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

.docs/batch-import-design.md

@@ -0,0 +1,153 @@
+# Recipe Batch Import Feature Design
+
+## Overview
+Enable users to import multiple images as recipes in a single operation, rather than processing them individually. This feature addresses the need for efficient bulk recipe creation from existing image collections.
+
+## Architecture
+
+```
+┌─────────────────────────────────────────────────────────────────┐
+│                        Frontend                                  │
+├─────────────────────────────────────────────────────────────────┤
+│  BatchImportManager.js                                          │
+│  ├── InputCollector (收集URL列表/目录路径)                        │
+│  ├── ConcurrencyController (自适应并发控制)                       │
+│  ├── ProgressTracker (进度追踪)                                   │
+│  └── ResultAggregator (结果汇总)                                  │
+├─────────────────────────────────────────────────────────────────┤
+│  batch_import_modal.html                                        │
+│  └── 批量导入UI组件                                              │
+├─────────────────────────────────────────────────────────────────┤
+│  batch_import_progress.css                                      │
+│  └── 进度显示样式                                                │
+└─────────────────────────────────────────────────────────────────┘
+                              │
+                              ▼
+┌─────────────────────────────────────────────────────────────────┐
+│                        Backend                                   │
+├─────────────────────────────────────────────────────────────────┤
+│  py/routes/handlers/recipe_handlers.py                          │
+│  ├── start_batch_import() - 启动批量导入                         │
+│  ├── get_batch_import_progress() - 查询进度                      │
+│  └── cancel_batch_import() - 取消导入                            │
+├─────────────────────────────────────────────────────────────────┤
+│  py/services/batch_import_service.py                            │
+│  ├── 自适应并发执行                                              │
+│  ├── 结果汇总                                                    │
+│  └── WebSocket进度广播                                           │
+└─────────────────────────────────────────────────────────────────┘
+```
+
+## API Endpoints
+
+| 端点 | 方法 | 说明 |
+|------|------|------|
+| `/api/lm/recipes/batch-import/start` | POST | 启动批量导入，返回 operation_id |
+| `/api/lm/recipes/batch-import/progress` | GET | 查询进度状态 |
+| `/api/lm/recipes/batch-import/cancel` | POST | 取消导入 |
+
+## Backend Implementation Details
+
+### BatchImportService
+
+Location: `py/services/batch_import_service.py`
+
+Key classes:
+- `BatchImportItem`: Dataclass for individual import item
+- `BatchImportProgress`: Dataclass for tracking progress
+- `BatchImportService`: Main service class
+
+Features:
+- Adaptive concurrency control (adjusts based on success/failure rate)
+- WebSocket progress broadcasting
+- Graceful error handling (individual failures don't stop the batch)
+- Result aggregation
+
+### WebSocket Message Format
+
+```json
+{
+    "type": "batch_import_progress",
+    "operation_id": "xxx",
+    "total": 50,
+    "completed": 23,
+    "success": 21,
+    "failed": 2,
+    "skipped": 0,
+    "current_item": "image_024.png",
+    "status": "running"
+}
+```
+
+### Input Types
+
+1. **URL List**: Array of URLs (http/https)
+2. **Local Paths**: Array of local file paths
+3. **Directory**: Path to directory with optional recursive flag
+
+### Error Handling
+
+- Invalid URLs/paths: Skip and record error
+- Download failures: Record error, continue
+- Metadata extraction failures: Mark as "no metadata"
+- Duplicate detection: Option to skip duplicates
+
+## Frontend Implementation Details (TODO)
+
+### UI Components
+
+1. **BatchImportModal**: Main modal with tabs for URLs/Directory input
+2. **ProgressDisplay**: Real-time progress bar and status
+3. **ResultsSummary**: Final results with success/failure breakdown
+
+### Adaptive Concurrency Controller
+
+```javascript
+class AdaptiveConcurrencyController {
+    constructor(options = {}) {
+        this.minConcurrency = options.minConcurrency || 1;
+        this.maxConcurrency = options.maxConcurrency || 5;
+        this.currentConcurrency = options.initialConcurrency || 3;
+    }
+
+    adjustConcurrency(taskDuration, success) {
+        if (success && taskDuration < 1000 && this.currentConcurrency < this.maxConcurrency) {
+            this.currentConcurrency = Math.min(this.currentConcurrency + 1, this.maxConcurrency);
+        }
+        if (!success || taskDuration > 10000) {
+            this.currentConcurrency = Math.max(this.currentConcurrency - 1, this.minConcurrency);
+        }
+        return this.currentConcurrency;
+    }
+}
+```
+
+## File Structure
+
+```
+Backend (implemented):
+├── py/services/batch_import_service.py            # 后端服务
+├── py/routes/handlers/batch_import_handler.py     # API处理器 (added to recipe_handlers.py)
+├── tests/services/test_batch_import_service.py    # 单元测试
+└── tests/routes/test_batch_import_routes.py       # API集成测试
+
+Frontend (TODO):
+├── static/js/managers/BatchImportManager.js       # 主管理器
+├── static/js/managers/batch/                       # 子模块
+│   ├── ConcurrencyController.js                    # 并发控制
+│   ├── ProgressTracker.js                          # 进度追踪
+│   └── ResultAggregator.js                         # 结果汇总
+├── static/css/components/batch-import-modal.css   # 样式
+└── templates/components/batch_import_modal.html    # Modal模板
+```
+
+## Implementation Status
+
+- [x] Backend BatchImportService
+- [x] Backend API handlers
+- [x] WebSocket progress broadcasting
+- [x] Unit tests
+- [x] Integration tests
+- [ ] Frontend BatchImportManager
+- [ ] Frontend UI components
+- [ ] E2E tests

.specs/metadata.schema.json

@@ -0,0 +1,464 @@
+{
+  "$schema": "http://json-schema.org/draft-07/schema#",
+  "$id": "https://github.com/willmiao/ComfyUI-Lora-Manager/.specs/metadata.schema.json",
+  "title": "ComfyUI LoRa Manager Model Metadata",
+  "description": "Schema for .metadata.json sidecar files used by ComfyUI LoRa Manager",
+  "type": "object",
+  "oneOf": [
+    {
+      "title": "LoRA Model Metadata",
+      "properties": {
+        "file_name": {
+          "type": "string",
+          "description": "Filename without extension"
+        },
+        "model_name": {
+          "type": "string",
+          "description": "Display name of the model"
+        },
+        "file_path": {
+          "type": "string",
+          "description": "Full absolute path to the model file"
+        },
+        "size": {
+          "type": "integer",
+          "minimum": 0,
+          "description": "File size in bytes at time of import/download"
+        },
+        "modified": {
+          "type": "number",
+          "description": "Unix timestamp when model was imported/added (Date Added)"
+        },
+        "sha256": {
+          "type": "string",
+          "pattern": "^[a-f0-9]{64}$",
+          "description": "SHA256 hash of the model file (lowercase)"
+        },
+        "base_model": {
+          "type": "string",
+          "description": "Base model type (SD1.5, SD2.1, SDXL, SD3, Flux, Unknown, etc.)"
+        },
+        "preview_url": {
+          "type": "string",
+          "description": "Path to preview image file"
+        },
+        "preview_nsfw_level": {
+          "type": "integer",
+          "minimum": 0,
+          "default": 0,
+          "description": "NSFW level using bitmask values: 0 (none), 1 (PG), 2 (PG13), 4 (R), 8 (X), 16 (XXX), 32 (Blocked)"
+        },
+        "notes": {
+          "type": "string",
+          "default": "",
+          "description": "User-defined notes"
+        },
+        "from_civitai": {
+          "type": "boolean",
+          "default": true,
+          "description": "Whether the model originated from Civitai"
+        },
+        "civitai": {
+          "$ref": "#/definitions/civitaiObject"
+        },
+        "tags": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "default": [],
+          "description": "Model tags"
+        },
+        "modelDescription": {
+          "type": "string",
+          "default": "",
+          "description": "Full model description"
+        },
+        "civitai_deleted": {
+          "type": "boolean",
+          "default": false,
+          "description": "Whether the model was deleted from Civitai"
+        },
+        "favorite": {
+          "type": "boolean",
+          "default": false,
+          "description": "Whether the model is marked as favorite"
+        },
+        "exclude": {
+          "type": "boolean",
+          "default": false,
+          "description": "Whether to exclude from cache/scanning"
+        },
+        "db_checked": {
+          "type": "boolean",
+          "default": false,
+          "description": "Whether checked against archive database"
+        },
+        "skip_metadata_refresh": {
+          "type": "boolean",
+          "default": false,
+          "description": "Skip this model during bulk metadata refresh"
+        },
+        "metadata_source": {
+          "type": ["string", "null"],
+          "enum": ["civitai_api", "civarchive", "archive_db", null],
+          "default": null,
+          "description": "Last provider that supplied metadata"
+        },
+        "last_checked_at": {
+          "type": "number",
+          "default": 0,
+          "description": "Unix timestamp of last metadata check"
+        },
+        "hash_status": {
+          "type": "string",
+          "enum": ["pending", "calculating", "completed", "failed"],
+          "default": "completed",
+          "description": "Hash calculation status"
+        },
+        "usage_tips": {
+          "type": "string",
+          "default": "{}",
+          "description": "JSON string containing recommended usage parameters (LoRA only)"
+        }
+      },
+      "required": [
+        "file_name",
+        "model_name",
+        "file_path",
+        "size",
+        "modified",
+        "sha256",
+        "base_model"
+      ],
+      "additionalProperties": true
+    },
+    {
+      "title": "Checkpoint Model Metadata",
+      "properties": {
+        "file_name": {
+          "type": "string"
+        },
+        "model_name": {
+          "type": "string"
+        },
+        "file_path": {
+          "type": "string"
+        },
+        "size": {
+          "type": "integer",
+          "minimum": 0
+        },
+        "modified": {
+          "type": "number"
+        },
+        "sha256": {
+          "type": "string",
+          "pattern": "^[a-f0-9]{64}$"
+        },
+        "base_model": {
+          "type": "string"
+        },
+        "preview_url": {
+          "type": "string"
+        },
+        "preview_nsfw_level": {
+          "type": "integer",
+          "minimum": 0,
+          "maximum": 3,
+          "default": 0
+        },
+        "notes": {
+          "type": "string",
+          "default": ""
+        },
+        "from_civitai": {
+          "type": "boolean",
+          "default": true
+        },
+        "civitai": {
+          "$ref": "#/definitions/civitaiObject"
+        },
+        "tags": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "default": []
+        },
+        "modelDescription": {
+          "type": "string",
+          "default": ""
+        },
+        "civitai_deleted": {
+          "type": "boolean",
+          "default": false
+        },
+        "favorite": {
+          "type": "boolean",
+          "default": false
+        },
+        "exclude": {
+          "type": "boolean",
+          "default": false
+        },
+        "db_checked": {
+          "type": "boolean",
+          "default": false
+        },
+        "skip_metadata_refresh": {
+          "type": "boolean",
+          "default": false
+        },
+        "metadata_source": {
+          "type": ["string", "null"],
+          "enum": ["civitai_api", "civarchive", "archive_db", null],
+          "default": null
+        },
+        "last_checked_at": {
+          "type": "number",
+          "default": 0
+        },
+        "hash_status": {
+          "type": "string",
+          "enum": ["pending", "calculating", "completed", "failed"],
+          "default": "completed"
+        },
+        "sub_type": {
+          "type": "string",
+          "default": "checkpoint",
+          "description": "Model sub-type (checkpoint, diffusion_model, etc.)"
+        }
+      },
+      "required": [
+        "file_name",
+        "model_name",
+        "file_path",
+        "size",
+        "modified",
+        "sha256",
+        "base_model"
+      ],
+      "additionalProperties": true
+    },
+    {
+      "title": "Embedding Model Metadata",
+      "properties": {
+        "file_name": {
+          "type": "string"
+        },
+        "model_name": {
+          "type": "string"
+        },
+        "file_path": {
+          "type": "string"
+        },
+        "size": {
+          "type": "integer",
+          "minimum": 0
+        },
+        "modified": {
+          "type": "number"
+        },
+        "sha256": {
+          "type": "string",
+          "pattern": "^[a-f0-9]{64}$"
+        },
+        "base_model": {
+          "type": "string"
+        },
+        "preview_url": {
+          "type": "string"
+        },
+        "preview_nsfw_level": {
+          "type": "integer",
+          "minimum": 0,
+          "maximum": 3,
+          "default": 0
+        },
+        "notes": {
+          "type": "string",
+          "default": ""
+        },
+        "from_civitai": {
+          "type": "boolean",
+          "default": true
+        },
+        "civitai": {
+          "$ref": "#/definitions/civitaiObject"
+        },
+        "tags": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "default": []
+        },
+        "modelDescription": {
+          "type": "string",
+          "default": ""
+        },
+        "civitai_deleted": {
+          "type": "boolean",
+          "default": false
+        },
+        "favorite": {
+          "type": "boolean",
+          "default": false
+        },
+        "exclude": {
+          "type": "boolean",
+          "default": false
+        },
+        "db_checked": {
+          "type": "boolean",
+          "default": false
+        },
+        "skip_metadata_refresh": {
+          "type": "boolean",
+          "default": false
+        },
+        "metadata_source": {
+          "type": ["string", "null"],
+          "enum": ["civitai_api", "civarchive", "archive_db", null],
+          "default": null
+        },
+        "last_checked_at": {
+          "type": "number",
+          "default": 0
+        },
+        "hash_status": {
+          "type": "string",
+          "enum": ["pending", "calculating", "completed", "failed"],
+          "default": "completed"
+        },
+        "sub_type": {
+          "type": "string",
+          "default": "embedding",
+          "description": "Model sub-type"
+        }
+      },
+      "required": [
+        "file_name",
+        "model_name",
+        "file_path",
+        "size",
+        "modified",
+        "sha256",
+        "base_model"
+      ],
+      "additionalProperties": true
+    }
+  ],
+  "definitions": {
+    "civitaiObject": {
+      "type": "object",
+      "default": {},
+      "description": "Civitai/CivArchive API data and user-defined fields",
+      "properties": {
+        "id": {
+          "type": "integer",
+          "description": "Version ID from Civitai"
+        },
+        "modelId": {
+          "type": "integer",
+          "description": "Model ID from Civitai"
+        },
+        "name": {
+          "type": "string",
+          "description": "Version name"
+        },
+        "description": {
+          "type": "string",
+          "description": "Version description"
+        },
+        "baseModel": {
+          "type": "string",
+          "description": "Base model type from Civitai"
+        },
+        "type": {
+          "type": "string",
+          "description": "Model type (checkpoint, embedding, etc.)"
+        },
+        "trainedWords": {
+          "type": "array",
+          "items": {
+            "type": "string"
+          },
+          "description": "Trigger words for the model (from API or user-defined)"
+        },
+        "customImages": {
+          "type": "array",
+          "items": {
+            "type": "object"
+          },
+          "description": "Custom example images added by user"
+        },
+        "model": {
+          "type": "object",
+          "properties": {
+            "name": {
+              "type": "string"
+            },
+            "description": {
+              "type": "string"
+            },
+            "tags": {
+              "type": "array",
+              "items": {
+                "type": "string"
+              }
+            }
+          }
+        },
+        "files": {
+          "type": "array",
+          "items": {
+            "type": "object"
+          }
+        },
+        "images": {
+          "type": "array",
+          "items": {
+            "type": "object"
+          }
+        },
+        "creator": {
+          "type": "object"
+        }
+      },
+      "additionalProperties": true
+    },
+    "usageTips": {
+      "type": "object",
+      "description": "Structure for usage_tips JSON string (LoRA models)",
+      "properties": {
+        "strength_min": {
+          "type": "number",
+          "description": "Minimum recommended model strength"
+        },
+        "strength_max": {
+          "type": "number",
+          "description": "Maximum recommended model strength"
+        },
+        "strength_range": {
+          "type": "string",
+          "description": "Human-readable strength range"
+        },
+        "strength": {
+          "type": "number",
+          "description": "Single recommended strength value"
+        },
+        "clip_strength": {
+          "type": "number",
+          "description": "Recommended CLIP/embedding strength"
+        },
+        "clip_skip": {
+          "type": "integer",
+          "description": "Recommended CLIP skip value"
+        }
+      },
+      "additionalProperties": true
+    }
+  }
+}

README.md

@@ -321,6 +321,12 @@ npm run test:coverage
 
 ---
 
+## Documentation
+
+- **[metadata.json Schema Documentation](docs/metadata-json-schema.md)** — Complete reference for the `.metadata.json` sidecar file format, including all fields, types, and examples for LoRA, Checkpoint, and Embedding models.
+
+---
+
 ## Contributing
 
 Thank you for your interest in contributing to ComfyUI LoRA Manager! As this project is currently in its early stages and undergoing rapid development and refactoring, we are temporarily not accepting pull requests.

__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,

docs/metadata-json-schema.md

@@ -0,0 +1,363 @@
+# metadata.json Schema Documentation
+
+This document defines the complete schema for `.metadata.json` files used by Lora Manager. These sidecar files store model metadata alongside model files (LoRA, Checkpoint, Embedding).
+
+## Overview
+
+- **File naming**: `<model_name>.metadata.json` (e.g., `my_lora.safetensors` → `my_lora.metadata.json`)
+- **Format**: JSON with UTF-8 encoding
+- **Purpose**: Store model metadata, tags, descriptions, preview images, and Civitai/CivArchive integration data
+- **Extensibility**: Unknown fields are preserved via `_unknown_fields` mechanism for forward compatibility
+
+---
+
+## Base Fields (All Model Types)
+
+These fields are present in all model metadata files.
+
+| Field | Type | Required | Auto-Updated | Description |
+|-------|------|----------|--------------|-------------|
+| `file_name` | string | ✅ Yes | ✅ Yes | Filename without extension (e.g., `"my_lora"`) |
+| `model_name` | string | ✅ Yes | ❌ No | Display name of the model. **Default**: `file_name` if no other source |
+| `file_path` | string | ✅ Yes | ✅ Yes | Full absolute path to the model file (normalized with `/` separators) |
+| `size` | integer | ✅ Yes | ❌ No | File size in bytes. **Set at**: Initial scan or download completion. Does not change thereafter. |
+| `modified` | float | ✅ Yes | ❌ No | **Import timestamp** — Unix timestamp when the model was first imported/added to the system. Used for "Date Added" sorting. Does not change after initial creation. |
+| `sha256` | string | ⚠️ Conditional | ✅ Yes | SHA256 hash of the model file (lowercase). **LoRA**: Required. **Checkpoint**: May be empty when `hash_status="pending"` (lazy hash calculation) |
+| `base_model` | string | ❌ No | ❌ No | Base model type. **Examples**: `"SD 1.5"`, `"SDXL 1.0"`, `"SDXL Lightning"`, `"Flux.1 D"`, `"Flux.1 S"`, `"Flux.1 Krea"`, `"Illustrious"`, `"Pony"`, `"AuraFlow"`, `"Kolors"`, `"ZImageTurbo"`, `"Wan Video"`, etc. **Default**: `"Unknown"` or `""` |
+| `preview_url` | string | ❌ No | ✅ Yes | Path to preview image file |
+| `preview_nsfw_level` | integer | ❌ No | ❌ No | NSFW level using **bitmask values** from Civitai: `1` (PG), `2` (PG13), `4` (R), `8` (X), `16` (XXX), `32` (Blocked). **Default**: `0` (none) |
+| `notes` | string | ❌ No | ❌ No | User-defined notes |
+| `from_civitai` | boolean | ❌ No (default: `true`) | ❌ No | Whether the model originated from Civitai |
+| `civitai` | object | ❌ No | ⚠️ Partial | Civitai/CivArchive API data and user-defined fields |
+| `tags` | array[string] | ❌ No | ⚠️ Partial | Model tags (merged from API and user input) |
+| `modelDescription` | string | ❌ No | ⚠️ Partial | Full model description (from API or user) |
+| `civitai_deleted` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model was deleted from Civitai |
+| `favorite` | boolean | ❌ No (default: `false`) | ❌ No | Whether the model is marked as favorite |
+| `exclude` | boolean | ❌ No (default: `false`) | ❌ No | Whether to exclude from cache/scanning. User can set from `false` to `true` (currently no UI to revert) |
+| `db_checked` | boolean | ❌ No (default: `false`) | ❌ No | Whether checked against archive database |
+| `skip_metadata_refresh` | boolean | ❌ No (default: `false`) | ❌ No | Skip this model during bulk metadata refresh |
+| `metadata_source` | string\|null | ❌ No | ✅ Yes | Last provider that supplied metadata (see below) |
+| `last_checked_at` | float | ❌ No (default: `0`) | ✅ Yes | Unix timestamp of last metadata check |
+| `hash_status` | string | ❌ No (default: `"completed"`) | ✅ Yes | Hash calculation status: `"pending"`, `"calculating"`, `"completed"`, `"failed"` |
+
+---
+
+## Model-Specific Fields
+
+### LoRA Models
+
+LoRA models do not have a `model_type` field in metadata.json. The type is inferred from context or `civitai.type` (e.g., `"LoRA"`, `"LoCon"`, `"DoRA"`).
+
+| Field | Type | Required | Auto-Updated | Description |
+|-------|------|----------|--------------|-------------|
+| `usage_tips` | string (JSON) | ❌ No (default: `"{}"`) | ❌ No | JSON string containing recommended usage parameters |
+
+**`usage_tips` JSON structure:**
+
+```json
+{
+  "strength_min": 0.3,
+  "strength_max": 0.8,
+  "strength_range": "0.3-0.8",
+  "strength": 0.6,
+  "clip_strength": 0.5,
+  "clip_skip": 2
+}
+```
+
+| Key | Type | Description |
+|-----|------|-------------|
+| `strength_min` | number | Minimum recommended model strength |
+| `strength_max` | number | Maximum recommended model strength |
+| `strength_range` | string | Human-readable strength range |
+| `strength` | number | Single recommended strength value |
+| `clip_strength` | number | Recommended CLIP/embedding strength |
+| `clip_skip` | integer | Recommended CLIP skip value |
+
+---
+
+### Checkpoint Models
+
+| Field | Type | Required | Auto-Updated | Description |
+|-------|------|----------|--------------|-------------|
+| `model_type` | string | ❌ No (default: `"checkpoint"`) | ❌ No | Model type: `"checkpoint"`, `"diffusion_model"` |
+
+---
+
+### Embedding Models
+
+| Field | Type | Required | Auto-Updated | Description |
+|-------|------|----------|--------------|-------------|
+| `model_type` | string | ❌ No (default: `"embedding"`) | ❌ No | Model type: `"embedding"` |
+
+---
+
+## The `civitai` Field Structure
+
+The `civitai` object stores the complete Civitai/CivArchive API response. Lora Manager preserves all fields from the API for future compatibility and extracts specific fields for use in the application.
+
+### Version-Level Fields (Civitai API)
+
+**Fields Used by Lora Manager:**
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | integer | Version ID |
+| `modelId` | integer | Parent model ID |
+| `name` | string | Version name (e.g., `"v1.0"`, `"v2.0-pruned"`) |
+| `nsfwLevel` | integer | NSFW level (bitmask: 1=PG, 2=PG13, 4=R, 8=X, 16=XXX, 32=Blocked) |
+| `baseModel` | string | Base model (e.g., `"SDXL 1.0"`, `"Flux.1 D"`, `"Illustrious"`, `"Pony"`) |
+| `trainedWords` | array[string] | **Trigger words** for the model |
+| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
+| `earlyAccessEndsAt` | string\|null | Early access end date (used for update notifications) |
+| `description` | string | Version description (HTML) |
+| `model` | object | Parent model object (see Model-Level Fields below) |
+| `creator` | object | Creator information (see Creator Fields below) |
+| `files` | array[object] | File list with hashes, sizes, download URLs (used for metadata extraction) |
+| `images` | array[object] | Image list with metadata, prompts, NSFW levels (used for preview/examples) |
+
+**Fields Stored but Not Currently Used:**
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `createdAt` | string (ISO 8601) | Creation timestamp |
+| `updatedAt` | string (ISO 8601) | Last update timestamp |
+| `status` | string | Version status (e.g., `"Published"`, `"Draft"`) |
+| `publishedAt` | string (ISO 8601) | Publication timestamp |
+| `baseModelType` | string | Base model type (e.g., `"Standard"`, `"Inpaint"`, `"Refiner"`) |
+| `earlyAccessConfig` | object | Early access configuration |
+| `uploadType` | string | Upload type (`"Created"`, `"FineTuned"`, etc.) |
+| `usageControl` | string | Usage control setting |
+| `air` | string | Artifact ID (URN format: `urn:air:sdxl:lora:civitai:122359@135867`) |
+| `stats` | object | Download count, ratings, thumbs up count |
+| `videos` | array[object] | Video list |
+| `downloadUrl` | string | Direct download URL |
+| `trainingStatus` | string\|null | Training status (for on-site training) |
+| `trainingDetails` | object\|null | Training configuration |
+
+### Model-Level Fields (`civitai.model.*`)
+
+**Fields Used by Lora Manager:**
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `name` | string | Model name |
+| `type` | string | Model type (`"LoRA"`, `"Checkpoint"`, `"TextualInversion"`) |
+| `description` | string | Model description (HTML, used for `modelDescription`) |
+| `tags` | array[string] | Model tags (used for `tags` field) |
+| `allowNoCredit` | boolean | License: allow use without credit |
+| `allowCommercialUse` | array[string] | License: allowed commercial uses. **Values**: `"Image"` (sell generated images), `"Video"` (sell generated videos), `"RentCivit"` (rent on Civitai), `"Rent"` (rent elsewhere) |
+| `allowDerivatives` | boolean | License: allow derivatives |
+| `allowDifferentLicense` | boolean | License: allow different license |
+
+**Fields Stored but Not Currently Used:**
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `nsfw` | boolean | Model NSFW flag |
+| `poi` | boolean | Person of Interest flag |
+
+### Creator Fields (`civitai.creator.*`)
+
+Both fields are used by Lora Manager:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `username` | string | Creator username (used for author display and search) |
+| `image` | string | Creator avatar URL (used for display) |
+
+### Model Type Field (Top-Level, Outside `civitai`)
+
+| Field | Type | Values | Description |
+|-------|------|--------|-------------|
+| `model_type` | string | `"checkpoint"`, `"diffusion_model"`, `"embedding"` | Stored in metadata.json for Checkpoint and Embedding models. **Note**: LoRA models do not have this field; type is inferred from `civitai.type` or context. |
+
+### User-Defined Fields (Within `civitai`)
+
+For models not from Civitai or user-added data:
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `trainedWords` | array[string] | **Trigger words** — manually added by user |
+| `customImages` | array[object] | Custom example images added by user |
+
+### customImages Structure
+
+Each custom image entry has the following structure:
+
+```json
+{
+  "url": "",
+  "id": "short_id",
+  "nsfwLevel": 0,
+  "width": 832,
+  "height": 1216,
+  "type": "image",
+  "meta": {
+    "prompt": "...",
+    "negativePrompt": "...",
+    "steps": 20,
+    "cfgScale": 7,
+    "seed": 123456
+  },
+  "hasMeta": true,
+  "hasPositivePrompt": true
+}
+```
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `url` | string | Empty for local custom images |
+| `id` | string | Short ID or filename |
+| `nsfwLevel` | integer | NSFW level (bitmask) |
+| `width` | integer | Image width in pixels |
+| `height` | integer | Image height in pixels |
+| `type` | string | `"image"` or `"video"` |
+| `meta` | object\|null | Generation metadata (prompt, seed, etc.) extracted from image |
+| `hasMeta` | boolean | Whether metadata is available |
+| `hasPositivePrompt` | boolean | Whether a positive prompt is available |
+
+### Minimal Non-Civitai Example
+
+```json
+{
+  "civitai": {
+    "trainedWords": ["my_trigger_word"]
+  }
+}
+```
+
+### Non-Civitai Example Without Trigger Words
+
+```json
+{
+  "civitai": {}
+}
+```
+
+### Example: User-Added Custom Images
+
+```json
+{
+  "civitai": {
+    "trainedWords": ["custom_style"],
+    "customImages": [
+      {
+        "url": "",
+        "id": "example_1",
+        "nsfwLevel": 0,
+        "width": 832,
+        "height": 1216,
+        "type": "image",
+        "meta": {
+          "prompt": "example prompt",
+          "seed": 12345
+        },
+        "hasMeta": true,
+        "hasPositivePrompt": true
+      }
+    ]
+  }
+}
+```
+
+---
+
+## Metadata Source Values
+
+The `metadata_source` field indicates which provider last updated the metadata:
+
+| Value | Source |
+|-------|--------|
+| `"civitai_api"` | Civitai API |
+| `"civarchive"` | CivArchive API |
+| `"archive_db"` | Metadata Archive Database |
+| `null` | No external source (user-defined only) |
+
+---
+
+## Auto-Update Behavior
+
+### Fields Updated During Scanning
+
+These fields are automatically synchronized with the filesystem:
+
+- `file_name` — Updated if actual filename differs
+- `file_path` — Normalized and updated if path changes
+- `preview_url` — Updated if preview file is moved/removed
+- `sha256` — Updated during hash calculation (when `hash_status="pending"`)
+- `hash_status` — Updated during hash calculation
+- `last_checked_at` — Timestamp of scan
+- `metadata_source` — Set based on metadata provider
+
+### Fields Set Once (Immutable After Import)
+
+These fields are set when the model is first imported/scanned and **never change** thereafter:
+
+- `modified` — Import timestamp (used for "Date Added" sorting)
+- `size` — File size at time of import/download
+
+### User-Editable Fields
+
+These fields can be edited by users at any time through the Lora Manager UI or by manually editing the metadata.json file:
+
+- `model_name` — Display name
+- `tags` — Model tags
+- `modelDescription` — Model description
+- `notes` — User notes
+- `favorite` — Favorite flag
+- `exclude` — Exclude from scanning (user can set `false`→`true`, currently no UI to revert)
+- `skip_metadata_refresh` — Skip during bulk refresh
+- `civitai.trainedWords` — Trigger words
+- `civitai.customImages` — Custom example images
+- `usage_tips` — Usage recommendations (LoRA only)
+
+---
+
+
+## Field Reference by Behavior
+
+### Required Fields (Must Always Exist)
+
+- `file_name`
+- `model_name` (defaults to `file_name` if not provided)
+- `file_path`
+- `size`
+- `modified`
+- `sha256` (LoRA: always required; Checkpoint: may be empty when `hash_status="pending"`)
+
+### Optional Fields with Defaults
+
+| Field | Default |
+|-------|---------|
+| `base_model` | `"Unknown"` or `""` |
+| `preview_nsfw_level` | `0` |
+| `from_civitai` | `true` |
+| `civitai` | `{}` |
+| `tags` | `[]` |
+| `modelDescription` | `""` |
+| `notes` | `""` |
+| `civitai_deleted` | `false` |
+| `favorite` | `false` |
+| `exclude` | `false` |
+| `db_checked` | `false` |
+| `skip_metadata_refresh` | `false` |
+| `metadata_source` | `null` |
+| `last_checked_at` | `0` |
+| `hash_status` | `"completed"` |
+| `usage_tips` | `"{}"` (LoRA only) |
+| `model_type` | `"checkpoint"` or `"embedding"` (not present in LoRA models) |
+
+---
+
+## Version History
+
+| Version | Date | Changes |
+|---------|------|---------|
+| 1.0 | 2026-03 | Initial schema documentation |
+
+---
+
+## See Also
+
+- [JSON Schema Definition](../.specs/metadata.schema.json) — Formal JSON Schema for validation

locales/de.json

@@ -14,7 +14,8 @@
             "backToTop": "Nach oben",
             "settings": "Einstellungen",
             "help": "Hilfe",
-            "add": "Hinzufügen"
+            "add": "Hinzufügen",
+            "close": "Schließen"
         },
         "status": {
             "loading": "Wird geladen...",
@@ -729,6 +730,64 @@
                 "failed": "Rezept-Reparatur fehlgeschlagen: {message}",
                 "missingId": "Rezept kann nicht repariert werden: Fehlende Rezept-ID"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "Fehler beim Speichern des Rezepts: {error}",
             "importFailed": "Import fehlgeschlagen: {message}",
             "folderTreeFailed": "Fehler beim Laden des Ordnerbaums",
-            "folderTreeError": "Fehler beim Laden des Ordnerbaums"
+            "folderTreeError": "Fehler beim Laden des Ordnerbaums",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "Keine Modelle ausgewählt",

locales/en.json

@@ -14,7 +14,8 @@
             "backToTop": "Back to top",
             "settings": "Settings",
             "help": "Help",
-            "add": "Add"
+            "add": "Add",
+            "close": "Close"
         },
         "status": {
             "loading": "Loading...",
@@ -729,6 +730,64 @@
                 "failed": "Failed to repair recipe: {message}",
                 "missingId": "Cannot repair recipe: Missing recipe ID"
             }
+        },
+        "batchImport": {
+            "title": "Batch Import Recipes",
+            "action": "Batch Import",
+            "urlList": "URL List",
+            "directory": "Directory",
+            "urlDescription": "Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "Enter a directory path to import all images from that folder.",
+            "urlsLabel": "Image URLs or Local Paths",
+            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "Enter one URL or path per line",
+            "directoryPath": "Directory Path",
+            "directoryPlaceholder": "/path/to/images/folder",
+            "browse": "Browse",
+            "recursive": "Include subdirectories",
+            "tagsOptional": "Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "Enter tags separated by commas",
+            "tagsHint": "Tags will be added to all imported recipes",
+            "skipNoMetadata": "Skip images without metadata",
+            "skipNoMetadataHelp": "Images without LoRA metadata will be skipped automatically.",
+            "start": "Start Import",
+            "startImport": "Start Import",
+            "importing": "Importing...",
+            "progress": "Progress",
+            "total": "Total",
+            "success": "Success",
+            "failed": "Failed",
+            "skipped": "Skipped",
+            "current": "Current",
+            "currentItem": "Current",
+            "preparing": "Preparing...",
+            "cancel": "Cancel",
+            "cancelImport": "Cancel",
+            "cancelled": "Import cancelled",
+            "completed": "Import completed",
+            "completedWithErrors": "Completed with errors",
+            "completedSuccess": "Successfully imported {count} recipe(s)",
+            "successCount": "Successful",
+            "failedCount": "Failed",
+            "skippedCount": "Skipped",
+            "totalProcessed": "Total processed",
+            "viewDetails": "View Details",
+            "newImport": "New Import",
+            "manualPathEntry": "Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "Directory selected: {path}",
+            "batchImportManualEntryRequired": "File browser not available. Please enter the directory path manually.",
+            "backToParent": "Back to parent directory",
+            "folders": "Folders",
+            "folderCount": "{count} folders",
+            "imageFiles": "Image Files",
+            "images": "images",
+            "imageCount": "{count} images",
+            "selectFolder": "Select This Folder",
+            "errors": {
+                "enterUrls": "Please enter at least one URL or path",
+                "enterDirectory": "Please enter a directory path",
+                "startFailed": "Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "Failed to save recipe: {error}",
             "importFailed": "Import failed: {message}",
             "folderTreeFailed": "Failed to load folder tree",
-            "folderTreeError": "Error loading folder tree"
+            "folderTreeError": "Error loading folder tree",
+            "batchImportFailed": "Failed to start batch import: {message}",
+            "batchImportCancelling": "Cancelling batch import...",
+            "batchImportCancelFailed": "Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "Please enter at least one URL or file path",
+            "batchImportNoDirectory": "Please enter a directory path",
+            "batchImportBrowseFailed": "Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "No models selected",

locales/es.json

@@ -14,7 +14,8 @@
             "backToTop": "Volver arriba",
             "settings": "Configuración",
             "help": "Ayuda",
-            "add": "Añadir"
+            "add": "Añadir",
+            "close": "Cerrar"
         },
         "status": {
             "loading": "Cargando...",
@@ -729,6 +730,64 @@
                 "failed": "Error al reparar la receta: {message}",
                 "missingId": "No se puede reparar la receta: falta el ID de la receta"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "Error al guardar receta: {error}",
             "importFailed": "Importación falló: {message}",
             "folderTreeFailed": "Error al cargar árbol de carpetas",
-            "folderTreeError": "Error cargando árbol de carpetas"
+            "folderTreeError": "Error cargando árbol de carpetas",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "No hay modelos seleccionados",

locales/fr.json

@@ -14,7 +14,8 @@
             "backToTop": "Retour en haut",
             "settings": "Paramètres",
             "help": "Aide",
-            "add": "Ajouter"
+            "add": "Ajouter",
+            "close": "Fermer"
         },
         "status": {
             "loading": "Chargement...",
@@ -729,6 +730,64 @@
                 "failed": "Échec de la réparation de la recette : {message}",
                 "missingId": "Impossible de réparer la recette : ID de recette manquant"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "Échec de la sauvegarde de la recipe : {error}",
             "importFailed": "Échec de l'importation : {message}",
             "folderTreeFailed": "Échec du chargement de l'arborescence des dossiers",
-            "folderTreeError": "Erreur lors du chargement de l'arborescence des dossiers"
+            "folderTreeError": "Erreur lors du chargement de l'arborescence des dossiers",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "Aucun modèle sélectionné",

locales/he.json

@@ -14,7 +14,8 @@
             "backToTop": "חזרה למעלה",
             "settings": "הגדרות",
             "help": "עזרה",
-            "add": "הוספה"
+            "add": "הוספה",
+            "close": "סגור"
         },
         "status": {
             "loading": "טוען...",
@@ -729,6 +730,64 @@
                 "failed": "תיקון המתכון נכשל: {message}",
                 "missingId": "לא ניתן לתקן את המתכון: חסר מזהה מתכון"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "שמירת המתכון נכשלה: {error}",
             "importFailed": "הייבוא נכשל: {message}",
             "folderTreeFailed": "טעינת עץ התיקיות נכשלה",
-            "folderTreeError": "שגיאה בטעינת עץ התיקיות"
+            "folderTreeError": "שגיאה בטעינת עץ התיקיות",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "לא נבחרו מודלים",

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...",
@@ -729,6 +730,64 @@
                 "failed": "レシピの修復に失敗しました: {message}",
                 "missingId": "レシピを修復できません: レシピIDがありません"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "レシピの保存に失敗しました：{error}",
             "importFailed": "インポートに失敗しました：{message}",
             "folderTreeFailed": "フォルダツリーの読み込みに失敗しました",
-            "folderTreeError": "フォルダツリー読み込みエラー"
+            "folderTreeError": "フォルダツリー読み込みエラー",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "モデルが選択されていません",

locales/ko.json

@@ -14,7 +14,8 @@
             "backToTop": "맨 위로",
             "settings": "설정",
             "help": "도움말",
-            "add": "추가"
+            "add": "추가",
+            "close": "닫기"
         },
         "status": {
             "loading": "로딩 중...",
@@ -729,6 +730,64 @@
                 "failed": "레시피 복구 실패: {message}",
                 "missingId": "레시피를 복구할 수 없음: 레시피 ID 누락"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "레시피 저장 실패: {error}",
             "importFailed": "가져오기 실패: {message}",
             "folderTreeFailed": "폴더 트리 로딩 실패",
-            "folderTreeError": "폴더 트리 로딩 오류"
+            "folderTreeError": "폴더 트리 로딩 오류",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "선택된 모델이 없습니다",

locales/ru.json

@@ -14,7 +14,8 @@
             "backToTop": "Наверх",
             "settings": "Настройки",
             "help": "Справка",
-            "add": "Добавить"
+            "add": "Добавить",
+            "close": "Закрыть"
         },
         "status": {
             "loading": "Загрузка...",
@@ -729,6 +730,64 @@
                 "failed": "Не удалось восстановить рецепт: {message}",
                 "missingId": "Не удалось восстановить рецепт: отсутствует ID рецепта"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "Не удалось сохранить рецепт: {error}",
             "importFailed": "Импорт не удался: {message}",
             "folderTreeFailed": "Не удалось загрузить дерево папок",
-            "folderTreeError": "Ошибка загрузки дерева папок"
+            "folderTreeError": "Ошибка загрузки дерева папок",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "Модели не выбраны",

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...",
@@ -729,6 +730,64 @@
                 "failed": "修复配方失败：{message}",
                 "missingId": "无法修复配方：缺少配方 ID"
             }
+        },
+        "batchImport": {
+            "title": "批量导入配方",
+            "action": "批量导入",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "输入目录路径以导入该文件夹中的所有图片。",
+            "urlsLabel": "图片 URL 或本地路径",
+            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "/图片/文件夹/路径",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "标签（可选，应用于所有配方）",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "跳过无元数据的图片",
+            "skipNoMetadataHelp": "没有 LoRA 元数据的图片将自动跳过。",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "开始导入",
+            "importing": "正在导入配方...",
+            "progress": "进度",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "当前",
+            "preparing": "准备中...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "取消",
+            "cancelled": "批量导入已取消",
+            "completed": "导入完成",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "成功导入 {count} 个配方",
+            "successCount": "成功",
+            "failedCount": "失败",
+            "skippedCount": "跳过",
+            "totalProcessed": "总计处理",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "请至少输入一个 URL 或路径",
+                "enterDirectory": "请输入目录路径",
+                "startFailed": "启动导入失败：{message}"
+            }
         }
     },
     "checkpoints": {
@@ -764,7 +823,7 @@
             "emptyFolderName": "请输入文件夹名称",
             "invalidFolderName": "文件夹名称包含无效字符",
             "noDragState": "未找到待处理的拖放操作"
-            },
+        },
         "empty": {
             "noFolders": "未找到文件夹",
             "dragHint": "拖拽项目到此处以创建文件夹"
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "保存配方失败：{error}",
             "importFailed": "导入失败：{message}",
             "folderTreeFailed": "加载文件夹树失败",
-            "folderTreeError": "加载文件夹树出错"
+            "folderTreeError": "加载文件夹树出错",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "未选中模型",

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...",
@@ -729,6 +730,64 @@
                 "failed": "修復配方失敗：{message}",
                 "missingId": "無法修復配方：缺少配方 ID"
             }
+        },
+        "batchImport": {
+            "title": "[TODO: Translate] Batch Import Recipes",
+            "action": "[TODO: Translate] Batch Import",
+            "urlList": "[TODO: Translate] URL List",
+            "directory": "[TODO: Translate] Directory",
+            "urlDescription": "[TODO: Translate] Enter image URLs or local file paths (one per line). Each will be imported as a recipe.",
+            "directoryDescription": "[TODO: Translate] Enter a directory path to import all images from that folder.",
+            "urlsLabel": "[TODO: Translate] Image URLs or Local Paths",
+            "urlsPlaceholder": "[TODO: Translate] https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "[TODO: Translate] Enter one URL or path per line",
+            "directoryPath": "[TODO: Translate] Directory Path",
+            "directoryPlaceholder": "[TODO: Translate] /path/to/images/folder",
+            "browse": "[TODO: Translate] Browse",
+            "recursive": "[TODO: Translate] Include subdirectories",
+            "tagsOptional": "[TODO: Translate] Tags (optional, applied to all recipes)",
+            "tagsPlaceholder": "[TODO: Translate] Enter tags separated by commas",
+            "tagsHint": "[TODO: Translate] Tags will be added to all imported recipes",
+            "skipNoMetadata": "[TODO: Translate] Skip images without metadata",
+            "skipNoMetadataHelp": "[TODO: Translate] Images without LoRA metadata will be skipped automatically.",
+            "start": "[TODO: Translate] Start Import",
+            "startImport": "[TODO: Translate] Start Import",
+            "importing": "[TODO: Translate] Importing...",
+            "progress": "[TODO: Translate] Progress",
+            "total": "[TODO: Translate] Total",
+            "success": "[TODO: Translate] Success",
+            "failed": "[TODO: Translate] Failed",
+            "skipped": "[TODO: Translate] Skipped",
+            "current": "[TODO: Translate] Current",
+            "currentItem": "[TODO: Translate] Current",
+            "preparing": "[TODO: Translate] Preparing...",
+            "cancel": "[TODO: Translate] Cancel",
+            "cancelImport": "[TODO: Translate] Cancel",
+            "cancelled": "[TODO: Translate] Import cancelled",
+            "completed": "[TODO: Translate] Import completed",
+            "completedWithErrors": "[TODO: Translate] Completed with errors",
+            "completedSuccess": "[TODO: Translate] Successfully imported {count} recipe(s)",
+            "successCount": "[TODO: Translate] Successful",
+            "failedCount": "[TODO: Translate] Failed",
+            "skippedCount": "[TODO: Translate] Skipped",
+            "totalProcessed": "[TODO: Translate] Total processed",
+            "viewDetails": "[TODO: Translate] View Details",
+            "newImport": "[TODO: Translate] New Import",
+            "manualPathEntry": "[TODO: Translate] Please enter the directory path manually. File browser is not available in this browser.",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {name}. You may need to enter the full path manually.",
+            "batchImportManualEntryRequired": "[TODO: Translate] File browser not available. Please enter the directory path manually.",
+            "backToParent": "[TODO: Translate] Back to parent directory",
+            "folders": "[TODO: Translate] Folders",
+            "folderCount": "[TODO: Translate] {count} folders",
+            "imageFiles": "[TODO: Translate] Image Files",
+            "images": "[TODO: Translate] images",
+            "imageCount": "[TODO: Translate] {count} images",
+            "selectFolder": "[TODO: Translate] Select This Folder",
+            "errors": {
+                "enterUrls": "[TODO: Translate] Please enter at least one URL or path",
+                "enterDirectory": "[TODO: Translate] Please enter a directory path",
+                "startFailed": "[TODO: Translate] Failed to start import: {message}"
+            }
         }
     },
     "checkpoints": {
@@ -1438,7 +1497,14 @@
             "recipeSaveFailed": "儲存配方失敗：{error}",
             "importFailed": "匯入失敗：{message}",
             "folderTreeFailed": "載入資料夾樹狀結構失敗",
-            "folderTreeError": "載入資料夾樹狀結構錯誤"
+            "folderTreeError": "載入資料夾樹狀結構錯誤",
+            "batchImportFailed": "[TODO: Translate] Failed to start batch import: {message}",
+            "batchImportCancelling": "[TODO: Translate] Cancelling batch import...",
+            "batchImportCancelFailed": "[TODO: Translate] Failed to cancel batch import: {message}",
+            "batchImportNoUrls": "[TODO: Translate] Please enter at least one URL or file path",
+            "batchImportNoDirectory": "[TODO: Translate] Please enter a directory path",
+            "batchImportBrowseFailed": "[TODO: Translate] Failed to browse directory: {message}",
+            "batchImportDirectorySelected": "[TODO: Translate] Directory selected: {path}"
         },
         "models": {
             "noModelsSelected": "未選擇模型",

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/base_recipe_routes.py

@@ -1,4 +1,5 @@
 """Base infrastructure shared across recipe routes."""
+
 from __future__ import annotations
 
 import logging
@@ -16,12 +17,14 @@ from ..services.recipes import (
     RecipePersistenceService,
     RecipeSharingService,
 )
+from ..services.batch_import_service import BatchImportService
 from ..services.server_i18n import server_i18n
 from ..services.service_registry import ServiceRegistry
 from ..services.settings_manager import get_settings_manager
 from ..utils.constants import CARD_PREVIEW_WIDTH
 from ..utils.exif_utils import ExifUtils
 from .handlers.recipe_handlers import (
+    BatchImportHandler,
     RecipeAnalysisHandler,
     RecipeHandlerSet,
     RecipeListingHandler,
@@ -116,7 +119,10 @@ class BaseRecipeRoutes:
         recipe_scanner_getter = lambda: self.recipe_scanner
         civitai_client_getter = lambda: self.civitai_client
 
-        standalone_mode = os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1" or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
+        standalone_mode = (
+            os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"
+            or os.environ.get("HF_HUB_DISABLE_TELEMETRY", "0") == "0"
+        )
         if not standalone_mode:
             from ..metadata_collector import get_metadata  # type: ignore[import-not-found]
             from ..metadata_collector.metadata_processor import (  # type: ignore[import-not-found]
@@ -190,6 +196,22 @@ class BaseRecipeRoutes:
             sharing_service=sharing_service,
         )
 
+        from ..services.websocket_manager import ws_manager
+
+        batch_import_service = BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+        batch_import = BatchImportHandler(
+            ensure_dependencies_ready=self.ensure_dependencies_ready,
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            logger=logger,
+            batch_import_service=batch_import_service,
+        )
+
         return RecipeHandlerSet(
             page_view=page_view,
             listing=listing,
@@ -197,4 +219,5 @@ class BaseRecipeRoutes:
             management=management,
             analysis=analysis,
             sharing=sharing,
+            batch_import=batch_import,
         )

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/routes/handlers/recipe_handlers.py

@@ -1,4 +1,5 @@
 """Dedicated handler objects for recipe-related routes."""
+
 from __future__ import annotations
 
 import json
@@ -8,6 +9,7 @@ import re
 import asyncio
 import tempfile
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Any, Awaitable, Callable, Dict, List, Mapping, Optional
 
 from aiohttp import web
@@ -29,6 +31,7 @@ from ...utils.exif_utils import ExifUtils
 from ...recipes.merger import GenParamsMerger
 from ...recipes.enrichment import RecipeEnricher
 from ...services.websocket_manager import ws_manager as default_ws_manager
+from ...services.batch_import_service import BatchImportService
 
 Logger = logging.Logger
 EnsureDependenciesCallable = Callable[[], Awaitable[None]]
@@ -46,8 +49,11 @@ class RecipeHandlerSet:
     management: "RecipeManagementHandler"
     analysis: "RecipeAnalysisHandler"
     sharing: "RecipeSharingHandler"
+    batch_import: "BatchImportHandler"
 
-    def to_route_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
+    def to_route_mapping(
+        self,
+    ) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         """Expose handler coroutines keyed by registrar handler names."""
 
         return {
@@ -81,6 +87,11 @@ class RecipeHandlerSet:
             "cancel_repair": self.management.cancel_repair,
             "repair_recipe": self.management.repair_recipe,
             "get_repair_progress": self.management.get_repair_progress,
+            "start_batch_import": self.batch_import.start_batch_import,
+            "get_batch_import_progress": self.batch_import.get_batch_import_progress,
+            "cancel_batch_import": self.batch_import.cancel_batch_import,
+            "start_directory_import": self.batch_import.start_directory_import,
+            "browse_directory": self.batch_import.browse_directory,
         }
 
 
@@ -170,8 +181,10 @@ class RecipeListingHandler:
             search_options = {
                 "title": request.query.get("search_title", "true").lower() == "true",
                 "tags": request.query.get("search_tags", "true").lower() == "true",
-                "lora_name": request.query.get("search_lora_name", "true").lower() == "true",
-                "lora_model": request.query.get("search_lora_model", "true").lower() == "true",
+                "lora_name": request.query.get("search_lora_name", "true").lower()
+                == "true",
+                "lora_model": request.query.get("search_lora_model", "true").lower()
+                == "true",
                 "prompt": request.query.get("search_prompt", "true").lower() == "true",
             }
 
@@ -246,7 +259,9 @@ class RecipeListingHandler:
                 return web.json_response({"error": "Recipe not found"}, status=404)
             return web.json_response(recipe)
         except Exception as exc:
-            self._logger.error("Error retrieving recipe details: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error retrieving recipe details: %s", exc, exc_info=True
+            )
             return web.json_response({"error": str(exc)}, status=500)
 
     def format_recipe_file_url(self, file_path: str) -> str:
@@ -256,7 +271,9 @@ class RecipeListingHandler:
             if static_url:
                 return static_url
         except Exception as exc:  # pragma: no cover - logging path
-            self._logger.error("Error formatting recipe file URL: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error formatting recipe file URL: %s", exc, exc_info=True
+            )
             return "/loras_static/images/no-preview.png"
 
         return "/loras_static/images/no-preview.png"
@@ -293,7 +310,9 @@ class RecipeQueryHandler:
                 for tag in recipe.get("tags", []) or []:
                     tag_counts[tag] = tag_counts.get(tag, 0) + 1
 
-            sorted_tags = [{"tag": tag, "count": count} for tag, count in tag_counts.items()]
+            sorted_tags = [
+                {"tag": tag, "count": count} for tag, count in tag_counts.items()
+            ]
             sorted_tags.sort(key=lambda entry: entry["count"], reverse=True)
             return web.json_response({"success": True, "tags": sorted_tags[:limit]})
         except Exception as exc:
@@ -313,9 +332,14 @@ class RecipeQueryHandler:
             for recipe in getattr(cache, "raw_data", []):
                 base_model = recipe.get("base_model")
                 if base_model:
-                    base_model_counts[base_model] = base_model_counts.get(base_model, 0) + 1
+                    base_model_counts[base_model] = (
+                        base_model_counts.get(base_model, 0) + 1
+                    )
 
-            sorted_models = [{"name": model, "count": count} for model, count in base_model_counts.items()]
+            sorted_models = [
+                {"name": model, "count": count}
+                for model, count in base_model_counts.items()
+            ]
             sorted_models.sort(key=lambda entry: entry["count"], reverse=True)
             return web.json_response({"success": True, "base_models": sorted_models})
         except Exception as exc:
@@ -345,7 +369,9 @@ class RecipeQueryHandler:
             folders = await recipe_scanner.get_folders()
             return web.json_response({"success": True, "folders": folders})
         except Exception as exc:
-            self._logger.error("Error retrieving recipe folders: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error retrieving recipe folders: %s", exc, exc_info=True
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def get_folder_tree(self, request: web.Request) -> web.Response:
@@ -358,7 +384,9 @@ class RecipeQueryHandler:
             folder_tree = await recipe_scanner.get_folder_tree()
             return web.json_response({"success": True, "tree": folder_tree})
         except Exception as exc:
-            self._logger.error("Error retrieving recipe folder tree: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error retrieving recipe folder tree: %s", exc, exc_info=True
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def get_unified_folder_tree(self, request: web.Request) -> web.Response:
@@ -371,7 +399,9 @@ class RecipeQueryHandler:
             folder_tree = await recipe_scanner.get_folder_tree()
             return web.json_response({"success": True, "tree": folder_tree})
         except Exception as exc:
-            self._logger.error("Error retrieving unified recipe folder tree: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error retrieving unified recipe folder tree: %s", exc, exc_info=True
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def get_recipes_for_lora(self, request: web.Request) -> web.Response:
@@ -383,7 +413,9 @@ class RecipeQueryHandler:
 
             lora_hash = request.query.get("hash")
             if not lora_hash:
-                return web.json_response({"success": False, "error": "Lora hash is required"}, status=400)
+                return web.json_response(
+                    {"success": False, "error": "Lora hash is required"}, status=400
+                )
 
             matching_recipes = await recipe_scanner.get_recipes_for_lora(lora_hash)
             return web.json_response({"success": True, "recipes": matching_recipes})
@@ -400,7 +432,9 @@ class RecipeQueryHandler:
 
             self._logger.info("Manually triggering recipe cache rebuild")
             await recipe_scanner.get_cached_data(force_refresh=True)
-            return web.json_response({"success": True, "message": "Recipe cache refreshed successfully"})
+            return web.json_response(
+                {"success": True, "message": "Recipe cache refreshed successfully"}
+            )
         except Exception as exc:
             self._logger.error("Error refreshing recipe cache: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -429,7 +463,9 @@ class RecipeQueryHandler:
                                 "id": recipe.get("id"),
                                 "title": recipe.get("title"),
                                 "file_url": recipe.get("file_url")
-                                or self._format_recipe_file_url(recipe.get("file_path", "")),
+                                or self._format_recipe_file_url(
+                                    recipe.get("file_path", "")
+                                ),
                                 "modified": recipe.get("modified"),
                                 "created_date": recipe.get("created_date"),
                                 "lora_count": len(recipe.get("loras", [])),
@@ -437,7 +473,9 @@ class RecipeQueryHandler:
                         )
 
                 if len(recipes) >= 2:
-                    recipes.sort(key=lambda entry: entry.get("modified", 0), reverse=True)
+                    recipes.sort(
+                        key=lambda entry: entry.get("modified", 0), reverse=True
+                    )
                     response_data.append(
                         {
                             "type": "fingerprint",
@@ -460,7 +498,9 @@ class RecipeQueryHandler:
                                 "id": recipe.get("id"),
                                 "title": recipe.get("title"),
                                 "file_url": recipe.get("file_url")
-                                or self._format_recipe_file_url(recipe.get("file_path", "")),
+                                or self._format_recipe_file_url(
+                                    recipe.get("file_path", "")
+                                ),
                                 "modified": recipe.get("modified"),
                                 "created_date": recipe.get("created_date"),
                                 "lora_count": len(recipe.get("loras", [])),
@@ -468,7 +508,9 @@ class RecipeQueryHandler:
                         )
 
                 if len(recipes) >= 2:
-                    recipes.sort(key=lambda entry: entry.get("modified", 0), reverse=True)
+                    recipes.sort(
+                        key=lambda entry: entry.get("modified", 0), reverse=True
+                    )
                     response_data.append(
                         {
                             "type": "source_url",
@@ -479,9 +521,13 @@ class RecipeQueryHandler:
                     )
 
             response_data.sort(key=lambda entry: entry["count"], reverse=True)
-            return web.json_response({"success": True, "duplicate_groups": response_data})
+            return web.json_response(
+                {"success": True, "duplicate_groups": response_data}
+            )
         except Exception as exc:
-            self._logger.error("Error finding duplicate recipes: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error finding duplicate recipes: %s", exc, exc_info=True
+            )
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
     async def get_recipe_syntax(self, request: web.Request) -> web.Response:
@@ -498,9 +544,13 @@ class RecipeQueryHandler:
                 return web.json_response({"error": "Recipe not found"}, status=404)
 
             if not syntax_parts:
-                return web.json_response({"error": "No LoRAs found in this recipe"}, status=400)
+                return web.json_response(
+                    {"error": "No LoRAs found in this recipe"}, status=400
+                )
 
-            return web.json_response({"success": True, "syntax": " ".join(syntax_parts)})
+            return web.json_response(
+                {"success": True, "syntax": " ".join(syntax_parts)}
+            )
         except Exception as exc:
             self._logger.error("Error generating recipe syntax: %s", exc, exc_info=True)
             return web.json_response({"error": str(exc)}, status=500)
@@ -561,11 +611,17 @@ class RecipeManagementHandler:
             await self._ensure_dependencies_ready()
             recipe_scanner = self._recipe_scanner_getter()
             if recipe_scanner is None:
-                return web.json_response({"success": False, "error": "Recipe scanner unavailable"}, status=503)
+                return web.json_response(
+                    {"success": False, "error": "Recipe scanner unavailable"},
+                    status=503,
+                )
 
             # Check if already running
             if self._ws_manager.is_recipe_repair_running():
-                return web.json_response({"success": False, "error": "Recipe repair already in progress"}, status=409)
+                return web.json_response(
+                    {"success": False, "error": "Recipe repair already in progress"},
+                    status=409,
+                )
 
             recipe_scanner.reset_cancellation()
 
@@ -579,11 +635,12 @@ class RecipeManagementHandler:
                         progress_callback=progress_callback
                     )
                 except Exception as e:
-                    self._logger.error(f"Error in recipe repair task: {e}", exc_info=True)
-                    await self._ws_manager.broadcast_recipe_repair_progress({
-                        "status": "error",
-                        "error": str(e)
-                    })
+                    self._logger.error(
+                        f"Error in recipe repair task: {e}", exc_info=True
+                    )
+                    await self._ws_manager.broadcast_recipe_repair_progress(
+                        {"status": "error", "error": str(e)}
+                    )
                 finally:
                     # Keep the final status for a while so the UI can see it
                     await asyncio.sleep(5)
@@ -593,7 +650,9 @@ class RecipeManagementHandler:
 
             asyncio.create_task(run_repair())
 
-            return web.json_response({"success": True, "message": "Recipe repair started"})
+            return web.json_response(
+                {"success": True, "message": "Recipe repair started"}
+            )
         except Exception as exc:
             self._logger.error("Error starting recipe repair: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -603,10 +662,15 @@ class RecipeManagementHandler:
             await self._ensure_dependencies_ready()
             recipe_scanner = self._recipe_scanner_getter()
             if recipe_scanner is None:
-                return web.json_response({"success": False, "error": "Recipe scanner unavailable"}, status=503)
+                return web.json_response(
+                    {"success": False, "error": "Recipe scanner unavailable"},
+                    status=503,
+                )
 
             recipe_scanner.cancel_task()
-            return web.json_response({"success": True, "message": "Cancellation requested"})
+            return web.json_response(
+                {"success": True, "message": "Cancellation requested"}
+            )
         except Exception as exc:
             self._logger.error("Error cancelling recipe repair: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
@@ -616,7 +680,10 @@ class RecipeManagementHandler:
             await self._ensure_dependencies_ready()
             recipe_scanner = self._recipe_scanner_getter()
             if recipe_scanner is None:
-                return web.json_response({"success": False, "error": "Recipe scanner unavailable"}, status=503)
+                return web.json_response(
+                    {"success": False, "error": "Recipe scanner unavailable"},
+                    status=503,
+                )
 
             recipe_id = request.match_info["recipe_id"]
             result = await recipe_scanner.repair_recipe_by_id(recipe_id)
@@ -632,25 +699,26 @@ class RecipeManagementHandler:
             progress = self._ws_manager.get_recipe_repair_progress()
             if progress:
                 return web.json_response({"success": True, "progress": progress})
-            return web.json_response({"success": False, "message": "No repair in progress"}, status=404)
+            return web.json_response(
+                {"success": False, "message": "No repair in progress"}, status=404
+            )
         except Exception as exc:
             self._logger.error("Error getting repair progress: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
-
     async def import_remote_recipe(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
             recipe_scanner = self._recipe_scanner_getter()
             if recipe_scanner is None:
                 raise RuntimeError("Recipe scanner unavailable")
-            
+
             # 1. Parse Parameters
             params = request.rel_url.query
             image_url = params.get("image_url")
             name = params.get("name")
             resources_raw = params.get("resources")
-            
+
             if not image_url:
                 raise RecipeValidationError("Missing required field: image_url")
             if not name:
@@ -658,64 +726,80 @@ class RecipeManagementHandler:
             if not resources_raw:
                 raise RecipeValidationError("Missing required field: resources")
 
-            checkpoint_entry, lora_entries = self._parse_resources_payload(resources_raw)
+            checkpoint_entry, lora_entries = self._parse_resources_payload(
+                resources_raw
+            )
             gen_params_request = self._parse_gen_params(params.get("gen_params"))
-            
+
             # 2. Initial Metadata Construction
             metadata: Dict[str, Any] = {
                 "base_model": params.get("base_model", "") or "",
                 "loras": lora_entries,
                 "gen_params": gen_params_request or {},
-                "source_url": image_url
+                "source_url": image_url,
             }
-            
+
             source_path = params.get("source_path")
             if source_path:
                 metadata["source_path"] = source_path
-                
+
             # Checkpoint handling
             if checkpoint_entry:
                 metadata["checkpoint"] = checkpoint_entry
                 # Ensure checkpoint is also in gen_params for consistency if needed by enricher?
                 # Actually enricher looks at metadata['checkpoint'], so this is fine.
-                
+
                 # Try to resolve base model from checkpoint if not explicitly provided
                 if not metadata["base_model"]:
-                    base_model_from_metadata = await self._resolve_base_model_from_checkpoint(checkpoint_entry)
+                    base_model_from_metadata = (
+                        await self._resolve_base_model_from_checkpoint(checkpoint_entry)
+                    )
                     if base_model_from_metadata:
                         metadata["base_model"] = base_model_from_metadata
 
             tags = self._parse_tags(params.get("tags"))
-            
+
             # 3. Download Image
-            image_bytes, extension, civitai_meta_from_download = await self._download_remote_media(image_url)
+            (
+                image_bytes,
+                extension,
+                civitai_meta_from_download,
+            ) = await self._download_remote_media(image_url)
 
             # 4. Extract Embedded Metadata
-            # Note: We still extract this here because Enricher currently expects 'gen_params' to already be populated 
-            # with embedded data if we want it to merge it. 
+            # Note: We still extract this here because Enricher currently expects 'gen_params' to already be populated
+            # with embedded data if we want it to merge it.
             # However, logic in Enricher merges: request > civitai > embedded.
-            # So we should gather embedded params and put them into the recipe's gen_params (as initial state) 
+            # So we should gather embedded params and put them into the recipe's gen_params (as initial state)
             # OR pass them to enricher to handle?
             # The interface of Enricher.enrich_recipe takes `recipe` (with gen_params) and `request_params`.
             # So let's extract embedded and put it into recipe['gen_params'] but careful not to overwrite request params.
             # Actually, `GenParamsMerger` which `Enricher` uses handles 3 layers.
             # But `Enricher` interface is: recipe['gen_params'] (as embedded) + request_params + civitai (fetched internally).
-            # Wait, `Enricher` fetches Civitai info internally based on URL. 
+            # Wait, `Enricher` fetches Civitai info internally based on URL.
             # `civitai_meta_from_download` is returned by `_download_remote_media` which might be useful if URL didn't have ID.
-            
+
             # Let's extract embedded metadata first
             embedded_gen_params = {}
             try:
-                with tempfile.NamedTemporaryFile(suffix=extension, delete=False) as temp_img:
+                with tempfile.NamedTemporaryFile(
+                    suffix=extension, delete=False
+                ) as temp_img:
                     temp_img.write(image_bytes)
                     temp_img_path = temp_img.name
-                
+
                 try:
                     raw_embedded = ExifUtils.extract_image_metadata(temp_img_path)
                     if raw_embedded:
-                        parser = self._analysis_service._recipe_parser_factory.create_parser(raw_embedded)
+                        parser = (
+                            self._analysis_service._recipe_parser_factory.create_parser(
+                                raw_embedded
+                            )
+                        )
                         if parser:
-                            parsed_embedded = await parser.parse_metadata(raw_embedded, recipe_scanner=recipe_scanner)
+                            parsed_embedded = await parser.parse_metadata(
+                                raw_embedded, recipe_scanner=recipe_scanner
+                            )
                             if parsed_embedded and "gen_params" in parsed_embedded:
                                 embedded_gen_params = parsed_embedded["gen_params"]
                         else:
@@ -724,7 +808,9 @@ class RecipeManagementHandler:
                     if os.path.exists(temp_img_path):
                         os.unlink(temp_img_path)
             except Exception as exc:
-                self._logger.warning("Failed to extract embedded metadata during import: %s", exc)
+                self._logger.warning(
+                    "Failed to extract embedded metadata during import: %s", exc
+                )
 
             # Pre-populate gen_params with embedded data so Enricher treats it as the "base" layer
             if embedded_gen_params:
@@ -732,18 +818,18 @@ class RecipeManagementHandler:
                 # But wait, we want request params to override everything.
                 # So we should set recipe['gen_params'] = embedded, and pass request params to enricher.
                 metadata["gen_params"] = embedded_gen_params
-            
+
             # 5. Enrich with unified logic
             # This will fetch Civitai info (if URL matches) and merge: request > civitai > embedded
             civitai_client = self._civitai_client_getter()
             await RecipeEnricher.enrich_recipe(
-                recipe=metadata, 
+                recipe=metadata,
                 civitai_client=civitai_client,
-                request_params=gen_params_request # Pass explicit request params here to override
+                request_params=gen_params_request,  # Pass explicit request params here to override
             )
-            
+
             # If we got civitai_meta from download but Enricher didn't fetch it (e.g. not a civitai URL or failed),
-            # we might want to manually merge it? 
+            # we might want to manually merge it?
             # But usually `import_remote_recipe` is used with Civitai URLs.
             # For now, relying on Enricher's internal fetch is consistent with repair.
 
@@ -762,7 +848,9 @@ class RecipeManagementHandler:
         except RecipeDownloadError as exc:
             return web.json_response({"error": str(exc)}, status=400)
         except Exception as exc:
-            self._logger.error("Error importing recipe from remote source: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error importing recipe from remote source: %s", exc, exc_info=True
+            )
             return web.json_response({"error": str(exc)}, status=500)
 
     async def delete_recipe(self, request: web.Request) -> web.Response:
@@ -816,7 +904,11 @@ class RecipeManagementHandler:
             target_path = data.get("target_path")
             if not recipe_id or not target_path:
                 return web.json_response(
-                    {"success": False, "error": "recipe_id and target_path are required"}, status=400
+                    {
+                        "success": False,
+                        "error": "recipe_id and target_path are required",
+                    },
+                    status=400,
                 )
 
             result = await self._persistence_service.move_recipe(
@@ -845,7 +937,11 @@ class RecipeManagementHandler:
             target_path = data.get("target_path")
             if not recipe_ids or not target_path:
                 return web.json_response(
-                    {"success": False, "error": "recipe_ids and target_path are required"}, status=400
+                    {
+                        "success": False,
+                        "error": "recipe_ids and target_path are required",
+                    },
+                    status=400,
                 )
 
             result = await self._persistence_service.move_recipes_bulk(
@@ -934,7 +1030,9 @@ class RecipeManagementHandler:
         except RecipeValidationError as exc:
             return web.json_response({"error": str(exc)}, status=400)
         except Exception as exc:
-            self._logger.error("Error saving recipe from widget: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error saving recipe from widget: %s", exc, exc_info=True
+            )
             return web.json_response({"error": str(exc)}, status=500)
 
     async def _parse_save_payload(self, reader) -> dict[str, Any]:
@@ -1006,7 +1104,9 @@ class RecipeManagementHandler:
             raise RecipeValidationError("gen_params payload must be an object")
         return parsed
 
-    def _parse_resources_payload(self, payload_raw: str) -> tuple[Optional[Dict[str, Any]], List[Dict[str, Any]]]:
+    def _parse_resources_payload(
+        self, payload_raw: str
+    ) -> tuple[Optional[Dict[str, Any]], List[Dict[str, Any]]]:
         try:
             payload = json.loads(payload_raw)
         except json.JSONDecodeError as exc:
@@ -1066,15 +1166,19 @@ class RecipeManagementHandler:
             civitai_match = re.match(r"https://civitai\.com/images/(\d+)", image_url)
             if civitai_match:
                 if civitai_client is None:
-                    raise RecipeDownloadError("Civitai client unavailable for image download")
+                    raise RecipeDownloadError(
+                        "Civitai client unavailable for image download"
+                    )
                 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"
+                    )
+
                 media_url = image_info.get("url")
                 if not media_url:
                     raise RecipeDownloadError("No image URL found in Civitai response")
-                
+
                 # Use optimized preview URLs if possible
                 media_type = image_info.get("type")
                 rewritten_url, _ = rewrite_preview_url(media_url, media_type=media_type)
@@ -1083,18 +1187,24 @@ class RecipeManagementHandler:
                 else:
                     download_url = media_url
 
-            success, result = await downloader.download_file(download_url, temp_path, use_auth=False)
+            success, result = await downloader.download_file(
+                download_url, temp_path, use_auth=False
+            )
             if not success:
                 raise RecipeDownloadError(f"Failed to download image: {result}")
-            
+
             # Extract extension from URL
-            url_path = download_url.split('?')[0].split('#')[0]
+            url_path = download_url.split("?")[0].split("#")[0]
             extension = os.path.splitext(url_path)[1].lower()
             if not extension:
-                extension = ".webp" # Default to webp if unknown
+                extension = ".webp"  # Default to webp if unknown
 
             with open(temp_path, "rb") as file_obj:
-                return file_obj.read(), extension, image_info.get("meta") if civitai_match and image_info else None
+                return (
+                    file_obj.read(),
+                    extension,
+                    image_info.get("meta") if civitai_match and image_info else None,
+                )
         except RecipeDownloadError:
             raise
         except RecipeValidationError:
@@ -1108,14 +1218,15 @@ class RecipeManagementHandler:
                 except FileNotFoundError:
                     pass
 
-
     def _safe_int(self, value: Any) -> int:
         try:
             return int(value)
         except (TypeError, ValueError):
             return 0
 
-    async def _resolve_base_model_from_checkpoint(self, checkpoint_entry: Dict[str, Any]) -> str:
+    async def _resolve_base_model_from_checkpoint(
+        self, checkpoint_entry: Dict[str, Any]
+    ) -> str:
         version_id = self._safe_int(checkpoint_entry.get("modelVersionId"))
 
         if not version_id:
@@ -1134,7 +1245,9 @@ class RecipeManagementHandler:
                 base_model = version_info.get("baseModel") or ""
                 return str(base_model) if base_model is not None else ""
         except Exception as exc:  # pragma: no cover - defensive logging
-            self._logger.warning("Failed to resolve base model from checkpoint metadata: %s", exc)
+            self._logger.warning(
+                "Failed to resolve base model from checkpoint metadata: %s", exc
+            )
 
         return ""
 
@@ -1279,5 +1392,311 @@ class RecipeSharingHandler:
         except RecipeNotFoundError as exc:
             return web.json_response({"error": str(exc)}, status=404)
         except Exception as exc:
-            self._logger.error("Error downloading shared recipe: %s", exc, exc_info=True)
+            self._logger.error(
+                "Error downloading shared recipe: %s", exc, exc_info=True
+            )
             return web.json_response({"error": str(exc)}, status=500)
+
+
+class BatchImportHandler:
+    """Handle batch import operations for recipes."""
+
+    def __init__(
+        self,
+        *,
+        ensure_dependencies_ready: EnsureDependenciesCallable,
+        recipe_scanner_getter: RecipeScannerGetter,
+        civitai_client_getter: CivitaiClientGetter,
+        logger: Logger,
+        batch_import_service: BatchImportService,
+    ) -> None:
+        self._ensure_dependencies_ready = ensure_dependencies_ready
+        self._recipe_scanner_getter = recipe_scanner_getter
+        self._civitai_client_getter = civitai_client_getter
+        self._logger = logger
+        self._batch_import_service = batch_import_service
+
+    async def start_batch_import(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+
+            if self._batch_import_service.is_import_running():
+                return web.json_response(
+                    {"success": False, "error": "Batch import already in progress"},
+                    status=409,
+                )
+
+            data = await request.json()
+            items = data.get("items", [])
+            tags = data.get("tags", [])
+            skip_no_metadata = data.get("skip_no_metadata", False)
+
+            if not items:
+                return web.json_response(
+                    {"success": False, "error": "No items provided"},
+                    status=400,
+                )
+
+            for item in items:
+                if not item.get("source"):
+                    return web.json_response(
+                        {
+                            "success": False,
+                            "error": "Each item must have a 'source' field",
+                        },
+                        status=400,
+                    )
+
+            operation_id = await self._batch_import_service.start_batch_import(
+                recipe_scanner_getter=self._recipe_scanner_getter,
+                civitai_client_getter=self._civitai_client_getter,
+                items=items,
+                tags=tags,
+                skip_no_metadata=skip_no_metadata,
+            )
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "operation_id": operation_id,
+                }
+            )
+        except RecipeValidationError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error("Error starting batch import: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def start_directory_import(self, request: web.Request) -> web.Response:
+        try:
+            await self._ensure_dependencies_ready()
+
+            if self._batch_import_service.is_import_running():
+                return web.json_response(
+                    {"success": False, "error": "Batch import already in progress"},
+                    status=409,
+                )
+
+            data = await request.json()
+            directory = data.get("directory")
+            recursive = data.get("recursive", True)
+            tags = data.get("tags", [])
+            skip_no_metadata = data.get("skip_no_metadata", True)
+
+            if not directory:
+                return web.json_response(
+                    {"success": False, "error": "Directory path is required"},
+                    status=400,
+                )
+
+            operation_id = await self._batch_import_service.start_directory_import(
+                recipe_scanner_getter=self._recipe_scanner_getter,
+                civitai_client_getter=self._civitai_client_getter,
+                directory=directory,
+                recursive=recursive,
+                tags=tags,
+                skip_no_metadata=skip_no_metadata,
+            )
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "operation_id": operation_id,
+                }
+            )
+        except RecipeValidationError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error(
+                "Error starting directory import: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def get_batch_import_progress(self, request: web.Request) -> web.Response:
+        try:
+            operation_id = request.query.get("operation_id")
+            if not operation_id:
+                return web.json_response(
+                    {"success": False, "error": "operation_id is required"},
+                    status=400,
+                )
+
+            progress = self._batch_import_service.get_progress(operation_id)
+            if not progress:
+                return web.json_response(
+                    {"success": False, "error": "Operation not found"},
+                    status=404,
+                )
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "progress": progress.to_dict(),
+                }
+            )
+        except Exception as exc:
+            self._logger.error(
+                "Error getting batch import progress: %s", exc, exc_info=True
+            )
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def cancel_batch_import(self, request: web.Request) -> web.Response:
+        try:
+            data = await request.json()
+            operation_id = data.get("operation_id")
+
+            if not operation_id:
+                return web.json_response(
+                    {"success": False, "error": "operation_id is required"},
+                    status=400,
+                )
+
+            cancelled = self._batch_import_service.cancel_import(operation_id)
+            if not cancelled:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": "Operation not found or already completed",
+                    },
+                    status=404,
+                )
+
+            return web.json_response(
+                {"success": True, "message": "Cancellation requested"}
+            )
+        except Exception as exc:
+            self._logger.error("Error cancelling batch import: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+    async def browse_directory(self, request: web.Request) -> web.Response:
+        """Browse a directory and return its contents (subdirectories and files)."""
+        try:
+            data = await request.json()
+            directory_path = data.get("path", "")
+
+            if not directory_path:
+                return web.json_response(
+                    {"success": False, "error": "Directory path is required"},
+                    status=400,
+                )
+
+            # Normalize the path
+            path = Path(directory_path).expanduser().resolve()
+
+            # Security check: ensure path is within allowed directories
+            # Allow common image/model directories
+            allowed_roots = [
+                Path.home(),
+                Path("/"),  # Allow browsing from root for flexibility
+            ]
+
+            # Check if path is within any allowed root
+            is_allowed = False
+            for root in allowed_roots:
+                try:
+                    path.relative_to(root)
+                    is_allowed = True
+                    break
+                except ValueError:
+                    continue
+
+            if not is_allowed:
+                return web.json_response(
+                    {"success": False, "error": "Access denied to this directory"},
+                    status=403,
+                )
+
+            if not path.exists():
+                return web.json_response(
+                    {"success": False, "error": "Directory does not exist"},
+                    status=404,
+                )
+
+            if not path.is_dir():
+                return web.json_response(
+                    {"success": False, "error": "Path is not a directory"},
+                    status=400,
+                )
+
+            # List directory contents
+            directories = []
+            image_files = []
+
+            image_extensions = {
+                ".jpg",
+                ".jpeg",
+                ".png",
+                ".gif",
+                ".webp",
+                ".bmp",
+                ".tiff",
+                ".tif",
+            }
+
+            try:
+                for item in path.iterdir():
+                    try:
+                        if item.is_dir():
+                            # Skip hidden directories and common system folders
+                            if not item.name.startswith(".") and item.name not in [
+                                "__pycache__",
+                                "node_modules",
+                            ]:
+                                directories.append(
+                                    {
+                                        "name": item.name,
+                                        "path": str(item),
+                                        "is_parent": False,
+                                    }
+                                )
+                        elif item.is_file() and item.suffix.lower() in image_extensions:
+                            image_files.append(
+                                {
+                                    "name": item.name,
+                                    "path": str(item),
+                                    "size": item.stat().st_size,
+                                }
+                            )
+                    except (PermissionError, OSError):
+                        # Skip files/directories we can't access
+                        continue
+
+                # Sort directories and files alphabetically
+                directories.sort(key=lambda x: x["name"].lower())
+                image_files.sort(key=lambda x: x["name"].lower())
+
+                # Add parent directory if not at root
+                parent_path = path.parent
+                show_parent = str(path) != str(path.root)
+
+                return web.json_response(
+                    {
+                        "success": True,
+                        "current_path": str(path),
+                        "parent_path": str(parent_path) if show_parent else None,
+                        "directories": directories,
+                        "image_files": image_files,
+                        "image_count": len(image_files),
+                        "directory_count": len(directories),
+                    }
+                )
+
+            except PermissionError:
+                return web.json_response(
+                    {"success": False, "error": "Permission denied"},
+                    status=403,
+                )
+            except OSError as exc:
+                return web.json_response(
+                    {"success": False, "error": f"Error reading directory: {str(exc)}"},
+                    status=500,
+                )
+
+        except json.JSONDecodeError:
+            return web.json_response(
+                {"success": False, "error": "Invalid JSON"},
+                status=400,
+            )
+        except Exception as exc:
+            self._logger.error("Error browsing directory: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)

py/routes/recipe_route_registrar.py

@@ -1,4 +1,5 @@
 """Route registrar for recipe endpoints."""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -22,7 +23,9 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/recipe/{recipe_id}", "get_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/import-remote", "import_remote_recipe"),
     RouteDefinition("POST", "/api/lm/recipes/analyze-image", "analyze_uploaded_image"),
-    RouteDefinition("POST", "/api/lm/recipes/analyze-local-image", "analyze_local_image"),
+    RouteDefinition(
+        "POST", "/api/lm/recipes/analyze-local-image", "analyze_local_image"
+    ),
     RouteDefinition("POST", "/api/lm/recipes/save", "save_recipe"),
     RouteDefinition("DELETE", "/api/lm/recipe/{recipe_id}", "delete_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/top-tags", "get_top_tags"),
@@ -30,9 +33,13 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/recipes/roots", "get_roots"),
     RouteDefinition("GET", "/api/lm/recipes/folders", "get_folders"),
     RouteDefinition("GET", "/api/lm/recipes/folder-tree", "get_folder_tree"),
-    RouteDefinition("GET", "/api/lm/recipes/unified-folder-tree", "get_unified_folder_tree"),
+    RouteDefinition(
+        "GET", "/api/lm/recipes/unified-folder-tree", "get_unified_folder_tree"
+    ),
     RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/share", "share_recipe"),
-    RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/share/download", "download_shared_recipe"),
+    RouteDefinition(
+        "GET", "/api/lm/recipe/{recipe_id}/share/download", "download_shared_recipe"
+    ),
     RouteDefinition("GET", "/api/lm/recipe/{recipe_id}/syntax", "get_recipe_syntax"),
     RouteDefinition("PUT", "/api/lm/recipe/{recipe_id}/update", "update_recipe"),
     RouteDefinition("POST", "/api/lm/recipe/move", "move_recipe"),
@@ -40,13 +47,26 @@ ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/recipe/lora/reconnect", "reconnect_lora"),
     RouteDefinition("GET", "/api/lm/recipes/find-duplicates", "find_duplicates"),
     RouteDefinition("POST", "/api/lm/recipes/bulk-delete", "bulk_delete"),
-    RouteDefinition("POST", "/api/lm/recipes/save-from-widget", "save_recipe_from_widget"),
+    RouteDefinition(
+        "POST", "/api/lm/recipes/save-from-widget", "save_recipe_from_widget"
+    ),
     RouteDefinition("GET", "/api/lm/recipes/for-lora", "get_recipes_for_lora"),
     RouteDefinition("GET", "/api/lm/recipes/scan", "scan_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/repair", "repair_recipes"),
     RouteDefinition("POST", "/api/lm/recipes/cancel-repair", "cancel_repair"),
     RouteDefinition("POST", "/api/lm/recipe/{recipe_id}/repair", "repair_recipe"),
     RouteDefinition("GET", "/api/lm/recipes/repair-progress", "get_repair_progress"),
+    RouteDefinition("POST", "/api/lm/recipes/batch-import/start", "start_batch_import"),
+    RouteDefinition(
+        "GET", "/api/lm/recipes/batch-import/progress", "get_batch_import_progress"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/recipes/batch-import/cancel", "cancel_batch_import"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/recipes/batch-import/directory", "start_directory_import"
+    ),
+    RouteDefinition("POST", "/api/lm/recipes/browse-directory", "browse_directory"),
 )
 
 
@@ -63,7 +83,9 @@ class RecipeRouteRegistrar:
     def __init__(self, app: web.Application) -> None:
         self._app = app
 
-    def register_routes(self, handler_lookup: Mapping[str, Callable[[web.Request], object]]) -> None:
+    def register_routes(
+        self, handler_lookup: Mapping[str, Callable[[web.Request], object]]
+    ) -> None:
         for definition in ROUTE_DEFINITIONS:
             handler = handler_lookup[definition.handler_name]
             self._bind_route(definition.method, definition.path, handler)

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/batch_import_service.py

@@ -0,0 +1,593 @@
+"""Batch import service for importing multiple images as recipes."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import time
+import uuid
+from dataclasses import dataclass, field
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional, Set
+
+from aiohttp import web
+
+from .recipes import (
+    RecipeAnalysisService,
+    RecipePersistenceService,
+    RecipeValidationError,
+    RecipeDownloadError,
+    RecipeNotFoundError,
+)
+
+
+class ImportItemType(Enum):
+    """Type of import item."""
+
+    URL = "url"
+    LOCAL_PATH = "local_path"
+
+
+class ImportStatus(Enum):
+    """Status of an individual import item."""
+
+    PENDING = "pending"
+    PROCESSING = "processing"
+    SUCCESS = "success"
+    FAILED = "failed"
+    SKIPPED = "skipped"
+
+
+@dataclass
+class BatchImportItem:
+    """Represents a single item to import."""
+
+    id: str
+    source: str
+    item_type: ImportItemType
+    status: ImportStatus = ImportStatus.PENDING
+    error_message: Optional[str] = None
+    recipe_name: Optional[str] = None
+    recipe_id: Optional[str] = None
+    duration: float = 0.0
+
+
+@dataclass
+class BatchImportProgress:
+    """Tracks progress of a batch import operation."""
+
+    operation_id: str
+    total: int
+    completed: int = 0
+    success: int = 0
+    failed: int = 0
+    skipped: int = 0
+    current_item: str = ""
+    status: str = "pending"
+    started_at: float = field(default_factory=time.time)
+    finished_at: Optional[float] = None
+    items: List[BatchImportItem] = field(default_factory=list)
+    tags: List[str] = field(default_factory=list)
+    skip_no_metadata: bool = False
+    skip_duplicates: bool = False
+
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            "operation_id": self.operation_id,
+            "total": self.total,
+            "completed": self.completed,
+            "success": self.success,
+            "failed": self.failed,
+            "skipped": self.skipped,
+            "current_item": self.current_item,
+            "status": self.status,
+            "started_at": self.started_at,
+            "finished_at": self.finished_at,
+            "progress_percent": round((self.completed / self.total) * 100, 1)
+            if self.total > 0
+            else 0,
+            "items": [
+                {
+                    "id": item.id,
+                    "source": item.source,
+                    "item_type": item.item_type.value,
+                    "status": item.status.value,
+                    "error_message": item.error_message,
+                    "recipe_name": item.recipe_name,
+                    "recipe_id": item.recipe_id,
+                    "duration": item.duration,
+                }
+                for item in self.items
+            ],
+        }
+
+
+class AdaptiveConcurrencyController:
+    """Adjusts concurrency based on task performance."""
+
+    def __init__(
+        self,
+        min_concurrency: int = 1,
+        max_concurrency: int = 5,
+        initial_concurrency: int = 3,
+    ) -> None:
+        self.min_concurrency = min_concurrency
+        self.max_concurrency = max_concurrency
+        self.current_concurrency = initial_concurrency
+        self._task_durations: List[float] = []
+        self._recent_errors = 0
+        self._recent_successes = 0
+
+    def record_result(self, duration: float, success: bool) -> None:
+        self._task_durations.append(duration)
+        if len(self._task_durations) > 10:
+            self._task_durations.pop(0)
+
+        if success:
+            self._recent_successes += 1
+            if duration < 1.0 and self.current_concurrency < self.max_concurrency:
+                self.current_concurrency = min(
+                    self.current_concurrency + 1, self.max_concurrency
+                )
+            elif duration > 10.0 and self.current_concurrency > self.min_concurrency:
+                self.current_concurrency = max(
+                    self.current_concurrency - 1, self.min_concurrency
+                )
+        else:
+            self._recent_errors += 1
+            if self.current_concurrency > self.min_concurrency:
+                self.current_concurrency = max(
+                    self.current_concurrency - 1, self.min_concurrency
+                )
+
+    def reset_counters(self) -> None:
+        self._recent_errors = 0
+        self._recent_successes = 0
+
+    def get_semaphore(self) -> asyncio.Semaphore:
+        return asyncio.Semaphore(self.current_concurrency)
+
+
+class BatchImportService:
+    """Service for batch importing images as recipes."""
+
+    SUPPORTED_EXTENSIONS: Set[str] = {".jpg", ".jpeg", ".png", ".webp", ".gif", ".bmp"}
+
+    def __init__(
+        self,
+        *,
+        analysis_service: RecipeAnalysisService,
+        persistence_service: RecipePersistenceService,
+        ws_manager: Any,
+        logger: logging.Logger,
+    ) -> None:
+        self._analysis_service = analysis_service
+        self._persistence_service = persistence_service
+        self._ws_manager = ws_manager
+        self._logger = logger
+        self._active_operations: Dict[str, BatchImportProgress] = {}
+        self._cancellation_flags: Dict[str, bool] = {}
+        self._concurrency_controller = AdaptiveConcurrencyController()
+
+    def is_import_running(self, operation_id: Optional[str] = None) -> bool:
+        if operation_id:
+            progress = self._active_operations.get(operation_id)
+            return progress is not None and progress.status in ("pending", "running")
+        return any(
+            p.status in ("pending", "running") for p in self._active_operations.values()
+        )
+
+    def get_progress(self, operation_id: str) -> Optional[BatchImportProgress]:
+        return self._active_operations.get(operation_id)
+
+    def cancel_import(self, operation_id: str) -> bool:
+        if operation_id in self._active_operations:
+            self._cancellation_flags[operation_id] = True
+            return True
+        return False
+
+    def _validate_url(self, url: str) -> bool:
+        import re
+
+        url_pattern = re.compile(
+            r"^https?://"
+            r"(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+[A-Z]{2,6}\.?|"
+            r"localhost|"
+            r"\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})"
+            r"(?::\d+)?"
+            r"(?:/?|[/?]\S+)$",
+            re.IGNORECASE,
+        )
+        return url_pattern.match(url) is not None
+
+    def _validate_local_path(self, path: str) -> bool:
+        try:
+            normalized = os.path.normpath(path)
+            if not os.path.isabs(normalized):
+                return False
+            if ".." in normalized:
+                return False
+            return True
+        except Exception:
+            return False
+
+    def _is_duplicate_source(
+        self,
+        source: str,
+        item_type: ImportItemType,
+        recipe_scanner: Any,
+    ) -> bool:
+        try:
+            cache = recipe_scanner.get_cached_data_sync()
+            if not cache:
+                return False
+
+            for recipe in getattr(cache, "raw_data", []):
+                source_path = recipe.get("source_path") or recipe.get("source_url")
+                if source_path and source_path == source:
+                    return True
+            return False
+        except Exception:
+            self._logger.warning("Failed to check for duplicates", exc_info=True)
+            return False
+
+    async def start_batch_import(
+        self,
+        *,
+        recipe_scanner_getter: Callable[[], Any],
+        civitai_client_getter: Callable[[], Any],
+        items: List[Dict[str, str]],
+        tags: Optional[List[str]] = None,
+        skip_no_metadata: bool = False,
+        skip_duplicates: bool = False,
+    ) -> str:
+        operation_id = str(uuid.uuid4())
+
+        import_items = []
+        for idx, item in enumerate(items):
+            source = item.get("source", "")
+            item_type_str = item.get("type", "url")
+
+            if item_type_str == "url" or source.startswith(("http://", "https://")):
+                item_type = ImportItemType.URL
+            else:
+                item_type = ImportItemType.LOCAL_PATH
+
+            batch_import_item = BatchImportItem(
+                id=f"{operation_id}_{idx}",
+                source=source,
+                item_type=item_type,
+            )
+            import_items.append(batch_import_item)
+
+        progress = BatchImportProgress(
+            operation_id=operation_id,
+            total=len(import_items),
+            items=import_items,
+            tags=tags or [],
+            skip_no_metadata=skip_no_metadata,
+            skip_duplicates=skip_duplicates,
+        )
+
+        self._active_operations[operation_id] = progress
+        self._cancellation_flags[operation_id] = False
+
+        asyncio.create_task(
+            self._run_batch_import(
+                operation_id=operation_id,
+                recipe_scanner_getter=recipe_scanner_getter,
+                civitai_client_getter=civitai_client_getter,
+            )
+        )
+
+        return operation_id
+
+    async def start_directory_import(
+        self,
+        *,
+        recipe_scanner_getter: Callable[[], Any],
+        civitai_client_getter: Callable[[], Any],
+        directory: str,
+        recursive: bool = True,
+        tags: Optional[List[str]] = None,
+        skip_no_metadata: bool = False,
+        skip_duplicates: bool = False,
+    ) -> str:
+        image_paths = await self._discover_images(directory, recursive)
+
+        items = [{"source": path, "type": "local_path"} for path in image_paths]
+
+        return await self.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=items,
+            tags=tags,
+            skip_no_metadata=skip_no_metadata,
+            skip_duplicates=skip_duplicates,
+        )
+
+    async def _discover_images(
+        self,
+        directory: str,
+        recursive: bool = True,
+    ) -> List[str]:
+        if not os.path.isdir(directory):
+            raise RecipeValidationError(f"Directory not found: {directory}")
+
+        image_paths: List[str] = []
+
+        if recursive:
+            for root, _, files in os.walk(directory):
+                for filename in files:
+                    if self._is_supported_image(filename):
+                        image_paths.append(os.path.join(root, filename))
+        else:
+            for filename in os.listdir(directory):
+                filepath = os.path.join(directory, filename)
+                if os.path.isfile(filepath) and self._is_supported_image(filename):
+                    image_paths.append(filepath)
+
+        return sorted(image_paths)
+
+    def _is_supported_image(self, filename: str) -> bool:
+        ext = os.path.splitext(filename)[1].lower()
+        return ext in self.SUPPORTED_EXTENSIONS
+
+    async def _run_batch_import(
+        self,
+        *,
+        operation_id: str,
+        recipe_scanner_getter: Callable[[], Any],
+        civitai_client_getter: Callable[[], Any],
+    ) -> None:
+        progress = self._active_operations.get(operation_id)
+        if not progress:
+            return
+
+        progress.status = "running"
+        await self._broadcast_progress(progress)
+
+        self._concurrency_controller = AdaptiveConcurrencyController()
+
+        async def process_item(item: BatchImportItem) -> None:
+            if self._cancellation_flags.get(operation_id, False):
+                return
+
+            progress.current_item = (
+                os.path.basename(item.source)
+                if item.item_type == ImportItemType.LOCAL_PATH
+                else item.source[:50]
+            )
+            item.status = ImportStatus.PROCESSING
+            await self._broadcast_progress(progress)
+
+            start_time = time.time()
+            try:
+                result = await self._import_single_item(
+                    item=item,
+                    recipe_scanner_getter=recipe_scanner_getter,
+                    civitai_client_getter=civitai_client_getter,
+                    tags=progress.tags,
+                    skip_no_metadata=progress.skip_no_metadata,
+                    skip_duplicates=progress.skip_duplicates,
+                    semaphore=self._concurrency_controller.get_semaphore(),
+                )
+
+                duration = time.time() - start_time
+                item.duration = duration
+                self._concurrency_controller.record_result(
+                    duration, result.get("success", False)
+                )
+
+                if result.get("success"):
+                    item.status = ImportStatus.SUCCESS
+                    item.recipe_name = result.get("recipe_name")
+                    item.recipe_id = result.get("recipe_id")
+                    progress.success += 1
+                elif result.get("skipped"):
+                    item.status = ImportStatus.SKIPPED
+                    item.error_message = result.get("error")
+                    progress.skipped += 1
+                else:
+                    item.status = ImportStatus.FAILED
+                    item.error_message = result.get("error")
+                    progress.failed += 1
+
+            except Exception as e:
+                self._logger.error(f"Error importing {item.source}: {e}")
+                item.status = ImportStatus.FAILED
+                item.error_message = str(e)
+                item.duration = time.time() - start_time
+                progress.failed += 1
+                self._concurrency_controller.record_result(item.duration, False)
+
+            progress.completed += 1
+            await self._broadcast_progress(progress)
+
+        tasks = [process_item(item) for item in progress.items]
+        await asyncio.gather(*tasks, return_exceptions=True)
+
+        if self._cancellation_flags.get(operation_id, False):
+            progress.status = "cancelled"
+        else:
+            progress.status = "completed"
+
+        progress.finished_at = time.time()
+        progress.current_item = ""
+        await self._broadcast_progress(progress)
+
+        await asyncio.sleep(5)
+        self._cleanup_operation(operation_id)
+
+    async def _import_single_item(
+        self,
+        *,
+        item: BatchImportItem,
+        recipe_scanner_getter: Callable[[], Any],
+        civitai_client_getter: Callable[[], Any],
+        tags: List[str],
+        skip_no_metadata: bool,
+        skip_duplicates: bool,
+        semaphore: asyncio.Semaphore,
+    ) -> Dict[str, Any]:
+        async with semaphore:
+            recipe_scanner = recipe_scanner_getter()
+            if recipe_scanner is None:
+                return {"success": False, "error": "Recipe scanner unavailable"}
+
+            try:
+                if item.item_type == ImportItemType.URL:
+                    if not self._validate_url(item.source):
+                        return {
+                            "success": False,
+                            "error": f"Invalid URL format: {item.source}",
+                        }
+
+                    if skip_duplicates:
+                        if self._is_duplicate_source(
+                            item.source, item.item_type, recipe_scanner
+                        ):
+                            return {
+                                "success": False,
+                                "skipped": True,
+                                "error": "Duplicate source URL",
+                            }
+
+                    civitai_client = civitai_client_getter()
+                    analysis_result = await self._analysis_service.analyze_remote_image(
+                        url=item.source,
+                        recipe_scanner=recipe_scanner,
+                        civitai_client=civitai_client,
+                    )
+                else:
+                    if not self._validate_local_path(item.source):
+                        return {
+                            "success": False,
+                            "error": f"Invalid or unsafe path: {item.source}",
+                        }
+
+                    if not os.path.exists(item.source):
+                        return {
+                            "success": False,
+                            "error": f"File not found: {item.source}",
+                        }
+
+                    if skip_duplicates:
+                        if self._is_duplicate_source(
+                            item.source, item.item_type, recipe_scanner
+                        ):
+                            return {
+                                "success": False,
+                                "skipped": True,
+                                "error": "Duplicate source path",
+                            }
+
+                    analysis_result = await self._analysis_service.analyze_local_image(
+                        file_path=item.source,
+                        recipe_scanner=recipe_scanner,
+                    )
+
+                payload = analysis_result.payload
+
+                if payload.get("error"):
+                    if skip_no_metadata and "No metadata" in payload.get("error", ""):
+                        return {
+                            "success": False,
+                            "skipped": True,
+                            "error": payload["error"],
+                        }
+                    return {"success": False, "error": payload["error"]}
+
+                loras = payload.get("loras", [])
+                if not loras:
+                    if skip_no_metadata:
+                        return {
+                            "success": False,
+                            "skipped": True,
+                            "error": "No LoRAs found in image",
+                        }
+                    # When skip_no_metadata is False, allow importing images without LoRAs
+                    # Continue with empty loras list
+
+                recipe_name = self._generate_recipe_name(item, payload)
+                all_tags = list(set(tags + (payload.get("tags", []) or [])))
+
+                metadata = {
+                    "base_model": payload.get("base_model", ""),
+                    "loras": loras,
+                    "gen_params": payload.get("gen_params", {}),
+                    "source_path": item.source,
+                }
+
+                if payload.get("checkpoint"):
+                    metadata["checkpoint"] = payload["checkpoint"]
+
+                image_bytes = None
+                image_base64 = payload.get("image_base64")
+
+                if item.item_type == ImportItemType.LOCAL_PATH:
+                    with open(item.source, "rb") as f:
+                        image_bytes = f.read()
+                    image_base64 = None
+
+                save_result = await self._persistence_service.save_recipe(
+                    recipe_scanner=recipe_scanner,
+                    image_bytes=image_bytes,
+                    image_base64=image_base64,
+                    name=recipe_name,
+                    tags=all_tags,
+                    metadata=metadata,
+                    extension=payload.get("extension"),
+                )
+
+                if save_result.status == 200:
+                    return {
+                        "success": True,
+                        "recipe_name": recipe_name,
+                        "recipe_id": save_result.payload.get("id"),
+                    }
+                else:
+                    return {
+                        "success": False,
+                        "error": save_result.payload.get(
+                            "error", "Failed to save recipe"
+                        ),
+                    }
+
+            except RecipeValidationError as e:
+                return {"success": False, "error": str(e)}
+            except RecipeDownloadError as e:
+                return {"success": False, "error": str(e)}
+            except RecipeNotFoundError as e:
+                return {"success": False, "skipped": True, "error": str(e)}
+            except Exception as e:
+                self._logger.error(
+                    f"Unexpected error importing {item.source}: {e}", exc_info=True
+                )
+                return {"success": False, "error": str(e)}
+
+    def _generate_recipe_name(
+        self, item: BatchImportItem, payload: Dict[str, Any]
+    ) -> str:
+        if item.item_type == ImportItemType.LOCAL_PATH:
+            base_name = os.path.splitext(os.path.basename(item.source))[0]
+            return base_name[:100]
+        else:
+            loras = payload.get("loras", [])
+            if loras:
+                first_lora = loras[0].get("name", "Recipe")
+                return f"Import - {first_lora}"[:100]
+            return f"Imported Recipe {item.id[:8]}"
+
+    async def _broadcast_progress(self, progress: BatchImportProgress) -> None:
+        await self._ws_manager.broadcast(
+            {
+                "type": "batch_import_progress",
+                **progress.to_dict(),
+            }
+        )
+
+    def _cleanup_operation(self, operation_id: str) -> None:
+        if operation_id in self._cancellation_flags:
+            del self._cancellation_flags[operation_id]

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/download_manager.py

@@ -10,7 +10,11 @@ import uuid
 from typing import Dict, List, Optional, Set, Tuple
 from urllib.parse import urlparse
 from ..utils.models import LoraMetadata, CheckpointMetadata, EmbeddingMetadata
-from ..utils.constants import CARD_PREVIEW_WIDTH, DIFFUSION_MODEL_BASE_MODELS, VALID_LORA_TYPES
+from ..utils.constants import (
+    CARD_PREVIEW_WIDTH,
+    DIFFUSION_MODEL_BASE_MODELS,
+    VALID_LORA_TYPES,
+)
 from ..utils.civitai_utils import rewrite_preview_url
 from ..utils.preview_selection import select_preview_media
 from ..utils.utils import sanitize_folder_name
@@ -352,10 +356,12 @@ class DownloadManager:
             # Check if this checkpoint should be treated as a diffusion model based on baseModel
             is_diffusion_model = False
             if model_type == "checkpoint":
-                base_model_value = version_info.get('baseModel', '')
+                base_model_value = version_info.get("baseModel", "")
                 if base_model_value in DIFFUSION_MODEL_BASE_MODELS:
                     is_diffusion_model = True
-                    logger.info(f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder")
+                    logger.info(
+                        f"baseModel '{base_model_value}' is a known diffusion model, routing to unet folder"
+                    )
 
             # Case 2: model_version_id was None, check after getting version_info
             if model_version_id is None:
@@ -464,7 +470,7 @@ class DownloadManager:
             # 2. Get file information
             files = version_info.get("files", [])
             file_info = None
-            
+
             # If file_params is provided, try to find matching file
             if file_params and model_version_id:
                 target_type = file_params.get("type", "Model")
@@ -472,23 +478,28 @@ class DownloadManager:
                 target_size = file_params.get("size", "full")
                 target_fp = file_params.get("fp")
                 is_primary = file_params.get("isPrimary", False)
-                
+
                 if is_primary:
                     # Find primary file
                     file_info = next(
-                        (f for f in files if f.get("primary") and f.get("type") in ("Model", "Negative")),
-                        None
+                        (
+                            f
+                            for f in files
+                            if f.get("primary")
+                            and f.get("type") in ("Model", "Negative")
+                        ),
+                        None,
                     )
                 else:
                     # Match by metadata
                     for f in files:
                         f_type = f.get("type", "")
                         f_meta = f.get("metadata", {})
-                        
+
                         # Check type match
                         if f_type != target_type:
                             continue
-                        
+
                         # Check metadata match
                         if f_meta.get("format") != target_format:
                             continue
@@ -496,10 +507,10 @@ class DownloadManager:
                             continue
                         if target_fp and f_meta.get("fp") != target_fp:
                             continue
-                        
+
                         file_info = f
                         break
-            
+
             # Fallback to primary file if no match found
             if not file_info:
                 file_info = next(
@@ -510,7 +521,7 @@ class DownloadManager:
                     ),
                     None,
                 )
-            
+
             if not file_info:
                 return {"success": False, "error": "No suitable file found in metadata"}
             mirrors = file_info.get("mirrors") or []
@@ -1220,7 +1231,13 @@ class DownloadManager:
         entries: List = []
         for index, file_path in enumerate(file_paths):
             entry = base_metadata if index == 0 else copy.deepcopy(base_metadata)
-            entry.update_file_info(file_path)
+            # Update file paths without modifying size and modified timestamps
+            # modified should remain as the download start time (import time)
+            # size will be updated below to reflect actual downloaded file size
+            entry.file_path = file_path.replace(os.sep, "/")
+            entry.file_name = os.path.splitext(os.path.basename(file_path))[0]
+            # Update size to actual downloaded file size
+            entry.size = os.path.getsize(file_path)
             entry.sha256 = await calculate_sha256(file_path)
             entries.append(entry)
 

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/services/tag_fts_index.py

@@ -449,6 +449,11 @@ class TagFTSIndex:
         Supports alias search: if the query matches an alias rather than
         the tag_name, the result will include a "matched_alias" field.
 
+        Ranking is based on a combination of:
+        1. FTS5 bm25 relevance score (how well the text matches)
+        2. Post count (popularity)
+        3. Exact prefix match boost (tag_name starts with query)
+
         Args:
             query: The search query string.
             categories: Optional list of category IDs to filter by.
@@ -457,7 +462,7 @@ class TagFTSIndex:
 
         Returns:
             List of dictionaries with tag_name, category, post_count,
-            and optionally matched_alias.
+            rank_score, and optionally matched_alias.
         """
         # Ensure index is ready (lazy initialization)
         if not self.ensure_ready():
@@ -473,35 +478,67 @@ class TagFTSIndex:
         if not fts_query:
             return []
 
+        query_lower = query.lower().strip()
+
         try:
             with self._lock:
                 conn = self._connect(readonly=True)
                 try:
-                    # Build the SQL query - now also fetch aliases for matched_alias detection
-                    # Use subquery for category filter to ensure FTS is evaluated first
+                    # Build the SQL query with bm25 ranking
+                    # FTS5 bm25() returns negative scores, lower is better
+                    # We use -bm25() to get higher=better scores
+                    # Weights: -100.0 for exact matches, 1.0 for others
+                    # Add LOG10(post_count) weighting to boost popular tags
+                    # Use CASE to boost tag_name prefix matches above alias matches
                     if categories:
                         placeholders = ",".join("?" * len(categories))
                         sql = f"""
-                            SELECT t.tag_name, t.category, t.post_count, t.aliases
-                            FROM tags t
-                            WHERE t.rowid IN (
-                                SELECT rowid FROM tag_fts WHERE searchable_text MATCH ?
-                            )
+                            SELECT t.tag_name, t.category, t.post_count, t.aliases,
+                                   CASE 
+                                       WHEN t.tag_name LIKE ? ESCAPE '\\' THEN 1
+                                       ELSE 0
+                                   END AS is_tag_name_match,
+                                   bm25(tag_fts, -100.0, 1.0, 1.0) + LOG10(t.post_count + 1) * 10.0 AS rank_score
+                            FROM tag_fts
+                            JOIN tags t ON tag_fts.rowid = t.rowid
+                            WHERE tag_fts.searchable_text MATCH ?
                             AND t.category IN ({placeholders})
-                            ORDER BY t.post_count DESC
+                            ORDER BY is_tag_name_match DESC, rank_score DESC
                             LIMIT ? OFFSET ?
                         """
-                        params = [fts_query] + categories + [limit, offset]
+                        # Escape special LIKE characters and add wildcard
+                        query_escaped = (
+                            query_lower.lstrip("/")
+                            .replace("\\", "\\\\")
+                            .replace("%", "\\%")
+                            .replace("_", "\\_")
+                        )
+                        params = (
+                            [query_escaped + "%", fts_query]
+                            + categories
+                            + [limit, offset]
+                        )
                     else:
                         sql = """
-                            SELECT t.tag_name, t.category, t.post_count, t.aliases
-                            FROM tag_fts f
-                            JOIN tags t ON f.rowid = t.rowid
-                            WHERE f.searchable_text MATCH ?
-                            ORDER BY t.post_count DESC
+                            SELECT t.tag_name, t.category, t.post_count, t.aliases,
+                                   CASE 
+                                       WHEN t.tag_name LIKE ? ESCAPE '\\' THEN 1
+                                       ELSE 0
+                                   END AS is_tag_name_match,
+                                   bm25(tag_fts, -100.0, 1.0, 1.0) + LOG10(t.post_count + 1) * 10.0 AS rank_score
+                            FROM tag_fts
+                            JOIN tags t ON tag_fts.rowid = t.rowid
+                            WHERE tag_fts.searchable_text MATCH ?
+                            ORDER BY is_tag_name_match DESC, rank_score DESC
                             LIMIT ? OFFSET ?
                         """
-                        params = [fts_query, limit, offset]
+                        query_escaped = (
+                            query_lower.lstrip("/")
+                            .replace("\\", "\\\\")
+                            .replace("%", "\\%")
+                            .replace("_", "\\_")
+                        )
+                        params = [query_escaped + "%", fts_query, limit, offset]
 
                     cursor = conn.execute(sql, params)
                     results = []
@@ -510,8 +547,17 @@ class TagFTSIndex:
                             "tag_name": row[0],
                             "category": row[1],
                             "post_count": row[2],
+                            "is_tag_name_match": row[4] == 1,
+                            "rank_score": row[5],
                         }
 
+                        # Set is_exact_prefix based on tag_name match
+                        tag_name = row[0]
+                        if tag_name.lower().startswith(query_lower.lstrip("/")):
+                            result["is_exact_prefix"] = True
+                        else:
+                            result["is_exact_prefix"] = result["is_tag_name_match"]
+
                         # Check if search matched an alias rather than the tag_name
                         matched_alias = self._find_matched_alias(query, row[0], row[3])
                         if matched_alias:

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/models.py

@@ -4,32 +4,40 @@ from datetime import datetime
 import os
 from .model_utils import determine_base_model
 
+
 @dataclass
 class BaseModelMetadata:
     """Base class for all model metadata structures"""
-    file_name: str              # The filename without extension
-    model_name: str             # The model's name defined by the creator
-    file_path: str              # Full path to the model file
-    size: int                   # File size in bytes
-    modified: float             # Timestamp when the model was added to the management system
-    sha256: str                 # SHA256 hash of the file
-    base_model: str             # Base model type (SD1.5/SD2.1/SDXL/etc.)
-    preview_url: str            # Preview image URL
-    preview_nsfw_level: int = 0 # NSFW level of the preview image
-    notes: str = ""             # Additional notes
-    from_civitai: bool = True   # Whether from Civitai
-    civitai: Dict[str, Any] = field(default_factory=dict)  # Civitai API data if available
-    tags: List[str] = None      # Model tags
+
+    file_name: str  # The filename without extension
+    model_name: str  # The model's name defined by the creator
+    file_path: str  # Full path to the model file
+    size: int  # File size in bytes
+    modified: float  # Timestamp when the model was added to the management system
+    sha256: str  # SHA256 hash of the file
+    base_model: str  # Base model type (SD1.5/SD2.1/SDXL/etc.)
+    preview_url: str  # Preview image URL
+    preview_nsfw_level: int = 0  # NSFW level of the preview image
+    notes: str = ""  # Additional notes
+    from_civitai: bool = True  # Whether from Civitai
+    civitai: Dict[str, Any] = field(
+        default_factory=dict
+    )  # Civitai API data if available
+    tags: List[str] = None  # Model tags
     modelDescription: str = ""  # Full model description
     civitai_deleted: bool = False  # Whether deleted from Civitai
-    favorite: bool = False      # Whether the model is a favorite
-    exclude: bool = False       # Whether to exclude this model from the cache
-    db_checked: bool = False    # Whether checked in archive DB
-    skip_metadata_refresh: bool = False  # Whether to skip this model during bulk metadata refresh
+    favorite: bool = False  # Whether the model is a favorite
+    exclude: bool = False  # Whether to exclude this model from the cache
+    db_checked: bool = False  # Whether checked in archive DB
+    skip_metadata_refresh: bool = (
+        False  # Whether to skip this model during bulk metadata refresh
+    )
     metadata_source: Optional[str] = None  # Last provider that supplied metadata
     last_checked_at: float = 0  # Last checked timestamp
     hash_status: str = "completed"  # Hash calculation status: pending | calculating | completed | failed
-    _unknown_fields: Dict[str, Any] = field(default_factory=dict, repr=False, compare=False)  # Store unknown fields
+    _unknown_fields: Dict[str, Any] = field(
+        default_factory=dict, repr=False, compare=False
+    )  # Store unknown fields
 
     def __post_init__(self):
         # Initialize empty lists to avoid mutable default parameter issue
@@ -40,211 +48,238 @@ class BaseModelMetadata:
             self.tags = []
 
     @classmethod
-    def from_dict(cls, data: Dict) -> 'BaseModelMetadata':
+    def from_dict(cls, data: Dict) -> "BaseModelMetadata":
         """Create instance from dictionary"""
         data_copy = data.copy()
-        
+
         # Use cached fields if available, otherwise compute them
-        if not hasattr(cls, '_known_fields_cache'):
+        if not hasattr(cls, "_known_fields_cache"):
             known_fields = set()
             for c in cls.mro():
-                if hasattr(c, '__annotations__'):
+                if hasattr(c, "__annotations__"):
                     known_fields.update(c.__annotations__.keys())
             cls._known_fields_cache = known_fields
-        
+
         known_fields = cls._known_fields_cache
-        
+
         # Extract fields that match our class attributes
         fields_to_use = {k: v for k, v in data_copy.items() if k in known_fields}
-        
+
         # Store unknown fields separately
-        unknown_fields = {k: v for k, v in data_copy.items() if k not in known_fields and not k.startswith('_')}
-        
+        unknown_fields = {
+            k: v
+            for k, v in data_copy.items()
+            if k not in known_fields and not k.startswith("_")
+        }
+
         # Create instance with known fields
         instance = cls(**fields_to_use)
-        
+
         # Add unknown fields as a separate attribute
         instance._unknown_fields = unknown_fields
-        
+
         return instance
 
     def to_dict(self) -> Dict:
         """Convert to dictionary for JSON serialization"""
         result = asdict(self)
-        
+
         # Remove private fields
-        result = {k: v for k, v in result.items() if not k.startswith('_')}
-        
+        result = {k: v for k, v in result.items() if not k.startswith("_")}
+
         # Add back unknown fields if they exist
-        if hasattr(self, '_unknown_fields'):
+        if hasattr(self, "_unknown_fields"):
             result.update(self._unknown_fields)
-            
+
         return result
 
     def update_civitai_info(self, civitai_data: Dict) -> None:
         """Update Civitai information"""
         self.civitai = civitai_data
 
-    def update_file_info(self, file_path: str) -> None:
-        """Update metadata with actual file information"""
+    def update_file_info(self, file_path: str, update_timestamps: bool = False) -> None:
+        """
+        Update metadata with actual file information.
+
+        Args:
+            file_path: Path to the model file
+            update_timestamps: If True, update size and modified from filesystem.
+                              If False (default), only update file_path and file_name.
+                              Set to True only when file has been moved/relocated.
+        """
         if os.path.exists(file_path):
-            self.size = os.path.getsize(file_path)
-            self.modified = os.path.getmtime(file_path)
-            self.file_path = file_path.replace(os.sep, '/')
-            # Update file_name when file_path changes
+            if update_timestamps:
+                # Only update size and modified when file has been relocated
+                self.size = os.path.getsize(file_path)
+                self.modified = os.path.getmtime(file_path)
+            # Always update paths when this method is called
+            self.file_path = file_path.replace(os.sep, "/")
             self.file_name = os.path.splitext(os.path.basename(file_path))[0]
 
     @staticmethod
-    def generate_unique_filename(target_dir: str, base_name: str, extension: str, hash_provider: callable = None) -> str:
+    def generate_unique_filename(
+        target_dir: str, base_name: str, extension: str, hash_provider: callable = None
+    ) -> str:
         """Generate a unique filename to avoid conflicts
-        
+
         Args:
             target_dir: Target directory path
             base_name: Base filename without extension
             extension: File extension including the dot
             hash_provider: A callable that returns the SHA256 hash when needed
-            
+
         Returns:
             str: Unique filename that doesn't conflict with existing files
         """
         original_filename = f"{base_name}{extension}"
         target_path = os.path.join(target_dir, original_filename)
-        
+
         # If no conflict, return original filename
         if not os.path.exists(target_path):
             return original_filename
-            
+
         # Only compute hash when needed
         if hash_provider:
             sha256_hash = hash_provider()
         else:
             sha256_hash = "0000"
-        
+
         # Generate short hash (first 4 characters of SHA256)
         short_hash = sha256_hash[:4] if sha256_hash else "0000"
-        
+
         # Try with short hash suffix
         unique_filename = f"{base_name}-{short_hash}{extension}"
         unique_path = os.path.join(target_dir, unique_filename)
-        
+
         # If still conflicts, add incremental number
         counter = 1
         while os.path.exists(unique_path):
             unique_filename = f"{base_name}-{short_hash}-{counter}{extension}"
             unique_path = os.path.join(target_dir, unique_filename)
             counter += 1
-            
+
         return unique_filename
 
+
 @dataclass
 class LoraMetadata(BaseModelMetadata):
     """Represents the metadata structure for a Lora model"""
-    usage_tips: str = "{}"      # Usage tips for the model, json string
+
+    usage_tips: str = "{}"  # Usage tips for the model, json string
 
     @classmethod
-    def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'LoraMetadata':
+    def from_civitai_info(
+        cls, version_info: Dict, file_info: Dict, save_path: str
+    ) -> "LoraMetadata":
         """Create LoraMetadata instance from Civitai version info"""
-        file_name = file_info.get('name', '')
-        base_model = determine_base_model(version_info.get('baseModel', ''))
+        file_name = file_info.get("name", "")
+        base_model = determine_base_model(version_info.get("baseModel", ""))
 
         # Extract tags and description if available
         tags = []
         description = ""
-        model_data = version_info.get('model') or {}
-        if 'tags' in model_data:
-            tags = model_data['tags']
-        if 'description' in model_data:
-            description = model_data['description']
+        model_data = version_info.get("model") or {}
+        if "tags" in model_data:
+            tags = model_data["tags"]
+        if "description" in model_data:
+            description = model_data["description"]
 
         return cls(
             file_name=os.path.splitext(file_name)[0],
-            model_name=model_data.get('name', os.path.splitext(file_name)[0]),
-            file_path=save_path.replace(os.sep, '/'),
-            size=file_info.get('sizeKB', 0) * 1024,
+            model_name=model_data.get("name", os.path.splitext(file_name)[0]),
+            file_path=save_path.replace(os.sep, "/"),
+            size=file_info.get("sizeKB", 0) * 1024,
             modified=datetime.now().timestamp(),
-            sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
+            sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
             base_model=base_model,
-            preview_url='',  # Will be updated after preview download
-            preview_nsfw_level=0, # Will be updated after preview download
+            preview_url="",  # Will be updated after preview download
+            preview_nsfw_level=0,  # Will be updated after preview download
             from_civitai=True,
             civitai=version_info,
             tags=tags,
-            modelDescription=description
+            modelDescription=description,
         )
 
+
 @dataclass
 class CheckpointMetadata(BaseModelMetadata):
     """Represents the metadata structure for a Checkpoint model"""
+
     sub_type: str = "checkpoint"  # Model sub-type (checkpoint, diffusion_model, etc.)
 
     @classmethod
-    def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'CheckpointMetadata':
+    def from_civitai_info(
+        cls, version_info: Dict, file_info: Dict, save_path: str
+    ) -> "CheckpointMetadata":
         """Create CheckpointMetadata instance from Civitai version info"""
-        file_name = file_info.get('name', '')
-        base_model = determine_base_model(version_info.get('baseModel', ''))
-        sub_type = version_info.get('type', 'checkpoint')
+        file_name = file_info.get("name", "")
+        base_model = determine_base_model(version_info.get("baseModel", ""))
+        sub_type = version_info.get("type", "checkpoint")
 
         # Extract tags and description if available
         tags = []
         description = ""
-        model_data = version_info.get('model') or {}
-        if 'tags' in model_data:
-            tags = model_data['tags']
-        if 'description' in model_data:
-            description = model_data['description']
+        model_data = version_info.get("model") or {}
+        if "tags" in model_data:
+            tags = model_data["tags"]
+        if "description" in model_data:
+            description = model_data["description"]
 
         return cls(
             file_name=os.path.splitext(file_name)[0],
-            model_name=model_data.get('name', os.path.splitext(file_name)[0]),
-            file_path=save_path.replace(os.sep, '/'),
-            size=file_info.get('sizeKB', 0) * 1024,
+            model_name=model_data.get("name", os.path.splitext(file_name)[0]),
+            file_path=save_path.replace(os.sep, "/"),
+            size=file_info.get("sizeKB", 0) * 1024,
             modified=datetime.now().timestamp(),
-            sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
+            sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
             base_model=base_model,
-            preview_url='',  # Will be updated after preview download
+            preview_url="",  # Will be updated after preview download
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,
             sub_type=sub_type,
             tags=tags,
-            modelDescription=description
+            modelDescription=description,
         )
 
+
 @dataclass
 class EmbeddingMetadata(BaseModelMetadata):
     """Represents the metadata structure for an Embedding model"""
+
     sub_type: str = "embedding"
 
     @classmethod
-    def from_civitai_info(cls, version_info: Dict, file_info: Dict, save_path: str) -> 'EmbeddingMetadata':
+    def from_civitai_info(
+        cls, version_info: Dict, file_info: Dict, save_path: str
+    ) -> "EmbeddingMetadata":
         """Create EmbeddingMetadata instance from Civitai version info"""
-        file_name = file_info.get('name', '')
-        base_model = determine_base_model(version_info.get('baseModel', ''))
-        sub_type = version_info.get('type', 'embedding')
+        file_name = file_info.get("name", "")
+        base_model = determine_base_model(version_info.get("baseModel", ""))
+        sub_type = version_info.get("type", "embedding")
 
         # Extract tags and description if available
         tags = []
         description = ""
-        model_data = version_info.get('model') or {}
-        if 'tags' in model_data:
-            tags = model_data['tags']
-        if 'description' in model_data:
-            description = model_data['description']
+        model_data = version_info.get("model") or {}
+        if "tags" in model_data:
+            tags = model_data["tags"]
+        if "description" in model_data:
+            description = model_data["description"]
 
         return cls(
             file_name=os.path.splitext(file_name)[0],
-            model_name=model_data.get('name', os.path.splitext(file_name)[0]),
-            file_path=save_path.replace(os.sep, '/'),
-            size=file_info.get('sizeKB', 0) * 1024,
+            model_name=model_data.get("name", os.path.splitext(file_name)[0]),
+            file_path=save_path.replace(os.sep, "/"),
+            size=file_info.get("sizeKB", 0) * 1024,
             modified=datetime.now().timestamp(),
-            sha256=(file_info.get('hashes') or {}).get('SHA256', '').lower(),
+            sha256=(file_info.get("hashes") or {}).get("SHA256", "").lower(),
             base_model=base_model,
-            preview_url='',  # Will be updated after preview download
+            preview_url="",  # Will be updated after preview download
             preview_nsfw_level=0,
             from_civitai=True,
             civitai=version_info,
             sub_type=sub_type,
             tags=tags,
-            modelDescription=description
+            modelDescription=description,
         )
-

py/utils/utils.py

@@ -7,33 +7,47 @@ from ..config import config
 from ..services.settings_manager import get_settings_manager
 import asyncio
 
+
 def get_lora_info(lora_name):
     """Get the lora path and trigger words from cache"""
+
     async def _get_lora_info_async():
         scanner = await ServiceRegistry.get_lora_scanner()
         cache = await scanner.get_cached_data()
-        
+
         for item in cache.raw_data:
-            if item.get('file_name') == lora_name:
-                file_path = item.get('file_path')
+            if item.get("file_name") == lora_name:
+                file_path = item.get("file_path")
                 if file_path:
-                    for root in config.loras_roots:
-                        root = root.replace(os.sep, '/')
+                    # Check all lora roots including extra paths
+                    all_roots = list(config.loras_roots or []) + list(
+                        config.extra_loras_roots or []
+                    )
+                    for root in all_roots:
+                        root = root.replace(os.sep, "/")
                         if file_path.startswith(root):
-                            relative_path = os.path.relpath(file_path, root).replace(os.sep, '/')
+                            relative_path = os.path.relpath(file_path, root).replace(
+                                os.sep, "/"
+                            )
                             # Get trigger words from civitai metadata
-                            civitai = item.get('civitai', {})
-                            trigger_words = civitai.get('trainedWords', []) if civitai else []
+                            civitai = item.get("civitai", {})
+                            trigger_words = (
+                                civitai.get("trainedWords", []) if civitai else []
+                            )
                             return relative_path, trigger_words
+                    # If not found in any root, return path with trigger words from cache
+                    civitai = item.get("civitai", {})
+                    trigger_words = civitai.get("trainedWords", []) if civitai else []
+                    return file_path, trigger_words
         return lora_name, []
-    
+
     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)
@@ -41,11 +55,11 @@ def get_lora_info(lora_name):
                 return new_loop.run_until_complete(_get_lora_info_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_lora_info_async())
@@ -53,33 +67,34 @@ def get_lora_info(lora_name):
 
 def get_lora_info_absolute(lora_name):
     """Get the absolute lora path and trigger words from cache
-    
+
     Returns:
-        tuple: (absolute_path, trigger_words) where absolute_path is the full 
+        tuple: (absolute_path, trigger_words) where absolute_path is the full
                file system path to the LoRA file, or original lora_name if not found
     """
+
     async def _get_lora_info_absolute_async():
         scanner = await ServiceRegistry.get_lora_scanner()
         cache = await scanner.get_cached_data()
-        
+
         for item in cache.raw_data:
-            if item.get('file_name') == lora_name:
-                file_path = item.get('file_path')
+            if item.get("file_name") == lora_name:
+                file_path = item.get("file_path")
                 if file_path:
                     # Return absolute path directly
                     # Get trigger words from civitai metadata
-                    civitai = item.get('civitai', {})
-                    trigger_words = civitai.get('trainedWords', []) if civitai else []
+                    civitai = item.get("civitai", {})
+                    trigger_words = civitai.get("trainedWords", []) if civitai else []
                     return file_path, trigger_words
         return lora_name, []
-    
+
     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)
@@ -87,50 +102,161 @@ def get_lora_info_absolute(lora_name):
                 return new_loop.run_until_complete(_get_lora_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_lora_info_absolute_async())
 
-def fuzzy_match(text: str, pattern: str, threshold: float = 0.85) -> bool:
-        """
-        Check if text matches pattern using fuzzy matching.
-        Returns True if similarity ratio is above threshold.
-        """
-        if not pattern or not text:
-            return False
-        
-        # Convert both to lowercase for case-insensitive matching
-        text = text.lower()
-        pattern = pattern.lower()
-        
-        # Split pattern into words
-        search_words = pattern.split()
-        
-        # Check each word
-        for word in search_words:
-            # First check if word is a substring (faster)
-            if word in text:
+
+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
-            
-            # If not found as substring, try fuzzy matching
-            # Check if any part of the text matches this word
-            found_match = False
-            for text_part in text.split():
-                ratio = SequenceMatcher(None, text_part, word).ratio()
-                if ratio >= threshold:
-                    found_match = True
-                    break
-                
-            if not found_match:
-                return False
-        
-        # All words found either as substrings or fuzzy matches
-        return True
+
+            # 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.
+    Returns True if similarity ratio is above threshold.
+    """
+    if not pattern or not text:
+        return False
+
+    # Convert both to lowercase for case-insensitive matching
+    text = text.lower()
+    pattern = pattern.lower()
+
+    # Split pattern into words
+    search_words = pattern.split()
+
+    # Check each word
+    for word in search_words:
+        # First check if word is a substring (faster)
+        if word in text:
+            continue
+
+        # If not found as substring, try fuzzy matching
+        # Check if any part of the text matches this word
+        found_match = False
+        for text_part in text.split():
+            ratio = SequenceMatcher(None, text_part, word).ratio()
+            if ratio >= threshold:
+                found_match = True
+                break
+
+        if not found_match:
+            return False
+
+    # All words found either as substrings or fuzzy matches
+    return True
+
 
 def sanitize_folder_name(name: str, replacement: str = "_") -> str:
     """Sanitize a folder name by removing or replacing invalid characters.
@@ -156,10 +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"
@@ -170,25 +299,25 @@ def sanitize_folder_name(name: str, replacement: str = "_") -> str:
 def calculate_recipe_fingerprint(loras):
     """
     Calculate a unique fingerprint for a recipe based on its LoRAs.
-    
+
     The fingerprint is created by sorting LoRA hashes, filtering invalid entries,
     normalizing strength values to 2 decimal places, and joining in format:
     hash1:strength1|hash2:strength2|...
-    
+
     Args:
         loras (list): List of LoRA dictionaries with hash and strength values
-        
+
     Returns:
         str: The calculated fingerprint
     """
     if not loras:
         return ""
-    
+
     valid_loras = []
     for lora in loras:
         if lora.get("exclude", False):
             continue
-            
+
         hash_value = lora.get("hash", "")
         if isinstance(hash_value, str):
             hash_value = hash_value.lower()
@@ -206,18 +335,23 @@ def calculate_recipe_fingerprint(loras):
             strength = round(float(strength_val), 2)
         except (ValueError, TypeError):
             strength = 1.0
-        
+
         valid_loras.append((hash_value, strength))
-    
+
     # Sort by hash
     valid_loras.sort()
-    
+
     # Join in format hash1:strength1|hash2:strength2|...
-    fingerprint = "|".join([f"{hash_value}:{strength}" for hash_value, strength in valid_loras])
-    
+    fingerprint = "|".join(
+        [f"{hash_value}:{strength}" for hash_value, strength in valid_loras]
+    )
+
     return fingerprint
 
-def calculate_relative_path_for_model(model_data: Dict, model_type: str = 'lora') -> str:
+
+def calculate_relative_path_for_model(
+    model_data: Dict, model_type: str = "lora"
+) -> str:
     """Calculate relative path for existing model using template from settings
 
     Args:
@@ -233,77 +367,80 @@ def calculate_relative_path_for_model(model_data: Dict, model_type: str = 'lora'
 
     # If template is empty, return empty path (flat structure)
     if not path_template:
-        return ''
+        return ""
 
     # Get base model name from model metadata
-    civitai_data = model_data.get('civitai', {})
+    civitai_data = model_data.get("civitai", {})
 
     # For CivitAI models, prefer civitai data only if 'id' exists; for non-CivitAI models, use model_data directly
-    if civitai_data and civitai_data.get('id') is not None:
-        base_model = model_data.get('base_model', '')
+    if civitai_data and civitai_data.get("id") is not None:
+        base_model = model_data.get("base_model", "")
         # Get author from civitai creator data
-        creator_info = civitai_data.get('creator') or {}
-        author = creator_info.get('username') or 'Anonymous'
+        creator_info = civitai_data.get("creator") or {}
+        author = creator_info.get("username") or "Anonymous"
     else:
         # Fallback to model_data fields for non-CivitAI models
-        base_model = model_data.get('base_model', '')
-        author = 'Anonymous'  # Default for non-CivitAI models
+        base_model = model_data.get("base_model", "")
+        author = "Anonymous"  # Default for non-CivitAI models
 
-    model_tags = model_data.get('tags', [])
+    model_tags = model_data.get("tags", [])
 
     # Apply mapping if available
-    base_model_mappings = settings_manager.get('base_model_path_mappings', {})
+    base_model_mappings = settings_manager.get("base_model_path_mappings", {})
     mapped_base_model = base_model_mappings.get(base_model, base_model)
 
     # Convert all tags to lowercase to avoid case sensitivity issues on Windows
     lowercase_tags = [tag.lower() for tag in model_tags if isinstance(tag, str)]
-    first_tag = settings_manager.resolve_priority_tag_for_model(lowercase_tags, model_type)
+    first_tag = settings_manager.resolve_priority_tag_for_model(
+        lowercase_tags, model_type
+    )
 
     if not first_tag:
-        first_tag = 'no tags'  # Default if no tags available
+        first_tag = "no tags"  # Default if no tags available
 
     # Format the template with available data
-    model_name = sanitize_folder_name(model_data.get('model_name', ''))
-    version_name = ''
+    model_name = sanitize_folder_name(model_data.get("model_name", ""))
+    version_name = ""
 
     if isinstance(civitai_data, dict):
-        version_name = sanitize_folder_name(civitai_data.get('name') or '')
+        version_name = sanitize_folder_name(civitai_data.get("name") or "")
 
     formatted_path = path_template
-    formatted_path = formatted_path.replace('{base_model}', mapped_base_model)
-    formatted_path = formatted_path.replace('{first_tag}', first_tag)
-    formatted_path = formatted_path.replace('{author}', author)
-    formatted_path = formatted_path.replace('{model_name}', model_name)
-    formatted_path = formatted_path.replace('{version_name}', version_name)
+    formatted_path = formatted_path.replace("{base_model}", mapped_base_model)
+    formatted_path = formatted_path.replace("{first_tag}", first_tag)
+    formatted_path = formatted_path.replace("{author}", author)
+    formatted_path = formatted_path.replace("{model_name}", model_name)
+    formatted_path = formatted_path.replace("{version_name}", version_name)
 
-    if model_type == 'embedding':
-        formatted_path = formatted_path.replace(' ', '_')
+    if model_type == "embedding":
+        formatted_path = formatted_path.replace(" ", "_")
 
     return formatted_path
 
+
 def remove_empty_dirs(path):
     """Recursively remove empty directories starting from the given path.
-    
+
     Args:
         path (str): Root directory to start cleaning from
-        
+
     Returns:
         int: Number of empty directories removed
     """
     removed_count = 0
-    
+
     if not os.path.isdir(path):
         return removed_count
-        
+
     # List all files in directory
     files = os.listdir(path)
-    
+
     # Process all subdirectories first
     for file in files:
         full_path = os.path.join(path, file)
         if os.path.isdir(full_path):
             removed_count += remove_empty_dirs(full_path)
-    
+
     # Check if directory is now empty (after processing subdirectories)
     if not os.listdir(path):
         try:
@@ -311,5 +448,5 @@ def remove_empty_dirs(path):
             removed_count += 1
         except OSError:
             pass
-    
+
     return removed_count

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*

standalone.py

@@ -345,6 +345,7 @@ class StandaloneLoraManager(LoraManager):
             "/ws/download-progress", ws_manager.handle_download_connection
         )
         app.router.add_get("/ws/init-progress", ws_manager.handle_init_connection)
+        app.router.add_get("/ws/batch-import-progress", ws_manager.handle_connection)
 
         # Schedule service initialization
         app.on_startup.append(lambda app: cls._initialize_services())

static/css/components/batch-import-modal.css

@@ -0,0 +1,677 @@
+/* Batch Import Modal Styles */
+
+/* Step Containers */
+.batch-import-step {
+    margin: var(--space-2) 0;
+}
+
+/* Section Description */
+.section-description {
+    color: var(--text-color);
+    opacity: 0.8;
+    margin-bottom: var(--space-2);
+    font-size: 0.95em;
+}
+
+/* Hint Text */
+.input-hint {
+    display: flex;
+    align-items: center;
+    gap: 6px;
+    color: var(--text-color);
+    opacity: 0.7;
+    font-size: 0.85em;
+    margin-top: 6px;
+}
+
+.input-hint i {
+    color: var(--lora-accent);
+}
+
+/* Textarea Styling */
+#batchUrlInput {
+    width: 100%;
+    min-height: 120px;
+    padding: 12px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--bg-color);
+    color: var(--text-color);
+    font-family: inherit;
+    font-size: 0.9em;
+    resize: vertical;
+    transition: border-color 0.2s, box-shadow 0.2s;
+}
+
+#batchUrlInput:focus {
+    outline: none;
+    border-color: var(--lora-accent);
+    box-shadow: 0 0 0 2px oklch(from var(--lora-accent) l c h / 0.2);
+}
+
+/* Checkbox Group */
+.checkbox-group {
+    margin-top: var(--space-2);
+}
+
+.checkbox-label {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    cursor: pointer;
+    color: var(--text-color);
+    font-size: 0.95em;
+    user-select: none;
+}
+
+.checkbox-label input[type="checkbox"] {
+    display: none;
+}
+
+.checkmark {
+    width: 18px;
+    height: 18px;
+    border: 2px solid var(--border-color);
+    border-radius: 4px;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    transition: all 0.2s;
+    background: var(--bg-color);
+}
+
+.checkbox-label input[type="checkbox"]:checked + .checkmark {
+    background: var(--lora-accent);
+    border-color: var(--lora-accent);
+}
+
+.checkbox-label input[type="checkbox"]:checked + .checkmark::after {
+    content: '\f00c';
+    font-family: 'Font Awesome 6 Free';
+    font-weight: 900;
+    color: var(--lora-text);
+    font-size: 12px;
+}
+
+/* Batch Options */
+.batch-options {
+    margin-top: var(--space-3);
+    padding-top: var(--space-3);
+    border-top: 1px solid var(--border-color);
+}
+
+/* Input with Button */
+.input-with-button {
+    display: flex;
+    gap: 8px;
+}
+
+.input-with-button input {
+    flex: 1;
+    min-width: 0;
+}
+
+.input-with-button button {
+    flex-shrink: 0;
+    white-space: nowrap;
+    padding: 8px 16px;
+    background: var(--lora-accent);
+    color: var(--lora-text);
+    border: none;
+    border-radius: var(--border-radius-xs);
+    cursor: pointer;
+    transition: background-color 0.2s;
+}
+
+.input-with-button button:hover {
+    background: oklch(from var(--lora-accent) l c h / 0.9);
+}
+
+/* Dark theme adjustments for input-with-button */
+[data-theme="dark"] .input-with-button button {
+    background: var(--lora-accent);
+    color: var(--lora-text);
+}
+
+[data-theme="dark"] .input-with-button button:hover {
+    background: oklch(from var(--lora-accent) calc(l - 0.1) c h);
+}
+
+/* Directory Browser */
+.directory-browser {
+    margin-top: var(--space-3);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--lora-surface);
+    overflow: hidden;
+}
+
+.browser-header {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    padding: 10px 12px;
+    background: var(--bg-color);
+    border-bottom: 1px solid var(--border-color);
+}
+
+.back-btn {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    width: 32px;
+    height: 32px;
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    background: var(--card-bg);
+    color: var(--text-color);
+    cursor: pointer;
+    transition: all 0.2s;
+}
+
+.back-btn:hover {
+    border-color: var(--lora-accent);
+    background: var(--bg-color);
+}
+
+.back-btn:disabled {
+    opacity: 0.5;
+    cursor: not-allowed;
+}
+
+.current-path {
+    flex: 1;
+    padding: 6px 10px;
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: var(--border-radius-xs);
+    font-size: 0.9em;
+    color: var(--text-color);
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.browser-content {
+    max-height: 300px;
+    overflow-y: auto;
+    padding: 12px;
+}
+
+.browser-section {
+    margin-bottom: 16px;
+}
+
+.browser-section:last-child {
+    margin-bottom: 0;
+}
+
+.section-label {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    font-weight: 600;
+    font-size: 0.85em;
+    color: var(--text-color);
+    margin-bottom: 8px;
+    padding-bottom: 6px;
+    border-bottom: 1px solid var(--border-color);
+}
+
+.section-label i {
+    color: var(--lora-accent);
+}
+
+.folder-list,
+.file-list {
+    display: flex;
+    flex-direction: column;
+    gap: 4px;
+}
+
+.folder-item,
+.file-item {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    padding: 8px 10px;
+    border-radius: var(--border-radius-xs);
+    cursor: pointer;
+    transition: all 0.2s;
+    border: 1px solid transparent;
+}
+
+.folder-item:hover,
+.file-item:hover {
+    background: var(--lora-surface-hover, oklch(from var(--lora-accent) l c h / 0.1));
+    border-color: var(--lora-accent);
+}
+
+.folder-item.selected,
+.file-item.selected {
+    background: oklch(from var(--lora-accent) l c h / 0.15);
+    border-color: var(--lora-accent);
+}
+
+.folder-item i {
+    color: #fbbf24;
+    font-size: 1.1em;
+}
+
+.file-item i {
+    color: var(--text-color);
+    opacity: 0.6;
+    font-size: 1em;
+}
+
+.item-name {
+    flex: 1;
+    font-size: 0.9em;
+    color: var(--text-color);
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.item-size {
+    font-size: 0.8em;
+    color: var(--text-color);
+    opacity: 0.6;
+}
+
+.browser-footer {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    padding: 10px 12px;
+    background: var(--bg-color);
+    border-top: 1px solid var(--border-color);
+}
+
+.stats {
+    font-size: 0.85em;
+    color: var(--text-color);
+    opacity: 0.8;
+}
+
+.stats span {
+    font-weight: 600;
+    color: var(--lora-accent);
+}
+
+/* Dark theme adjustments */
+[data-theme="dark"] .directory-browser {
+    background: var(--card-bg);
+}
+
+[data-theme="dark"] .browser-header,
+[data-theme="dark"] .browser-footer {
+    background: var(--lora-surface);
+}
+
+[data-theme="dark"] .folder-item i {
+    color: #fcd34d;
+}
+
+/* Progress Container */
+.batch-progress-container {
+    padding: var(--space-3);
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-sm);
+    margin-bottom: var(--space-3);
+}
+
+.progress-header {
+    display: flex;
+    justify-content: space-between;
+    align-items: center;
+    margin-bottom: var(--space-2);
+}
+
+.progress-status {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+}
+
+.status-icon {
+    color: var(--lora-accent);
+    font-size: 1.1em;
+}
+
+.status-icon i {
+    animation: fa-spin 2s infinite linear;
+}
+
+.status-text {
+    font-weight: 500;
+    color: var(--text-color);
+}
+
+.progress-percentage {
+    font-size: 1.2em;
+    font-weight: 600;
+    color: var(--lora-accent);
+}
+
+/* Progress Bar */
+.progress-bar-container {
+    height: 8px;
+    background: var(--bg-color);
+    border-radius: 4px;
+    overflow: hidden;
+    margin-bottom: var(--space-3);
+}
+
+.progress-bar {
+    height: 100%;
+    background: linear-gradient(90deg, var(--lora-accent), oklch(from var(--lora-accent) calc(l + 0.1) c h));
+    border-radius: 4px;
+    transition: width 0.3s ease;
+}
+
+/* Progress Stats */
+.progress-stats {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    gap: var(--space-2);
+    margin-bottom: var(--space-2);
+}
+
+.stat-item {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    padding: var(--space-2);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+}
+
+.stat-item.success {
+    border-left: 3px solid #00B87A;
+}
+
+.stat-item.failed {
+    border-left: 3px solid var(--lora-error);
+}
+
+.stat-item.skipped {
+    border-left: 3px solid var(--lora-warning);
+}
+
+.stat-label {
+    font-size: 0.8em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-bottom: 4px;
+}
+
+.stat-value {
+    font-size: 1.4em;
+    font-weight: 600;
+    color: var(--text-color);
+}
+
+/* Current Item */
+.current-item {
+    display: flex;
+    align-items: baseline;
+    gap: 10px;
+    padding: var(--space-2);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+    font-size: 0.9em;
+}
+
+.current-item-label {
+    color: var(--text-color);
+    opacity: 0.7;
+    flex-shrink: 0;
+}
+
+.current-item-name {
+    color: var(--text-color);
+    font-weight: 500;
+    flex: 1;
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+    line-height: 1.2;
+}
+
+/* Results Container */
+.batch-results-container {
+    padding: var(--space-3);
+    background: var(--lora-surface);
+    border-radius: var(--border-radius-sm);
+    margin-bottom: var(--space-3);
+}
+
+.results-header {
+    text-align: center;
+    margin-bottom: var(--space-3);
+}
+
+.results-icon {
+    font-size: 3em;
+    color: #00B87A;
+    margin-bottom: var(--space-1);
+}
+
+.results-icon.warning {
+    color: var(--lora-warning);
+}
+
+.results-icon.error {
+    color: var(--lora-error);
+}
+
+.results-title {
+    font-size: 1.3em;
+    font-weight: 600;
+    color: var(--text-color);
+}
+
+/* Results Summary - Matches progress-stats styling */
+.results-summary {
+    display: grid;
+    grid-template-columns: repeat(4, 1fr);
+    gap: var(--space-2);
+    margin-bottom: var(--space-3);
+}
+
+.result-card {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    padding: var(--space-2);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+    text-align: center;
+}
+
+.result-card.success {
+    border-left: 3px solid #00B87A;
+}
+
+.result-card.failed {
+    border-left: 3px solid var(--lora-error);
+}
+
+.result-card.skipped {
+    border-left: 3px solid var(--lora-warning);
+}
+
+.result-label {
+    font-size: 0.8em;
+    color: var(--text-color);
+    opacity: 0.7;
+    margin-bottom: 4px;
+}
+
+.result-value {
+    font-size: 1.4em;
+    font-weight: 600;
+    color: var(--text-color);
+}
+
+/* Results Details */
+.results-details {
+    border-top: 1px solid var(--border-color);
+    padding-top: var(--space-2);
+}
+
+.details-toggle {
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    gap: 8px;
+    padding: 10px;
+    cursor: pointer;
+    color: var(--lora-accent);
+    font-weight: 500;
+    border-radius: var(--border-radius-xs);
+    transition: background 0.2s;
+}
+
+.details-toggle:hover {
+    background: oklch(from var(--lora-accent) l c h / 0.1);
+}
+
+.details-toggle i {
+    transition: transform 0.2s;
+}
+
+.details-toggle.expanded i {
+    transform: rotate(180deg);
+}
+
+.details-list {
+    max-height: 250px;
+    overflow-y: auto;
+    margin-top: var(--space-2);
+    background: var(--bg-color);
+    border-radius: var(--border-radius-xs);
+    border: 1px solid var(--border-color);
+}
+
+/* Result Item in Details */
+.result-item {
+    display: flex;
+    align-items: center;
+    gap: 10px;
+    padding: 10px 12px;
+    border-bottom: 1px solid var(--border-color);
+    font-size: 0.9em;
+}
+
+.result-item:last-child {
+    border-bottom: none;
+}
+
+.result-item-status {
+    width: 24px;
+    height: 24px;
+    border-radius: 50%;
+    display: flex;
+    align-items: center;
+    justify-content: center;
+    font-size: 0.8em;
+}
+
+.result-item-status.success {
+    background: oklch(from #00B87A l c h / 0.2);
+    color: #00B87A;
+}
+
+.result-item-status.failed {
+    background: oklch(from var(--lora-error) l c h / 0.2);
+    color: var(--lora-error);
+}
+
+.result-item-status.skipped {
+    background: oklch(from var(--lora-warning) l c h / 0.2);
+    color: var(--lora-warning);
+}
+
+.result-item-info {
+    flex: 1;
+    min-width: 0;
+}
+
+.result-item-name {
+    font-weight: 500;
+    color: var(--text-color);
+    white-space: nowrap;
+    overflow: hidden;
+    text-overflow: ellipsis;
+}
+
+.result-item-error {
+    font-size: 0.8em;
+    color: var(--lora-error);
+    margin-top: 2px;
+}
+
+/* Responsive Adjustments */
+@media (max-width: 768px) {
+    .progress-stats,
+    .results-summary {
+        grid-template-columns: repeat(2, 1fr);
+    }
+    
+    .batch-progress-container,
+    .batch-results-container {
+        padding: var(--space-2);
+    }
+}
+
+/* Dark Theme Adjustments */
+[data-theme="dark"] .batch-progress-container,
+[data-theme="dark"] .batch-results-container {
+    background: var(--card-bg);
+}
+
+[data-theme="dark"] .stat-item,
+[data-theme="dark"] .result-card,
+[data-theme="dark"] .current-item,
+[data-theme="dark"] .details-list {
+    background: var(--lora-surface);
+}
+
+/* Cancelled State */
+.batch-progress-container.cancelled .progress-bar {
+    background: var(--lora-warning);
+}
+
+.batch-progress-container.cancelled .status-icon {
+    color: var(--lora-warning);
+}
+
+/* Error State */
+.batch-progress-container.error .progress-bar {
+    background: var(--lora-error);
+}
+
+.batch-progress-container.error .status-icon {
+    color: var(--lora-error);
+}
+
+/* Completed State */
+.batch-progress-container.completed .progress-bar {
+    background: #00B87A;
+}
+
+.batch-progress-container.completed .status-icon {
+    color: #00B87A;
+}
+
+.batch-progress-container.completed .status-icon i {
+    animation: none;
+}
+
+.batch-progress-container.completed .status-icon i::before {
+    content: '\f00c';
+}

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/components/ContextMenu/BulkContextMenu.js

@@ -117,7 +117,10 @@ export class BulkContextMenu extends BaseContextMenu {
     countSkipStatus(skipState) {
         let count = 0;
         for (const filePath of state.selectedModels) {
-            const card = document.querySelector(`.model-card[data-filepath="${filePath}"]`);
+            const escapedPath = window.CSS && typeof window.CSS.escape === 'function'
+                ? window.CSS.escape(filePath)
+                : filePath.replace(/["\\]/g, '\\$&');
+            const card = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
             if (card) {
                 const isSkipped = card.dataset.skip_metadata_refresh === 'true';
                 if (isSkipped === skipState) {

static/js/components/RecipeCard.js

@@ -201,8 +201,9 @@ class RecipeCard {
             this.recipe.favorite = isFavorite;
 
             // Re-find star icon in case of re-render during fault
+            const filePathForXpath = this.recipe.file_path.replace(/"/g, '"');
             const currentCard = card.ownerDocument.evaluate(
-                `.//*[@data-filepath="${this.recipe.file_path}"]`,
+                `.//*[@data-filepath="${filePathForXpath}"]`,
                 card.ownerDocument, null, XPathResult.FIRST_ORDERED_NODE_TYPE, null
             ).singleNodeValue || card;
 

static/js/components/shared/ModelModal.js

@@ -846,8 +846,14 @@ function setupLoraSpecificFields(filePath) {
 
         const currentPath = resolveFilePath();
         if (!currentPath) return;
-        const loraCard = document.querySelector(`.model-card[data-filepath="${currentPath}"]`) ||
-            document.querySelector(`.model-card[data-filepath="${filePath}"]`);
+        const escapedCurrentPath = window.CSS && typeof window.CSS.escape === 'function'
+            ? window.CSS.escape(currentPath)
+            : currentPath.replace(/["\\]/g, '\\$&');
+        const escapedFilePath = window.CSS && typeof window.CSS.escape === 'function'
+            ? window.CSS.escape(filePath)
+            : filePath.replace(/["\\]/g, '\\$&');
+        const loraCard = document.querySelector(`.model-card[data-filepath="${escapedCurrentPath}"]`) ||
+            document.querySelector(`.model-card[data-filepath="${escapedFilePath}"]`);
         const currentPresets = parsePresets(loraCard?.dataset.usage_tips);
 
         if (key === 'strength_range') {

static/js/components/shared/PresetTags.js

@@ -49,7 +49,10 @@ function formatPresetKey(key) {
  */
 window.removePreset = async function(key) {
     const filePath = document.querySelector('#modelModal .modal-content .file-path').dataset.filepath;
-    const loraCard = document.querySelector(`.model-card[data-filepath="${filePath}"]`);
+    const escapedPath = window.CSS && typeof window.CSS.escape === 'function'
+        ? window.CSS.escape(filePath)
+        : filePath.replace(/["\\]/g, '\\$&');
+    const loraCard = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
     const currentPresets = parsePresets(loraCard.dataset.usage_tips);
     
     delete currentPresets[key];

static/js/components/shared/showcase/MetadataPanel.js

@@ -2,6 +2,7 @@
  * MetadataPanel.js
  * Generates metadata panels for showcase media items
  */
+import { escapeHtml } from '../utils.js';
 
 /**
  * Generate metadata panel HTML
@@ -49,6 +50,7 @@ export function generateMetadataPanel(hasParams, hasPrompts, prompt, negativePro
     }
     
     if (prompt) {
+        prompt = escapeHtml(prompt);
         content += `
             <div class="metadata-row prompt-row">
                 <span class="metadata-label">Prompt:</span>
@@ -64,6 +66,7 @@ export function generateMetadataPanel(hasParams, hasPrompts, prompt, negativePro
     }
     
     if (negativePrompt) {
+        negativePrompt = escapeHtml(negativePrompt);
         content += `
             <div class="metadata-row prompt-row">
                 <span class="metadata-label">Negative Prompt:</span>
@@ -80,4 +83,4 @@ export function generateMetadataPanel(hasParams, hasPrompts, prompt, negativePro
     
     content += '</div></div>';
     return content;
-}
+}

static/js/managers/BatchImportManager.js

@@ -0,0 +1,815 @@
+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
+ */
+export class BatchImportManager {
+    constructor() {
+        this.initialized = false;
+        this.inputMode = 'urls'; // 'urls' or 'directory'
+        this.operationId = null;
+        this.wsConnection = null;
+        this.pollingInterval = null;
+        this.progress = null;
+        this.results = null;
+        this.isCancelled = false;
+    }
+
+    /**
+     * Show the batch import modal
+     */
+    showModal() {
+        if (!this.initialized) {
+            this.initialize();
+        }
+        this.resetState();
+        modalManager.showModal('batchImportModal');
+    }
+
+    /**
+     * Initialize the manager
+     */
+    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);
+            });
+        }
+    }
+
+    /**
+     * Reset all state to initial values
+     */
+    resetState() {
+        this.inputMode = 'urls';
+        this.operationId = null;
+        this.progress = null;
+        this.results = null;
+        this.isCancelled = false;
+
+        // Reset UI
+        this.showStep('batchInputStep');
+        this.toggleInputMode('urls');
+        
+        // Clear inputs
+        const urlInput = document.getElementById('batchUrlInput');
+        if (urlInput) urlInput.value = '';
+        
+        const directoryInput = document.getElementById('batchDirectoryInput');
+        if (directoryInput) directoryInput.value = '';
+        
+        const tagsInput = document.getElementById('batchTagsInput');
+        if (tagsInput) tagsInput.value = '';
+        
+        const skipNoMetadata = document.getElementById('batchSkipNoMetadata');
+        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;
+
+        // Reset progress UI
+        this.updateProgressUI({
+            total: 0,
+            completed: 0,
+            success: 0,
+            failed: 0,
+            skipped: 0,
+            progress_percent: 0,
+            current_item: '',
+            status: 'pending'
+        });
+
+        // Reset results
+        const detailsList = document.getElementById('batchDetailsList');
+        if (detailsList) {
+            detailsList.innerHTML = '';
+            detailsList.style.display = 'none';
+        }
+
+        const toggleIcon = document.getElementById('resultsToggleIcon');
+        if (toggleIcon) {
+            toggleIcon.classList.remove('expanded');
+        }
+
+        // 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);
+    }
+
+    /**
+     * Show a specific step in the modal
+     */
+    showStep(stepId) {
+        document.querySelectorAll('.batch-import-step').forEach(step => {
+            step.style.display = 'none';
+        });
+        
+        const step = document.getElementById(stepId);
+        if (step) {
+            step.style.display = 'block';
+        }
+    }
+
+    /**
+     * Toggle between URL list and directory input modes
+     */
+    toggleInputMode(mode) {
+        this.inputMode = mode;
+
+        // Update toggle buttons
+        document.querySelectorAll('.toggle-btn[data-mode]').forEach(btn => {
+            btn.classList.remove('active');
+        });
+        
+        const activeBtn = document.querySelector(`.toggle-btn[data-mode="${mode}"]`);
+        if (activeBtn) {
+            activeBtn.classList.add('active');
+        }
+
+        // Show/hide appropriate sections
+        const urlSection = document.getElementById('urlListSection');
+        const directorySection = document.getElementById('directorySection');
+
+        if (urlSection && directorySection) {
+            if (mode === 'urls') {
+                urlSection.style.display = 'block';
+                directorySection.style.display = 'none';
+            } else {
+                urlSection.style.display = 'none';
+                directorySection.style.display = 'block';
+            }
+        }
+    }
+
+    /**
+     * Start the batch import process
+     */
+    async startImport() {
+        const data = this.collectInputData();
+        
+        if (!this.validateInput(data)) {
+            return;
+        }
+
+        try {
+            // Show progress step
+            this.showStep('batchProgressStep');
+            
+            // Start the import
+            const response = await this.sendStartRequest(data);
+            
+            if (response.success) {
+                this.operationId = response.operation_id;
+                this.isCancelled = false;
+                
+                // Connect to WebSocket for real-time updates
+                this.connectWebSocket();
+                
+                // Start polling as fallback
+                this.startPolling();
+            } else {
+                showToast('toast.recipes.batchImportFailed', { message: response.error }, 'error');
+                this.showStep('batchInputStep');
+            }
+        } catch (error) {
+            console.error('Error starting batch import:', error);
+            showToast('toast.recipes.batchImportFailed', { message: error.message }, 'error');
+            this.showStep('batchInputStep');
+        }
+    }
+
+    /**
+     * Collect input data from the form
+     */
+    collectInputData() {
+        const data = {
+            mode: this.inputMode,
+            tags: [],
+            skip_no_metadata: false
+        };
+
+        // Collect tags
+        const tagsInput = document.getElementById('batchTagsInput');
+        if (tagsInput && tagsInput.value.trim()) {
+            data.tags = tagsInput.value.split(',').map(t => t.trim()).filter(t => t);
+        }
+
+        // Collect skip_no_metadata
+        const skipNoMetadata = document.getElementById('batchSkipNoMetadata');
+        if (skipNoMetadata) {
+            data.skip_no_metadata = skipNoMetadata.checked;
+        }
+
+        if (this.inputMode === 'urls') {
+            const urlInput = document.getElementById('batchUrlInput');
+            if (urlInput) {
+                const urls = urlInput.value.split('\n')
+                    .map(line => line.trim())
+                    .filter(line => line.length > 0);
+                
+                // Convert to items format
+                data.items = urls.map(url => ({
+                    source: url,
+                    type: this.detectUrlType(url)
+                }));
+            }
+        } else {
+            const directoryInput = document.getElementById('batchDirectoryInput');
+            if (directoryInput) {
+                data.directory = directoryInput.value.trim();
+            }
+            
+            const recursiveCheck = document.getElementById('batchRecursiveCheck');
+            if (recursiveCheck) {
+                data.recursive = recursiveCheck.checked;
+            }
+        }
+
+        return data;
+    }
+
+    /**
+     * Detect if a URL is http or local path
+     */
+    detectUrlType(url) {
+        if (url.startsWith('http://') || url.startsWith('https://')) {
+            return 'url';
+        }
+        return 'local_path';
+    }
+
+    /**
+     * Validate the input data
+     */
+    validateInput(data) {
+        if (data.mode === 'urls') {
+            if (!data.items || data.items.length === 0) {
+                showToast('toast.recipes.batchImportNoUrls', {}, 'error');
+                return false;
+            }
+        } else {
+            if (!data.directory) {
+                showToast('toast.recipes.batchImportNoDirectory', {}, 'error');
+                return false;
+            }
+        }
+        return true;
+    }
+
+    /**
+     * Send the start batch import request
+     */
+    async sendStartRequest(data) {
+        const endpoint = data.mode === 'urls' 
+            ? '/api/lm/recipes/batch-import/start'
+            : '/api/lm/recipes/batch-import/directory';
+
+        const response = await fetch(endpoint, {
+            method: 'POST',
+            headers: {
+                'Content-Type': 'application/json'
+            },
+            body: JSON.stringify(data)
+        });
+
+        return await response.json();
+    }
+
+    /**
+     * Connect to WebSocket for real-time progress updates
+     */
+    connectWebSocket() {
+        const wsProtocol = window.location.protocol === 'https:' ? 'wss:' : 'ws:';
+        const wsUrl = `${wsProtocol}//${window.location.host}/ws/batch-import-progress?id=${this.operationId}`;
+        
+        this.wsConnection = new WebSocket(wsUrl);
+        
+        this.wsConnection.onopen = () => {
+            console.log('Connected to batch import progress WebSocket');
+        };
+        
+        this.wsConnection.onmessage = (event) => {
+            try {
+                const data = JSON.parse(event.data);
+                if (data.type === 'batch_import_progress') {
+                    this.handleProgressUpdate(data);
+                }
+            } catch (error) {
+                console.error('Error parsing WebSocket message:', error);
+            }
+        };
+        
+        this.wsConnection.onerror = (error) => {
+            console.error('WebSocket error:', error);
+        };
+        
+        this.wsConnection.onclose = () => {
+            console.log('WebSocket connection closed');
+        };
+    }
+
+    /**
+     * Start polling for progress updates (fallback)
+     */
+    startPolling() {
+        this.pollingInterval = setInterval(async () => {
+            if (!this.operationId || this.isCancelled) {
+                return;
+            }
+            
+            try {
+                const response = await fetch(`/api/lm/recipes/batch-import/progress?operation_id=${this.operationId}`);
+                const data = await response.json();
+                
+                if (data.success && data.progress) {
+                    this.handleProgressUpdate(data.progress);
+                }
+            } catch (error) {
+                console.error('Error polling progress:', error);
+            }
+        }, 1000);
+    }
+
+    /**
+     * Handle progress update from WebSocket or polling
+     */
+    handleProgressUpdate(progress) {
+        this.progress = progress;
+        this.updateProgressUI(progress);
+        
+        // Check if import is complete
+        if (progress.status === 'completed' || progress.status === 'cancelled' || 
+            (progress.total > 0 && progress.completed >= progress.total)) {
+            this.importComplete(progress);
+        }
+    }
+
+    /**
+     * Update the progress UI
+     */
+    updateProgressUI(progress) {
+        // Update progress bar
+        const progressBar = document.getElementById('batchProgressBar');
+        if (progressBar) {
+            progressBar.style.width = `${progress.progress_percent || 0}%`;
+        }
+        
+        // Update percentage
+        const progressPercent = document.getElementById('batchProgressPercent');
+        if (progressPercent) {
+            progressPercent.textContent = `${Math.round(progress.progress_percent || 0)}%`;
+        }
+        
+        // Update stats
+        const totalCount = document.getElementById('batchTotalCount');
+        if (totalCount) totalCount.textContent = progress.total || 0;
+        
+        const successCount = document.getElementById('batchSuccessCount');
+        if (successCount) successCount.textContent = progress.success || 0;
+        
+        const failedCount = document.getElementById('batchFailedCount');
+        if (failedCount) failedCount.textContent = progress.failed || 0;
+        
+        const skippedCount = document.getElementById('batchSkippedCount');
+        if (skippedCount) skippedCount.textContent = progress.skipped || 0;
+        
+        // Update current item
+        const currentItem = document.getElementById('batchCurrentItem');
+        if (currentItem) {
+            currentItem.textContent = progress.current_item || '-';
+        }
+        
+        // Update status text
+        const statusText = document.getElementById('batchStatusText');
+        if (statusText) {
+            if (progress.status === 'running') {
+                statusText.textContent = translate('recipes.batchImport.importing', {}, 'Importing...');
+            } else if (progress.status === 'completed') {
+                statusText.textContent = translate('recipes.batchImport.completed', {}, 'Import completed');
+            } else if (progress.status === 'cancelled') {
+                statusText.textContent = translate('recipes.batchImport.cancelled', {}, 'Import cancelled');
+            }
+        }
+
+        // Update container classes
+        const progressContainer = document.querySelector('.batch-progress-container');
+        if (progressContainer) {
+            progressContainer.classList.remove('completed', 'cancelled', 'error');
+            if (progress.status === 'completed') {
+                progressContainer.classList.add('completed');
+            } else if (progress.status === 'cancelled') {
+                progressContainer.classList.add('cancelled');
+            } else if (progress.failed > 0 && progress.failed === progress.total) {
+                progressContainer.classList.add('error');
+            }
+        }
+    }
+
+    /**
+     * Handle import completion
+     */
+    importComplete(progress) {
+        this.cleanupConnections();
+        this.results = progress;
+        
+        // Refresh recipes list to show newly imported recipes
+        if (window.recipeManager && typeof window.recipeManager.loadRecipes === 'function') {
+            window.recipeManager.loadRecipes();
+        }
+        
+        // Show results step
+        this.showStep('batchResultsStep');
+        this.updateResultsUI(progress);
+    }
+
+    /**
+     * Update the results UI
+     */
+    updateResultsUI(progress) {
+        // Update summary cards
+        const resultsTotal = document.getElementById('resultsTotal');
+        if (resultsTotal) resultsTotal.textContent = progress.total || 0;
+        
+        const resultsSuccess = document.getElementById('resultsSuccess');
+        if (resultsSuccess) resultsSuccess.textContent = progress.success || 0;
+        
+        const resultsFailed = document.getElementById('resultsFailed');
+        if (resultsFailed) resultsFailed.textContent = progress.failed || 0;
+        
+        const resultsSkipped = document.getElementById('resultsSkipped');
+        if (resultsSkipped) resultsSkipped.textContent = progress.skipped || 0;
+        
+        // Update header based on results
+        const resultsHeader = document.getElementById('batchResultsHeader');
+        if (resultsHeader) {
+            const icon = resultsHeader.querySelector('.results-icon i');
+            const title = resultsHeader.querySelector('.results-title');
+            
+                if (this.isCancelled) {
+                if (icon) {
+                    icon.className = 'fas fa-stop-circle';
+                    icon.parentElement.classList.add('warning');
+                }
+                if (title) title.textContent = translate('recipes.batchImport.cancelled', {}, 'Import cancelled');
+            } else if (progress.failed === 0 && progress.success > 0) {
+                if (icon) {
+                    icon.className = 'fas fa-check-circle';
+                    icon.parentElement.classList.remove('warning', 'error');
+                }
+                if (title) title.textContent = translate('recipes.batchImport.completed', {}, 'Import completed');
+            } else if (progress.failed > 0 && progress.success === 0) {
+                if (icon) {
+                    icon.className = 'fas fa-times-circle';
+                    icon.parentElement.classList.add('error');
+                }
+                if (title) title.textContent = translate('recipes.batchImport.failed', {}, 'Import failed');
+            } else {
+                if (icon) {
+                    icon.className = 'fas fa-exclamation-circle';
+                    icon.parentElement.classList.add('warning');
+                }
+                if (title) title.textContent = translate('recipes.batchImport.completedWithErrors', {}, 'Completed with errors');
+            }
+        }
+    }
+
+    /**
+     * Toggle the results details visibility
+     */
+    toggleResultsDetails() {
+        const detailsList = document.getElementById('batchDetailsList');
+        const toggleIcon = document.getElementById('resultsToggleIcon');
+        const toggle = document.querySelector('.details-toggle');
+        
+        if (detailsList && toggleIcon) {
+            if (detailsList.style.display === 'none') {
+                detailsList.style.display = 'block';
+                toggleIcon.classList.add('expanded');
+                if (toggle) toggle.classList.add('expanded');
+                
+                // Load details if not loaded
+                if (detailsList.children.length === 0 && this.results && this.results.items) {
+                    this.loadResultsDetails(this.results.items);
+                }
+            } else {
+                detailsList.style.display = 'none';
+                toggleIcon.classList.remove('expanded');
+                if (toggle) toggle.classList.remove('expanded');
+            }
+        }
+    }
+
+    /**
+     * Load results details into the list
+     */
+    loadResultsDetails(items) {
+        const detailsList = document.getElementById('batchDetailsList');
+        if (!detailsList) return;
+        
+        detailsList.innerHTML = '';
+        
+        items.forEach(item => {
+            const resultItem = document.createElement('div');
+            resultItem.className = 'result-item';
+            
+            const statusClass = item.status === 'success' ? 'success' : 
+                               item.status === 'failed' ? 'failed' : 'skipped';
+            const statusIcon = item.status === 'success' ? 'check' : 
+                              item.status === 'failed' ? 'times' : 'forward';
+            
+            resultItem.innerHTML = `
+                <div class="result-item-status ${statusClass}">
+                    <i class="fas fa-${statusIcon}"></i>
+                </div>
+                <div class="result-item-info">
+                    <div class="result-item-name">${this.escapeHtml(item.source || item.current_item || 'Unknown')}</div>
+                    ${item.error_message ? `<div class="result-item-error">${this.escapeHtml(item.error_message)}</div>` : ''}
+                </div>
+            `;
+            
+            detailsList.appendChild(resultItem);
+        });
+    }
+
+    /**
+     * Cancel the current import
+     */
+    async cancelImport() {
+        if (!this.operationId) return;
+        
+        this.isCancelled = true;
+        
+        try {
+            const response = await fetch('/api/lm/recipes/batch-import/cancel', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ operation_id: this.operationId })
+            });
+            
+            const data = await response.json();
+            
+            if (data.success) {
+                showToast('toast.recipes.batchImportCancelling', {}, 'info');
+            } else {
+                showToast('toast.recipes.batchImportCancelFailed', { message: data.error }, 'error');
+            }
+        } catch (error) {
+            console.error('Error cancelling import:', error);
+            showToast('toast.recipes.batchImportCancelFailed', { message: error.message }, 'error');
+        }
+    }
+
+    /**
+     * Close modal and reset state
+     */
+    closeAndReset() {
+        this.cleanupConnections();
+        this.resetState();
+        modalManager.closeModal('batchImportModal');
+    }
+
+    /**
+     * Start a new import (from results step)
+     */
+    startNewImport() {
+        this.resetState();
+        this.showStep('batchInputStep');
+    }
+
+    /**
+     * Toggle directory browser visibility
+     */
+    toggleDirectoryBrowser() {
+        const browser = document.getElementById('batchDirectoryBrowser');
+        if (browser) {
+            const isVisible = browser.style.display !== 'none';
+            browser.style.display = isVisible ? 'none' : 'block';
+            
+            if (!isVisible) {
+                // Load initial directory when opening
+                const currentPath = document.getElementById('batchDirectoryInput').value;
+                this.loadDirectory(currentPath || '/');
+            }
+        }
+    }
+
+    /**
+     * Load directory contents
+     */
+    async loadDirectory(path) {
+        try {
+            const response = await fetch('/api/lm/recipes/browse-directory', {
+                method: 'POST',
+                headers: {
+                    'Content-Type': 'application/json'
+                },
+                body: JSON.stringify({ path })
+            });
+
+            const data = await response.json();
+
+            if (data.success) {
+                this.renderDirectoryBrowser(data);
+            } else {
+                showToast('toast.recipes.batchImportBrowseFailed', { message: data.error }, 'error');
+            }
+        } catch (error) {
+            console.error('Error loading directory:', error);
+            showToast('toast.recipes.batchImportBrowseFailed', { message: error.message }, 'error');
+        }
+    }
+
+    /**
+     * Render directory browser UI
+     */
+    renderDirectoryBrowser(data) {
+        const currentPathEl = document.getElementById('batchCurrentPath');
+        const folderList = document.getElementById('batchFolderList');
+        const fileList = document.getElementById('batchFileList');
+        const directoryCount = document.getElementById('batchDirectoryCount');
+        const imageCount = document.getElementById('batchImageCount');
+
+        if (currentPathEl) {
+            currentPathEl.textContent = data.current_path;
+        }
+
+        // Render folders
+        if (folderList) {
+            folderList.innerHTML = '';
+            
+            // Add parent directory if available
+            if (data.parent_path) {
+                const parentItem = this.createFolderItem('..', data.parent_path, true);
+                folderList.appendChild(parentItem);
+            }
+
+            data.directories.forEach(dir => {
+                folderList.appendChild(this.createFolderItem(dir.name, dir.path));
+            });
+        }
+
+        // Render files
+        if (fileList) {
+            fileList.innerHTML = '';
+            data.image_files.forEach(file => {
+                fileList.appendChild(this.createFileItem(file.name, file.path, file.size));
+            });
+        }
+
+        // Update stats
+        if (directoryCount) {
+            directoryCount.textContent = data.directory_count;
+        }
+        if (imageCount) {
+            imageCount.textContent = data.image_count;
+        }
+    }
+
+    /**
+     * Create folder item element
+     */
+    createFolderItem(name, path, isParent = false) {
+        const item = document.createElement('div');
+        item.className = 'folder-item';
+        item.dataset.path = path;
+        
+        item.innerHTML = `
+            <i class="fas fa-folder${isParent ? '' : ''}"></i>
+            <span class="item-name">${this.escapeHtml(name)}</span>
+        `;
+        
+        item.addEventListener('click', () => {
+            if (isParent) {
+                this.navigateToParentDirectory();
+            } else {
+                this.loadDirectory(path);
+            }
+        });
+        
+        return item;
+    }
+
+    /**
+     * Create file item element
+     */
+    createFileItem(name, path, size) {
+        const item = document.createElement('div');
+        item.className = 'file-item';
+        item.dataset.path = path;
+        
+        item.innerHTML = `
+            <i class="fas fa-image"></i>
+            <span class="item-name">${this.escapeHtml(name)}</span>
+            <span class="item-size">${this.formatFileSize(size)}</span>
+        `;
+        
+        return item;
+    }
+
+    /**
+     * Navigate to parent directory
+     */
+    navigateToParentDirectory() {
+        const currentPath = document.getElementById('batchCurrentPath')?.textContent;
+        if (currentPath) {
+            // Get parent path using path manipulation
+            const lastSeparator = currentPath.lastIndexOf('/');
+            const parentPath = lastSeparator > 0 ? currentPath.substring(0, lastSeparator) : currentPath;
+            this.loadDirectory(parentPath);
+        }
+    }
+
+    /**
+     * Select current directory
+     */
+    selectCurrentDirectory() {
+        const currentPath = document.getElementById('batchCurrentPath')?.textContent;
+        const directoryInput = document.getElementById('batchDirectoryInput');
+        
+        if (currentPath && directoryInput) {
+            directoryInput.value = currentPath;
+            this.toggleDirectoryBrowser(); // Close browser
+            showToast('toast.recipes.batchImportDirectorySelected', { path: currentPath }, 'success');
+        }
+    }
+
+    /**
+     * Format file size for display
+     */
+    formatFileSize(bytes) {
+        if (bytes === 0) return '0 B';
+        const k = 1024;
+        const sizes = ['B', 'KB', 'MB', 'GB'];
+        const i = Math.floor(Math.log(bytes) / Math.log(k));
+        return Math.round(bytes / Math.pow(k, i) * 10) / 10 + ' ' + sizes[i];
+    }
+
+    /**
+     * Escape HTML to prevent XSS
+     */
+    escapeHtml(text) {
+        if (!text) return '';
+        const div = document.createElement('div');
+        div.textContent = text;
+        return div.innerHTML;
+    }
+
+    /**
+     * Browse for directory using File System Access API (deprecated - kept for compatibility)
+     */
+    async browseDirectory() {
+        // Now redirects to the new directory browser
+        this.toggleDirectoryBrowser();
+    }
+
+    /**
+     * Clean up WebSocket and polling connections
+     */
+    cleanupConnections() {
+        if (this.wsConnection) {
+            if (this.wsConnection.readyState === WebSocket.OPEN || 
+                this.wsConnection.readyState === WebSocket.CONNECTING) {
+                this.wsConnection.close();
+            }
+            this.wsConnection = null;
+        }
+        
+        if (this.pollingInterval) {
+            clearInterval(this.pollingInterval);
+            this.pollingInterval = null;
+        }
+    }
+
+    /**
+     * Escape HTML to prevent XSS
+     */
+    escapeHtml(text) {
+        if (!text) return '';
+        const div = document.createElement('div');
+        div.textContent = text;
+        return div.innerHTML;
+    }
+}
+
+// Create singleton instance
+export const batchImportManager = new BatchImportManager();

static/js/managers/BulkManager.js

@@ -568,7 +568,8 @@ export class BulkManager {
     }
 
     deselectItem(filepath) {
-        const card = document.querySelector(`.model-card[data-filepath="${filepath}"]`);
+        const escapedPath = this.escapeAttributeValue(filepath);
+        const card = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
         if (card) {
             card.classList.remove('selected');
         }
@@ -632,7 +633,8 @@ export class BulkManager {
                 for (const filepath of state.selectedModels) {
                     const metadata = metadataCache.get(filepath);
                     if (metadata) {
-                        const card = document.querySelector(`.model-card[data-filepath="${filepath}"]`);
+                        const escapedPath = this.escapeAttributeValue(filepath);
+                        const card = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
                         if (card) {
                             this.updateMetadataCacheFromCard(filepath, card);
                         }

static/js/managers/ModalManager.js

@@ -134,6 +134,19 @@ export class ModalManager {
             });
         }
 
+        // Add batchImportModal registration
+        const batchImportModal = document.getElementById('batchImportModal');
+        if (batchImportModal) {
+            this.registerModal('batchImportModal', {
+                element: batchImportModal,
+                onClose: () => {
+                    this.getModal('batchImportModal').element.style.display = 'none';
+                    document.body.classList.remove('modal-open');
+                },
+                closeOnOutsideClick: true
+            });
+        }
+
         // Add recipeModal registration
         const recipeModal = document.getElementById('recipeModal');
         if (recipeModal) {

static/js/recipes.js

@@ -1,6 +1,7 @@
 // Recipe manager module
 import { appCore } from './core.js';
 import { ImportManager } from './managers/ImportManager.js';
+import { BatchImportManager } from './managers/BatchImportManager.js';
 import { RecipeModal } from './components/RecipeModal.js';
 import { state, getCurrentPageState } from './state/index.js';
 import { getSessionItem, removeSessionItem } from './utils/storageHelpers.js';
@@ -46,6 +47,10 @@ class RecipeManager {
         // Initialize ImportManager
         this.importManager = new ImportManager();
 
+        // Initialize BatchImportManager and make it globally accessible
+        this.batchImportManager = new BatchImportManager();
+        window.batchImportManager = this.batchImportManager;
+
         // Initialize RecipeModal
         this.recipeModal = new RecipeModal();
 

static/js/utils/modalUtils.js

@@ -7,7 +7,10 @@ let pendingExcludePath = null;
 export function showDeleteModal(filePath) {
     pendingDeletePath = filePath;
     
-    const card = document.querySelector(`.model-card[data-filepath="${filePath}"]`);
+    const escapedPath = window.CSS && typeof window.CSS.escape === 'function'
+        ? window.CSS.escape(filePath)
+        : filePath.replace(/["\\]/g, '\\$&');
+    const card = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
     const modelName = card ? card.dataset.name : filePath.split('/').pop();
     const modal = modalManager.getModal('deleteModal').element;
     const modelInfo = modal.querySelector('.delete-model-info');
@@ -47,7 +50,10 @@ export function closeDeleteModal() {
 export function showExcludeModal(filePath) {
     pendingExcludePath = filePath;
     
-    const card = document.querySelector(`.model-card[data-filepath="${filePath}"]`);
+    const escapedPath = window.CSS && typeof window.CSS.escape === 'function'
+        ? window.CSS.escape(filePath)
+        : filePath.replace(/["\\]/g, '\\$&');
+    const card = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
     const modelName = card ? card.dataset.name : filePath.split('/').pop();
     const modal = modalManager.getModal('excludeModal').element;
     const modelInfo = modal.querySelector('.exclude-model-info');

static/js/utils/uiHelpers.js

@@ -197,7 +197,10 @@ export function openCivitaiByMetadata(civitaiId, versionId, modelName = null) {
 }
 
 export function openCivitai(filePath) {
-  const loraCard = document.querySelector(`.model-card[data-filepath="${filePath}"]`);
+  const escapedPath = window.CSS && typeof window.CSS.escape === 'function'
+    ? window.CSS.escape(filePath)
+    : filePath.replace(/["\\]/g, '\\$&');
+  const loraCard = document.querySelector(`.model-card[data-filepath="${escapedPath}"]`);
   if (!loraCard) return;
 
   const metaData = JSON.parse(loraCard.dataset.meta);

templates/components/batch_import_modal.html

@@ -0,0 +1,206 @@
+<div id="batchImportModal" class="modal">
+    <div class="modal-content">
+        <div class="modal-header">
+            <button class="close" onclick="modalManager.closeModal('batchImportModal')">&times;</button>
+            <h2>{{ t('recipes.batchImport.title') }}</h2>
+        </div>
+        
+        <!-- Step 1: Input Selection -->
+        <div class="batch-import-step" id="batchInputStep">
+            <div class="import-mode-toggle">
+                <button class="toggle-btn active" data-mode="urls" onclick="batchImportManager.toggleInputMode('urls')">
+                    <i class="fas fa-link"></i> {{ t('recipes.batchImport.urlList') }}
+                </button>
+                <button class="toggle-btn" data-mode="directory" onclick="batchImportManager.toggleInputMode('directory')">
+                    <i class="fas fa-folder"></i> {{ t('recipes.batchImport.directory') }}
+                </button>
+            </div>
+            
+            <!-- URL List Section -->
+            <div class="import-section" id="urlListSection">
+                <p class="section-description">{{ t('recipes.batchImport.urlDescription') }}</p>
+                <div class="input-group">
+                    <label for="batchUrlInput">{{ t('recipes.batchImport.urlsLabel') }}</label>
+                    <textarea id="batchUrlInput" rows="8" placeholder="{{ t('recipes.batchImport.urlsPlaceholder') }}"></textarea>
+                    <div class="input-hint">
+                        <i class="fas fa-info-circle"></i>
+                        {{ t('recipes.batchImport.urlsHint') }}
+                    </div>
+                </div>
+            </div>
+            
+            <!-- Directory Section -->
+            <div class="import-section" id="directorySection" style="display: none;">
+                <p class="section-description">{{ t('recipes.batchImport.directoryDescription') }}</p>
+                <div class="input-group">
+                    <label for="batchDirectoryInput">{{ t('recipes.batchImport.directoryPath') }}</label>
+                    <div class="input-with-button">
+                        <input type="text" id="batchDirectoryInput" placeholder="{{ t('recipes.batchImport.directoryPlaceholder') }}" autocomplete="off">
+                        <button class="secondary-btn" onclick="batchImportManager.toggleDirectoryBrowser()">
+                            <i class="fas fa-folder-open"></i> {{ t('recipes.batchImport.browse') }}
+                        </button>
+                    </div>
+                </div>
+                
+                <!-- Directory Browser -->
+                <div class="directory-browser" id="batchDirectoryBrowser" style="display: none;">
+                    <div class="browser-header">
+                        <button class="back-btn" onclick="batchImportManager.navigateToParentDirectory()" title="{{ t('recipes.batchImport.backToParent') }}">
+                            <i class="fas fa-arrow-up"></i>
+                        </button>
+                        <div class="current-path" id="batchCurrentPath"></div>
+                    </div>
+                    <div class="browser-content">
+                        <div class="browser-section">
+                            <div class="section-label"><i class="fas fa-folder"></i> {{ t('recipes.batchImport.folders') }}</div>
+                            <div class="folder-list" id="batchFolderList"></div>
+                        </div>
+                        <div class="browser-section">
+                            <div class="section-label"><i class="fas fa-image"></i> {{ t('recipes.batchImport.imageFiles') }}</div>
+                            <div class="file-list" id="batchFileList"></div>
+                        </div>
+                    </div>
+                    <div class="browser-footer">
+                        <div class="stats">
+                            <span id="batchDirectoryCount">0</span> {{ t('recipes.batchImport.folders') }}, 
+                            <span id="batchImageCount">0</span> {{ t('recipes.batchImport.images') }}
+                        </div>
+                        <button class="primary-btn" onclick="batchImportManager.selectCurrentDirectory()">
+                            <i class="fas fa-check"></i> {{ t('recipes.batchImport.selectFolder') }}
+                        </button>
+                    </div>
+                </div>
+                
+                <div class="checkbox-group">
+                    <label class="checkbox-label">
+                        <input type="checkbox" id="batchRecursiveCheck" checked>
+                        <span class="checkmark"></span>
+                        {{ t('recipes.batchImport.recursive') }}
+                    </label>
+                </div>
+            </div>
+            
+            <!-- Common Options -->
+            <div class="batch-options">
+                <div class="input-group">
+                    <label for="batchTagsInput">{{ t('recipes.batchImport.tagsOptional') }}</label>
+                    <input type="text" id="batchTagsInput" placeholder="{{ t('recipes.batchImport.tagsPlaceholder') }}">
+                    <div class="input-hint">
+                        <i class="fas fa-info-circle"></i>
+                        {{ t('recipes.batchImport.tagsHint') }}
+                    </div>
+                </div>
+                
+                <div class="checkbox-group">
+                    <label class="checkbox-label">
+                        <input type="checkbox" id="batchSkipNoMetadata">
+                        <span class="checkmark"></span>
+                        {{ t('recipes.batchImport.skipNoMetadata') }}
+                    </label>
+                </div>
+            </div>
+            
+            <div class="modal-actions">
+                <button class="secondary-btn" onclick="modalManager.closeModal('batchImportModal')">{{ t('common.actions.cancel') }}</button>
+                <button class="primary-btn" id="batchImportStartBtn" onclick="batchImportManager.startImport()">
+                    <i class="fas fa-play"></i> {{ t('recipes.batchImport.start') }}
+                </button>
+            </div>
+        </div>
+        
+        <!-- Step 2: Progress -->
+        <div class="batch-import-step" id="batchProgressStep" style="display: none;">
+            <div class="batch-progress-container">
+                <div class="progress-header">
+                    <div class="progress-status">
+                        <span class="status-icon"><i class="fas fa-spinner fa-spin"></i></span>
+                        <span class="status-text" id="batchStatusText">{{ t('recipes.batchImport.importing') }}</span>
+                    </div>
+                    <div class="progress-percentage" id="batchProgressPercent">0%</div>
+                </div>
+                
+                <div class="progress-bar-container">
+                    <div class="progress-bar" id="batchProgressBar" style="width: 0%"></div>
+                </div>
+                
+                <div class="progress-stats">
+                    <div class="stat-item">
+                        <span class="stat-label">{{ t('recipes.batchImport.total') }}</span>
+                        <span class="stat-value" id="batchTotalCount">0</span>
+                    </div>
+                    <div class="stat-item success">
+                        <span class="stat-label">{{ t('recipes.batchImport.success') }}</span>
+                        <span class="stat-value" id="batchSuccessCount">0</span>
+                    </div>
+                    <div class="stat-item failed">
+                        <span class="stat-label">{{ t('recipes.batchImport.failed') }}</span>
+                        <span class="stat-value" id="batchFailedCount">0</span>
+                    </div>
+                    <div class="stat-item skipped">
+                        <span class="stat-label">{{ t('recipes.batchImport.skipped') }}</span>
+                        <span class="stat-value" id="batchSkippedCount">0</span>
+                    </div>
+                </div>
+                
+                <div class="current-item" id="batchCurrentItemContainer">
+                    <span class="current-item-label">{{ t('recipes.batchImport.current') }}</span>
+                    <span class="current-item-name" id="batchCurrentItem">-</span>
+                </div>
+            </div>
+            
+            <div class="modal-actions">
+                <button class="secondary-btn" id="batchCancelBtn" onclick="batchImportManager.cancelImport()">
+                    <i class="fas fa-stop"></i> {{ t('recipes.batchImport.cancel') }}
+                </button>
+            </div>
+        </div>
+        
+        <!-- Step 3: Results -->
+        <div class="batch-import-step" id="batchResultsStep" style="display: none;">
+            <div class="batch-results-container">
+                <div class="results-header" id="batchResultsHeader">
+                    <div class="results-icon">
+                        <i class="fas fa-check-circle"></i>
+                    </div>
+                    <div class="results-title">{{ t('recipes.batchImport.completed') }}</div>
+                </div>
+                
+                <div class="results-summary">
+                    <div class="result-card total">
+                        <span class="result-label">{{ t('recipes.batchImport.total') }}</span>
+                        <span class="result-value" id="resultsTotal">0</span>
+                    </div>
+                    <div class="result-card success">
+                        <span class="result-label">{{ t('recipes.batchImport.success') }}</span>
+                        <span class="result-value" id="resultsSuccess">0</span>
+                    </div>
+                    <div class="result-card failed">
+                        <span class="result-label">{{ t('recipes.batchImport.failed') }}</span>
+                        <span class="result-value" id="resultsFailed">0</span>
+                    </div>
+                    <div class="result-card skipped">
+                        <span class="result-label">{{ t('recipes.batchImport.skipped') }}</span>
+                        <span class="result-value" id="resultsSkipped">0</span>
+                    </div>
+                </div>
+                
+                <div class="results-details" id="batchResultsDetails">
+                    <div class="details-toggle" onclick="batchImportManager.toggleResultsDetails()">
+                        <i class="fas fa-chevron-down" id="resultsToggleIcon"></i>
+                        <span>{{ t('recipes.batchImport.viewDetails') }}</span>
+                    </div>
+                    <div class="details-list" id="batchDetailsList" style="display: none;">
+                        <!-- Details will be populated dynamically -->
+                    </div>
+                </div>
+            </div>
+            
+            <div class="modal-actions">
+                <button class="secondary-btn" onclick="batchImportManager.closeAndReset()">{{ t('common.actions.close') }}</button>
+                <button class="primary-btn" onclick="batchImportManager.startNewImport()">
+                    <i class="fas fa-plus"></i> {{ t('recipes.batchImport.newImport') }}
+                </button>
+            </div>
+        </div>
+    </div>
+</div>

templates/recipes.html

@@ -7,10 +7,12 @@
 <link rel="stylesheet" href="/loras_static/css/components/card.css?v={{ version }}">
 <link rel="stylesheet" href="/loras_static/css/components/recipe-modal.css?v={{ version }}">
 <link rel="stylesheet" href="/loras_static/css/components/import-modal.css?v={{ version }}">
+<link rel="stylesheet" href="/loras_static/css/components/batch-import-modal.css?v={{ version }}">
 {% endblock %}
 
 {% block additional_components %}
 {% include 'components/import_modal.html' %}
+{% include 'components/batch_import_modal.html' %}
 {% include 'components/recipe_modal.html' %}
 
 <div id="recipeContextMenu" class="context-menu" style="display: none;">
@@ -85,6 +87,10 @@
                 <button onclick="importManager.showImportModal()"><i class="fas fa-file-import"></i> {{
                     t('recipes.controls.import.action') }}</button>
             </div>
+            <div title="{{ t('recipes.batchImport.title') }}" class="control-group">
+                <button onclick="batchImportManager.showModal()"><i class="fas fa-layer-group"></i> {{
+                    t('recipes.batchImport.action') }}</button>
+            </div>
             <div class="control-group" title="{{ t('loras.controls.bulk.title') }}">
                 <button id="bulkOperationsBtn" data-action="bulk" title="{{ t('loras.controls.bulk.title') }}">
                     <i class="fas fa-th-large"></i> <span><span>{{ t('loras.controls.bulk.action') }}</span>

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

@@ -156,4 +156,542 @@ describe('AutoComplete widget interactions', () => {
     expect(highlighted).toContain('detail');
     expect(highlighted).not.toMatch(/beta<\/span>/i);
   });
+
+  it('handles arrow key navigation with virtual scrolling', async () => {
+    vi.useFakeTimers();
+
+    const mockItems = Array.from({ length: 50 }, (_, i) => `model_${i.toString().padStart(2, '0')}.safetensors`);
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, relative_paths: mockItems }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('model');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'loras', {
+      debounceDelay: 0,
+      showPreview: false,
+      enableVirtualScroll: true,
+      itemHeight: 40,
+      visibleItems: 15,
+      pageSize: 20,
+    });
+
+    input.value = 'model';
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+
+    await vi.runAllTimersAsync();
+    await Promise.resolve();
+
+    expect(autoComplete.items.length).toBeGreaterThan(0);
+    expect(autoComplete.selectedIndex).toBe(0);
+
+    const initialSelectedEl = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
+    expect(initialSelectedEl).toBeDefined();
+
+    const arrowDownEvent = new KeyboardEvent('keydown', { key: 'ArrowDown', bubbles: true });
+    input.dispatchEvent(arrowDownEvent);
+
+    expect(autoComplete.selectedIndex).toBe(1);
+
+    const secondSelectedEl = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
+    expect(secondSelectedEl).toBeDefined();
+    expect(secondSelectedEl?.dataset.index).toBe('1');
+
+    const arrowUpEvent = new KeyboardEvent('keydown', { key: 'ArrowUp', bubbles: true });
+    input.dispatchEvent(arrowUpEvent);
+
+    expect(autoComplete.selectedIndex).toBe(0);
+
+    const firstSelectedElAgain = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
+    expect(firstSelectedElAgain).toBeDefined();
+    expect(firstSelectedElAgain?.dataset.index).toBe('0');
+  });
+
+  it('maintains selection when scrolling to invisible items', async () => {
+    vi.useFakeTimers();
+
+    const mockItems = Array.from({ length: 100 }, (_, i) => `item_${i.toString().padStart(3, '0')}.safetensors`);
+
+    fetchApiMock.mockResolvedValue({
+      json: () => Promise.resolve({ success: true, relative_paths: mockItems }),
+    });
+
+    caretHelperInstance.getBeforeCursor.mockReturnValue('item');
+    caretHelperInstance.getCursorOffset.mockReturnValue({ left: 15, top: 25 });
+
+    const input = document.createElement('textarea');
+    input.style.width = '400px';
+    input.style.height = '200px';
+    document.body.append(input);
+
+    const { AutoComplete } = await import(AUTOCOMPLETE_MODULE);
+    const autoComplete = new AutoComplete(input, 'loras', {
+      debounceDelay: 0,
+      showPreview: false,
+      enableVirtualScroll: true,
+      itemHeight: 40,
+      visibleItems: 15,
+      pageSize: 20,
+    });
+
+    input.value = 'item';
+    input.dispatchEvent(new Event('input', { bubbles: true }));
+
+    await vi.runAllTimersAsync();
+    await Promise.resolve();
+
+    expect(autoComplete.items.length).toBeGreaterThan(0);
+
+    autoComplete.selectedIndex = 14;
+
+    const scrollTopBefore = autoComplete.scrollContainer?.scrollTop || 0;
+
+    const arrowDownEvent = new KeyboardEvent('keydown', { key: 'ArrowDown', bubbles: true });
+    input.dispatchEvent(arrowDownEvent);
+
+    await vi.runAllTimersAsync();
+    await Promise.resolve();
+
+    expect(autoComplete.selectedIndex).toBe(15);
+
+    const selectedEl = autoComplete.contentContainer?.querySelector('.comfy-autocomplete-item-selected');
+    expect(selectedEl).toBeDefined();
+    expect(selectedEl?.dataset.index).toBe('15');
+
+    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/frontend/versionDetection.test.js

@@ -0,0 +1,75 @@
+import { describe, it, expect } from 'vitest';
+
+describe('Version Detection Logic', () => {
+    const parseVersion = (versionStr) => {
+        if (!versionStr || typeof versionStr !== 'string') {
+            return [0, 0, 0];
+        }
+        
+        const cleanVersion = versionStr.replace(/^[vV]/, '').split('-')[0];
+        const parts = cleanVersion.split('.').map(part => parseInt(part, 10) || 0);
+        
+        while (parts.length < 3) {
+            parts.push(0);
+        }
+        
+        return parts;
+    };
+    
+    const compareVersions = (version1, version2) => {
+        const v1 = typeof version1 === 'string' ? parseVersion(version1) : version1;
+        const v2 = typeof version2 === 'string' ? parseVersion(version2) : version2;
+        
+        for (let i = 0; i < 3; i++) {
+            if (v1[i] > v2[i]) return 1;
+            if (v1[i] < v2[i]) return -1;
+        }
+        
+        return 0;
+    };
+    
+    const MIN_VERSION_FOR_ACTION_BAR = [1, 33, 9];
+    
+    const supportsActionBarButtons = (version) => {
+        return compareVersions(version, MIN_VERSION_FOR_ACTION_BAR) >= 0;
+    };
+    
+    it('should parse version strings correctly', () => {
+        expect(parseVersion('1.33.9')).toEqual([1, 33, 9]);
+        expect(parseVersion('v1.33.9')).toEqual([1, 33, 9]);
+        expect(parseVersion('1.33.9-beta')).toEqual([1, 33, 9]);
+        expect(parseVersion('1.33')).toEqual([1, 33, 0]);
+        expect(parseVersion('1')).toEqual([1, 0, 0]);
+        expect(parseVersion('')).toEqual([0, 0, 0]);
+        expect(parseVersion(null)).toEqual([0, 0, 0]);
+    });
+    
+    it('should compare versions correctly', () => {
+        expect(compareVersions('1.33.9', '1.33.9')).toBe(0);
+        expect(compareVersions('1.33.10', '1.33.9')).toBe(1);
+        expect(compareVersions('1.34.0', '1.33.9')).toBe(1);
+        expect(compareVersions('2.0.0', '1.33.9')).toBe(1);
+        expect(compareVersions('1.33.8', '1.33.9')).toBe(-1);
+        expect(compareVersions('1.32.0', '1.33.9')).toBe(-1);
+        expect(compareVersions('0.9.9', '1.33.9')).toBe(-1);
+    });
+    
+    it('should return false for versions below 1.33.9', () => {
+        expect(supportsActionBarButtons('1.33.8')).toBe(false);
+        expect(supportsActionBarButtons('1.32.0')).toBe(false);
+        expect(supportsActionBarButtons('0.9.9')).toBe(false);
+    });
+    
+    it('should return true for versions 1.33.9 and above', () => {
+        expect(supportsActionBarButtons('1.33.9')).toBe(true);
+        expect(supportsActionBarButtons('1.33.10')).toBe(true);
+        expect(supportsActionBarButtons('1.34.0')).toBe(true);
+        expect(supportsActionBarButtons('2.0.0')).toBe(true);
+    });
+    
+    it('should handle edge cases in version parsing', () => {
+        expect(supportsActionBarButtons('v1.33.9')).toBe(true);
+        expect(supportsActionBarButtons('1.33.9-rc.1')).toBe(true);
+        expect(supportsActionBarButtons('1.33.9-beta')).toBe(true);
+    });
+});

tests/routes/test_recipe_routes.py

@@ -1,4 +1,5 @@
 """Integration smoke tests for the recipe route stack."""
+
 from __future__ import annotations
 
 import json
@@ -94,19 +95,25 @@ class StubAnalysisService:
         self._recipe_parser_factory = None
         StubAnalysisService.instances.append(self)
 
-    async def analyze_uploaded_image(self, *, image_bytes: bytes | None, recipe_scanner) -> SimpleNamespace:  # noqa: D401 - mirrors real signature
+    async def analyze_uploaded_image(
+        self, *, image_bytes: bytes | None, recipe_scanner
+    ) -> SimpleNamespace:  # noqa: D401 - mirrors real signature
         if self.raise_for_uploaded:
             raise self.raise_for_uploaded
         self.upload_calls.append(image_bytes or b"")
         return self.result
 
-    async def analyze_remote_image(self, *, url: Optional[str], recipe_scanner, civitai_client) -> SimpleNamespace:  # noqa: D401
+    async def analyze_remote_image(
+        self, *, url: Optional[str], recipe_scanner, civitai_client
+    ) -> SimpleNamespace:  # noqa: D401
         if self.raise_for_remote:
             raise self.raise_for_remote
         self.remote_calls.append(url)
         return self.result
 
-    async def analyze_local_image(self, *, file_path: Optional[str], recipe_scanner) -> SimpleNamespace:  # noqa: D401
+    async def analyze_local_image(
+        self, *, file_path: Optional[str], recipe_scanner
+    ) -> SimpleNamespace:  # noqa: D401
         if self.raise_for_local:
             raise self.raise_for_local
         self.local_calls.append(file_path)
@@ -125,11 +132,23 @@ class StubPersistenceService:
         self.save_calls: List[Dict[str, Any]] = []
         self.delete_calls: List[str] = []
         self.move_calls: List[Dict[str, str]] = []
-        self.save_result = SimpleNamespace(payload={"success": True, "recipe_id": "stub-id"}, status=200)
+        self.save_result = SimpleNamespace(
+            payload={"success": True, "recipe_id": "stub-id"}, status=200
+        )
         self.delete_result = SimpleNamespace(payload={"success": True}, status=200)
         StubPersistenceService.instances.append(self)
 
-    async def save_recipe(self, *, recipe_scanner, image_bytes, image_base64, name, tags, metadata, extension=None) -> SimpleNamespace:  # noqa: D401
+    async def save_recipe(
+        self,
+        *,
+        recipe_scanner,
+        image_bytes,
+        image_base64,
+        name,
+        tags,
+        metadata,
+        extension=None,
+    ) -> SimpleNamespace:  # noqa: D401
         self.save_calls.append(
             {
                 "recipe_scanner": recipe_scanner,
@@ -148,22 +167,42 @@ class StubPersistenceService:
         await recipe_scanner.remove_recipe(recipe_id)
         return self.delete_result
 
-    async def move_recipe(self, *, recipe_scanner, recipe_id: str, target_path: str) -> SimpleNamespace:  # noqa: D401
+    async def move_recipe(
+        self, *, recipe_scanner, recipe_id: str, target_path: str
+    ) -> SimpleNamespace:  # noqa: D401
         self.move_calls.append({"recipe_id": recipe_id, "target_path": target_path})
         return SimpleNamespace(
-            payload={"success": True, "recipe_id": recipe_id, "new_file_path": target_path}, status=200
+            payload={
+                "success": True,
+                "recipe_id": recipe_id,
+                "new_file_path": target_path,
+            },
+            status=200,
         )
 
-    async def update_recipe(self, *, recipe_scanner, recipe_id: str, updates: Dict[str, Any]) -> SimpleNamespace:  # pragma: no cover - unused by smoke tests
-        return SimpleNamespace(payload={"success": True, "recipe_id": recipe_id, "updates": updates}, status=200)
+    async def update_recipe(
+        self, *, recipe_scanner, recipe_id: str, updates: Dict[str, Any]
+    ) -> SimpleNamespace:  # pragma: no cover - unused by smoke tests
+        return SimpleNamespace(
+            payload={"success": True, "recipe_id": recipe_id, "updates": updates},
+            status=200,
+        )
 
-    async def reconnect_lora(self, *, recipe_scanner, recipe_id: str, lora_index: int, target_name: str) -> SimpleNamespace:  # pragma: no cover
+    async def reconnect_lora(
+        self, *, recipe_scanner, recipe_id: str, lora_index: int, target_name: str
+    ) -> SimpleNamespace:  # pragma: no cover
         return SimpleNamespace(payload={"success": True}, status=200)
 
-    async def bulk_delete(self, *, recipe_scanner, recipe_ids: List[str]) -> SimpleNamespace:  # pragma: no cover
-        return SimpleNamespace(payload={"success": True, "deleted": recipe_ids}, status=200)
+    async def bulk_delete(
+        self, *, recipe_scanner, recipe_ids: List[str]
+    ) -> SimpleNamespace:  # pragma: no cover
+        return SimpleNamespace(
+            payload={"success": True, "deleted": recipe_ids}, status=200
+        )
 
-    async def save_recipe_from_widget(self, *, recipe_scanner, metadata: Dict[str, Any], image_bytes: bytes) -> SimpleNamespace:  # pragma: no cover
+    async def save_recipe_from_widget(
+        self, *, recipe_scanner, metadata: Dict[str, Any], image_bytes: bytes
+    ) -> SimpleNamespace:  # pragma: no cover
         return SimpleNamespace(payload={"success": True}, status=200)
 
 
@@ -176,7 +215,11 @@ class StubSharingService:
         self.share_calls: List[str] = []
         self.download_calls: List[str] = []
         self.share_result = SimpleNamespace(
-            payload={"success": True, "download_url": "/share/stub", "filename": "recipe.png"},
+            payload={
+                "success": True,
+                "download_url": "/share/stub",
+                "filename": "recipe.png",
+            },
             status=200,
         )
         self.download_info = SimpleNamespace(file_path="", download_filename="")
@@ -186,7 +229,9 @@ class StubSharingService:
         self.share_calls.append(recipe_id)
         return self.share_result
 
-    async def prepare_download(self, *, recipe_scanner, recipe_id: str) -> SimpleNamespace:
+    async def prepare_download(
+        self, *, recipe_scanner, recipe_id: str
+    ) -> SimpleNamespace:
         self.download_calls.append(recipe_id)
         return self.download_info
 
@@ -214,7 +259,9 @@ class StubCivitaiClient:
 
 
 @asynccontextmanager
-async def recipe_harness(monkeypatch, tmp_path: Path) -> AsyncIterator[RecipeRouteHarness]:
+async def recipe_harness(
+    monkeypatch, tmp_path: Path
+) -> AsyncIterator[RecipeRouteHarness]:
     """Context manager that yields a fully wired recipe route harness."""
 
     StubAnalysisService.instances.clear()
@@ -237,8 +284,12 @@ async def recipe_harness(monkeypatch, tmp_path: Path) -> AsyncIterator[RecipeRou
 
     monkeypatch.setattr(ServiceRegistry, "get_recipe_scanner", fake_get_recipe_scanner)
     monkeypatch.setattr(ServiceRegistry, "get_civitai_client", fake_get_civitai_client)
-    monkeypatch.setattr(base_recipe_routes, "RecipeAnalysisService", StubAnalysisService)
-    monkeypatch.setattr(base_recipe_routes, "RecipePersistenceService", StubPersistenceService)
+    monkeypatch.setattr(
+        base_recipe_routes, "RecipeAnalysisService", StubAnalysisService
+    )
+    monkeypatch.setattr(
+        base_recipe_routes, "RecipePersistenceService", StubPersistenceService
+    )
     monkeypatch.setattr(base_recipe_routes, "RecipeSharingService", StubSharingService)
     monkeypatch.setattr(base_recipe_routes, "get_downloader", fake_get_downloader)
     monkeypatch.setattr(config, "loras_roots", [str(tmp_path)], raising=False)
@@ -294,7 +345,9 @@ async def test_list_recipes_provides_file_urls(monkeypatch, tmp_path: Path) -> N
 async def test_save_and_delete_recipe_round_trip(monkeypatch, tmp_path: Path) -> None:
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         form = FormData()
-        form.add_field("image", b"stub", filename="sample.png", content_type="image/png")
+        form.add_field(
+            "image", b"stub", filename="sample.png", content_type="image/png"
+        )
         form.add_field("name", "Test Recipe")
         form.add_field("tags", json.dumps(["tag-a"]))
         form.add_field("metadata", json.dumps({"loras": []}))
@@ -312,7 +365,9 @@ async def test_save_and_delete_recipe_round_trip(monkeypatch, tmp_path: Path) ->
         assert save_payload["recipe_id"] == "saved-id"
         assert harness.persistence.save_calls[-1]["name"] == "Test Recipe"
 
-        harness.persistence.delete_result = SimpleNamespace(payload={"success": True}, status=200)
+        harness.persistence.delete_result = SimpleNamespace(
+            payload={"success": True}, status=200
+        )
 
         delete_response = await harness.client.delete("/api/lm/recipe/saved-id")
         delete_payload = await delete_response.json()
@@ -326,14 +381,20 @@ async def test_move_recipe_invokes_persistence(monkeypatch, tmp_path: Path) -> N
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         response = await harness.client.post(
             "/api/lm/recipe/move",
-            json={"recipe_id": "move-me", "target_path": str(tmp_path / "recipes" / "subdir")},
+            json={
+                "recipe_id": "move-me",
+                "target_path": str(tmp_path / "recipes" / "subdir"),
+            },
         )
 
         payload = await response.json()
         assert response.status == 200
         assert payload["recipe_id"] == "move-me"
         assert harness.persistence.move_calls == [
-            {"recipe_id": "move-me", "target_path": str(tmp_path / "recipes" / "subdir")}
+            {
+                "recipe_id": "move-me",
+                "target_path": str(tmp_path / "recipes" / "subdir"),
+            }
         ]
 
 
@@ -348,7 +409,10 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
     async def fake_get_default_metadata_provider():
         return Provider()
 
-    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr(
+        "py.recipes.enrichment.get_default_metadata_provider",
+        fake_get_default_metadata_provider,
+    )
 
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         resources = [
@@ -397,7 +461,9 @@ async def test_import_remote_recipe(monkeypatch, tmp_path: Path) -> None:
         assert harness.downloader.urls == ["https://example.com/images/1"]
 
 
-async def test_import_remote_recipe_falls_back_to_request_base_model(monkeypatch, tmp_path: Path) -> None:
+async def test_import_remote_recipe_falls_back_to_request_base_model(
+    monkeypatch, tmp_path: Path
+) -> None:
     provider_calls: list[str | int] = []
 
     class Provider:
@@ -408,7 +474,10 @@ async def test_import_remote_recipe_falls_back_to_request_base_model(monkeypatch
     async def fake_get_default_metadata_provider():
         return Provider()
 
-    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr(
+        "py.recipes.enrichment.get_default_metadata_provider",
+        fake_get_default_metadata_provider,
+    )
 
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         resources = [
@@ -444,13 +513,16 @@ async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
     async def fake_get_default_metadata_provider():
         return SimpleNamespace(get_model_version_info=lambda id: ({}, None))
 
-    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr(
+        "py.recipes.enrichment.get_default_metadata_provider",
+        fake_get_default_metadata_provider,
+    )
 
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         harness.civitai.image_info["12345"] = {
             "id": 12345,
             "url": "https://image.civitai.com/x/y/original=true/video.mp4",
-            "type": "video"
+            "type": "video",
         }
 
         response = await harness.client.get(
@@ -469,7 +541,7 @@ async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
 
         # Verify downloader was called with rewritten URL
         assert "transcode=true" in harness.downloader.urls[0]
-        
+
         # Verify persistence was called with correct extension
         call = harness.persistence.save_calls[-1]
         assert call["extension"] == ".mp4"
@@ -477,7 +549,9 @@ async def test_import_remote_video_recipe(monkeypatch, tmp_path: Path) -> None:
 
 async def test_analyze_uploaded_image_error_path(monkeypatch, tmp_path: Path) -> None:
     async with recipe_harness(monkeypatch, tmp_path) as harness:
-        harness.analysis.raise_for_uploaded = RecipeValidationError("No image data provided")
+        harness.analysis.raise_for_uploaded = RecipeValidationError(
+            "No image data provided"
+        )
 
         form = FormData()
         form.add_field("image", b"", filename="empty.png", content_type="image/png")
@@ -504,7 +578,11 @@ async def test_share_and_download_recipe(monkeypatch, tmp_path: Path) -> None:
         }
 
         harness.sharing.share_result = SimpleNamespace(
-            payload={"success": True, "download_url": "/api/share", "filename": "share.png"},
+            payload={
+                "success": True,
+                "download_url": "/api/share",
+                "filename": "share.png",
+            },
             status=200,
         )
         harness.sharing.download_info = SimpleNamespace(
@@ -519,15 +597,24 @@ async def test_share_and_download_recipe(monkeypatch, tmp_path: Path) -> None:
         assert share_payload["filename"] == "share.png"
         assert harness.sharing.share_calls == [recipe_id]
 
-        download_response = await harness.client.get(f"/api/lm/recipe/{recipe_id}/share/download")
+        download_response = await harness.client.get(
+            f"/api/lm/recipe/{recipe_id}/share/download"
+        )
         body = await download_response.read()
 
         assert download_response.status == 200
-        assert download_response.headers["Content-Disposition"] == 'attachment; filename="share.png"'
+        assert (
+            download_response.headers["Content-Disposition"]
+            == 'attachment; filename="share.png"'
+        )
         assert body == b"stub"
 
         download_path.unlink(missing_ok=True)
-async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path) -> None:
+
+
+async def test_import_remote_recipe_merges_metadata(
+    monkeypatch, tmp_path: Path
+) -> None:
     # 1. Mock Metadata Provider
     class Provider:
         async def get_model_version_info(self, model_version_id):
@@ -536,22 +623,25 @@ async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path)
     async def fake_get_default_metadata_provider():
         return Provider()
 
-    monkeypatch.setattr("py.recipes.enrichment.get_default_metadata_provider", fake_get_default_metadata_provider)
+    monkeypatch.setattr(
+        "py.recipes.enrichment.get_default_metadata_provider",
+        fake_get_default_metadata_provider,
+    )
 
     # 2. Mock ExifUtils to return some embedded metadata
     class MockExifUtils:
         @staticmethod
         def extract_image_metadata(path):
-            return "Recipe metadata: " + json.dumps({
-                "gen_params": {"prompt": "from embedded", "seed": 123}
-            })
+            return "Recipe metadata: " + json.dumps(
+                {"gen_params": {"prompt": "from embedded", "seed": 123}}
+            )
 
     monkeypatch.setattr(recipe_handlers, "ExifUtils", MockExifUtils)
 
     # 3. Mock Parser Factory for StubAnalysisService
     class MockParser:
         async def parse_metadata(self, raw, recipe_scanner=None):
-            return json.loads(raw[len("Recipe metadata: "):])
+            return json.loads(raw[len("Recipe metadata: ") :])
 
     class MockFactory:
         def create_parser(self, raw):
@@ -562,12 +652,12 @@ async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path)
     # 4. Setup Harness and run test
     async with recipe_harness(monkeypatch, tmp_path) as harness:
         harness.analysis._recipe_parser_factory = MockFactory()
-        
+
         # Civitai meta via image_info
         harness.civitai.image_info["1"] = {
             "id": 1,
             "url": "https://example.com/images/1.jpg",
-            "meta": {"prompt": "from civitai", "cfg": 7.0}
+            "meta": {"prompt": "from civitai", "cfg": 7.0},
         }
 
         resources = []
@@ -583,11 +673,11 @@ async def test_import_remote_recipe_merges_metadata(monkeypatch, tmp_path: Path)
 
         payload = await response.json()
         assert response.status == 200
-        
+
         call = harness.persistence.save_calls[-1]
         metadata = call["metadata"]
         gen_params = metadata["gen_params"]
-        
+
         assert gen_params["seed"] == 123
 
 
@@ -619,3 +709,142 @@ async def test_get_recipe_syntax(monkeypatch, tmp_path: Path) -> None:
         response_404 = await harness.client.get("/api/lm/recipe/non-existent/syntax")
         assert response_404.status == 404
 
+
+async def test_batch_import_start_success(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipes/batch-import/start",
+            json={
+                "items": [
+                    {"source": "https://example.com/image1.png"},
+                    {"source": "https://example.com/image2.png"},
+                ],
+                "tags": ["batch", "import"],
+                "skip_no_metadata": True,
+            },
+        )
+        payload = await response.json()
+        assert response.status == 200
+        assert payload["success"] is True
+        assert "operation_id" in payload
+
+
+async def test_batch_import_start_empty_items(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipes/batch-import/start",
+            json={"items": [], "tags": []},
+        )
+        payload = await response.json()
+        assert response.status == 400
+        assert payload["success"] is False
+        assert "No items provided" in payload["error"]
+
+
+async def test_batch_import_start_missing_source(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipes/batch-import/start",
+            json={"items": [{"source": ""}]},
+        )
+        payload = await response.json()
+        assert response.status == 400
+        assert payload["success"] is False
+        assert "source" in payload["error"].lower()
+
+
+async def test_batch_import_start_already_running(monkeypatch, tmp_path: Path) -> None:
+    import asyncio
+
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        original_analyze = harness.analysis.analyze_remote_image
+
+        async def slow_analyze(*, url, recipe_scanner, civitai_client):
+            await asyncio.sleep(0.5)
+            return await original_analyze(
+                url=url, recipe_scanner=recipe_scanner, civitai_client=civitai_client
+            )
+
+        harness.analysis.analyze_remote_image = slow_analyze
+
+        items = [{"source": f"https://example.com/image{i}.png"} for i in range(10)]
+
+        response1 = await harness.client.post(
+            "/api/lm/recipes/batch-import/start",
+            json={"items": items},
+        )
+        assert response1.status == 200
+
+        payload1 = await response1.json()
+        assert payload1["success"] is True
+
+        await asyncio.sleep(0.1)
+
+        response2 = await harness.client.post(
+            "/api/lm/recipes/batch-import/start",
+            json={"items": [{"source": "https://example.com/other.png"}]},
+        )
+        payload2 = await response2.json()
+        assert response2.status == 409
+        assert "already in progress" in payload2["error"].lower()
+
+
+async def test_batch_import_get_progress_not_found(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.get(
+            "/api/lm/recipes/batch-import/progress",
+            params={"operation_id": "nonexistent-id"},
+        )
+        payload = await response.json()
+        assert response.status == 404
+        assert payload["success"] is False
+
+
+async def test_batch_import_get_progress_missing_id(
+    monkeypatch, tmp_path: Path
+) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.get("/api/lm/recipes/batch-import/progress")
+        payload = await response.json()
+        assert response.status == 400
+        assert payload["success"] is False
+
+
+async def test_batch_import_cancel_success(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        start_response = await harness.client.post(
+            "/api/lm/recipes/batch-import/start",
+            json={"items": [{"source": "https://example.com/image.png"}]},
+        )
+        start_payload = await start_response.json()
+        operation_id = start_payload["operation_id"]
+
+        cancel_response = await harness.client.post(
+            "/api/lm/recipes/batch-import/cancel",
+            json={"operation_id": operation_id},
+        )
+        cancel_payload = await cancel_response.json()
+        assert cancel_response.status == 200
+        assert cancel_payload["success"] is True
+
+
+async def test_batch_import_cancel_not_found(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipes/batch-import/cancel",
+            json={"operation_id": "nonexistent-id"},
+        )
+        payload = await response.json()
+        assert response.status == 404
+        assert payload["success"] is False
+
+
+async def test_batch_import_cancel_missing_id(monkeypatch, tmp_path: Path) -> None:
+    async with recipe_harness(monkeypatch, tmp_path) as harness:
+        response = await harness.client.post(
+            "/api/lm/recipes/batch-import/cancel",
+            json={},
+        )
+        payload = await response.json()
+        assert response.status == 400
+        assert payload["success"] is False

tests/services/test_batch_import_service.py

@@ -0,0 +1,597 @@
+"""Unit tests for BatchImportService."""
+
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import tempfile
+from dataclasses import dataclass
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Any, Dict, List, Optional
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+from py.services.batch_import_service import (
+    AdaptiveConcurrencyController,
+    BatchImportItem,
+    BatchImportProgress,
+    BatchImportService,
+    ImportItemType,
+    ImportStatus,
+)
+
+
+class MockWebSocketManager:
+    def __init__(self):
+        self.broadcasts: List[Dict[str, Any]] = []
+
+    async def broadcast(self, data: Dict[str, Any]):
+        self.broadcasts.append(data)
+
+
+@dataclass
+class MockAnalysisResult:
+    payload: Dict[str, Any]
+    status: int = 200
+
+
+class MockAnalysisService:
+    def __init__(self, results: Optional[Dict[str, MockAnalysisResult]] = None):
+        self.results = results or {}
+        self.call_count = 0
+        self.last_url = None
+        self.last_path = None
+
+    async def analyze_remote_image(self, *, url: str, recipe_scanner, civitai_client):
+        self.call_count += 1
+        self.last_url = url
+        if url in self.results:
+            return self.results[url]
+        return MockAnalysisResult({"error": "No metadata found", "loras": []})
+
+    async def analyze_local_image(self, *, file_path: str, recipe_scanner):
+        self.call_count += 1
+        self.last_path = file_path
+        if file_path in self.results:
+            return self.results[file_path]
+        return MockAnalysisResult({"error": "No metadata found", "loras": []})
+
+
+@dataclass
+class MockSaveResult:
+    payload: Dict[str, Any]
+    status: int = 200
+
+
+class MockPersistenceService:
+    def __init__(self, should_succeed: bool = True):
+        self.should_succeed = should_succeed
+        self.saved_recipes: List[Dict[str, Any]] = []
+        self.call_count = 0
+
+    async def save_recipe(
+        self,
+        *,
+        recipe_scanner,
+        image_bytes: Optional[bytes] = None,
+        image_base64: Optional[str] = None,
+        name: str,
+        tags: List[str],
+        metadata: Dict[str, Any],
+        extension: Optional[str] = None,
+    ):
+        self.call_count += 1
+        self.saved_recipes.append(
+            {
+                "name": name,
+                "tags": tags,
+                "metadata": metadata,
+            }
+        )
+        if self.should_succeed:
+            return MockSaveResult({"success": True, "id": f"recipe_{self.call_count}"})
+        return MockSaveResult({"success": False, "error": "Save failed"}, status=400)
+
+
+class TestAdaptiveConcurrencyController:
+    def test_initial_values(self):
+        controller = AdaptiveConcurrencyController()
+        assert controller.current_concurrency == 3
+        assert controller.min_concurrency == 1
+        assert controller.max_concurrency == 5
+
+    def test_custom_initial_values(self):
+        controller = AdaptiveConcurrencyController(
+            min_concurrency=2,
+            max_concurrency=10,
+            initial_concurrency=5,
+        )
+        assert controller.current_concurrency == 5
+        assert controller.min_concurrency == 2
+        assert controller.max_concurrency == 10
+
+    def test_increase_concurrency_on_success(self):
+        controller = AdaptiveConcurrencyController(initial_concurrency=3)
+        controller.record_result(duration=0.5, success=True)
+        assert controller.current_concurrency == 4
+
+    def test_do_not_exceed_max(self):
+        controller = AdaptiveConcurrencyController(
+            max_concurrency=5,
+            initial_concurrency=5,
+        )
+        controller.record_result(duration=0.5, success=True)
+        assert controller.current_concurrency == 5
+
+    def test_decrease_concurrency_on_failure(self):
+        controller = AdaptiveConcurrencyController(initial_concurrency=3)
+        controller.record_result(duration=1.0, success=False)
+        assert controller.current_concurrency == 2
+
+    def test_do_not_go_below_min(self):
+        controller = AdaptiveConcurrencyController(
+            min_concurrency=1,
+            initial_concurrency=1,
+        )
+        controller.record_result(duration=1.0, success=False)
+        assert controller.current_concurrency == 1
+
+    def test_slow_task_decreases_concurrency(self):
+        controller = AdaptiveConcurrencyController(initial_concurrency=3)
+        controller.record_result(duration=11.0, success=True)
+        assert controller.current_concurrency == 2
+
+    def test_fast_task_increases_concurrency(self):
+        controller = AdaptiveConcurrencyController(initial_concurrency=3)
+        controller.record_result(duration=0.5, success=True)
+        assert controller.current_concurrency == 4
+
+    def test_moderate_task_no_change(self):
+        controller = AdaptiveConcurrencyController(initial_concurrency=3)
+        controller.record_result(duration=5.0, success=True)
+        assert controller.current_concurrency == 3
+
+
+class TestBatchImportProgress:
+    def test_to_dict(self):
+        progress = BatchImportProgress(
+            operation_id="test-123",
+            total=10,
+            completed=5,
+            success=3,
+            failed=2,
+            skipped=0,
+            current_item="image.png",
+            status="running",
+        )
+        result = progress.to_dict()
+        assert result["operation_id"] == "test-123"
+        assert result["total"] == 10
+        assert result["completed"] == 5
+        assert result["success"] == 3
+        assert result["failed"] == 2
+        assert result["progress_percent"] == 50.0
+
+    def test_progress_percent_zero_total(self):
+        progress = BatchImportProgress(
+            operation_id="test-123",
+            total=0,
+        )
+        assert progress.to_dict()["progress_percent"] == 0
+
+
+class TestBatchImportItem:
+    def test_defaults(self):
+        item = BatchImportItem(
+            id="item-1",
+            source="https://example.com/image.png",
+            item_type=ImportItemType.URL,
+        )
+        assert item.status == ImportStatus.PENDING
+        assert item.error_message is None
+        assert item.recipe_name is None
+
+
+class TestBatchImportService:
+    @pytest.fixture
+    def mock_services(self):
+        ws_manager = MockWebSocketManager()
+        analysis_service = MockAnalysisService()
+        persistence_service = MockPersistenceService()
+        logger = logging.getLogger("test")
+        return ws_manager, analysis_service, persistence_service, logger
+
+    @pytest.fixture
+    def service(self, mock_services):
+        ws_manager, analysis_service, persistence_service, logger = mock_services
+        return BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+
+    def test_is_import_running_no_operations(self, service):
+        assert not service.is_import_running()
+
+    @pytest.mark.asyncio
+    async def test_start_batch_import_creates_operation(self, service):
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[{"source": "https://example.com/image.png"}],
+        )
+
+        assert operation_id is not None
+        assert service.is_import_running(operation_id)
+
+    @pytest.mark.asyncio
+    async def test_get_progress(self, service):
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[
+                {"source": "https://example.com/1.png"},
+                {"source": "https://example.com/2.png"},
+            ],
+        )
+
+        progress = service.get_progress(operation_id)
+        assert progress is not None
+        assert progress.total == 2
+        assert progress.status in ("pending", "running")
+
+    @pytest.mark.asyncio
+    async def test_cancel_import(self, service):
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[{"source": "https://example.com/image.png"}],
+        )
+
+        assert service.cancel_import(operation_id) is True
+        assert service.cancel_import("nonexistent") is False
+
+    @pytest.mark.asyncio
+    async def test_discover_images_non_recursive(self, service, tmp_path):
+        for i in range(3):
+            (tmp_path / f"image{i}.png").write_bytes(b"fake-image")
+
+        (tmp_path / "subdir").mkdir()
+        (tmp_path / "subdir" / "hidden.png").write_bytes(b"fake-image")
+
+        images = await service._discover_images(str(tmp_path), recursive=False)
+        assert len(images) == 3
+
+    @pytest.mark.asyncio
+    async def test_discover_images_recursive(self, service, tmp_path):
+        for i in range(2):
+            (tmp_path / f"image{i}.png").write_bytes(b"fake-image")
+
+        subdir = tmp_path / "subdir"
+        subdir.mkdir()
+        for i in range(2):
+            (subdir / f"nested{i}.jpg").write_bytes(b"fake-image")
+
+        images = await service._discover_images(str(tmp_path), recursive=True)
+        assert len(images) == 4
+
+    @pytest.mark.asyncio
+    async def test_discover_images_filters_by_extension(self, service, tmp_path):
+        (tmp_path / "image.png").write_bytes(b"fake-image")
+        (tmp_path / "image.jpg").write_bytes(b"fake-image")
+        (tmp_path / "image.webp").write_bytes(b"fake-image")
+        (tmp_path / "document.pdf").write_bytes(b"fake-doc")
+        (tmp_path / "script.py").write_bytes(b"print('hello')")
+
+        images = await service._discover_images(str(tmp_path), recursive=False)
+        assert len(images) == 3
+
+    @pytest.mark.asyncio
+    async def test_discover_images_invalid_directory(self, service):
+        from py.services.recipes.errors import RecipeValidationError
+
+        with pytest.raises(RecipeValidationError):
+            await service._discover_images("/nonexistent/path", recursive=False)
+
+    def test_is_supported_image(self, service):
+        assert service._is_supported_image("test.png") is True
+        assert service._is_supported_image("test.jpg") is True
+        assert service._is_supported_image("test.jpeg") is True
+        assert service._is_supported_image("test.webp") is True
+        assert service._is_supported_image("test.gif") is True
+        assert service._is_supported_image("test.bmp") is True
+        assert service._is_supported_image("test.pdf") is False
+        assert service._is_supported_image("test.txt") is False
+
+    @pytest.mark.asyncio
+    async def test_batch_import_processes_items(self, mock_services, tmp_path):
+        ws_manager, _, persistence_service, logger = mock_services
+
+        analysis_service = MockAnalysisService(
+            {
+                "https://example.com/valid.png": MockAnalysisResult(
+                    {
+                        "loras": [{"name": "test-lora", "weight": 1.0}],
+                        "base_model": "SD1.5",
+                        "gen_params": {"steps": 20},
+                    }
+                ),
+            }
+        )
+
+        service = BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+
+        recipe_scanner_getter = lambda: SimpleNamespace(
+            find_recipes_by_fingerprint=lambda x: [],
+            add_recipe=lambda x: None,
+        )
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[
+                {"source": "https://example.com/valid.png"},
+                {"source": "https://example.com/no-meta.png"},
+            ],
+            skip_no_metadata=True,
+        )
+
+        await asyncio.sleep(0.5)
+
+        progress = service.get_progress(operation_id)
+        assert progress is not None or persistence_service.call_count == 1
+
+    @pytest.mark.asyncio
+    async def test_start_directory_import(self, service, tmp_path):
+        for i in range(5):
+            (tmp_path / f"image{i}.png").write_bytes(b"fake-image")
+
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_directory_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            directory=str(tmp_path),
+            recursive=False,
+        )
+
+        progress = service.get_progress(operation_id)
+        assert progress is not None
+        assert progress.total == 5
+
+    @pytest.mark.asyncio
+    async def test_websocket_broadcasts_progress(self, mock_services):
+        ws_manager, analysis_service, persistence_service, logger = mock_services
+
+        service = BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[{"source": "https://example.com/test.png"}],
+        )
+
+        await asyncio.sleep(0.3)
+
+        assert len(ws_manager.broadcasts) > 0
+        assert any(
+            b.get("type") == "batch_import_progress" for b in ws_manager.broadcasts
+        )
+
+    @pytest.mark.asyncio
+    async def test_cancellation_stops_processing(self, mock_services):
+        ws_manager, analysis_service, persistence_service, logger = mock_services
+
+        service = BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        items = [{"source": f"https://example.com/{i}.png"} for i in range(10)]
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=items,
+        )
+
+        service.cancel_import(operation_id)
+        await asyncio.sleep(0.3)
+
+        progress = service.get_progress(operation_id)
+        if progress:
+            assert progress.status == "cancelled"
+
+
+class TestBatchImportServiceEdgeCases:
+    @pytest.fixture
+    def service(self):
+        ws_manager = MockWebSocketManager()
+        analysis_service = MockAnalysisService()
+        persistence_service = MockPersistenceService()
+        logger = logging.getLogger("test")
+
+        return BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+
+    @pytest.mark.asyncio
+    async def test_empty_items_list(self, service):
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[],
+        )
+
+        progress = service.get_progress(operation_id)
+        assert progress is not None
+        assert progress.total == 0
+
+    @pytest.mark.asyncio
+    async def test_mixed_url_and_path_items(self, service, tmp_path):
+        (tmp_path / "local.png").write_bytes(b"fake-image")
+
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[
+                {"source": "https://example.com/remote.png", "type": "url"},
+                {"source": str(tmp_path / "local.png"), "type": "local_path"},
+            ],
+        )
+
+        progress = service.get_progress(operation_id)
+        assert progress is not None
+        assert progress.total == 2
+        assert progress.items[0].item_type == ImportItemType.URL
+        assert progress.items[1].item_type == ImportItemType.LOCAL_PATH
+
+    @pytest.mark.asyncio
+    async def test_tags_are_passed_to_persistence(self, tmp_path):
+        ws_manager = MockWebSocketManager()
+        analysis_service = MockAnalysisService(
+            {
+                str(tmp_path / "test.png"): MockAnalysisResult(
+                    {
+                        "loras": [{"name": "test-lora"}],
+                    }
+                ),
+            }
+        )
+        persistence_service = MockPersistenceService()
+        logger = logging.getLogger("test")
+
+        (tmp_path / "test.png").write_bytes(b"fake-image")
+
+        service = BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+
+        recipe_scanner_getter = lambda: SimpleNamespace(
+            find_recipes_by_fingerprint=lambda x: [],
+        )
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[{"source": str(tmp_path / "test.png")}],
+            tags=["batch-import", "test"],
+        )
+
+        await asyncio.sleep(0.3)
+
+        if persistence_service.saved_recipes:
+            assert "batch-import" in persistence_service.saved_recipes[0]["tags"]
+            assert "test" in persistence_service.saved_recipes[0]["tags"]
+
+    @pytest.mark.asyncio
+    async def test_skip_duplicates_parameter(self, service):
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[{"source": "https://example.com/test.png"}],
+            skip_duplicates=True,
+        )
+
+        progress = service.get_progress(operation_id)
+        assert progress is not None
+        assert progress.skip_duplicates is True
+
+    @pytest.mark.asyncio
+    async def test_skip_duplicates_false_by_default(self, service):
+        recipe_scanner_getter = lambda: SimpleNamespace()
+        civitai_client_getter = lambda: SimpleNamespace()
+
+        operation_id = await service.start_batch_import(
+            recipe_scanner_getter=recipe_scanner_getter,
+            civitai_client_getter=civitai_client_getter,
+            items=[{"source": "https://example.com/test.png"}],
+        )
+
+        progress = service.get_progress(operation_id)
+        assert progress is not None
+        assert progress.skip_duplicates is False
+
+
+class TestInputValidation:
+    @pytest.fixture
+    def service(self):
+        ws_manager = MockWebSocketManager()
+        analysis_service = MockAnalysisService()
+        persistence_service = MockPersistenceService()
+        logger = logging.getLogger("test")
+
+        return BatchImportService(
+            analysis_service=analysis_service,
+            persistence_service=persistence_service,
+            ws_manager=ws_manager,
+            logger=logger,
+        )
+
+    def test_validate_valid_url(self, service):
+        assert service._validate_url("https://example.com/image.png") is True
+        assert service._validate_url("http://example.com/image.png") is True
+        assert service._validate_url("https://civitai.com/images/123") is True
+
+    def test_validate_invalid_url(self, service):
+        assert service._validate_url("not-a-url") is False
+        assert service._validate_url("ftp://example.com/file") is False
+        assert service._validate_url("") is False
+
+    def test_validate_valid_local_path(self, service, tmp_path):
+        valid_path = str(tmp_path / "image.png")
+        assert service._validate_local_path(valid_path) is True
+
+    def test_validate_invalid_local_path(self, service):
+        assert service._validate_local_path("../etc/passwd") is False
+        assert service._validate_local_path("relative/path.png") is False
+        assert service._validate_local_path("") is False

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

@@ -31,10 +31,27 @@ def temp_db_path():
 @pytest.fixture
 def temp_csv_path():
     """Create a temporary CSV file with test data."""
-    with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False, encoding="utf-8") as f:
+    with tempfile.NamedTemporaryFile(
+        mode="w", suffix=".csv", delete=False, encoding="utf-8"
+    ) as f:
         # Write test data in the same format as danbooru_e621_merged.csv
         # Format: tag_name,category,post_count,aliases
+        # Include multiple tags starting with "1" to test popularity-based ranking
         f.write('1girl,0,6008644,"1girls,sole_female"\n')
+        f.write('1boy,0,1405457,"1boys,sole_male"\n')
+        f.write('1:1,14,377032,""\n')
+        f.write('16:9,14,152866,""\n')
+        f.write('1other,0,70962,""\n')
+        f.write('16:10,14,14739,""\n')
+        f.write('1990s_(style),0,9369,""\n')
+        f.write('1_eye,0,7179,""\n')
+        f.write('1:2,14,5865,""\n')
+        f.write('1980s_(style),0,5665,""\n')
+        f.write('1koma,0,4384,""\n')
+        f.write('1_horn,0,2122,""\n')
+        f.write('101_dalmatian_street,3,1933,""\n')
+        f.write('1upgobbo,3,1731,""\n')
+        f.write('14:9,14,1038,""\n')
         f.write('highres,5,5256195,"high_res,high_resolution,hires"\n')
         f.write('solo,0,5000954,"alone,female_solo,single"\n')
         f.write('hatsune_miku,4,500000,"miku"\n')
@@ -86,7 +103,7 @@ class TestTagFTSIndexBuild:
         fts.build_index()
 
         assert fts.is_ready() is True
-        assert fts.get_indexed_count() == 10
+        assert fts.get_indexed_count() == 24
 
     def test_build_index_nonexistent_csv(self, temp_db_path):
         """Test that build_index handles missing CSV gracefully."""
@@ -187,6 +204,110 @@ class TestTagFTSIndexSearch:
         results = populated_fts.search("girl", limit=1)
         assert len(results) <= 1
 
+    def test_search_tag_name_prefix_match_priority(self, populated_fts):
+        """Test that tag_name prefix matches rank higher than alias matches."""
+        results = populated_fts.search("1", limit=20)
+
+        assert len(results) > 0, "Should return results for '1'"
+
+        # Find first alias match (if any)
+        first_alias_idx = None
+        for i, result in enumerate(results):
+            if result.get("matched_alias"):
+                first_alias_idx = i
+                break
+
+        # All tag_name prefix matches should come before alias matches
+        if first_alias_idx is not None:
+            for i in range(first_alias_idx):
+                assert results[i]["tag_name"].lower().startswith("1"), (
+                    f"Tag at index {i} should start with '1' before alias matches"
+                )
+
+    def test_search_ranks_popular_tags_higher(self, populated_fts):
+        """Test that tags with higher post_count rank higher among prefix matches."""
+        results = populated_fts.search("1", limit=20)
+
+        # Filter to only tag_name prefix matches
+        prefix_matches = [r for r in results if r["tag_name"].lower().startswith("1")]
+
+        assert len(prefix_matches) > 1, "Should have multiple prefix matches"
+
+        # Verify descending post_count order among prefix matches
+        for i in range(len(prefix_matches) - 1):
+            assert (
+                prefix_matches[i]["post_count"] >= prefix_matches[i + 1]["post_count"]
+            ), (
+                f"Tags should be sorted by post_count: {prefix_matches[i]['tag_name']} ({prefix_matches[i]['post_count']}) >= {prefix_matches[i + 1]['tag_name']} ({prefix_matches[i + 1]['post_count']})"
+            )
+
+    def test_search_pagination_ordering_consistency(self, populated_fts):
+        """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 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_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_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 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["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']})"
+            )
+
 
 class TestAliasSearch:
     """Tests for alias search functionality."""
@@ -204,7 +325,9 @@ class TestAliasSearch:
         results = populated_fts.search("miku")
 
         assert len(results) >= 1
-        hatsune_result = next((r for r in results if r["tag_name"] == "hatsune_miku"), None)
+        hatsune_result = next(
+            (r for r in results if r["tag_name"] == "hatsune_miku"), None
+        )
         assert hatsune_result is not None
         assert hatsune_result["matched_alias"] == "miku"
 
@@ -214,7 +337,9 @@ class TestAliasSearch:
         results = populated_fts.search("hatsune")
 
         assert len(results) >= 1
-        hatsune_result = next((r for r in results if r["tag_name"] == "hatsune_miku"), None)
+        hatsune_result = next(
+            (r for r in results if r["tag_name"] == "hatsune_miku"), None
+        )
         assert hatsune_result is not None
         assert "matched_alias" not in hatsune_result
 
@@ -301,7 +426,9 @@ class TestSlashPrefixAliases:
     @pytest.fixture
     def fts_with_slash_aliases(self, temp_db_path):
         """Create an FTS index with slash-prefixed aliases."""
-        with tempfile.NamedTemporaryFile(mode="w", suffix=".csv", delete=False, encoding="utf-8") as f:
+        with tempfile.NamedTemporaryFile(
+            mode="w", suffix=".csv", delete=False, encoding="utf-8"
+        ) as f:
             # Format: tag_name,category,post_count,aliases
             f.write('long_hair,0,4350743,"/lh,longhair,very_long_hair"\n')
             f.write('breasts,0,3439214,"/b,boobs,oppai"\n')
@@ -380,7 +507,15 @@ class TestCategoryMappings:
 
     def test_category_name_to_ids_complete(self):
         """Test that CATEGORY_NAME_TO_IDS includes all expected names."""
-        expected_names = ["general", "artist", "copyright", "character", "meta", "species", "lore"]
+        expected_names = [
+            "general",
+            "artist",
+            "copyright",
+            "character",
+            "meta",
+            "species",
+            "lore",
+        ]
         for name in expected_names:
             assert name in CATEGORY_NAME_TO_IDS
             assert isinstance(CATEGORY_NAME_TO_IDS[name], list)

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

@@ -742,6 +742,14 @@ class AutoComplete {
         try {
             this.currentSearchTerm = term;
 
+            // Save current search type to detect mode changes during async search
+            const searchTypeAtStart = this.searchType;
+
+            // Clear items before starting new search to avoid stale data
+            // This is critical for preventing command suggestions from persisting
+            // when switching from command mode to regular tag search
+            this.items = [];
+
             if (!endpoint) {
                 endpoint = `/lm/${this.modelType}/relative-paths`;
             }
@@ -776,7 +784,15 @@ class AutoComplete {
 
             const resultsArrays = await Promise.all(searchPromises);
 
-            // Merge and deduplicate results
+            // Check if search type changed during async operation
+            // If so, skip updating items to prevent stale data from showing
+            if (this.searchType !== searchTypeAtStart) {
+                console.log('[Lora Manager] Search type changed during search, skipping update');
+                return;
+            }
+
+            // Merge and deduplicate results while preserving order from backend
+            // Backend returns results sorted by relevance, so we maintain that order
             const seen = new Set();
             const mergedItems = [];
 
@@ -793,39 +809,10 @@ class AutoComplete {
                 }
             }
 
-            // Score and sort results: exact matches first, then by match quality
-            const scoredItems = mergedItems.map(item => {
-                let bestScore = -1;
-                let isExact = false;
-
-                for (const query of queriesToExecute) {
-                    const match = this._matchItem(item, query);
-                    if (match.matched) {
-                        // Higher score for exact matches
-                        const score = match.isExactMatch ? 1000 : 100;
-                        if (score > bestScore) {
-                            bestScore = score;
-                            isExact = match.isExactMatch;
-                        }
-                    }
-                }
-
-                return { item, score: bestScore, isExact };
-            });
-
-            // Sort by score (descending), exact matches first
-            scoredItems.sort((a, b) => {
-                if (b.isExact !== a.isExact) {
-                    return b.isExact ? 1 : -1;
-                }
-                return b.score - a.score;
-            });
-
-            // Extract just the items
-            const sortedItems = scoredItems.map(s => s.item);
-
-            if (sortedItems.length > 0) {
-                this.items = sortedItems;
+            // Use backend-sorted results directly without re-scoring
+            // Backend already ranks by: FTS5 bm25 score + post count + exact prefix boost
+            if (mergedItems.length > 0) {
+                this.items = mergedItems;
                 this.render();
                 this.show();
             } else {
@@ -908,6 +895,12 @@ class AutoComplete {
      * @param {string} filter - Optional filter for commands
      */
     _showCommandList(filter = '') {
+        // Only show command list if we're in command mode
+        // This prevents stale command suggestions from appearing after switching to tag search
+        if (this.searchType !== 'commands' && this.showingCommands !== true) {
+            return;
+        }
+        
         const filterLower = filter.toLowerCase();
 
         // Get unique commands (avoid duplicates like /char and /character)
@@ -942,12 +935,20 @@ class AutoComplete {
      * Render the command list dropdown
      */
     _renderCommandList() {
-        this.dropdown.innerHTML = '';
+        // Clear command list items properly based on rendering mode
+        if (this.contentContainer) {
+            // Virtual scrolling mode - clear content container
+            this.contentContainer.innerHTML = '';
+        } else {
+            // Non-virtual scrolling mode - clear dropdown direct children
+            this.dropdown.innerHTML = '';
+        }
         this.selectedIndex = -1;
 
         this.items.forEach((item, index) => {
             const itemEl = document.createElement('div');
             itemEl.className = 'comfy-autocomplete-item comfy-autocomplete-command';
+            itemEl.dataset.index = index.toString();
 
             const cmdSpan = document.createElement('span');
             cmdSpan.className = 'lm-autocomplete-command-name';
@@ -973,6 +974,8 @@ class AutoComplete {
                 justify-content: space-between;
                 align-items: center;
                 gap: 12px;
+                height: ${this.options.itemHeight}px;
+                box-sizing: border-box;
             `;
 
             itemEl.addEventListener('mouseenter', () => {
@@ -983,18 +986,29 @@ class AutoComplete {
                 this._insertCommand(item.command);
             });
 
-            this.dropdown.appendChild(itemEl);
+            // Append to correct container based on rendering mode
+            if (this.contentContainer) {
+                this.contentContainer.appendChild(itemEl);
+            } else {
+                this.dropdown.appendChild(itemEl);
+            }
         });
 
         // Remove border from last item
-        if (this.dropdown.lastChild) {
-            this.dropdown.lastChild.style.borderBottom = 'none';
+        const lastChild = this.contentContainer ? this.contentContainer.lastChild : this.dropdown.lastChild;
+        if (lastChild) {
+            lastChild.style.borderBottom = 'none';
         }
 
         // Auto-select first item
         if (this.items.length > 0) {
             setTimeout(() => this.selectItem(0), 100);
         }
+        
+        // Update virtual scroll height for virtual scrolling mode
+        if (this.contentContainer) {
+            this.updateVirtualScrollHeight();
+        }
     }
 
     /**
@@ -1057,28 +1071,49 @@ class AutoComplete {
         }
 
         if (this.options.enableVirtualScroll && this.contentContainer) {
-            // Use virtual scrolling - only update visible items if dropdown is already visible
-            // If not visible, updateVisibleItems() will be called from show() after display:block
+            // Use virtual scrolling - always update visible items to ensure content is fresh
+            // The dropdown visibility is controlled by show()/hide()
             this.updateVirtualScrollHeight();
-            if (this.isVisible && this.dropdown.style.display !== 'none') {
-                this.updateVisibleItems();
-            }
+            this.updateVisibleItems();
         } else {
             // Traditional rendering (fallback)
             this.dropdown.innerHTML = '';
 
-            // Check if items are enriched (have tag_name, category, post_count)
+            // Check if items are enriched (have tag_name, category, post_count) or command objects
             const isEnriched = this.items[0] && typeof this.items[0] === 'object' && 'tag_name' in this.items[0];
+            const isCommand = this.items[0] && typeof this.items[0] === 'object' && 'command' in this.items[0];
 
             this.items.forEach((itemData, index) => {
                 const item = document.createElement('div');
                 item.className = 'comfy-autocomplete-item';
 
-                // Get the display text and path for insertion
-                const displayText = isEnriched ? itemData.tag_name : itemData;
-                const insertPath = isEnriched ? itemData.tag_name : itemData;
+                if (isCommand) {
+                    // Render command item
+                    const cmdSpan = document.createElement('span');
+                    cmdSpan.className = 'lm-autocomplete-command-name';
+                    cmdSpan.textContent = itemData.command;
 
-                if (isEnriched) {
+                    const labelSpan = document.createElement('span');
+                    labelSpan.className = 'lm-autocomplete-command-label';
+                    labelSpan.textContent = itemData.label;
+
+                    item.appendChild(cmdSpan);
+                    item.appendChild(labelSpan);
+                    item.style.cssText = `
+                        padding: 8px 12px;
+                        cursor: pointer;
+                        color: rgba(226, 232, 240, 0.8);
+                        border-bottom: 1px solid rgba(226, 232, 240, 0.1);
+                        transition: all 0.2s ease;
+                        white-space: nowrap;
+                        overflow: hidden;
+                        text-overflow: ellipsis;
+                        display: flex;
+                        justify-content: space-between;
+                        align-items: center;
+                        gap: 12px;
+                    `;
+                } else if (isEnriched) {
                     // Render enriched item with category badge and post count
                     this._renderEnrichedItem(item, itemData, this.currentSearchTerm);
                 } else {
@@ -1087,7 +1122,7 @@ class AutoComplete {
                     const nameSpan = document.createElement('span');
                     nameSpan.className = 'lm-autocomplete-name';
                     // Use display text without extension for cleaner UI
-                    const displayTextWithoutExt = this._getDisplayText(displayText);
+                    const displayTextWithoutExt = this._getDisplayText(itemData);
                     nameSpan.innerHTML = this.highlightMatch(displayTextWithoutExt, this.currentSearchTerm);
                     nameSpan.style.cssText = `
                         flex: 1;
@@ -1096,25 +1131,25 @@ class AutoComplete {
                         text-overflow: ellipsis;
                     `;
                     item.appendChild(nameSpan);
+                    
+                    // Apply item styles with new color scheme
+                    item.style.cssText = `
+                        padding: 8px 12px;
+                        cursor: pointer;
+                        color: rgba(226, 232, 240, 0.8);
+                        border-bottom: 1px solid rgba(226, 232, 240, 0.1);
+                        transition: all 0.2s ease;
+                        white-space: nowrap;
+                        overflow: hidden;
+                        text-overflow: ellipsis;
+                        position: relative;
+                        display: flex;
+                        justify-content: space-between;
+                        align-items: center;
+                        gap: 8px;
+                    `;
                 }
 
-                // Apply item styles with new color scheme
-                item.style.cssText = `
-                    padding: 8px 12px;
-                    cursor: pointer;
-                    color: rgba(226, 232, 240, 0.8);
-                    border-bottom: 1px solid rgba(226, 232, 240, 0.1);
-                    transition: all 0.2s ease;
-                    white-space: nowrap;
-                    overflow: hidden;
-                    text-overflow: ellipsis;
-                    position: relative;
-                    display: flex;
-                    justify-content: space-between;
-                    align-items: center;
-                    gap: 8px;
-                `;
-
                 // Hover and selection handlers
                 item.addEventListener('mouseenter', () => {
                     this.selectItem(index);
@@ -1126,7 +1161,12 @@ class AutoComplete {
 
                 // Click handler
                 item.addEventListener('click', () => {
-                    this.insertSelection(insertPath);
+                    if (isCommand) {
+                        this._insertCommand(itemData.command);
+                    } else {
+                        const insertPath = isEnriched ? itemData.tag_name : itemData;
+                        this.insertSelection(insertPath);
+                    }
                 });
 
                 this.dropdown.appendChild(item);
@@ -1369,39 +1409,11 @@ class AutoComplete {
                 this.hasMoreItems = false;
             }
 
-            // If we got new items, add them and re-render
+            // If we got new items, append them and re-render
+            // IMPORTANT: Do NOT re-sort! Backend already returns results sorted by relevance
             if (newItems.length > 0) {
-                const currentLength = this.items.length;
                 this.items.push(...newItems);
 
-                // Re-score and sort all items
-                const scoredItems = this.items.map(item => {
-                    let bestScore = -1;
-                    let isExact = false;
-
-                    for (const query of queriesToExecute) {
-                        const match = this._matchItem(item, query);
-                        if (match.matched) {
-                            const score = match.isExactMatch ? 1000 : 100;
-                            if (score > bestScore) {
-                                bestScore = score;
-                                isExact = match.isExactMatch;
-                            }
-                        }
-                    }
-
-                    return { item, score: bestScore, isExact };
-                });
-
-                scoredItems.sort((a, b) => {
-                    if (b.isExact !== a.isExact) {
-                        return b.isExact ? 1 : -1;
-                    }
-                    return b.score - a.score;
-                });
-
-                this.items = scoredItems.map(s => s.item);
-
                 // Update render
                 if (this.options.enableVirtualScroll) {
                     this.updateVirtualScrollHeight();
@@ -1458,10 +1470,18 @@ class AutoComplete {
      * Update the total height of the virtual scroll container
      */
     updateVirtualScrollHeight() {
-        if (!this.contentContainer) return;
+        if (!this.contentContainer || !this.scrollContainer) return;
 
         this.totalHeight = this.items.length * this.options.itemHeight;
         this.contentContainer.style.height = `${this.totalHeight}px`;
+        
+        // Adjust scroll container max-height based on actual content
+        // Only show scrollbar when content exceeds visibleItems limit
+        const maxHeight = this.options.visibleItems * this.options.itemHeight;
+        const shouldShowScrollbar = this.totalHeight > maxHeight;
+        
+        this.scrollContainer.style.maxHeight = shouldShowScrollbar ? `${maxHeight}px` : `${this.totalHeight}px`;
+        this.scrollContainer.style.overflowY = shouldShowScrollbar ? 'auto' : 'hidden';
     }
 
     /**
@@ -1473,11 +1493,12 @@ class AutoComplete {
         const scrollTop = this.scrollContainer.scrollTop;
         const containerHeight = this.scrollContainer.clientHeight;
 
-        // Calculate which items should be visible
-        const startIndex = Math.max(0, Math.floor(scrollTop / this.options.itemHeight) - 2);
+        // Calculate which items should be visible with a larger buffer for smoother rendering
+        // Use a fixed buffer of 5 items to ensure selected item is always rendered
+        const startIndex = Math.max(0, Math.floor(scrollTop / this.options.itemHeight) - 5);
         const endIndex = Math.min(
             this.items.length - 1,
-            Math.ceil((scrollTop + containerHeight) / this.options.itemHeight) + 2
+            Math.ceil((scrollTop + containerHeight) / this.options.itemHeight) + 5
         );
 
         // Clear current content
@@ -1492,10 +1513,11 @@ class AutoComplete {
 
         // Render visible items
         const isEnriched = this.items[0] && typeof this.items[0] === 'object' && 'tag_name' in this.items[0];
+        const isCommand = this.items[0] && typeof this.items[0] === 'object' && 'command' in this.items[0];
 
         for (let i = startIndex; i <= endIndex; i++) {
             const itemData = this.items[i];
-            const itemEl = this.createItemElement(itemData, i, isEnriched);
+            const itemEl = this.createItemElement(itemData, i, isEnriched, isCommand);
             this.contentContainer.appendChild(itemEl);
         }
 
@@ -1505,12 +1527,22 @@ class AutoComplete {
             bottomSpacer.style.height = `${(this.items.length - 1 - endIndex) * this.options.itemHeight}px`;
             this.contentContainer.appendChild(bottomSpacer);
         }
+        
+        // Re-apply selection styling after re-rendering
+        // This ensures the selected item remains highlighted even after DOM updates
+        if (this.selectedIndex >= startIndex && this.selectedIndex <= endIndex) {
+            const selectedEl = this.contentContainer.querySelector(`.comfy-autocomplete-item[data-index="${this.selectedIndex}"]`);
+            if (selectedEl) {
+                selectedEl.classList.add('comfy-autocomplete-item-selected');
+                selectedEl.style.backgroundColor = 'rgba(66, 153, 225, 0.2)';
+            }
+        }
     }
 
     /**
      * Create a single item element
      */
-    createItemElement(itemData, index, isEnriched) {
+    createItemElement(itemData, index, isEnriched, isCommand = false) {
         const item = document.createElement('div');
         item.className = 'comfy-autocomplete-item';
         item.dataset.index = index.toString();
@@ -1532,16 +1564,31 @@ class AutoComplete {
             box-sizing: border-box;
         `;
 
-        const displayText = isEnriched ? itemData.tag_name : itemData;
-        const insertPath = isEnriched ? itemData.tag_name : itemData;
+        // Check if this is a command object (override parameter if needed)
+        if (!isCommand && itemData && typeof itemData === 'object' && 'command' in itemData) {
+            isCommand = true;
+        }
 
-        if (isEnriched) {
+        if (isCommand) {
+            // Render command item
+            const cmdSpan = document.createElement('span');
+            cmdSpan.className = 'lm-autocomplete-command-name';
+            cmdSpan.textContent = itemData.command;
+
+            const labelSpan = document.createElement('span');
+            labelSpan.className = 'lm-autocomplete-command-label';
+            labelSpan.textContent = itemData.label;
+
+            item.appendChild(cmdSpan);
+            item.appendChild(labelSpan);
+            item.style.gap = '12px';
+        } else if (isEnriched) {
             this._renderEnrichedItem(item, itemData, this.currentSearchTerm);
         } else {
             const nameSpan = document.createElement('span');
             nameSpan.className = 'lm-autocomplete-name';
             // Use display text without extension for cleaner UI
-            const displayTextWithoutExt = this._getDisplayText(displayText);
+            const displayTextWithoutExt = this._getDisplayText(itemData);
             nameSpan.innerHTML = this.highlightMatch(displayTextWithoutExt, this.currentSearchTerm);
             nameSpan.style.cssText = `
                 flex: 1;
@@ -1561,8 +1608,14 @@ class AutoComplete {
             this.hidePreview();
         });
 
+        // Click handler
         item.addEventListener('click', () => {
-            this.insertSelection(insertPath);
+            if (isCommand) {
+                this._insertCommand(itemData.command);
+            } else {
+                const insertPath = isEnriched ? itemData.tag_name : itemData;
+                this.insertSelection(insertPath);
+            }
         });
 
         return item;
@@ -1578,7 +1631,10 @@ class AutoComplete {
         if (this.options.enableVirtualScroll && this.contentContainer) {
             this.dropdown.style.display = 'block';
             this.isVisible = true;
-            this.updateVisibleItems();
+            // Skip updateVisibleItems if showing commands (already rendered by _renderCommandList)
+            if (!this.showingCommands) {
+                this.updateVisibleItems();
+            }
             this.positionAtCursor();
         } else {
             // Position dropdown at cursor position using TextAreaCaretHelper
@@ -1638,6 +1694,19 @@ class AutoComplete {
         this.isVisible = false;
         this.selectedIndex = -1;
         this.showingCommands = false;
+        
+        // Clear items to prevent stale data from being displayed
+        // when autocomplete is shown again
+        this.items = [];
+        
+        // Clear content container to prevent stale items from showing
+        if (this.contentContainer) {
+            // Virtual scrolling mode - clear content container
+            this.contentContainer.innerHTML = '';
+        } else {
+            // Non-virtual scrolling mode - clear dropdown direct children
+            this.dropdown.innerHTML = '';
+        }
 
         // Reset virtual scrolling state
         this.virtualScrollOffset = 0;
@@ -1688,26 +1757,22 @@ class AutoComplete {
 
                 // If item is not visible, scroll to make it visible
                 if (itemTop < scrollTop || itemBottom > scrollBottom) {
-                    this.scrollContainer.scrollTop = itemTop - containerHeight / 2;
+                    // Scroll to position the item in the visible area
+                    // Position item at 1/3 from top for better visibility
+                    const targetScrollTop = Math.max(0, itemTop - containerHeight / 3);
+                    this.scrollContainer.scrollTop = targetScrollTop;
+                    
                     // Re-render visible items after scroll
                     this.updateVisibleItems();
-                }
-
-                // Find the item element using data-index attribute
-                const selectedEl = container.querySelector(`.comfy-autocomplete-item[data-index="${index}"]`);
-
-                if (selectedEl) {
-                    selectedEl.classList.add('comfy-autocomplete-item-selected');
-                    selectedEl.style.backgroundColor = 'rgba(66, 153, 225, 0.2)';
-
-                    // Show preview for selected item
-                    if (this.options.showPreview) {
-                        if (typeof this.behavior.showPreview === 'function') {
-                            this.behavior.showPreview(this, this.items[index], selectedEl);
-                        } else if (this.previewTooltip) {
-                            this.showPreviewForItem(this.items[index], selectedEl);
-                        }
-                    }
+                    
+                    // Apply selection after DOM is updated
+                    // Use setTimeout to ensure DOM has been re-rendered
+                    setTimeout(() => {
+                        this._applyItemSelection(index);
+                    }, 0);
+                } else {
+                    // Item is already visible, apply selection immediately
+                    this._applyItemSelection(index);
                 }
             } else {
                 // Traditional rendering
@@ -1731,6 +1796,31 @@ class AutoComplete {
             }
         }
     }
+
+    /**
+     * Apply selection styling to an item (used after virtual scroll re-render)
+     * @param {number} index - Index of item to select
+     */
+    _applyItemSelection(index) {
+        if (!this.contentContainer) return;
+
+        // Find the item element using data-index attribute
+        const selectedEl = this.contentContainer.querySelector(`.comfy-autocomplete-item[data-index="${index}"]`);
+
+        if (selectedEl) {
+            selectedEl.classList.add('comfy-autocomplete-item-selected');
+            selectedEl.style.backgroundColor = 'rgba(66, 153, 225, 0.2)';
+
+            // Show preview for selected item
+            if (this.options.showPreview) {
+                if (typeof this.behavior.showPreview === 'function') {
+                    this.behavior.showPreview(this, this.items[index], selectedEl);
+                } else if (this.previewTooltip) {
+                    this.showPreviewForItem(this.items[index], selectedEl);
+                }
+            }
+        }
+    }
     
     handleKeyDown(e) {
         if (!this.isVisible) {
@@ -1740,12 +1830,39 @@ class AutoComplete {
         switch (e.key) {
             case 'ArrowDown':
                 e.preventDefault();
-                this.selectItem(Math.min(this.selectedIndex + 1, this.items.length - 1));
+                if (this.options.enableVirtualScroll && this.scrollContainer) {
+                    // For virtual scrolling, handle boundary cases
+                    if (this.selectedIndex >= this.items.length - 1) {
+                        // Already at last item, try to load more
+                        if (this.hasMoreItems && !this.isLoadingMore) {
+                            this.loadMoreItems().then(() => {
+                                // After loading more, select the next item
+                                if (this.selectedIndex < this.items.length - 1) {
+                                    this.selectItem(this.selectedIndex + 1);
+                                }
+                            });
+                        }
+                    } else {
+                        this.selectItem(this.selectedIndex + 1);
+                    }
+                } else {
+                    this.selectItem(Math.min(this.selectedIndex + 1, this.items.length - 1));
+                }
                 break;
                 
             case 'ArrowUp':
                 e.preventDefault();
-                this.selectItem(Math.max(this.selectedIndex - 1, 0));
+                if (this.options.enableVirtualScroll && this.scrollContainer) {
+                    // For virtual scrolling, handle top boundary
+                    if (this.selectedIndex <= 0) {
+                        // Already at first item, ensure it's selected
+                        this.selectItem(0);
+                    } else {
+                        this.selectItem(this.selectedIndex - 1);
+                    }
+                } else {
+                    this.selectItem(Math.max(this.selectedIndex - 1, 0));
+                }
                 break;
                 
             case 'Enter':
@@ -1788,10 +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,
 };

web/comfyui/top_menu_extension.js

@@ -3,6 +3,10 @@ import { app } from "../../scripts/app.js";
 const BUTTON_TOOLTIP = "Launch LoRA Manager (Shift+Click opens in new window)";
 const LORA_MANAGER_PATH = "/loras";
 const NEW_WINDOW_FEATURES = "width=1200,height=800,resizable=yes,scrollbars=yes,status=yes";
+const MAX_ATTACH_ATTEMPTS = 120;
+const BUTTON_GROUP_CLASS = "lora-manager-top-menu-group";
+
+const MIN_VERSION_FOR_ACTION_BAR = [1, 33, 9];
 
 const openLoraManager = (event) => {
     const url = `${window.location.origin}${LORA_MANAGER_PATH}`;
@@ -15,6 +19,65 @@ const openLoraManager = (event) => {
     window.open(url, "_blank");
 };
 
+const getComfyUIFrontendVersion = async () => {
+    try {
+        if (window['__COMFYUI_FRONTEND_VERSION__']) {
+            return window['__COMFYUI_FRONTEND_VERSION__'];
+        }
+    } catch (error) {
+        console.warn("LoRA Manager: unable to read __COMFYUI_FRONTEND_VERSION__:", error);
+    }
+
+    try {
+        const response = await fetch("/system_stats");
+        const data = await response.json();
+        
+        if (data?.system?.comfyui_frontend_version) {
+            return data.system.comfyui_frontend_version;
+        }
+        
+        if (data?.system?.required_frontend_version) {
+            return data.system.required_frontend_version;
+        }
+    } catch (error) {
+        console.warn("LoRA Manager: unable to fetch system_stats:", error);
+    }
+
+    return "0.0.0";
+};
+
+const parseVersion = (versionStr) => {
+    if (!versionStr || typeof versionStr !== 'string') {
+        return [0, 0, 0];
+    }
+    
+    const cleanVersion = versionStr.replace(/^[vV]/, '').split('-')[0];
+    const parts = cleanVersion.split('.').map(part => parseInt(part, 10) || 0);
+    
+    while (parts.length < 3) {
+        parts.push(0);
+    }
+    
+    return parts;
+};
+
+const compareVersions = (version1, version2) => {
+    const v1 = typeof version1 === 'string' ? parseVersion(version1) : version1;
+    const v2 = typeof version2 === 'string' ? parseVersion(version2) : version2;
+    
+    for (let i = 0; i < 3; i++) {
+        if (v1[i] > v2[i]) return 1;
+        if (v1[i] < v2[i]) return -1;
+    }
+    
+    return 0;
+};
+
+const supportsActionBarButtons = async () => {
+    const version = await getComfyUIFrontendVersion();
+    return compareVersions(version, MIN_VERSION_FOR_ACTION_BAR) >= 0;
+};
+
 const fetchVersionInfo = async () => {
     try {
         const response = await fetch("/api/lm/version-info");
@@ -30,6 +93,55 @@ const fetchVersionInfo = async () => {
     return "";
 };
 
+const createTopMenuButton = async () => {
+    const { ComfyButton } = await import("../../scripts/ui/components/button.js");
+    
+    const button = new ComfyButton({
+        icon: "loramanager",
+        tooltip: BUTTON_TOOLTIP,
+        app,
+        enabled: true,
+        classList: "comfyui-button comfyui-menu-mobile-collapse primary",
+    });
+
+    button.element.setAttribute("aria-label", BUTTON_TOOLTIP);
+    button.element.title = BUTTON_TOOLTIP;
+
+    if (button.iconElement) {
+        button.iconElement.innerHTML = getLoraManagerIcon();
+        button.iconElement.style.width = "1.2rem";
+        button.iconElement.style.height = "1.2rem";
+    }
+
+    button.element.addEventListener("click", openLoraManager);
+    return button;
+};
+
+const attachTopMenuButton = async (attempt = 0) => {
+    if (document.querySelector(`.${BUTTON_GROUP_CLASS}`)) {
+        return;
+    }
+
+    const settingsGroup = app.menu?.settingsGroup;
+    if (!settingsGroup?.element?.parentElement) {
+        if (attempt >= MAX_ATTACH_ATTEMPTS) {
+            console.warn("LoRA Manager: unable to locate the ComfyUI settings button group.");
+            return;
+        }
+
+        requestAnimationFrame(() => attachTopMenuButton(attempt + 1));
+        return;
+    }
+
+    const loraManagerButton = await createTopMenuButton();
+    const { ComfyButtonGroup } = await import("../../scripts/ui/components/buttonGroup.js");
+    
+    const buttonGroup = new ComfyButtonGroup(loraManagerButton);
+    buttonGroup.element.classList.add(BUTTON_GROUP_CLASS);
+
+    settingsGroup.element.before(buttonGroup.element);
+};
+
 const createAboutBadge = (version) => {
     const label = version ? `LoRA Manager v${version}` : "LoRA Manager";
 
@@ -40,60 +152,80 @@ const createAboutBadge = (version) => {
     };
 };
 
-app.registerExtension({
-    name: "LoraManager.TopMenu",
-    actionBarButtons: [
-        {
-            icon: "icon-[mdi--alpha-l-box] size-4",
-            tooltip: BUTTON_TOOLTIP,
-            onClick: openLoraManager
-        }
-    ],
-    aboutPageBadges: [createAboutBadge()],
-    async setup() {
-        const version = await fetchVersionInfo();
-        this.aboutPageBadges = [createAboutBadge(version)];
-
-        const injectStyles = () => {
-            const styleId = 'lm-top-menu-button-styles';
-            if (document.getElementById(styleId)) return;
-
-            const style = document.createElement('style');
-            style.id = styleId;
-            style.textContent = `
-                button[aria-label="Launch LoRA Manager (Shift+Click opens in new window)"].lm-top-menu-button {
-                    transition: all 0.2s ease;
-                    border: 1px solid transparent;
-                }
-                button[aria-label="Launch LoRA Manager (Shift+Click opens in new window)"].lm-top-menu-button:hover {
-                    background-color: var(--primary-hover-bg) !important;
-                }
-            `;
-            document.head.appendChild(style);
-        };
-        injectStyles();
-
-        const replaceButtonIcon = () => {
-            const buttons = document.querySelectorAll('button[aria-label="Launch LoRA Manager (Shift+Click opens in new window)"]');
-            buttons.forEach(button => {
-                button.classList.add('lm-top-menu-button');
-                button.innerHTML = getLoraManagerIcon();
-                button.style.borderRadius = '4px';
-                button.style.padding = '6px';
-                button.style.backgroundColor = 'var(--primary-bg)';
-                const svg = button.querySelector('svg');
-                if (svg) {
-                    svg.style.width = '20px';
-                    svg.style.height = '20px';
-                }
-            });
-            if (buttons.length === 0) {
-                requestAnimationFrame(replaceButtonIcon);
+const createExtensionObject = (useActionBar) => {
+    const extensionObj = {
+        name: "LoraManager.TopMenu",
+        async setup() {
+            const version = await fetchVersionInfo();
+            
+            if (!useActionBar) {
+                console.log("LoRA Manager: using legacy button attachment (frontend version < 1.33.9)");
+                await attachTopMenuButton();
+            } else {
+                console.log("LoRA Manager: using actionBarButtons API (frontend version >= 1.33.9)");
             }
-        };
-        requestAnimationFrame(replaceButtonIcon);
-    },
-});
+            
+            this.aboutPageBadges = [createAboutBadge(version)];
+
+            const injectStyles = () => {
+                const styleId = 'lm-top-menu-button-styles';
+                if (document.getElementById(styleId)) return;
+
+                const style = document.createElement('style');
+                style.id = styleId;
+                style.textContent = `
+                    button[aria-label="Launch LoRA Manager (Shift+Click opens in new window)"].lm-top-menu-button {
+                        transition: all 0.2s ease;
+                        border: 1px solid transparent;
+                    }
+                    button[aria-label="Launch LoRA Manager (Shift+Click opens in new window)"].lm-top-menu-button:hover {
+                        background-color: var(--primary-hover-bg) !important;
+                    }
+                `;
+                document.head.appendChild(style);
+            };
+            injectStyles();
+
+            const replaceButtonIcon = () => {
+                const buttons = document.querySelectorAll('button[aria-label="Launch LoRA Manager (Shift+Click opens in new window)"]');
+                buttons.forEach(button => {
+                    button.classList.add('lm-top-menu-button');
+                    button.innerHTML = getLoraManagerIcon();
+                    button.style.borderRadius = '4px';
+                    button.style.padding = '6px';
+                    button.style.backgroundColor = 'var(--primary-bg)';
+                    const svg = button.querySelector('svg');
+                    if (svg) {
+                        svg.style.width = '20px';
+                        svg.style.height = '20px';
+                    }
+                });
+                if (buttons.length === 0) {
+                    requestAnimationFrame(replaceButtonIcon);
+                }
+            };
+            requestAnimationFrame(replaceButtonIcon);
+        },
+    };
+    
+    if (useActionBar) {
+        extensionObj.actionBarButtons = [
+            {
+                icon: "icon-[mdi--alpha-l-box] size-4",
+                tooltip: BUTTON_TOOLTIP,
+                onClick: openLoraManager
+            }
+        ];
+    }
+    
+    return extensionObj;
+};
+
+(async () => {
+    const useActionBar = await supportsActionBarButtons();
+    const extensionObj = createExtensionObject(useActionBar);
+    app.registerExtension(extensionObj);
+})();
 
 const getLoraManagerIcon = () => {
     return `