": "Einstellungen",
             "help": "Hilfe",
-            "add": "Hinzufügen"
+            "add": "Hinzufügen",
+            "close": "Schließen"
         },
         "status": {
             "loading": "Wird geladen...",

locales/en.json

@@ -14,7 +14,8 @@
             "backToTop": "Back to top",
             "settings": "Settings",
             "help": "Help",
-            "add": "Add"
+            "add": "Add",
+            "close": "Close"
         },
         "status": {
             "loading": "Loading...",

locales/es.json

@@ -14,7 +14,8 @@
             "backToTop": "Volver arriba",
             "settings": "Configuración",
             "help": "Ayuda",
-            "add": "Añadir"
+            "add": "Añadir",
+            "close": "Cerrar"
         },
         "status": {
             "loading": "Cargando...",

locales/fr.json

@@ -14,7 +14,8 @@
             "backToTop": "Retour en haut",
             "settings": "Paramètres",
             "help": "Aide",
-            "add": "Ajouter"
+            "add": "Ajouter",
+            "close": "Fermer"
         },
         "status": {
             "loading": "Chargement...",

locales/he.json

@@ -14,7 +14,8 @@
             "backToTop": "חזרה למעלה",
             "settings": "הגדרות",
             "help": "עזרה",
-            "add": "הוספה"
+            "add": "הוספה",
+            "close": "סגור"
         },
         "status": {
             "loading": "טוען...",

locales/ja.json

@@ -14,7 +14,8 @@
             "backToTop": "トップへ戻る",
             "settings": "設定",
             "help": "ヘルプ",
-            "add": "追加"
+            "add": "追加",
+            "close": "閉じる"
         },
         "status": {
             "loading": "読み込み中...",

locales/ko.json

@@ -14,7 +14,8 @@
             "backToTop": "맨 위로",
             "settings": "설정",
             "help": "도움말",
-            "add": "추가"
+            "add": "추가",
+            "close": "닫기"
         },
         "status": {
             "loading": "로딩 중...",

locales/ru.json

@@ -14,7 +14,8 @@
             "backToTop": "Наверх",
             "settings": "Настройки",
             "help": "Справка",
-            "add": "Добавить"
+            "add": "Добавить",
+            "close": "Закрыть"
         },
         "status": {
             "loading": "Загрузка...",

locales/zh-CN.json

@@ -14,7 +14,8 @@
             "backToTop": "返回顶部",
             "settings": "设置",
             "help": "帮助",
-            "add": "添加"
+            "add": "添加",
+            "close": "关闭"
         },
         "status": {
             "loading": "加载中...",

locales/zh-TW.json

@@ -14,7 +14,8 @@
             "backToTop": "回到頂部",
             "settings": "設定",
             "help": "說明",
-            "add": "新增"
+            "add": "新增",
+            "close": "關閉"
         },
         "status": {
             "loading": "載入中...",

package-lock.json

@@ -114,7 +114,6 @@
         }
       ],
       "license": "MIT",
-      "peer": true,
       "engines": {
         "node": ">=18"
       },
@@ -138,7 +137,6 @@
         }
       ],
       "license": "MIT",
-      "peer": true,
       "engines": {
         "node": ">=18"
       }
@@ -1613,7 +1611,6 @@
       "integrity": "sha512-MyL55p3Ut3cXbeBEG7Hcv0mVM8pp8PBNWxRqchZnSfAiES1v1mRnMeFfaHWIPULpwsYfvO+ZmMZz5tGCnjzDUQ==",
       "dev": true,
       "license": "MIT",
-      "peer": true,
       "dependencies": {
         "cssstyle": "^4.0.1",
         "data-urls": "^5.0.0",

py/config.py

@@ -707,7 +707,13 @@ class Config:
 
     def _prepare_checkpoint_paths(
         self, checkpoint_paths: Iterable[str], unet_paths: Iterable[str]
-    ) -> List[str]:
+    ) -> Tuple[List[str], List[str], List[str]]:
+        """Prepare checkpoint paths and return (all_roots, checkpoint_roots, unet_roots).
+
+        Returns:
+            Tuple of (all_unique_paths, checkpoint_only_paths, unet_only_paths)
+            This method does NOT modify instance variables - callers must set them.
+        """
         checkpoint_map = self._dedupe_existing_paths(checkpoint_paths)
         unet_map = self._dedupe_existing_paths(unet_paths)
 
@@ -737,8 +743,8 @@ class Config:
 
         checkpoint_values = set(checkpoint_map.values())
         unet_values = set(unet_map.values())
-        self.checkpoints_roots = [p for p in unique_paths if p in checkpoint_values]
-        self.unet_roots = [p for p in unique_paths if p in unet_values]
+        checkpoint_roots = [p for p in unique_paths if p in checkpoint_values]
+        unet_roots = [p for p in unique_paths if p in unet_values]
 
         for original_path in unique_paths:
             real_path = os.path.normpath(os.path.realpath(original_path)).replace(
@@ -747,7 +753,7 @@ class Config:
             if real_path != original_path:
                 self.add_path_mapping(original_path, real_path)
 
-        return unique_paths
+        return unique_paths, checkpoint_roots, unet_roots
 
     def _prepare_embedding_paths(self, raw_paths: Iterable[str]) -> List[str]:
         path_map = self._dedupe_existing_paths(raw_paths)
@@ -776,9 +782,11 @@ class Config:
         embedding_paths = folder_paths.get("embeddings", []) or []
 
         self.loras_roots = self._prepare_lora_paths(lora_paths)
-        self.base_models_roots = self._prepare_checkpoint_paths(
-            checkpoint_paths, unet_paths
-        )
+        (
+            self.base_models_roots,
+            self.checkpoints_roots,
+            self.unet_roots,
+        ) = self._prepare_checkpoint_paths(checkpoint_paths, unet_paths)
         self.embeddings_roots = self._prepare_embedding_paths(embedding_paths)
 
         # Process extra paths (only for LoRA Manager, not shared with ComfyUI)
@@ -789,18 +797,11 @@ class Config:
         extra_embedding_paths = extra_paths.get("embeddings", []) or []
 
         self.extra_loras_roots = self._prepare_lora_paths(extra_lora_paths)
-        # Save main paths before processing extra paths ( _prepare_checkpoint_paths overwrites them)
-        saved_checkpoints_roots = self.checkpoints_roots
-        saved_unet_roots = self.unet_roots
-        self.extra_checkpoints_roots = self._prepare_checkpoint_paths(
-            extra_checkpoint_paths, extra_unet_paths
-        )
-        self.extra_unet_roots = (
-            self.unet_roots if self.unet_roots is not None else []
-        )  # unet_roots was set by _prepare_checkpoint_paths
-        # Restore main paths
-        self.checkpoints_roots = saved_checkpoints_roots
-        self.unet_roots = saved_unet_roots
+        (
+            _,
+            self.extra_checkpoints_roots,
+            self.extra_unet_roots,
+        ) = self._prepare_checkpoint_paths(extra_checkpoint_paths, extra_unet_paths)
         self.extra_embeddings_roots = self._prepare_embedding_paths(
             extra_embedding_paths
         )
@@ -857,9 +858,11 @@ class Config:
         try:
             raw_checkpoint_paths = folder_paths.get_folder_paths("checkpoints")
             raw_unet_paths = folder_paths.get_folder_paths("unet")
-            unique_paths = self._prepare_checkpoint_paths(
-                raw_checkpoint_paths, raw_unet_paths
-            )
+            (
+                unique_paths,
+                self.checkpoints_roots,
+                self.unet_roots,
+            ) = self._prepare_checkpoint_paths(raw_checkpoint_paths, raw_unet_paths)
 
             logger.info(
                 "Found checkpoint roots:"

py/nodes/checkpoint_loader.py

@@ -0,0 +1,118 @@
+import logging
+from typing import List, Tuple
+import comfy.sd # type: ignore
+import folder_paths # type: ignore
+from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
+
+logger = logging.getLogger(__name__)
+
+
+class CheckpointLoaderLM:
+    """Checkpoint Loader with support for extra folder paths
+
+    Loads checkpoints from both standard ComfyUI folders and LoRA Manager's
+    extra folder paths, providing a unified interface for checkpoint loading.
+    """
+
+    NAME = "Checkpoint Loader (LoraManager)"
+    CATEGORY = "Lora Manager/loaders"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        # Get list of checkpoint names from scanner (includes extra folder paths)
+        checkpoint_names = s._get_checkpoint_names()
+        return {
+            "required": {
+                "ckpt_name": (
+                    checkpoint_names,
+                    {"tooltip": "The name of the checkpoint (model) to load."},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL", "CLIP", "VAE")
+    RETURN_NAMES = ("MODEL", "CLIP", "VAE")
+    OUTPUT_TOOLTIPS = (
+        "The model used for denoising latents.",
+        "The CLIP model used for encoding text prompts.",
+        "The VAE model used for encoding and decoding images to and from latent space.",
+    )
+    FUNCTION = "load_checkpoint"
+
+    @classmethod
+    def _get_checkpoint_names(cls) -> List[str]:
+        """Get list of checkpoint names from scanner cache in ComfyUI format (relative path with extension)"""
+        try:
+            from ..services.service_registry import ServiceRegistry
+            import asyncio
+
+            async def _get_names():
+                scanner = await ServiceRegistry.get_checkpoint_scanner()
+                cache = await scanner.get_cached_data()
+
+                # Get all model roots for calculating relative paths
+                model_roots = scanner.get_model_roots()
+
+                # Filter only checkpoint type (not diffusion_model) and format names
+                names = []
+                for item in cache.raw_data:
+                    if item.get("sub_type") == "checkpoint":
+                        file_path = item.get("file_path", "")
+                        if file_path:
+                            # Format using relative path with OS-native separator
+                            formatted_name = _format_model_name_for_comfyui(
+                                file_path, model_roots
+                            )
+                            if formatted_name:
+                                names.append(formatted_name)
+
+                return sorted(names)
+
+            try:
+                loop = asyncio.get_running_loop()
+                import concurrent.futures
+
+                def run_in_thread():
+                    new_loop = asyncio.new_event_loop()
+                    asyncio.set_event_loop(new_loop)
+                    try:
+                        return new_loop.run_until_complete(_get_names())
+                    finally:
+                        new_loop.close()
+
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    future = executor.submit(run_in_thread)
+                    return future.result()
+            except RuntimeError:
+                return asyncio.run(_get_names())
+        except Exception as e:
+            logger.error(f"Error getting checkpoint names: {e}")
+            return []
+
+    def load_checkpoint(self, ckpt_name: str) -> Tuple:
+        """Load a checkpoint by name, supporting extra folder paths
+
+        Args:
+            ckpt_name: The name of the checkpoint to load (relative path with extension)
+
+        Returns:
+            Tuple of (MODEL, CLIP, VAE)
+        """
+        # Get absolute path from cache using ComfyUI-style name
+        ckpt_path, metadata = get_checkpoint_info_absolute(ckpt_name)
+
+        if metadata is None:
+            raise FileNotFoundError(
+                f"Checkpoint '{ckpt_name}' not found in LoRA Manager cache. "
+                "Make sure the checkpoint is indexed and try again."
+            )
+
+        # Load regular checkpoint using ComfyUI's API
+        logger.info(f"Loading checkpoint from: {ckpt_path}")
+        out = comfy.sd.load_checkpoint_guess_config(
+            ckpt_path,
+            output_vae=True,
+            output_clip=True,
+            embedding_directory=folder_paths.get_folder_paths("embeddings"),
+        )
+        return out[:3]

py/nodes/gguf_import_helper.py

@@ -0,0 +1,161 @@
+"""
+Helper module to safely import ComfyUI-GGUF modules.
+
+This module provides a robust way to import ComfyUI-GGUF functionality
+regardless of how ComfyUI loaded it.
+"""
+
+import sys
+import os
+import importlib.util
+import logging
+from typing import Optional, Tuple, Any
+
+logger = logging.getLogger(__name__)
+
+
+def _get_gguf_path() -> str:
+    """Get the path to ComfyUI-GGUF based on this file's location.
+
+    Since ComfyUI-Lora-Manager and ComfyUI-GGUF are both in custom_nodes/,
+    we can derive the GGUF path from our own location.
+    """
+    # This file is at: custom_nodes/ComfyUI-Lora-Manager/py/nodes/gguf_import_helper.py
+    # ComfyUI-GGUF is at: custom_nodes/ComfyUI-GGUF
+    current_file = os.path.abspath(__file__)
+    # Go up 4 levels: nodes -> py -> ComfyUI-Lora-Manager -> custom_nodes
+    custom_nodes_dir = os.path.dirname(
+        os.path.dirname(os.path.dirname(os.path.dirname(current_file)))
+    )
+    return os.path.join(custom_nodes_dir, "ComfyUI-GGUF")
+
+
+def _find_gguf_module() -> Optional[Any]:
+    """Find ComfyUI-GGUF module in sys.modules.
+
+    ComfyUI registers modules using the full path with dots replaced by _x_.
+    """
+    gguf_path = _get_gguf_path()
+    sys_module_name = gguf_path.replace(".", "_x_")
+
+    logger.debug(f"[GGUF Import] Looking for module '{sys_module_name}' in sys.modules")
+    if sys_module_name in sys.modules:
+        logger.info(f"[GGUF Import] Found module: '{sys_module_name}'")
+        return sys.modules[sys_module_name]
+
+    logger.debug(f"[GGUF Import] Module not found: '{sys_module_name}'")
+    return None
+
+
+def _load_gguf_modules_directly() -> Optional[Any]:
+    """Load ComfyUI-GGUF modules directly from file paths."""
+    gguf_path = _get_gguf_path()
+
+    logger.info(f"[GGUF Import] Direct Load: Attempting to load from '{gguf_path}'")
+
+    if not os.path.exists(gguf_path):
+        logger.warning(f"[GGUF Import] Path does not exist: {gguf_path}")
+        return None
+
+    try:
+        namespace = "ComfyUI_GGUF_Dynamic"
+        init_path = os.path.join(gguf_path, "__init__.py")
+
+        if not os.path.exists(init_path):
+            logger.warning(f"[GGUF Import] __init__.py not found at '{init_path}'")
+            return None
+
+        logger.debug(f"[GGUF Import] Loading from '{init_path}'")
+        spec = importlib.util.spec_from_file_location(namespace, init_path)
+        if not spec or not spec.loader:
+            logger.error(f"[GGUF Import] Failed to create spec for '{init_path}'")
+            return None
+
+        package = importlib.util.module_from_spec(spec)
+        package.__path__ = [gguf_path]
+        sys.modules[namespace] = package
+        spec.loader.exec_module(package)
+        logger.debug(f"[GGUF Import] Loaded main package '{namespace}'")
+
+        # Load submodules
+        loaded = []
+        for submod_name in ["loader", "ops", "nodes"]:
+            submod_path = os.path.join(gguf_path, f"{submod_name}.py")
+            if os.path.exists(submod_path):
+                submod_spec = importlib.util.spec_from_file_location(
+                    f"{namespace}.{submod_name}", submod_path
+                )
+                if submod_spec and submod_spec.loader:
+                    submod = importlib.util.module_from_spec(submod_spec)
+                    submod.__package__ = namespace
+                    sys.modules[f"{namespace}.{submod_name}"] = submod
+                    submod_spec.loader.exec_module(submod)
+                    setattr(package, submod_name, submod)
+                    loaded.append(submod_name)
+                    logger.debug(f"[GGUF Import] Loaded submodule '{submod_name}'")
+
+        logger.info(f"[GGUF Import] Direct Load success: {loaded}")
+        return package
+
+    except Exception as e:
+        logger.error(f"[GGUF Import] Direct Load failed: {e}", exc_info=True)
+        return None
+
+
+def get_gguf_modules() -> Tuple[Any, Any, Any]:
+    """Get ComfyUI-GGUF modules (loader, ops, nodes).
+
+    Returns:
+        Tuple of (loader_module, ops_module, nodes_module)
+
+    Raises:
+        RuntimeError: If ComfyUI-GGUF cannot be found or loaded.
+    """
+    logger.debug("[GGUF Import] Starting module search...")
+
+    # Try to find already loaded module first
+    gguf_module = _find_gguf_module()
+
+    if gguf_module is None:
+        logger.info("[GGUF Import] Not found in sys.modules, trying direct load...")
+        gguf_module = _load_gguf_modules_directly()
+
+    if gguf_module is None:
+        raise RuntimeError(
+            "ComfyUI-GGUF is not installed. "
+            "Please install from https://github.com/city96/ComfyUI-GGUF"
+        )
+
+    # Extract submodules
+    loader = getattr(gguf_module, "loader", None)
+    ops = getattr(gguf_module, "ops", None)
+    nodes = getattr(gguf_module, "nodes", None)
+
+    if loader is None or ops is None or nodes is None:
+        missing = [
+            name
+            for name, mod in [("loader", loader), ("ops", ops), ("nodes", nodes)]
+            if mod is None
+        ]
+        raise RuntimeError(f"ComfyUI-GGUF missing submodules: {missing}")
+
+    logger.debug("[GGUF Import] All modules loaded successfully")
+    return loader, ops, nodes
+
+
+def get_gguf_sd_loader():
+    """Get the gguf_sd_loader function from ComfyUI-GGUF."""
+    loader, _, _ = get_gguf_modules()
+    return getattr(loader, "gguf_sd_loader")
+
+
+def get_ggml_ops():
+    """Get the GGMLOps class from ComfyUI-GGUF."""
+    _, ops, _ = get_gguf_modules()
+    return getattr(ops, "GGMLOps")
+
+
+def get_gguf_model_patcher():
+    """Get the GGUFModelPatcher class from ComfyUI-GGUF."""
+    _, _, nodes = get_gguf_modules()
+    return getattr(nodes, "GGUFModelPatcher")

py/nodes/lora_cycler.py

@@ -56,6 +56,9 @@ class LoraCyclerLM:
         clip_strength = float(cycler_config.get("clip_strength", 1.0))
         sort_by = "filename"
 
+        # Include "no lora" option
+        include_no_lora = cycler_config.get("include_no_lora", False)
+
         # Dual-index mechanism for batch queue synchronization
         execution_index = cycler_config.get("execution_index")  # Can be None
         # next_index_from_config = cycler_config.get("next_index")  # Not used on backend
@@ -71,7 +74,10 @@ class LoraCyclerLM:
 
         total_count = len(lora_list)
 
-        if total_count == 0:
+        # Calculate effective total count (includes no lora option if enabled)
+        effective_total_count = total_count + 1 if include_no_lora else total_count
+
+        if total_count == 0 and not include_no_lora:
             logger.warning("[LoraCyclerLM] No LoRAs available in pool")
             return {
                 "result": ([],),
@@ -93,42 +99,66 @@ class LoraCyclerLM:
         else:
             actual_index = current_index
 
-        # Clamp index to valid range (1-based)
-        clamped_index = max(1, min(actual_index, total_count))
+        # Clamp index to valid range (1-based, includes no lora if enabled)
+        clamped_index = max(1, min(actual_index, effective_total_count))
 
-        # Get LoRA at current index (convert to 0-based for list access)
-        current_lora = lora_list[clamped_index - 1]
+        # Check if current index is the "no lora" option (last position when include_no_lora is True)
+        is_no_lora = include_no_lora and clamped_index == effective_total_count
 
-        # Build LORA_STACK with single LoRA
-        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 is_no_lora:
+            # "No LoRA" option - return empty stack
             lora_stack = []
+            current_lora_name = "No LoRA"
+            current_lora_filename = "No LoRA"
         else:
-            # Normalize path separators
-            lora_path = lora_path.replace("/", os.sep)
-            lora_stack = [(lora_path, model_strength, clip_strength)]
+            # Get LoRA at current index (convert to 0-based for list access)
+            current_lora = lora_list[clamped_index - 1]
+            current_lora_name = current_lora["file_name"]
+            current_lora_filename = current_lora["file_name"]
+
+            # Build LORA_STACK with single LoRA
+            if current_lora["file_name"] == "None":
+                lora_path = None
+            else:
+                lora_path, _ = get_lora_info(current_lora["file_name"])
+
+            if not lora_path:
+                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
+                lora_path = lora_path.replace("/", os.sep)
+                lora_stack = [(lora_path, model_strength, clip_strength)]
 
         # Calculate next index (wrap to 1 if at end)
         next_index = clamped_index + 1
-        if next_index > total_count:
+        if next_index > effective_total_count:
             next_index = 1
 
         # Get next LoRA for UI display (what will be used next generation)
-        next_lora = lora_list[next_index - 1]
-        next_display_name = next_lora["file_name"]
+        is_next_no_lora = include_no_lora and next_index == effective_total_count
+        if is_next_no_lora:
+            next_display_name = "No LoRA"
+            next_lora_filename = "No LoRA"
+        else:
+            next_lora = lora_list[next_index - 1]
+            next_display_name = next_lora["file_name"]
+            next_lora_filename = next_lora["file_name"]
 
         return {
             "result": (lora_stack,),
             "ui": {
                 "current_index": [clamped_index],
                 "next_index": [next_index],
-                "total_count": [total_count],
-                "current_lora_name": [current_lora["file_name"]],
-                "current_lora_filename": [current_lora["file_name"]],
+                "total_count": [
+                    total_count
+                ],  # Return actual LoRA count, not effective_total_count
+                "current_lora_name": [current_lora_name],
+                "current_lora_filename": [current_lora_filename],
                 "next_lora_name": [next_display_name],
-                "next_lora_filename": [next_lora["file_name"]],
+                "next_lora_filename": [next_lora_filename],
             },
         }

py/nodes/lora_pool.py

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

py/nodes/lora_randomizer.py

@@ -7,10 +7,8 @@ and tracks the last used combination for reuse.
 """
 
 import logging
-import random
 import os
 from ..utils.utils import get_lora_info
-from .utils import extract_lora_name
 
 logger = logging.getLogger(__name__)
 

py/nodes/unet_loader.py

@@ -0,0 +1,205 @@
+import logging
+import os
+from typing import List, Tuple
+import comfy.sd # type: ignore
+from ..utils.utils import get_checkpoint_info_absolute, _format_model_name_for_comfyui
+
+logger = logging.getLogger(__name__)
+
+
+class UNETLoaderLM:
+    """UNET Loader with support for extra folder paths
+
+    Loads diffusion models/UNets from both standard ComfyUI folders and LoRA Manager's
+    extra folder paths, providing a unified interface for UNET loading.
+    Supports both regular diffusion models and GGUF format models.
+    """
+
+    NAME = "Unet Loader (LoraManager)"
+    CATEGORY = "Lora Manager/loaders"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        # Get list of unet names from scanner (includes extra folder paths)
+        unet_names = s._get_unet_names()
+        return {
+            "required": {
+                "unet_name": (
+                    unet_names,
+                    {"tooltip": "The name of the diffusion model to load."},
+                ),
+                "weight_dtype": (
+                    ["default", "fp8_e4m3fn", "fp8_e4m3fn_fast", "fp8_e5m2"],
+                    {"tooltip": "The dtype to use for the model weights."},
+                ),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("MODEL",)
+    OUTPUT_TOOLTIPS = ("The model used for denoising latents.",)
+    FUNCTION = "load_unet"
+
+    @classmethod
+    def _get_unet_names(cls) -> List[str]:
+        """Get list of diffusion model names from scanner cache in ComfyUI format (relative path with extension)"""
+        try:
+            from ..services.service_registry import ServiceRegistry
+            import asyncio
+
+            async def _get_names():
+                scanner = await ServiceRegistry.get_checkpoint_scanner()
+                cache = await scanner.get_cached_data()
+
+                # Get all model roots for calculating relative paths
+                model_roots = scanner.get_model_roots()
+
+                # Filter only diffusion_model type and format names
+                names = []
+                for item in cache.raw_data:
+                    if item.get("sub_type") == "diffusion_model":
+                        file_path = item.get("file_path", "")
+                        if file_path:
+                            # Format using relative path with OS-native separator
+                            formatted_name = _format_model_name_for_comfyui(
+                                file_path, model_roots
+                            )
+                            if formatted_name:
+                                names.append(formatted_name)
+
+                return sorted(names)
+
+            try:
+                loop = asyncio.get_running_loop()
+                import concurrent.futures
+
+                def run_in_thread():
+                    new_loop = asyncio.new_event_loop()
+                    asyncio.set_event_loop(new_loop)
+                    try:
+                        return new_loop.run_until_complete(_get_names())
+                    finally:
+                        new_loop.close()
+
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    future = executor.submit(run_in_thread)
+                    return future.result()
+            except RuntimeError:
+                return asyncio.run(_get_names())
+        except Exception as e:
+            logger.error(f"Error getting unet names: {e}")
+            return []
+
+    def load_unet(self, unet_name: str, weight_dtype: str) -> Tuple:
+        """Load a diffusion model by name, supporting extra folder paths
+
+        Args:
+            unet_name: The name of the diffusion model to load (relative path with extension)
+            weight_dtype: The dtype to use for model weights
+
+        Returns:
+            Tuple of (MODEL,)
+        """
+        import torch
+
+        # Get absolute path from cache using ComfyUI-style name
+        unet_path, metadata = get_checkpoint_info_absolute(unet_name)
+
+        if metadata is None:
+            raise FileNotFoundError(
+                f"Diffusion model '{unet_name}' not found in LoRA Manager cache. "
+                "Make sure the model is indexed and try again."
+            )
+
+        # Check if it's a GGUF model
+        if unet_path.endswith(".gguf"):
+            return self._load_gguf_unet(unet_path, unet_name, weight_dtype)
+
+        # Load regular diffusion model using ComfyUI's API
+        logger.info(f"Loading diffusion model from: {unet_path}")
+
+        # Build model options based on weight_dtype
+        model_options = {}
+        if weight_dtype == "fp8_e4m3fn":
+            model_options["dtype"] = torch.float8_e4m3fn
+        elif weight_dtype == "fp8_e4m3fn_fast":
+            model_options["dtype"] = torch.float8_e4m3fn
+            model_options["fp8_optimizations"] = True
+        elif weight_dtype == "fp8_e5m2":
+            model_options["dtype"] = torch.float8_e5m2
+
+        model = comfy.sd.load_diffusion_model(unet_path, model_options=model_options)
+        return (model,)
+
+    def _load_gguf_unet(
+        self, unet_path: str, unet_name: str, weight_dtype: str
+    ) -> Tuple:
+        """Load a GGUF format diffusion model
+
+        Args:
+            unet_path: Absolute path to the GGUF file
+            unet_name: Name of the model for error messages
+            weight_dtype: The dtype to use for model weights
+
+        Returns:
+            Tuple of (MODEL,)
+        """
+        import torch
+        from .gguf_import_helper import get_gguf_modules
+
+        # Get ComfyUI-GGUF modules using helper (handles various import scenarios)
+        try:
+            loader_module, ops_module, nodes_module = get_gguf_modules()
+            gguf_sd_loader = getattr(loader_module, "gguf_sd_loader")
+            GGMLOps = getattr(ops_module, "GGMLOps")
+            GGUFModelPatcher = getattr(nodes_module, "GGUFModelPatcher")
+        except RuntimeError as e:
+            raise RuntimeError(f"Cannot load GGUF model '{unet_name}'. {str(e)}")
+
+        logger.info(f"Loading GGUF diffusion model from: {unet_path}")
+
+        try:
+            # Load GGUF state dict
+            sd, extra = gguf_sd_loader(unet_path)
+
+            # Prepare kwargs for metadata if supported
+            kwargs = {}
+            import inspect
+
+            valid_params = inspect.signature(
+                comfy.sd.load_diffusion_model_state_dict
+            ).parameters
+            if "metadata" in valid_params:
+                kwargs["metadata"] = extra.get("metadata", {})
+
+            # Setup custom operations with GGUF support
+            ops = GGMLOps()
+
+            # Handle weight_dtype for GGUF models
+            if weight_dtype in ("default", None):
+                ops.Linear.dequant_dtype = None
+            elif weight_dtype in ["target"]:
+                ops.Linear.dequant_dtype = weight_dtype
+            else:
+                ops.Linear.dequant_dtype = getattr(torch, weight_dtype, None)
+
+            # Load the model
+            model = comfy.sd.load_diffusion_model_state_dict(
+                sd, model_options={"custom_operations": ops}, **kwargs
+            )
+
+            if model is None:
+                raise RuntimeError(
+                    f"Could not detect model type for GGUF diffusion model: {unet_path}"
+                )
+
+            # Wrap with GGUFModelPatcher
+            model = GGUFModelPatcher.clone(model)
+
+            return (model,)
+
+        except Exception as e:
+            logger.error(f"Error loading GGUF diffusion model '{unet_name}': {e}")
+            raise RuntimeError(
+                f"Failed to load GGUF diffusion model '{unet_name}': {str(e)}"
+            )

py/routes/handlers/model_handlers.py

@@ -309,6 +309,13 @@ class ModelListingHandler:
         else:
             allow_selling_generated_content = None  # None means no filter applied
 
+        # Name pattern filters for LoRA Pool
+        name_pattern_include = request.query.getall("name_pattern_include", [])
+        name_pattern_exclude = request.query.getall("name_pattern_exclude", [])
+        name_pattern_use_regex = (
+            request.query.get("name_pattern_use_regex", "false").lower() == "true"
+        )
+
         return {
             "page": page,
             "page_size": page_size,
@@ -328,6 +335,9 @@ class ModelListingHandler:
             "credit_required": credit_required,
             "allow_selling_generated_content": allow_selling_generated_content,
             "model_types": model_types,
+            "name_pattern_include": name_pattern_include,
+            "name_pattern_exclude": name_pattern_exclude,
+            "name_pattern_use_regex": name_pattern_use_regex,
             **self._parse_specific_params(request),
         }
 

py/services/base_model_service.py

@@ -208,7 +208,11 @@ class BaseModelService(ABC):
 
         reverse = sort_params.order == "desc"
         annotated.sort(
-            key=lambda x: (x.get("usage_count", 0), x.get("model_name", "").lower()),
+            key=lambda x: (
+                x.get("usage_count", 0),
+                x.get("model_name", "").lower(),
+                x.get("file_path", "").lower()
+            ),
             reverse=reverse,
         )
         return annotated

py/services/cache_entry_validator.py

@@ -58,6 +58,7 @@ class CacheEntryValidator:
         'preview_nsfw_level': (0, False),
         'notes': ('', False),
         'usage_tips': ('', False),
+        'hash_status': ('completed', False),
     }
 
     @classmethod
@@ -90,13 +91,31 @@ class CacheEntryValidator:
 
         errors: List[str] = []
         repaired = False
+        
+        # If auto_repair is on, we work on a copy. If not, we still need a safe way to check fields.
         working_entry = dict(entry) if auto_repair else entry
 
+        # Determine effective hash_status for validation logic
+        hash_status = entry.get('hash_status')
+        if hash_status is None:
+            if auto_repair:
+                working_entry['hash_status'] = 'completed'
+                repaired = True
+            hash_status = 'completed'
+
         for field_name, (default_value, is_required) in cls.CORE_FIELDS.items():
-            value = working_entry.get(field_name)
+            # Get current value from the original entry to avoid side effects during validation
+            value = entry.get(field_name)
 
             # Check if field is missing or None
             if value is None:
+                # Special case: sha256 can be None/empty if hash_status is pending
+                if field_name == 'sha256' and hash_status == 'pending':
+                    if auto_repair:
+                        working_entry[field_name] = ''
+                        repaired = True
+                    continue
+
                 if is_required:
                     errors.append(f"Required field '{field_name}' is missing or None")
                 if auto_repair:
@@ -107,6 +126,10 @@ class CacheEntryValidator:
             # Validate field type and value
             field_error = cls._validate_field(field_name, value, default_value)
             if field_error:
+                # Special case: allow empty string for sha256 if pending
+                if field_name == 'sha256' and hash_status == 'pending' and value == '':
+                    continue
+
                 errors.append(field_error)
                 if auto_repair:
                     working_entry[field_name] = cls._get_default_copy(default_value)
@@ -127,7 +150,7 @@ class CacheEntryValidator:
         # Special validation: sha256 must not be empty for required field
         # BUT allow empty sha256 when hash_status is pending (lazy hash calculation)
         sha256 = working_entry.get('sha256', '')
-        hash_status = working_entry.get('hash_status', 'completed')
+        # Use the effective hash_status we determined earlier
         if not sha256 or (isinstance(sha256, str) and not sha256.strip()):
             # Allow empty sha256 for lazy hash calculation (checkpoints)
             if hash_status != 'pending':
@@ -144,8 +167,13 @@ class CacheEntryValidator:
         if isinstance(sha256, str):
             normalized_sha = sha256.lower().strip()
             if normalized_sha != sha256:
-                working_entry['sha256'] = normalized_sha
-                repaired = True
+                if auto_repair:
+                    working_entry['sha256'] = normalized_sha
+                    repaired = True
+                else:
+                    # If not auto-repairing, we don't consider case difference as a "critical error" 
+                    # that invalidates the entry, but we also don't mark it repaired.
+                    pass
 
         # Determine if entry is valid
         # Entry is valid if no critical required field errors remain after repair

py/services/checkpoint_scanner.py

@@ -13,22 +13,35 @@ from .model_hash_index import ModelHashIndex
 
 logger = logging.getLogger(__name__)
 
+
 class CheckpointScanner(ModelScanner):
     """Service for scanning and managing checkpoint files"""
-    
+
     def __init__(self):
         # Define supported file extensions
-        file_extensions = {'.ckpt', '.pt', '.pt2', '.bin', '.pth', '.safetensors', '.pkl', '.sft', '.gguf'}
+        file_extensions = {
+            ".ckpt",
+            ".pt",
+            ".pt2",
+            ".bin",
+            ".pth",
+            ".safetensors",
+            ".pkl",
+            ".sft",
+            ".gguf",
+        }
         super().__init__(
             model_type="checkpoint",
             model_class=CheckpointMetadata,
             file_extensions=file_extensions,
-            hash_index=ModelHashIndex()
+            hash_index=ModelHashIndex(),
         )
 
-    async def _create_default_metadata(self, file_path: str) -> Optional[CheckpointMetadata]:
+    async def _create_default_metadata(
+        self, file_path: str
+    ) -> Optional[CheckpointMetadata]:
         """Create default metadata for checkpoint without calculating hash (lazy hash).
-        
+
         Checkpoints are typically large (10GB+), so we skip hash calculation during initial
         scanning to improve startup performance. Hash will be calculated on-demand when
         fetching metadata from Civitai.
@@ -38,13 +51,13 @@ class CheckpointScanner(ModelScanner):
             if not os.path.exists(real_path):
                 logger.error(f"File not found: {file_path}")
                 return None
-            
+
             base_name = os.path.splitext(os.path.basename(file_path))[0]
             dir_path = os.path.dirname(file_path)
-            
+
             # Find preview image
             preview_url = find_preview_file(base_name, dir_path)
-            
+
             # Create metadata WITHOUT calculating hash
             metadata = CheckpointMetadata(
                 file_name=base_name,
@@ -59,70 +72,76 @@ class CheckpointScanner(ModelScanner):
                 modelDescription="",
                 sub_type="checkpoint",
                 from_civitai=False,  # Mark as local model since no hash yet
-                hash_status="pending"  # Mark hash as pending
+                hash_status="pending",  # Mark hash as pending
             )
-            
+
             # Save the created metadata
             logger.info(f"Creating checkpoint metadata (hash pending) for {file_path}")
             await MetadataManager.save_metadata(file_path, metadata)
-            
+
             return metadata
-            
+
         except Exception as e:
-            logger.error(f"Error creating default checkpoint metadata for {file_path}: {e}")
+            logger.error(
+                f"Error creating default checkpoint metadata for {file_path}: {e}"
+            )
             return None
 
     async def calculate_hash_for_model(self, file_path: str) -> Optional[str]:
         """Calculate hash for a checkpoint on-demand.
-        
+
         Args:
             file_path: Path to the model file
-            
+
         Returns:
             SHA256 hash string, or None if calculation failed
         """
         from ..utils.file_utils import calculate_sha256
-        
+
         try:
             real_path = os.path.realpath(file_path)
             if not os.path.exists(real_path):
                 logger.error(f"File not found for hash calculation: {file_path}")
                 return None
-            
+
             # Load current metadata
-            metadata, _ = await MetadataManager.load_metadata(file_path, self.model_class)
+            metadata, _ = await MetadataManager.load_metadata(
+                file_path, self.model_class
+            )
             if metadata is None:
                 logger.error(f"No metadata found for {file_path}")
                 return None
-            
+
             # Check if hash is already calculated
             if metadata.hash_status == "completed" and metadata.sha256:
                 return metadata.sha256
-            
+
             # Update status to calculating
             metadata.hash_status = "calculating"
             await MetadataManager.save_metadata(file_path, metadata)
-            
+
             # Calculate hash
             logger.info(f"Calculating hash for checkpoint: {file_path}")
             sha256 = await calculate_sha256(real_path)
-            
+
             # Update metadata with hash
             metadata.sha256 = sha256
             metadata.hash_status = "completed"
             await MetadataManager.save_metadata(file_path, metadata)
-            
+
             # Update hash index
             self._hash_index.add_entry(sha256.lower(), file_path)
-            
+
             logger.info(f"Hash calculated for checkpoint: {file_path}")
             return sha256
-            
+
         except Exception as e:
             logger.error(f"Error calculating hash for {file_path}: {e}")
             # Update status to failed
             try:
-                metadata, _ = await MetadataManager.load_metadata(file_path, self.model_class)
+                metadata, _ = await MetadataManager.load_metadata(
+                    file_path, self.model_class
+                )
                 if metadata:
                     metadata.hash_status = "failed"
                     await MetadataManager.save_metadata(file_path, metadata)
@@ -130,43 +149,46 @@ class CheckpointScanner(ModelScanner):
                 pass
             return None
 
-    async def calculate_all_pending_hashes(self, progress_callback=None) -> Dict[str, int]:
+    async def calculate_all_pending_hashes(
+        self, progress_callback=None
+    ) -> Dict[str, int]:
         """Calculate hashes for all checkpoints with pending hash status.
-        
+
         If cache is not initialized, scans filesystem directly for metadata files
         with hash_status != 'completed'.
-        
+
         Args:
             progress_callback: Optional callback(progress, total, current_file)
-            
+
         Returns:
             Dict with 'completed', 'failed', 'total' counts
         """
         # Try to get from cache first
         cache = await self.get_cached_data()
-        
+
         if cache and cache.raw_data:
             # Use cache if available
             pending_models = [
-                item for item in cache.raw_data 
-                if item.get('hash_status') != 'completed' or not item.get('sha256')
+                item
+                for item in cache.raw_data
+                if item.get("hash_status") != "completed" or not item.get("sha256")
             ]
         else:
             # Cache not initialized, scan filesystem directly
             pending_models = await self._find_pending_models_from_filesystem()
-        
+
         if not pending_models:
-            return {'completed': 0, 'failed': 0, 'total': 0}
-        
+            return {"completed": 0, "failed": 0, "total": 0}
+
         total = len(pending_models)
         completed = 0
         failed = 0
-        
+
         for i, model_data in enumerate(pending_models):
-            file_path = model_data.get('file_path')
+            file_path = model_data.get("file_path")
             if not file_path:
                 continue
-            
+
             try:
                 sha256 = await self.calculate_hash_for_model(file_path)
                 if sha256:
@@ -176,77 +198,102 @@ class CheckpointScanner(ModelScanner):
             except Exception as e:
                 logger.error(f"Error calculating hash for {file_path}: {e}")
                 failed += 1
-            
+
             if progress_callback:
                 try:
                     await progress_callback(i + 1, total, file_path)
                 except Exception:
                     pass
-        
-        return {
-            'completed': completed,
-            'failed': failed,
-            'total': total
-        }
-    
+
+        return {"completed": completed, "failed": failed, "total": total}
+
     async def _find_pending_models_from_filesystem(self) -> List[Dict[str, Any]]:
         """Scan filesystem for checkpoint metadata files with pending hash status."""
         pending_models = []
-        
+
         for root_path in self.get_model_roots():
             if not os.path.exists(root_path):
                 continue
-            
+
             for dirpath, _dirnames, filenames in os.walk(root_path):
                 for filename in filenames:
-                    if not filename.endswith('.metadata.json'):
+                    if not filename.endswith(".metadata.json"):
                         continue
-                    
+
                     metadata_path = os.path.join(dirpath, filename)
                     try:
-                        with open(metadata_path, 'r', encoding='utf-8') as f:
+                        with open(metadata_path, "r", encoding="utf-8") as f:
                             data = json.load(f)
-                        
+
                         # Check if hash is pending
-                        hash_status = data.get('hash_status', 'completed')
-                        sha256 = data.get('sha256', '')
-                        
-                        if hash_status != 'completed' or not sha256:
+                        hash_status = data.get("hash_status", "completed")
+                        sha256 = data.get("sha256", "")
+
+                        if hash_status != "completed" or not sha256:
                             # Find corresponding model file
-                            model_name = filename.replace('.metadata.json', '')
+                            model_name = filename.replace(".metadata.json", "")
                             model_path = None
-                            
+
                             # Look for model file with matching name
                             for ext in self.file_extensions:
                                 potential_path = os.path.join(dirpath, model_name + ext)
                                 if os.path.exists(potential_path):
                                     model_path = potential_path
                                     break
-                            
+
                             if model_path:
-                                pending_models.append({
-                                    'file_path': model_path.replace(os.sep, '/'),
-                                    'hash_status': hash_status,
-                                    'sha256': sha256,
-                                    **{k: v for k, v in data.items() if k not in ['file_path', 'hash_status', 'sha256']}
-                                })
+                                pending_models.append(
+                                    {
+                                        "file_path": model_path.replace(os.sep, "/"),
+                                        "hash_status": hash_status,
+                                        "sha256": sha256,
+                                        **{
+                                            k: v
+                                            for k, v in data.items()
+                                            if k
+                                            not in [
+                                                "file_path",
+                                                "hash_status",
+                                                "sha256",
+                                            ]
+                                        },
+                                    }
+                                )
                     except (json.JSONDecodeError, Exception) as e:
-                        logger.debug(f"Error reading metadata file {metadata_path}: {e}")
+                        logger.debug(
+                            f"Error reading metadata file {metadata_path}: {e}"
+                        )
                         continue
-        
+
         return pending_models
 
     def _resolve_sub_type(self, root_path: Optional[str]) -> Optional[str]:
-        """Resolve the sub-type based on the root path."""
+        """Resolve the sub-type based on the root path.
+
+        Checks both standard ComfyUI paths and LoRA Manager's extra folder paths.
+        """
         if not root_path:
             return None
 
+        # Check standard ComfyUI checkpoint paths
         if config.checkpoints_roots and root_path in config.checkpoints_roots:
             return "checkpoint"
 
+        # Check extra checkpoint paths
+        if (
+            config.extra_checkpoints_roots
+            and root_path in config.extra_checkpoints_roots
+        ):
+            return "checkpoint"
+
+        # Check standard ComfyUI unet paths
         if config.unet_roots and root_path in config.unet_roots:
             return "diffusion_model"
 
+        # Check extra unet paths
+        if config.extra_unet_roots and root_path in config.extra_unet_roots:
+            return "diffusion_model"
+
         return None
 
     def adjust_metadata(self, metadata, file_path, root_path):

py/services/lora_service.py

@@ -27,7 +27,7 @@ class LoraService(BaseModelService):
         # Resolve sub_type using priority: sub_type > model_type > civitai.model.type > default
         # Normalize to lowercase for consistent API responses
         sub_type = resolve_sub_type(lora_data).lower()
-        
+
         return {
             "model_name": lora_data["model_name"],
             "file_name": lora_data["file_name"],
@@ -48,7 +48,9 @@ class LoraService(BaseModelService):
             "notes": lora_data.get("notes", ""),
             "favorite": lora_data.get("favorite", False),
             "update_available": bool(lora_data.get("update_available", False)),
-            "skip_metadata_refresh": bool(lora_data.get("skip_metadata_refresh", False)),
+            "skip_metadata_refresh": bool(
+                lora_data.get("skip_metadata_refresh", False)
+            ),
             "sub_type": sub_type,
             "civitai": self.filter_civitai_data(
                 lora_data.get("civitai", {}), minimal=True
@@ -62,6 +64,68 @@ class LoraService(BaseModelService):
         if first_letter:
             data = self._filter_by_first_letter(data, first_letter)
 
+        # Handle name pattern filters
+        name_pattern_include = kwargs.get("name_pattern_include", [])
+        name_pattern_exclude = kwargs.get("name_pattern_exclude", [])
+        name_pattern_use_regex = kwargs.get("name_pattern_use_regex", False)
+
+        if name_pattern_include or name_pattern_exclude:
+            import re
+
+            def matches_pattern(name, pattern, use_regex):
+                """Check if name matches pattern (regex or substring)"""
+                if not name:
+                    return False
+                if use_regex:
+                    try:
+                        return bool(re.search(pattern, name, re.IGNORECASE))
+                    except re.error:
+                        # Invalid regex, fall back to substring match
+                        return pattern.lower() in name.lower()
+                else:
+                    return pattern.lower() in name.lower()
+
+            def matches_any_pattern(name, patterns, use_regex):
+                """Check if name matches any of the patterns"""
+                if not patterns:
+                    return True
+                return any(matches_pattern(name, p, use_regex) for p in patterns)
+
+            filtered = []
+            for lora in data:
+                model_name = lora.get("model_name", "")
+                file_name = lora.get("file_name", "")
+                names_to_check = [n for n in [model_name, file_name] if n]
+
+                # Check exclude patterns first
+                excluded = False
+                if name_pattern_exclude:
+                    for name in names_to_check:
+                        if matches_any_pattern(
+                            name, name_pattern_exclude, name_pattern_use_regex
+                        ):
+                            excluded = True
+                            break
+
+                if excluded:
+                    continue
+
+                # Check include patterns
+                if name_pattern_include:
+                    included = False
+                    for name in names_to_check:
+                        if matches_any_pattern(
+                            name, name_pattern_include, name_pattern_use_regex
+                        ):
+                            included = True
+                            break
+                    if not included:
+                        continue
+
+                filtered.append(lora)
+
+            data = filtered
+
         return data
 
     def _filter_by_first_letter(self, data: List[Dict], letter: str) -> List[Dict]:
@@ -368,9 +432,7 @@ class LoraService(BaseModelService):
                         rng.uniform(clip_strength_min, clip_strength_max), 2
                     )
             else:
-                clip_str = round(
-                    rng.uniform(clip_strength_min, clip_strength_max), 2
-                )
+                clip_str = round(rng.uniform(clip_strength_min, clip_strength_max), 2)
 
             result_loras.append(
                 {
@@ -485,12 +547,69 @@ class LoraService(BaseModelService):
                 if bool(lora.get("license_flags", 127) & (1 << 1))
             ]
 
+        # Apply name pattern filters
+        name_patterns = filter_section.get("namePatterns", {})
+        include_patterns = name_patterns.get("include", [])
+        exclude_patterns = name_patterns.get("exclude", [])
+        use_regex = name_patterns.get("useRegex", False)
+
+        if include_patterns or exclude_patterns:
+            import re
+
+            def matches_pattern(name, pattern, use_regex):
+                """Check if name matches pattern (regex or substring)"""
+                if not name:
+                    return False
+                if use_regex:
+                    try:
+                        return bool(re.search(pattern, name, re.IGNORECASE))
+                    except re.error:
+                        # Invalid regex, fall back to substring match
+                        return pattern.lower() in name.lower()
+                else:
+                    return pattern.lower() in name.lower()
+
+            def matches_any_pattern(name, patterns, use_regex):
+                """Check if name matches any of the patterns"""
+                if not patterns:
+                    return True
+                return any(matches_pattern(name, p, use_regex) for p in patterns)
+
+            filtered = []
+            for lora in available_loras:
+                model_name = lora.get("model_name", "")
+                file_name = lora.get("file_name", "")
+                names_to_check = [n for n in [model_name, file_name] if n]
+
+                # Check exclude patterns first
+                excluded = False
+                if exclude_patterns:
+                    for name in names_to_check:
+                        if matches_any_pattern(name, exclude_patterns, use_regex):
+                            excluded = True
+                            break
+
+                if excluded:
+                    continue
+
+                # Check include patterns
+                if include_patterns:
+                    included = False
+                    for name in names_to_check:
+                        if matches_any_pattern(name, include_patterns, use_regex):
+                            included = True
+                            break
+                    if not included:
+                        continue
+
+                filtered.append(lora)
+
+            available_loras = filtered
+
         return available_loras
 
     async def get_cycler_list(
-        self,
-        pool_config: Optional[Dict] = None,
-        sort_by: str = "filename"
+        self, pool_config: Optional[Dict] = None, sort_by: str = "filename"
     ) -> List[Dict]:
         """
         Get filtered and sorted LoRA list for cycling.
@@ -516,12 +635,18 @@ class LoraService(BaseModelService):
         if sort_by == "model_name":
             available_loras = sorted(
                 available_loras,
-                key=lambda x: (x.get("model_name") or x.get("file_name", "")).lower()
+                key=lambda x: (
+                    (x.get("model_name") or x.get("file_name", "")).lower(),
+                    x.get("file_path", "").lower(),
+                ),
             )
         else:  # Default to filename
             available_loras = sorted(
                 available_loras,
-                key=lambda x: x.get("file_name", "").lower()
+                key=lambda x: (
+                    x.get("file_name", "").lower(),
+                    x.get("file_path", "").lower(),
+                ),
             )
 
         # Return minimal data needed for cycling

py/services/model_cache.py

@@ -221,33 +221,45 @@ class ModelCache:
         start_time = time.perf_counter()
         reverse = (order == 'desc')
         if sort_key == 'name':
-            # Natural sort by configured display name, case-insensitive
+            # Natural sort by configured display name, case-insensitive, with file_path as tie-breaker
             result = natsorted(
                 data,
-                key=lambda x: self._get_display_name(x).lower(),
+                key=lambda x: (
+                    self._get_display_name(x).lower(),
+                    x.get('file_path', '').lower()
+                ),
                 reverse=reverse
             )
         elif sort_key == 'date':
-            # Sort by modified timestamp (use .get() with default to handle missing fields)
+            # Sort by modified timestamp, fallback to name and path for stability
             result = sorted(
                 data,
-                key=lambda x: x.get('modified', 0.0),
+                key=lambda x: (
+                    x.get('modified', 0.0),
+                    self._get_display_name(x).lower(),
+                    x.get('file_path', '').lower()
+                ),
                 reverse=reverse
             )
         elif sort_key == 'size':
-            # Sort by file size (use .get() with default to handle missing fields)
+            # Sort by file size, fallback to name and path for stability
             result = sorted(
                 data,
-                key=lambda x: x.get('size', 0),
+                key=lambda x: (
+                    x.get('size', 0),
+                    self._get_display_name(x).lower(),
+                    x.get('file_path', '').lower()
+                ),
                 reverse=reverse
             )
         elif sort_key == 'usage':
-            # Sort by usage count, fallback to 0, then name for stability
+            # Sort by usage count, fallback to 0, then name and path for stability
             return sorted(
                 data,
                 key=lambda x: (
                     x.get('usage_count', 0),
-                    self._get_display_name(x).lower()
+                    self._get_display_name(x).lower(),
+                    x.get('file_path', '').lower()
                 ),
                 reverse=reverse
             )

py/services/model_scanner.py

@@ -14,7 +14,6 @@ from ..utils.metadata_manager import MetadataManager
 from ..utils.civitai_utils import resolve_license_info
 from .model_cache import ModelCache
 from .model_hash_index import ModelHashIndex
-from ..utils.constants import PREVIEW_EXTENSIONS
 from .model_lifecycle_service import delete_model_artifacts
 from .service_registry import ServiceRegistry
 from .websocket_manager import ws_manager
@@ -1442,14 +1441,13 @@ class ModelScanner:
         file_path = self._hash_index.get_path(sha256.lower())
         if not file_path:
             return None
-            
-        base_name = os.path.splitext(file_path)[0]
-        
-        for ext in PREVIEW_EXTENSIONS:
-            preview_path = f"{base_name}{ext}"
-            if os.path.exists(preview_path):
-                return config.get_preview_static_url(preview_path)
-        
+
+        dir_path = os.path.dirname(file_path)
+        base_name = os.path.splitext(os.path.basename(file_path))[0]
+        preview_path = find_preview_file(base_name, dir_path)
+        if preview_path:
+            return config.get_preview_static_url(preview_path)
+
         return None
         
     async def get_top_tags(self, limit: int = 20) -> List[Dict[str, any]]:

py/services/persistent_model_cache.py

@@ -56,6 +56,7 @@ class PersistentModelCache:
         "exclude",
         "db_checked",
         "last_checked_at",
+        "hash_status",
     )
     _MODEL_UPDATE_COLUMNS: Tuple[str, ...] = _MODEL_COLUMNS[2:]
     _instances: Dict[str, "PersistentModelCache"] = {}
@@ -186,6 +187,7 @@ class PersistentModelCache:
                 "civitai_deleted": bool(row["civitai_deleted"]),
                 "skip_metadata_refresh": bool(row["skip_metadata_refresh"]),
                 "license_flags": int(license_value),
+                "hash_status": row["hash_status"] or "completed",
             }
             raw_data.append(item)
 
@@ -449,6 +451,7 @@ class PersistentModelCache:
                             exclude INTEGER,
                             db_checked INTEGER,
                             last_checked_at REAL,
+                            hash_status TEXT,
                             PRIMARY KEY (model_type, file_path)
                         );
 
@@ -496,6 +499,7 @@ class PersistentModelCache:
             "skip_metadata_refresh": "INTEGER DEFAULT 0",
             # Persisting without explicit flags should assume CivitAI's documented defaults (0b111001 == 57).
             "license_flags": f"INTEGER DEFAULT {DEFAULT_LICENSE_FLAGS}",
+            "hash_status": "TEXT DEFAULT 'completed'",
         }
 
         for column, definition in required_columns.items():
@@ -570,6 +574,7 @@ class PersistentModelCache:
             1 if item.get("exclude") else 0,
             1 if item.get("db_checked") else 0,
             float(item.get("last_checked_at") or 0.0),
+            item.get("hash_status", "completed"),
         )
 
     def _insert_model_sql(self) -> str:

py/services/recipe_cache.py

@@ -135,7 +135,8 @@ class RecipeCache:
         """Sort cached views. Caller must hold ``_lock``."""
 
         self.sorted_by_name = natsorted(
-            self.raw_data, key=lambda x: x.get("title", "").lower()
+            self.raw_data,
+            key=lambda x: (x.get("title", "").lower(), x.get("file_path", "").lower()),
         )
         if not name_only:
             self.sorted_by_date = sorted(

py/services/recipes/analysis_service.py

@@ -1,4 +1,5 @@
 """Services responsible for recipe metadata analysis."""
+
 from __future__ import annotations
 
 import base64
@@ -69,7 +70,9 @@ class RecipeAnalysisService:
         try:
             metadata = self._exif_utils.extract_image_metadata(temp_path)
             if not metadata:
-                return AnalysisResult({"error": "No metadata found in this image", "loras": []})
+                return AnalysisResult(
+                    {"error": "No metadata found in this image", "loras": []}
+                )
 
             return await self._parse_metadata(
                 metadata,
@@ -105,29 +108,33 @@ class RecipeAnalysisService:
             if civitai_match:
                 image_info = await civitai_client.get_image_info(civitai_match.group(1))
                 if not image_info:
-                    raise RecipeDownloadError("Failed to fetch image information from Civitai")
-                
+                    raise RecipeDownloadError(
+                        "Failed to fetch image information from Civitai"
+                    )
+
                 image_url = image_info.get("url")
                 if not image_url:
                     raise RecipeDownloadError("No image URL found in Civitai response")
-                
+
                 is_video = image_info.get("type") == "video"
-                
+
                 # Use optimized preview URLs if possible
-                rewritten_url, _ = rewrite_preview_url(image_url, media_type=image_info.get("type"))
+                rewritten_url, _ = rewrite_preview_url(
+                    image_url, media_type=image_info.get("type")
+                )
                 if rewritten_url:
                     image_url = rewritten_url
 
                 if is_video:
                     # Extract extension from URL
-                    url_path = image_url.split('?')[0].split('#')[0]
+                    url_path = image_url.split("?")[0].split("#")[0]
                     extension = os.path.splitext(url_path)[1].lower() or ".mp4"
                 else:
                     extension = ".jpg"
 
                 temp_path = self._create_temp_path(suffix=extension)
                 await self._download_image(image_url, temp_path)
-                
+
                 metadata = image_info.get("meta") if "meta" in image_info else None
                 if (
                     isinstance(metadata, dict)
@@ -135,15 +142,23 @@ class RecipeAnalysisService:
                     and isinstance(metadata["meta"], dict)
                 ):
                     metadata = metadata["meta"]
+
+                # Validate that metadata contains meaningful recipe fields
+                # If not, treat as None to trigger EXIF extraction from downloaded image
+                if isinstance(metadata, dict) and not self._has_recipe_fields(metadata):
+                    self._logger.debug(
+                        "Civitai API metadata lacks recipe fields, will extract from EXIF"
+                    )
+                    metadata = None
             else:
                 # Basic extension detection for non-Civitai URLs
-                url_path = url.split('?')[0].split('#')[0]
+                url_path = url.split("?")[0].split("#")[0]
                 extension = os.path.splitext(url_path)[1].lower()
                 if extension in [".mp4", ".webm"]:
                     is_video = True
                 else:
                     extension = ".jpg"
-                
+
                 temp_path = self._create_temp_path(suffix=extension)
                 await self._download_image(url, temp_path)
 
@@ -211,7 +226,9 @@ class RecipeAnalysisService:
 
         image_bytes = self._convert_tensor_to_png_bytes(latest_image)
         if image_bytes is None:
-            raise RecipeValidationError("Cannot handle this data shape from metadata registry")
+            raise RecipeValidationError(
+                "Cannot handle this data shape from metadata registry"
+            )
 
         return AnalysisResult(
             {
@@ -222,6 +239,22 @@ class RecipeAnalysisService:
 
     # Internal helpers -------------------------------------------------
 
+    def _has_recipe_fields(self, metadata: dict[str, Any]) -> bool:
+        """Check if metadata contains meaningful recipe-related fields."""
+        recipe_fields = {
+            "prompt",
+            "negative_prompt",
+            "resources",
+            "hashes",
+            "params",
+            "generationData",
+            "Workflow",
+            "prompt_type",
+            "positive",
+            "negative",
+        }
+        return any(field in metadata for field in recipe_fields)
+
     async def _parse_metadata(
         self,
         metadata: dict[str, Any],
@@ -234,7 +267,12 @@ class RecipeAnalysisService:
     ) -> AnalysisResult:
         parser = self._recipe_parser_factory.create_parser(metadata)
         if parser is None:
-            payload = {"error": "No parser found for this image", "loras": []}
+            # Provide more specific error message based on metadata source
+            if not metadata:
+                error_msg = "This image does not contain any generation metadata (prompt, models, or parameters)"
+            else:
+                error_msg = "No parser found for this image"
+            payload = {"error": error_msg, "loras": []}
             if include_image_base64 and image_path:
                 payload["image_base64"] = self._encode_file(image_path)
             payload["is_video"] = is_video
@@ -257,7 +295,9 @@ class RecipeAnalysisService:
 
         matching_recipes: list[str] = []
         if fingerprint:
-            matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(fingerprint)
+            matching_recipes = await recipe_scanner.find_recipes_by_fingerprint(
+                fingerprint
+            )
         result["matching_recipes"] = matching_recipes
 
         return AnalysisResult(result)
@@ -269,7 +309,10 @@ class RecipeAnalysisService:
             raise RecipeDownloadError(f"Failed to download image from URL: {result}")
 
     def _metadata_not_found_response(self, path: str) -> AnalysisResult:
-        payload: dict[str, Any] = {"error": "No metadata found in this image", "loras": []}
+        payload: dict[str, Any] = {
+            "error": "No metadata found in this image",
+            "loras": [],
+        }
         if os.path.exists(path):
             payload["image_base64"] = self._encode_file(path)
         return AnalysisResult(payload)
@@ -305,7 +348,9 @@ class RecipeAnalysisService:
 
             if hasattr(tensor_image, "shape"):
                 self._logger.debug(
-                    "Tensor shape: %s, dtype: %s", tensor_image.shape, getattr(tensor_image, "dtype", None)
+                    "Tensor shape: %s, dtype: %s",
+                    tensor_image.shape,
+                    getattr(tensor_image, "dtype", None),
                 )
 
             import torch  # type: ignore[import-not-found]

py/utils/file_utils.py

@@ -40,49 +40,39 @@ async def calculate_sha256(file_path: str) -> str:
     return sha256_hash.hexdigest()
 
 def find_preview_file(base_name: str, dir_path: str) -> str:
-    """Find preview file for given base name in directory"""
-    
+    """Find preview file for given base name in directory.
+
+    Performs an exact-case check first (fast path), then falls back to a
+    case-insensitive scan so that files like ``model.WEBP`` or ``model.Png``
+    are discovered on case-sensitive filesystems.
+    """
+
     temp_extensions = PREVIEW_EXTENSIONS.copy()
     # Add example extension for compatibility
     # https://github.com/willmiao/ComfyUI-Lora-Manager/issues/225
     # The preview image will be optimized to lora-name.webp, so it won't affect other logic
     temp_extensions.append(".example.0.jpeg")
+
+    # Fast path: exact-case match
     for ext in temp_extensions:
         full_pattern = os.path.join(dir_path, f"{base_name}{ext}")
         if os.path.exists(full_pattern):
-            # Check if this is an image and not already webp
-            # TODO: disable the optimization for now, maybe add a config option later
-            # if ext.lower().endswith(('.jpg', '.jpeg', '.png')) and not ext.lower().endswith('.webp'):
-            #     try:
-            #         # Optimize the image to webp format
-            #         webp_path = os.path.join(dir_path, f"{base_name}.webp")
-                    
-            #         # Use ExifUtils to optimize the image
-            #         with open(full_pattern, 'rb') as f:
-            #             image_data = f.read()
-                    
-            #         optimized_data, _ = ExifUtils.optimize_image(
-            #             image_data=image_data,
-            #             target_width=CARD_PREVIEW_WIDTH,
-            #             format='webp',
-            #             quality=85,
-            #             preserve_metadata=False
-            #         )
-                    
-            #         # Save the optimized webp file
-            #         with open(webp_path, 'wb') as f:
-            #             f.write(optimized_data)
-                    
-            #         logger.debug(f"Optimized preview image from {full_pattern} to {webp_path}")
-            #         return webp_path.replace(os.sep, "/")
-            #     except Exception as e:
-            #         logger.error(f"Error optimizing preview image {full_pattern}: {e}")
-            #         # Fall back to original file if optimization fails
-            #         return full_pattern.replace(os.sep, "/")
-            
-            # Return the original path for webp images or non-image files
             return full_pattern.replace(os.sep, "/")
-    
+
+    # Slow path: case-insensitive match for systems with mixed-case extensions
+    # (e.g. .WEBP, .Png, .JPG placed manually or by external tools)
+    try:
+        dir_entries = os.listdir(dir_path)
+    except OSError:
+        return ""
+
+    base_lower = base_name.lower()
+    for ext in temp_extensions:
+        target = f"{base_lower}{ext}"  # ext is already lowercase
+        for entry in dir_entries:
+            if entry.lower() == target:
+                return os.path.join(dir_path, entry).replace(os.sep, "/")
+
     return ""
 
 def get_preview_extension(preview_path: str) -> str:

py/utils/utils.py

@@ -112,6 +112,115 @@ def get_lora_info_absolute(lora_name):
         return asyncio.run(_get_lora_info_absolute_async())
 
 
+def get_checkpoint_info_absolute(checkpoint_name):
+    """Get the absolute checkpoint path and metadata from cache
+
+    Supports ComfyUI-style model names (e.g., "folder/model_name.ext")
+
+    Args:
+        checkpoint_name: The model name, can be:
+            - ComfyUI format: "folder/model_name.safetensors"
+            - Simple name: "model_name"
+
+    Returns:
+        tuple: (absolute_path, metadata) where absolute_path is the full
+               file system path to the checkpoint file, or original checkpoint_name if not found,
+               metadata is the full model metadata dict or None
+    """
+
+    async def _get_checkpoint_info_absolute_async():
+        from ..services.service_registry import ServiceRegistry
+
+        scanner = await ServiceRegistry.get_checkpoint_scanner()
+        cache = await scanner.get_cached_data()
+
+        # Get model roots for matching
+        model_roots = scanner.get_model_roots()
+
+        # Normalize the checkpoint name
+        normalized_name = checkpoint_name.replace(os.sep, "/")
+
+        for item in cache.raw_data:
+            file_path = item.get("file_path", "")
+            if not file_path:
+                continue
+
+            # Format the stored path as ComfyUI-style name
+            formatted_name = _format_model_name_for_comfyui(file_path, model_roots)
+
+            # Match by formatted name (normalize separators for robust comparison)
+            if formatted_name.replace(os.sep, "/") == normalized_name or formatted_name == checkpoint_name:
+                return file_path, item
+
+            # Also try matching by basename only (for backward compatibility)
+            file_name = item.get("file_name", "")
+            if (
+                file_name == checkpoint_name
+                or file_name == os.path.splitext(normalized_name)[0]
+            ):
+                return file_path, item
+
+        return checkpoint_name, None
+
+    try:
+        # Check if we're already in an event loop
+        loop = asyncio.get_running_loop()
+        # If we're in a running loop, we need to use a different approach
+        # Create a new thread to run the async code
+        import concurrent.futures
+
+        def run_in_thread():
+            new_loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(new_loop)
+            try:
+                return new_loop.run_until_complete(
+                    _get_checkpoint_info_absolute_async()
+                )
+            finally:
+                new_loop.close()
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            future = executor.submit(run_in_thread)
+            return future.result()
+
+    except RuntimeError:
+        # No event loop is running, we can use asyncio.run()
+        return asyncio.run(_get_checkpoint_info_absolute_async())
+
+
+def _format_model_name_for_comfyui(file_path: str, model_roots: list) -> str:
+    """Format file path to ComfyUI-style model name (relative path with extension)
+
+    Example: /path/to/checkpoints/Illustrious/model.safetensors -> Illustrious/model.safetensors
+
+    Args:
+        file_path: Absolute path to the model file
+        model_roots: List of model root directories
+
+    Returns:
+        ComfyUI-style model name with relative path and extension
+    """
+    # Find the matching root and get relative path
+    for root in model_roots:
+        try:
+            # Normalize paths for comparison
+            norm_file = os.path.normcase(os.path.abspath(file_path))
+            norm_root = os.path.normcase(os.path.abspath(root))
+
+            # Add trailing separator for prefix check
+            if not norm_root.endswith(os.sep):
+                norm_root += os.sep
+
+            if norm_file.startswith(norm_root):
+                # Use os.path.relpath to get relative path with OS-native separator
+                return os.path.relpath(file_path, root)
+        except (ValueError, TypeError):
+            continue
+
+    # If no root matches, just return the basename with extension
+    return os.path.basename(file_path)
+
+
 def fuzzy_match(text: str, pattern: str, threshold: float = 0.85) -> bool:
     """
     Check if text matches pattern using fuzzy matching.
@@ -173,10 +282,13 @@ def sanitize_folder_name(name: str, replacement: str = "_") -> str:
     # Collapse repeated replacement characters to a single instance
     if replacement:
         sanitized = re.sub(f"{re.escape(replacement)}+", replacement, sanitized)
-        sanitized = sanitized.strip(replacement)
-
-    # Remove trailing spaces or periods which are invalid on Windows
-    sanitized = sanitized.rstrip(" .")
+        # Combine stripping to be idempotent:
+        # Right side: strip replacement, space, and dot (Windows restriction)
+        # Left side: strip replacement and space (leading dots are allowed)
+        sanitized = sanitized.rstrip(" ." + replacement).lstrip(" " + replacement)
+    else:
+        # If no replacement, just strip spaces and dots from right, spaces from left
+        sanitized = sanitized.rstrip(" .").lstrip(" ")
 
     if not sanitized:
         return "unnamed"

pytest.ini

@@ -1,5 +1,5 @@
 [pytest]
-addopts = -v --import-mode=importlib -m "not performance"
+addopts = -v --import-mode=importlib -m "not performance" --ignore=__init__.py
 testpaths = tests
 python_files = test_*.py
 python_classes = Test*

static/js/api/baseModelApi.js

@@ -251,7 +251,7 @@ export class BaseModelApiClient {
     replaceModelPreview(filePath) {
         const input = document.createElement('input');
         input.type = 'file';
-        input.accept = 'image/*,video/mp4';
+        input.accept = 'image/*,image/webp,video/mp4';
 
         input.onchange = async () => {
             if (!input.files || !input.files[0]) return;

static/js/managers/BatchImportManager.js

@@ -2,6 +2,7 @@ import { modalManager } from './ModalManager.js';
 import { showToast } from '../utils/uiHelpers.js';
 import { translate } from '../utils/i18nHelpers.js';
 import { WS_ENDPOINTS } from '../api/apiConfig.js';
+import { getStorageItem, setStorageItem } from '../utils/storageHelpers.js';
 
 /**
  * Manager for batch importing recipes from multiple images
@@ -34,6 +35,14 @@ export class BatchImportManager {
      */
     initialize() {
         this.initialized = true;
+
+        // Add event listener for persisting "Skip images without metadata" choice
+        const skipNoMetadata = document.getElementById('batchSkipNoMetadata');
+        if (skipNoMetadata) {
+            skipNoMetadata.addEventListener('change', (e) => {
+                setStorageItem('batch_import_skip_no_metadata', e.target.checked);
+            });
+        }
     }
 
     /**
@@ -61,7 +70,10 @@ export class BatchImportManager {
         if (tagsInput) tagsInput.value = '';
         
         const skipNoMetadata = document.getElementById('batchSkipNoMetadata');
-        if (skipNoMetadata) skipNoMetadata.checked = true;
+        if (skipNoMetadata) {
+            // Load preference from storage, defaulting to true
+            skipNoMetadata.checked = getStorageItem('batch_import_skip_no_metadata', true);
+        }
         
         const recursiveCheck = document.getElementById('batchRecursiveCheck');
         if (recursiveCheck) recursiveCheck.checked = true;
@@ -92,6 +104,14 @@ export class BatchImportManager {
 
         // Clean up any existing connections
         this.cleanupConnections();
+
+        // Focus on the URL input field for better UX
+        setTimeout(() => {
+            const urlInput = document.getElementById('batchUrlInput');
+            if (urlInput) {
+                urlInput.focus();
+            }
+        }, 100);
     }
 
     /**

tests/config/test_checkpoint_path_overlap.py

@@ -36,8 +36,8 @@ class TestCheckpointPathOverlap:
             config._preview_root_paths = set()
             config._cached_fingerprint = None
 
-            # Call the method under test
-            result = config._prepare_checkpoint_paths(
+            # Call the method under test - now returns a tuple
+            all_paths, checkpoint_roots, unet_roots = config._prepare_checkpoint_paths(
                 [str(checkpoints_link)], [str(unet_link)]
             )
 
@@ -50,21 +50,27 @@ class TestCheckpointPathOverlap:
         ]
         assert len(warning_messages) == 1
         assert "checkpoints" in warning_messages[0].lower()
-        assert "diffusion_models" in warning_messages[0].lower() or "unet" in warning_messages[0].lower()
+        assert (
+            "diffusion_models" in warning_messages[0].lower()
+            or "unet" in warning_messages[0].lower()
+        )
         # Verify warning mentions backward compatibility fallback
-        assert "falling back" in warning_messages[0].lower() or "backward compatibility" in warning_messages[0].lower()
+        assert (
+            "falling back" in warning_messages[0].lower()
+            or "backward compatibility" in warning_messages[0].lower()
+        )
 
         # Verify only one path is returned (deduplication still works)
-        assert len(result) == 1
+        assert len(all_paths) == 1
         # Prioritizes checkpoints path for backward compatibility
-        assert _normalize(result[0]) == _normalize(str(checkpoints_link))
+        assert _normalize(all_paths[0]) == _normalize(str(checkpoints_link))
 
-        # Verify checkpoints_roots has the path (prioritized)
-        assert len(config.checkpoints_roots) == 1
-        assert _normalize(config.checkpoints_roots[0]) == _normalize(str(checkpoints_link))
+        # Verify checkpoint_roots has the path (prioritized)
+        assert len(checkpoint_roots) == 1
+        assert _normalize(checkpoint_roots[0]) == _normalize(str(checkpoints_link))
 
         # Verify unet_roots is empty (overlapping paths removed)
-        assert config.unet_roots == []
+        assert unet_roots == []
 
     def test_non_overlapping_paths_no_warning(
         self, monkeypatch: pytest.MonkeyPatch, tmp_path, caplog
@@ -83,7 +89,7 @@ class TestCheckpointPathOverlap:
             config._preview_root_paths = set()
             config._cached_fingerprint = None
 
-            result = config._prepare_checkpoint_paths(
+            all_paths, checkpoint_roots, unet_roots = config._prepare_checkpoint_paths(
                 [str(checkpoints_dir)], [str(unet_dir)]
             )
 
@@ -97,14 +103,14 @@ class TestCheckpointPathOverlap:
         assert len(warning_messages) == 0
 
         # Verify both paths are returned
-        assert len(result) == 2
-        normalized_result = [_normalize(p) for p in result]
+        assert len(all_paths) == 2
+        normalized_result = [_normalize(p) for p in all_paths]
         assert _normalize(str(checkpoints_dir)) in normalized_result
         assert _normalize(str(unet_dir)) in normalized_result
 
         # Verify both roots are properly set
-        assert len(config.checkpoints_roots) == 1
-        assert len(config.unet_roots) == 1
+        assert len(checkpoint_roots) == 1
+        assert len(unet_roots) == 1
 
     def test_partial_overlap_prioritizes_checkpoints(
         self, monkeypatch: pytest.MonkeyPatch, tmp_path, caplog
@@ -129,9 +135,9 @@ class TestCheckpointPathOverlap:
             config._cached_fingerprint = None
 
             # One checkpoint path overlaps with one unet path
-            result = config._prepare_checkpoint_paths(
+            all_paths, checkpoint_roots, unet_roots = config._prepare_checkpoint_paths(
                 [str(shared_link), str(separate_checkpoint)],
-                [str(shared_link), str(separate_unet)]
+                [str(shared_link), str(separate_unet)],
             )
 
         # Verify warning was logged for the overlapping path
@@ -144,17 +150,20 @@ class TestCheckpointPathOverlap:
         assert len(warning_messages) == 1
 
         # Verify 3 unique paths (shared counted once as checkpoint, plus separate ones)
-        assert len(result) == 3
+        assert len(all_paths) == 3
 
         # Verify the overlapping path appears in warning message
-        assert str(shared_link.name) in warning_messages[0] or str(shared_dir.name) in warning_messages[0]
+        assert (
+            str(shared_link.name) in warning_messages[0]
+            or str(shared_dir.name) in warning_messages[0]
+        )
 
-        # Verify checkpoints_roots includes both checkpoint paths (including the shared one)
-        assert len(config.checkpoints_roots) == 2
-        checkpoint_normalized = [_normalize(p) for p in config.checkpoints_roots]
+        # Verify checkpoint_roots includes both checkpoint paths (including the shared one)
+        assert len(checkpoint_roots) == 2
+        checkpoint_normalized = [_normalize(p) for p in checkpoint_roots]
         assert _normalize(str(shared_link)) in checkpoint_normalized
         assert _normalize(str(separate_checkpoint)) in checkpoint_normalized
 
         # Verify unet_roots only includes the non-overlapping unet path
-        assert len(config.unet_roots) == 1
-        assert _normalize(config.unet_roots[0]) == _normalize(str(separate_unet))
+        assert len(unet_roots) == 1
+        assert _normalize(unet_roots[0]) == _normalize(str(separate_unet))

tests/frontend/components/autocomplete.behavior.test.js

@@ -267,4 +267,431 @@ describe('AutoComplete widget interactions', () => {
     const scrollTopAfter = autoComplete.scrollContainer?.scrollTop || 0;
     expect(scrollTopAfter).toBeGreaterThanOrEqual(scrollTopBefore);
   });
+
+  it('replaces entire multi-word phrase when it matches selected tag (Danbooru convention)', async () => {
+    const mockTags = [
+      { tag_name: 'looking_to_the_side', category: 0, post_count: 1234 },
+      { tag_name: 'looking_away', category: 0, post_count: 5678 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('looking to the side');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'looking to the side';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+
+    await autoComplete.insertSelection('looking_to_the_side');
+
+    expect(input.value).toBe('looking_to_the_side, ');
+    expect(autoComplete.dropdown.style.display).toBe('none');
+    expect(input.focus).toHaveBeenCalled();
+  });
+
+  it('replaces only last token when typing partial match (e.g., "hello 1gi" -> "1girl")', async () => {
+    const mockTags = [
+      { tag_name: '1girl', category: 4, post_count: 500000 },
+      { tag_name: '1boy', category: 4, post_count: 300000 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('hello 1gi');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'hello 1gi';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'hello 1gi';
+
+    await autoComplete.insertSelection('1girl');
+
+    expect(input.value).toBe('hello 1girl, ');
+  });
+
+  it('replaces entire phrase for underscore tag match (e.g., "blue hair" -> "blue_hair")', async () => {
+    const mockTags = [
+      { tag_name: 'blue_hair', category: 0, post_count: 45000 },
+      { tag_name: 'blue_eyes', category: 0, post_count: 80000 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('blue hair');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'blue hair';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'blue hair';
+
+    await autoComplete.insertSelection('blue_hair');
+
+    expect(input.value).toBe('blue_hair, ');
+  });
+
+  it('handles multi-word phrase with preceding text correctly', async () => {
+    const mockTags = [
+      { tag_name: 'looking_to_the_side', category: 0, post_count: 1234 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('1girl, looking to the side');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = '1girl, looking to the side';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'looking to the side';
+
+    await autoComplete.insertSelection('looking_to_the_side');
+
+    expect(input.value).toBe('1girl, looking_to_the_side, ');
+  });
+
+  it('replaces entire command and search term when using command mode with multi-word phrase', async () => {
+    const mockTags = [
+      { tag_name: 'looking_to_the_side', category: 4, post_count: 1234 },
+      { tag_name: 'looking_away', category: 4, post_count: 5678 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    // Simulate "/char looking to the side" input
+    caretHelperInstance.getBeforeCursor.mockReturnValue('/char looking to the side');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = '/char looking to the side';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    // Set up command mode state
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = { categories: [4, 11], label: 'Character' };
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = '/char looking to the side';
+
+    await autoComplete.insertSelection('looking_to_the_side');
+
+    // Command part should be replaced along with search term
+    expect(input.value).toBe('looking_to_the_side, ');
+  });
+
+  it('replaces only last token when multi-word query does not exactly match selected tag', async () => {
+    const mockTags = [
+      { tag_name: 'blue_hair', category: 0, post_count: 45000 },
+      { tag_name: 'blue_eyes', category: 0, post_count: 80000 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    // User types "looking to the blue" but selects "blue_hair" (doesn't match entire phrase)
+    caretHelperInstance.getBeforeCursor.mockReturnValue('looking to the blue');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'looking to the blue';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'looking to the blue';
+
+    await autoComplete.insertSelection('blue_hair');
+
+    // Only "blue" should be replaced, not the entire phrase
+    expect(input.value).toBe('looking to the blue_hair, ');
+  });
+
+  it('handles multiple consecutive spaces in multi-word phrase correctly', async () => {
+    const mockTags = [
+      { tag_name: 'looking_to_the_side', category: 0, post_count: 1234 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    // Input with multiple spaces between words
+    caretHelperInstance.getBeforeCursor.mockReturnValue('looking  to   the side');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'looking  to   the side';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'looking  to   the side';
+
+    await autoComplete.insertSelection('looking_to_the_side');
+
+    // Multiple spaces should be normalized to single underscores for matching
+    expect(input.value).toBe('looking_to_the_side, ');
+  });
+
+  it('handles command mode with partial match replacing only last token', async () => {
+    const mockTags = [
+      { tag_name: 'blue_hair', category: 0, post_count: 45000 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    // Command mode but selected tag doesn't match entire search phrase
+    caretHelperInstance.getBeforeCursor.mockReturnValue('/general looking to the blue');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = '/general looking to the blue';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    // Command mode with activeCommand
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = { categories: [0, 7], label: 'General' };
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = '/general looking to the blue';
+
+    await autoComplete.insertSelection('blue_hair');
+
+    // In command mode, the entire command + search term should be replaced
+    expect(input.value).toBe('blue_hair, ');
+  });
+
+  it('replaces entire phrase when selected tag starts with underscore version of search term (prefix match)', async () => {
+    const mockTags = [
+      { tag_name: 'looking_to_the_side', category: 0, post_count: 1234 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    // User types partial phrase "looking to the" and selects "looking_to_the_side"
+    caretHelperInstance.getBeforeCursor.mockReturnValue('looking to the');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'looking to the';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'looking to the';
+
+    await autoComplete.insertSelection('looking_to_the_side');
+
+    // Entire phrase should be replaced with selected tag (with underscores)
+    expect(input.value).toBe('looking_to_the_side, ');
+  });
+
+  it('inserts tag with underscores regardless of space replacement setting', async () => {
+    const mockTags = [
+      { tag_name: 'blue_hair', category: 0, post_count: 45000 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('blue');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'blue';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+
+    await autoComplete.insertSelection('blue_hair');
+
+    // Tag should be inserted with underscores, not spaces
+    expect(input.value).toBe('blue_hair, ');
+  });
+
+  it('replaces entire phrase when selected tag ends with underscore version of search term (suffix match)', async () => {
+    const mockTags = [
+      { tag_name: 'looking_to_the_side', category: 0, post_count: 1234 },
+    ];
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, words: mockTags }),
+    });
+
+    // User types suffix "to the side" and selects "looking_to_the_side"
+    caretHelperInstance.getBeforeCursor.mockReturnValue('to the side');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.value = 'to the side';
+    input.selectionStart = input.value.length;
+    input.focus = vi.fn();
+    input.setSelectionRange = vi.fn();
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'prompt', {
+      debounceDelay: 0,
+      showPreview: false,
+      minChars: 1,
+    });
+
+    autoComplete.searchType = 'custom_words';
+    autoComplete.activeCommand = null;
+    autoComplete.items = mockTags;
+    autoComplete.selectedIndex = 0;
+    autoComplete.currentSearchTerm = 'to the side';
+
+    await autoComplete.insertSelection('looking_to_the_side');
+
+    // Entire phrase should be replaced with selected tag
+    expect(input.value).toBe('looking_to_the_side, ');
+  });
 });

tests/services/test_cache_health_monitor.py

@@ -194,6 +194,7 @@ class TestCacheHealthMonitor:
             'preview_nsfw_level': 0,
             'notes': '',
             'usage_tips': '',
+            'hash_status': 'completed',
         }
         incomplete_entry = {
             'file_path': '/models/test2.safetensors',

tests/services/test_lora_pool_filters.py

@@ -369,3 +369,289 @@ async def test_pool_filter_combined_all_filters(lora_service):
     # - tags: tag1 ✓
     assert len(filtered) == 1
     assert filtered[0]["file_name"] == "match_all.safetensors"
+
+
+@pytest.mark.asyncio
+async def test_pool_filter_name_patterns_include_text(lora_service):
+    """Test filtering by name patterns with text matching (useRegex=False)."""
+    sample_loras = [
+        {
+            "file_name": "character_anime_v1.safetensors",
+            "model_name": "Anime Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "character_realistic_v1.safetensors",
+            "model_name": "Realistic Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "style_watercolor_v1.safetensors",
+            "model_name": "Watercolor Style",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+    ]
+
+    # Test include patterns with text matching
+    pool_config = {
+        "baseModels": [],
+        "tags": {"include": [], "exclude": []},
+        "folders": {"include": [], "exclude": []},
+        "license": {"noCreditRequired": False, "allowSelling": False},
+        "namePatterns": {"include": ["character"], "exclude": [], "useRegex": False},
+    }
+
+    filtered = await lora_service._apply_pool_filters(sample_loras, pool_config)
+    assert len(filtered) == 2
+    file_names = {lora["file_name"] for lora in filtered}
+    assert file_names == {
+        "character_anime_v1.safetensors",
+        "character_realistic_v1.safetensors",
+    }
+
+
+@pytest.mark.asyncio
+async def test_pool_filter_name_patterns_exclude_text(lora_service):
+    """Test excluding by name patterns with text matching (useRegex=False)."""
+    sample_loras = [
+        {
+            "file_name": "character_anime_v1.safetensors",
+            "model_name": "Anime Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "character_realistic_v1.safetensors",
+            "model_name": "Realistic Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "style_watercolor_v1.safetensors",
+            "model_name": "Watercolor Style",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+    ]
+
+    # Test exclude patterns with text matching
+    pool_config = {
+        "baseModels": [],
+        "tags": {"include": [], "exclude": []},
+        "folders": {"include": [], "exclude": []},
+        "license": {"noCreditRequired": False, "allowSelling": False},
+        "namePatterns": {"include": [], "exclude": ["anime"], "useRegex": False},
+    }
+
+    filtered = await lora_service._apply_pool_filters(sample_loras, pool_config)
+    assert len(filtered) == 2
+    file_names = {lora["file_name"] for lora in filtered}
+    assert file_names == {
+        "character_realistic_v1.safetensors",
+        "style_watercolor_v1.safetensors",
+    }
+
+
+@pytest.mark.asyncio
+async def test_pool_filter_name_patterns_include_regex(lora_service):
+    """Test filtering by name patterns with regex matching (useRegex=True)."""
+    sample_loras = [
+        {
+            "file_name": "character_anime_v1.safetensors",
+            "model_name": "Anime Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "character_realistic_v1.safetensors",
+            "model_name": "Realistic Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "style_watercolor_v1.safetensors",
+            "model_name": "Watercolor Style",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+    ]
+
+    # Test include patterns with regex matching - match files starting with "character_"
+    pool_config = {
+        "baseModels": [],
+        "tags": {"include": [], "exclude": []},
+        "folders": {"include": [], "exclude": []},
+        "license": {"noCreditRequired": False, "allowSelling": False},
+        "namePatterns": {"include": ["^character_"], "exclude": [], "useRegex": True},
+    }
+
+    filtered = await lora_service._apply_pool_filters(sample_loras, pool_config)
+    assert len(filtered) == 2
+    file_names = {lora["file_name"] for lora in filtered}
+    assert file_names == {
+        "character_anime_v1.safetensors",
+        "character_realistic_v1.safetensors",
+    }
+
+
+@pytest.mark.asyncio
+async def test_pool_filter_name_patterns_exclude_regex(lora_service):
+    """Test excluding by name patterns with regex matching (useRegex=True)."""
+    sample_loras = [
+        {
+            "file_name": "character_anime_v1.safetensors",
+            "model_name": "Anime Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "character_realistic_v1.safetensors",
+            "model_name": "Realistic Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "style_watercolor_v1.safetensors",
+            "model_name": "Watercolor Style",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+    ]
+
+    # Test exclude patterns with regex matching - exclude files ending with "_v1.safetensors"
+    pool_config = {
+        "baseModels": [],
+        "tags": {"include": [], "exclude": []},
+        "folders": {"include": [], "exclude": []},
+        "license": {"noCreditRequired": False, "allowSelling": False},
+        "namePatterns": {
+            "include": [],
+            "exclude": ["_v1\\.safetensors$"],
+            "useRegex": True,
+        },
+    }
+
+    filtered = await lora_service._apply_pool_filters(sample_loras, pool_config)
+    assert len(filtered) == 0  # All files match the exclude pattern
+
+
+@pytest.mark.asyncio
+async def test_pool_filter_name_patterns_combined(lora_service):
+    """Test combining include and exclude name patterns."""
+    sample_loras = [
+        {
+            "file_name": "character_anime_v1.safetensors",
+            "model_name": "Anime Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "character_realistic_v1.safetensors",
+            "model_name": "Realistic Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "style_watercolor_v1.safetensors",
+            "model_name": "Watercolor Style",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+    ]
+
+    # Test include "character" but exclude "anime"
+    pool_config = {
+        "baseModels": [],
+        "tags": {"include": [], "exclude": []},
+        "folders": {"include": [], "exclude": []},
+        "license": {"noCreditRequired": False, "allowSelling": False},
+        "namePatterns": {
+            "include": ["character"],
+            "exclude": ["anime"],
+            "useRegex": False,
+        },
+    }
+
+    filtered = await lora_service._apply_pool_filters(sample_loras, pool_config)
+    assert len(filtered) == 1
+    assert filtered[0]["file_name"] == "character_realistic_v1.safetensors"
+
+
+@pytest.mark.asyncio
+async def test_pool_filter_name_patterns_model_name_fallback(lora_service):
+    """Test that name pattern filtering falls back to model_name when file_name doesn't match."""
+    sample_loras = [
+        {
+            "file_name": "abc123.safetensors",
+            "model_name": "Super Anime Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+        {
+            "file_name": "def456.safetensors",
+            "model_name": "Realistic Portrait",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+    ]
+
+    # Should match model_name even if file_name doesn't contain the pattern
+    pool_config = {
+        "baseModels": [],
+        "tags": {"include": [], "exclude": []},
+        "folders": {"include": [], "exclude": []},
+        "license": {"noCreditRequired": False, "allowSelling": False},
+        "namePatterns": {"include": ["anime"], "exclude": [], "useRegex": False},
+    }
+
+    filtered = await lora_service._apply_pool_filters(sample_loras, pool_config)
+    assert len(filtered) == 1
+    assert filtered[0]["file_name"] == "abc123.safetensors"
+
+
+@pytest.mark.asyncio
+async def test_pool_filter_name_patterns_invalid_regex(lora_service):
+    """Test that invalid regex falls back to substring matching."""
+    sample_loras = [
+        {
+            "file_name": "character_anime[test]_v1.safetensors",
+            "model_name": "Anime Character",
+            "base_model": "Illustrious",
+            "folder": "",
+            "license_flags": build_license_flags(None),
+        },
+    ]
+
+    # Invalid regex pattern (unclosed character class) should fall back to substring matching
+    # The pattern "anime[" is invalid regex but valid substring - it exists in the filename
+    pool_config = {
+        "baseModels": [],
+        "tags": {"include": [], "exclude": []},
+        "folders": {"include": [], "exclude": []},
+        "license": {"noCreditRequired": False, "allowSelling": False},
+        "namePatterns": {"include": ["anime["], "exclude": [], "useRegex": True},
+    }
+
+    # Should not crash and should match using substring fallback
+    filtered = await lora_service._apply_pool_filters(sample_loras, pool_config)
+    assert len(filtered) == 1  # Substring match works even with invalid regex

tests/test_checkpoint_loaders.py

@@ -0,0 +1,158 @@
+"""Tests for checkpoint and unet loaders with extra folder paths support"""
+
+import pytest
+import os
+
+
+# Get project root directory (ComfyUI-Lora-Manager folder)
+PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+
+
+class TestCheckpointLoaderLM:
+    """Test CheckpointLoaderLM node"""
+
+    def test_class_attributes(self):
+        """Test that CheckpointLoaderLM has required class attributes"""
+        # Import in a way that doesn't require ComfyUI
+        import ast
+
+        filepath = os.path.join(PROJECT_ROOT, "py", "nodes", "checkpoint_loader.py")
+
+        with open(filepath, "r") as f:
+            tree = ast.parse(f.read())
+
+        # Find CheckpointLoaderLM class
+        classes = {
+            node.name: node for node in ast.walk(tree) if isinstance(node, ast.ClassDef)
+        }
+        assert "CheckpointLoaderLM" in classes
+
+        cls = classes["CheckpointLoaderLM"]
+
+        # Check for NAME attribute
+        name_attr = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.Assign)
+            and any(t.id == "NAME" for t in n.targets if isinstance(t, ast.Name))
+        ]
+        assert len(name_attr) > 0, "CheckpointLoaderLM should have NAME attribute"
+
+        # Check for CATEGORY attribute
+        cat_attr = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.Assign)
+            and any(t.id == "CATEGORY" for t in n.targets if isinstance(t, ast.Name))
+        ]
+        assert len(cat_attr) > 0, "CheckpointLoaderLM should have CATEGORY attribute"
+
+        # Check for INPUT_TYPES method
+        input_types = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.FunctionDef) and n.name == "INPUT_TYPES"
+        ]
+        assert len(input_types) > 0, "CheckpointLoaderLM should have INPUT_TYPES method"
+
+        # Check for load_checkpoint method
+        load_method = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.FunctionDef) and n.name == "load_checkpoint"
+        ]
+        assert len(load_method) > 0, (
+            "CheckpointLoaderLM should have load_checkpoint method"
+        )
+
+
+class TestUNETLoaderLM:
+    """Test UNETLoaderLM node"""
+
+    def test_class_attributes(self):
+        """Test that UNETLoaderLM has required class attributes"""
+        # Import in a way that doesn't require ComfyUI
+        import ast
+
+        filepath = os.path.join(PROJECT_ROOT, "py", "nodes", "unet_loader.py")
+
+        with open(filepath, "r") as f:
+            tree = ast.parse(f.read())
+
+        # Find UNETLoaderLM class
+        classes = {
+            node.name: node for node in ast.walk(tree) if isinstance(node, ast.ClassDef)
+        }
+        assert "UNETLoaderLM" in classes
+
+        cls = classes["UNETLoaderLM"]
+
+        # Check for NAME attribute
+        name_attr = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.Assign)
+            and any(t.id == "NAME" for t in n.targets if isinstance(t, ast.Name))
+        ]
+        assert len(name_attr) > 0, "UNETLoaderLM should have NAME attribute"
+
+        # Check for CATEGORY attribute
+        cat_attr = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.Assign)
+            and any(t.id == "CATEGORY" for t in n.targets if isinstance(t, ast.Name))
+        ]
+        assert len(cat_attr) > 0, "UNETLoaderLM should have CATEGORY attribute"
+
+        # Check for INPUT_TYPES method
+        input_types = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.FunctionDef) and n.name == "INPUT_TYPES"
+        ]
+        assert len(input_types) > 0, "UNETLoaderLM should have INPUT_TYPES method"
+
+        # Check for load_unet method
+        load_method = [
+            n
+            for n in cls.body
+            if isinstance(n, ast.FunctionDef) and n.name == "load_unet"
+        ]
+        assert len(load_method) > 0, "UNETLoaderLM should have load_unet method"
+
+
+class TestUtils:
+    """Test utility functions"""
+
+    def test_get_checkpoint_info_absolute_exists(self):
+        """Test that get_checkpoint_info_absolute function exists in utils"""
+        import ast
+
+        filepath = os.path.join(PROJECT_ROOT, "py", "utils", "utils.py")
+
+        with open(filepath, "r") as f:
+            tree = ast.parse(f.read())
+
+        functions = [
+            node.name for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)
+        ]
+        assert "get_checkpoint_info_absolute" in functions, (
+            "get_checkpoint_info_absolute should exist"
+        )
+
+    def test_format_model_name_for_comfyui_exists(self):
+        """Test that _format_model_name_for_comfyui function exists in utils"""
+        import ast
+
+        filepath = os.path.join(PROJECT_ROOT, "py", "utils", "utils.py")
+
+        with open(filepath, "r") as f:
+            tree = ast.parse(f.read())
+
+        functions = [
+            node.name for node in ast.walk(tree) if isinstance(node, ast.FunctionDef)
+        ]
+        assert "_format_model_name_for_comfyui" in functions, (
+            "_format_model_name_for_comfyui should exist"
+        )

tests/test_tag_fts_index.py

@@ -242,36 +242,70 @@ class TestTagFTSIndexSearch:
             )
 
     def test_search_pagination_ordering_consistency(self, populated_fts):
-        """Test that pagination maintains consistent ordering."""
+        """Test that pagination maintains consistent ordering by post_count."""
         page1 = populated_fts.search("1", limit=10, offset=0)
         page2 = populated_fts.search("1", limit=10, offset=10)
 
         assert len(page1) > 0, "Page 1 should have results"
         assert len(page2) > 0, "Page 2 should have results"
 
-        # Page 2 scores should all be <= Page 1 min score
-        page1_min_score = min(r["rank_score"] for r in page1)
-        page2_max_score = max(r["rank_score"] for r in page2)
+        # Page 2 max post_count should be <= Page 1 min post_count
+        page1_min_posts = min(r["post_count"] for r in page1)
+        page2_max_posts = max(r["post_count"] for r in page2)
 
-        assert page2_max_score <= page1_min_score, (
-            f"Page 2 max score ({page2_max_score}) should be <= Page 1 min score ({page1_min_score})"
+        assert page2_max_posts <= page1_min_posts, (
+            f"Page 2 max post_count ({page2_max_posts}) should be <= Page 1 min post_count ({page1_min_posts})"
         )
 
-    def test_search_rank_score_includes_popularity_weight(self, populated_fts):
-        """Test that rank_score includes post_count popularity weighting."""
+    def test_search_returns_popular_tags_higher(self, populated_fts):
+        """Test that search returns popular tags (higher post_count) first."""
         results = populated_fts.search("1", limit=5)
 
         assert len(results) >= 2, "Need at least 2 results to compare"
 
-        # 1girl has 6M posts, should have higher rank_score than tags with fewer posts
+        # 1girl has 6M posts, should be ranked first
         girl_result = next((r for r in results if r["tag_name"] == "1girl"), None)
         assert girl_result is not None, "1girl should be in results"
+        assert results[0]["tag_name"] == "1girl", (
+            "1girl should be first due to highest post_count"
+        )
 
         # Find a tag with significantly fewer posts
         low_post_result = next((r for r in results if r["post_count"] < 10000), None)
         if low_post_result:
-            assert girl_result["rank_score"] > low_post_result["rank_score"], (
-                f"1girl (6M posts) should have higher score than {low_post_result['tag_name']} ({low_post_result['post_count']} posts)"
+            assert girl_result["post_count"] > low_post_result["post_count"], (
+                f"1girl (6M posts) should have higher post_count than {low_post_result['tag_name']} ({low_post_result['post_count']} posts)"
+            )
+
+    def test_search_popularity_ordering(self, populated_fts):
+        """Test that results are ordered by post_count (popularity)."""
+        results = populated_fts.search("1", limit=20)
+
+        # Get 1girl and 1boy results for comparison
+        girl_result = next((r for r in results if r["tag_name"] == "1girl"), None)
+        boy_result = next((r for r in results if r["tag_name"] == "1boy"), None)
+
+        assert girl_result is not None, "1girl should be in results"
+        assert boy_result is not None, "1boy should be in results"
+
+        # 1girl: 6M posts, 1boy: 1.4M posts
+        assert girl_result["post_count"] == 6008644, "1girl should have 6M posts"
+        assert boy_result["post_count"] == 1405457, "1boy should have 1.4M posts"
+
+        # 1girl should rank higher due to higher post_count
+        girl_rank = results.index(girl_result)
+        boy_rank = results.index(boy_result)
+        assert girl_rank < boy_rank, (
+            f"1girl should rank higher than 1boy due to higher post_count "
+            f"(girl rank: {girl_rank}, boy rank: {boy_rank})"
+        )
+
+        # Verify results are sorted by post_count descending
+        for i in range(len(results) - 1):
+            assert results[i]["post_count"] >= results[i + 1]["post_count"], (
+                f"Results should be sorted by post_count descending: "
+                f"{results[i]['tag_name']} ({results[i]['post_count']}) >= "
+                f"{results[i + 1]['tag_name']} ({results[i + 1]['post_count']})"
             )
 
 

vue-widgets/src/components/LoraCyclerWidget.vue

@@ -2,8 +2,8 @@
   <div class="lora-cycler-widget">
     <LoraCyclerSettingsView
       :current-index="state.currentIndex.value"
-      :total-count="state.totalCount.value"
-      :current-lora-name="state.currentLoraName.value"
+      :total-count="displayTotalCount"
+      :current-lora-name="displayLoraName"
       :current-lora-filename="state.currentLoraFilename.value"
       :model-strength="state.modelStrength.value"
       :clip-strength="state.clipStrength.value"
@@ -16,11 +16,14 @@
       :is-pause-disabled="hasQueuedPrompts"
       :is-workflow-executing="state.isWorkflowExecuting.value"
       :executing-repeat-step="state.executingRepeatStep.value"
+      :include-no-lora="state.includeNoLora.value"
+      :is-no-lora="isNoLora"
       @update:current-index="handleIndexUpdate"
       @update:model-strength="state.modelStrength.value = $event"
       @update:clip-strength="state.clipStrength.value = $event"
       @update:use-custom-clip-range="handleUseCustomClipRangeChange"
       @update:repeat-count="handleRepeatCountChange"
+      @update:include-no-lora="handleIncludeNoLoraChange"
       @toggle-pause="handleTogglePause"
       @reset-index="handleResetIndex"
       @open-lora-selector="isModalOpen = true"
@@ -30,6 +33,7 @@
       :visible="isModalOpen"
       :lora-list="cachedLoraList"
       :current-index="state.currentIndex.value"
+      :include-no-lora="state.includeNoLora.value"
       @close="isModalOpen = false"
       @select="handleModalSelect"
     />
@@ -37,7 +41,7 @@
 </template>
 
 <script setup lang="ts">
-import { onMounted, ref } from 'vue'
+import { onMounted, ref, computed } from 'vue'
 import LoraCyclerSettingsView from './lora-cycler/LoraCyclerSettingsView.vue'
 import LoraListModal from './lora-cycler/LoraListModal.vue'
 import { useLoraCyclerState } from '../composables/useLoraCyclerState'
@@ -102,6 +106,31 @@ const isModalOpen = ref(false)
 // Cache for LoRA list (used by modal)
 const cachedLoraList = ref<LoraItem[]>([])
 
+// Computed: display total count (includes no lora option if enabled)
+const displayTotalCount = computed(() => {
+  const baseCount = state.totalCount.value
+  return state.includeNoLora.value ? baseCount + 1 : baseCount
+})
+
+// Computed: display LoRA name (shows "No LoRA" if on the last index and includeNoLora is enabled)
+const displayLoraName = computed(() => {
+  const currentIndex = state.currentIndex.value
+  const totalCount = state.totalCount.value
+  
+  // If includeNoLora is enabled and we're on the last position (no lora slot)
+  if (state.includeNoLora.value && currentIndex === totalCount + 1) {
+    return 'No LoRA'
+  }
+  
+  // Otherwise show the normal LoRA name
+  return state.currentLoraName.value
+})
+
+// Computed: check if currently on "No LoRA" option
+const isNoLora = computed(() => {
+  return state.includeNoLora.value && state.currentIndex.value === state.totalCount.value + 1
+})
+
 // Get pool config from connected node
 const getPoolConfig = (): LoraPoolConfig | null => {
   // Check if getPoolConfig method exists on node (added by main.ts)
@@ -113,7 +142,17 @@ const getPoolConfig = (): LoraPoolConfig | null => {
 
 // Update display from LoRA list and index
 const updateDisplayFromLoraList = (loraList: LoraItem[], index: number) => {
-  if (loraList.length > 0 && index > 0 && index <= loraList.length) {
+  const actualLoraCount = loraList.length
+  
+  // If index is beyond actual LoRA count, it means we're on the "no lora" option
+  if (state.includeNoLora.value && index === actualLoraCount + 1) {
+    state.currentLoraName.value = 'No LoRA'
+    state.currentLoraFilename.value = 'No LoRA'
+    return
+  }
+  
+  // Otherwise, show normal LoRA info
+  if (actualLoraCount > 0 && index > 0 && index <= actualLoraCount) {
     const currentLora = loraList[index - 1]
     if (currentLora) {
       state.currentLoraName.value = currentLora.file_name
@@ -124,6 +163,14 @@ const updateDisplayFromLoraList = (loraList: LoraItem[], index: number) => {
 
 // Handle index update from user
 const handleIndexUpdate = async (newIndex: number) => {
+  // Calculate max valid index (includes no lora slot if enabled)
+  const maxIndex = state.includeNoLora.value 
+    ? state.totalCount.value + 1 
+    : state.totalCount.value
+  
+  // Clamp index to valid range
+  const clampedIndex = Math.max(1, Math.min(newIndex, maxIndex || 1))
+  
   // Reset execution state when user manually changes index
   // This ensures the next execution starts from the user-set index
   ;(props.widget as any)[HAS_EXECUTED] = false
@@ -134,14 +181,14 @@ const handleIndexUpdate = async (newIndex: number) => {
   executionQueue.length = 0
   hasQueuedPrompts.value = false
 
-  state.setIndex(newIndex)
+  state.setIndex(clampedIndex)
 
   // Refresh list to update current LoRA display
   try {
     const poolConfig = getPoolConfig()
     const loraList = await state.fetchCyclerList(poolConfig)
     cachedLoraList.value = loraList
-    updateDisplayFromLoraList(loraList, newIndex)
+    updateDisplayFromLoraList(loraList, clampedIndex)
   } catch (error) {
     console.error('[LoraCyclerWidget] Error updating index:', error)
   }
@@ -169,6 +216,17 @@ const handleRepeatCountChange = (newValue: number) => {
   state.displayRepeatUsed.value = 0
 }
 
+// Handle include no lora toggle
+const handleIncludeNoLoraChange = (newValue: boolean) => {
+  state.includeNoLora.value = newValue
+  
+  // If turning off and current index is beyond the actual LoRA count,
+  // clamp it to the last valid LoRA index
+  if (!newValue && state.currentIndex.value > state.totalCount.value) {
+    state.currentIndex.value = Math.max(1, state.totalCount.value)
+  }
+}
+
 // Handle pause toggle
 const handleTogglePause = () => {
   state.togglePause()

vue-widgets/src/components/LoraPoolWidget.vue

@@ -8,6 +8,9 @@
       :exclude-tags="state.excludeTags.value"
       :include-folders="state.includeFolders.value"
       :exclude-folders="state.excludeFolders.value"
+      :include-patterns="state.includePatterns.value"
+      :exclude-patterns="state.excludePatterns.value"
+      :use-regex="state.useRegex.value"
       :no-credit-required="state.noCreditRequired.value"
       :allow-selling="state.allowSelling.value"
       :preview-items="state.previewItems.value"
@@ -16,6 +19,9 @@
       @open-modal="openModal"
       @update:include-folders="state.includeFolders.value = $event"
       @update:exclude-folders="state.excludeFolders.value = $event"
+      @update:include-patterns="state.includePatterns.value = $event"
+      @update:exclude-patterns="state.excludePatterns.value = $event"
+      @update:use-regex="state.useRegex.value = $event"
       @update:no-credit-required="state.noCreditRequired.value = $event"
       @update:allow-selling="state.allowSelling.value = $event"
       @refresh="state.refreshPreview"

vue-widgets/src/components/lora-cycler/LoraCyclerSettingsView.vue

@@ -13,7 +13,9 @@
           @click="handleOpenSelector"
         >
           <span class="progress-label">{{ isWorkflowExecuting ? 'Using LoRA:' : 'Next LoRA:' }}</span>
-          <span class="progress-name clickable" :class="{ disabled: isPauseDisabled }" :title="currentLoraFilename">
+          <span class="progress-name clickable" 
+                :class="{ disabled: isPauseDisabled, 'no-lora': isNoLora }" 
+                :title="currentLoraFilename">
             {{ currentLoraName || 'None' }}
             <svg class="selector-icon" viewBox="0 0 24 24" fill="currentColor">
               <path d="M7 10l5 5 5-5z"/>
@@ -160,6 +162,27 @@
         />
       </div>
     </div>
+
+    <!-- Include No LoRA Toggle -->
+    <div class="setting-section">
+      <div class="section-header-with-toggle">
+        <label class="setting-label">
+          Add "No LoRA" step
+        </label>
+        <button
+          type="button"
+          class="toggle-switch"
+          :class="{ 'toggle-switch--active': includeNoLora }"
+          @click="$emit('update:includeNoLora', !includeNoLora)"
+          role="switch"
+          :aria-checked="includeNoLora"
+          title="Add an iteration without LoRA for comparison"
+        >
+          <span class="toggle-switch__track"></span>
+          <span class="toggle-switch__thumb"></span>
+        </button>
+      </div>
+    </div>
   </div>
 </template>
 
@@ -182,6 +205,8 @@ const props = defineProps<{
   isPauseDisabled: boolean
   isWorkflowExecuting: boolean
   executingRepeatStep: number
+  includeNoLora: boolean
+  isNoLora?: boolean
 }>()
 
 const emit = defineEmits<{
@@ -190,6 +215,7 @@ const emit = defineEmits<{
   'update:clipStrength': [value: number]
   'update:useCustomClipRange': [value: boolean]
   'update:repeatCount': [value: number]
+  'update:includeNoLora': [value: boolean]
   'toggle-pause': []
   'reset-index': []
   'open-lora-selector': []
@@ -346,6 +372,16 @@ const onRepeatBlur = (event: Event) => {
   color: rgba(191, 219, 254, 1);
 }
 
+.progress-name.no-lora {
+  font-style: italic;
+  color: rgba(226, 232, 240, 0.6);
+}
+
+.progress-name.clickable.no-lora:hover:not(.disabled) {
+  background: rgba(160, 174, 192, 0.2);
+  color: rgba(226, 232, 240, 0.8);
+}
+
 .progress-name.clickable.disabled {
   cursor: not-allowed;
   opacity: 0.5;

vue-widgets/src/components/lora-cycler/LoraListModal.vue

@@ -35,7 +35,10 @@
         v-for="item in filteredList"
         :key="item.index"
         class="lora-item"
-        :class="{ active: currentIndex === item.index }"
+        :class="{ 
+          active: currentIndex === item.index,
+          'no-lora-item': item.lora.file_name === 'No LoRA'
+        }"
         @mouseenter="showPreview(item.lora.file_name, $event)"
         @mouseleave="hidePreview"
         @click="selectLora(item.index)"
@@ -65,6 +68,7 @@ const props = defineProps<{
   visible: boolean
   loraList: LoraItem[]
   currentIndex: number
+  includeNoLora?: boolean
 }>()
 
 const emit = defineEmits<{
@@ -79,7 +83,8 @@ const searchInputRef = ref<HTMLInputElement | null>(null)
 let previewTooltip: any = null
 
 const subtitleText = computed(() => {
-  const total = props.loraList.length
+  const baseTotal = props.loraList.length
+  const total = props.includeNoLora ? baseTotal + 1 : baseTotal
   const filtered = filteredList.value.length
   if (filtered === total) {
     return `Total: ${total} LoRA${total !== 1 ? 's' : ''}`
@@ -88,11 +93,19 @@ const subtitleText = computed(() => {
 })
 
 const filteredList = computed<LoraListItem[]>(() => {
-  const list = props.loraList.map((lora, idx) => ({
+  const list: LoraListItem[] = props.loraList.map((lora, idx) => ({
     index: idx + 1,
     lora
   }))
 
+  // Add "No LoRA" option at the end if includeNoLora is enabled
+  if (props.includeNoLora) {
+    list.push({
+      index: list.length + 1,
+      lora: { file_name: 'No LoRA' } as LoraItem
+    })
+  }
+
   if (!searchQuery.value.trim()) {
     return list
   }
@@ -303,6 +316,15 @@ onUnmounted(() => {
   font-weight: 500;
 }
 
+.lora-item.no-lora-item .lora-name {
+  font-style: italic;
+  color: rgba(226, 232, 240, 0.6);
+}
+
+.lora-item.no-lora-item:hover .lora-name {
+  color: rgba(226, 232, 240, 0.8);
+}
+
 .no-results {
   padding: 32px 20px;
   text-align: center;

vue-widgets/src/components/lora-pool/LoraPoolSummaryView.vue

@@ -24,6 +24,15 @@
         @edit-exclude="$emit('open-modal', 'excludeFolders')"
       />
 
+      <NamePatternsSection
+        :include-patterns="includePatterns"
+        :exclude-patterns="excludePatterns"
+        :use-regex="useRegex"
+        @update:include-patterns="$emit('update:includePatterns', $event)"
+        @update:exclude-patterns="$emit('update:excludePatterns', $event)"
+        @update:use-regex="$emit('update:useRegex', $event)"
+      />
+
       <LicenseSection
         :no-credit-required="noCreditRequired"
         :allow-selling="allowSelling"
@@ -46,6 +55,7 @@
 import BaseModelSection from './sections/BaseModelSection.vue'
 import TagsSection from './sections/TagsSection.vue'
 import FoldersSection from './sections/FoldersSection.vue'
+import NamePatternsSection from './sections/NamePatternsSection.vue'
 import LicenseSection from './sections/LicenseSection.vue'
 import LoraPoolPreview from './LoraPoolPreview.vue'
 import type { BaseModelOption, LoraItem } from '../../composables/types'
@@ -61,6 +71,10 @@ defineProps<{
   // Folders
   includeFolders: string[]
   excludeFolders: string[]
+  // Name patterns
+  includePatterns: string[]
+  excludePatterns: string[]
+  useRegex: boolean
   // License
   noCreditRequired: boolean
   allowSelling: boolean
@@ -74,6 +88,9 @@ defineEmits<{
   'open-modal': [modal: ModalType]
   'update:includeFolders': [value: string[]]
   'update:excludeFolders': [value: string[]]
+  'update:includePatterns': [value: string[]]
+  'update:excludePatterns': [value: string[]]
+  'update:useRegex': [value: boolean]
   'update:noCreditRequired': [value: boolean]
   'update:allowSelling': [value: boolean]
   refresh: []

vue-widgets/src/components/lora-pool/sections/NamePatternsSection.vue

@@ -0,0 +1,255 @@
+<template>
+  <div class="section">
+    <div class="section__header">
+      <span class="section__title">NAME PATTERNS</span>
+      <label class="section__toggle">
+        <input
+          type="checkbox"
+          :checked="useRegex"
+          @change="$emit('update:useRegex', ($event.target as HTMLInputElement).checked)"
+        />
+        <span class="section__toggle-label">Use Regex</span>
+      </label>
+    </div>
+    <div class="section__columns">
+      <!-- Include column -->
+      <div class="section__column">
+        <div class="section__column-header">
+          <span class="section__column-title section__column-title--include">INCLUDE</span>
+        </div>
+        <div class="section__input-wrapper">
+          <input
+            type="text"
+            v-model="includeInput"
+            :placeholder="useRegex ? 'Add regex pattern...' : 'Add text pattern...'"
+            class="section__input"
+            @keydown.enter="addInclude"
+          />
+          <button type="button" class="section__add-btn" @click="addInclude">+</button>
+        </div>
+        <div class="section__patterns">
+          <FilterChip
+            v-for="pattern in includePatterns"
+            :key="pattern"
+            :label="pattern"
+            variant="include"
+            removable
+            @remove="removeInclude(pattern)"
+          />
+          <div v-if="includePatterns.length === 0" class="section__empty">
+            {{ useRegex ? 'No regex patterns' : 'No text patterns' }}
+          </div>
+        </div>
+      </div>
+
+      <!-- Exclude column -->
+      <div class="section__column">
+        <div class="section__column-header">
+          <span class="section__column-title section__column-title--exclude">EXCLUDE</span>
+        </div>
+        <div class="section__input-wrapper">
+          <input
+            type="text"
+            v-model="excludeInput"
+            :placeholder="useRegex ? 'Add regex pattern...' : 'Add text pattern...'"
+            class="section__input"
+            @keydown.enter="addExclude"
+          />
+          <button type="button" class="section__add-btn" @click="addExclude">+</button>
+        </div>
+        <div class="section__patterns">
+          <FilterChip
+            v-for="pattern in excludePatterns"
+            :key="pattern"
+            :label="pattern"
+            variant="exclude"
+            removable
+            @remove="removeExclude(pattern)"
+          />
+          <div v-if="excludePatterns.length === 0" class="section__empty">
+            {{ useRegex ? 'No regex patterns' : 'No text patterns' }}
+          </div>
+        </div>
+      </div>
+    </div>
+  </div>
+</template>
+
+<script setup lang="ts">
+import { ref } from 'vue'
+import FilterChip from '../shared/FilterChip.vue'
+
+const props = defineProps<{
+  includePatterns: string[]
+  excludePatterns: string[]
+  useRegex: boolean
+}>()
+
+const emit = defineEmits<{
+  'update:includePatterns': [value: string[]]
+  'update:excludePatterns': [value: string[]]
+  'update:useRegex': [value: boolean]
+}>()
+
+const includeInput = ref('')
+const excludeInput = ref('')
+
+const addInclude = () => {
+  const pattern = includeInput.value.trim()
+  if (pattern && !props.includePatterns.includes(pattern)) {
+    emit('update:includePatterns', [...props.includePatterns, pattern])
+    includeInput.value = ''
+  }
+}
+
+const addExclude = () => {
+  const pattern = excludeInput.value.trim()
+  if (pattern && !props.excludePatterns.includes(pattern)) {
+    emit('update:excludePatterns', [...props.excludePatterns, pattern])
+    excludeInput.value = ''
+  }
+}
+
+const removeInclude = (pattern: string) => {
+  emit('update:includePatterns', props.includePatterns.filter(p => p !== pattern))
+}
+
+const removeExclude = (pattern: string) => {
+  emit('update:excludePatterns', props.excludePatterns.filter(p => p !== pattern))
+}
+</script>
+
+<style scoped>
+.section {
+  margin-bottom: 16px;
+}
+
+.section__header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 8px;
+}
+
+.section__title {
+  font-size: 10px;
+  font-weight: 600;
+  text-transform: uppercase;
+  letter-spacing: 0.05em;
+  color: var(--fg-color, #fff);
+  opacity: 0.6;
+}
+
+.section__toggle {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  cursor: pointer;
+  font-size: 11px;
+  color: var(--fg-color, #fff);
+  opacity: 0.7;
+}
+
+.section__toggle input[type="checkbox"] {
+  margin: 0;
+  width: 14px;
+  height: 14px;
+  cursor: pointer;
+}
+
+.section__toggle-label {
+  font-weight: 500;
+}
+
+.section__columns {
+  display: grid;
+  grid-template-columns: 1fr 1fr;
+  gap: 12px;
+}
+
+.section__column {
+  min-width: 0;
+}
+
+.section__column-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  margin-bottom: 6px;
+}
+
+.section__column-title {
+  font-size: 9px;
+  font-weight: 500;
+  text-transform: uppercase;
+  letter-spacing: 0.03em;
+}
+
+.section__column-title--include {
+  color: #4299e1;
+}
+
+.section__column-title--exclude {
+  color: #ef4444;
+}
+
+.section__input-wrapper {
+  display: flex;
+  gap: 4px;
+  margin-bottom: 8px;
+}
+
+.section__input {
+  flex: 1;
+  min-width: 0;
+  padding: 6px 8px;
+  background: var(--comfy-input-bg, #333);
+  border: 1px solid var(--comfy-input-border, #444);
+  border-radius: 4px;
+  color: var(--fg-color, #fff);
+  font-size: 12px;
+  outline: none;
+}
+
+.section__input:focus {
+  border-color: #4299e1;
+}
+
+.section__add-btn {
+  width: 28px;
+  height: 28px;
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  background: var(--comfy-input-bg, #333);
+  border: 1px solid var(--comfy-input-border, #444);
+  border-radius: 4px;
+  color: var(--fg-color, #fff);
+  font-size: 16px;
+  font-weight: 500;
+  cursor: pointer;
+  transition: all 0.15s;
+}
+
+.section__add-btn:hover {
+  background: var(--comfy-input-bg-hover, #444);
+  border-color: #4299e1;
+}
+
+.section__patterns {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 4px;
+  min-height: 22px;
+}
+
+.section__empty {
+  font-size: 10px;
+  color: var(--fg-color, #fff);
+  opacity: 0.3;
+  font-style: italic;
+  min-height: 22px;
+  display: flex;
+  align-items: center;
+}
+</style>

vue-widgets/src/composables/types.ts

@@ -10,6 +10,12 @@ export interface LoraPoolConfig {
       noCreditRequired: boolean
       allowSelling: boolean
     }
+    namePatterns: {
+      include: string[]
+      exclude: string[]
+      useRegex: boolean
+    }
+    includeEmptyLora?: boolean  // Optional, deprecated (moved to Cycler)
   }
   preview: { matchCount: number; lastUpdated: number }
 }
@@ -84,6 +90,8 @@ export interface CyclerConfig {
   repeat_count: number        // How many times each LoRA should repeat (default: 1)
   repeat_used: number         // How many times current index has been used
   is_paused: boolean          // Whether iteration is paused
+  // Include "no LoRA" option in cycle
+  include_no_lora: boolean    // Whether to include empty LoRA option
 }
 
 // Widget config union type

vue-widgets/src/composables/useLoraCyclerState.ts

@@ -4,6 +4,7 @@ import type { ComponentWidget, CyclerConfig, LoraPoolConfig } from './types'
 export interface CyclerLoraItem {
   file_name: string
   model_name: string
+  file_path: string
 }
 
 export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
@@ -34,6 +35,7 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
   const repeatUsed = ref(0)         // How many times current index has been used (internal tracking)
   const displayRepeatUsed = ref(0)  // For UI display, deferred updates like currentIndex
   const isPaused = ref(false)       // Whether iteration is paused
+  const includeNoLora = ref(false)  // Whether to include empty LoRA option in cycle
 
   // Execution progress tracking (visual feedback)
   const isWorkflowExecuting = ref(false)    // Workflow is currently running
@@ -58,6 +60,7 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
         repeat_count: repeatCount.value,
         repeat_used: repeatUsed.value,
         is_paused: isPaused.value,
+        include_no_lora: includeNoLora.value,
       }
     }
     return {
@@ -75,6 +78,7 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
       repeat_count: repeatCount.value,
       repeat_used: repeatUsed.value,
       is_paused: isPaused.value,
+      include_no_lora: includeNoLora.value,
     }
   }
 
@@ -93,12 +97,13 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
       sortBy.value = config.sort_by || 'filename'
       currentLoraName.value = config.current_lora_name || ''
       currentLoraFilename.value = config.current_lora_filename || ''
-      // Advanced index control features
-      repeatCount.value = config.repeat_count ?? 1
-      repeatUsed.value = config.repeat_used ?? 0
-      isPaused.value = config.is_paused ?? false
-      // Note: execution_index and next_index are not restored from config
-      // as they are transient values used only during batch execution
+    // Advanced index control features
+    repeatCount.value = config.repeat_count ?? 1
+    repeatUsed.value = config.repeat_used ?? 0
+    isPaused.value = config.is_paused ?? false
+    includeNoLora.value = config.include_no_lora ?? false
+    // Note: execution_index and next_index are not restored from config
+    // as they are transient values used only during batch execution
     } finally {
       isRestoring = false
     }
@@ -111,7 +116,9 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
     // Calculate the next index (wrap to 1 if at end)
     const current = executionIndex.value ?? currentIndex.value
     let next = current + 1
-    if (totalCount.value > 0 && next > totalCount.value) {
+    // Total count includes no lora option if enabled
+    const effectiveTotalCount = includeNoLora.value ? totalCount.value + 1 : totalCount.value
+    if (effectiveTotalCount > 0 && next > effectiveTotalCount) {
       next = 1
     }
     nextIndex.value = next
@@ -122,7 +129,9 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
     if (nextIndex.value === null) {
       // First execution uses current_index, so next is current + 1
       let next = currentIndex.value + 1
-      if (totalCount.value > 0 && next > totalCount.value) {
+      // Total count includes no lora option if enabled
+      const effectiveTotalCount = includeNoLora.value ? totalCount.value + 1 : totalCount.value
+      if (effectiveTotalCount > 0 && next > effectiveTotalCount) {
         next = 1
       }
       nextIndex.value = next
@@ -230,7 +239,9 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
 
   // Set index manually
   const setIndex = (index: number) => {
-    if (index >= 1 && index <= totalCount.value) {
+    // Total count includes no lora option if enabled
+    const effectiveTotalCount = includeNoLora.value ? totalCount.value + 1 : totalCount.value
+    if (index >= 1 && index <= effectiveTotalCount) {
       currentIndex.value = index
     }
   }
@@ -272,6 +283,7 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
     repeatCount,
     repeatUsed,
     isPaused,
+    includeNoLora,
   ], () => {
     widget.value = buildConfig()
   }, { deep: true })
@@ -294,6 +306,7 @@ export function useLoraCyclerState(widget: ComponentWidget<CyclerConfig>) {
     repeatUsed,
     displayRepeatUsed,
     isPaused,
+    includeNoLora,
     isWorkflowExecuting,
     executingRepeatStep,
 

vue-widgets/src/composables/useLoraPoolApi.ts

@@ -62,6 +62,9 @@ export function useLoraPoolApi() {
     foldersExclude?: string[]
     noCreditRequired?: boolean
     allowSelling?: boolean
+    namePatternsInclude?: string[]
+    namePatternsExclude?: string[]
+    namePatternsUseRegex?: boolean
     page?: number
     pageSize?: number
   }
@@ -92,6 +95,13 @@ export function useLoraPoolApi() {
         urlParams.set('allow_selling_generated_content', String(params.allowSelling))
       }
 
+      // Name pattern filters
+      params.namePatternsInclude?.forEach(pattern => urlParams.append('name_pattern_include', pattern))
+      params.namePatternsExclude?.forEach(pattern => urlParams.append('name_pattern_exclude', pattern))
+      if (params.namePatternsUseRegex !== undefined) {
+        urlParams.set('name_pattern_use_regex', String(params.namePatternsUseRegex))
+      }
+
       const response = await fetch(`/api/lm/loras/list?${urlParams}`)
       const data = await response.json()
 

vue-widgets/src/composables/useLoraPoolState.ts

@@ -24,6 +24,9 @@ export function useLoraPoolState(widget: ComponentWidget<LoraPoolConfig>) {
   const excludeFolders = ref<string[]>([])
   const noCreditRequired = ref(false)
   const allowSelling = ref(false)
+  const includePatterns = ref<string[]>([])
+  const excludePatterns = ref<string[]>([])
+  const useRegex = ref(false)
 
   // Available options from API
   const availableBaseModels = ref<BaseModelOption[]>([])
@@ -52,6 +55,11 @@ export function useLoraPoolState(widget: ComponentWidget<LoraPoolConfig>) {
         license: {
           noCreditRequired: noCreditRequired.value,
           allowSelling: allowSelling.value
+        },
+        namePatterns: {
+          include: includePatterns.value,
+          exclude: excludePatterns.value,
+          useRegex: useRegex.value
         }
       },
       preview: {
@@ -94,6 +102,9 @@ export function useLoraPoolState(widget: ComponentWidget<LoraPoolConfig>) {
       updateIfChanged(excludeFolders, filters.folders?.exclude || [])
       updateIfChanged(noCreditRequired, filters.license?.noCreditRequired ?? false)
       updateIfChanged(allowSelling, filters.license?.allowSelling ?? false)
+      updateIfChanged(includePatterns, filters.namePatterns?.include || [])
+      updateIfChanged(excludePatterns, filters.namePatterns?.exclude || [])
+      updateIfChanged(useRegex, filters.namePatterns?.useRegex ?? false)
 
       // matchCount doesn't trigger watchers, so direct assignment is fine
       matchCount.value = preview?.matchCount || 0
@@ -125,6 +136,9 @@ export function useLoraPoolState(widget: ComponentWidget<LoraPoolConfig>) {
       foldersExclude: excludeFolders.value,
       noCreditRequired: noCreditRequired.value || undefined,
       allowSelling: allowSelling.value || undefined,
+      namePatternsInclude: includePatterns.value,
+      namePatternsExclude: excludePatterns.value,
+      namePatternsUseRegex: useRegex.value,
       pageSize: 6
     })
 
@@ -150,7 +164,10 @@ export function useLoraPoolState(widget: ComponentWidget<LoraPoolConfig>) {
     includeFolders,
     excludeFolders,
     noCreditRequired,
-    allowSelling
+    allowSelling,
+    includePatterns,
+    excludePatterns,
+    useRegex
   ], onFilterChange, { deep: true })
 
   return {
@@ -162,6 +179,9 @@ export function useLoraPoolState(widget: ComponentWidget<LoraPoolConfig>) {
     excludeFolders,
     noCreditRequired,
     allowSelling,
+    includePatterns,
+    excludePatterns,
+    useRegex,
 
     // Available options
     availableBaseModels,

vue-widgets/src/main.ts

@@ -13,12 +13,12 @@ import {
 } from './mode-change-handler'
 
 const LORA_POOL_WIDGET_MIN_WIDTH = 500
-const LORA_POOL_WIDGET_MIN_HEIGHT = 400
+const LORA_POOL_WIDGET_MIN_HEIGHT = 520
 const LORA_RANDOMIZER_WIDGET_MIN_WIDTH = 500
 const LORA_RANDOMIZER_WIDGET_MIN_HEIGHT = 448
 const LORA_RANDOMIZER_WIDGET_MAX_HEIGHT = LORA_RANDOMIZER_WIDGET_MIN_HEIGHT
 const LORA_CYCLER_WIDGET_MIN_WIDTH = 380
-const LORA_CYCLER_WIDGET_MIN_HEIGHT = 314
+const LORA_CYCLER_WIDGET_MIN_HEIGHT = 344
 const LORA_CYCLER_WIDGET_MAX_HEIGHT = LORA_CYCLER_WIDGET_MIN_HEIGHT
 const JSON_DISPLAY_WIDGET_MIN_WIDTH = 300
 const JSON_DISPLAY_WIDGET_MIN_HEIGHT = 200

vue-widgets/tests/composables/useLoraCyclerState.test.ts

@@ -84,7 +84,8 @@ describe('useLoraCyclerState', () => {
         current_lora_filename: '',
         repeat_count: 1,
         repeat_used: 0,
-        is_paused: false
+        is_paused: false,
+        include_no_lora: false
       })
 
       expect(state.currentIndex.value).toBe(5)

vue-widgets/tests/fixtures/mockConfigs.ts

@@ -24,6 +24,7 @@ export function createMockCyclerConfig(overrides: Partial<CyclerConfig> = {}): C
     repeat_count: 1,
     repeat_used: 0,
     is_paused: false,
+    include_no_lora: false,
     ...overrides
   }
 }
@@ -54,7 +55,8 @@ export function createMockPoolConfig(overrides: Partial<LoraPoolConfig> = {}): L
 export function createMockLoraList(count: number = 5): CyclerLoraItem[] {
   return Array.from({ length: count }, (_, i) => ({
     file_name: `lora${i + 1}.safetensors`,
-    model_name: `LoRA Model ${i + 1}`
+    model_name: `LoRA Model ${i + 1}`,
+    file_path: `/models/loras/lora${i + 1}.safetensors`
   }))
 }
 

web/comfyui/autocomplete.js

@@ -1905,10 +1905,38 @@ class AutoComplete {
 
         // For regular tag autocomplete (no command), only replace the last space-separated token
         // This allows "hello 1gi" + selecting "1girl" to become "hello 1girl, "
+        // However, if the user typed a multi-word phrase that matches a tag (e.g., "looking to the side"
+        // matching "looking_to_the_side"), replace the entire phrase instead of just the last word.
         // Command mode (e.g., "/char miku") should replace the entire command+search
         let searchTerm = fullSearchTerm;
         if (this.modelType === 'prompt' && this.searchType === 'custom_words' && !this.activeCommand) {
-            searchTerm = this._getLastSpaceToken(fullSearchTerm);
+            // Check if the selectedItem exists and its tag_name matches the full search term
+            // when converted to underscore format (Danbooru convention)
+            const selectedItem = this.selectedIndex >= 0 ? this.items[this.selectedIndex] : null;
+            const selectedTagName = selectedItem && typeof selectedItem === 'object' && 'tag_name' 
+                ? selectedItem.tag_name 
+                : null;
+            
+            // Convert full search term to underscore format and check if it matches selected tag
+            // Normalize multiple spaces to single underscore for matching (e.g., "looking  to   the side" -> "looking_to_the_side")
+            const underscoreVersion = fullSearchTerm.replace(/ +/g, '_').toLowerCase();
+            const selectedTagLower = selectedTagName?.toLowerCase() ?? '';
+            
+            // If multi-word search term is a prefix or suffix of the selected tag,
+            // replace the entire phrase. This handles cases where user types partial tag name.
+            // Examples:
+            // - "looking to the" -> "looking_to_the_side" (prefix match)
+            // - "to the side" -> "looking_to_the_side" (suffix match)
+            // - "looking to the side" -> "looking_to_the_side" (exact match)
+            if (fullSearchTerm.includes(' ') && (
+                selectedTagLower.startsWith(underscoreVersion) ||
+                selectedTagLower.endsWith(underscoreVersion) ||
+                underscoreVersion === selectedTagLower
+            )) {
+                searchTerm = fullSearchTerm;
+            } else {
+                searchTerm = this._getLastSpaceToken(fullSearchTerm);
+            }
         }
 
         const searchStartPos = caretPos - searchTerm.length;

web/comfyui/loras_widget.js

@@ -14,6 +14,7 @@ import { initDrag, createContextMenu, initHeaderDrag, initReorderDrag, handleKey
 import { forwardMiddleMouseToCanvas } from "./utils.js";
 import { PreviewTooltip } from "./preview_tooltip.js";
 import { ensureLmStyles } from "./lm_styles_loader.js";
+import { getStrengthStepPreference } from "./settings.js";
 
 export function addLorasWidget(node, name, opts, callback) {
   ensureLmStyles();
@@ -416,7 +417,7 @@ export function addLorasWidget(node, name, opts, callback) {
         const loraIndex = lorasData.findIndex(l => l.name === name);
         
         if (loraIndex >= 0) {
-          lorasData[loraIndex].strength = (parseFloat(lorasData[loraIndex].strength) - 0.05).toFixed(2);
+          lorasData[loraIndex].strength = (parseFloat(lorasData[loraIndex].strength) - getStrengthStepPreference()).toFixed(2);
           // Sync clipStrength if collapsed
           syncClipStrengthIfCollapsed(lorasData[loraIndex]);
           
@@ -488,7 +489,7 @@ export function addLorasWidget(node, name, opts, callback) {
         const loraIndex = lorasData.findIndex(l => l.name === name);
         
         if (loraIndex >= 0) {
-          lorasData[loraIndex].strength = (parseFloat(lorasData[loraIndex].strength) + 0.05).toFixed(2);
+          lorasData[loraIndex].strength = (parseFloat(lorasData[loraIndex].strength) + getStrengthStepPreference()).toFixed(2);
           // Sync clipStrength if collapsed
           syncClipStrengthIfCollapsed(lorasData[loraIndex]);
           
@@ -541,7 +542,7 @@ export function addLorasWidget(node, name, opts, callback) {
           const loraIndex = lorasData.findIndex(l => l.name === name);
           
           if (loraIndex >= 0) {
-            lorasData[loraIndex].clipStrength = (parseFloat(lorasData[loraIndex].clipStrength) - 0.05).toFixed(2);
+            lorasData[loraIndex].clipStrength = (parseFloat(lorasData[loraIndex].clipStrength) - getStrengthStepPreference()).toFixed(2);
             
             const newValue = formatLoraValue(lorasData);
             updateWidgetValue(newValue);
@@ -611,7 +612,7 @@ export function addLorasWidget(node, name, opts, callback) {
           const loraIndex = lorasData.findIndex(l => l.name === name);
           
           if (loraIndex >= 0) {
-            lorasData[loraIndex].clipStrength = (parseFloat(lorasData[loraIndex].clipStrength) + 0.05).toFixed(2);
+            lorasData[loraIndex].clipStrength = (parseFloat(lorasData[loraIndex].clipStrength) + getStrengthStepPreference()).toFixed(2);
             
             const newValue = formatLoraValue(lorasData);
             updateWidgetValue(newValue);

web/comfyui/settings.js

@@ -24,6 +24,9 @@ const NEW_TAB_TEMPLATE_DEFAULT = "Default";
 
 const NEW_TAB_ZOOM_LEVEL = 0.8;
 
+const STRENGTH_STEP_SETTING_ID = "loramanager.strength_step";
+const STRENGTH_STEP_DEFAULT = 0.05;
+
 // ============================================================================
 // Helper Functions
 // ============================================================================
@@ -232,6 +235,32 @@ const getNewTabTemplatePreference = (() => {
     };
 })();
 
+const getStrengthStepPreference = (() => {
+    let settingsUnavailableLogged = false;
+
+    return () => {
+        const settingManager = app?.extensionManager?.setting;
+        if (!settingManager || typeof settingManager.get !== "function") {
+            if (!settingsUnavailableLogged) {
+                console.warn("LoRA Manager: settings API unavailable, using default strength step.");
+                settingsUnavailableLogged = true;
+            }
+            return STRENGTH_STEP_DEFAULT;
+        }
+
+        try {
+            const value = settingManager.get(STRENGTH_STEP_SETTING_ID);
+            return value ?? STRENGTH_STEP_DEFAULT;
+        } catch (error) {
+            if (!settingsUnavailableLogged) {
+                console.warn("LoRA Manager: unable to read strength step setting, using default.", error);
+                settingsUnavailableLogged = true;
+            }
+            return STRENGTH_STEP_DEFAULT;
+        }
+    };
+})();
+
 // ============================================================================
 // Register Extension with All Settings
 // ============================================================================
@@ -293,6 +322,19 @@ app.registerExtension({
             tooltip: "Choose a template workflow to load when creating a new workflow tab. 'Default (Blank)' keeps ComfyUI's original blank workflow behavior.",
             category: ["LoRA Manager", "Workflow", "New Tab Template"],
         },
+        {
+            id: STRENGTH_STEP_SETTING_ID,
+            name: "Strength Adjustment Step",
+            type: "slider",
+            attrs: {
+                min: 0.01,
+                max: 0.1,
+                step: 0.01,
+            },
+            defaultValue: STRENGTH_STEP_DEFAULT,
+            tooltip: "Step size for adjusting LoRA strength via arrow buttons or keyboard (default: 0.05)",
+            category: ["LoRA Manager", "LoRA Widget", "Strength Step"],
+        },
     ],
     async setup() {
         await loadWorkflowOptions();
@@ -375,4 +417,5 @@ export {
     getTagSpaceReplacementPreference,
     getUsageStatisticsPreference,
     getNewTabTemplatePreference,
+    getStrengthStepPreference,
 };