fix(network): restore destination-scoped memory download guard

fix(network): return friendly offline message for memory downloads

.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

.github/workflows/update-supporters.yml

@@ -0,0 +1,31 @@
+name: Update Supporters in README
+
+on:
+  push:
+    paths:
+      - 'data/supporters.json'
+    branches:
+      - main
+  workflow_dispatch: # Allow manual trigger
+
+jobs:
+  update-readme:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+    steps:
+      - uses: actions/checkout@v4
+      
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.10'
+          
+      - name: Update README
+        run: python scripts/update_supporters.py
+        
+      - name: Commit and push changes
+        uses: stefanzweifel/git-auto-commit-action@v5
+        with:
+          commit_message: "docs: auto-update supporters list in README"
+          file_pattern: "README.md"

.gitignore

@@ -14,6 +14,8 @@ model_cache/
 
 # agent
 .opencode/
+.claude/
+.codex
 
 # Vue widgets development cache (but keep build output)
 vue-widgets/node_modules/

.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
+    }
+  }
+}

AGENTS.md

@@ -135,9 +135,16 @@ npm run test:coverage       # Generate coverage report
 - ALWAYS use English for comments (per copilot-instructions.md)
 - Dual mode: ComfyUI plugin (folder_paths) vs standalone (settings.json)
 - Detection: `os.environ.get("LORA_MANAGER_STANDALONE", "0") == "1"`
-- Run `python scripts/sync_translation_keys.py` after UI string updates
+- Run `python scripts/sync_translation_keys.py` after adding UI strings to `locales/en.json`
 - Symlinks require normalized paths
 
+## Git / Commit Messages
+
+- Follow the style of recent repository commits when writing commit messages
+- Prefer the repo's existing `feat(...)`, `fix(...)`, `chore:` style where applicable
+- If the user has provided a GitHub issue link or issue ID for the task, mention that issue in the commit message, for example `(#871)`
+- When unrelated local changes exist, stage and commit only the files relevant to the requested task
+
 ## Frontend UI Architecture
 
 ### 1. Standalone Web UI

__init__.py

@@ -1,10 +1,13 @@
 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
     from .py.nodes.lora_stacker import LoraStackerLM
+    from .py.nodes.lora_stack_combiner import LoraStackCombinerLM
     from .py.nodes.save_image import SaveImageLM
     from .py.nodes.debug_metadata import DebugMetadataLM
     from .py.nodes.wanvideo_lora_select import WanVideoLoraSelectLM
@@ -27,16 +30,19 @@ 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
     LoraStackerLM = importlib.import_module("py.nodes.lora_stacker").LoraStackerLM
+    LoraStackCombinerLM = importlib.import_module(
+        "py.nodes.lora_stack_combiner"
+    ).LoraStackCombinerLM
     SaveImageLM = importlib.import_module("py.nodes.save_image").SaveImageLM
     DebugMetadataLM = importlib.import_module("py.nodes.debug_metadata").DebugMetadataLM
     WanVideoLoraSelectLM = importlib.import_module(
@@ -49,9 +55,7 @@ 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 = {
@@ -59,8 +63,11 @@ NODE_CLASS_MAPPINGS = {
     TextLM.NAME: TextLM,
     LoraLoaderLM.NAME: LoraLoaderLM,
     LoraTextLoaderLM.NAME: LoraTextLoaderLM,
+    CheckpointLoaderLM.NAME: CheckpointLoaderLM,
+    UNETLoaderLM.NAME: UNETLoaderLM,
     TriggerWordToggleLM.NAME: TriggerWordToggleLM,
     LoraStackerLM.NAME: LoraStackerLM,
+    LoraStackCombinerLM.NAME: LoraStackCombinerLM,
     SaveImageLM.NAME: SaveImageLM,
     DebugMetadataLM.NAME: DebugMetadataLM,
     WanVideoLoraSelectLM.NAME: WanVideoLoraSelectLM,

data/supporters.json

@@ -0,0 +1,673 @@
+{
+  "specialThanks": [
+    "dispenser",
+    "EbonEagle",
+    "DanielMagPizza",
+    "Scott R"
+  ],
+  "allSupporters": [
+    "Insomnia Art Designs",
+    "megakirbs",
+    "Brennok",
+    "2018cfh",
+    "W+K+White",
+    "wackop",
+    "Takkan",
+    "Carl G.",
+    "$MetaSamsara",
+    "itismyelement",
+    "onesecondinosaur",
+    "stone9k",
+    "Rosenthal",
+    "Francisco Tatis",
+    "Andrew Wilson",
+    "Greybush",
+    "Gooohokrbe",
+    "Ricky Carter",
+    "JongWon Han",
+    "OldBones",
+    "VantAI",
+    "runte3221",
+    "FreelancerZ",
+    "Edgar Tejeda",
+    "Liam MacDougal",
+    "Fraser Cross",
+    "Polymorphic Indeterminate",
+    "Birdy",
+    "Marc Whiffen",
+    "Jorge Hussni",
+    "Kiba",
+    "Skalabananen",
+    "Reno Lam",
+    "sig",
+    "Christian Byrne",
+    "DM",
+    "Sen314",
+    "Estragon",
+    "J\\B/ 8r0wns0n",
+    "Snaggwort",
+    "Arlecchino Shion",
+    "Charles Blakemore",
+    "Rob Williams",
+    "ClockDaemon",
+    "KD",
+    "Omnidex",
+    "Tyler Trebuchon",
+    "Release Cabrakan",
+    "Tobi_Swagg",
+    "SG",
+    "carozzz",
+    "James Dooley",
+    "zenbound",
+    "Buzzard",
+    "jmack",
+    "Mark Corneglio",
+    "SarcasticHashtag",
+    "Cosmosis",
+    "iamresist",
+    "RedrockVP",
+    "Wolffen",
+    "FloPro4Sho",
+    "James Todd",
+    "Steven Pfeiffer",
+    "Tim",
+    "Lisster",
+    "Michael Wong",
+    "Illrigger",
+    "Tom Corrigan",
+    "JackieWang",
+    "fnkylove",
+    "Julian V",
+    "Steven Owens",
+    "Yushio",
+    "Vik71it",
+    "Echo",
+    "Lilleman",
+    "Robert Stacey",
+    "PM",
+    "Todd Keck",
+    "Mozzel",
+    "Gingko Biloba",
+    "Sterilized",
+    "BadassArabianMofo",
+    "Pascal Dahle",
+    "quarz",
+    "Greg",
+    "Penfore",
+    "JSST",
+    "esthe",
+    "lmsupporter",
+    "IamAyam",
+    "wfpearl",
+    "Baekdoosixt",
+    "Jonathan Ross",
+    "Jack B Nimble",
+    "Nazono_hito",
+    "Melville Parrish",
+    "daniel dove",
+    "Lustre",
+    "JW Sin",
+    "contrite831",
+    "Alex",
+    "bh",
+    "confiscated Zyra",
+    "Marlon Daniels",
+    "Starkselle",
+    "Aaron Bleuer",
+    "LacesOut!",
+    "greebles",
+    "Adam Shaw",
+    "Tee Gee",
+    "Anthony Rizzo",
+    "tarek helmi",
+    "M Postkasse",
+    "ASLPro3D",
+    "Jacob Hoehler",
+    "FinalyFree",
+    "Weasyl",
+    "Timmy",
+    "Johnny",
+    "Cory Paza",
+    "Tak",
+    "Gonzalo Andre Allendes Lopez",
+    "Zach Gonser",
+    "Big Red",
+    "whudunit",
+    "Luc Job",
+    "dl0901dm",
+    "Philip Hempel",
+    "corde",
+    "Nick Walker",
+    "lh qwe",
+    "Bishoujoker",
+    "conner",
+    "aai",
+    "Briton Heilbrun",
+    "Tori",
+    "wildnut",
+    "Princess Bright Eyes",
+    "AbstractAss",
+    "Felipe dos Santos",
+    "ViperC",
+    "jean jahren",
+    "Aleksander Wujczyk",
+    "AM Kuro",
+    "Markus",
+    "S Sang",
+    "Karl P.",
+    "Akira_HentAI",
+    "MagnaInsomnia",
+    "Gordon Cole",
+    "yuxz69",
+    "Douglas Gaspar",
+    "AlexDuKaNa",
+    "George",
+    "andrew.tappan",
+    "dw",
+    "N/A",
+    "The Spawn",
+    "Phil",
+    "graysock",
+    "Greenmoustache",
+    "zounic",
+    "fancypants",
+    "Digital",
+    "JaxMax",
+    "takyamtom",
+    "奚明 刘",
+    "Jwk0205",
+    "Bro Xie",
+    "준희 김",
+    "batblue",
+    "carey6409",
+    "Olive",
+    "太郎 ゲーム",
+    "Some Guy Named Barry",
+    "Max Marklund",
+    "Tomohiro Baba",
+    "David Ortega",
+    "AELOX",
+    "Nicfit23",
+    "Noora",
+    "wamekukyouzin",
+    "drum matthieu",
+    "Dogmaster",
+    "Matt Wenzel",
+    "Mattssn",
+    "Lex Song",
+    "John Saveas",
+    "Christopher Michel",
+    "Serge Bekenkamp",
+    "Jimmy Ledbetter",
+    "LeoZero",
+    "Antonio Pontes",
+    "ApathyJones",
+    "nahinahi9",
+    "Dustin Chen",
+    "dan",
+    "Yaboi",
+    "Mouthlessman",
+    "Steam Steam",
+    "Damon Cunliffe",
+    "CryptoTraderJK",
+    "Davaitamin",
+    "otaku fra",
+    "Ran C",
+    "tedcor",
+    "Fotek Design",
+    "Adam Taylor",
+    "Weird_With_A_Beard",
+    "MadSpin",
+    "Pozadine1",
+    "Qarob",
+    "AIGooner",
+    "inbijiburu",
+    "Luc",
+    "ProtonPrince",
+    "DiffDuck",
+    "elu3199",
+    "Nick “Loadstone” D",
+    "Hasturkun",
+    "Jon Sandman",
+    "Ubivis",
+    "CloudValley",
+    "thesoftwaredruid",
+    "wundershark",
+    "mr_dinosaur",
+    "linnfrey",
+    "Gamalonia",
+    "Vir",
+    "Pkrsky",
+    "Joboshy",
+    "Bohemian Corporal",
+    "Dan",
+    "Josef Lanzl",
+    "Seth Christensen",
+    "Griffin Dahlberg",
+    "Draven T",
+    "yer fey",
+    "Error_Rule34_Not_found",
+    "Gerald Welly",
+    "Roslynd",
+    "Geolog",
+    "jinxedx",
+    "Neco28",
+    "Aquatic Coffee",
+    "Dankin",
+    "ethanfel",
+    "Cristian Vazquez",
+    "Frank Nitty",
+    "Magic Noob",
+    "Focuschannel",
+    "DougPeterson",
+    "Jeff",
+    "Bruce",
+    "Kevin John Duck",
+    "Anthony Faxlandez",
+    "Kevin Christopher",
+    "Ouro Boros",
+    "Blackfish95",
+    "dd",
+    "Paul Kroll",
+    "MiraiKuriyamaSy",
+    "semicolon drainpipe",
+    "Thesharingbrother",
+    "Bas Imagineer",
+    "Pat Hen",
+    "John Statham",
+    "ResidentDeviant",
+    "Nihongasuki",
+    "JC",
+    "Prompt Pirate",
+    "uwutismxd",
+    "decoy",
+    "Tyrswood",
+    "Ray Wing",
+    "Ranzitho",
+    "Gus",
+    "地獄の禄",
+    "MJG",
+    "David LaVallee",
+    "ae",
+    "Tr4shP4nda",
+    "WRL_SPR",
+    "capn",
+    "Joseph",
+    "Mirko Katzula",
+    "dan",
+    "Piccio08",
+    "kumakichi",
+    "cppbel",
+    "starbugx",
+    "Moon Knight",
+    "몽타주",
+    "Kland",
+    "zenobeus",
+    "Jackthemind",
+    "ryoma",
+    "Stryker",
+    "raf8osz",
+    "ElitaSSJ4",
+    "blikkies",
+    "Chris",
+    "Brian M",
+    "Nerezza",
+    "sanborondon",
+    "Taylor Funk",
+    "aezin",
+    "Thought2Form",
+    "jcay015",
+    "Kevin Picco",
+    "Erik Lopez",
+    "Shock Shockor",
+    "Mateo Curić",
+    "Goldwaters",
+    "Zude",
+    "Eris3D",
+    "m",
+    "Pierce McBride",
+    "Joshua Gray",
+    "Kyler",
+    "Mikko Hemilä",
+    "aRtFuL_DodGeR",
+    "Jamie Ogletree",
+    "a _",
+    "James Coleman",
+    "CrimsonDX",
+    "Martial",
+    "battu",
+    "Emil Andersson",
+    "Chad Idk",
+    "DarkSunset",
+    "Billy Gladky",
+    "Yuji Kaneko",
+    "Probis",
+    "Dušan Ryban",
+    "ItsGeneralButtNaked",
+    "Jordan Shaw",
+    "Rops Alot",
+    "Sam",
+    "sjon kreutz",
+    "Nimess",
+    "SRDB",
+    "Ace Ventura",
+    "g unit",
+    "Youguang",
+    "Metryman55",
+    "andrewzpong",
+    "FrxzenSnxw",
+    "BossGame",
+    "lrdchs",
+    "momokai",
+    "Hailshem",
+    "kudari",
+    "Naomi Hale Danchi",
+    "dc7431",
+    "ken",
+    "Inversity",
+    "AIVORY3D",
+    "epicgamer0020690",
+    "Joshua Porrata",
+    "keemun",
+    "SuBu",
+    "RedPIXel",
+    "Kevinj",
+    "Wind",
+    "Nexus",
+    "Ramneek“Guy”Ashok",
+    "squid_actually",
+    "Nat_20",
+    "Edward Weeks",
+    "kyoumei",
+    "RadStorm04",
+    "JohnDoe42054",
+    "BillyHill",
+    "emyth",
+    "chriphost",
+    "KitKatM",
+    "socrasteeze",
+    "ResidentDeviant",
+    "gzmzmvp",
+    "Welkor",
+    "John Martin",
+    "Richard",
+    "Andrew",
+    "Robert Wegemund",
+    "Littlehuggy",
+    "moranqianlong",
+    "Gregory Kozhemiak",
+    "mrjuan",
+    "Brian Buie",
+    "Sadlip",
+    "Haru Yotu",
+    "Eric Whitney",
+    "Joey Callahan",
+    "Ivan Tadic",
+    "Mike Simone",
+    "Morgandel",
+    "Kyron Mahan",
+    "Matura Arbeit",
+    "Noah",
+    "Jacob McDaniel",
+    "X",
+    "Sloan Steddy",
+    "TBitz33",
+    "Anonym dkjglfleeoeldldldlkf",
+    "Temikus",
+    "Artokun",
+    "Michael Taylor",
+    "SendingRavens",
+    "Derek Baker",
+    "Michael Anthony Scott",
+    "Atilla Berke Pekduyar",
+    "Michael Docherty",
+    "Nathan",
+    "Decx _",
+    "Paul Hartsuyker",
+    "elitassj",
+    "Jacob Winter",
+    "Distortik",
+    "David",
+    "Meilo",
+    "Pen Bouryoung",
+    "四糸凜音",
+    "shinonomeiro",
+    "Snille",
+    "MaartenAlbers",
+    "khanh duy",
+    "xybrightsummer",
+    "jreedatchison",
+    "PhilW",
+    "Tree Tagger",
+    "Janik",
+    "Crocket",
+    "Cruel",
+    "MRBlack",
+    "Mitchell Robson",
+    "Kiyoe",
+    "humptynutz",
+    "michael.isaza",
+    "Kalnei",
+    "Whitepinetrader",
+    "OrganicArtifact",
+    "Scott",
+    "MudkipMedkitz",
+    "deanbrian",
+    "POPPIN",
+    "Alex Wortman",
+    "Cody",
+    "Raku",
+    "smart.edge5178",
+    "emadsultan",
+    "InformedViewz",
+    "CHKeeho80",
+    "Bubbafett",
+    "leaf",
+    "Menard",
+    "Skyfire83",
+    "Adam Rinehart",
+    "D",
+    "Pitpe11",
+    "TheD1rtyD03",
+    "moonpetal",
+    "SomeDude",
+    "g9p0o",
+    "nanana",
+    "TheHolySheep",
+    "Monte Won",
+    "SpringBootisTrash",
+    "carsten",
+    "ikok",
+    "Buecyb99",
+    "4IXplr0r3r",
+    "dfklsjfkljslfjd",
+    "hayden",
+    "ahoystan",
+    "Leland Saunders",
+    "Wolfe7D1",
+    "Ink Temptation",
+    "Bob Barker",
+    "edk",
+    "Kalli Core",
+    "Aeternyx",
+    "elleshar666",
+    "YOU SINWOO",
+    "ja s",
+    "Doug Mason",
+    "Kauffy",
+    "Jeremy Townsend",
+    "EpicElric",
+    "Sean voets",
+    "Owen Gwosdz",
+    "John J Linehan",
+    "Elliot E",
+    "Thomas Wanner",
+    "Theerat Jiramate",
+    "Edward Kennedy",
+    "Justin Blaylock",
+    "Devil Lude",
+    "Nick Kage",
+    "kevin stoddard",
+    "Jack Dole",
+    "Vane Holzer",
+    "psytrax",
+    "Ezokewn",
+    "hexxish",
+    "CptNeo",
+    "notedfakes",
+    "Maso",
+    "Eric Ketchum",
+    "NICHOLAS BAXLEY",
+    "Michael Scott",
+    "Kevin Wallace",
+    "Matheus Couto",
+    "Saya",
+    "ChicRic",
+    "mercur",
+    "J C",
+    "Ed Wang",
+    "Ryan Presley Ng",
+    "Wes Sims",
+    "Donor4115",
+    "Yves Poezevara",
+    "Teriak47",
+    "Just me",
+    "Raf Stahelin",
+    "Вячеслав Маринин",
+    "Lyavph",
+    "Filippo Ferrari",
+    "Cola Matthew",
+    "OniNoKen",
+    "Iain Wisely",
+    "Zertens",
+    "NOHOW",
+    "Apo",
+    "nekotxt",
+    "choowkee",
+    "Clusters",
+    "ibrahim",
+    "Highlandrise",
+    "philcoraz",
+    "mztn",
+    "ImagineerNL",
+    "MrAcrtosSursus",
+    "al300680",
+    "pixl",
+    "Robin",
+    "chahknoir",
+    "Marcus thronico",
+    "nd",
+    "keno94d",
+    "James Melzer",
+    "Bartleby",
+    "Renvertere",
+    "Rahuy",
+    "Hermann003",
+    "D",
+    "Foolish",
+    "RevyHiep",
+    "Captain_Swag",
+    "obkircher",
+    "gwyar",
+    "D",
+    "edgecase",
+    "Neoxena",
+    "mrmhalo",
+    "dg",
+    "Maarten Harms",
+    "Israel",
+    "Muratoraccio",
+    "SelfishMedic",
+    "Ginnie",
+    "adderleighn",
+    "EnragedAntelope",
+    "Alan+Cano",
+    "FeralOpticsAI",
+    "Pavlaki",
+    "generic404",
+    "Mateusz+Kosela",
+    "Doug+Rintoul",
+    "Noor",
+    "Yorunai",
+    "Bula",
+    "quantenmecha",
+    "abattoirblues",
+    "Jason+Nash",
+    "BillyBoy84",
+    "DarkRoast",
+    "zounik",
+    "letzte",
+    "Nasty+Hobbit",
+    "SgtFluffles",
+    "lrdchs2",
+    "Duk3+Rand0m",
+    "KUJYAKU",
+    "NathenChoi",
+    "Thomas+Reck",
+    "Larses",
+    "cocona",
+    "Coeur+de+cochon",
+    "David Schenck",
+    "han b",
+    "Nico",
+    "Banana Joe",
+    "_ G3n",
+    "Donovan Jenkins",
+    "JBsuede",
+    "Michael Eid",
+    "beersandbacon",
+    "Maximilian Pyko",
+    "Invis",
+    "Justin Houston",
+    "Time Valentine",
+    "james",
+    "OrochiNights",
+    "Michael Zhu",
+    "ACTUALLY_the_Real_Willem_Dafoe",
+    "gonzalo",
+    "Seraphy",
+    "Михал Михалыч",
+    "雨の心 落",
+    "Matt",
+    "AllTimeNoobie",
+    "jumpd",
+    "John C",
+    "Rim",
+    "Dismem",
+    "Frogmilk",
+    "SPJ",
+    "Xan Dionysus",
+    "Nathan lee",
+    "Mewtora",
+    "Middo",
+    "Forbidden Atelier",
+    "Bryan Rutkowski",
+    "Adictedtohumping",
+    "Towelie",
+    "Cyrus Fett",
+    "Jean-françois SEMA",
+    "Kurt",
+    "max blo",
+    "Xenon Xue",
+    "JackJohnnyJim",
+    "Edward Ten Eyck",
+    "Chase Kwon",
+    "Inyoshu",
+    "Goober719",
+    "Chad Barnes",
+    "James Ming",
+    "vanditking",
+    "kripitonga",
+    "Rizzi",
+    "nimin",
+    "OMAR LUCIANO",
+    "hannibal",
+    "Jo+Example",
+    "BrentBertram",
+    "eumelzocker",
+    "dxjaymz",
+    "L C",
+    "Dude"
+  ],
+  "totalCount": 666
+}

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

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "Abbrechen",
+        "confirm": "Bestätigen",
         "actions": {
             "save": "Speichern",
             "cancel": "Abbrechen",
+            "confirm": "Bestätigen",
             "delete": "Löschen",
             "move": "Verschieben",
             "refresh": "Aktualisieren",
@@ -11,7 +14,8 @@
             "backToTop": "Nach oben",
             "settings": "Einstellungen",
             "help": "Hilfe",
-            "add": "Hinzufügen"
+            "add": "Hinzufügen",
+            "close": "Schließen"
         },
         "status": {
             "loading": "Wird geladen...",
@@ -171,6 +175,9 @@
             "success": "{count} Rezepte erfolgreich repariert.",
             "cancelled": "Reparatur abgebrochen. {count} Rezepte wurden repariert.",
             "error": "Recipe-Reparatur fehlgeschlagen: {message}"
+        },
+        "manageExcludedModels": {
+            "label": "Ausgeschlossene Modelle verwalten"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "Voreinstellung \"{name}\" existiert bereits. Überschreiben?",
             "presetNamePlaceholder": "Voreinstellungsname...",
             "baseModel": "Basis-Modell",
+            "baseModelSearchPlaceholder": "Basismodelle durchsuchen...",
             "modelTags": "Tags (Top 20)",
-            "modelTypes": "Model Types",
+            "modelTypes": "Modelltypen",
             "license": "Lizenz",
             "noCreditRequired": "Kein Credit erforderlich",
             "allowSellingGeneratedContent": "Verkauf erlaubt",
             "noTags": "Keine Tags",
+            "noBaseModelMatches": "Keine Basismodelle entsprechen der aktuellen Suche.",
             "clearAll": "Alle Filter löschen",
             "any": "Beliebig",
             "all": "Alle",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Civitai API Key",
         "civitaiApiKeyPlaceholder": "Geben Sie Ihren Civitai API Key ein",
         "civitaiApiKeyHelp": "Wird für die Authentifizierung beim Herunterladen von Modellen von Civitai verwendet",
+        "civitaiHost": {
+            "label": "Civitai-Host",
+            "help": "Wählen Sie aus, welche Civitai-Seite geöffnet wird, wenn Sie „View on Civitai“-Links verwenden.",
+            "options": {
+                "com": "civitai.com (nur SFW)",
+                "red": "civitai.red (uneingeschränkt)"
+            }
+        },
+        "downloadBackend": {
+            "label": "Download-Backend",
+            "help": "Wähle aus, wie Modelldateien heruntergeladen werden. Python verwendet den eingebauten Downloader. aria2 verwendet den experimentellen externen Downloader-Prozess.",
+            "options": {
+                "python": "Python (integriert)",
+                "aria2": "aria2 (experimentell)"
+            }
+        },
+        "aria2cPath": {
+            "label": "aria2c-Pfad",
+            "help": "Optionaler Pfad zur ausführbaren aria2c-Datei. Leer lassen, um aria2c aus dem System-PATH zu verwenden.",
+            "placeholder": "Leer lassen, um aria2c aus dem PATH zu verwenden"
+        },
+        "civitaiHostBanner": {
+            "title": "Civitai-Host-Einstellung verfügbar",
+            "content": "Civitai verwendet jetzt civitai.com für SFW-Inhalte und civitai.red für uneingeschränkte Inhalte. In den Einstellungen können Sie ändern, welche Seite standardmäßig geöffnet wird.",
+            "openSettings": "Einstellungen öffnen"
+        },
         "openSettingsFileLocation": {
             "label": "Einstellungsordner öffnen",
             "tooltip": "Den Ordner mit der settings.json öffnen",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "Inhaltsfilterung",
+            "downloads": "Downloads",
             "videoSettings": "Video-Einstellungen",
             "layoutSettings": "Layout-Einstellungen",
             "misc": "Verschiedenes",
+            "backup": "Backups",
             "folderSettings": "Standard-Roots",
+            "recipeSettings": "Rezepte",
             "extraFolderPaths": "Zusätzliche Ordnerpfade",
             "downloadPathTemplates": "Download-Pfad-Vorlagen",
             "priorityTags": "Prioritäts-Tags",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "NSFW-Inhalte unscharf stellen",
             "blurNsfwContentHelp": "Nicht jugendfreie (NSFW) Vorschaubilder unscharf stellen",
             "showOnlySfw": "Nur SFW-Ergebnisse anzeigen",
-            "showOnlySfwHelp": "Alle NSFW-Inhalte beim Durchsuchen und Suchen herausfiltern"
+            "showOnlySfwHelp": "Alle NSFW-Inhalte beim Durchsuchen und Suchen herausfiltern",
+            "matureBlurThreshold": "Schwelle für Unschärfe bei jugendgefährdenden Inhalten",
+            "matureBlurThresholdHelp": "Legen Sie fest, ab welcher Altersfreigabe die Unschärfe beginnt, wenn NSFW-Unschärfe aktiviert ist.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 und höher",
+                "r": "R und höher (Standard)",
+                "x": "X und höher",
+                "xxx": "Nur XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Videos bei Hover automatisch abspielen",
@@ -311,6 +357,54 @@
                 "saveFailed": "Übersprungene Pfade konnten nicht gespeichert werden: {message}"
             }
         },
+        "backup": {
+            "autoEnabled": "Automatische Backups",
+            "autoEnabledHelp": "Erstellt einmal täglich einen lokalen Schnappschuss und behält die neuesten Schnappschüsse gemäß der Aufbewahrungsrichtlinie.",
+            "retention": "Aufbewahrungsanzahl",
+            "retentionHelp": "Wie viele automatische Schnappschüsse behalten werden, bevor ältere entfernt werden.",
+            "management": "Backup-Verwaltung",
+            "managementHelp": "Exportiere deinen aktuellen Benutzerstatus oder stelle ihn aus einem Backup-Archiv wieder her.",
+            "scopeHelp": "Sichert deine Einstellungen, den Downloadverlauf und den Status der Modellaktualisierung. Modelldateien und neu erzeugbare Caches sind nicht enthalten.",
+            "locationSummary": "Aktueller Backup-Speicherort",
+            "openFolderButton": "Backup-Ordner öffnen",
+            "openFolderSuccess": "Backup-Ordner geöffnet",
+            "openFolderFailed": "Backup-Ordner konnte nicht geöffnet werden",
+            "locationCopied": "Backup-Pfad in die Zwischenablage kopiert: {{path}}",
+            "locationClipboardFallback": "Backup-Pfad: {{path}}",
+            "exportButton": "Backup exportieren",
+            "exportSuccess": "Backup erfolgreich exportiert.",
+            "exportFailed": "Backup konnte nicht exportiert werden: {message}",
+            "importButton": "Backup importieren",
+            "importConfirm": "Dieses Backup importieren und den lokalen Benutzerstatus überschreiben?",
+            "importSuccess": "Backup erfolgreich importiert.",
+            "importFailed": "Backup konnte nicht importiert werden: {message}",
+            "latestSnapshot": "Neuester Schnappschuss",
+            "latestAutoSnapshot": "Neuester automatischer Schnappschuss",
+            "snapshotCount": "Gespeicherte Schnappschüsse",
+            "noneAvailable": "Noch keine Schnappschüsse vorhanden"
+        },
+        "downloadSkipBaseModels": {
+            "label": "Downloads für Basismodelle überspringen",
+            "help": "Gilt für alle Download-Abläufe. Hier können nur unterstützte Basismodelle ausgewählt werden.",
+            "searchPlaceholder": "Basismodelle filtern...",
+            "empty": "Keine Basismodelle entsprechen der aktuellen Suche.",
+            "summary": {
+                "none": "Nichts ausgewählt",
+                "count": "{count} ausgewählt"
+            },
+            "actions": {
+                "edit": "Bearbeiten",
+                "collapse": "Einklappen",
+                "clear": "Löschen"
+            },
+            "validation": {
+                "saveFailed": "Ausgeschlossene Basismodelle konnten nicht gespeichert werden: {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "Bereits heruntergeladene Modellversionen überspringen",
+            "help": "Wenn aktiviert, überspringt LoRA Manager den Download einer Modellversion, wenn der Download-Verlaufsdienst diese spezifische Version als bereits heruntergeladen erfasst hat. Gilt für alle Download-Abläufe."
+        },
         "layoutSettings": {
             "displayDensity": "Anzeige-Dichte",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "Legen Sie den Standard-Diffusion-Modell-(UNET)-Stammordner für Downloads, Importe und Verschiebungen fest",
             "defaultEmbeddingRoot": "Embedding-Stammordner",
             "defaultEmbeddingRootHelp": "Legen Sie den Standard-Embedding-Stammordner für Downloads, Importe und Verschiebungen fest",
+            "recipesPath": "Rezepte-Speicherpfad",
+            "recipesPathHelp": "Optionales benutzerdefiniertes Verzeichnis für gespeicherte Rezepte. Leer lassen, um den recipes-Ordner im ersten LoRA-Stammverzeichnis zu verwenden.",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "Rezepte-Speicher wird verschoben...",
             "noDefault": "Kein Standard"
         },
+        "extraFolderPaths": {
+            "title": "Zusätzliche Ordnerpfade",
+            "description": "Zusätzliche Modellstammverzeichnisse, die ausschließlich für LoRA Manager gelten. Laden Sie Modelle von Speicherorten außerhalb der Standardordner von ComfyUI – ideal für große Bibliotheken, die ComfyUI sonst verlangsamen würden.",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "LoRA-Pfade",
+                "checkpoint": "Checkpoint-Pfade",
+                "unet": "Diffusionsmodell-Pfade",
+                "embedding": "Embedding-Pfade"
+            },
+            "pathPlaceholder": "/pfad/zu/extra/modellen",
+            "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert. Neustart erforderlich, um Änderungen anzuwenden.",
+            "saveError": "Fehler beim Aktualisieren der zusätzlichen Ordnerpfade: {message}",
+            "validation": {
+                "duplicatePath": "Dieser Pfad ist bereits konfiguriert"
+            }
+        },
         "priorityTags": {
             "title": "Prioritäts-Tags",
             "description": "Passen Sie die Tag-Prioritätsreihenfolge für jeden Modelltyp an (z. B. character, concept, style(toon|toon_style))",
@@ -485,23 +600,6 @@
             "proxyPassword": "Passwort (optional)",
             "proxyPasswordPlaceholder": "passwort",
             "proxyPasswordHelp": "Passwort für die Proxy-Authentifizierung (falls erforderlich)"
-        },
-        "extraFolderPaths": {
-            "title": "Zusätzliche Ordnerpfade",
-            "help": "Fügen Sie zusätzliche Modellordner außerhalb der Standardpfade von ComfyUI hinzu. Diese Pfade werden separat gespeichert und zusammen mit den Standardordnern gescannt.",
-            "description": "Konfigurieren Sie zusätzliche Ordner zum Scannen von Modellen. Diese Pfade sind spezifisch für LoRA Manager und werden mit den Standardpfaden von ComfyUI zusammengeführt.",
-            "modelTypes": {
-                "lora": "LoRA-Pfade",
-                "checkpoint": "Checkpoint-Pfade",
-                "unet": "Diffusionsmodell-Pfade",
-                "embedding": "Embedding-Pfade"
-            },
-            "pathPlaceholder": "/pfad/zu/extra/modellen",
-            "saveSuccess": "Zusätzliche Ordnerpfade aktualisiert.",
-            "saveError": "Fehler beim Aktualisieren der zusätzlichen Ordnerpfade: {message}",
-            "validation": {
-                "duplicatePath": "Dieser Pfad ist bereits konfiguriert"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle überspringen",
             "resumeMetadataRefresh": "Metadaten-Aktualisierung für ausgewählte Modelle fortsetzen",
             "deleteAll": "Alle Modelle löschen",
+            "downloadMissingLoras": "Fehlende LoRAs herunterladen",
             "clear": "Auswahl löschen",
             "skipMetadataRefreshCount": "Überspringen（{count} Modelle）",
             "resumeMetadataRefreshCount": "Fortsetzen（{count} Modelle）",
@@ -600,6 +699,7 @@
             "moveToFolder": "In Ordner verschieben",
             "repairMetadata": "Metadaten reparieren",
             "excludeModel": "Modell ausschließen",
+            "restoreModel": "Modell wiederherstellen",
             "deleteModel": "Modell löschen",
             "shareRecipe": "Rezept teilen",
             "viewAllLoras": "Alle LoRAs anzeigen",
@@ -641,6 +741,8 @@
                 "root": "Stammverzeichnis",
                 "browseFolders": "Ordner durchsuchen:",
                 "downloadAndSaveRecipe": "Herunterladen & Rezept speichern",
+                "importRecipeOnly": "Nur Rezept importieren",
+                "importAndDownload": "Importieren & Herunterladen",
                 "downloadMissingLoras": "Fehlende LoRAs herunterladen",
                 "saveRecipe": "Rezept speichern",
                 "loraCountInfo": "({existing}/{total} in Bibliothek)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "Wenigste"
             },
             "refresh": {
-                "title": "Rezeptliste aktualisieren"
+                "title": "Rezeptliste aktualisieren",
+                "quick": "Änderungen synchronisieren",
+                "quickTooltip": "Änderungen synchronisieren - schnelle Aktualisierung ohne Cache-Neubau",
+                "full": "Cache neu aufbauen",
+                "fullTooltip": "Cache neu aufbauen - vollständiger Rescan aller Rezeptdateien"
             },
             "filteredByLora": "Gefiltert nach LoRA",
             "favorites": {
@@ -722,6 +828,64 @@
                 "failed": "Rezept-Reparatur fehlgeschlagen: {message}",
                 "missingId": "Rezept kann nicht repariert werden: Fehlende Rezept-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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "In {otherType}-Ordner verschieben"
+            "moveToOtherTypeFolder": "In {otherType}-Ordner verschieben",
+            "sendToWorkflow": "An Workflow senden"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "Sidebar lösen",
         "switchToListView": "Zur Listenansicht wechseln",
         "switchToTreeView": "Zur Baumansicht wechseln",
-        "recursiveOn": "Unterordner durchsuchen",
-        "recursiveOff": "Nur aktuellen Ordner durchsuchen",
+        "recursiveOn": "Unterordner einbeziehen",
+        "recursiveOff": "Nur aktueller Ordner",
         "recursiveUnavailable": "Rekursive Suche ist nur in der Baumansicht verfügbar",
         "collapseAllDisabled": "Im Listenmodus nicht verfügbar",
         "dragDrop": {
             "unableToResolveRoot": "Zielpfad für das Verschieben konnte nicht ermittelt werden.",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "Verschieben wird für dieses Element nicht unterstützt.",
+            "createFolderHint": "Loslassen, um einen neuen Ordner zu erstellen",
+            "newFolderName": "Neuer Ordnername",
+            "folderNameHint": "Eingabetaste zum Bestätigen, Escape zum Abbrechen",
+            "emptyFolderName": "Bitte geben Sie einen Ordnernamen ein",
+            "invalidFolderName": "Ordnername enthält ungültige Zeichen",
+            "noDragState": "Kein ausstehender Ziehvorgang gefunden"
+        },
+        "empty": {
+            "noFolders": "Keine Ordner gefunden",
+            "dragHint": "Elemente hierher ziehen, um Ordner zu erstellen"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "Early Access",
             "earlyAccessTooltip": "Early Access erforderlich",
             "inLibrary": "In Bibliothek",
+            "downloaded": "Heruntergeladen",
+            "downloadedTooltip": "Zuvor heruntergeladen, aber derzeit nicht in Ihrer Bibliothek.",
             "alreadyInLibrary": "Bereits in Bibliothek",
             "autoOrganizedPath": "[Automatisch organisiert durch Pfadvorlage]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "Basis-Modell aktualisieren",
             "cancel": "Abbrechen"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Fehlende LoRAs herunterladen",
+            "message": "{uniqueCount} einzigartige fehlende LoRAs gefunden (von insgesamt {totalCount} in ausgewählten Rezepten).",
+            "previewTitle": "Zu herunterladende LoRAs:",
+            "moreItems": "...und {count} weitere",
+            "note": "Dateien werden mit Standard-Pfad-Vorlagen heruntergeladen. Dies kann je nach Anzahl der LoRAs eine Weile dauern.",
+            "downloadButton": "{count} LoRA(s) herunterladen"
+        },
         "exampleAccess": {
             "title": "Lokale Beispielbilder",
             "message": "Keine lokalen Beispielbilder für dieses Modell gefunden. Ansichtsoptionen:",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "Auf Civitai anzeigen",
                 "viewOnCivitaiText": "Auf Civitai anzeigen",
                 "viewCreatorProfile": "Ersteller-Profil anzeigen",
-                "openFileLocation": "Dateispeicherort öffnen"
+                "openFileLocation": "Dateispeicherort öffnen",
+                "sendToWorkflow": "An ComfyUI senden",
+                "sendToWorkflowText": "An ComfyUI senden"
             },
             "openFileLocation": {
                 "success": "Dateispeicherort erfolgreich geöffnet",
@@ -964,6 +1151,9 @@
                 "copied": "Pfad in die Zwischenablage kopiert: {{path}}",
                 "clipboardFallback": "Pfad: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Kann nicht an ComfyUI senden: Kein Dateipfad verfügbar"
+            },
             "metadata": {
                 "version": "Version",
                 "fileName": "Dateiname",
@@ -1071,17 +1261,30 @@
                     "days": "in {count}d"
                 },
                 "badges": {
-                    "current": "Aktuelle Version",
+                    "current": "Geöffnete Version",
+                    "currentTooltip": "Das ist die Version, mit der dieses Modal geöffnet wurde",
                     "inLibrary": "In der Bibliothek",
+                    "inLibraryTooltip": "Diese Version befindet sich in Ihrer lokalen Bibliothek",
+                    "downloaded": "Heruntergeladen",
+                    "downloadedTooltip": "Diese Version wurde bereits heruntergeladen, befindet sich aber derzeit nicht in Ihrer Bibliothek",
                     "newer": "Neuere Version",
+                    "newerTooltip": "Diese Version ist neuer als Ihre neueste lokale Version",
                     "earlyAccess": "Früher Zugriff",
-                    "ignored": "Ignoriert"
+                    "earlyAccessTooltip": "Für diese Version ist derzeit Civitai Early Access erforderlich",
+                    "ignored": "Ignoriert",
+                    "ignoredTooltip": "Für diese Version sind Update-Benachrichtigungen deaktiviert"
                 },
                 "actions": {
                     "download": "Herunterladen",
+                    "downloadTooltip": "Diese Version herunterladen",
+                    "downloadEarlyAccessTooltip": "Diese Early-Access-Version von Civitai herunterladen",
                     "delete": "Löschen",
+                    "deleteTooltip": "Diese lokale Version löschen",
                     "ignore": "Ignorieren",
                     "unignore": "Ignorierung aufheben",
+                    "ignoreTooltip": "Update-Benachrichtigungen für diese Version ignorieren",
+                    "unignoreTooltip": "Update-Benachrichtigungen für diese Version fortsetzen",
+                    "viewVersionOnCivitai": "Version auf Civitai anzeigen",
                     "earlyAccessTooltip": "Erfordert Early-Access-Kauf",
                     "resumeModelUpdates": "Aktualisierungen für dieses Modell fortsetzen",
                     "ignoreModelUpdates": "Aktualisierungen für dieses Modell ignorieren",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "Rezept im Workflow ersetzt",
             "recipeFailedToSend": "Fehler beim Senden des Rezepts an den Workflow",
             "noMatchingNodes": "Keine kompatiblen Knoten im aktuellen Workflow verfügbar",
-            "noTargetNodeSelected": "Kein Zielknoten ausgewählt"
+            "noTargetNodeSelected": "Kein Zielknoten ausgewählt",
+            "modelUpdated": "Modell im Workflow aktualisiert",
+            "modelFailed": "Fehler beim Aktualisieren des Modellknotens"
         },
         "nodeSelector": {
             "recipe": "Rezept",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "WeChat QR-Code anzeigen",
             "hideWechatQR": "WeChat QR-Code ausblenden"
         },
-        "footer": "Vielen Dank, dass Sie LoRA Manager verwenden! ❤️"
+        "footer": "Vielen Dank, dass Sie LoRA Manager verwenden! ❤️",
+        "supporters": {
+            "title": "Danke an alle Unterstützer",
+            "subtitle": "Danke an {count} Unterstützer, die dieses Projekt möglich gemacht haben",
+            "specialThanks": "Besonderer Dank",
+            "allSupporters": "Alle Unterstützer",
+            "totalCount": "{count} Unterstützer insgesamt"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "Bitte wählen Sie eine Version aus",
             "versionExists": "Diese Version existiert bereits in Ihrer Bibliothek",
             "downloadCompleted": "Download erfolgreich abgeschlossen",
+            "downloadSkippedByBaseModel": "Download übersprungen, weil das Basismodell {baseModel} ausgeschlossen ist",
             "autoOrganizeSuccess": "Automatische Organisation für {count} {type} erfolgreich abgeschlossen",
             "autoOrganizePartialSuccess": "Automatische Organisation abgeschlossen: {success} verschoben, {failures} fehlgeschlagen von insgesamt {total} Modellen",
             "autoOrganizeFailed": "Automatische Organisation fehlgeschlagen: {error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "Fehler beim Laden der {modelType}s: {message}",
             "refreshComplete": "Aktualisierung abgeschlossen",
             "refreshFailed": "Fehler beim Aktualisieren der Rezepte: {message}",
+            "syncComplete": "Synchronisation abgeschlossen",
+            "syncFailed": "Fehler beim Synchronisieren der Rezepte: {message}",
             "updateFailed": "Fehler beim Aktualisieren des Rezepts: {error}",
             "updateError": "Fehler beim Aktualisieren des Rezepts: {message}",
             "nameSaved": "Rezept \"{name}\" erfolgreich gespeichert",
             "nameUpdated": "Rezeptname erfolgreich aktualisiert",
             "tagsUpdated": "Rezept-Tags erfolgreich aktualisiert",
             "sourceUrlUpdated": "Quell-URL erfolgreich aktualisiert",
+            "promptUpdated": "Prompt erfolgreich aktualisiert",
+            "negativePromptUpdated": "Negativer Prompt erfolgreich aktualisiert",
+            "promptEditorHint": "Drücken Sie Enter zum Speichern, Shift+Enter für neue Zeile",
             "noRecipeId": "Keine Rezept-ID verfügbar",
+            "sendToWorkflowFailed": "Fehler beim Senden des Rezepts an den Workflow: {message}",
             "copyFailed": "Fehler beim Kopieren der Rezept-Syntax: {message}",
             "noMissingLoras": "Keine fehlenden LoRAs zum Herunterladen",
             "missingLorasInfoFailed": "Fehler beim Abrufen der Informationen für fehlende LoRAs",
@@ -1410,9 +1629,20 @@
             "processingError": "Verarbeitungsfehler: {message}",
             "folderBrowserError": "Fehler beim Laden des Ordner-Browsers: {message}",
             "recipeSaveFailed": "Fehler beim Speichern des Rezepts: {error}",
+            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Import fehlgeschlagen: {message}",
             "folderTreeFailed": "Fehler beim Laden des Ordnerbaums",
-            "folderTreeError": "Fehler beim Laden des Ordnerbaums"
+            "folderTreeError": "Fehler beim Laden des Ordnerbaums",
+            "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}",
+            "noRecipesSelected": "Keine Rezepte ausgewählt",
+            "noMissingLorasInSelection": "Keine fehlenden LoRAs in ausgewählten Rezepten gefunden",
+            "noLoraRootConfigured": "Kein LoRA-Stammverzeichnis konfiguriert. Bitte legen Sie ein Standard-LoRA-Stammverzeichnis in den Einstellungen fest."
         },
         "models": {
             "noModelsSelected": "Keine Modelle ausgewählt",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "Fehler beim Speichern der Basis-Modell-Zuordnungen: {message}",
             "downloadTemplatesUpdated": "Download-Pfad-Vorlagen aktualisiert",
             "downloadTemplatesFailed": "Fehler beim Speichern der Download-Pfad-Vorlagen: {message}",
+            "recipesPathUpdated": "Rezepte-Speicherpfad aktualisiert",
+            "recipesPathSaveFailed": "Fehler beim Aktualisieren des Rezepte-Speicherpfads: {message}",
             "settingsUpdated": "Einstellungen aktualisiert: {setting}",
             "compactModeToggled": "Kompakt-Modus {state}",
             "settingSaveFailed": "Fehler beim Speichern der Einstellung: {message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "Fehler beim Löschen von {type}: {message}",
             "excludeSuccess": "{type} erfolgreich ausgeschlossen",
             "excludeFailed": "Fehler beim Ausschließen von {type}: {message}",
+            "restoreSuccess": "{type} erfolgreich wiederhergestellt",
+            "restoreFailed": "{type} konnte nicht wiederhergestellt werden: {message}",
             "fileNameUpdated": "Dateiname erfolgreich aktualisiert",
             "fileRenameFailed": "Fehler beim Umbenennen der Datei: {error}",
             "previewUpdated": "Vorschau erfolgreich aktualisiert",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "Systemdiagnose",
+        "title": "Doktor",
+        "buttonTitle": "Diagnose und häufige Fehlerbehebungen ausführen",
+        "loading": "Umgebung wird geprüft...",
+        "footer": "Exportiere ein Diagnosepaket, falls das Problem nach der Reparatur weiterhin besteht.",
+        "summary": {
+            "idle": "Führe eine Überprüfung von Einstellungen, Cache-Integrität und UI-Konsistenz durch.",
+            "ok": "Keine aktiven Probleme wurden in der aktuellen Umgebung gefunden.",
+            "warning": "{count} Problem(e) wurden gefunden. Die meisten lassen sich direkt über dieses Panel beheben.",
+            "error": "Bevor die App vollständig fehlerfrei ist, müssen {count} Problem(e) behoben werden."
+        },
+        "status": {
+            "ok": "Gesund",
+            "warning": "Handlungsbedarf",
+            "error": "Aktion erforderlich"
+        },
+        "actions": {
+            "runAgain": "Erneut ausführen",
+            "exportBundle": "Paket exportieren"
+        },
+        "toast": {
+            "loadFailed": "Diagnose konnte nicht geladen werden: {message}",
+            "repairSuccess": "Cache-Neuaufbau abgeschlossen.",
+            "repairFailed": "Cache-Neuaufbau fehlgeschlagen: {message}",
+            "exportSuccess": "Diagnosepaket exportiert.",
+            "exportFailed": "Export des Diagnosepakets fehlgeschlagen: {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "Anwendungs-Update erkannt",
@@ -1651,4 +1914,4 @@
             "retry": "Wiederholen"
         }
     }
-}
+}

locales/en.json

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "Cancel",
+        "confirm": "Confirm",
         "actions": {
             "save": "Save",
             "cancel": "Cancel",
+            "confirm": "Confirm",
             "delete": "Delete",
             "move": "Move",
             "refresh": "Refresh",
@@ -11,7 +14,8 @@
             "backToTop": "Back to top",
             "settings": "Settings",
             "help": "Help",
-            "add": "Add"
+            "add": "Add",
+            "close": "Close"
         },
         "status": {
             "loading": "Loading...",
@@ -171,6 +175,9 @@
             "success": "Successfully repaired {count} recipes.",
             "cancelled": "Repair cancelled. {count} recipes were repaired.",
             "error": "Recipe repair failed: {message}"
+        },
+        "manageExcludedModels": {
+            "label": "Manage Excluded Models"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "Preset \"{name}\" already exists. Overwrite?",
             "presetNamePlaceholder": "Preset name...",
             "baseModel": "Base Model",
+            "baseModelSearchPlaceholder": "Search base models...",
             "modelTags": "Tags (Top 20)",
             "modelTypes": "Model Types",
             "license": "License",
             "noCreditRequired": "No Credit Required",
             "allowSellingGeneratedContent": "Allow Selling",
             "noTags": "No tags",
+            "noBaseModelMatches": "No base models match the current search.",
             "clearAll": "Clear All Filters",
             "any": "Any",
             "all": "All",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Civitai API Key",
         "civitaiApiKeyPlaceholder": "Enter your Civitai API key",
         "civitaiApiKeyHelp": "Used for authentication when downloading models from Civitai",
+        "civitaiHost": {
+            "label": "Civitai host",
+            "help": "Choose which Civitai site opens when using View on Civitai links.",
+            "options": {
+                "com": "civitai.com (SFW)",
+                "red": "civitai.red (unrestricted)"
+            }
+        },
+        "downloadBackend": {
+            "label": "Download backend",
+            "help": "Choose how model files are downloaded. Python uses the built-in downloader. aria2 uses the experimental external downloader process.",
+            "options": {
+                "python": "Python (built-in)",
+                "aria2": "aria2 (experimental)"
+            }
+        },
+        "aria2cPath": {
+            "label": "aria2c path",
+            "help": "Optional path to the aria2c executable. Leave empty to use aria2c from your system PATH.",
+            "placeholder": "Leave empty to use aria2c from PATH"
+        },
+        "civitaiHostBanner": {
+            "title": "Civitai host preference available",
+            "content": "Civitai now uses civitai.com for SFW content and civitai.red for unrestricted content. You can change which site opens by default in Settings.",
+            "openSettings": "Open Settings"
+        },
         "openSettingsFileLocation": {
             "label": "Open settings folder",
             "tooltip": "Open folder containing settings.json",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "Content Filtering",
+            "downloads": "Downloads",
             "videoSettings": "Video Settings",
             "layoutSettings": "Layout Settings",
             "misc": "Miscellaneous",
+            "backup": "Backups",
             "folderSettings": "Default Roots",
+            "recipeSettings": "Recipes",
             "extraFolderPaths": "Extra Folder Paths",
             "downloadPathTemplates": "Download Path Templates",
             "priorityTags": "Priority Tags",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "Blur NSFW Content",
             "blurNsfwContentHelp": "Blur mature (NSFW) content preview images",
             "showOnlySfw": "Show Only SFW Results",
-            "showOnlySfwHelp": "Filter out all NSFW content when browsing and searching"
+            "showOnlySfwHelp": "Filter out all NSFW content when browsing and searching",
+            "matureBlurThreshold": "Mature Blur Threshold",
+            "matureBlurThresholdHelp": "Set which rating level starts blur filtering when NSFW blur is enabled.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 and above",
+                "r": "R and above (default)",
+                "x": "X and above",
+                "xxx": "XXX only"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Autoplay Videos on Hover",
@@ -311,6 +357,54 @@
                 "saveFailed": "Unable to save skip paths: {message}"
             }
         },
+        "backup": {
+            "autoEnabled": "Automatic backups",
+            "autoEnabledHelp": "Create a local snapshot once per day and keep the latest snapshots according to the retention policy.",
+            "retention": "Retention count",
+            "retentionHelp": "How many automatic snapshots to keep before older ones are pruned.",
+            "management": "Backup management",
+            "managementHelp": "Export your current user state or restore it from a backup archive.",
+            "scopeHelp": "Backs up your settings, download history, and model update state. It does not include model files or rebuildable caches.",
+            "locationSummary": "Current backup location",
+            "openFolderButton": "Open backup folder",
+            "openFolderSuccess": "Opened backup folder",
+            "openFolderFailed": "Failed to open backup folder",
+            "locationCopied": "Backup path copied to clipboard: {{path}}",
+            "locationClipboardFallback": "Backup path: {{path}}",
+            "exportButton": "Export backup",
+            "exportSuccess": "Backup exported successfully.",
+            "exportFailed": "Failed to export backup: {message}",
+            "importButton": "Import backup",
+            "importConfirm": "Import this backup and overwrite local user state?",
+            "importSuccess": "Backup imported successfully.",
+            "importFailed": "Failed to import backup: {message}",
+            "latestSnapshot": "Latest snapshot",
+            "latestAutoSnapshot": "Latest automatic snapshot",
+            "snapshotCount": "Saved snapshots",
+            "noneAvailable": "No snapshots yet"
+        },
+        "downloadSkipBaseModels": {
+            "label": "Skip downloads for base models",
+            "help": "When enabled, versions using the selected base models will be skipped.",
+            "searchPlaceholder": "Filter base models...",
+            "empty": "No base models match the current search.",
+            "summary": {
+                "none": "None selected",
+                "count": "{count} selected"
+            },
+            "actions": {
+                "edit": "Edit",
+                "collapse": "Collapse",
+                "clear": "Clear"
+            },
+            "validation": {
+                "saveFailed": "Unable to save excluded base models: {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "Skip previously downloaded model versions",
+            "help": "When enabled, versions downloaded before will be skipped."
+        },
         "layoutSettings": {
             "displayDensity": "Display Density",
             "displayDensityOptions": {
@@ -359,12 +453,16 @@
             "defaultUnetRootHelp": "Set default diffusion model (UNET) root directory for downloads, imports and moves",
             "defaultEmbeddingRoot": "Embedding Root",
             "defaultEmbeddingRootHelp": "Set default embedding root directory for downloads, imports and moves",
+            "recipesPath": "Recipes Storage Path",
+            "recipesPathHelp": "Optional custom directory for stored recipes. Leave empty to use the first LoRA root's recipes folder.",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "Migrating recipes storage...",
             "noDefault": "No Default"
         },
         "extraFolderPaths": {
             "title": "Extra Folder Paths",
-            "help": "Add additional model folders outside of ComfyUI's standard paths. These paths are stored separately and scanned alongside the default folders.",
-            "description": "Configure additional folders to scan for models. These paths are specific to LoRA Manager and will be merged with ComfyUI's default paths.",
+            "description": "Additional model root paths exclusive to LoRA Manager. Load models from locations outside ComfyUI's standard folders—ideal for large libraries that would otherwise slow down ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
             "modelTypes": {
                 "lora": "LoRA Paths",
                 "checkpoint": "Checkpoint Paths",
@@ -372,7 +470,7 @@
                 "embedding": "Embedding Paths"
             },
             "pathPlaceholder": "/path/to/extra/models",
-            "saveSuccess": "Extra folder paths updated.",
+            "saveSuccess": "Extra folder paths updated. Restart required to apply changes.",
             "saveError": "Failed to update extra folder paths: {message}",
             "validation": {
                 "duplicatePath": "This path is already configured"
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "Skip Metadata Refresh for Selected",
             "resumeMetadataRefresh": "Resume Metadata Refresh for Selected",
             "deleteAll": "Delete Selected Models",
+            "downloadMissingLoras": "Download Missing LoRAs",
             "clear": "Clear Selection",
             "skipMetadataRefreshCount": "Skip ({count} models)",
             "resumeMetadataRefreshCount": "Resume ({count} models)",
@@ -600,6 +699,7 @@
             "moveToFolder": "Move to Folder",
             "repairMetadata": "Repair metadata",
             "excludeModel": "Exclude Model",
+            "restoreModel": "Restore Model",
             "deleteModel": "Delete Model",
             "shareRecipe": "Share Recipe",
             "viewAllLoras": "View All LoRAs",
@@ -618,9 +718,9 @@
                 "title": "Import a recipe from image or URL",
                 "urlLocalPath": "URL / Local Path",
                 "uploadImage": "Upload Image",
-                "urlSectionDescription": "Input a Civitai image URL or local file path to import as a recipe.",
+                "urlSectionDescription": "Input a Civitai image URL from civitai.com or civitai.red, or a local file path, to import as a recipe.",
                 "imageUrlOrPath": "Image URL or File Path:",
-                "urlPlaceholder": "https://civitai.com/images/... or C:/path/to/image.png",
+                "urlPlaceholder": "https://civitai.com/images/... or https://civitai.red/images/... or C:/path/to/image.png",
                 "fetchImage": "Fetch Image",
                 "uploadSectionDescription": "Upload an image with LoRA metadata to import as a recipe.",
                 "selectImage": "Select Image",
@@ -641,6 +741,8 @@
                 "root": "Root",
                 "browseFolders": "Browse Folders:",
                 "downloadAndSaveRecipe": "Download & Save Recipe",
+                "importRecipeOnly": "Import Recipe Only",
+                "importAndDownload": "Import & Download",
                 "downloadMissingLoras": "Download Missing LoRAs",
                 "saveRecipe": "Save Recipe",
                 "loraCountInfo": "({existing}/{total} in library)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "Least"
             },
             "refresh": {
-                "title": "Refresh recipe list"
+                "title": "Refresh recipe list",
+                "quick": "Sync Changes",
+                "quickTooltip": "Sync changes - quick refresh without rebuilding cache",
+                "full": "Rebuild Cache",
+                "fullTooltip": "Rebuild cache - full rescan of all recipe files"
             },
             "filteredByLora": "Filtered by LoRA",
             "favorites": {
@@ -722,6 +828,64 @@
                 "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Move to {otherType} Folder"
+            "moveToOtherTypeFolder": "Move to {otherType} Folder",
+            "sendToWorkflow": "Send to Workflow"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "Unpin Sidebar",
         "switchToListView": "Switch to List View",
         "switchToTreeView": "Switch to Tree View",
-        "recursiveOn": "Search subfolders",
-        "recursiveOff": "Search current folder only",
+        "recursiveOn": "Include subfolders",
+        "recursiveOff": "Current folder only",
         "recursiveUnavailable": "Recursive search is available in tree view only",
         "collapseAllDisabled": "Not available in list view",
         "dragDrop": {
             "unableToResolveRoot": "Unable to determine destination path for move.",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "Move is not supported for this item.",
+            "createFolderHint": "Release to create new folder",
+            "newFolderName": "New folder name",
+            "folderNameHint": "Press Enter to confirm, Escape to cancel",
+            "emptyFolderName": "Please enter a folder name",
+            "invalidFolderName": "Folder name contains invalid characters",
+            "noDragState": "No pending drag operation found"
+        },
+        "empty": {
+            "noFolders": "No folders found",
+            "dragHint": "Drag items here to create folders"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "Early Access",
             "earlyAccessTooltip": "Early access required",
             "inLibrary": "In Library",
+            "downloaded": "Downloaded",
+            "downloadedTooltip": "Previously downloaded, but it is not currently in your library.",
             "alreadyInLibrary": "Already in Library",
             "autoOrganizedPath": "[Auto-organized by path template]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "Update Base Model",
             "cancel": "Cancel"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Download Missing LoRAs",
+            "message": "Found {uniqueCount} unique missing LoRAs (from {totalCount} total across selected recipes).",
+            "previewTitle": "LoRAs to download:",
+            "moreItems": "...and {count} more",
+            "note": "Files will be downloaded using default path templates. This may take a while depending on the number of LoRAs.",
+            "downloadButton": "Download {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Local Example Images",
             "message": "No local example images found for this model. View options:",
@@ -938,9 +1123,9 @@
             },
             "proceedText": "Only proceed if you're sure this is what you want.",
             "urlLabel": "Civitai Model URL:",
-            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
+            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676 or https://civitai.red/models/649516/model-name?modelVersionId=726676",
             "helpText": {
-                "title": "Paste any Civitai model URL. Supported formats:",
+                "title": "Paste any Civitai model URL from civitai.com or civitai.red. Supported formats:",
                 "format1": "https://civitai.com/models/649516",
                 "format2": "https://civitai.com/models/649516?modelVersionId=726676",
                 "format3": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "View on Civitai",
                 "viewOnCivitaiText": "View on Civitai",
                 "viewCreatorProfile": "View Creator Profile",
-                "openFileLocation": "Open File Location"
+                "openFileLocation": "Open File Location",
+                "sendToWorkflow": "Send to ComfyUI",
+                "sendToWorkflowText": "Send to ComfyUI"
             },
             "openFileLocation": {
                 "success": "File location opened successfully",
@@ -964,6 +1151,9 @@
                 "copied": "Path copied to clipboard: {{path}}",
                 "clipboardFallback": "Path: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Unable to send to ComfyUI: No file path available"
+            },
             "metadata": {
                 "version": "Version",
                 "fileName": "File Name",
@@ -1071,17 +1261,30 @@
                     "days": "in {count}d"
                 },
                 "badges": {
-                    "current": "Current Version",
+                    "current": "Opened Version",
+                    "currentTooltip": "This is the version you opened this modal from",
                     "inLibrary": "In Library",
+                    "inLibraryTooltip": "This version exists in your local library",
+                    "downloaded": "Downloaded",
+                    "downloadedTooltip": "This version was downloaded before, but is not currently in your library",
                     "newer": "Newer Version",
+                    "newerTooltip": "This version is newer than your latest local version",
                     "earlyAccess": "Early Access",
-                    "ignored": "Ignored"
+                    "earlyAccessTooltip": "This version currently requires Civitai early access",
+                    "ignored": "Ignored",
+                    "ignoredTooltip": "Update notifications are disabled for this version"
                 },
                 "actions": {
                     "download": "Download",
+                    "downloadTooltip": "Download this version",
+                    "downloadEarlyAccessTooltip": "Download this early access version from Civitai",
                     "delete": "Delete",
+                    "deleteTooltip": "Delete this local version",
                     "ignore": "Ignore",
                     "unignore": "Unignore",
+                    "ignoreTooltip": "Ignore update notifications for this version",
+                    "unignoreTooltip": "Resume update notifications for this version",
+                    "viewVersionOnCivitai": "View version on Civitai",
                     "earlyAccessTooltip": "Requires early access purchase",
                     "resumeModelUpdates": "Resume updates for this model",
                     "ignoreModelUpdates": "Ignore updates for this model",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "Recipe replaced in workflow",
             "recipeFailedToSend": "Failed to send recipe to workflow",
             "noMatchingNodes": "No compatible nodes available in the current workflow",
-            "noTargetNodeSelected": "No target node selected"
+            "noTargetNodeSelected": "No target node selected",
+            "modelUpdated": "Model updated in workflow",
+            "modelFailed": "Failed to update model node"
         },
         "nodeSelector": {
             "recipe": "Recipe",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "Show WeChat QR Code",
             "hideWechatQR": "Hide WeChat QR Code"
         },
-        "footer": "Thank you for using LoRA Manager! ❤️"
+        "footer": "Thank you for using LoRA Manager! ❤️",
+        "supporters": {
+            "title": "Thank You To Our Supporters",
+            "subtitle": "Thanks to {count} supporters who made this project possible",
+            "specialThanks": "Special Thanks",
+            "allSupporters": "All Supporters",
+            "totalCount": "{count} supporters in total"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "Please select a version",
             "versionExists": "This version already exists in your library",
             "downloadCompleted": "Download completed successfully",
+            "downloadSkippedByBaseModel": "Skipped download because base model {baseModel} is excluded",
             "autoOrganizeSuccess": "Auto-organize completed successfully for {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organize completed with {success} moved, {failures} failed out of {total} models",
             "autoOrganizeFailed": "Auto-organize failed: {error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "Failed to load {modelType}s: {message}",
             "refreshComplete": "Refresh complete",
             "refreshFailed": "Failed to refresh recipes: {message}",
+            "syncComplete": "Sync complete",
+            "syncFailed": "Failed to sync recipes: {message}",
             "updateFailed": "Failed to update recipe: {error}",
             "updateError": "Error updating recipe: {message}",
             "nameSaved": "Recipe \"{name}\" saved successfully",
             "nameUpdated": "Recipe name updated successfully",
             "tagsUpdated": "Recipe tags updated successfully",
             "sourceUrlUpdated": "Source URL updated successfully",
+            "promptUpdated": "Prompt updated successfully",
+            "negativePromptUpdated": "Negative prompt updated successfully",
+            "promptEditorHint": "Press Enter to save, Shift+Enter for new line",
             "noRecipeId": "No recipe ID available",
+            "sendToWorkflowFailed": "Failed to send recipe to workflow: {message}",
             "copyFailed": "Error copying recipe syntax: {message}",
             "noMissingLoras": "No missing LoRAs to download",
             "missingLorasInfoFailed": "Failed to get information for missing LoRAs",
@@ -1410,9 +1629,20 @@
             "processingError": "Processing error: {message}",
             "folderBrowserError": "Error loading folder browser: {message}",
             "recipeSaveFailed": "Failed to save recipe: {error}",
+            "recipeSaved": "Recipe saved successfully",
             "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}",
+            "noRecipesSelected": "No recipes selected",
+            "noMissingLorasInSelection": "No missing LoRAs found in selected recipes",
+            "noLoraRootConfigured": "No LoRA root directory configured. Please set a default LoRA root in settings."
         },
         "models": {
             "noModelsSelected": "No models selected",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "Failed to save base model mappings: {message}",
             "downloadTemplatesUpdated": "Download path templates updated",
             "downloadTemplatesFailed": "Failed to save download path templates: {message}",
+            "recipesPathUpdated": "Recipes storage path updated",
+            "recipesPathSaveFailed": "Failed to update recipes storage path: {message}",
             "settingsUpdated": "Settings updated: {setting}",
             "compactModeToggled": "Compact Mode {state}",
             "settingSaveFailed": "Failed to save setting: {message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "Failed to delete {type}: {message}",
             "excludeSuccess": "{type} excluded successfully",
             "excludeFailed": "Failed to exclude {type}: {message}",
+            "restoreSuccess": "{type} restored successfully",
+            "restoreFailed": "Failed to restore {type}: {message}",
             "fileNameUpdated": "File name updated successfully",
             "fileRenameFailed": "Failed to rename file: {error}",
             "previewUpdated": "Preview updated successfully",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "System diagnostics",
+        "title": "Doctor",
+        "buttonTitle": "Run diagnostics and common fixes",
+        "loading": "Checking environment...",
+        "footer": "Export a diagnostics bundle if the issue still persists after repair.",
+        "summary": {
+            "idle": "Run a health check for settings, cache integrity, and UI consistency.",
+            "ok": "No active issues were found in the current environment.",
+            "warning": "{count} issue(s) were found. Most can be fixed directly from this panel.",
+            "error": "{count} issue(s) need attention before the app is fully healthy."
+        },
+        "status": {
+            "ok": "Healthy",
+            "warning": "Needs Attention",
+            "error": "Action Required"
+        },
+        "actions": {
+            "runAgain": "Run Again",
+            "exportBundle": "Export Bundle"
+        },
+        "toast": {
+            "loadFailed": "Failed to load diagnostics: {message}",
+            "repairSuccess": "Cache rebuild completed.",
+            "repairFailed": "Cache rebuild failed: {message}",
+            "exportSuccess": "Diagnostics bundle exported.",
+            "exportFailed": "Failed to export diagnostics bundle: {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "Application Update Detected",
@@ -1651,4 +1914,4 @@
             "retry": "Retry"
         }
     }
-}
+}

locales/es.json

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "Cancelar",
+        "confirm": "Confirmar",
         "actions": {
             "save": "Guardar",
             "cancel": "Cancelar",
+            "confirm": "Confirmar",
             "delete": "Eliminar",
             "move": "Mover",
             "refresh": "Actualizar",
@@ -11,7 +14,8 @@
             "backToTop": "Volver arriba",
             "settings": "Configuración",
             "help": "Ayuda",
-            "add": "Añadir"
+            "add": "Añadir",
+            "close": "Cerrar"
         },
         "status": {
             "loading": "Cargando...",
@@ -171,6 +175,9 @@
             "success": "Se repararon con éxito {count} recetas.",
             "cancelled": "Reparación cancelada. {count} recetas fueron reparadas.",
             "error": "Error al reparar recetas: {message}"
+        },
+        "manageExcludedModels": {
+            "label": "Gestionar modelos excluidos"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "El preset \"{name}\" ya existe. ¿Sobrescribir?",
             "presetNamePlaceholder": "Nombre del preajuste...",
             "baseModel": "Modelo base",
+            "baseModelSearchPlaceholder": "Buscar modelos base...",
             "modelTags": "Etiquetas (Top 20)",
-            "modelTypes": "Model Types",
+            "modelTypes": "Tipos de modelos",
             "license": "Licencia",
             "noCreditRequired": "Sin crédito requerido",
             "allowSellingGeneratedContent": "Venta permitida",
             "noTags": "Sin etiquetas",
+            "noBaseModelMatches": "Ningún modelo base coincide con la búsqueda actual.",
             "clearAll": "Limpiar todos los filtros",
             "any": "Cualquiera",
             "all": "Todos",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Clave API de Civitai",
         "civitaiApiKeyPlaceholder": "Introduce tu clave API de Civitai",
         "civitaiApiKeyHelp": "Utilizada para autenticación al descargar modelos de Civitai",
+        "civitaiHost": {
+            "label": "Host de Civitai",
+            "help": "Elige qué sitio de Civitai se abre al usar los enlaces de \"View on Civitai\".",
+            "options": {
+                "com": "civitai.com (solo SFW)",
+                "red": "civitai.red (sin restricciones)"
+            }
+        },
+        "downloadBackend": {
+            "label": "Backend de descarga",
+            "help": "Elige cómo se descargan los archivos del modelo. Python usa el descargador integrado. aria2 usa el proceso externo experimental de descarga.",
+            "options": {
+                "python": "Python (integrado)",
+                "aria2": "aria2 (experimental)"
+            }
+        },
+        "aria2cPath": {
+            "label": "Ruta de aria2c",
+            "help": "Ruta opcional al ejecutable aria2c. Déjalo vacío para usar aria2c desde el PATH del sistema.",
+            "placeholder": "Déjalo vacío para usar aria2c desde el PATH"
+        },
+        "civitaiHostBanner": {
+            "title": "Preferencia de host de Civitai disponible",
+            "content": "Civitai ahora usa civitai.com para contenido SFW y civitai.red para contenido sin restricciones. Puedes cambiar en Ajustes qué sitio se abre por defecto.",
+            "openSettings": "Abrir ajustes"
+        },
         "openSettingsFileLocation": {
             "label": "Abrir carpeta de ajustes",
             "tooltip": "Abrir la carpeta que contiene settings.json",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "Filtrado de contenido",
+            "downloads": "Descargas",
             "videoSettings": "Configuración de video",
             "layoutSettings": "Configuración de diseño",
             "misc": "Varios",
+            "backup": "Copias de seguridad",
             "folderSettings": "Raíces predeterminadas",
+            "recipeSettings": "Recetas",
             "extraFolderPaths": "Rutas de carpetas adicionales",
             "downloadPathTemplates": "Plantillas de rutas de descarga",
             "priorityTags": "Etiquetas prioritarias",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "Difuminar contenido NSFW",
             "blurNsfwContentHelp": "Difuminar imágenes de vista previa de contenido para adultos (NSFW)",
             "showOnlySfw": "Mostrar solo resultados SFW",
-            "showOnlySfwHelp": "Filtrar todo el contenido NSFW al navegar y buscar"
+            "showOnlySfwHelp": "Filtrar todo el contenido NSFW al navegar y buscar",
+            "matureBlurThreshold": "Umbral de difuminado para contenido adulto",
+            "matureBlurThresholdHelp": "Establecer a partir de qué nivel de clasificación comienza el filtrado por difuminado cuando el difuminado NSFW está habilitado.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 y superior",
+                "r": "R y superior (predeterminado)",
+                "x": "X y superior",
+                "xxx": "Solo XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Reproducir videos automáticamente al pasar el ratón",
@@ -311,6 +357,54 @@
                 "saveFailed": "No se pudieron guardar las rutas a omitir: {message}"
             }
         },
+        "backup": {
+            "autoEnabled": "Copias de seguridad automáticas",
+            "autoEnabledHelp": "Crea una instantánea local una vez al día y conserva las más recientes según la política de retención.",
+            "retention": "Cantidad de retención",
+            "retentionHelp": "Cuántas instantáneas automáticas conservar antes de eliminar las antiguas.",
+            "management": "Gestión de copias",
+            "managementHelp": "Exporta tu estado de usuario actual o restáuralo desde un archivo de copia de seguridad.",
+            "scopeHelp": "Incluye tu configuración, el historial de descargas y el estado de actualización de los modelos. No incluye los archivos de modelo ni las cachés que se pueden regenerar.",
+            "locationSummary": "Ubicación actual de la copia",
+            "openFolderButton": "Abrir carpeta de copias",
+            "openFolderSuccess": "Carpeta de copias abierta",
+            "openFolderFailed": "No se pudo abrir la carpeta de copias",
+            "locationCopied": "Ruta de la copia copiada al portapapeles: {{path}}",
+            "locationClipboardFallback": "Ruta de la copia: {{path}}",
+            "exportButton": "Exportar copia",
+            "exportSuccess": "Copia exportada correctamente.",
+            "exportFailed": "No se pudo exportar la copia: {message}",
+            "importButton": "Importar copia",
+            "importConfirm": "¿Importar esta copia y sobrescribir el estado local del usuario?",
+            "importSuccess": "Copia importada correctamente.",
+            "importFailed": "No se pudo importar la copia: {message}",
+            "latestSnapshot": "Última instantánea",
+            "latestAutoSnapshot": "Última instantánea automática",
+            "snapshotCount": "Instantáneas guardadas",
+            "noneAvailable": "Aún no hay instantáneas"
+        },
+        "downloadSkipBaseModels": {
+            "label": "Omitir descargas para modelos base",
+            "help": "Se aplica a todos los flujos de descarga. Aquí solo se pueden seleccionar modelos base compatibles.",
+            "searchPlaceholder": "Filtrar modelos base...",
+            "empty": "Ningún modelo base coincide con la búsqueda actual.",
+            "summary": {
+                "none": "Ninguno seleccionado",
+                "count": "{count} seleccionados"
+            },
+            "actions": {
+                "edit": "Editar",
+                "collapse": "Contraer",
+                "clear": "Limpiar"
+            },
+            "validation": {
+                "saveFailed": "No se pudieron guardar los modelos base excluidos: {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "Omitir versiones de modelos previamente descargadas",
+            "help": "Cuando está habilitado, LoRA Manager omitirá la descarga de una versión de modelo si el servicio de historial de descargas registra esa versión exacta como ya descargada. Aplica a todos los flujos de descarga."
+        },
         "layoutSettings": {
             "displayDensity": "Densidad de visualización",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "Establecer el directorio raíz predeterminado de Diffusion Model (UNET) para descargas, importaciones y movimientos",
             "defaultEmbeddingRoot": "Raíz de embedding",
             "defaultEmbeddingRootHelp": "Establecer el directorio raíz predeterminado de embedding para descargas, importaciones y movimientos",
+            "recipesPath": "Ruta de almacenamiento de recetas",
+            "recipesPathHelp": "Directorio personalizado opcional para las recetas guardadas. Déjalo vacío para usar la carpeta recipes del primer directorio raíz de LoRA.",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "Migrando el almacenamiento de recetas...",
             "noDefault": "Sin predeterminado"
         },
+        "extraFolderPaths": {
+            "title": "Rutas de carpetas adicionales",
+            "description": "Rutas raíz de modelos adicionales exclusivas para LoRA Manager. Cargue modelos desde ubicaciones fuera de las carpetas estándar de ComfyUI, ideal para bibliotecas grandes que de otro modo ralentizarían ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "Rutas de LoRA",
+                "checkpoint": "Rutas de Checkpoint",
+                "unet": "Rutas de modelo de difusión",
+                "embedding": "Rutas de Embedding"
+            },
+            "pathPlaceholder": "/ruta/a/modelos/extra",
+            "saveSuccess": "Rutas de carpetas adicionales actualizadas. Se requiere reinicio para aplicar los cambios.",
+            "saveError": "Error al actualizar las rutas de carpetas adicionales: {message}",
+            "validation": {
+                "duplicatePath": "Esta ruta ya está configurada"
+            }
+        },
         "priorityTags": {
             "title": "Etiquetas prioritarias",
             "description": "Personaliza el orden de prioridad de etiquetas para cada tipo de modelo (p. ej., character, concept, style(toon|toon_style))",
@@ -485,23 +600,6 @@
             "proxyPassword": "Contraseña (opcional)",
             "proxyPasswordPlaceholder": "contraseña",
             "proxyPasswordHelp": "Contraseña para autenticación de proxy (si es necesario)"
-        },
-        "extraFolderPaths": {
-            "title": "Rutas de carpetas adicionales",
-            "help": "Agregue carpetas de modelos adicionales fuera de las rutas estándar de ComfyUI. Estas rutas se almacenan por separado y se escanean junto con las carpetas predeterminadas.",
-            "description": "Configure carpetas adicionales para escanear modelos. Estas rutas son específicas de LoRA Manager y se fusionarán con las rutas predeterminadas de ComfyUI.",
-            "modelTypes": {
-                "lora": "Rutas de LoRA",
-                "checkpoint": "Rutas de Checkpoint",
-                "unet": "Rutas de modelo de difusión",
-                "embedding": "Rutas de Embedding"
-            },
-            "pathPlaceholder": "/ruta/a/modelos/extra",
-            "saveSuccess": "Rutas de carpetas adicionales actualizadas.",
-            "saveError": "Error al actualizar las rutas de carpetas adicionales: {message}",
-            "validation": {
-                "duplicatePath": "Esta ruta ya está configurada"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "Omitir actualización de metadatos para seleccionados",
             "resumeMetadataRefresh": "Reanudar actualización de metadatos para seleccionados",
             "deleteAll": "Eliminar todos los modelos",
+            "downloadMissingLoras": "Descargar LoRAs faltantes",
             "clear": "Limpiar selección",
             "skipMetadataRefreshCount": "Omitir（{count} modelos）",
             "resumeMetadataRefreshCount": "Reanudar（{count} modelos）",
@@ -600,6 +699,7 @@
             "moveToFolder": "Mover a carpeta",
             "repairMetadata": "Reparar metadatos",
             "excludeModel": "Excluir modelo",
+            "restoreModel": "Restaurar modelo",
             "deleteModel": "Eliminar modelo",
             "shareRecipe": "Compartir receta",
             "viewAllLoras": "Ver todos los LoRAs",
@@ -641,6 +741,8 @@
                 "root": "Raíz",
                 "browseFolders": "Explorar carpetas:",
                 "downloadAndSaveRecipe": "Descargar y guardar receta",
+                "importRecipeOnly": "Importar solo la receta",
+                "importAndDownload": "Importar y descargar",
                 "downloadMissingLoras": "Descargar LoRAs faltantes",
                 "saveRecipe": "Guardar receta",
                 "loraCountInfo": "({existing}/{total} en la biblioteca)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "Menos"
             },
             "refresh": {
-                "title": "Actualizar lista de recetas"
+                "title": "Actualizar lista de recetas",
+                "quick": "Sincronizar cambios",
+                "quickTooltip": "Sincronizar cambios - actualización rápida sin reconstruir caché",
+                "full": "Reconstruir caché",
+                "fullTooltip": "Reconstruir caché - reescaneo completo de todos los archivos de recetas"
             },
             "filteredByLora": "Filtrado por LoRA",
             "favorites": {
@@ -722,6 +828,64 @@
                 "failed": "Error al reparar la receta: {message}",
                 "missingId": "No se puede reparar la receta: falta el ID de la receta"
             }
+        },
+        "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Mover a la carpeta {otherType}"
+            "moveToOtherTypeFolder": "Mover a la carpeta {otherType}",
+            "sendToWorkflow": "Enviar al flujo de trabajo"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "Desfijar barra lateral",
         "switchToListView": "Cambiar a vista de lista",
         "switchToTreeView": "Cambiar a vista de árbol",
-        "recursiveOn": "Buscar en subcarpetas",
-        "recursiveOff": "Buscar solo en la carpeta actual",
+        "recursiveOn": "Incluir subcarpetas",
+        "recursiveOff": "Solo carpeta actual",
         "recursiveUnavailable": "La búsqueda recursiva solo está disponible en la vista en árbol",
         "collapseAllDisabled": "No disponible en vista de lista",
         "dragDrop": {
             "unableToResolveRoot": "No se puede determinar la ruta de destino para el movimiento.",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "El movimiento no es compatible con este elemento.",
+            "createFolderHint": "Suelta para crear una nueva carpeta",
+            "newFolderName": "Nombre de la nueva carpeta",
+            "folderNameHint": "Presiona Enter para confirmar, Escape para cancelar",
+            "emptyFolderName": "Por favor, introduce un nombre de carpeta",
+            "invalidFolderName": "El nombre de la carpeta contiene caracteres no válidos",
+            "noDragState": "No se encontró ninguna operación de arrastre pendiente"
+        },
+        "empty": {
+            "noFolders": "No se encontraron carpetas",
+            "dragHint": "Arrastra elementos aquí para crear carpetas"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "Acceso temprano",
             "earlyAccessTooltip": "Acceso temprano requerido",
             "inLibrary": "En la biblioteca",
+            "downloaded": "Descargado",
+            "downloadedTooltip": "Descargado anteriormente, pero actualmente no está en tu biblioteca.",
             "alreadyInLibrary": "Ya en la biblioteca",
             "autoOrganizedPath": "[Auto-organizado por plantilla de ruta]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "Actualizar modelo base",
             "cancel": "Cancelar"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Descargar LoRAs faltantes",
+            "message": "Se encontraron {uniqueCount} LoRAs faltantes únicos (de {totalCount} en total entre las recetas seleccionadas).",
+            "previewTitle": "LoRAs para descargar:",
+            "moreItems": "...y {count} más",
+            "note": "Los archivos se descargarán usando las plantillas de ruta predeterminadas. Esto puede tomar un tiempo dependiendo del número de LoRAs.",
+            "downloadButton": "Descargar {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Imágenes de ejemplo locales",
             "message": "No se encontraron imágenes de ejemplo locales para este modelo. Opciones de visualización:",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "Ver en Civitai",
                 "viewOnCivitaiText": "Ver en Civitai",
                 "viewCreatorProfile": "Ver perfil del creador",
-                "openFileLocation": "Abrir ubicación del archivo"
+                "openFileLocation": "Abrir ubicación del archivo",
+                "sendToWorkflow": "Enviar a ComfyUI",
+                "sendToWorkflowText": "Enviar a ComfyUI"
             },
             "openFileLocation": {
                 "success": "Ubicación del archivo abierta exitosamente",
@@ -964,6 +1151,9 @@
                 "copied": "Ruta copiada al portapapeles: {{path}}",
                 "clipboardFallback": "Ruta: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "No se puede enviar a ComfyUI: no hay ruta de archivo disponible"
+            },
             "metadata": {
                 "version": "Versión",
                 "fileName": "Nombre de archivo",
@@ -1071,17 +1261,30 @@
                     "days": "en {count}d"
                 },
                 "badges": {
-                    "current": "Versión actual",
+                    "current": "Versión abierta",
+                    "currentTooltip": "Es la versión con la que abriste este modal",
                     "inLibrary": "En la biblioteca",
+                    "inLibraryTooltip": "Esta versión existe en tu biblioteca local",
+                    "downloaded": "Descargado",
+                    "downloadedTooltip": "Esta versión se descargó antes, pero ahora no está en tu biblioteca",
                     "newer": "Versión más reciente",
+                    "newerTooltip": "Esta versión es más reciente que tu última versión local",
                     "earlyAccess": "Acceso temprano",
-                    "ignored": "Ignorada"
+                    "earlyAccessTooltip": "Esta versión requiere actualmente acceso temprano de Civitai",
+                    "ignored": "Ignorada",
+                    "ignoredTooltip": "Las notificaciones de actualización están desactivadas para esta versión"
                 },
                 "actions": {
                     "download": "Descargar",
+                    "downloadTooltip": "Descargar esta versión",
+                    "downloadEarlyAccessTooltip": "Descargar esta versión de acceso temprano desde Civitai",
                     "delete": "Eliminar",
+                    "deleteTooltip": "Eliminar esta versión local",
                     "ignore": "Ignorar",
                     "unignore": "Dejar de ignorar",
+                    "ignoreTooltip": "Ignorar las notificaciones de actualización de esta versión",
+                    "unignoreTooltip": "Reanudar las notificaciones de actualización de esta versión",
+                    "viewVersionOnCivitai": "Ver versión en Civitai",
                     "earlyAccessTooltip": "Requiere compra de acceso temprano",
                     "resumeModelUpdates": "Reanudar actualizaciones para este modelo",
                     "ignoreModelUpdates": "Ignorar actualizaciones para este modelo",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "Receta reemplazada en el flujo de trabajo",
             "recipeFailedToSend": "Error al enviar receta al flujo de trabajo",
             "noMatchingNodes": "No hay nodos compatibles disponibles en el flujo de trabajo actual",
-            "noTargetNodeSelected": "No se ha seleccionado ningún nodo de destino"
+            "noTargetNodeSelected": "No se ha seleccionado ningún nodo de destino",
+            "modelUpdated": "Modelo actualizado en el flujo de trabajo",
+            "modelFailed": "Error al actualizar nodo de modelo"
         },
         "nodeSelector": {
             "recipe": "Receta",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "Mostrar código QR de WeChat",
             "hideWechatQR": "Ocultar código QR de WeChat"
         },
-        "footer": "¡Gracias por usar el gestor de LoRA! ❤️"
+        "footer": "¡Gracias por usar el gestor de LoRA! ❤️",
+        "supporters": {
+            "title": "Gracias a todos los seguidores",
+            "subtitle": "Gracias a {count} seguidores que hicieron este proyecto posible",
+            "specialThanks": "Agradecimientos especiales",
+            "allSupporters": "Todos los seguidores",
+            "totalCount": "{count} seguidores en total"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "Por favor selecciona una versión",
             "versionExists": "Esta versión ya existe en tu biblioteca",
             "downloadCompleted": "Descarga completada exitosamente",
+            "downloadSkippedByBaseModel": "Descarga omitida porque el modelo base {baseModel} está excluido",
             "autoOrganizeSuccess": "Auto-organización completada exitosamente para {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organización completada con {success} movidos, {failures} fallidos de un total de {total} modelos",
             "autoOrganizeFailed": "Auto-organización fallida: {error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "Error al cargar {modelType}s: {message}",
             "refreshComplete": "Actualización completa",
             "refreshFailed": "Error al actualizar recetas: {message}",
+            "syncComplete": "Sincronización completa",
+            "syncFailed": "Error al sincronizar recetas: {message}",
             "updateFailed": "Error al actualizar receta: {error}",
             "updateError": "Error actualizando receta: {message}",
             "nameSaved": "Receta \"{name}\" guardada exitosamente",
             "nameUpdated": "Nombre de receta actualizado exitosamente",
             "tagsUpdated": "Etiquetas de receta actualizadas exitosamente",
             "sourceUrlUpdated": "URL de origen actualizada exitosamente",
+            "promptUpdated": "Prompt actualizado exitosamente",
+            "negativePromptUpdated": "Prompt negativo actualizado exitosamente",
+            "promptEditorHint": "Presiona Enter para guardar, Shift+Enter para nueva línea",
             "noRecipeId": "No hay ID de receta disponible",
+            "sendToWorkflowFailed": "Error al enviar la receta al flujo de trabajo: {message}",
             "copyFailed": "Error copiando sintaxis de receta: {message}",
             "noMissingLoras": "No hay LoRAs faltantes para descargar",
             "missingLorasInfoFailed": "Error al obtener información de LoRAs faltantes",
@@ -1410,9 +1629,20 @@
             "processingError": "Error de procesamiento: {message}",
             "folderBrowserError": "Error cargando explorador de carpetas: {message}",
             "recipeSaveFailed": "Error al guardar receta: {error}",
+            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Importación falló: {message}",
             "folderTreeFailed": "Error al cargar árbol de carpetas",
-            "folderTreeError": "Error cargando árbol de carpetas"
+            "folderTreeError": "Error cargando árbol de carpetas",
+            "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}",
+            "noRecipesSelected": "No se han seleccionado recetas",
+            "noMissingLorasInSelection": "No se encontraron LoRAs faltantes en las recetas seleccionadas",
+            "noLoraRootConfigured": "No se ha configurado el directorio raíz de LoRA. Por favor, establezca un directorio raíz de LoRA predeterminado en la configuración."
         },
         "models": {
             "noModelsSelected": "No hay modelos seleccionados",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "Error al guardar mapeos de modelo base: {message}",
             "downloadTemplatesUpdated": "Plantillas de rutas de descarga actualizadas",
             "downloadTemplatesFailed": "Error al guardar plantillas de rutas de descarga: {message}",
+            "recipesPathUpdated": "Ruta de almacenamiento de recetas actualizada",
+            "recipesPathSaveFailed": "Error al actualizar la ruta de almacenamiento de recetas: {message}",
             "settingsUpdated": "Configuración actualizada: {setting}",
             "compactModeToggled": "Modo compacto {state}",
             "settingSaveFailed": "Error al guardar configuración: {message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "Error al eliminar {type}: {message}",
             "excludeSuccess": "{type} excluido exitosamente",
             "excludeFailed": "Error al excluir {type}: {message}",
+            "restoreSuccess": "{type} restaurado correctamente",
+            "restoreFailed": "No se pudo restaurar {type}: {message}",
             "fileNameUpdated": "Nombre de archivo actualizado exitosamente",
             "fileRenameFailed": "Error al renombrar archivo: {error}",
             "previewUpdated": "Vista previa actualizada exitosamente",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "Diagnósticos del sistema",
+        "title": "Doctor",
+        "buttonTitle": "Ejecutar diagnósticos y correcciones comunes",
+        "loading": "Comprobando el entorno...",
+        "footer": "Exporta un paquete de diagnóstico si el problema persiste después de la reparación.",
+        "summary": {
+            "idle": "Ejecuta una comprobación del estado de la configuración, la integridad de la caché y la coherencia de la interfaz.",
+            "ok": "No se encontraron problemas activos en el entorno actual.",
+            "warning": "Se encontraron {count} problema(s). La mayoría se puede solucionar directamente desde este panel.",
+            "error": "Se encontraron {count} problema(s). Deben atenderse antes de que la aplicación esté completamente saludable."
+        },
+        "status": {
+            "ok": "Saludable",
+            "warning": "Requiere atención",
+            "error": "Se requiere acción"
+        },
+        "actions": {
+            "runAgain": "Ejecutar de nuevo",
+            "exportBundle": "Exportar paquete"
+        },
+        "toast": {
+            "loadFailed": "Error al cargar los diagnósticos: {message}",
+            "repairSuccess": "Reconstrucción de caché completada.",
+            "repairFailed": "Error al reconstruir la caché: {message}",
+            "exportSuccess": "Paquete de diagnósticos exportado.",
+            "exportFailed": "Error al exportar el paquete de diagnósticos: {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "Actualización de la aplicación detectada",
@@ -1651,4 +1914,4 @@
             "retry": "Reintentar"
         }
     }
-}
+}

locales/fr.json

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "Annuler",
+        "confirm": "Confirmer",
         "actions": {
             "save": "Enregistrer",
             "cancel": "Annuler",
+            "confirm": "Confirmer",
             "delete": "Supprimer",
             "move": "Déplacer",
             "refresh": "Actualiser",
@@ -11,7 +14,8 @@
             "backToTop": "Retour en haut",
             "settings": "Paramètres",
             "help": "Aide",
-            "add": "Ajouter"
+            "add": "Ajouter",
+            "close": "Fermer"
         },
         "status": {
             "loading": "Chargement...",
@@ -171,6 +175,9 @@
             "success": "{count} recettes réparées avec succès.",
             "cancelled": "Réparation annulée. {count} recettes ont été réparées.",
             "error": "Échec de la réparation des recettes : {message}"
+        },
+        "manageExcludedModels": {
+            "label": "Gérer les modèles exclus"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "Le préréglage \"{name}\" existe déjà. Remplacer?",
             "presetNamePlaceholder": "Nom du préréglage...",
             "baseModel": "Modèle de base",
+            "baseModelSearchPlaceholder": "Rechercher des modèles de base...",
             "modelTags": "Tags (Top 20)",
-            "modelTypes": "Model Types",
+            "modelTypes": "Types de modèles",
             "license": "Licence",
             "noCreditRequired": "Crédit non requis",
             "allowSellingGeneratedContent": "Vente autorisée",
             "noTags": "Aucun tag",
+            "noBaseModelMatches": "Aucun modèle de base ne correspond à la recherche actuelle.",
             "clearAll": "Effacer tous les filtres",
             "any": "N'importe quel",
             "all": "Tous",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Clé API Civitai",
         "civitaiApiKeyPlaceholder": "Entrez votre clé API Civitai",
         "civitaiApiKeyHelp": "Utilisée pour l'authentification lors du téléchargement de modèles depuis Civitai",
+        "civitaiHost": {
+            "label": "Hôte Civitai",
+            "help": "Choisissez quel site Civitai s'ouvre lorsque vous utilisez les liens « View on Civitai ».",
+            "options": {
+                "com": "civitai.com (SFW uniquement)",
+                "red": "civitai.red (sans restriction)"
+            }
+        },
+        "downloadBackend": {
+            "label": "Moteur de téléchargement",
+            "help": "Choisissez comment les fichiers de modèles sont téléchargés. Python utilise le téléchargeur intégré. aria2 utilise le processus externe expérimental de téléchargement.",
+            "options": {
+                "python": "Python (intégré)",
+                "aria2": "aria2 (expérimental)"
+            }
+        },
+        "aria2cPath": {
+            "label": "Chemin vers aria2c",
+            "help": "Chemin facultatif vers l’exécutable aria2c. Laissez vide pour utiliser aria2c depuis le PATH système.",
+            "placeholder": "Laisser vide pour utiliser aria2c depuis le PATH"
+        },
+        "civitaiHostBanner": {
+            "title": "Préférence d’hôte Civitai disponible",
+            "content": "Civitai utilise désormais civitai.com pour le contenu SFW et civitai.red pour le contenu sans restriction. Vous pouvez modifier dans les paramètres le site ouvert par défaut.",
+            "openSettings": "Ouvrir les paramètres"
+        },
         "openSettingsFileLocation": {
             "label": "Ouvrir le dossier des paramètres",
             "tooltip": "Ouvrir le dossier contenant settings.json",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "Filtrage du contenu",
+            "downloads": "Téléchargements",
             "videoSettings": "Paramètres vidéo",
             "layoutSettings": "Paramètres d'affichage",
             "misc": "Divers",
+            "backup": "Sauvegardes",
             "folderSettings": "Racines par défaut",
+            "recipeSettings": "Recipes",
             "extraFolderPaths": "Chemins de dossiers supplémentaires",
             "downloadPathTemplates": "Modèles de chemin de téléchargement",
             "priorityTags": "Étiquettes prioritaires",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "Flouter le contenu NSFW",
             "blurNsfwContentHelp": "Flouter les images d'aperçu de contenu pour adultes (NSFW)",
             "showOnlySfw": "Afficher uniquement les résultats SFW",
-            "showOnlySfwHelp": "Filtrer tout le contenu NSFW lors de la navigation et de la recherche"
+            "showOnlySfwHelp": "Filtrer tout le contenu NSFW lors de la navigation et de la recherche",
+            "matureBlurThreshold": "Seuil de floutage pour contenu adulte",
+            "matureBlurThresholdHelp": "Définir à partir de quel niveau de classification le floutage s'applique lorsque le floutage NSFW est activé.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 et plus",
+                "r": "R et plus (par défaut)",
+                "x": "X et plus",
+                "xxx": "XXX uniquement"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Lecture automatique vidéo au survol",
@@ -311,6 +357,54 @@
                 "saveFailed": "Impossible d'enregistrer les chemins à ignorer : {message}"
             }
         },
+        "backup": {
+            "autoEnabled": "Sauvegardes automatiques",
+            "autoEnabledHelp": "Crée un instantané local une fois par jour et conserve les plus récents selon la politique de rétention.",
+            "retention": "Nombre de rétention",
+            "retentionHelp": "Combien d'instantanés automatiques conserver avant de supprimer les plus anciens.",
+            "management": "Gestion des sauvegardes",
+            "managementHelp": "Exporte l'état actuel de l'utilisateur ou restaure-le depuis une archive de sauvegarde.",
+            "scopeHelp": "Inclut vos paramètres, l'historique des téléchargements et l'état des mises à jour des modèles. Les fichiers de modèle et les caches régénérables ne sont pas inclus.",
+            "locationSummary": "Emplacement actuel des sauvegardes",
+            "openFolderButton": "Ouvrir le dossier de sauvegarde",
+            "openFolderSuccess": "Dossier de sauvegarde ouvert",
+            "openFolderFailed": "Impossible d'ouvrir le dossier de sauvegarde",
+            "locationCopied": "Chemin de sauvegarde copié dans le presse-papiers : {{path}}",
+            "locationClipboardFallback": "Chemin de sauvegarde : {{path}}",
+            "exportButton": "Exporter la sauvegarde",
+            "exportSuccess": "Sauvegarde exportée avec succès.",
+            "exportFailed": "Échec de l'export de la sauvegarde : {message}",
+            "importButton": "Importer la sauvegarde",
+            "importConfirm": "Importer cette sauvegarde et écraser l'état local de l'utilisateur ?",
+            "importSuccess": "Sauvegarde importée avec succès.",
+            "importFailed": "Échec de l'import de la sauvegarde : {message}",
+            "latestSnapshot": "Dernier instantané",
+            "latestAutoSnapshot": "Dernier instantané automatique",
+            "snapshotCount": "Instantanés enregistrés",
+            "noneAvailable": "Aucun instantané pour le moment"
+        },
+        "downloadSkipBaseModels": {
+            "label": "Ignorer les téléchargements pour certains modèles de base",
+            "help": "S’applique à tous les flux de téléchargement. Seuls les modèles de base pris en charge peuvent être sélectionnés ici.",
+            "searchPlaceholder": "Filtrer les modèles de base...",
+            "empty": "Aucun modèle de base ne correspond à la recherche actuelle.",
+            "summary": {
+                "none": "Aucune sélection",
+                "count": "{count} sélectionnés"
+            },
+            "actions": {
+                "edit": "Modifier",
+                "collapse": "Réduire",
+                "clear": "Effacer"
+            },
+            "validation": {
+                "saveFailed": "Impossible d’enregistrer les modèles de base exclus : {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "Ignorer les versions de modèles précédemment téléchargées",
+            "help": "Lorsque activé, LoRA Manager ignorera le téléchargement d'une version de modèle si le service d'historique des téléchargements enregistre cette version exacte comme déjà téléchargée. S'applique à tous les flux de téléchargement."
+        },
         "layoutSettings": {
             "displayDensity": "Densité d'affichage",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "Définir le répertoire racine Diffusion Model (UNET) par défaut pour les téléchargements, imports et déplacements",
             "defaultEmbeddingRoot": "Racine Embedding",
             "defaultEmbeddingRootHelp": "Définir le répertoire racine embedding par défaut pour les téléchargements, imports et déplacements",
+            "recipesPath": "Recipes Storage Path",
+            "recipesPathHelp": "Optional custom directory for stored recipes. Leave empty to use the first LoRA root's recipes folder.",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "Migrating recipes storage...",
             "noDefault": "Aucun par défaut"
         },
+        "extraFolderPaths": {
+            "title": "Chemins de dossiers supplémentaires",
+            "description": "Chemins racine de modèles supplémentaires exclusifs à LoRA Manager. Chargez des modèles depuis des emplacements en dehors des dossiers standard de ComfyUI, idéal pour les grandes bibliothèques qui ralentiraient autrement ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "Chemins LoRA",
+                "checkpoint": "Chemins Checkpoint",
+                "unet": "Chemins de modèle de diffusion",
+                "embedding": "Chemins Embedding"
+            },
+            "pathPlaceholder": "/chemin/vers/modèles/supplémentaires",
+            "saveSuccess": "Chemins de dossiers supplémentaires mis à jour. Redémarrage requis pour appliquer les changements.",
+            "saveError": "Échec de la mise à jour des chemins de dossiers supplémentaires: {message}",
+            "validation": {
+                "duplicatePath": "Ce chemin est déjà configuré"
+            }
+        },
         "priorityTags": {
             "title": "Étiquettes prioritaires",
             "description": "Personnalisez l'ordre de priorité des étiquettes pour chaque type de modèle (par ex. : character, concept, style(toon|toon_style))",
@@ -485,23 +600,6 @@
             "proxyPassword": "Mot de passe (optionnel)",
             "proxyPasswordPlaceholder": "mot_de_passe",
             "proxyPasswordHelp": "Mot de passe pour l'authentification proxy (si nécessaire)"
-        },
-        "extraFolderPaths": {
-            "title": "Chemins de dossiers supplémentaires",
-            "help": "Ajoutez des dossiers de modèles supplémentaires en dehors des chemins standard de ComfyUI. Ces chemins sont stockés séparément et analysés aux côtés des dossiers par défaut.",
-            "description": "Configurez des dossiers supplémentaires pour l'analyse de modèles. Ces chemins sont spécifiques à LoRA Manager et seront fusionnés avec les chemins par défaut de ComfyUI.",
-            "modelTypes": {
-                "lora": "Chemins LoRA",
-                "checkpoint": "Chemins Checkpoint",
-                "unet": "Chemins de modèle de diffusion",
-                "embedding": "Chemins Embedding"
-            },
-            "pathPlaceholder": "/chemin/vers/modèles/supplémentaires",
-            "saveSuccess": "Chemins de dossiers supplémentaires mis à jour.",
-            "saveError": "Échec de la mise à jour des chemins de dossiers supplémentaires: {message}",
-            "validation": {
-                "duplicatePath": "Ce chemin est déjà configuré"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "Ignorer l'actualisation des métadonnées pour la sélection",
             "resumeMetadataRefresh": "Reprendre l'actualisation des métadonnées pour la sélection",
             "deleteAll": "Supprimer tous les modèles",
+            "downloadMissingLoras": "Télécharger les LoRAs manquants",
             "clear": "Effacer la sélection",
             "skipMetadataRefreshCount": "Ignorer（{count} modèles）",
             "resumeMetadataRefreshCount": "Reprendre（{count} modèles）",
@@ -600,6 +699,7 @@
             "moveToFolder": "Déplacer vers un dossier",
             "repairMetadata": "Réparer les métadonnées",
             "excludeModel": "Exclure le modèle",
+            "restoreModel": "Restaurer le modèle",
             "deleteModel": "Supprimer le modèle",
             "shareRecipe": "Partager la recipe",
             "viewAllLoras": "Voir tous les LoRAs",
@@ -641,6 +741,8 @@
                 "root": "Racine",
                 "browseFolders": "Parcourir les dossiers :",
                 "downloadAndSaveRecipe": "Télécharger et sauvegarder la recipe",
+                "importRecipeOnly": "Importer uniquement la recette",
+                "importAndDownload": "Importer et télécharger",
                 "downloadMissingLoras": "Télécharger les LoRAs manquants",
                 "saveRecipe": "Sauvegarder la recipe",
                 "loraCountInfo": "({existing}/{total} dans la bibliothèque)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "Moins"
             },
             "refresh": {
-                "title": "Actualiser la liste des recipes"
+                "title": "Actualiser la liste des recipes",
+                "quick": "Synchroniser les changements",
+                "quickTooltip": "Synchroniser les changements - actualisation rapide sans reconstruire le cache",
+                "full": "Reconstruire le cache",
+                "fullTooltip": "Reconstruire le cache - rescan complet de tous les fichiers de recipes"
             },
             "filteredByLora": "Filtré par LoRA",
             "favorites": {
@@ -722,6 +828,64 @@
                 "failed": "Échec de la réparation de la recette : {message}",
                 "missingId": "Impossible de réparer la recette : ID de recette manquant"
             }
+        },
+        "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Déplacer vers le dossier {otherType}"
+            "moveToOtherTypeFolder": "Déplacer vers le dossier {otherType}",
+            "sendToWorkflow": "Envoyer vers le workflow"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "Désépingler la barre latérale",
         "switchToListView": "Passer en vue liste",
         "switchToTreeView": "Passer en vue arborescence",
-        "recursiveOn": "Rechercher dans les sous-dossiers",
-        "recursiveOff": "Rechercher uniquement dans le dossier actuel",
+        "recursiveOn": "Inclure les sous-dossiers",
+        "recursiveOff": "Dossier actuel uniquement",
         "recursiveUnavailable": "La recherche récursive n'est disponible qu'en vue arborescente",
         "collapseAllDisabled": "Non disponible en vue liste",
         "dragDrop": {
             "unableToResolveRoot": "Impossible de déterminer le chemin de destination pour le déplacement.",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "Le déplacement n'est pas pris en charge pour cet élément.",
+            "createFolderHint": "Relâcher pour créer un nouveau dossier",
+            "newFolderName": "Nom du nouveau dossier",
+            "folderNameHint": "Appuyez sur Entrée pour confirmer, Échap pour annuler",
+            "emptyFolderName": "Veuillez saisir un nom de dossier",
+            "invalidFolderName": "Le nom du dossier contient des caractères invalides",
+            "noDragState": "Aucune opération de glissement en attente trouvée"
+        },
+        "empty": {
+            "noFolders": "Aucun dossier trouvé",
+            "dragHint": "Faites glisser des éléments ici pour créer des dossiers"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "Accès anticipé",
             "earlyAccessTooltip": "Accès anticipé requis",
             "inLibrary": "Dans la bibliothèque",
+            "downloaded": "Téléchargé",
+            "downloadedTooltip": "Déjà téléchargé, mais il n'est actuellement pas dans votre bibliothèque.",
             "alreadyInLibrary": "Déjà dans la bibliothèque",
             "autoOrganizedPath": "[Auto-organisé par modèle de chemin]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "Mettre à jour le modèle de base",
             "cancel": "Annuler"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Télécharger les LoRAs manquants",
+            "message": "{uniqueCount} LoRAs manquants uniques trouvés (sur un total de {totalCount} dans les recettes sélectionnées).",
+            "previewTitle": "LoRAs à télécharger :",
+            "moreItems": "...et {count} de plus",
+            "note": "Les fichiers seront téléchargés en utilisant les modèles de chemins par défaut. Cela peut prendre un certain temps selon le nombre de LoRAs.",
+            "downloadButton": "Télécharger {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Images d'exemple locales",
             "message": "Aucune image d'exemple locale trouvée pour ce modèle. Options d'affichage :",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "Voir sur Civitai",
                 "viewOnCivitaiText": "Voir sur Civitai",
                 "viewCreatorProfile": "Voir le profil du créateur",
-                "openFileLocation": "Ouvrir l'emplacement du fichier"
+                "openFileLocation": "Ouvrir l'emplacement du fichier",
+                "sendToWorkflow": "Envoyer vers ComfyUI",
+                "sendToWorkflowText": "Envoyer vers ComfyUI"
             },
             "openFileLocation": {
                 "success": "Emplacement du fichier ouvert avec succès",
@@ -964,6 +1151,9 @@
                 "copied": "Chemin copié dans le presse-papiers: {{path}}",
                 "clipboardFallback": "Chemin: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Impossible d'envoyer vers ComfyUI : aucun chemin de fichier disponible"
+            },
             "metadata": {
                 "version": "Version",
                 "fileName": "Nom de fichier",
@@ -1071,17 +1261,30 @@
                     "days": "dans {count}j"
                 },
                 "badges": {
-                    "current": "Version actuelle",
+                    "current": "Version ouverte",
+                    "currentTooltip": "C'est la version à partir de laquelle cette fenêtre a été ouverte",
                     "inLibrary": "Dans la bibliothèque",
+                    "inLibraryTooltip": "Cette version existe dans votre bibliothèque locale",
+                    "downloaded": "Téléchargé",
+                    "downloadedTooltip": "Cette version a déjà été téléchargée, mais n'est pas actuellement dans votre bibliothèque",
                     "newer": "Version plus récente",
+                    "newerTooltip": "Cette version est plus récente que votre dernière version locale",
                     "earlyAccess": "Accès anticipé",
-                    "ignored": "Ignorée"
+                    "earlyAccessTooltip": "Cette version nécessite actuellement l'accès anticipé Civitai",
+                    "ignored": "Ignorée",
+                    "ignoredTooltip": "Les notifications de mise à jour sont désactivées pour cette version"
                 },
                 "actions": {
                     "download": "Télécharger",
+                    "downloadTooltip": "Télécharger cette version",
+                    "downloadEarlyAccessTooltip": "Télécharger cette version en accès anticipé depuis Civitai",
                     "delete": "Supprimer",
+                    "deleteTooltip": "Supprimer cette version locale",
                     "ignore": "Ignorer",
                     "unignore": "Ne plus ignorer",
+                    "ignoreTooltip": "Ignorer les notifications de mise à jour pour cette version",
+                    "unignoreTooltip": "Reprendre les notifications de mise à jour pour cette version",
+                    "viewVersionOnCivitai": "Voir la version sur Civitai",
                     "earlyAccessTooltip": "Nécessite l'achat de l'accès anticipé",
                     "resumeModelUpdates": "Reprendre les mises à jour pour ce modèle",
                     "ignoreModelUpdates": "Ignorer les mises à jour pour ce modèle",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "Recipe remplacée dans le workflow",
             "recipeFailedToSend": "Échec de l'envoi de la recipe au workflow",
             "noMatchingNodes": "Aucun nœud compatible disponible dans le workflow actuel",
-            "noTargetNodeSelected": "Aucun nœud cible sélectionné"
+            "noTargetNodeSelected": "Aucun nœud cible sélectionné",
+            "modelUpdated": "Modèle mis à jour dans le workflow",
+            "modelFailed": "Échec de la mise à jour du nœud modèle"
         },
         "nodeSelector": {
             "recipe": "Recipe",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "Afficher le QR Code WeChat",
             "hideWechatQR": "Masquer le QR Code WeChat"
         },
-        "footer": "Merci d'utiliser le Gestionnaire LoRA ! ❤️"
+        "footer": "Merci d'utiliser le Gestionnaire LoRA ! ❤️",
+        "supporters": {
+            "title": "Merci à tous les supporters",
+            "subtitle": "Merci aux {count} supporters qui ont rendu ce projet possible",
+            "specialThanks": "Remerciements spéciaux",
+            "allSupporters": "Tous les supporters",
+            "totalCount": "{count} supporters au total"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "Veuillez sélectionner une version",
             "versionExists": "Cette version existe déjà dans votre bibliothèque",
             "downloadCompleted": "Téléchargement terminé avec succès",
+            "downloadSkippedByBaseModel": "Téléchargement ignoré, car le modèle de base {baseModel} est exclu",
             "autoOrganizeSuccess": "Auto-organisation terminée avec succès pour {count} {type}",
             "autoOrganizePartialSuccess": "Auto-organisation terminée avec {success} déplacés, {failures} échecs sur {total} modèles",
             "autoOrganizeFailed": "Échec de l'auto-organisation : {error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "Échec du chargement des {modelType}s : {message}",
             "refreshComplete": "Actualisation terminée",
             "refreshFailed": "Échec de l'actualisation des recipes : {message}",
+            "syncComplete": "Synchronisation terminée",
+            "syncFailed": "Échec de la synchronisation des recipes : {message}",
             "updateFailed": "Échec de la mise à jour de la recipe : {error}",
             "updateError": "Erreur lors de la mise à jour de la recipe : {message}",
             "nameSaved": "Recipe \"{name}\" sauvegardée avec succès",
             "nameUpdated": "Nom de la recipe mis à jour avec succès",
             "tagsUpdated": "Tags de la recipe mis à jour avec succès",
             "sourceUrlUpdated": "URL source mise à jour avec succès",
+            "promptUpdated": "Prompt mis à jour avec succès",
+            "negativePromptUpdated": "Prompt négatif mis à jour avec succès",
+            "promptEditorHint": "Appuyez sur Entrée pour sauvegarder, Maj+Entrée pour nouvelle ligne",
             "noRecipeId": "Aucun ID de recipe disponible",
+            "sendToWorkflowFailed": "Échec de l'envoi de la recette vers le workflow : {message}",
             "copyFailed": "Erreur lors de la copie de la syntaxe de la recipe : {message}",
             "noMissingLoras": "Aucun LoRA manquant à télécharger",
             "missingLorasInfoFailed": "Échec de l'obtention des informations pour les LoRAs manquants",
@@ -1410,9 +1629,20 @@
             "processingError": "Erreur de traitement : {message}",
             "folderBrowserError": "Erreur lors du chargement du navigateur de dossiers : {message}",
             "recipeSaveFailed": "Échec de la sauvegarde de la recipe : {error}",
+            "recipeSaved": "Recipe saved successfully",
             "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": "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}",
+            "noRecipesSelected": "Aucune recette sélectionnée",
+            "noMissingLorasInSelection": "Aucun LoRA manquant trouvé dans les recettes sélectionnées",
+            "noLoraRootConfigured": "Aucun répertoire racine LoRA configuré. Veuillez définir un répertoire racine LoRA par défaut dans les paramètres."
         },
         "models": {
             "noModelsSelected": "Aucun modèle sélectionné",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "Échec de la sauvegarde des mappages de modèle de base : {message}",
             "downloadTemplatesUpdated": "Modèles de chemin de téléchargement mis à jour",
             "downloadTemplatesFailed": "Échec de la sauvegarde des modèles de chemin de téléchargement : {message}",
+            "recipesPathUpdated": "Recipes storage path updated",
+            "recipesPathSaveFailed": "Failed to update recipes storage path: {message}",
             "settingsUpdated": "Paramètres mis à jour : {setting}",
             "compactModeToggled": "Mode compact {state}",
             "settingSaveFailed": "Échec de la sauvegarde du paramètre : {message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "Échec de la suppression de {type} : {message}",
             "excludeSuccess": "{type} exclu avec succès",
             "excludeFailed": "Échec de l'exclusion de {type} : {message}",
+            "restoreSuccess": "{type} restauré avec succès",
+            "restoreFailed": "Échec de la restauration de {type} : {message}",
             "fileNameUpdated": "Nom de fichier mis à jour avec succès",
             "fileRenameFailed": "Échec du renommage du fichier : {error}",
             "previewUpdated": "Aperçu mis à jour avec succès",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "Diagnostics système",
+        "title": "Docteur",
+        "buttonTitle": "Lancer les diagnostics et les corrections courantes",
+        "loading": "Vérification de l'environnement...",
+        "footer": "Exportez un lot de diagnostic si le problème persiste après la réparation.",
+        "summary": {
+            "idle": "Lancez une vérification de l'état des paramètres, de l'intégrité du cache et de la cohérence de l'interface.",
+            "ok": "Aucun problème actif n'a été trouvé dans l'environnement actuel.",
+            "warning": "{count} problème(s) ont été trouvés. La plupart peuvent être corrigés directement depuis ce panneau.",
+            "error": "{count} problème(s) nécessitent une attention avant que l'application soit entièrement saine."
+        },
+        "status": {
+            "ok": "Sain",
+            "warning": "Nécessite une attention",
+            "error": "Action requise"
+        },
+        "actions": {
+            "runAgain": "Relancer",
+            "exportBundle": "Exporter le lot"
+        },
+        "toast": {
+            "loadFailed": "Échec du chargement des diagnostics : {message}",
+            "repairSuccess": "Reconstruction du cache terminée.",
+            "repairFailed": "Échec de la reconstruction du cache : {message}",
+            "exportSuccess": "Lot de diagnostics exporté.",
+            "exportFailed": "Échec de l'export du lot de diagnostics : {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "Mise à jour de l'application détectée",
@@ -1651,4 +1914,4 @@
             "retry": "Réessayer"
         }
     }
-}
+}

locales/he.json

@@ -1,17 +1,21 @@
 {
     "common": {
+        "cancel": "ביטול",
+        "confirm": "אישור",
         "actions": {
-            "save": "שמור",
+            "save": "שמירה",
             "cancel": "ביטול",
-            "delete": "מחק",
-            "move": "העבר",
-            "refresh": "רענן",
-            "back": "חזור",
+            "confirm": "אישור",
+            "delete": "מחיקה",
+            "move": "העברה",
+            "refresh": "רענון",
+            "back": "חזרה",
             "next": "הבא",
-            "backToTop": "חזור למעלה",
+            "backToTop": "חזרה למעלה",
             "settings": "הגדרות",
             "help": "עזרה",
-            "add": "הוסף"
+            "add": "הוספה",
+            "close": "סגור"
         },
         "status": {
             "loading": "טוען...",
@@ -171,6 +175,9 @@
             "success": "תוקנו בהצלחה {count} מתכונים.",
             "cancelled": "תיקון בוטל. {count} מתכונים תוקנו.",
             "error": "תיקון המתכונים נכשל: {message}"
+        },
+        "manageExcludedModels": {
+            "label": "ניהול מודלים מוחרגים"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "הפריסט \"{name}\" כבר קיים. לדרוס?",
             "presetNamePlaceholder": "שם קביעה מראש...",
             "baseModel": "מודל בסיס",
+            "baseModelSearchPlaceholder": "חפש מודלי בסיס...",
             "modelTags": "תגיות (20 המובילות)",
-            "modelTypes": "Model Types",
+            "modelTypes": "סוגי מודלים",
             "license": "רישיון",
             "noCreditRequired": "ללא קרדיט נדרש",
             "allowSellingGeneratedContent": "אפשר מכירה",
             "noTags": "ללא תגיות",
+            "noBaseModelMatches": "אין מודלי בסיס התואמים לחיפוש הנוכחי.",
             "clearAll": "נקה את כל המסננים",
             "any": "כלשהו",
             "all": "כל התגים",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "מפתח API של Civitai",
         "civitaiApiKeyPlaceholder": "הזן את מפתח ה-API שלך מ-Civitai",
         "civitaiApiKeyHelp": "משמש לאימות בעת הורדת מודלים מ-Civitai",
+        "civitaiHost": {
+            "label": "מארח Civitai",
+            "help": "בחר איזה אתר של Civitai ייפתח בעת שימוש בקישורי \"View on Civitai\".",
+            "options": {
+                "com": "civitai.com (SFW בלבד)",
+                "red": "civitai.red (ללא הגבלות)"
+            }
+        },
+        "downloadBackend": {
+            "label": "מנגנון הורדה",
+            "help": "בחר כיצד יורדים קבצי המודל. Python משתמש במוריד המובנה. aria2 משתמש בתהליך הורדה חיצוני ניסיוני.",
+            "options": {
+                "python": "Python (מובנה)",
+                "aria2": "aria2 (ניסיוני)"
+            }
+        },
+        "aria2cPath": {
+            "label": "נתיב aria2c",
+            "help": "נתיב אופציונלי לקובץ ההפעלה aria2c. השאר ריק כדי להשתמש ב-aria2c מתוך ה-PATH של המערכת.",
+            "placeholder": "השאר ריק כדי להשתמש ב-aria2c מתוך ה-PATH"
+        },
+        "civitaiHostBanner": {
+            "title": "העדפת מארח Civitai זמינה",
+            "content": "Civitai משתמש כעת ב-civitai.com עבור תוכן SFW וב-civitai.red עבור תוכן ללא הגבלות. ניתן לשנות בהגדרות איזה אתר ייפתח כברירת מחדל.",
+            "openSettings": "פתח הגדרות"
+        },
         "openSettingsFileLocation": {
             "label": "פתח תיקיית הגדרות",
             "tooltip": "פתח את התיקייה שמכילה את ‎settings.json",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "סינון תוכן",
+            "downloads": "הורדות",
             "videoSettings": "הגדרות וידאו",
             "layoutSettings": "הגדרות פריסה",
             "misc": "שונות",
+            "backup": "גיבויים",
             "folderSettings": "תיקיות ברירת מחדל",
+            "recipeSettings": "מתכונים",
             "extraFolderPaths": "נתיבי תיקיות נוספים",
             "downloadPathTemplates": "תבניות נתיב הורדה",
             "priorityTags": "תגיות עדיפות",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "טשטש תוכן NSFW",
             "blurNsfwContentHelp": "טשטש תמונות תצוגה מקדימה של תוכן למבוגרים (NSFW)",
             "showOnlySfw": "הצג רק תוצאות SFW",
-            "showOnlySfwHelp": "סנן את כל התוכן ה-NSFW בעת גלישה וחיפוש"
+            "showOnlySfwHelp": "סנן את כל התוכן ה-NSFW בעת גלישה וחיפוש",
+            "matureBlurThreshold": "סף טשטוש תוכן מבוגרים",
+            "matureBlurThresholdHelp": "הגדר מאיזו רמת דירוג מתחיל סינון הטשטוש כאשר טשטוש NSFW מופעל.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 ומעלה",
+                "r": "R ומעלה (ברירת מחדל)",
+                "x": "X ומעלה",
+                "xxx": "XXX בלבד"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "נגן וידאו אוטומטית בריחוף",
@@ -311,6 +357,54 @@
                 "saveFailed": "לא ניתן לשמור נתיבי דילוג: {message}"
             }
         },
+        "backup": {
+            "autoEnabled": "גיבויים אוטומטיים",
+            "autoEnabledHelp": "יוצר צילום מצב מקומי פעם ביום ושומר את הצילומים האחרונים לפי מדיניות השמירה.",
+            "retention": "כמות שמירה",
+            "retentionHelp": "כמה צילומי מצב אוטומטיים לשמור לפני שמסירים ישנים.",
+            "management": "ניהול גיבויים",
+            "managementHelp": "ייצא את מצב המשתמש הנוכחי או שחזר אותו מארכיון גיבוי.",
+            "scopeHelp": "כולל את ההגדרות שלך, היסטוריית ההורדות ומצב עדכוני המודלים. אינו כולל קובצי מודל או מטמונים שניתן לשחזר.",
+            "locationSummary": "מיקום הגיבוי הנוכחי",
+            "openFolderButton": "פתח את תיקיית הגיבויים",
+            "openFolderSuccess": "תיקיית הגיבויים נפתחה",
+            "openFolderFailed": "לא ניתן היה לפתוח את תיקיית הגיבויים",
+            "locationCopied": "נתיב הגיבוי הועתק ללוח: {{path}}",
+            "locationClipboardFallback": "נתיב הגיבוי: {{path}}",
+            "exportButton": "ייצא גיבוי",
+            "exportSuccess": "הגיבוי יוצא בהצלחה.",
+            "exportFailed": "נכשל ייצוא הגיבוי: {message}",
+            "importButton": "ייבא גיבוי",
+            "importConfirm": "לייבא את הגיבוי הזה ולדרוס את מצב המשתמש המקומי?",
+            "importSuccess": "הגיבוי יובא בהצלחה.",
+            "importFailed": "נכשל ייבוא הגיבוי: {message}",
+            "latestSnapshot": "צילום המצב האחרון",
+            "latestAutoSnapshot": "צילום המצב האוטומטי האחרון",
+            "snapshotCount": "צילומי מצב שמורים",
+            "noneAvailable": "עדיין אין צילומי מצב"
+        },
+        "downloadSkipBaseModels": {
+            "label": "דלג על הורדות עבור מודלי בסיס",
+            "help": "חל על כל תהליכי ההורדה. ניתן לבחור כאן רק מודלי בסיס נתמכים.",
+            "searchPlaceholder": "סנן מודלי בסיס...",
+            "empty": "אין מודלי בסיס התואמים לחיפוש הנוכחי.",
+            "summary": {
+                "none": "לא נבחר דבר",
+                "count": "{count} נבחרו"
+            },
+            "actions": {
+                "edit": "עריכה",
+                "collapse": "כווץ",
+                "clear": "נקה"
+            },
+            "validation": {
+                "saveFailed": "לא ניתן לשמור את מודלי הבסיס המוחרגים: {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "דלג על גרסאות מודלים שהורדו בעבר",
+            "help": "כאשר מופעל, LoRA Manager ידלג על הורדת גרסת מודל אם שירות היסטוריית ההורדות רושם את הגרסה המדויקת הזו ככבר שהורדה. חל על כל תהליכי ההורדה."
+        },
         "layoutSettings": {
             "displayDensity": "צפיפות תצוגה",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של Diffusion Model (UNET) להורדות, ייבוא והעברות",
             "defaultEmbeddingRoot": "תיקיית שורש Embedding",
             "defaultEmbeddingRootHelp": "הגדר את ספריית השורש המוגדרת כברירת מחדל של embedding להורדות, ייבוא והעברות",
+            "recipesPath": "נתיב אחסון מתכונים",
+            "recipesPathHelp": "ספרייה מותאמת אישית אופציונלית למתכונים שנשמרו. השאר ריק כדי להשתמש בתיקיית recipes של שורש LoRA הראשון.",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "מעביר את אחסון המתכונים...",
             "noDefault": "אין ברירת מחדל"
         },
+        "extraFolderPaths": {
+            "title": "נתיבי תיקיות נוספים",
+            "description": "נתיבי שורש מודלים נוספים בלעדיים ל-LoRA Manager. טען מודלים ממיקומים מחוץ לתיקיות הסטנדרטיות של ComfyUI - אידיאלי לספריות גדולות שאחרת יאטו את ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "נתיבי LoRA",
+                "checkpoint": "נתיבי Checkpoint",
+                "unet": "נתיבי מודל דיפוזיה",
+                "embedding": "נתיבי Embedding"
+            },
+            "pathPlaceholder": "/נתיב/למודלים/נוספים",
+            "saveSuccess": "נתיבי תיקיות נוספים עודכנו. נדרשת הפעלה מחדש כדי להחיל את השינויים.",
+            "saveError": "נכשל בעדכון נתיבי תיקיות נוספים: {message}",
+            "validation": {
+                "duplicatePath": "נתיב זה כבר מוגדר"
+            }
+        },
         "priorityTags": {
             "title": "תגיות עדיפות",
             "description": "התאם את סדר העדיפות של התגיות עבור כל סוג מודל (לדוגמה: character, concept, style(toon|toon_style))",
@@ -485,23 +600,6 @@
             "proxyPassword": "סיסמה (אופציונלי)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "סיסמה לאימות מול הפרוקסי (אם נדרש)"
-        },
-        "extraFolderPaths": {
-            "title": "נתיבי תיקיות נוספים",
-            "help": "הוסף תיקיות מודלים נוספות מחוץ לנתיבים הסטנדרטיים של ComfyUI. נתיבים אלה נשמרים בנפרד ונסרקים לצד תיקיות ברירת המחדל.",
-            "description": "הגדר תיקיות נוספות לסריקת מודלים. נתיבים אלה ספציפיים ל-LoRA Manager וימוזגו עם נתיבי ברירת המחדל של ComfyUI.",
-            "modelTypes": {
-                "lora": "נתיבי LoRA",
-                "checkpoint": "נתיבי Checkpoint",
-                "unet": "נתיבי מודל דיפוזיה",
-                "embedding": "נתיבי Embedding"
-            },
-            "pathPlaceholder": "/נתיב/למודלים/נוספים",
-            "saveSuccess": "נתיבי תיקיות נוספים עודכנו.",
-            "saveError": "נכשל בעדכון נתיבי תיקיות נוספים: {message}",
-            "validation": {
-                "duplicatePath": "נתיב זה כבר מוגדר"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "דילוג על רענון מטא-נתונים לנבחרים",
             "resumeMetadataRefresh": "המשך רענון מטא-נתונים לנבחרים",
             "deleteAll": "מחק את כל המודלים",
+            "downloadMissingLoras": "הורדת LoRAs חסרים",
             "clear": "נקה בחירה",
             "skipMetadataRefreshCount": "דילוג（{count} מודלים）",
             "resumeMetadataRefreshCount": "המשך（{count} מודלים）",
@@ -600,6 +699,7 @@
             "moveToFolder": "העבר לתיקייה",
             "repairMetadata": "תיקון מטא-דאטה",
             "excludeModel": "החרג מודל",
+            "restoreModel": "שחזור מודל",
             "deleteModel": "מחק מודל",
             "shareRecipe": "שתף מתכון",
             "viewAllLoras": "הצג את כל ה-LoRAs",
@@ -641,6 +741,8 @@
                 "root": "שורש",
                 "browseFolders": "דפדף בתיקיות:",
                 "downloadAndSaveRecipe": "הורד ושמור מתכון",
+                "importRecipeOnly": "יבא רק מתכון",
+                "importAndDownload": "יבא והורד",
                 "downloadMissingLoras": "הורד LoRAs חסרים",
                 "saveRecipe": "שמור מתכון",
                 "loraCountInfo": "({existing}/{total} בספרייה)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "הכי פחות"
             },
             "refresh": {
-                "title": "רענן רשימת מתכונים"
+                "title": "רענן רשימת מתכונים",
+                "quick": "סנכרן שינויים",
+                "quickTooltip": "סנכרן שינויים - רענון מהיר ללא בניית מטמון מחדש",
+                "full": "בנה מטמון מחדש",
+                "fullTooltip": "בנה מטמון מחדש - סריקה מחדש מלאה של כל קבצי המתכונים"
             },
             "filteredByLora": "מסונן לפי LoRA",
             "favorites": {
@@ -722,6 +828,64 @@
                 "failed": "תיקון המתכון נכשל: {message}",
                 "missingId": "לא ניתן לתקן את המתכון: חסר מזהה מתכון"
             }
+        },
+        "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "העבר לתיקיית {otherType}"
+            "moveToOtherTypeFolder": "העבר לתיקיית {otherType}",
+            "sendToWorkflow": "שלח ל-workflow"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "שחרר סרגל צד",
         "switchToListView": "עבור לתצוגת רשימה",
         "switchToTreeView": "תצוגת עץ",
-        "recursiveOn": "חיפוש בתיקיות משנה",
-        "recursiveOff": "חיפוש רק בתיקייה הנוכחית",
+        "recursiveOn": "כלול תיקיות משנה",
+        "recursiveOff": "רק התיקייה הנוכחית",
         "recursiveUnavailable": "חיפוש רקורסיבי זמין רק בתצוגת עץ",
         "collapseAllDisabled": "לא זמין בתצוגת רשימה",
         "dragDrop": {
             "unableToResolveRoot": "לא ניתן לקבוע את נתיב היעד להעברה.",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "העברה אינה נתמכת עבור פריט זה.",
+            "createFolderHint": "שחרר כדי ליצור תיקייה חדשה",
+            "newFolderName": "שם תיקייה חדשה",
+            "folderNameHint": "הקש Enter לאישור, Escape לביטול",
+            "emptyFolderName": "אנא הזן שם תיקייה",
+            "invalidFolderName": "שם התיקייה מכיל תווים לא חוקיים",
+            "noDragState": "לא נמצאה פעולת גרירה ממתינה"
+        },
+        "empty": {
+            "noFolders": "לא נמצאו תיקיות",
+            "dragHint": "גרור פריטים לכאן כדי ליצור תיקיות"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "גישה מוקדמת",
             "earlyAccessTooltip": "נדרשת גישה מוקדמת",
             "inLibrary": "בספרייה",
+            "downloaded": "הורד",
+            "downloadedTooltip": "הורד בעבר, אך הוא אינו נמצא כרגע בספרייה שלך.",
             "alreadyInLibrary": "כבר בספרייה",
             "autoOrganizedPath": "[מאורגן אוטומטית לפי תבנית נתיב]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "עדכן מודל בסיס",
             "cancel": "ביטול"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "הורדת LoRAs חסרים",
+            "message": "נמצאו {uniqueCount} LoRAs חסרים ייחודיים (מתוך {totalCount} בסך הכל במתכונים שנבחרו).",
+            "previewTitle": "LoRAs להורדה:",
+            "moreItems": "...ועוד {count}",
+            "note": "הקבצים יורדו באמצעות תבניות נתיב ברירת מחדל. זה עשוי לקחת זמן בהתאם למספר ה-LoRAs.",
+            "downloadButton": "הורד {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "תמונות דוגמה מקומיות",
             "message": "לא נמצאו תמונות דוגמה מקומיות למודל זה. אפשרויות צפייה:",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "הצג ב-Civitai",
                 "viewOnCivitaiText": "הצג ב-Civitai",
                 "viewCreatorProfile": "הצג פרופיל יוצר",
-                "openFileLocation": "פתח מיקום קובץ"
+                "openFileLocation": "פתח מיקום קובץ",
+                "sendToWorkflow": "שלח ל-ComfyUI",
+                "sendToWorkflowText": "שלח ל-ComfyUI"
             },
             "openFileLocation": {
                 "success": "מיקום הקובץ נפתח בהצלחה",
@@ -964,6 +1151,9 @@
                 "copied": "הנתיב הועתק ללוח העריכה: {{path}}",
                 "clipboardFallback": "נתיב: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "לא ניתן לשלוח ל-ComfyUI: אין נתיב קובץ זמין"
+            },
             "metadata": {
                 "version": "גרסה",
                 "fileName": "שם קובץ",
@@ -1071,17 +1261,30 @@
                     "days": "בעוד {count} ימים"
                 },
                 "badges": {
-                    "current": "גרסה נוכחית",
+                    "current": "גרסה שנפתחה",
+                    "currentTooltip": "זוהי הגרסה שממנה נפתח החלון הזה",
                     "inLibrary": "בספרייה",
+                    "inLibraryTooltip": "גרסה זו קיימת בספרייה המקומית שלך",
+                    "downloaded": "הורד",
+                    "downloadedTooltip": "גרסה זו הורדה בעבר, אך אינה נמצאת כרגע בספרייה שלך",
                     "newer": "גרסה חדשה יותר",
+                    "newerTooltip": "גרסה זו חדשה יותר מהגרסה המקומית האחרונה שלך",
                     "earlyAccess": "גישה מוקדמת",
-                    "ignored": "התעלם"
+                    "earlyAccessTooltip": "גרסה זו דורשת כרגע גישת Early Access של Civitai",
+                    "ignored": "התעלם",
+                    "ignoredTooltip": "התראות העדכון מושבתות עבור גרסה זו"
                 },
                 "actions": {
                     "download": "הורדה",
+                    "downloadTooltip": "הורד את הגרסה הזו",
+                    "downloadEarlyAccessTooltip": "הורד את גרסת ה-Early Access הזו מ-Civitai",
                     "delete": "מחיקה",
+                    "deleteTooltip": "מחק את הגרסה המקומית הזו",
                     "ignore": "התעלם",
                     "unignore": "בטל התעלמות",
+                    "ignoreTooltip": "התעלם מהתראות העדכון עבור גרסה זו",
+                    "unignoreTooltip": "חזור לקבל התראות עדכון עבור גרסה זו",
+                    "viewVersionOnCivitai": "הצג את הגרסה ב-Civitai",
                     "earlyAccessTooltip": "נדרש רכישת גישה מוקדמת",
                     "resumeModelUpdates": "המשך עדכונים עבור מודל זה",
                     "ignoreModelUpdates": "התעלם מעדכונים עבור מודל זה",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "מתכון הוחלף ב-workflow",
             "recipeFailedToSend": "שליחת מתכון ל-workflow נכשלה",
             "noMatchingNodes": "אין צמתים תואמים זמינים ב-workflow הנוכחי",
-            "noTargetNodeSelected": "לא נבחר צומת יעד"
+            "noTargetNodeSelected": "לא נבחר צומת יעד",
+            "modelUpdated": "מודל עודכן ב-workflow",
+            "modelFailed": "עדכון צומת המודל נכשל"
         },
         "nodeSelector": {
             "recipe": "מתכון",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "הצג קוד QR של WeChat",
             "hideWechatQR": "הסתר קוד QR של WeChat"
         },
-        "footer": "תודה על השימוש במנהל LoRA! ❤️"
+        "footer": "תודה על השימוש במנהל LoRA! ❤️",
+        "supporters": {
+            "title": "תודה לכל התומכים",
+            "subtitle": "תודה ל־{count} תומכים שהפכו את הפרויקט הזה לאפשרי",
+            "specialThanks": "תודה מיוחדת",
+            "allSupporters": "כל התומכים",
+            "totalCount": "{count} תומכים בסך הכל"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "אנא בחר גרסה",
             "versionExists": "גרסה זו כבר קיימת בספרייה שלך",
             "downloadCompleted": "ההורדה הושלמה בהצלחה",
+            "downloadSkippedByBaseModel": "ההורדה דולגה כי מודל הבסיס {baseModel} מוחרג",
             "autoOrganizeSuccess": "הארגון האוטומטי הושלם בהצלחה עבור {count} {type}",
             "autoOrganizePartialSuccess": "הארגון האוטומטי הושלם עם {success} שהועברו, {failures} שנכשלו מתוך {total} מודלים",
             "autoOrganizeFailed": "הארגון האוטומטי נכשל: {error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "טעינת {modelType}s נכשלה: {message}",
             "refreshComplete": "הרענון הושלם",
             "refreshFailed": "רענון המתכונים נכשל: {message}",
+            "syncComplete": "הסנכרון הושלם",
+            "syncFailed": "סנכרון המתכונים נכשל: {message}",
             "updateFailed": "עדכון המתכון נכשל: {error}",
             "updateError": "שגיאה בעדכון המתכון: {message}",
             "nameSaved": "המתכון \"{name}\" נשמר בהצלחה",
             "nameUpdated": "שם המתכון עודכן בהצלחה",
             "tagsUpdated": "תגיות המתכון עודכנו בהצלחה",
             "sourceUrlUpdated": "כתובת ה-URL המקורית עודכנה בהצלחה",
+            "promptUpdated": "הפרומפט עודכן בהצלחה",
+            "negativePromptUpdated": "הפרומפט השלילי עודכן בהצלחה",
+            "promptEditorHint": "לחץ Enter לשמירה, Shift+Enter לשורה חדשה",
             "noRecipeId": "אין מזהה מתכון זמין",
+            "sendToWorkflowFailed": "נכשל שליחת המתכון ל-workflow: {message}",
             "copyFailed": "שגיאה בהעתקת תחביר המתכון: {message}",
             "noMissingLoras": "אין LoRAs חסרים להורדה",
             "missingLorasInfoFailed": "קבלת מידע עבור LoRAs חסרים נכשלה",
@@ -1410,9 +1629,20 @@
             "processingError": "שגיאת עיבוד: {message}",
             "folderBrowserError": "שגיאה בטעינת דפדפן התיקיות: {message}",
             "recipeSaveFailed": "שמירת המתכון נכשלה: {error}",
+            "recipeSaved": "Recipe saved successfully",
             "importFailed": "הייבוא נכשל: {message}",
             "folderTreeFailed": "טעינת עץ התיקיות נכשלה",
-            "folderTreeError": "שגיאה בטעינת עץ התיקיות"
+            "folderTreeError": "שגיאה בטעינת עץ התיקיות",
+            "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}",
+            "noRecipesSelected": "לא נבחרו מתכונים",
+            "noMissingLorasInSelection": "לא נמצאו LoRAs חסרים במתכונים שנבחרו",
+            "noLoraRootConfigured": "תיקיית השורש של LoRA לא מוגדרת. אנא הגדר תיקיית שורש LoRA ברירת מחדל בהגדרות."
         },
         "models": {
             "noModelsSelected": "לא נבחרו מודלים",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "שמירת מיפויי מודל בסיס נכשלה: {message}",
             "downloadTemplatesUpdated": "תבניות נתיב הורדה עודכנו",
             "downloadTemplatesFailed": "שמירת תבניות נתיב הורדה נכשלה: {message}",
+            "recipesPathUpdated": "נתיב אחסון המתכונים עודכן",
+            "recipesPathSaveFailed": "עדכון נתיב אחסון המתכונים נכשל: {message}",
             "settingsUpdated": "הגדרות עודכנו: {setting}",
             "compactModeToggled": "מצב קומפקטי {state}",
             "settingSaveFailed": "שמירת ההגדרה נכשלה: {message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "מחיקת {type} נכשלה: {message}",
             "excludeSuccess": "{type} הוחרג בהצלחה",
             "excludeFailed": "החרגת {type} נכשלה: {message}",
+            "restoreSuccess": "{type} שוחזר בהצלחה",
+            "restoreFailed": "שחזור {type} נכשל: {message}",
             "fileNameUpdated": "שם הקובץ עודכן בהצלחה",
             "fileRenameFailed": "שינוי שם הקובץ נכשל: {error}",
             "previewUpdated": "התצוגה המקדימה עודכנה בהצלחה",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "אבחון מערכת",
+        "title": "דוקטור",
+        "buttonTitle": "הפעלת אבחון ותיקונים נפוצים",
+        "loading": "בודק את הסביבה...",
+        "footer": "ייצא חבילת אבחון אם הבעיה עדיין נמשכת לאחר התיקון.",
+        "summary": {
+            "idle": "הרץ בדיקת תקינות עבור הגדרות, שלמות המטמון ועקביות הממשק.",
+            "ok": "לא נמצאו בעיות פעילות בסביבה הנוכחית.",
+            "warning": "נמצאה/נמצאו {count} בעיה/בעיות. את רובן אפשר לתקן ישירות מלוח זה.",
+            "error": "יש לטפל ב-{count} בעיה/בעיות לפני שהאפליקציה תהיה תקינה לחלוטין."
+        },
+        "status": {
+            "ok": "תקין",
+            "warning": "דורש תשומת לב",
+            "error": "נדרשת פעולה"
+        },
+        "actions": {
+            "runAgain": "הפעל שוב",
+            "exportBundle": "ייצוא חבילה"
+        },
+        "toast": {
+            "loadFailed": "טעינת האבחון נכשלה: {message}",
+            "repairSuccess": "בניית המטמון מחדש הושלמה.",
+            "repairFailed": "בניית המטמון מחדש נכשלה: {message}",
+            "exportSuccess": "חבילת האבחון יוצאה.",
+            "exportFailed": "ייצוא חבילת האבחון נכשל: {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "זוהה עדכון יישום",
@@ -1651,4 +1914,4 @@
             "retry": "נסה שוב"
         }
     }
-}
+}

locales/ja.json

@@ -1,17 +1,21 @@
 {
     "common": {
+        "cancel": "キャンセル",
+        "confirm": "確認",
         "actions": {
             "save": "保存",
             "cancel": "キャンセル",
+            "confirm": "確認",
             "delete": "削除",
             "move": "移動",
             "refresh": "更新",
             "back": "戻る",
             "next": "次へ",
-            "backToTop": "トップに戻る",
+            "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...",
@@ -171,6 +175,9 @@
             "success": "{count} 件のレシピを正常に修復しました。",
             "cancelled": "修復がキャンセルされました。{count}個のレシピが修復されました。",
             "error": "レシピの修復に失敗しました: {message}"
+        },
+        "manageExcludedModels": {
+            "label": "除外モデルを管理"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "プリセット「{name}」は既に存在します。上書きしますか？",
             "presetNamePlaceholder": "プリセット名...",
             "baseModel": "ベースモデル",
+            "baseModelSearchPlaceholder": "ベースモデルを検索...",
             "modelTags": "タグ（上位20）",
-            "modelTypes": "Model Types",
+            "modelTypes": "モデルタイプ",
             "license": "ライセンス",
             "noCreditRequired": "クレジット不要",
             "allowSellingGeneratedContent": "販売許可",
             "noTags": "タグなし",
+            "noBaseModelMatches": "現在の検索に一致するベースモデルはありません。",
             "clearAll": "すべてのフィルタをクリア",
             "any": "いずれか",
             "all": "すべて",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Civitai APIキー",
         "civitaiApiKeyPlaceholder": "Civitai APIキーを入力してください",
         "civitaiApiKeyHelp": "Civitaiからモデルをダウンロードするときの認証に使用されます",
+        "civitaiHost": {
+            "label": "Civitai ホスト",
+            "help": "「View on Civitai」リンクを使うときに開く Civitai サイトを選択します。",
+            "options": {
+                "com": "civitai.com（SFW のみ）",
+                "red": "civitai.red（制限なし）"
+            }
+        },
+        "downloadBackend": {
+            "label": "ダウンロードバックエンド",
+            "help": "モデルファイルのダウンロード方法を選択します。Python は内蔵ダウンローダーを使用し、aria2 は実験的な外部ダウンローダープロセスを使用します。",
+            "options": {
+                "python": "Python（内蔵）",
+                "aria2": "aria2（実験的）"
+            }
+        },
+        "aria2cPath": {
+            "label": "aria2c のパス",
+            "help": "aria2c 実行ファイルへの任意のパスです。空欄のままにすると、システム PATH 上の aria2c を使用します。",
+            "placeholder": "空欄のままにすると PATH 上の aria2c を使用します"
+        },
+        "civitaiHostBanner": {
+            "title": "Civitai ホスト設定を利用できます",
+            "content": "Civitai は現在、SFW コンテンツには civitai.com、制限なしコンテンツには civitai.red を使用しています。設定で既定で開くサイトを変更できます。",
+            "openSettings": "設定を開く"
+        },
         "openSettingsFileLocation": {
             "label": "設定フォルダーを開く",
             "tooltip": "settings.json を含むフォルダーを開きます",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "コンテンツフィルタリング",
+            "downloads": "ダウンロード",
             "videoSettings": "動画設定",
             "layoutSettings": "レイアウト設定",
             "misc": "その他",
+            "backup": "バックアップ",
             "folderSettings": "デフォルトルート",
+            "recipeSettings": "レシピ",
             "extraFolderPaths": "追加フォルダーパス",
             "downloadPathTemplates": "ダウンロードパステンプレート",
             "priorityTags": "優先タグ",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "NSFWコンテンツをぼかす",
             "blurNsfwContentHelp": "成人向け（NSFW）コンテンツのプレビュー画像をぼかします",
             "showOnlySfw": "SFWコンテンツのみ表示",
-            "showOnlySfwHelp": "閲覧と検索時にすべてのNSFWコンテンツを除外します"
+            "showOnlySfwHelp": "閲覧と検索時にすべてのNSFWコンテンツを除外します",
+            "matureBlurThreshold": "成人コンテンツぼかし閾値",
+            "matureBlurThresholdHelp": "NSFWぼかしが有効な場合、どのレーティングレベルからぼかしフィルタリングを開始するかを設定します。",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 以上",
+                "r": "R 以上（デフォルト）",
+                "x": "X 以上",
+                "xxx": "XXX のみ"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "ホバー時に動画を自動再生",
@@ -311,6 +357,54 @@
                 "saveFailed": "スキップパスの保存に失敗しました：{message}"
             }
         },
+        "backup": {
+            "autoEnabled": "自動バックアップ",
+            "autoEnabledHelp": "1日1回ローカルのスナップショットを作成し、保持ポリシーに従って最新のものを残します。",
+            "retention": "保持数",
+            "retentionHelp": "古いものを削除する前に、何件の自動スナップショットを保持するかを指定します。",
+            "management": "バックアップ管理",
+            "managementHelp": "現在のユーザー状態をエクスポートするか、バックアップアーカイブから復元します。",
+            "scopeHelp": "設定、ダウンロード履歴、モデル更新の状態をバックアップします。モデルファイルや再生成できるキャッシュは含まれません。",
+            "locationSummary": "現在のバックアップ場所",
+            "openFolderButton": "バックアップフォルダを開く",
+            "openFolderSuccess": "バックアップフォルダを開きました",
+            "openFolderFailed": "バックアップフォルダを開けませんでした",
+            "locationCopied": "バックアップパスをクリップボードにコピーしました: {{path}}",
+            "locationClipboardFallback": "バックアップパス: {{path}}",
+            "exportButton": "バックアップをエクスポート",
+            "exportSuccess": "バックアップを正常にエクスポートしました。",
+            "exportFailed": "バックアップのエクスポートに失敗しました: {message}",
+            "importButton": "バックアップをインポート",
+            "importConfirm": "このバックアップをインポートして、ローカルのユーザー状態を上書きしますか?",
+            "importSuccess": "バックアップを正常にインポートしました。",
+            "importFailed": "バックアップのインポートに失敗しました: {message}",
+            "latestSnapshot": "最新のスナップショット",
+            "latestAutoSnapshot": "最新の自動スナップショット",
+            "snapshotCount": "保存済みスナップショット",
+            "noneAvailable": "まだスナップショットはありません"
+        },
+        "downloadSkipBaseModels": {
+            "label": "ベースモデルのダウンロードをスキップ",
+            "help": "すべてのダウンロードフローに適用されます。ここでは対応しているベースモデルのみ選択できます。",
+            "searchPlaceholder": "ベースモデルを絞り込む...",
+            "empty": "現在の検索に一致するベースモデルはありません。",
+            "summary": {
+                "none": "未選択",
+                "count": "{count} 件を選択"
+            },
+            "actions": {
+                "edit": "編集",
+                "collapse": "折りたたむ",
+                "clear": "クリア"
+            },
+            "validation": {
+                "saveFailed": "除外するベースモデルを保存できませんでした: {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "以前にダウンロードしたモデルバージョンをスキップ",
+            "help": "有効にすると、ダウンロード履歴サービスがそのバージョンが既にダウンロード済みと記録している場合、LoRA Managerはそのモデルバージョンのダウンロードをスキップします。すべてのダウンロードフローに適用されます。"
+        },
         "layoutSettings": {
             "displayDensity": "表示密度",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "ダウンロード、インポート、移動用のデフォルトDiffusion Model (UNET)ルートディレクトリを設定",
             "defaultEmbeddingRoot": "Embeddingルート",
             "defaultEmbeddingRootHelp": "ダウンロード、インポート、移動用のデフォルトembeddingルートディレクトリを設定",
+            "recipesPath": "レシピ保存先",
+            "recipesPathHelp": "保存済みレシピ用の任意のカスタムディレクトリです。空欄にすると最初のLoRAルートのrecipesフォルダーを使用します。",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "レシピ保存先を移動中...",
             "noDefault": "デフォルトなし"
         },
+        "extraFolderPaths": {
+            "title": "追加フォルダーパス",
+            "description": "LoRA Manager専用の追加モデルルートパス。ComfyUIの標準フォルダー外の場所からモデルを読み込みます。ComfyUIの動作を低下させる可能性のある大規模ライブラリに最適です。",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "LoRAパス",
+                "checkpoint": "Checkpointパス",
+                "unet": "Diffusionモデルパス",
+                "embedding": "Embeddingパス"
+            },
+            "pathPlaceholder": "/追加モデルへのパス",
+            "saveSuccess": "追加フォルダーパスを更新しました。変更を適用するには再起動が必要です。",
+            "saveError": "追加フォルダーパスの更新に失敗しました: {message}",
+            "validation": {
+                "duplicatePath": "このパスはすでに設定されています"
+            }
+        },
         "priorityTags": {
             "title": "優先タグ",
             "description": "各モデルタイプのタグ優先順位をカスタマイズします (例: character, concept, style(toon|toon_style))",
@@ -485,23 +600,6 @@
             "proxyPassword": "パスワード（任意）",
             "proxyPasswordPlaceholder": "パスワード",
             "proxyPasswordHelp": "プロキシ認証用のパスワード（必要な場合）"
-        },
-        "extraFolderPaths": {
-            "title": "追加フォルダーパス",
-            "help": "ComfyUIの標準パスの外部に追加のモデルフォルダを追加します。これらのパスは別々に保存され、デフォルトのフォルダと一緒にスキャンされます。",
-            "description": "モデルをスキャンするための追加フォルダを設定します。これらのパスはLoRA Manager固有であり、ComfyUIのデフォルトパスとマージされます。",
-            "modelTypes": {
-                "lora": "LoRAパス",
-                "checkpoint": "Checkpointパス",
-                "unet": "Diffusionモデルパス",
-                "embedding": "Embeddingパス"
-            },
-            "pathPlaceholder": "/追加モデルへのパス",
-            "saveSuccess": "追加フォルダーパスを更新しました。",
-            "saveError": "追加フォルダーパスの更新に失敗しました: {message}",
-            "validation": {
-                "duplicatePath": "このパスはすでに設定されています"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "選択したモデルのメタデータ更新をスキップ",
             "resumeMetadataRefresh": "選択したモデルのメタデータ更新を再開",
             "deleteAll": "すべてのモデルを削除",
+            "downloadMissingLoras": "不足している LoRA をダウンロード",
             "clear": "選択をクリア",
             "skipMetadataRefreshCount": "スキップ（{count}モデル）",
             "resumeMetadataRefreshCount": "再開（{count}モデル）",
@@ -600,6 +699,7 @@
             "moveToFolder": "フォルダに移動",
             "repairMetadata": "メタデータを修復",
             "excludeModel": "モデルを除外",
+            "restoreModel": "モデルを復元",
             "deleteModel": "モデルを削除",
             "shareRecipe": "レシピを共有",
             "viewAllLoras": "すべてのLoRAを表示",
@@ -641,6 +741,8 @@
                 "root": "ルート",
                 "browseFolders": "フォルダを参照：",
                 "downloadAndSaveRecipe": "ダウンロード & レシピ保存",
+                "importRecipeOnly": "レシピのみインポート",
+                "importAndDownload": "インポートとダウンロード",
                 "downloadMissingLoras": "不足しているLoRAをダウンロード",
                 "saveRecipe": "レシピを保存",
                 "loraCountInfo": "（{existing}/{total} ライブラリ内）",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "少ない順"
             },
             "refresh": {
-                "title": "レシピリストを更新"
+                "title": "レシピリストを更新",
+                "quick": "変更を同期",
+                "quickTooltip": "変更を同期 - キャッシュを再構築せずにクイック更新",
+                "full": "キャッシュを再構築",
+                "fullTooltip": "キャッシュを再構築 - すべてのレシピファイルを完全に再スキャン"
             },
             "filteredByLora": "LoRAでフィルタ済み",
             "favorites": {
@@ -722,6 +828,64 @@
                 "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": "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "{otherType} フォルダに移動"
+            "moveToOtherTypeFolder": "{otherType} フォルダに移動",
+            "sendToWorkflow": "ワークフローに送信"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "サイドバーの固定を解除",
         "switchToListView": "リストビューに切り替え",
         "switchToTreeView": "ツリー表示に切り替え",
-        "recursiveOn": "サブフォルダーを検索",
-        "recursiveOff": "現在のフォルダーのみを検索",
+        "recursiveOn": "サブフォルダーを含める",
+        "recursiveOff": "現在のフォルダーのみ",
         "recursiveUnavailable": "再帰検索はツリービューでのみ利用できます",
         "collapseAllDisabled": "リストビューでは利用できません",
         "dragDrop": {
             "unableToResolveRoot": "移動先のパスを特定できません。",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "この項目の移動はサポートされていません。",
+            "createFolderHint": "放して新しいフォルダを作成",
+            "newFolderName": "新しいフォルダ名",
+            "folderNameHint": "Enterで確定、Escでキャンセル",
+            "emptyFolderName": "フォルダ名を入力してください",
+            "invalidFolderName": "フォルダ名に無効な文字が含まれています",
+            "noDragState": "保留中のドラッグ操作が見つかりません"
+        },
+        "empty": {
+            "noFolders": "フォルダが見つかりません",
+            "dragHint": "ここへアイテムをドラッグしてフォルダを作成します"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "アーリーアクセス",
             "earlyAccessTooltip": "アーリーアクセスが必要",
             "inLibrary": "ライブラリ内",
+            "downloaded": "ダウンロード済み",
+            "downloadedTooltip": "以前にダウンロード済みですが、現在はライブラリにありません。",
             "alreadyInLibrary": "既にライブラリ内",
             "autoOrganizedPath": "[パステンプレートによる自動整理]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "ベースモデルを更新",
             "cancel": "キャンセル"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "不足している LoRA をダウンロード",
+            "message": "選択したレシピから合計 {totalCount} 個中 {uniqueCount} 個のユニークな不足している LoRA が見つかりました。",
+            "previewTitle": "ダウンロードする LoRA:",
+            "moreItems": "...あと {count} 個",
+            "note": "ファイルはデフォルトのパステンプレートを使用してダウンロードされます。LoRA の数によっては時間がかかる場合があります。",
+            "downloadButton": "{count} 個の LoRA をダウンロード"
+        },
         "exampleAccess": {
             "title": "ローカル例画像",
             "message": "このモデルのローカル例画像が見つかりませんでした。表示オプション：",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "Civitaiで表示",
                 "viewOnCivitaiText": "Civitaiで表示",
                 "viewCreatorProfile": "作成者プロフィールを表示",
-                "openFileLocation": "ファイルの場所を開く"
+                "openFileLocation": "ファイルの場所を開く",
+                "sendToWorkflow": "ComfyUI に送信",
+                "sendToWorkflowText": "ComfyUI に送信"
             },
             "openFileLocation": {
                 "success": "ファイルの場所を正常に開きました",
@@ -964,6 +1151,9 @@
                 "copied": "パスをクリップボードにコピーしました: {{path}}",
                 "clipboardFallback": "パス: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "ComfyUI に送信できません：ファイルパスがありません"
+            },
             "metadata": {
                 "version": "バージョン",
                 "fileName": "ファイル名",
@@ -1071,17 +1261,30 @@
                     "days": "{count}日後"
                 },
                 "badges": {
-                    "current": "現在のバージョン",
+                    "current": "開いたバージョン",
+                    "currentTooltip": "このモーダルを開くために選択したバージョンです",
                     "inLibrary": "ライブラリにあります",
+                    "inLibraryTooltip": "このバージョンはローカルライブラリに存在します",
+                    "downloaded": "ダウンロード済み",
+                    "downloadedTooltip": "このバージョンは以前ダウンロードされましたが、現在はライブラリにありません",
                     "newer": "新しいバージョン",
+                    "newerTooltip": "このバージョンはローカルの最新バージョンより新しいです",
                     "earlyAccess": "早期アクセス",
-                    "ignored": "無視中"
+                    "earlyAccessTooltip": "このバージョンは現在 Civitai の早期アクセスが必要です",
+                    "ignored": "無視中",
+                    "ignoredTooltip": "このバージョンの更新通知は無効です"
                 },
                 "actions": {
                     "download": "ダウンロード",
+                    "downloadTooltip": "このバージョンをダウンロード",
+                    "downloadEarlyAccessTooltip": "Civitai からこの早期アクセス版をダウンロード",
                     "delete": "削除",
+                    "deleteTooltip": "このローカルバージョンを削除",
                     "ignore": "無視",
                     "unignore": "無視を解除",
+                    "ignoreTooltip": "このバージョンの更新通知を無視",
+                    "unignoreTooltip": "このバージョンの更新通知を再開",
+                    "viewVersionOnCivitai": "Civitai でバージョンを表示",
                     "earlyAccessTooltip": "早期アクセス購入が必要",
                     "resumeModelUpdates": "このモデルの更新を再開",
                     "ignoreModelUpdates": "このモデルの更新を無視",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "レシピがワークフローで置換されました",
             "recipeFailedToSend": "レシピをワークフローに送信できませんでした",
             "noMatchingNodes": "現在のワークフローには互換性のあるノードがありません",
-            "noTargetNodeSelected": "ターゲットノードが選択されていません"
+            "noTargetNodeSelected": "ターゲットノードが選択されていません",
+            "modelUpdated": "モデルがワークフローで更新されました",
+            "modelFailed": "モデルノードの更新に失敗しました"
         },
         "nodeSelector": {
             "recipe": "レシピ",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "WeChat QRコードを表示",
             "hideWechatQR": "WeChat QRコードを非表示"
         },
-        "footer": "LoRA Managerをご利用いただきありがとうございます！ ❤️"
+        "footer": "LoRA Managerをご利用いただきありがとうございます！ ❤️",
+        "supporters": {
+            "title": "サポーターの皆様に感謝",
+            "subtitle": "{count} 名のサポーターの皆様に、このプロジェクトを実現していただきありがとうございます",
+            "specialThanks": "特別感謝",
+            "allSupporters": "全サポーター",
+            "totalCount": "サポーター {count} 名"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "バージョンを選択してください",
             "versionExists": "このバージョンは既にライブラリに存在します",
             "downloadCompleted": "ダウンロードが正常に完了しました",
+            "downloadSkippedByBaseModel": "ベースモデル {baseModel} が除外されているため、ダウンロードをスキップしました",
             "autoOrganizeSuccess": "{count} {type} の自動整理が正常に完了しました",
             "autoOrganizePartialSuccess": "自動整理が完了しました：{total} モデル中 {success} 移動、{failures} 失敗",
             "autoOrganizeFailed": "自動整理に失敗しました：{error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "{modelType}の読み込みに失敗しました：{message}",
             "refreshComplete": "更新完了",
             "refreshFailed": "レシピの更新に失敗しました：{message}",
+            "syncComplete": "同期完了",
+            "syncFailed": "レシピの同期に失敗しました：{message}",
             "updateFailed": "レシピの更新に失敗しました：{error}",
             "updateError": "レシピ更新エラー：{message}",
             "nameSaved": "レシピ\"{name}\"が正常に保存されました",
             "nameUpdated": "レシピ名が正常に更新されました",
             "tagsUpdated": "レシピタグが正常に更新されました",
             "sourceUrlUpdated": "ソースURLが正常に更新されました",
+            "promptUpdated": "プロンプトが正常に更新されました",
+            "negativePromptUpdated": "ネガティブプロンプトが正常に更新されました",
+            "promptEditorHint": "Enterキーで保存、Shift+Enterで改行",
             "noRecipeId": "レシピIDが利用できません",
+            "sendToWorkflowFailed": "ワークフローへのレシピ送信に失敗しました：{message}",
             "copyFailed": "レシピ構文のコピーエラー：{message}",
             "noMissingLoras": "ダウンロードする不足LoRAがありません",
             "missingLorasInfoFailed": "不足LoRAの情報取得に失敗しました",
@@ -1410,9 +1629,20 @@
             "processingError": "処理エラー：{message}",
             "folderBrowserError": "フォルダブラウザの読み込みエラー：{message}",
             "recipeSaveFailed": "レシピの保存に失敗しました：{error}",
+            "recipeSaved": "Recipe saved successfully",
             "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": "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}",
+            "noRecipesSelected": "レシピが選択されていません",
+            "noMissingLorasInSelection": "選択したレシピに不足している LoRA が見つかりませんでした",
+            "noLoraRootConfigured": "LoRA ルートディレクトリが設定されていません。設定でデフォルトの LoRA ルートを設定してください。"
         },
         "models": {
             "noModelsSelected": "モデルが選択されていません",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "ベースモデルマッピングの保存に失敗しました：{message}",
             "downloadTemplatesUpdated": "ダウンロードパステンプレートが更新されました",
             "downloadTemplatesFailed": "ダウンロードパステンプレートの保存に失敗しました：{message}",
+            "recipesPathUpdated": "レシピ保存先を更新しました",
+            "recipesPathSaveFailed": "レシピ保存先の更新に失敗しました: {message}",
             "settingsUpdated": "設定が更新されました：{setting}",
             "compactModeToggled": "コンパクトモード {state}",
             "settingSaveFailed": "設定の保存に失敗しました：{message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "{type}の削除に失敗しました：{message}",
             "excludeSuccess": "{type}が正常に除外されました",
             "excludeFailed": "{type}の除外に失敗しました：{message}",
+            "restoreSuccess": "{type}を復元しました",
+            "restoreFailed": "{type}の復元に失敗しました: {message}",
             "fileNameUpdated": "ファイル名が正常に更新されました",
             "fileRenameFailed": "ファイル名の変更に失敗しました：{error}",
             "previewUpdated": "プレビューが正常に更新されました",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "システム診断",
+        "title": "ドクター",
+        "buttonTitle": "診断と一般的な修復を実行",
+        "loading": "環境を確認中...",
+        "footer": "修復後も問題が続く場合は、診断パッケージをエクスポートしてください。",
+        "summary": {
+            "idle": "設定、キャッシュ整合性、UI の一貫性をヘルスチェックします。",
+            "ok": "現在の環境でアクティブな問題は見つかりませんでした。",
+            "warning": "{count} 件の問題が見つかりました。ほとんどはこのパネルから直接修復できます。",
+            "error": "アプリが完全に正常になる前に、{count} 件の問題に対処する必要があります。"
+        },
+        "status": {
+            "ok": "正常",
+            "warning": "要注意",
+            "error": "対応が必要"
+        },
+        "actions": {
+            "runAgain": "再実行",
+            "exportBundle": "パッケージをエクスポート"
+        },
+        "toast": {
+            "loadFailed": "診断の読み込みに失敗しました: {message}",
+            "repairSuccess": "キャッシュの再構築が完了しました。",
+            "repairFailed": "キャッシュの再構築に失敗しました: {message}",
+            "exportSuccess": "診断パッケージをエクスポートしました。",
+            "exportFailed": "診断パッケージのエクスポートに失敗しました: {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "アプリケーション更新が検出されました",
@@ -1651,4 +1914,4 @@
             "retry": "再試行"
         }
     }
-}
+}

locales/ko.json

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "취소",
+        "confirm": "확인",
         "actions": {
             "save": "저장",
             "cancel": "취소",
+            "confirm": "확인",
             "delete": "삭제",
             "move": "이동",
             "refresh": "새로고침",
@@ -11,7 +14,8 @@
             "backToTop": "맨 위로",
             "settings": "설정",
             "help": "도움말",
-            "add": "추가"
+            "add": "추가",
+            "close": "닫기"
         },
         "status": {
             "loading": "로딩 중...",
@@ -171,6 +175,9 @@
             "success": "{count}개의 레시피가 성공적으로 복구되었습니다.",
             "cancelled": "수리가 취소되었습니다. {count}개의 레시피가 수리되었습니다.",
             "error": "레시피 복구 실패: {message}"
+        },
+        "manageExcludedModels": {
+            "label": "제외된 모델 관리"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "프리셋 \"{name}\"이(가) 이미 존재합니다. 덮어쓰시겠습니까?",
             "presetNamePlaceholder": "프리셋 이름...",
             "baseModel": "베이스 모델",
+            "baseModelSearchPlaceholder": "베이스 모델 검색...",
             "modelTags": "태그 (상위 20개)",
-            "modelTypes": "Model Types",
+            "modelTypes": "모델 유형",
             "license": "라이선스",
             "noCreditRequired": "크레딧 표기 없음",
             "allowSellingGeneratedContent": "판매 허용",
             "noTags": "태그 없음",
+            "noBaseModelMatches": "현재 검색과 일치하는 베이스 모델이 없습니다.",
             "clearAll": "모든 필터 지우기",
             "any": "아무",
             "all": "모두",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Civitai API 키",
         "civitaiApiKeyPlaceholder": "Civitai API 키를 입력하세요",
         "civitaiApiKeyHelp": "Civitai에서 모델을 다운로드할 때 인증에 사용됩니다",
+        "civitaiHost": {
+            "label": "Civitai 호스트",
+            "help": "\"View on Civitai\" 링크를 사용할 때 어떤 Civitai 사이트를 열지 선택합니다.",
+            "options": {
+                "com": "civitai.com(SFW 전용)",
+                "red": "civitai.red(무제한)"
+            }
+        },
+        "downloadBackend": {
+            "label": "다운로드 백엔드",
+            "help": "모델 파일을 다운로드하는 방식을 선택합니다. Python은 내장 다운로더를 사용하고, aria2는 실험적인 외부 다운로더 프로세스를 사용합니다.",
+            "options": {
+                "python": "Python(내장)",
+                "aria2": "aria2(실험적)"
+            }
+        },
+        "aria2cPath": {
+            "label": "aria2c 경로",
+            "help": "aria2c 실행 파일의 선택적 경로입니다. 비워 두면 시스템 PATH의 aria2c를 사용합니다.",
+            "placeholder": "비워 두면 PATH의 aria2c를 사용합니다"
+        },
+        "civitaiHostBanner": {
+            "title": "Civitai 호스트 기본 설정 사용 가능",
+            "content": "이제 Civitai는 SFW 콘텐츠에 civitai.com을, 무제한 콘텐츠에 civitai.red를 사용합니다. 설정에서 기본으로 열 사이트를 변경할 수 있습니다.",
+            "openSettings": "설정 열기"
+        },
         "openSettingsFileLocation": {
             "label": "설정 폴더 열기",
             "tooltip": "settings.json이 있는 폴더를 엽니다",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "콘텐츠 필터링",
+            "downloads": "다운로드",
             "videoSettings": "비디오 설정",
             "layoutSettings": "레이아웃 설정",
             "misc": "기타",
+            "backup": "백업",
             "folderSettings": "기본 루트",
+            "recipeSettings": "레시피",
             "extraFolderPaths": "추가 폴다 경로",
             "downloadPathTemplates": "다운로드 경로 템플릿",
             "priorityTags": "우선순위 태그",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "NSFW 콘텐츠 블러 처리",
             "blurNsfwContentHelp": "성인(NSFW) 콘텐츠 미리보기 이미지를 블러 처리합니다",
             "showOnlySfw": "SFW 결과만 표시",
-            "showOnlySfwHelp": "탐색 및 검색 시 모든 NSFW 콘텐츠를 필터링합니다"
+            "showOnlySfwHelp": "탐색 및 검색 시 모든 NSFW 콘텐츠를 필터링합니다",
+            "matureBlurThreshold": "성인 콘텐츠 블러 임계값",
+            "matureBlurThresholdHelp": "NSFW 블러가 활성화될 때 어떤 등급 레벨부터 블러 필터링을 시작할지 설정합니다.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 이상",
+                "r": "R 이상（기본값）",
+                "x": "X 이상",
+                "xxx": "XXX만"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "호버 시 비디오 자동 재생",
@@ -311,6 +357,54 @@
                 "saveFailed": "건너뛰기 경로를 저장할 수 없습니다: {message}"
             }
         },
+        "backup": {
+            "autoEnabled": "자동 백업",
+            "autoEnabledHelp": "하루에 한 번 로컬 스냅샷을 만들고 보존 정책에 따라 최신 스냅샷을 유지합니다.",
+            "retention": "보존 개수",
+            "retentionHelp": "오래된 자동 스냅샷을 삭제하기 전에 몇 개를 유지할지 지정합니다.",
+            "management": "백업 관리",
+            "managementHelp": "현재 사용자 상태를 내보내거나 백업 아카이브에서 복원합니다.",
+            "scopeHelp": "설정, 다운로드 기록, 모델 업데이트 상태를 백업합니다. 모델 파일과 다시 생성할 수 있는 캐시는 포함되지 않습니다.",
+            "locationSummary": "현재 백업 위치",
+            "openFolderButton": "백업 폴더 열기",
+            "openFolderSuccess": "백업 폴더를 열었습니다",
+            "openFolderFailed": "백업 폴더를 열지 못했습니다",
+            "locationCopied": "백업 경로를 클립보드에 복사했습니다: {{path}}",
+            "locationClipboardFallback": "백업 경로: {{path}}",
+            "exportButton": "백업 내보내기",
+            "exportSuccess": "백업을 성공적으로 내보냈습니다.",
+            "exportFailed": "백업 내보내기에 실패했습니다: {message}",
+            "importButton": "백업 가져오기",
+            "importConfirm": "이 백업을 가져와서 로컬 사용자 상태를 덮어쓰시겠습니까?",
+            "importSuccess": "백업을 성공적으로 가져왔습니다.",
+            "importFailed": "백업 가져오기에 실패했습니다: {message}",
+            "latestSnapshot": "최근 스냅샷",
+            "latestAutoSnapshot": "최근 자동 스냅샷",
+            "snapshotCount": "저장된 스냅샷",
+            "noneAvailable": "아직 스냅샷이 없습니다"
+        },
+        "downloadSkipBaseModels": {
+            "label": "기본 모델 다운로드 건너뛰기",
+            "help": "모든 다운로드 흐름에 적용됩니다. 여기서는 지원되는 기본 모델만 선택할 수 있습니다.",
+            "searchPlaceholder": "기본 모델 필터링...",
+            "empty": "현재 검색과 일치하는 기본 모델이 없습니다.",
+            "summary": {
+                "none": "선택 없음",
+                "count": "{count}개 선택됨"
+            },
+            "actions": {
+                "edit": "편집",
+                "collapse": "접기",
+                "clear": "지우기"
+            },
+            "validation": {
+                "saveFailed": "제외된 기본 모델을 저장할 수 없습니다: {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "이전에 다운로드한 모델 버전 건너뛰기",
+            "help": "활성화하면 다운로드 기록 서비스가 해당 버전이 이미 다운로드되었음을 기록한 경우 LoRA Manager는 해당 모델 버전 다운로드를 건너뜁니다. 모든 다운로드 플로우에 적용됩니다."
+        },
         "layoutSettings": {
             "displayDensity": "표시 밀도",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Diffusion Model (UNET) 루트 디렉토리를 설정합니다",
             "defaultEmbeddingRoot": "Embedding 루트",
             "defaultEmbeddingRootHelp": "다운로드, 가져오기 및 이동을 위한 기본 Embedding 루트 디렉토리를 설정합니다",
+            "recipesPath": "레시피 저장 경로",
+            "recipesPathHelp": "저장된 레시피를 위한 선택적 사용자 지정 디렉터리입니다. 비워 두면 첫 번째 LoRA 루트의 recipes 폴더를 사용합니다.",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "레시피 저장 경로를 이동 중...",
             "noDefault": "기본값 없음"
         },
+        "extraFolderPaths": {
+            "title": "추가 폴다 경로",
+            "description": "LoRA Manager 전용 추가 모델 루트 경로입니다. ComfyUI의 표준 폴더 외부 위치에서 모델을 로드하여 대규모 라이브러리로 인한 성능 저하를 방지합니다.",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "LoRA 경로",
+                "checkpoint": "Checkpoint 경로",
+                "unet": "Diffusion 모델 경로",
+                "embedding": "Embedding 경로"
+            },
+            "pathPlaceholder": "/추가/모델/경로",
+            "saveSuccess": "추가 폴다 경로가 업데이트되었습니다. 변경 사항을 적용하려면 재시작이 필요합니다.",
+            "saveError": "추가 폴다 경로 업데이트 실패: {message}",
+            "validation": {
+                "duplicatePath": "이 경로는 이미 구성되어 있습니다"
+            }
+        },
         "priorityTags": {
             "title": "우선순위 태그",
             "description": "모델 유형별 태그 우선순위를 사용자 지정합니다(예: character, concept, style(toon|toon_style)).",
@@ -485,23 +600,6 @@
             "proxyPassword": "비밀번호 (선택사항)",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "프록시 인증에 필요한 비밀번호 (필요한 경우)"
-        },
-        "extraFolderPaths": {
-            "title": "추가 폴다 경로",
-            "help": "ComfyUI의 표준 경로 외부에 추가 모델 폴드를 추가하세요. 이러한 경로는 별도로 저장되며 기본 폴와 함께 스캔됩니다.",
-            "description": "모델을 스캔하기 위한 추가 폴를 설정하세요. 이러한 경로는 LoRA Manager 특유의 것이며 ComfyUI의 기본 경로와 병합됩니다.",
-            "modelTypes": {
-                "lora": "LoRA 경로",
-                "checkpoint": "Checkpoint 경로",
-                "unet": "Diffusion 모델 경로",
-                "embedding": "Embedding 경로"
-            },
-            "pathPlaceholder": "/추가/모델/경로",
-            "saveSuccess": "추가 폴다 경로가 업데이트되었습니다.",
-            "saveError": "추가 폴다 경로 업데이트 실패: {message}",
-            "validation": {
-                "duplicatePath": "이 경로는 이미 구성되어 있습니다"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "선택한 모델의 메타데이터 새로고침 건너뛰기",
             "resumeMetadataRefresh": "선택한 모델의 메타데이터 새로고침 재개",
             "deleteAll": "모든 모델 삭제",
+            "downloadMissingLoras": "누락된 LoRA 다운로드",
             "clear": "선택 지우기",
             "skipMetadataRefreshCount": "건너뛰기（{count}개 모델）",
             "resumeMetadataRefreshCount": "재개（{count}개 모델）",
@@ -600,6 +699,7 @@
             "moveToFolder": "폴더로 이동",
             "repairMetadata": "메타데이터 복구",
             "excludeModel": "모델 제외",
+            "restoreModel": "모델 복원",
             "deleteModel": "모델 삭제",
             "shareRecipe": "레시피 공유",
             "viewAllLoras": "모든 LoRA 보기",
@@ -641,6 +741,8 @@
                 "root": "루트",
                 "browseFolders": "폴더 탐색:",
                 "downloadAndSaveRecipe": "다운로드 및 레시피 저장",
+                "importRecipeOnly": "레시피만 가져오기",
+                "importAndDownload": "가져오기 및 다운로드",
                 "downloadMissingLoras": "누락된 LoRA 다운로드",
                 "saveRecipe": "레시피 저장",
                 "loraCountInfo": "({existing}/{total} 라이브러리에 있음)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "적은순"
             },
             "refresh": {
-                "title": "레시피 목록 새로고침"
+                "title": "레시피 목록 새로고침",
+                "quick": "변경 사항 동기화",
+                "quickTooltip": "변경 사항 동기화 - 캐시를 재구성하지 않고 빠른 새로고침",
+                "full": "캐시 재구성",
+                "fullTooltip": "캐시 재구성 - 모든 레시피 파일을 완전히 다시 스캔"
             },
             "filteredByLora": "LoRA로 필터링됨",
             "favorites": {
@@ -722,6 +828,64 @@
                 "failed": "레시피 복구 실패: {message}",
                 "missingId": "레시피를 복구할 수 없음: 레시피 ID 누락"
             }
+        },
+        "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "{otherType} 폴더로 이동"
+            "moveToOtherTypeFolder": "{otherType} 폴더로 이동",
+            "sendToWorkflow": "워크플로우로 전송"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "사이드바 고정 해제",
         "switchToListView": "목록 보기로 전환",
         "switchToTreeView": "트리 보기로 전환",
-        "recursiveOn": "하위 폴더 검색",
-        "recursiveOff": "현재 폴더만 검색",
+        "recursiveOn": "하위 폴더 포함",
+        "recursiveOff": "현재 폴더만",
         "recursiveUnavailable": "재귀 검색은 트리 보기에서만 사용할 수 있습니다",
         "collapseAllDisabled": "목록 보기에서는 사용할 수 없습니다",
         "dragDrop": {
             "unableToResolveRoot": "이동할 대상 경로를 확인할 수 없습니다.",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "이 항목은 이동을 지원하지 않습니다.",
+            "createFolderHint": "놓아서 새 폴더 만들기",
+            "newFolderName": "새 폴더 이름",
+            "folderNameHint": "Enter를 눌러 확인, Escape를 눌러 취소",
+            "emptyFolderName": "폴더 이름을 입력하세요",
+            "invalidFolderName": "폴더 이름에 잘못된 문자가 포함되어 있습니다",
+            "noDragState": "보류 중인 드래그 작업을 찾을 수 없습니다"
+        },
+        "empty": {
+            "noFolders": "폴더를 찾을 수 없습니다",
+            "dragHint": "항목을 여기로 드래그하여 폴더를 만듭니다"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "얼리 액세스",
             "earlyAccessTooltip": "얼리 액세스 필요",
             "inLibrary": "라이브러리에 있음",
+            "downloaded": "다운로드됨",
+            "downloadedTooltip": "이전에 다운로드했지만 현재 라이브러리에 없습니다.",
             "alreadyInLibrary": "이미 라이브러리에 있음",
             "autoOrganizedPath": "[경로 템플릿으로 자동 정리됨]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "베이스 모델 업데이트",
             "cancel": "취소"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "누락된 LoRA 다운로드",
+            "message": "선택한 레시피에서 총 {totalCount}개 중 {uniqueCount}개의 고유한 누락된 LoRA를 찾았습니다.",
+            "previewTitle": "다운로드할 LoRA:",
+            "moreItems": "...그리고 {count}개 더",
+            "note": "파일은 기본 경로 템플릿을 사용하여 다운로드됩니다. LoRA의 수에 따라 다소 시간이 걸릴 수 있습니다.",
+            "downloadButton": "{count}개 LoRA 다운로드"
+        },
         "exampleAccess": {
             "title": "로컬 예시 이미지",
             "message": "이 모델의 로컬 예시 이미지를 찾을 수 없습니다. 보기 옵션:",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "Civitai에서 보기",
                 "viewOnCivitaiText": "Civitai에서 보기",
                 "viewCreatorProfile": "제작자 프로필 보기",
-                "openFileLocation": "파일 위치 열기"
+                "openFileLocation": "파일 위치 열기",
+                "sendToWorkflow": "ComfyUI로 보내기",
+                "sendToWorkflowText": "ComfyUI로 보내기"
             },
             "openFileLocation": {
                 "success": "파일 위치가 성공적으로 열렸습니다",
@@ -964,6 +1151,9 @@
                 "copied": "경로가 클립보드에 복사되었습니다: {{path}}",
                 "clipboardFallback": "경로: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "ComfyUI로 보낼 수 없습니다: 파일 경로가 없습니다"
+            },
             "metadata": {
                 "version": "버전",
                 "fileName": "파일명",
@@ -1071,17 +1261,30 @@
                     "days": "{count}일 후"
                 },
                 "badges": {
-                    "current": "현재 버전",
+                    "current": "열린 버전",
+                    "currentTooltip": "이 모달을 열 때 사용한 버전입니다",
                     "inLibrary": "라이브러리에 있음",
+                    "inLibraryTooltip": "이 버전은 로컬 라이브러리에 있습니다",
+                    "downloaded": "다운로드됨",
+                    "downloadedTooltip": "이 버전은 이전에 다운로드되었지만 현재는 라이브러리에 없습니다",
                     "newer": "최신 버전",
+                    "newerTooltip": "이 버전은 로컬의 최신 버전보다 더 새롭습니다",
                     "earlyAccess": "얼리 액세스",
-                    "ignored": "무시됨"
+                    "earlyAccessTooltip": "이 버전은 현재 Civitai 얼리 액세스가 필요합니다",
+                    "ignored": "무시됨",
+                    "ignoredTooltip": "이 버전은 업데이트 알림이 비활성화되어 있습니다"
                 },
                 "actions": {
                     "download": "다운로드",
+                    "downloadTooltip": "이 버전 다운로드",
+                    "downloadEarlyAccessTooltip": "Civitai에서 이 얼리 액세스 버전 다운로드",
                     "delete": "삭제",
+                    "deleteTooltip": "이 로컬 버전 삭제",
                     "ignore": "무시",
                     "unignore": "무시 해제",
+                    "ignoreTooltip": "이 버전의 업데이트 알림 무시",
+                    "unignoreTooltip": "이 버전의 업데이트 알림 다시 받기",
+                    "viewVersionOnCivitai": "Civitai에서 버전 보기",
                     "earlyAccessTooltip": "얼리 액세스 구매 필요",
                     "resumeModelUpdates": "이 모델 업데이트 재개",
                     "ignoreModelUpdates": "이 모델 업데이트 무시",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "레시피가 워크플로에서 교체되었습니다",
             "recipeFailedToSend": "레시피를 워크플로로 전송하지 못했습니다",
             "noMatchingNodes": "현재 워크플로에서 호환되는 노드가 없습니다",
-            "noTargetNodeSelected": "대상 노드가 선택되지 않았습니다"
+            "noTargetNodeSelected": "대상 노드가 선택되지 않았습니다",
+            "modelUpdated": "모델이 워크플로에서 업데이트되었습니다",
+            "modelFailed": "모델 노드 업데이트 실패"
         },
         "nodeSelector": {
             "recipe": "레시피",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "WeChat QR 코드 표시",
             "hideWechatQR": "WeChat QR 코드 숨기기"
         },
-        "footer": "LoRA Manager를 사용해주셔서 감사합니다! ❤️"
+        "footer": "LoRA Manager를 사용해주셔서 감사합니다! ❤️",
+        "supporters": {
+            "title": "후원자 분들께 감사드립니다",
+            "subtitle": "이 프로젝트를 가능하게 해준 {count}명의 후원자분들께 감사드립니다",
+            "specialThanks": "특별 감사",
+            "allSupporters": "모든 후원자",
+            "totalCount": "총 {count}명의 후원자"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "버전을 선택해주세요",
             "versionExists": "이 버전은 이미 라이브러리에 있습니다",
             "downloadCompleted": "다운로드가 성공적으로 완료되었습니다",
+            "downloadSkippedByBaseModel": "기본 모델 {baseModel}이(가) 제외되어 다운로드를 건너뛰었습니다",
             "autoOrganizeSuccess": "{count}개의 {type}에 대해 자동 정리가 성공적으로 완료되었습니다",
             "autoOrganizePartialSuccess": "자동 정리 완료: 전체 {total}개 중 {success}개 이동, {failures}개 실패",
             "autoOrganizeFailed": "자동 정리 실패: {error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "{modelType} 로딩 실패: {message}",
             "refreshComplete": "새로고침 완료",
             "refreshFailed": "레시피 새로고침 실패: {message}",
+            "syncComplete": "동기화 완료",
+            "syncFailed": "레시피 동기화 실패: {message}",
             "updateFailed": "레시피 업데이트 실패: {error}",
             "updateError": "레시피 업데이트 오류: {message}",
             "nameSaved": "레시피 \"{name}\"이 성공적으로 저장되었습니다",
             "nameUpdated": "레시피 이름이 성공적으로 업데이트되었습니다",
             "tagsUpdated": "레시피 태그가 성공적으로 업데이트되었습니다",
             "sourceUrlUpdated": "소스 URL이 성공적으로 업데이트되었습니다",
+            "promptUpdated": "프롬프트가 성공적으로 업데이트되었습니다",
+            "negativePromptUpdated": "네거티브 프롬프트가 성공적으로 업데이트되었습니다",
+            "promptEditorHint": "Enter 키를 눌러 저장, Shift+Enter로 새 줄",
             "noRecipeId": "사용 가능한 레시피 ID가 없습니다",
+            "sendToWorkflowFailed": "워크플로우에 레시피 보내기 실패: {message}",
             "copyFailed": "레시피 문법 복사 오류: {message}",
             "noMissingLoras": "다운로드할 누락된 LoRA가 없습니다",
             "missingLorasInfoFailed": "누락된 LoRA 정보를 가져오는데 실패했습니다",
@@ -1410,9 +1629,20 @@
             "processingError": "처리 오류: {message}",
             "folderBrowserError": "폴더 브라우저 로딩 오류: {message}",
             "recipeSaveFailed": "레시피 저장 실패: {error}",
+            "recipeSaved": "Recipe saved successfully",
             "importFailed": "가져오기 실패: {message}",
             "folderTreeFailed": "폴더 트리 로딩 실패",
-            "folderTreeError": "폴더 트리 로딩 오류"
+            "folderTreeError": "폴더 트리 로딩 오류",
+            "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}",
+            "noRecipesSelected": "선택한 레시피가 없습니다",
+            "noMissingLorasInSelection": "선택한 레시피에서 누락된 LoRA를 찾을 수 없습니다",
+            "noLoraRootConfigured": "LoRA 루트 디렉토리가 구성되지 않았습니다. 설정에서 기본 LoRA 루트를 설정하세요."
         },
         "models": {
             "noModelsSelected": "선택된 모델이 없습니다",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "베이스 모델 매핑 저장 실패: {message}",
             "downloadTemplatesUpdated": "다운로드 경로 템플릿이 업데이트되었습니다",
             "downloadTemplatesFailed": "다운로드 경로 템플릿 저장 실패: {message}",
+            "recipesPathUpdated": "레시피 저장 경로가 업데이트되었습니다",
+            "recipesPathSaveFailed": "레시피 저장 경로 업데이트 실패: {message}",
             "settingsUpdated": "설정 업데이트됨: {setting}",
             "compactModeToggled": "컴팩트 모드 {state}",
             "settingSaveFailed": "설정 저장 실패: {message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "{type} 삭제 실패: {message}",
             "excludeSuccess": "{type}이(가) 성공적으로 제외되었습니다",
             "excludeFailed": "{type} 제외 실패: {message}",
+            "restoreSuccess": "{type} 복원 완료",
+            "restoreFailed": "{type} 복원 실패: {message}",
             "fileNameUpdated": "파일명이 성공적으로 업데이트되었습니다",
             "fileRenameFailed": "파일 이름 변경 실패: {error}",
             "previewUpdated": "미리보기가 성공적으로 업데이트되었습니다",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "시스템 진단",
+        "title": "닥터",
+        "buttonTitle": "진단 및 일반적인 수정 실행",
+        "loading": "환경을 확인하는 중...",
+        "footer": "수리 후에도 문제가 계속되면 진단 번들을 내보내세요.",
+        "summary": {
+            "idle": "설정, 캐시 무결성, UI 일관성에 대한 상태 검사를 실행합니다.",
+            "ok": "현재 환경에서 활성 문제를 찾지 못했습니다.",
+            "warning": "{count}개의 문제가 발견되었습니다. 대부분은 이 패널에서 바로 해결할 수 있습니다.",
+            "error": "앱이 완전히 정상 상태가 되기 전에 {count}개의 문제를 처리해야 합니다."
+        },
+        "status": {
+            "ok": "정상",
+            "warning": "주의 필요",
+            "error": "조치 필요"
+        },
+        "actions": {
+            "runAgain": "다시 실행",
+            "exportBundle": "번들 내보내기"
+        },
+        "toast": {
+            "loadFailed": "진단 로드 실패: {message}",
+            "repairSuccess": "캐시 재구성이 완료되었습니다.",
+            "repairFailed": "캐시 재구성 실패: {message}",
+            "exportSuccess": "진단 번들이 내보내졌습니다.",
+            "exportFailed": "진단 번들 내보내기 실패: {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "애플리케이션 업데이트 감지",
@@ -1651,4 +1914,4 @@
             "retry": "다시 시도"
         }
     }
-}
+}

locales/ru.json

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "Отмена",
+        "confirm": "Подтвердить",
         "actions": {
             "save": "Сохранить",
             "cancel": "Отмена",
+            "confirm": "Подтвердить",
             "delete": "Удалить",
             "move": "Переместить",
             "refresh": "Обновить",
@@ -11,7 +14,8 @@
             "backToTop": "Наверх",
             "settings": "Настройки",
             "help": "Справка",
-            "add": "Добавить"
+            "add": "Добавить",
+            "close": "Закрыть"
         },
         "status": {
             "loading": "Загрузка...",
@@ -171,6 +175,9 @@
             "success": "Успешно восстановлено {count} рецептов.",
             "cancelled": "Восстановление отменено. {count} рецептов было восстановлено.",
             "error": "Ошибка восстановления рецептов: {message}"
+        },
+        "manageExcludedModels": {
+            "label": "Управление исключёнными моделями"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "Пресет \"{name}\" уже существует. Перезаписать?",
             "presetNamePlaceholder": "Имя пресета...",
             "baseModel": "Базовая модель",
+            "baseModelSearchPlaceholder": "Поиск базовых моделей...",
             "modelTags": "Теги (Топ 20)",
-            "modelTypes": "Model Types",
+            "modelTypes": "Типы моделей",
             "license": "Лицензия",
             "noCreditRequired": "Без указания авторства",
             "allowSellingGeneratedContent": "Продажа разрешена",
             "noTags": "Без тегов",
+            "noBaseModelMatches": "Нет базовых моделей, соответствующих текущему поиску.",
             "clearAll": "Очистить все фильтры",
             "any": "Любой",
             "all": "Все",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Ключ API Civitai",
         "civitaiApiKeyPlaceholder": "Введите ваш ключ API Civitai",
         "civitaiApiKeyHelp": "Используется для аутентификации при загрузке моделей с Civitai",
+        "civitaiHost": {
+            "label": "Хост Civitai",
+            "help": "Выберите, какой сайт Civitai будет открываться при использовании ссылок «View on Civitai».",
+            "options": {
+                "com": "civitai.com (только SFW)",
+                "red": "civitai.red (без ограничений)"
+            }
+        },
+        "downloadBackend": {
+            "label": "Бэкенд загрузки",
+            "help": "Выберите способ загрузки файлов моделей. Python использует встроенный загрузчик. aria2 использует экспериментальный внешний процесс загрузки.",
+            "options": {
+                "python": "Python (встроенный)",
+                "aria2": "aria2 (экспериментальный)"
+            }
+        },
+        "aria2cPath": {
+            "label": "Путь к aria2c",
+            "help": "Необязательный путь к исполняемому файлу aria2c. Оставьте пустым, чтобы использовать aria2c из системного PATH.",
+            "placeholder": "Оставьте пустым, чтобы использовать aria2c из PATH"
+        },
+        "civitaiHostBanner": {
+            "title": "Доступна настройка хоста Civitai",
+            "content": "Теперь Civitai использует civitai.com для контента SFW и civitai.red для контента без ограничений. В настройках можно изменить, какой сайт открывать по умолчанию.",
+            "openSettings": "Открыть настройки"
+        },
         "openSettingsFileLocation": {
             "label": "Открыть папку настроек",
             "tooltip": "Открыть папку, содержащую settings.json",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "Фильтрация контента",
+            "downloads": "Загрузки",
             "videoSettings": "Настройки видео",
             "layoutSettings": "Настройки макета",
             "misc": "Разное",
+            "backup": "Резервные копии",
             "folderSettings": "Корневые папки",
+            "recipeSettings": "Рецепты",
             "extraFolderPaths": "Дополнительные пути к папкам",
             "downloadPathTemplates": "Шаблоны путей загрузки",
             "priorityTags": "Приоритетные теги",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "Размывать NSFW контент",
             "blurNsfwContentHelp": "Размывать превью изображений контента для взрослых (NSFW)",
             "showOnlySfw": "Показывать только SFW результаты",
-            "showOnlySfwHelp": "Фильтровать весь NSFW контент при просмотре и поиске"
+            "showOnlySfwHelp": "Фильтровать весь NSFW контент при просмотре и поиске",
+            "matureBlurThreshold": "Порог размытия взрослого контента",
+            "matureBlurThresholdHelp": "Установить, с какого уровня рейтинга начинается размытие при включенном размытии NSFW.",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 и выше",
+                "r": "R и выше (по умолчанию)",
+                "x": "X и выше",
+                "xxx": "Только XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "Автовоспроизведение видео при наведении",
@@ -311,6 +357,54 @@
                 "saveFailed": "Не удалось сохранить пути для пропуска: {message}"
             }
         },
+        "backup": {
+            "autoEnabled": "Автоматические резервные копии",
+            "autoEnabledHelp": "Создаёт локальный снимок раз в день и хранит последние снимки согласно политике хранения.",
+            "retention": "Количество хранения",
+            "retentionHelp": "Сколько автоматических снимков сохранять перед удалением старых.",
+            "management": "Управление резервными копиями",
+            "managementHelp": "Экспортируйте текущее состояние пользователя или восстановите его из архива резервной копии.",
+            "scopeHelp": "Резервная копия включает ваши настройки, историю загрузок и состояние обновлений моделей. Файлы моделей и пересоздаваемые кэши не входят.",
+            "locationSummary": "Текущее расположение резервных копий",
+            "openFolderButton": "Открыть папку резервных копий",
+            "openFolderSuccess": "Папка резервных копий открыта",
+            "openFolderFailed": "Не удалось открыть папку резервных копий",
+            "locationCopied": "Путь к резервной копии скопирован в буфер обмена: {{path}}",
+            "locationClipboardFallback": "Путь к резервной копии: {{path}}",
+            "exportButton": "Экспортировать резервную копию",
+            "exportSuccess": "Резервная копия успешно экспортирована.",
+            "exportFailed": "Не удалось экспортировать резервную копию: {message}",
+            "importButton": "Импортировать резервную копию",
+            "importConfirm": "Импортировать эту резервную копию и перезаписать локальное состояние пользователя?",
+            "importSuccess": "Резервная копия успешно импортирована.",
+            "importFailed": "Не удалось импортировать резервную копию: {message}",
+            "latestSnapshot": "Последний снимок",
+            "latestAutoSnapshot": "Последний автоматический снимок",
+            "snapshotCount": "Сохранённые снимки",
+            "noneAvailable": "Снимков пока нет"
+        },
+        "downloadSkipBaseModels": {
+            "label": "Пропускать загрузки для базовых моделей",
+            "help": "Применяется ко всем сценариям загрузки. Здесь можно выбрать только поддерживаемые базовые модели.",
+            "searchPlaceholder": "Фильтровать базовые модели...",
+            "empty": "Нет базовых моделей, соответствующих текущему поиску.",
+            "summary": {
+                "none": "Ничего не выбрано",
+                "count": "Выбрано: {count}"
+            },
+            "actions": {
+                "edit": "Изменить",
+                "collapse": "Свернуть",
+                "clear": "Очистить"
+            },
+            "validation": {
+                "saveFailed": "Не удалось сохранить исключённые базовые модели: {message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "Пропускать ранее загруженные версии моделей",
+            "help": "Если включено, LoRA Manager будет пропускать загрузку версии модели, если сервис истории загрузок записал, что эта конкретная версия уже загружена. Применяется ко всем потокам загрузки."
+        },
         "layoutSettings": {
             "displayDensity": "Плотность отображения",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "Установить корневую папку Diffusion Model (UNET) по умолчанию для загрузок, импорта и перемещений",
             "defaultEmbeddingRoot": "Корневая папка Embedding",
             "defaultEmbeddingRootHelp": "Установить корневую папку embedding по умолчанию для загрузок, импорта и перемещений",
+            "recipesPath": "Путь хранения рецептов",
+            "recipesPathHelp": "Дополнительный пользовательский каталог для сохранённых рецептов. Оставьте пустым, чтобы использовать папку recipes в первом корне LoRA.",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "Перенос хранилища рецептов...",
             "noDefault": "Не задано"
         },
+        "extraFolderPaths": {
+            "title": "Дополнительные пути к папкам",
+            "description": "Дополнительные корневые пути моделей, эксклюзивные для LoRA Manager. Загружайте модели из расположений за пределами стандартных папок ComfyUI — идеально подходит для больших библиотек, которые иначе замедлили бы ComfyUI.",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "Пути LoRA",
+                "checkpoint": "Пути Checkpoint",
+                "unet": "Пути моделей диффузии",
+                "embedding": "Пути Embedding"
+            },
+            "pathPlaceholder": "/путь/к/дополнительным/моделям",
+            "saveSuccess": "Дополнительные пути к папкам обновлены. Требуется перезапуск для применения изменений.",
+            "saveError": "Не удалось обновить дополнительные пути к папкам: {message}",
+            "validation": {
+                "duplicatePath": "Этот путь уже настроен"
+            }
+        },
         "priorityTags": {
             "title": "Приоритетные теги",
             "description": "Настройте порядок приоритетов тегов для каждого типа моделей (например, character, concept, style(toon|toon_style)).",
@@ -485,23 +600,6 @@
             "proxyPassword": "Пароль (необязательно)",
             "proxyPasswordPlaceholder": "пароль",
             "proxyPasswordHelp": "Пароль для аутентификации на прокси (если требуется)"
-        },
-        "extraFolderPaths": {
-            "title": "Дополнительные пути к папкам",
-            "help": "Добавьте дополнительные папки моделей за пределами стандартных путей ComfyUI. Эти пути хранятся отдельно и сканируются вместе с папками по умолчанию.",
-            "description": "Настройте дополнительные папки для сканирования моделей. Эти пути специфичны для LoRA Manager и будут объединены с путями по умолчанию ComfyUI.",
-            "modelTypes": {
-                "lora": "Пути LoRA",
-                "checkpoint": "Пути Checkpoint",
-                "unet": "Пути моделей диффузии",
-                "embedding": "Пути Embedding"
-            },
-            "pathPlaceholder": "/путь/к/дополнительным/моделям",
-            "saveSuccess": "Дополнительные пути к папкам обновлены.",
-            "saveError": "Не удалось обновить дополнительные пути к папкам: {message}",
-            "validation": {
-                "duplicatePath": "Этот путь уже настроен"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "Пропустить обновление метаданных для выбранных",
             "resumeMetadataRefresh": "Возобновить обновление метаданных для выбранных",
             "deleteAll": "Удалить все модели",
+            "downloadMissingLoras": "Скачать отсутствующие LoRAs",
             "clear": "Очистить выбор",
             "skipMetadataRefreshCount": "Пропустить（{count} моделей）",
             "resumeMetadataRefreshCount": "Возобновить（{count} моделей）",
@@ -600,6 +699,7 @@
             "moveToFolder": "Переместить в папку",
             "repairMetadata": "Восстановить метаданные",
             "excludeModel": "Исключить модель",
+            "restoreModel": "Восстановить модель",
             "deleteModel": "Удалить модель",
             "shareRecipe": "Поделиться рецептом",
             "viewAllLoras": "Посмотреть все LoRAs",
@@ -641,6 +741,8 @@
                 "root": "Корень",
                 "browseFolders": "Обзор папок:",
                 "downloadAndSaveRecipe": "Скачать и сохранить рецепт",
+                "importRecipeOnly": "Импортировать только рецепт",
+                "importAndDownload": "Импорт и скачивание",
                 "downloadMissingLoras": "Скачать отсутствующие LoRAs",
                 "saveRecipe": "Сохранить рецепт",
                 "loraCountInfo": "({existing}/{total} в библиотеке)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "Меньше всего"
             },
             "refresh": {
-                "title": "Обновить список рецептов"
+                "title": "Обновить список рецептов",
+                "quick": "Синхронизировать изменения",
+                "quickTooltip": "Синхронизировать изменения - быстрое обновление без перестроения кэша",
+                "full": "Перестроить кэш",
+                "fullTooltip": "Перестроить кэш - полное повторное сканирование всех файлов рецептов"
             },
             "filteredByLora": "Фильтр по LoRA",
             "favorites": {
@@ -722,6 +828,64 @@
                 "failed": "Не удалось восстановить рецепт: {message}",
                 "missingId": "Не удалось восстановить рецепт: отсутствует ID рецепта"
             }
+        },
+        "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "Переместить в папку {otherType}"
+            "moveToOtherTypeFolder": "Переместить в папку {otherType}",
+            "sendToWorkflow": "Отправить в workflow"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "Открепить боковую панель",
         "switchToListView": "Переключить на вид списка",
         "switchToTreeView": "Переключить на древовидный вид",
-        "recursiveOn": "Искать во вложенных папках",
-        "recursiveOff": "Искать только в текущей папке",
+        "recursiveOn": "Включать вложенные папки",
+        "recursiveOff": "Только текущая папка",
         "recursiveUnavailable": "Рекурсивный поиск доступен только в режиме дерева",
         "collapseAllDisabled": "Недоступно в виде списка",
         "dragDrop": {
             "unableToResolveRoot": "Не удалось определить путь назначения для перемещения.",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "Перемещение этого элемента не поддерживается.",
+            "createFolderHint": "Отпустите, чтобы создать новую папку",
+            "newFolderName": "Имя новой папки",
+            "folderNameHint": "Нажмите Enter для подтверждения, Escape для отмены",
+            "emptyFolderName": "Пожалуйста, введите имя папки",
+            "invalidFolderName": "Имя папки содержит недопустимые символы",
+            "noDragState": "Ожидающая операция перетаскивания не найдена"
+        },
+        "empty": {
+            "noFolders": "Папки не найдены",
+            "dragHint": "Перетащите элементы сюда, чтобы создать папки"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "Ранний доступ",
             "earlyAccessTooltip": "Требуется ранний доступ",
             "inLibrary": "В библиотеке",
+            "downloaded": "Загружено",
+            "downloadedTooltip": "Ранее загружено, но сейчас этого нет в вашей библиотеке.",
             "alreadyInLibrary": "Уже в библиотеке",
             "autoOrganizedPath": "[Автоматически организовано по шаблону пути]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "Обновить базовую модель",
             "cancel": "Отмена"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "Скачать отсутствующие LoRAs",
+            "message": "Найдено {uniqueCount} уникальных отсутствующих LoRAs (из {totalCount} всего в выбранных рецептах).",
+            "previewTitle": "LoRAs для скачивания:",
+            "moreItems": "...и еще {count}",
+            "note": "Файлы будут скачаны с использованием шаблонов путей по умолчанию. Это может занять некоторое время в зависимости от количества LoRAs.",
+            "downloadButton": "Скачать {count} LoRA(s)"
+        },
         "exampleAccess": {
             "title": "Локальные примеры изображений",
             "message": "Локальные примеры изображений для этой модели не найдены. Варианты просмотра:",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "Посмотреть на Civitai",
                 "viewOnCivitaiText": "Посмотреть на Civitai",
                 "viewCreatorProfile": "Посмотреть профиль создателя",
-                "openFileLocation": "Открыть расположение файла"
+                "openFileLocation": "Открыть расположение файла",
+                "sendToWorkflow": "Отправить в ComfyUI",
+                "sendToWorkflowText": "Отправить в ComfyUI"
             },
             "openFileLocation": {
                 "success": "Расположение файла успешно открыто",
@@ -964,6 +1151,9 @@
                 "copied": "Путь скопирован в буфер обмена: {{path}}",
                 "clipboardFallback": "Путь: {{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "Невозможно отправить в ComfyUI: путь к файлу недоступен"
+            },
             "metadata": {
                 "version": "Версия",
                 "fileName": "Имя файла",
@@ -1071,17 +1261,30 @@
                     "days": "через {count}д"
                 },
                 "badges": {
-                    "current": "Текущая версия",
+                    "current": "Открытая версия",
+                    "currentTooltip": "Это версия, с которой было открыто это окно",
                     "inLibrary": "В библиотеке",
+                    "inLibraryTooltip": "Эта версия есть в вашей локальной библиотеке",
+                    "downloaded": "Загружено",
+                    "downloadedTooltip": "Эта версия уже загружалась, но сейчас отсутствует в вашей библиотеке",
                     "newer": "Более новая версия",
+                    "newerTooltip": "Эта версия новее вашей последней локальной версии",
                     "earlyAccess": "Ранний доступ",
-                    "ignored": "Игнорируется"
+                    "earlyAccessTooltip": "Для этой версии сейчас требуется ранний доступ Civitai",
+                    "ignored": "Игнорируется",
+                    "ignoredTooltip": "Уведомления об обновлениях для этой версии отключены"
                 },
                 "actions": {
                     "download": "Скачать",
+                    "downloadTooltip": "Скачать эту версию",
+                    "downloadEarlyAccessTooltip": "Скачать эту версию раннего доступа с Civitai",
                     "delete": "Удалить",
+                    "deleteTooltip": "Удалить эту локальную версию",
                     "ignore": "Игнорировать",
                     "unignore": "Перестать игнорировать",
+                    "ignoreTooltip": "Игнорировать уведомления об обновлениях для этой версии",
+                    "unignoreTooltip": "Возобновить уведомления об обновлениях для этой версии",
+                    "viewVersionOnCivitai": "Посмотреть версию на Civitai",
                     "earlyAccessTooltip": "Требуется покупка раннего доступа",
                     "resumeModelUpdates": "Возобновить обновления для этой модели",
                     "ignoreModelUpdates": "Игнорировать обновления для этой модели",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "Рецепт заменён в workflow",
             "recipeFailedToSend": "Не удалось отправить рецепт в workflow",
             "noMatchingNodes": "В текущем workflow нет совместимых узлов",
-            "noTargetNodeSelected": "Целевой узел не выбран"
+            "noTargetNodeSelected": "Целевой узел не выбран",
+            "modelUpdated": "Модель обновлена в workflow",
+            "modelFailed": "Не удалось обновить узел модели"
         },
         "nodeSelector": {
             "recipe": "Рецепт",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "Показать QR-код WeChat",
             "hideWechatQR": "Скрыть QR-код WeChat"
         },
-        "footer": "Спасибо за использование LoRA Manager! ❤️"
+        "footer": "Спасибо за использование LoRA Manager! ❤️",
+        "supporters": {
+            "title": "Спасибо всем сторонникам",
+            "subtitle": "Спасибо {count} сторонникам, которые сделали этот проект возможным",
+            "specialThanks": "Особая благодарность",
+            "allSupporters": "Все сторонники",
+            "totalCount": "Всего {count} сторонников"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "Пожалуйста, выберите версию",
             "versionExists": "Эта версия уже существует в вашей библиотеке",
             "downloadCompleted": "Загрузка успешно завершена",
+            "downloadSkippedByBaseModel": "Загрузка пропущена, потому что базовая модель {baseModel} исключена",
             "autoOrganizeSuccess": "Автоматическая организация успешно завершена для {count} {type}",
             "autoOrganizePartialSuccess": "Автоматическая организация завершена: перемещено {success}, не удалось {failures} из {total} моделей",
             "autoOrganizeFailed": "Ошибка автоматической организации: {error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "Не удалось загрузить {modelType}s: {message}",
             "refreshComplete": "Обновление завершено",
             "refreshFailed": "Не удалось обновить рецепты: {message}",
+            "syncComplete": "Синхронизация завершена",
+            "syncFailed": "Не удалось синхронизировать рецепты: {message}",
             "updateFailed": "Не удалось обновить рецепт: {error}",
             "updateError": "Ошибка обновления рецепта: {message}",
             "nameSaved": "Рецепт \"{name}\" успешно сохранен",
             "nameUpdated": "Название рецепта успешно обновлено",
             "tagsUpdated": "Теги рецепта успешно обновлены",
             "sourceUrlUpdated": "Исходный URL успешно обновлен",
+            "promptUpdated": "Промпт успешно обновлён",
+            "negativePromptUpdated": "Негативный промпт успешно обновлён",
+            "promptEditorHint": "Нажмите Enter для сохранения, Shift+Enter для новой строки",
             "noRecipeId": "ID рецепта недоступен",
+            "sendToWorkflowFailed": "Не удалось отправить рецепт в рабочий процесс: {message}",
             "copyFailed": "Ошибка копирования синтаксиса рецепта: {message}",
             "noMissingLoras": "Нет отсутствующих LoRAs для загрузки",
             "missingLorasInfoFailed": "Не удалось получить информацию для отсутствующих LoRAs",
@@ -1410,9 +1629,20 @@
             "processingError": "Ошибка обработки: {message}",
             "folderBrowserError": "Ошибка загрузки браузера папок: {message}",
             "recipeSaveFailed": "Не удалось сохранить рецепт: {error}",
+            "recipeSaved": "Recipe saved successfully",
             "importFailed": "Импорт не удался: {message}",
             "folderTreeFailed": "Не удалось загрузить дерево папок",
-            "folderTreeError": "Ошибка загрузки дерева папок"
+            "folderTreeError": "Ошибка загрузки дерева папок",
+            "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}",
+            "noRecipesSelected": "Рецепты не выбраны",
+            "noMissingLorasInSelection": "В выбранных рецептах не найдены отсутствующие LoRAs",
+            "noLoraRootConfigured": "Корневой каталог LoRA не настроен. Пожалуйста, установите корневой каталог LoRA по умолчанию в настройках."
         },
         "models": {
             "noModelsSelected": "Модели не выбраны",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "Не удалось сохранить сопоставления базовых моделей: {message}",
             "downloadTemplatesUpdated": "Шаблоны путей загрузки обновлены",
             "downloadTemplatesFailed": "Не удалось сохранить шаблоны путей загрузки: {message}",
+            "recipesPathUpdated": "Путь хранения рецептов обновлён",
+            "recipesPathSaveFailed": "Не удалось обновить путь хранения рецептов: {message}",
             "settingsUpdated": "Настройки обновлены: {setting}",
             "compactModeToggled": "Компактный режим {state}",
             "settingSaveFailed": "Не удалось сохранить настройку: {message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "Не удалось удалить {type}: {message}",
             "excludeSuccess": "{type} успешно исключен",
             "excludeFailed": "Не удалось исключить {type}: {message}",
+            "restoreSuccess": "{type} успешно восстановлен",
+            "restoreFailed": "Не удалось восстановить {type}: {message}",
             "fileNameUpdated": "Имя файла успешно обновлено",
             "fileRenameFailed": "Не удалось переименовать файл: {error}",
             "previewUpdated": "Превью успешно обновлено",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "Системная диагностика",
+        "title": "Доктор",
+        "buttonTitle": "Запустить диагностику и обычные исправления",
+        "loading": "Проверка окружения...",
+        "footer": "Экспортируйте диагностический пакет, если проблема сохраняется после исправления.",
+        "summary": {
+            "idle": "Выполнить проверку настроек, целостности кэша и согласованности интерфейса.",
+            "ok": "В текущем окружении активных проблем не обнаружено.",
+            "warning": "Обнаружено {count} проблем(ы). Большинство можно исправить прямо из этой панели.",
+            "error": "Перед тем как приложение станет полностью исправным, нужно устранить {count} проблем(ы)."
+        },
+        "status": {
+            "ok": "Исправно",
+            "warning": "Требует внимания",
+            "error": "Требуется действие"
+        },
+        "actions": {
+            "runAgain": "Запустить снова",
+            "exportBundle": "Экспортировать пакет"
+        },
+        "toast": {
+            "loadFailed": "Не удалось загрузить диагностику: {message}",
+            "repairSuccess": "Перестройка кэша завершена.",
+            "repairFailed": "Не удалось перестроить кэш: {message}",
+            "exportSuccess": "Диагностический пакет экспортирован.",
+            "exportFailed": "Не удалось экспортировать диагностический пакет: {message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "Обнаружено обновление приложения",
@@ -1651,4 +1914,4 @@
             "retry": "Повторить"
         }
     }
-}
+}

locales/zh-CN.json

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "取消",
+        "confirm": "确认",
         "actions": {
             "save": "保存",
             "cancel": "取消",
+            "confirm": "确认",
             "delete": "删除",
             "move": "移动",
             "refresh": "刷新",
@@ -11,7 +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...",
@@ -159,11 +163,11 @@
             "error": "清理示例图片文件夹失败：{message}"
         },
         "fetchMissingLicenses": {
-            "label": "Refresh license metadata",
-            "loading": "Refreshing license metadata for {typePlural}...",
-            "success": "Updated license metadata for {count} {typePlural}",
-            "none": "All {typePlural} already have license metadata",
-            "error": "Failed to refresh license metadata for {typePlural}: {message}"
+            "label": "刷新许可证元数据",
+            "loading": "正在刷新 {typePlural} 的许可证元数据...",
+            "success": "已更新 {count} 个 {typePlural} 的许可证元数据",
+            "none": "所有 {typePlural} 都已具备许可证元数据",
+            "error": "刷新 {typePlural} 的许可证元数据失败：{message}"
         },
         "repairRecipes": {
             "label": "修复配方数据",
@@ -171,6 +175,9 @@
             "success": "成功修复了 {count} 个配方。",
             "cancelled": "修复已取消。已修复 {count} 个配方。",
             "error": "配方修复失败：{message}"
+        },
+        "manageExcludedModels": {
+            "label": "管理已排除的模型"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "预设 \"{name}\" 已存在。是否覆盖？",
             "presetNamePlaceholder": "预设名称...",
             "baseModel": "基础模型",
+            "baseModelSearchPlaceholder": "搜索基础模型...",
             "modelTags": "标签（前20）",
-            "modelTypes": "Model Types",
+            "modelTypes": "模型类型",
             "license": "许可证",
             "noCreditRequired": "无需署名",
             "allowSellingGeneratedContent": "允许销售",
             "noTags": "无标签",
+            "noBaseModelMatches": "没有基础模型符合当前搜索。",
             "clearAll": "清除所有筛选",
             "any": "任一",
             "all": "全部",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Civitai API 密钥",
         "civitaiApiKeyPlaceholder": "请输入你的 Civitai API 密钥",
         "civitaiApiKeyHelp": "用于从 Civitai 下载模型时的身份验证",
+        "civitaiHost": {
+            "label": "Civitai 站点",
+            "help": "选择使用“在 Civitai 中查看”时默认打开的 Civitai 站点。",
+            "options": {
+                "com": "civitai.com（仅 SFW）",
+                "red": "civitai.red（无限制）"
+            }
+        },
+        "downloadBackend": {
+            "label": "下载后端",
+            "help": "选择模型文件的下载方式。Python 使用内置下载器。aria2 使用实验性的外部下载进程。",
+            "options": {
+                "python": "Python（内置）",
+                "aria2": "aria2（实验性）"
+            }
+        },
+        "aria2cPath": {
+            "label": "aria2c 路径",
+            "help": "可选的 aria2c 可执行文件路径。留空则使用系统 PATH 中的 aria2c。",
+            "placeholder": "留空则使用 PATH 中的 aria2c"
+        },
+        "civitaiHostBanner": {
+            "title": "已提供 Civitai 站点偏好设置",
+            "content": "Civitai 现在使用 civitai.com 提供 SFW 内容，使用 civitai.red 提供无限制内容。你可以在设置中更改默认打开的站点。",
+            "openSettings": "打开设置"
+        },
         "openSettingsFileLocation": {
             "label": "打开设置文件夹",
             "tooltip": "打开包含 settings.json 的文件夹",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "内容过滤",
+            "downloads": "下载",
             "videoSettings": "视频设置",
             "layoutSettings": "布局设置",
             "misc": "其他",
+            "backup": "备份",
             "folderSettings": "默认根目录",
+            "recipeSettings": "配方",
             "extraFolderPaths": "额外文件夹路径",
             "downloadPathTemplates": "下载路径模板",
             "priorityTags": "优先标签",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "模糊 NSFW 内容",
             "blurNsfwContentHelp": "模糊成熟（NSFW）内容预览图片",
             "showOnlySfw": "仅显示 SFW 结果",
-            "showOnlySfwHelp": "浏览和搜索时过滤所有 NSFW 内容"
+            "showOnlySfwHelp": "浏览和搜索时过滤所有 NSFW 内容",
+            "matureBlurThreshold": "成人内容模糊阈值",
+            "matureBlurThresholdHelp": "设置当启用 NSFW 模糊时，从哪个评级级别开始模糊过滤。",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 及以上",
+                "r": "R 及以上（默认）",
+                "x": "X 及以上",
+                "xxx": "仅 XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "悬停时自动播放视频",
@@ -311,6 +357,54 @@
                 "saveFailed": "无法保存跳过路径：{message}"
             }
         },
+        "backup": {
+            "autoEnabled": "自动备份",
+            "autoEnabledHelp": "每天创建一次本地快照，并按保留策略保留最新快照。",
+            "retention": "保留数量",
+            "retentionHelp": "在删除旧快照之前，要保留多少个自动快照。",
+            "management": "备份管理",
+            "managementHelp": "导出当前用户状态，或从备份归档中恢复。",
+            "scopeHelp": "备份你的设置、下载历史和模型更新状态。不包含模型文件或可重建的缓存。",
+            "locationSummary": "当前备份位置",
+            "openFolderButton": "打开备份文件夹",
+            "openFolderSuccess": "已打开备份文件夹",
+            "openFolderFailed": "无法打开备份文件夹",
+            "locationCopied": "备份路径已复制到剪贴板：{{path}}",
+            "locationClipboardFallback": "备份路径：{{path}}",
+            "exportButton": "导出备份",
+            "exportSuccess": "备份导出成功。",
+            "exportFailed": "备份导出失败：{message}",
+            "importButton": "导入备份",
+            "importConfirm": "导入此备份并覆盖本地用户状态吗？",
+            "importSuccess": "备份导入成功。",
+            "importFailed": "备份导入失败：{message}",
+            "latestSnapshot": "最近快照",
+            "latestAutoSnapshot": "最近自动快照",
+            "snapshotCount": "已保存快照",
+            "noneAvailable": "还没有快照"
+        },
+        "downloadSkipBaseModels": {
+            "label": "跳过这些基础模型的下载",
+            "help": "适用于所有下载流程。这里只能选择受支持的基础模型。",
+            "searchPlaceholder": "筛选基础模型...",
+            "empty": "没有与当前搜索匹配的基础模型。",
+            "summary": {
+                "none": "未选择",
+                "count": "已选择 {count} 项"
+            },
+            "actions": {
+                "edit": "编辑",
+                "collapse": "收起",
+                "clear": "清空"
+            },
+            "validation": {
+                "saveFailed": "无法保存已排除的基础模型：{message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "跳过已下载的模型版本",
+            "help": "启用后，如果下载历史服务记录显示该版本已下载，LoRA Manager 将跳过下载该模型版本。适用于所有下载流程。"
+        },
         "layoutSettings": {
             "displayDensity": "显示密度",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "设置下载、导入和移动时的默认 Diffusion Model (UNET) 根目录",
             "defaultEmbeddingRoot": "Embedding 根目录",
             "defaultEmbeddingRootHelp": "设置下载、导入和移动时的默认 Embedding 根目录",
+            "recipesPath": "配方存储路径",
+            "recipesPathHelp": "已保存配方的可选自定义目录。留空则使用第一个 LoRA 根目录下的 recipes 文件夹。",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "正在迁移配方存储...",
             "noDefault": "无默认"
         },
+        "extraFolderPaths": {
+            "title": "额外文件夹路径",
+            "description": "LoRA Manager 专属的额外模型根目录。从 ComfyUI 标准文件夹之外的位置加载模型，特别适合管理大型模型库，避免影响 ComfyUI 性能。",
+            "restartRequired": "需要重启才能生效",
+            "modelTypes": {
+                "lora": "LoRA 路径",
+                "checkpoint": "Checkpoint 路径",
+                "unet": "Diffusion 模型路径",
+                "embedding": "Embedding 路径"
+            },
+            "pathPlaceholder": "/额外/模型/路径",
+            "saveSuccess": "额外文件夹路径已更新，需要重启才能生效。",
+            "saveError": "更新额外文件夹路径失败：{message}",
+            "validation": {
+                "duplicatePath": "此路径已配置"
+            }
+        },
         "priorityTags": {
             "title": "优先标签",
             "description": "为每种模型类型自定义标签优先级顺序 (例如: character, concept, style(toon|toon_style))",
@@ -485,23 +600,6 @@
             "proxyPassword": "密码 (可选)",
             "proxyPasswordPlaceholder": "密码",
             "proxyPasswordHelp": "代理认证的密码 (如果需要)"
-        },
-        "extraFolderPaths": {
-            "title": "额外文件夹路径",
-            "help": "在 ComfyUI 的标准路径之外添加额外的模型文件夹。这些路径单独存储，并与默认文件夹一起扫描。",
-            "description": "配置额外的文件夹以扫描模型。这些路径是 LoRA Manager 特有的，将与 ComfyUI 的默认路径合并。",
-            "modelTypes": {
-                "lora": "LoRA 路径",
-                "checkpoint": "Checkpoint 路径",
-                "unet": "Diffusion 模型路径",
-                "embedding": "Embedding 路径"
-            },
-            "pathPlaceholder": "/额外/模型/路径",
-            "saveSuccess": "额外文件夹路径已更新。",
-            "saveError": "更新额外文件夹路径失败：{message}",
-            "validation": {
-                "duplicatePath": "此路径已配置"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "跳过所选模型的元数据刷新",
             "resumeMetadataRefresh": "恢复所选模型的元数据刷新",
             "deleteAll": "删除选中模型",
+            "downloadMissingLoras": "下载缺失的 LoRAs",
             "clear": "清除选择",
             "skipMetadataRefreshCount": "跳过（{count} 个模型）",
             "resumeMetadataRefreshCount": "恢复（{count} 个模型）",
@@ -600,6 +699,7 @@
             "moveToFolder": "移动到文件夹",
             "repairMetadata": "修复元数据",
             "excludeModel": "排除模型",
+            "restoreModel": "恢复模型",
             "deleteModel": "删除模型",
             "shareRecipe": "分享配方",
             "viewAllLoras": "查看所有 LoRA",
@@ -618,9 +718,9 @@
                 "title": "从图片或 URL 导入配方",
                 "urlLocalPath": "URL / 本地路径",
                 "uploadImage": "上传图片",
-                "urlSectionDescription": "输入 Civitai 图片 URL 或本地文件路径以导入为配方。",
+                "urlSectionDescription": "输入来自 civitai.com 或 civitai.red 的 Civitai 图片 URL，或本地文件路径以导入为配方。",
                 "imageUrlOrPath": "图片 URL 或文件路径：",
-                "urlPlaceholder": "https://civitai.com/images/... 或 C:/path/to/image.png",
+                "urlPlaceholder": "https://civitai.com/images/... 或 https://civitai.red/images/... 或 C:/path/to/image.png",
                 "fetchImage": "获取图片",
                 "uploadSectionDescription": "上传带有 LoRA 元数据的图片以导入为配方。",
                 "selectImage": "选择图片",
@@ -641,6 +741,8 @@
                 "root": "根目录",
                 "browseFolders": "浏览文件夹：",
                 "downloadAndSaveRecipe": "下载并保存配方",
+                "importRecipeOnly": "仅导入配方",
+                "importAndDownload": "导入并下载",
                 "downloadMissingLoras": "下载缺失的 LoRA",
                 "saveRecipe": "保存配方",
                 "loraCountInfo": "({existing}/{total} in library)",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "最少"
             },
             "refresh": {
-                "title": "刷新配方列表"
+                "title": "刷新配方列表",
+                "quick": "同步变更",
+                "quickTooltip": "同步变更 - 快速刷新而不重建缓存",
+                "full": "重建缓存",
+                "fullTooltip": "重建缓存 - 重新扫描所有配方文件"
             },
             "filteredByLora": "按 LoRA 筛选",
             "favorites": {
@@ -722,6 +828,64 @@
                 "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": "URL 列表",
+            "directory": "目录",
+            "urlDescription": "输入图像 URL 或本地文件路径（每行一个）。每个都将作为配方导入。",
+            "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": "每行输入一个 URL 或路径",
+            "directoryPath": "目录路径",
+            "directoryPlaceholder": "/图片/文件夹/路径",
+            "browse": "浏览",
+            "recursive": "包含子目录",
+            "tagsOptional": "标签（可选，应用于所有配方）",
+            "tagsPlaceholder": "输入以逗号分隔的标签",
+            "tagsHint": "标签将被添加到所有导入的配方中",
+            "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": "开始导入",
+            "startImport": "开始导入",
+            "importing": "正在导入配方...",
+            "progress": "进度",
+            "total": "总计",
+            "success": "成功",
+            "failed": "失败",
+            "skipped": "跳过",
+            "current": "当前",
+            "currentItem": "当前",
+            "preparing": "准备中...",
+            "cancel": "取消",
+            "cancelImport": "取消",
+            "cancelled": "批量导入已取消",
+            "completed": "导入完成",
+            "completedWithErrors": "导入完成但有错误",
+            "completedSuccess": "成功导入 {count} 个配方",
+            "successCount": "成功",
+            "failedCount": "失败",
+            "skippedCount": "跳过",
+            "totalProcessed": "总计处理",
+            "viewDetails": "查看详情",
+            "newImport": "新建导入",
+            "manualPathEntry": "请手动输入目录路径。此浏览器中文件浏览器不可用。",
+            "batchImportDirectorySelected": "已选择目录：{path}",
+            "batchImportManualEntryRequired": "文件浏览器不可用。请手动输入目录路径。",
+            "backToParent": "返回上级目录",
+            "folders": "文件夹",
+            "folderCount": "{count} 个文件夹",
+            "imageFiles": "图像文件",
+            "images": "图像",
+            "imageCount": "{count} 个图像",
+            "selectFolder": "选择此文件夹",
+            "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": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "移动到 {otherType} 文件夹"
+            "moveToOtherTypeFolder": "移动到 {otherType} 文件夹",
+            "sendToWorkflow": "发送到工作流"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "取消固定侧边栏",
         "switchToListView": "切换到列表视图",
         "switchToTreeView": "切换到树状视图",
-        "recursiveOn": "搜索子文件夹",
-        "recursiveOff": "仅搜索当前文件夹",
+        "recursiveOn": "包含子文件夹",
+        "recursiveOff": "仅当前文件夹",
         "recursiveUnavailable": "仅在树形视图中可使用递归搜索",
         "collapseAllDisabled": "列表视图下不可用",
         "dragDrop": {
             "unableToResolveRoot": "无法确定移动的目标路径。",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "Move is not supported for this item.",
+            "createFolderHint": "释放以创建新文件夹",
+            "newFolderName": "新文件夹名称",
+            "folderNameHint": "按 Enter 确认，Escape 取消",
+            "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"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "早期访问",
             "earlyAccessTooltip": "需要早期访问权限",
             "inLibrary": "已在库中",
+            "downloaded": "已下载",
+            "downloadedTooltip": "之前已下载，但当前不在你的库中。",
             "alreadyInLibrary": "已存在于库中",
             "autoOrganizedPath": "【已按路径模板自动整理】",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "更新基础模型",
             "cancel": "取消"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "下载缺失的 LoRAs",
+            "message": "发现 {uniqueCount} 个独特的缺失 LoRAs（从选定配方中的 {totalCount} 个总数）。",
+            "previewTitle": "要下载的 LoRAs：",
+            "moreItems": "...还有 {count} 个",
+            "note": "文件将使用默认路径模板下载。根据 LoRAs 的数量，这可能需要一些时间。",
+            "downloadButton": "下载 {count} 个 LoRA(s)"
+        },
         "exampleAccess": {
             "title": "本地示例图片",
             "message": "未找到此模型的本地示例图片。可选操作：",
@@ -938,9 +1123,9 @@
             },
             "proceedText": "仅在你确定需要此操作时继续。",
             "urlLabel": "Civitai 模型 URL：",
-            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
+            "urlPlaceholder": "https://civitai.com/models/649516/model-name?modelVersionId=726676 或 https://civitai.red/models/649516/model-name?modelVersionId=726676",
             "helpText": {
-                "title": "粘贴任意 Civitai 模型 URL。支持格式：",
+                "title": "粘贴任意来自 civitai.com 或 civitai.red 的 Civitai 模型 URL。支持格式：",
                 "format1": "https://civitai.com/models/649516",
                 "format2": "https://civitai.com/models/649516?modelVersionId=726676",
                 "format3": "https://civitai.com/models/649516/model-name?modelVersionId=726676",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
                 "viewCreatorProfile": "查看创作者主页",
-                "openFileLocation": "打开文件位置"
+                "openFileLocation": "打开文件位置",
+                "sendToWorkflow": "发送到 ComfyUI",
+                "sendToWorkflowText": "发送到 ComfyUI"
             },
             "openFileLocation": {
                 "success": "文件位置已成功打开",
@@ -964,6 +1151,9 @@
                 "copied": "路径已复制到剪贴板：{{path}}",
                 "clipboardFallback": "路径：{{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "无法发送到 ComfyUI：没有可用的文件路径"
+            },
             "metadata": {
                 "version": "版本",
                 "fileName": "文件名",
@@ -1071,17 +1261,30 @@
                     "days": "{count}天后"
                 },
                 "badges": {
-                    "current": "当前版本",
+                    "current": "已打开版本",
+                    "currentTooltip": "这是你用来打开此弹窗的版本",
                     "inLibrary": "已在库中",
+                    "inLibraryTooltip": "此版本已存在于你的本地库中",
+                    "downloaded": "已下载",
+                    "downloadedTooltip": "此版本之前下载过，但当前不在你的本地库中",
                     "newer": "较新的版本",
+                    "newerTooltip": "此版本比你本地的最新版本更新",
                     "earlyAccess": "抢先体验",
-                    "ignored": "已忽略"
+                    "earlyAccessTooltip": "此版本当前需要 Civitai 抢先体验权限",
+                    "ignored": "已忽略",
+                    "ignoredTooltip": "此版本已关闭更新通知"
                 },
                 "actions": {
                     "download": "下载",
+                    "downloadTooltip": "下载此版本",
+                    "downloadEarlyAccessTooltip": "从 Civitai 下载此抢先体验版本",
                     "delete": "删除",
+                    "deleteTooltip": "删除此本地版本",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
+                    "ignoreTooltip": "忽略此版本的更新通知",
+                    "unignoreTooltip": "恢复此版本的更新通知",
+                    "viewVersionOnCivitai": "在 Civitai 上查看版本",
                     "earlyAccessTooltip": "需要购买抢先体验",
                     "resumeModelUpdates": "继续跟踪该模型的更新",
                     "ignoreModelUpdates": "忽略该模型的更新",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "配方已替换到工作流",
             "recipeFailedToSend": "发送配方到工作流失败",
             "noMatchingNodes": "当前工作流中没有兼容的节点",
-            "noTargetNodeSelected": "未选择目标节点"
+            "noTargetNodeSelected": "未选择目标节点",
+            "modelUpdated": "模型已更新到工作流",
+            "modelFailed": "更新模型节点失败"
         },
         "nodeSelector": {
             "recipe": "配方",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "显示微信二维码",
             "hideWechatQR": "隐藏微信二维码"
         },
-        "footer": "感谢使用 LoRA 管理器！❤️"
+        "footer": "感谢使用 LoRA 管理器！❤️",
+        "supporters": {
+            "title": "感谢所有支持者",
+            "subtitle": "感谢 {count} 位支持者让这个项目成为可能",
+            "specialThanks": "特别感谢",
+            "allSupporters": "所有支持者",
+            "totalCount": "共 {count} 位支持者"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "请选择版本",
             "versionExists": "该版本已存在于你的库中",
             "downloadCompleted": "下载成功完成",
+            "downloadSkippedByBaseModel": "由于基础模型 {baseModel} 已被排除，已跳过下载",
             "autoOrganizeSuccess": "自动整理已成功完成，共 {count} 个 {type}",
             "autoOrganizePartialSuccess": "自动整理完成：已移动 {success} 个，{failures} 个失败，共 {total} 个模型",
             "autoOrganizeFailed": "自动整理失败：{error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "加载 {modelType} 失败：{message}",
             "refreshComplete": "刷新完成",
             "refreshFailed": "刷新配方失败：{message}",
+            "syncComplete": "同步完成",
+            "syncFailed": "同步配方失败：{message}",
             "updateFailed": "更新配方失败：{error}",
             "updateError": "更新配方出错：{message}",
             "nameSaved": "配方“{name}”保存成功",
             "nameUpdated": "配方名称更新成功",
             "tagsUpdated": "配方标签更新成功",
             "sourceUrlUpdated": "来源 URL 更新成功",
+            "promptUpdated": "提示词更新成功",
+            "negativePromptUpdated": "负面提示词更新成功",
+            "promptEditorHint": "按 Enter 保存，Shift+Enter 换行",
             "noRecipeId": "无配方 ID",
+            "sendToWorkflowFailed": "发送配方到工作流失败：{message}",
             "copyFailed": "复制配方语法出错：{message}",
             "noMissingLoras": "没有缺失的 LoRA 可下载",
             "missingLorasInfoFailed": "获取缺失 LoRA 信息失败",
@@ -1410,9 +1629,20 @@
             "processingError": "处理出错：{message}",
             "folderBrowserError": "加载文件夹浏览器出错：{message}",
             "recipeSaveFailed": "保存配方失败：{error}",
+            "recipeSaved": "配方保存成功",
             "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": "启动批量导入失败：{message}",
+            "batchImportCancelling": "正在取消批量导入...",
+            "batchImportCancelFailed": "取消批量导入失败：{message}",
+            "batchImportNoUrls": "请输入至少一个 URL 或文件路径",
+            "batchImportNoDirectory": "请输入目录路径",
+            "batchImportBrowseFailed": "浏览目录失败：{message}",
+            "batchImportDirectorySelected": "已选择目录：{path}",
+            "noRecipesSelected": "未选择任何配方",
+            "noMissingLorasInSelection": "在选定的配方中未找到缺失的 LoRAs",
+            "noLoraRootConfigured": "未配置 LoRA 根目录。请在设置中设置默认的 LoRA 根目录。"
         },
         "models": {
             "noModelsSelected": "未选中模型",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "保存基础模型映射失败：{message}",
             "downloadTemplatesUpdated": "下载路径模板已更新",
             "downloadTemplatesFailed": "保存下载路径模板失败：{message}",
+            "recipesPathUpdated": "配方存储路径已更新",
+            "recipesPathSaveFailed": "更新配方存储路径失败：{message}",
             "settingsUpdated": "设置已更新：{setting}",
             "compactModeToggled": "紧凑模式 {state}",
             "settingSaveFailed": "保存设置失败：{message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "删除 {type} 失败：{message}",
             "excludeSuccess": "{type} 排除成功",
             "excludeFailed": "排除 {type} 失败：{message}",
+            "restoreSuccess": "{type} 已成功恢复",
+            "restoreFailed": "恢复 {type} 失败：{message}",
             "fileNameUpdated": "文件名更新成功",
             "fileRenameFailed": "重命名文件失败：{error}",
             "previewUpdated": "预览图片更新成功",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "系统诊断",
+        "title": "医生",
+        "buttonTitle": "运行诊断并尝试修复常见问题",
+        "loading": "正在检查当前环境...",
+        "footer": "如果修复后问题仍然存在，可以导出诊断包进一步排查。",
+        "summary": {
+            "idle": "检查设置、缓存健康状况和前后端 UI 版本是否一致。",
+            "ok": "当前环境未发现活动问题。",
+            "warning": "发现 {count} 个问题，大多数可以直接在这里处理。",
+            "error": "发现 {count} 个需要尽快处理的问题。"
+        },
+        "status": {
+            "ok": "健康",
+            "warning": "需要关注",
+            "error": "需要处理"
+        },
+        "actions": {
+            "runAgain": "重新检查",
+            "exportBundle": "导出诊断包"
+        },
+        "toast": {
+            "loadFailed": "加载诊断结果失败：{message}",
+            "repairSuccess": "缓存重建完成。",
+            "repairFailed": "缓存重建失败：{message}",
+            "exportSuccess": "诊断包已导出。",
+            "exportFailed": "导出诊断包失败：{message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "检测到应用更新",
@@ -1651,4 +1914,4 @@
             "retry": "重试"
         }
     }
-}
+}

locales/zh-TW.json

@@ -1,8 +1,11 @@
 {
     "common": {
+        "cancel": "取消",
+        "confirm": "確認",
         "actions": {
             "save": "儲存",
             "cancel": "取消",
+            "confirm": "確認",
             "delete": "刪除",
             "move": "移動",
             "refresh": "重新整理",
@@ -11,7 +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...",
@@ -171,6 +175,9 @@
             "success": "成功修復 {count} 個配方。",
             "cancelled": "修復已取消。已修復 {count} 個配方。",
             "error": "配方修復失敗：{message}"
+        },
+        "manageExcludedModels": {
+            "label": "管理已排除的模型"
         }
     },
     "header": {
@@ -218,12 +225,14 @@
             "presetOverwriteConfirm": "預設 \"{name}\" 已存在。是否覆蓋？",
             "presetNamePlaceholder": "預設名稱...",
             "baseModel": "基礎模型",
+            "baseModelSearchPlaceholder": "搜尋基礎模型...",
             "modelTags": "標籤（前 20）",
-            "modelTypes": "Model Types",
+            "modelTypes": "模型類型",
             "license": "授權",
             "noCreditRequired": "無需署名",
             "allowSellingGeneratedContent": "允許銷售",
             "noTags": "無標籤",
+            "noBaseModelMatches": "沒有基礎模型符合目前的搜尋。",
             "clearAll": "清除所有篩選",
             "any": "任一",
             "all": "全部",
@@ -246,6 +255,32 @@
         "civitaiApiKey": "Civitai API 金鑰",
         "civitaiApiKeyPlaceholder": "請輸入您的 Civitai API 金鑰",
         "civitaiApiKeyHelp": "用於從 Civitai 下載模型時的身份驗證",
+        "civitaiHost": {
+            "label": "Civitai 站點",
+            "help": "選擇使用「在 Civitai 中查看」時預設開啟的 Civitai 站點。",
+            "options": {
+                "com": "civitai.com（僅 SFW）",
+                "red": "civitai.red（無限制）"
+            }
+        },
+        "downloadBackend": {
+            "label": "下載後端",
+            "help": "選擇模型檔案的下載方式。Python 使用內建下載器。aria2 使用實驗性的外部下載程序。",
+            "options": {
+                "python": "Python（內建）",
+                "aria2": "aria2（實驗性）"
+            }
+        },
+        "aria2cPath": {
+            "label": "aria2c 路徑",
+            "help": "可選的 aria2c 可執行檔路徑。留空則使用系統 PATH 中的 aria2c。",
+            "placeholder": "留空則使用 PATH 中的 aria2c"
+        },
+        "civitaiHostBanner": {
+            "title": "已提供 Civitai 站點偏好設定",
+            "content": "Civitai 現在使用 civitai.com 提供 SFW 內容，使用 civitai.red 提供無限制內容。你可以在設定中變更預設開啟的站點。",
+            "openSettings": "開啟設定"
+        },
         "openSettingsFileLocation": {
             "label": "開啟設定資料夾",
             "tooltip": "開啟包含 settings.json 的資料夾",
@@ -256,10 +291,13 @@
         },
         "sections": {
             "contentFiltering": "內容過濾",
+            "downloads": "下載",
             "videoSettings": "影片設定",
             "layoutSettings": "版面設定",
             "misc": "其他",
+            "backup": "備份",
             "folderSettings": "預設根目錄",
+            "recipeSettings": "配方",
             "extraFolderPaths": "額外資料夾路徑",
             "downloadPathTemplates": "下載路徑範本",
             "priorityTags": "優先標籤",
@@ -287,7 +325,15 @@
             "blurNsfwContent": "模糊 NSFW 內容",
             "blurNsfwContentHelp": "模糊成熟（NSFW）內容預覽圖片",
             "showOnlySfw": "僅顯示 SFW 結果",
-            "showOnlySfwHelp": "瀏覽和搜尋時過濾所有 NSFW 內容"
+            "showOnlySfwHelp": "瀏覽和搜尋時過濾所有 NSFW 內容",
+            "matureBlurThreshold": "成人內容模糊閾值",
+            "matureBlurThresholdHelp": "設定當啟用 NSFW 模糊時，從哪個評級級別開始模糊過濾。",
+            "matureBlurThresholdOptions": {
+                "pg13": "PG13 及以上",
+                "r": "R 及以上（預設）",
+                "x": "X 及以上",
+                "xxx": "僅 XXX"
+            }
         },
         "videoSettings": {
             "autoplayOnHover": "滑鼠懸停自動播放影片",
@@ -311,6 +357,54 @@
                 "saveFailed": "無法儲存跳過路徑：{message}"
             }
         },
+        "backup": {
+            "autoEnabled": "自動備份",
+            "autoEnabledHelp": "每天建立一次本地快照，並依保留政策保留最新快照。",
+            "retention": "保留數量",
+            "retentionHelp": "在刪除舊快照之前，要保留多少自動快照。",
+            "management": "備份管理",
+            "managementHelp": "匯出目前的使用者狀態，或從備份封存中還原。",
+            "scopeHelp": "備份你的設定、下載歷史與模型更新狀態。不包含模型檔案或可重建的快取。",
+            "locationSummary": "目前備份位置",
+            "openFolderButton": "開啟備份資料夾",
+            "openFolderSuccess": "已開啟備份資料夾",
+            "openFolderFailed": "無法開啟備份資料夾",
+            "locationCopied": "備份路徑已複製到剪貼簿：{{path}}",
+            "locationClipboardFallback": "備份路徑：{{path}}",
+            "exportButton": "匯出備份",
+            "exportSuccess": "備份匯出成功。",
+            "exportFailed": "備份匯出失敗：{message}",
+            "importButton": "匯入備份",
+            "importConfirm": "要匯入此備份並覆寫本機使用者狀態嗎？",
+            "importSuccess": "備份匯入成功。",
+            "importFailed": "備份匯入失敗：{message}",
+            "latestSnapshot": "最近快照",
+            "latestAutoSnapshot": "最近自動快照",
+            "snapshotCount": "已儲存快照",
+            "noneAvailable": "目前還沒有快照"
+        },
+        "downloadSkipBaseModels": {
+            "label": "跳過這些基礎模型的下載",
+            "help": "適用於所有下載流程。這裡只能選擇受支援的基礎模型。",
+            "searchPlaceholder": "篩選基礎模型...",
+            "empty": "沒有符合目前搜尋條件的基礎模型。",
+            "summary": {
+                "none": "未選擇",
+                "count": "已選擇 {count} 項"
+            },
+            "actions": {
+                "edit": "編輯",
+                "collapse": "收起",
+                "clear": "清空"
+            },
+            "validation": {
+                "saveFailed": "無法儲存已排除的基礎模型：{message}"
+            }
+        },
+        "skipPreviouslyDownloadedModelVersions": {
+            "label": "跳過已下載的模型版本",
+            "help": "啟用後，如果下載歷史服務記錄顯示該版本已下載，LoRA Manager 將跳過下載該模型版本。適用於所有下載流程。"
+        },
         "layoutSettings": {
             "displayDensity": "顯示密度",
             "displayDensityOptions": {
@@ -359,8 +453,29 @@
             "defaultUnetRootHelp": "設定下載、匯入和移動時的預設 Diffusion Model (UNET) 根目錄",
             "defaultEmbeddingRoot": "Embedding 根目錄",
             "defaultEmbeddingRootHelp": "設定下載、匯入和移動時的預設 Embedding 根目錄",
+            "recipesPath": "配方儲存路徑",
+            "recipesPathHelp": "已儲存配方的可選自訂目錄。留空則使用第一個 LoRA 根目錄下的 recipes 資料夾。",
+            "recipesPathPlaceholder": "/path/to/recipes",
+            "recipesPathMigrating": "正在遷移配方儲存...",
             "noDefault": "未設定預設"
         },
+        "extraFolderPaths": {
+            "title": "額外資料夾路徑",
+            "description": "LoRA Manager 專屬的額外模型根目錄。從 ComfyUI 標準資料夾之外的位置載入模型，特別適合管理大型模型庫，避免影響 ComfyUI 效能。",
+            "restartRequired": "Requires restart to take effect",
+            "modelTypes": {
+                "lora": "LoRA 路徑",
+                "checkpoint": "Checkpoint 路徑",
+                "unet": "Diffusion 模型路徑",
+                "embedding": "Embedding 路徑"
+            },
+            "pathPlaceholder": "/額外/模型/路徑",
+            "saveSuccess": "額外資料夾路徑已更新，需要重啟才能生效。",
+            "saveError": "更新額外資料夾路徑失敗：{message}",
+            "validation": {
+                "duplicatePath": "此路徑已設定"
+            }
+        },
         "priorityTags": {
             "title": "優先標籤",
             "description": "為每種模型類型自訂標籤的優先順序 (例如: character, concept, style(toon|toon_style))",
@@ -485,23 +600,6 @@
             "proxyPassword": "密碼（選填）",
             "proxyPasswordPlaceholder": "password",
             "proxyPasswordHelp": "代理驗證所需的密碼（如有需要）"
-        },
-        "extraFolderPaths": {
-            "title": "額外資料夾路徑",
-            "help": "在 ComfyUI 的標準路徑之外新增額外的模型資料夾。這些路徑單獨儲存，並與預設資料夾一起掃描。",
-            "description": "設定額外的資料夾以掃描模型。這些路徑是 LoRA Manager 特有的，將與 ComfyUI 的預設路徑合併。",
-            "modelTypes": {
-                "lora": "LoRA 路徑",
-                "checkpoint": "Checkpoint 路徑",
-                "unet": "Diffusion 模型路徑",
-                "embedding": "Embedding 路徑"
-            },
-            "pathPlaceholder": "/額外/模型/路徑",
-            "saveSuccess": "額外資料夾路徑已更新。",
-            "saveError": "更新額外資料夾路徑失敗：{message}",
-            "validation": {
-                "duplicatePath": "此路徑已設定"
-            }
         }
     },
     "loras": {
@@ -571,6 +669,7 @@
             "skipMetadataRefresh": "跳過所選模型的元數據更新",
             "resumeMetadataRefresh": "恢復所選模型的元數據更新",
             "deleteAll": "刪除全部模型",
+            "downloadMissingLoras": "下載缺失的 LoRAs",
             "clear": "清除選取",
             "skipMetadataRefreshCount": "跳過（{count} 個模型）",
             "resumeMetadataRefreshCount": "恢復（{count} 個模型）",
@@ -600,6 +699,7 @@
             "moveToFolder": "移動到資料夾",
             "repairMetadata": "修復元數據",
             "excludeModel": "排除模型",
+            "restoreModel": "還原模型",
             "deleteModel": "刪除模型",
             "shareRecipe": "分享配方",
             "viewAllLoras": "檢視全部 LoRA",
@@ -641,6 +741,8 @@
                 "root": "根目錄",
                 "browseFolders": "瀏覽資料夾：",
                 "downloadAndSaveRecipe": "下載並儲存配方",
+                "importRecipeOnly": "僅匯入配方",
+                "importAndDownload": "匯入並下載",
                 "downloadMissingLoras": "下載缺少的 LoRA",
                 "saveRecipe": "儲存配方",
                 "loraCountInfo": "（庫存 {existing}/{total}）",
@@ -682,7 +784,11 @@
                 "lorasCountAsc": "最少"
             },
             "refresh": {
-                "title": "重新整理配方列表"
+                "title": "重新整理配方列表",
+                "quick": "同步變更",
+                "quickTooltip": "同步變更 - 快速重新整理而不重建快取",
+                "full": "重建快取",
+                "fullTooltip": "重建快取 - 重新掃描所有配方檔案"
             },
             "filteredByLora": "已依 LoRA 篩選",
             "favorites": {
@@ -722,6 +828,64 @@
                 "failed": "修復配方失敗：{message}",
                 "missingId": "無法修復配方：缺少配方 ID"
             }
+        },
+        "batchImport": {
+            "title": "批量匯入配方",
+            "action": "批量匯入",
+            "urlList": "URL 列表",
+            "directory": "目錄",
+            "urlDescription": "輸入圖像 URL 或本地檔案路徑（每行一個）。每個都將作為配方匯入。",
+            "directoryDescription": "輸入目錄路徑以匯入該資料夾中的所有圖像。",
+            "urlsLabel": "圖像 URL 或本地路徑",
+            "urlsPlaceholder": "https://civitai.com/images/...\nhttps://civitai.com/images/...\nC:/path/to/image.png\n...",
+            "urlsHint": "每行輸入一個 URL 或路徑",
+            "directoryPath": "目錄路徑",
+            "directoryPlaceholder": "/path/to/images/folder",
+            "browse": "瀏覽",
+            "recursive": "包含子目錄",
+            "tagsOptional": "標籤（可選，應用於所有配方）",
+            "tagsPlaceholder": "輸入以逗號分隔的標籤",
+            "tagsHint": "標籤將被添加到所有匯入的配方中",
+            "skipNoMetadata": "跳過無元資料的圖像",
+            "skipNoMetadataHelp": "沒有 LoRA 元資料的圖像將被自動跳過。",
+            "start": "開始匯入",
+            "startImport": "開始匯入",
+            "importing": "匯入中...",
+            "progress": "進度",
+            "total": "總計",
+            "success": "成功",
+            "failed": "失敗",
+            "skipped": "跳過",
+            "current": "當前",
+            "currentItem": "當前項目",
+            "preparing": "準備中...",
+            "cancel": "取消",
+            "cancelImport": "取消匯入",
+            "cancelled": "匯入已取消",
+            "completed": "匯入完成",
+            "completedWithErrors": "匯入完成但有錯誤",
+            "completedSuccess": "成功匯入 {count} 個配方",
+            "successCount": "成功",
+            "failedCount": "失敗",
+            "skippedCount": "跳過",
+            "totalProcessed": "總計處理",
+            "viewDetails": "查看詳情",
+            "newImport": "新建匯入",
+            "manualPathEntry": "請手動輸入目錄路徑。此瀏覽器中檔案瀏覽器不可用。",
+            "batchImportDirectorySelected": "已選擇目錄：{path}",
+            "batchImportManualEntryRequired": "檔案瀏覽器不可用。請手動輸入目錄路徑。",
+            "backToParent": "返回上級目錄",
+            "folders": "資料夾",
+            "folderCount": "{count} 個資料夾",
+            "imageFiles": "圖像檔案",
+            "images": "圖像",
+            "imageCount": "{count} 個圖像",
+            "selectFolder": "選擇此資料夾",
+            "errors": {
+                "enterUrls": "請輸入至少一個 URL 或路徑",
+                "enterDirectory": "請輸入目錄路徑",
+                "startFailed": "啟動匯入失敗：{message}"
+            }
         }
     },
     "checkpoints": {
@@ -731,7 +895,8 @@
             "diffusion_model": "Diffusion Model"
         },
         "contextMenu": {
-            "moveToOtherTypeFolder": "移動到 {otherType} 資料夾"
+            "moveToOtherTypeFolder": "移動到 {otherType} 資料夾",
+            "sendToWorkflow": "傳送到工作流"
         }
     },
     "embeddings": {
@@ -744,13 +909,23 @@
         "unpinSidebar": "取消固定側邊欄",
         "switchToListView": "切換至列表檢視",
         "switchToTreeView": "切換到樹狀檢視",
-        "recursiveOn": "搜尋子資料夾",
-        "recursiveOff": "僅搜尋目前資料夾",
+        "recursiveOn": "包含子資料夾",
+        "recursiveOff": "僅目前資料夾",
         "recursiveUnavailable": "遞迴搜尋僅能在樹狀檢視中使用",
         "collapseAllDisabled": "列表檢視下不可用",
         "dragDrop": {
             "unableToResolveRoot": "無法確定移動的目標路徑。",
-            "moveUnsupported": "Move is not supported for this item."
+            "moveUnsupported": "Move is not supported for this item.",
+            "createFolderHint": "放開以建立新資料夾",
+            "newFolderName": "新資料夾名稱",
+            "folderNameHint": "按 Enter 確認，Escape 取消",
+            "emptyFolderName": "請輸入資料夾名稱",
+            "invalidFolderName": "資料夾名稱包含無效字元",
+            "noDragState": "未找到待處理的拖放操作"
+        },
+        "empty": {
+            "noFolders": "未找到資料夾",
+            "dragHint": "將項目拖到此處以建立資料夾"
         }
     },
     "statistics": {
@@ -815,6 +990,8 @@
             "earlyAccess": "早期存取",
             "earlyAccessTooltip": "需要早期存取",
             "inLibrary": "已在庫存",
+            "downloaded": "已下載",
+            "downloadedTooltip": "先前已下載，但目前不在你的庫中。",
             "alreadyInLibrary": "已在庫存",
             "autoOrganizedPath": "[依路徑範本自動整理]",
             "errors": {
@@ -905,6 +1082,14 @@
             "save": "更新基礎模型",
             "cancel": "取消"
         },
+        "bulkDownloadMissingLoras": {
+            "title": "下載缺失的 LoRAs",
+            "message": "發現 {uniqueCount} 個獨特的缺失 LoRAs（從選取食譜中的 {totalCount} 個總數）。",
+            "previewTitle": "要下載的 LoRAs：",
+            "moreItems": "...還有 {count} 個",
+            "note": "檔案將使用預設路徑模板下載。根據 LoRAs 的數量，這可能需要一些時間。",
+            "downloadButton": "下載 {count} 個 LoRA(s)"
+        },
         "exampleAccess": {
             "title": "本機範例圖片",
             "message": "此模型未找到本機範例圖片。可選擇：",
@@ -956,7 +1141,9 @@
                 "viewOnCivitai": "在 Civitai 查看",
                 "viewOnCivitaiText": "在 Civitai 查看",
                 "viewCreatorProfile": "查看創作者個人檔案",
-                "openFileLocation": "開啟檔案位置"
+                "openFileLocation": "開啟檔案位置",
+                "sendToWorkflow": "傳送到 ComfyUI",
+                "sendToWorkflowText": "傳送到 ComfyUI"
             },
             "openFileLocation": {
                 "success": "檔案位置已成功開啟",
@@ -964,6 +1151,9 @@
                 "copied": "路徑已複製到剪貼簿：{{path}}",
                 "clipboardFallback": "路徑：{{path}}"
             },
+            "sendToWorkflow": {
+                "noFilePath": "無法傳送到 ComfyUI：沒有可用的檔案路徑"
+            },
             "metadata": {
                 "version": "版本",
                 "fileName": "檔案名稱",
@@ -1071,17 +1261,30 @@
                     "days": "{count}天後"
                 },
                 "badges": {
-                    "current": "目前版本",
+                    "current": "已開啟版本",
+                    "currentTooltip": "這是你用來開啟此彈窗的版本",
                     "inLibrary": "已在庫中",
+                    "inLibraryTooltip": "此版本已存在於你的本地庫中",
+                    "downloaded": "已下載",
+                    "downloadedTooltip": "此版本之前下載過，但目前不在你的本地庫中",
                     "newer": "較新版本",
+                    "newerTooltip": "此版本比你本地的最新版本更新",
                     "earlyAccess": "搶先體驗",
-                    "ignored": "已忽略"
+                    "earlyAccessTooltip": "此版本目前需要 Civitai 搶先體驗權限",
+                    "ignored": "已忽略",
+                    "ignoredTooltip": "此版本已關閉更新通知"
                 },
                 "actions": {
                     "download": "下載",
+                    "downloadTooltip": "下載此版本",
+                    "downloadEarlyAccessTooltip": "從 Civitai 下載此搶先體驗版本",
                     "delete": "刪除",
+                    "deleteTooltip": "刪除此本地版本",
                     "ignore": "忽略",
                     "unignore": "取消忽略",
+                    "ignoreTooltip": "忽略此版本的更新通知",
+                    "unignoreTooltip": "恢復此版本的更新通知",
+                    "viewVersionOnCivitai": "在 Civitai 上查看版本",
                     "earlyAccessTooltip": "需要購買搶先體驗",
                     "resumeModelUpdates": "恢復追蹤此模型的更新",
                     "ignoreModelUpdates": "忽略此模型的更新",
@@ -1221,7 +1424,9 @@
             "recipeReplaced": "配方已取代於工作流",
             "recipeFailedToSend": "傳送配方到工作流失敗",
             "noMatchingNodes": "目前工作流程中沒有相容的節點",
-            "noTargetNodeSelected": "未選擇目標節點"
+            "noTargetNodeSelected": "未選擇目標節點",
+            "modelUpdated": "模型已更新到工作流",
+            "modelFailed": "更新模型節點失敗"
         },
         "nodeSelector": {
             "recipe": "配方",
@@ -1342,7 +1547,14 @@
             "showWechatQR": "顯示微信二維碼",
             "hideWechatQR": "隱藏微信二維碼"
         },
-        "footer": "感謝您使用 LoRA 管理器！❤️"
+        "footer": "感謝您使用 LoRA 管理器！❤️",
+        "supporters": {
+            "title": "感謝所有支持者",
+            "subtitle": "感謝 {count} 位支持者讓這個專案成為可能",
+            "specialThanks": "特別感謝",
+            "allSupporters": "所有支持者",
+            "totalCount": "共 {count} 位支持者"
+        }
     },
     "toast": {
         "general": {
@@ -1365,6 +1577,7 @@
             "pleaseSelectVersion": "請選擇一個版本",
             "versionExists": "此版本已存在於您的庫中",
             "downloadCompleted": "下載成功完成",
+            "downloadSkippedByBaseModel": "由於基礎模型 {baseModel} 已被排除，已跳過下載",
             "autoOrganizeSuccess": "自動整理已成功完成，共 {count} 個 {type} 已整理",
             "autoOrganizePartialSuccess": "自動整理完成：已移動 {success} 個，{failures} 個失敗，共 {total} 個模型",
             "autoOrganizeFailed": "自動整理失敗：{error}",
@@ -1376,13 +1589,19 @@
             "loadFailed": "載入 {modelType} 失敗：{message}",
             "refreshComplete": "刷新完成",
             "refreshFailed": "刷新配方失敗：{message}",
+            "syncComplete": "同步完成",
+            "syncFailed": "同步配方失敗：{message}",
             "updateFailed": "更新配方失敗：{error}",
             "updateError": "更新配方錯誤：{message}",
             "nameSaved": "配方「{name}」已成功儲存",
             "nameUpdated": "配方名稱已更新",
             "tagsUpdated": "配方標籤已更新",
             "sourceUrlUpdated": "來源網址已更新",
+            "promptUpdated": "提示詞更新成功",
+            "negativePromptUpdated": "負面提示詞更新成功",
+            "promptEditorHint": "按 Enter 儲存，Shift+Enter 換行",
             "noRecipeId": "無配方 ID",
+            "sendToWorkflowFailed": "傳送配方到工作流失敗：{message}",
             "copyFailed": "複製配方語法錯誤：{message}",
             "noMissingLoras": "無缺少的 LoRA 可下載",
             "missingLorasInfoFailed": "取得缺少 LoRA 資訊失敗",
@@ -1410,9 +1629,20 @@
             "processingError": "處理錯誤：{message}",
             "folderBrowserError": "載入資料夾瀏覽器錯誤：{message}",
             "recipeSaveFailed": "儲存配方失敗：{error}",
+            "recipeSaved": "配方儲存成功",
             "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": "啟動批量匯入失敗：{message}",
+            "batchImportCancelling": "正在取消批量匯入...",
+            "batchImportCancelFailed": "取消批量匯入失敗：{message}",
+            "batchImportNoUrls": "請輸入至少一個 URL 或檔案路徑",
+            "batchImportNoDirectory": "請輸入目錄路徑",
+            "batchImportBrowseFailed": "瀏覽目錄失敗：{message}",
+            "batchImportDirectorySelected": "已選擇目錄：{path}",
+            "noRecipesSelected": "未選取任何食譜",
+            "noMissingLorasInSelection": "在選取的食譜中未找到缺失的 LoRAs",
+            "noLoraRootConfigured": "未配置 LoRA 根目錄。請在設定中設定預設的 LoRA 根目錄。"
         },
         "models": {
             "noModelsSelected": "未選擇模型",
@@ -1479,6 +1709,8 @@
             "mappingSaveFailed": "儲存基礎模型對應失敗：{message}",
             "downloadTemplatesUpdated": "下載路徑範本已更新",
             "downloadTemplatesFailed": "儲存下載路徑範本失敗：{message}",
+            "recipesPathUpdated": "配方儲存路徑已更新",
+            "recipesPathSaveFailed": "更新配方儲存路徑失敗：{message}",
             "settingsUpdated": "設定已更新：{setting}",
             "compactModeToggled": "緊湊模式已{state}",
             "settingSaveFailed": "儲存設定失敗：{message}",
@@ -1591,6 +1823,8 @@
             "deleteFailed": "刪除 {type} 失敗：{message}",
             "excludeSuccess": "{type} 已成功排除",
             "excludeFailed": "排除 {type} 失敗：{message}",
+            "restoreSuccess": "{type} 已成功還原",
+            "restoreFailed": "還原 {type} 失敗：{message}",
             "fileNameUpdated": "檔案名稱已成功更新",
             "fileRenameFailed": "重新命名檔案失敗：{error}",
             "previewUpdated": "預覽圖片已成功更新",
@@ -1622,6 +1856,35 @@
             "moveFailed": "Failed to move item: {message}"
         }
     },
+    "doctor": {
+        "kicker": "系統診斷",
+        "title": "醫生",
+        "buttonTitle": "執行診斷與常見修復",
+        "loading": "正在檢查環境...",
+        "footer": "如果修復後問題仍然存在，請匯出診斷套件。",
+        "summary": {
+            "idle": "針對設定、快取完整性與 UI 一致性執行健康檢查。",
+            "ok": "目前環境中未發現任何活動中的問題。",
+            "warning": "找到 {count} 個問題。大多可以直接在此面板修復。",
+            "error": "應先處理 {count} 個問題，應用程式才能完全正常。"
+        },
+        "status": {
+            "ok": "健康",
+            "warning": "需要注意",
+            "error": "需要處理"
+        },
+        "actions": {
+            "runAgain": "重新執行",
+            "exportBundle": "匯出套件"
+        },
+        "toast": {
+            "loadFailed": "載入診斷失敗：{message}",
+            "repairSuccess": "快取重建完成。",
+            "repairFailed": "快取重建失敗：{message}",
+            "exportSuccess": "診斷套件已匯出。",
+            "exportFailed": "匯出診斷套件失敗：{message}"
+        }
+    },
     "banners": {
         "versionMismatch": {
             "title": "偵測到應用程式更新",
@@ -1651,4 +1914,4 @@
             "retry": "重試"
         }
     }
-}
+}

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

@@ -2,7 +2,7 @@ import os
 import platform
 import threading
 from pathlib import Path
-import folder_paths # type: ignore
+import folder_paths  # type: ignore
 from typing import Any, Dict, Iterable, List, Mapping, Optional, Set, Tuple
 import logging
 import json
@@ -10,16 +10,72 @@ import urllib.parse
 import time
 
 from .utils.cache_paths import CacheType, get_cache_file_path, get_legacy_cache_paths
-from .utils.settings_paths import ensure_settings_file, get_settings_dir, load_settings_template
+from .utils.settings_paths import (
+    ensure_settings_file,
+    get_settings_dir,
+    load_settings_template,
+)
 
 # Use an environment variable to control standalone mode
-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"
+)
 
 logger = logging.getLogger(__name__)
 
 
+def _resolve_valid_default_root(
+    current: str, primary_paths: List[str], allowed_paths: List[str], name: str
+) -> str:
+    """Return a valid default root from the current primary/extra path set."""
+
+    valid_paths = [path for path in primary_paths if isinstance(path, str) and path.strip()]
+    fallback_paths: List[str] = []
+    seen: Set[str] = set()
+    for path in allowed_paths:
+        if not isinstance(path, str):
+            continue
+        stripped = path.strip()
+        if not stripped or stripped in seen:
+            continue
+        seen.add(stripped)
+        fallback_paths.append(stripped)
+
+    allowed = set(fallback_paths)
+
+    if current and current in allowed:
+        return current
+
+    if not valid_paths:
+        if not fallback_paths:
+            return ""
+        if current:
+            logger.info(
+                "Repaired stale %s from '%s' to '%s' because it is not present in primary or extra roots",
+                name,
+                current,
+                fallback_paths[0],
+            )
+        else:
+            logger.info("Auto-setting %s to '%s'", name, fallback_paths[0])
+        return fallback_paths[0]
+
+    if current:
+        logger.info(
+            "Repaired stale %s from '%s' to '%s' because it is not present in primary or extra roots",
+            name,
+            current,
+            valid_paths[0],
+        )
+    else:
+        logger.info("Auto-setting %s to '%s'", name, valid_paths[0])
+
+    return valid_paths[0]
+
+
 def _normalize_folder_paths_for_comparison(
-    folder_paths: Mapping[str, Iterable[str]]
+    folder_paths: Mapping[str, Iterable[str]],
 ) -> Dict[str, Set[str]]:
     """Normalize folder paths for comparison across libraries."""
 
@@ -49,7 +105,7 @@ def _normalize_folder_paths_for_comparison(
 
 
 def _normalize_library_folder_paths(
-    library_payload: Mapping[str, Any]
+    library_payload: Mapping[str, Any],
 ) -> Dict[str, Set[str]]:
     """Return normalized folder paths extracted from a library payload."""
 
@@ -74,11 +130,17 @@ def _get_template_folder_paths() -> Dict[str, Set[str]]:
 
 class Config:
     """Global configuration for LoRA Manager"""
-    
+
     def __init__(self):
-        self.templates_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'templates')
-        self.static_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'static')
-        self.i18n_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'locales')
+        self.templates_path = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)), "templates"
+        )
+        self.static_path = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)), "static"
+        )
+        self.i18n_path = os.path.join(
+            os.path.dirname(os.path.dirname(__file__)), "locales"
+        )
         # Path mapping dictionary, target to link mapping
         self._path_mappings: Dict[str, str] = {}
         # Normalized preview root directories used to validate preview access
@@ -96,9 +158,10 @@ class Config:
         self.extra_checkpoints_roots: List[str] = []
         self.extra_unet_roots: List[str] = []
         self.extra_embeddings_roots: List[str] = []
+        self.recipes_path: str = ""
         # Scan symbolic links during initialization
         self._initialize_symlink_mappings()
-        
+
         if not standalone_mode:
             # Save the paths to settings.json when running in ComfyUI mode
             self.save_folder_paths_to_settings()
@@ -152,17 +215,21 @@ class Config:
                 default_library = libraries.get("default", {})
 
             target_folder_paths = {
-                'loras': list(self.loras_roots),
-                'checkpoints': list(self.checkpoints_roots or []),
-                'unet': list(self.unet_roots or []),
-                'embeddings': list(self.embeddings_roots or []),
+                "loras": list(self.loras_roots),
+                "checkpoints": list(self.checkpoints_roots or []),
+                "unet": list(self.unet_roots or []),
+                "embeddings": list(self.embeddings_roots or []),
             }
 
-            normalized_target_paths = _normalize_folder_paths_for_comparison(target_folder_paths)
+            normalized_target_paths = _normalize_folder_paths_for_comparison(
+                target_folder_paths
+            )
 
             normalized_default_paths: Optional[Dict[str, Set[str]]] = None
             if isinstance(default_library, Mapping):
-                normalized_default_paths = _normalize_library_folder_paths(default_library)
+                normalized_default_paths = _normalize_library_folder_paths(
+                    default_library
+                )
 
             if (
                 not comfy_library
@@ -180,47 +247,89 @@ class Config:
                         "Failed to rename legacy 'default' library: %s", rename_error
                     )
 
-            default_lora_root = comfy_library.get("default_lora_root", "")
-            if not default_lora_root and len(self.loras_roots) == 1:
-                default_lora_root = self.loras_roots[0]
+            default_lora_root = _resolve_valid_default_root(
+                comfy_library.get("default_lora_root", ""),
+                list(self.loras_roots or []),
+                list(self.loras_roots or [])
+                + list(comfy_library.get("extra_folder_paths", {}).get("loras", []) or []),
+                "default_lora_root",
+            )
 
-            default_checkpoint_root = comfy_library.get("default_checkpoint_root", "")
-            if (not default_checkpoint_root and self.checkpoints_roots and
-                    len(self.checkpoints_roots) == 1):
-                default_checkpoint_root = self.checkpoints_roots[0]
+            default_checkpoint_root = _resolve_valid_default_root(
+                comfy_library.get("default_checkpoint_root", ""),
+                list(self.checkpoints_roots or []),
+                list(self.checkpoints_roots or [])
+                + list(comfy_library.get("extra_folder_paths", {}).get("checkpoints", []) or []),
+                "default_checkpoint_root",
+            )
 
-            default_embedding_root = comfy_library.get("default_embedding_root", "")
-            if (not default_embedding_root and self.embeddings_roots and
-                    len(self.embeddings_roots) == 1):
-                default_embedding_root = self.embeddings_roots[0]
+            default_embedding_root = _resolve_valid_default_root(
+                comfy_library.get("default_embedding_root", ""),
+                list(self.embeddings_roots or []),
+                list(self.embeddings_roots or [])
+                + list(comfy_library.get("extra_folder_paths", {}).get("embeddings", []) or []),
+                "default_embedding_root",
+            )
 
             metadata = dict(comfy_library.get("metadata", {}))
             metadata.setdefault("display_name", "ComfyUI")
             metadata["source"] = "comfyui"
+            extra_folder_paths = {}
+            if isinstance(comfy_library, Mapping):
+                existing_extra_paths = comfy_library.get("extra_folder_paths", {})
+                if isinstance(existing_extra_paths, Mapping):
+                    extra_folder_paths = {
+                        key: list(value) if isinstance(value, list) else []
+                        for key, value in existing_extra_paths.items()
+                    }
+
+            active_library_name = settings_service.get_active_library_name()
+            should_activate = (
+                active_library_name == "comfyui"
+                or self._should_activate_comfy_library(libraries, libraries_changed)
+            )
 
             settings_service.upsert_library(
                 "comfyui",
                 folder_paths=target_folder_paths,
+                extra_folder_paths=extra_folder_paths,
                 default_lora_root=default_lora_root,
                 default_checkpoint_root=default_checkpoint_root,
                 default_embedding_root=default_embedding_root,
                 metadata=metadata,
-                activate=True,
+                activate=should_activate,
             )
 
-            logger.info("Updated 'comfyui' library with current folder paths")
+            if should_activate:
+                logger.info("Updated 'comfyui' library with current folder paths")
+            else:
+                logger.info(
+                    "Updated 'comfyui' library with current folder paths without activating it"
+                )
         except Exception as e:
             logger.warning(f"Failed to save folder paths: {e}")
 
+    def _should_activate_comfy_library(
+        self, libraries: Mapping[str, Any], libraries_changed: bool
+    ) -> bool:
+        """Return whether startup sync should make the ComfyUI library active."""
+
+        if libraries_changed:
+            return True
+        if not libraries:
+            return True
+        return "comfyui" in libraries and len(libraries) == 1
+
     def _is_link(self, path: str) -> bool:
         try:
             if os.path.islink(path):
                 return True
-            if platform.system() == 'Windows':
+            if platform.system() == "Windows":
                 try:
                     import ctypes
+
                     FILE_ATTRIBUTE_REPARSE_POINT = 0x400
-                    attrs = ctypes.windll.kernel32.GetFileAttributesW(str(path))
+                    attrs = ctypes.windll.kernel32.GetFileAttributesW(str(path))  # type: ignore[attr-defined]
                     return attrs != -1 and (attrs & FILE_ATTRIBUTE_REPARSE_POINT)
                 except Exception as e:
                     logger.error(f"Error checking Windows reparse point: {e}")
@@ -233,18 +342,19 @@ class Config:
         """Check if a directory entry is a symlink, including Windows junctions."""
         if entry.is_symlink():
             return True
-        if platform.system() == 'Windows':
+        if platform.system() == "Windows":
             try:
                 import ctypes
+
                 FILE_ATTRIBUTE_REPARSE_POINT = 0x400
-                attrs = ctypes.windll.kernel32.GetFileAttributesW(entry.path)
+                attrs = ctypes.windll.kernel32.GetFileAttributesW(entry.path)  # type: ignore[attr-defined]
                 return attrs != -1 and (attrs & FILE_ATTRIBUTE_REPARSE_POINT)
             except Exception:
                 pass
         return False
 
     def _normalize_path(self, path: str) -> str:
-        return os.path.normpath(path).replace(os.sep, '/')
+        return os.path.normpath(path).replace(os.sep, "/")
 
     def _get_symlink_cache_path(self) -> Path:
         canonical_path = get_cache_file_path(CacheType.SYMLINK, create_dir=True)
@@ -278,19 +388,18 @@ class Config:
                             if self._entry_is_symlink(entry):
                                 try:
                                     target = os.path.realpath(entry.path)
-                                    direct_symlinks.append([
-                                        self._normalize_path(entry.path),
-                                        self._normalize_path(target)
-                                    ])
+                                    direct_symlinks.append(
+                                        [
+                                            self._normalize_path(entry.path),
+                                            self._normalize_path(target),
+                                        ]
+                                    )
                                 except OSError:
                                     pass
             except (OSError, PermissionError):
                 pass
 
-        return {
-            "roots": unique_roots,
-            "direct_symlinks": sorted(direct_symlinks)
-        }
+        return {"roots": unique_roots, "direct_symlinks": sorted(direct_symlinks)}
 
     def _initialize_symlink_mappings(self) -> None:
         start = time.perf_counter()
@@ -307,10 +416,14 @@ class Config:
             cached_fingerprint = self._cached_fingerprint
 
             # Check 1: First-level symlinks unchanged (catches new symlinks at root)
-            fingerprint_valid = cached_fingerprint and current_fingerprint == cached_fingerprint
+            fingerprint_valid = (
+                cached_fingerprint and current_fingerprint == cached_fingerprint
+            )
 
             # Check 2: All cached mappings still valid (catches changes at any depth)
-            mappings_valid = self._validate_cached_mappings() if fingerprint_valid else False
+            mappings_valid = (
+                self._validate_cached_mappings() if fingerprint_valid else False
+            )
 
             if fingerprint_valid and mappings_valid:
                 return
@@ -370,7 +483,9 @@ class Config:
         for target, link in cached_mappings.items():
             if not isinstance(target, str) or not isinstance(link, str):
                 continue
-            normalized_mappings[self._normalize_path(target)] = self._normalize_path(link)
+            normalized_mappings[self._normalize_path(target)] = self._normalize_path(
+                link
+            )
 
         self._path_mappings = normalized_mappings
 
@@ -391,7 +506,9 @@ class Config:
                         parent_dir = loaded_path.parent
                         if parent_dir.name == "cache" and not any(parent_dir.iterdir()):
                             parent_dir.rmdir()
-                            logger.info("Removed empty legacy cache directory: %s", parent_dir)
+                            logger.info(
+                                "Removed empty legacy cache directory: %s", parent_dir
+                            )
                     except Exception:
                         pass
 
@@ -402,7 +519,9 @@ class Config:
                     exc,
                 )
         else:
-            logger.info("Symlink cache loaded with %d mappings", len(self._path_mappings))
+            logger.info(
+                "Symlink cache loaded with %d mappings", len(self._path_mappings)
+            )
 
         return True
 
@@ -414,7 +533,7 @@ class Config:
         """
         for target, link in self._path_mappings.items():
             # Convert normalized paths back to OS paths
-            link_path = link.replace('/', os.sep)
+            link_path = link.replace("/", os.sep)
 
             # Check if symlink still exists
             if not self._is_link(link_path):
@@ -427,7 +546,9 @@ class Config:
                 if actual_target != target:
                     logger.debug(
                         "Symlink target changed: %s -> %s (cached: %s)",
-                        link_path, actual_target, target
+                        link_path,
+                        actual_target,
+                        target,
                     )
                     return False
             except OSError:
@@ -446,7 +567,11 @@ class Config:
         try:
             with cache_path.open("w", encoding="utf-8") as handle:
                 json.dump(payload, handle, ensure_ascii=False, indent=2)
-            logger.debug("Symlink cache saved to %s with %d mappings", cache_path, len(self._path_mappings))
+            logger.debug(
+                "Symlink cache saved to %s with %d mappings",
+                cache_path,
+                len(self._path_mappings),
+            )
         except Exception as exc:
             logger.info("Failed to write symlink cache %s: %s", cache_path, exc)
 
@@ -458,7 +583,7 @@ class Config:
         at the root level only (not nested symlinks in subdirectories).
         """
         start = time.perf_counter()
-        
+
         # Reset mappings before rescanning to avoid stale entries
         self._path_mappings.clear()
         self._seed_root_symlink_mappings()
@@ -472,7 +597,7 @@ class Config:
 
     def _scan_first_level_symlinks(self, root: str):
         """Scan only the first level of a directory for symlinks.
-        
+
         This avoids traversing the entire directory tree which can be extremely
         slow for large model collections. Only symlinks directly under the root
         are detected.
@@ -494,13 +619,13 @@ class Config:
                         self.add_path_mapping(entry.path, target_path)
                     except Exception as inner_exc:
                         logger.debug(
-                            "Error processing directory entry %s: %s", entry.path, inner_exc
+                            "Error processing directory entry %s: %s",
+                            entry.path,
+                            inner_exc,
                         )
         except Exception as e:
             logger.error(f"Error scanning links in {root}: {e}")
 
-
-
     def add_path_mapping(self, link_path: str, target_path: str):
         """Add a symbolic link path mapping
         target_path: actual target path
@@ -589,46 +714,53 @@ class Config:
             preview_roots.update(self._expand_preview_root(root))
         for root in self.extra_embeddings_roots or []:
             preview_roots.update(self._expand_preview_root(root))
+        if self.recipes_path:
+            preview_roots.update(self._expand_preview_root(self.recipes_path))
 
         for target, link in self._path_mappings.items():
             preview_roots.update(self._expand_preview_root(target))
             preview_roots.update(self._expand_preview_root(link))
 
-        self._preview_root_paths = {path for path in preview_roots if path.is_absolute()}
+        self._preview_root_paths = {
+            path for path in preview_roots if path.is_absolute()
+        }
         logger.debug(
             "Preview roots rebuilt: %d paths from %d lora roots (%d extra), %d checkpoint roots (%d extra), %d embedding roots (%d extra), %d symlink mappings",
             len(self._preview_root_paths),
-            len(self.loras_roots or []), len(self.extra_loras_roots or []),
-            len(self.base_models_roots or []), len(self.extra_checkpoints_roots or []),
-            len(self.embeddings_roots or []), len(self.extra_embeddings_roots or []),
+            len(self.loras_roots or []),
+            len(self.extra_loras_roots or []),
+            len(self.base_models_roots or []),
+            len(self.extra_checkpoints_roots or []),
+            len(self.embeddings_roots or []),
+            len(self.extra_embeddings_roots or []),
             len(self._path_mappings),
         )
 
     def map_path_to_link(self, path: str) -> str:
         """Map a target path back to its symbolic link path"""
-        normalized_path = os.path.normpath(path).replace(os.sep, '/')
+        normalized_path = os.path.normpath(path).replace(os.sep, "/")
         # Check if the path is contained in any mapped target path
         for target_path, link_path in self._path_mappings.items():
             # Match whole path components to avoid prefix collisions (e.g., /a/b vs /a/bc)
             if normalized_path == target_path:
                 return link_path
-            
-            if normalized_path.startswith(target_path + '/'):
+
+            if normalized_path.startswith(target_path + "/"):
                 # If the path starts with the target path, replace with link path
                 mapped_path = normalized_path.replace(target_path, link_path, 1)
                 return mapped_path
         return normalized_path
-    
+
     def map_link_to_path(self, link_path: str) -> str:
         """Map a symbolic link path back to the actual path"""
-        normalized_link = os.path.normpath(link_path).replace(os.sep, '/')
+        normalized_link = os.path.normpath(link_path).replace(os.sep, "/")
         # Check if the path is contained in any mapped target path
         for target_path, link_path_mapped in self._path_mappings.items():
             # Match whole path components
             if normalized_link == link_path_mapped:
                 return target_path
 
-            if normalized_link.startswith(link_path_mapped + '/'):
+            if normalized_link.startswith(link_path_mapped + "/"):
                 # If the path starts with the link path, replace with actual path
                 mapped_path = normalized_link.replace(link_path_mapped, target_path, 1)
                 return mapped_path
@@ -641,8 +773,8 @@ class Config:
                 continue
             if not os.path.exists(path):
                 continue
-            real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, '/')
-            normalized = os.path.normpath(path).replace(os.sep, '/')
+            real_path = os.path.normpath(os.path.realpath(path)).replace(os.sep, "/")
+            normalized = os.path.normpath(path).replace(os.sep, "/")
             if real_path not in dedup:
                 dedup[real_path] = normalized
         return dedup
@@ -652,15 +784,139 @@ class Config:
         unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
 
         for original_path in unique_paths:
-            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(
+                os.sep, "/"
+            )
             if real_path != original_path:
                 self.add_path_mapping(original_path, real_path)
 
         return unique_paths
 
+    @staticmethod
+    def _normalize_path_for_comparison(
+        path: str, *, resolve_realpath: bool = False
+    ) -> str:
+        """Normalize a path for equality checks across platforms."""
+        candidate = os.path.realpath(path) if resolve_realpath else path
+        return os.path.normcase(os.path.normpath(candidate)).replace(os.sep, "/")
+
+    def _filter_overlapping_extra_lora_paths(
+        self,
+        primary_paths: Iterable[str],
+        extra_paths: Iterable[str],
+    ) -> List[str]:
+        """Drop extra LoRA paths that resolve to the same physical location as primary roots."""
+
+        primary_map = {
+            self._normalize_path_for_comparison(path, resolve_realpath=True): path
+            for path in primary_paths
+            if isinstance(path, str) and path.strip() and os.path.exists(path)
+        }
+        primary_symlink_map = self._collect_first_level_symlink_targets(primary_paths)
+        filtered: List[str] = []
+
+        for original_path in extra_paths:
+            if not isinstance(original_path, str):
+                continue
+
+            stripped = original_path.strip()
+            if not stripped:
+                continue
+            if not os.path.exists(stripped):
+                continue
+
+            real_path = self._normalize_path_for_comparison(
+                stripped,
+                resolve_realpath=True,
+            )
+            normalized_path = os.path.normpath(stripped).replace(os.sep, "/")
+            primary_path = primary_map.get(real_path)
+            if primary_path:
+                # Config loading should stay tolerant of existing invalid state and warn.
+                logger.warning(
+                    "Detected the same LoRA folder in both ComfyUI model paths and "
+                    "LoRA Manager Extra Folder Paths. This can cause duplicate items or "
+                    "other unexpected behavior, and it usually means the path setup is "
+                    "not doing what you intended. LoRA Manager will keep the ComfyUI "
+                    "path and ignore this Extra Folder Paths entry: '%s'. Please review "
+                    "your path settings and remove the duplicate entry.",
+                    normalized_path,
+                )
+                continue
+
+            symlink_path = primary_symlink_map.get(real_path)
+            if symlink_path:
+                # Config loading should stay tolerant of existing invalid state and warn.
+                logger.warning(
+                    "Detected the same LoRA folder in both ComfyUI model paths and "
+                    "LoRA Manager Extra Folder Paths. This can cause duplicate items or "
+                    "other unexpected behavior, and it usually means the path setup is "
+                    "not doing what you intended. LoRA Manager will keep the ComfyUI "
+                    "path and ignore this Extra Folder Paths entry: '%s'. Please review "
+                    "your path settings and remove the duplicate entry.",
+                    normalized_path,
+                )
+                continue
+
+            filtered.append(stripped)
+
+        return filtered
+
+    def _collect_first_level_symlink_targets(
+        self, roots: Iterable[str]
+    ) -> Dict[str, str]:
+        """Return real-path -> link-path mappings for first-level symlinks under the given roots."""
+
+        targets: Dict[str, str] = {}
+        for root in roots:
+            if not isinstance(root, str):
+                continue
+            stripped_root = root.strip()
+            if not stripped_root or not os.path.isdir(stripped_root):
+                continue
+
+            try:
+                with os.scandir(stripped_root) as iterator:
+                    for entry in iterator:
+                        try:
+                            if not self._entry_is_symlink(entry):
+                                continue
+                            target_path = os.path.realpath(entry.path)
+                            if not os.path.isdir(target_path):
+                                continue
+
+                            normalized_target = self._normalize_path_for_comparison(
+                                target_path,
+                                resolve_realpath=True,
+                            )
+                            normalized_link = os.path.normpath(entry.path).replace(
+                                os.sep, "/"
+                            )
+                            targets.setdefault(normalized_target, normalized_link)
+                        except Exception as inner_exc:
+                            logger.debug(
+                                "Error collecting LoRA symlink target for %s: %s",
+                                entry.path,
+                                inner_exc,
+                            )
+            except Exception as exc:
+                logger.debug(
+                    "Error scanning first-level LoRA symlinks in %s: %s",
+                    stripped_root,
+                    exc,
+                )
+
+        return targets
+
     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)
 
@@ -674,7 +930,7 @@ class Config:
                 "Please fix your ComfyUI path configuration to separate these folders. "
                 "Falling back to 'checkpoints' for backward compatibility. "
                 "Overlapping real paths: %s",
-                [checkpoint_map.get(rp, rp) for rp in overlapping_real_paths]
+                [checkpoint_map.get(rp, rp) for rp in overlapping_real_paths],
             )
             # Remove overlapping paths from unet_map to prioritize checkpoints
             for rp in overlapping_real_paths:
@@ -690,22 +946,26 @@ 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(os.sep, '/')
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(
+                os.sep, "/"
+            )
             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)
         unique_paths = sorted(path_map.values(), key=lambda p: p.lower())
 
         for original_path in unique_paths:
-            real_path = os.path.normpath(os.path.realpath(original_path)).replace(os.sep, '/')
+            real_path = os.path.normpath(os.path.realpath(original_path)).replace(
+                os.sep, "/"
+            )
             if real_path != original_path:
                 self.add_path_mapping(original_path, real_path)
 
@@ -715,32 +975,71 @@ class Config:
         self,
         folder_paths: Mapping[str, Iterable[str]],
         extra_folder_paths: Optional[Mapping[str, Iterable[str]]] = None,
+        recipes_path: str = "",
     ) -> None:
         self._path_mappings.clear()
         self._preview_root_paths = set()
+        self.recipes_path = recipes_path if isinstance(recipes_path, str) else ""
 
-        lora_paths = folder_paths.get('loras', []) or []
-        checkpoint_paths = folder_paths.get('checkpoints', []) or []
-        unet_paths = folder_paths.get('unet', []) or []
-        embedding_paths = folder_paths.get('embeddings', []) or []
+        lora_paths = folder_paths.get("loras", []) or []
+        checkpoint_paths = folder_paths.get("checkpoints", []) or []
+        unet_paths = folder_paths.get("unet", []) or []
+        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)
         extra_paths = extra_folder_paths or {}
-        extra_lora_paths = extra_paths.get('loras', []) or []
-        extra_checkpoint_paths = extra_paths.get('checkpoints', []) or []
-        extra_unet_paths = extra_paths.get('unet', []) or []
-        extra_embedding_paths = extra_paths.get('embeddings', []) or []
+        extra_lora_paths = extra_paths.get("loras", []) or []
+        extra_checkpoint_paths = extra_paths.get("checkpoints", []) or []
+        extra_unet_paths = extra_paths.get("unet", []) or []
+        extra_embedding_paths = extra_paths.get("embeddings", []) or []
 
-        self.extra_loras_roots = self._prepare_lora_paths(extra_lora_paths)
-        self.extra_checkpoints_roots = self._prepare_checkpoint_paths(extra_checkpoint_paths, extra_unet_paths)
-        self.extra_embeddings_roots = self._prepare_embedding_paths(extra_embedding_paths)
-        # extra_unet_roots is set by _prepare_checkpoint_paths (access unet_roots before it's reset)
-        unet_roots_value: List[str] = getattr(self, 'unet_roots', None) or []
-        self.extra_unet_roots = unet_roots_value
+        filtered_extra_lora_paths = self._filter_overlapping_extra_lora_paths(
+            self.loras_roots,
+            extra_lora_paths,
+        )
+        self.extra_loras_roots = self._prepare_lora_paths(filtered_extra_lora_paths)
+        (
+            _,
+            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
+        )
+
+        # Log extra folder paths
+        if self.extra_loras_roots:
+            logger.info(
+                "Found extra LoRA roots:"
+                + "\n - "
+                + "\n - ".join(self.extra_loras_roots)
+            )
+        if self.extra_checkpoints_roots:
+            logger.info(
+                "Found extra checkpoint roots:"
+                + "\n - "
+                + "\n - ".join(self.extra_checkpoints_roots)
+            )
+        if self.extra_unet_roots:
+            logger.info(
+                "Found extra diffusion model roots:"
+                + "\n - "
+                + "\n - ".join(self.extra_unet_roots)
+            )
+        if self.extra_embeddings_roots:
+            logger.info(
+                "Found extra embedding roots:"
+                + "\n - "
+                + "\n - ".join(self.extra_embeddings_roots)
+            )
 
         self._initialize_symlink_mappings()
 
@@ -749,7 +1048,10 @@ class Config:
         try:
             raw_paths = folder_paths.get_folder_paths("loras")
             unique_paths = self._prepare_lora_paths(raw_paths)
-            logger.info("Found LoRA roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
+            logger.info(
+                "Found LoRA roots:"
+                + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]")
+            )
 
             if not unique_paths:
                 logger.warning("No valid loras folders found in ComfyUI configuration")
@@ -765,12 +1067,21 @@ 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:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
+            logger.info(
+                "Found checkpoint roots:"
+                + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]")
+            )
 
             if not unique_paths:
-                logger.warning("No valid checkpoint folders found in ComfyUI configuration")
+                logger.warning(
+                    "No valid checkpoint folders found in ComfyUI configuration"
+                )
                 return []
 
             return unique_paths
@@ -783,10 +1094,15 @@ class Config:
         try:
             raw_paths = folder_paths.get_folder_paths("embeddings")
             unique_paths = self._prepare_embedding_paths(raw_paths)
-            logger.info("Found embedding roots:" + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]"))
+            logger.info(
+                "Found embedding roots:"
+                + ("\n - " + "\n - ".join(unique_paths) if unique_paths else "[]")
+            )
 
             if not unique_paths:
-                logger.warning("No valid embeddings folders found in ComfyUI configuration")
+                logger.warning(
+                    "No valid embeddings folders found in ComfyUI configuration"
+                )
                 return []
 
             return unique_paths
@@ -798,13 +1114,13 @@ class Config:
         if not preview_path:
             return ""
 
-        normalized = os.path.normpath(preview_path).replace(os.sep, '/')
-        encoded_path = urllib.parse.quote(normalized, safe='')
-        return f'/api/lm/previews?path={encoded_path}'
+        normalized = os.path.normpath(preview_path).replace(os.sep, "/")
+        encoded_path = urllib.parse.quote(normalized, safe="")
+        return f"/api/lm/previews?path={encoded_path}"
 
     def is_preview_path_allowed(self, preview_path: str) -> bool:
         """Return ``True`` if ``preview_path`` is within an allowed directory.
-        
+
         If the path is initially rejected, attempts to discover deep symlinks
         that were not scanned during initialization. If a symlink is found,
         updates the in-memory path mappings and retries the check.
@@ -875,14 +1191,18 @@ class Config:
                         normalized_link = self._normalize_path(str(current))
 
                         self._path_mappings[normalized_target] = normalized_link
-                        self._preview_root_paths.update(self._expand_preview_root(normalized_target))
-                        self._preview_root_paths.update(self._expand_preview_root(normalized_link))
+                        self._preview_root_paths.update(
+                            self._expand_preview_root(normalized_target)
+                        )
+                        self._preview_root_paths.update(
+                            self._expand_preview_root(normalized_link)
+                        )
 
                         logger.debug(
                             "Discovered deep symlink: %s -> %s (preview path: %s)",
                             normalized_link,
                             normalized_target,
-                            preview_path
+                            preview_path,
                         )
 
                         return True
@@ -900,20 +1220,36 @@ class Config:
 
     def apply_library_settings(self, library_config: Mapping[str, object]) -> None:
         """Update runtime paths to match the provided library configuration."""
-        folder_paths = library_config.get('folder_paths') if isinstance(library_config, Mapping) else {}
-        extra_folder_paths = library_config.get('extra_folder_paths') if isinstance(library_config, Mapping) else None
+        folder_paths = (
+            library_config.get("folder_paths")
+            if isinstance(library_config, Mapping)
+            else {}
+        )
+        extra_folder_paths = (
+            library_config.get("extra_folder_paths")
+            if isinstance(library_config, Mapping)
+            else None
+        )
         if not isinstance(folder_paths, Mapping):
             folder_paths = {}
         if not isinstance(extra_folder_paths, Mapping):
             extra_folder_paths = None
 
-        self._apply_library_paths(folder_paths, extra_folder_paths)
+        recipes_path = (
+            str(library_config.get("recipes_path", ""))
+            if isinstance(library_config, Mapping)
+            else ""
+        )
+        self._apply_library_paths(folder_paths, extra_folder_paths, recipes_path)
 
         logger.info(
             "Applied library settings with %d lora roots (%d extra), %d checkpoint roots (%d extra), and %d embedding roots (%d extra)",
-            len(self.loras_roots or []), len(self.extra_loras_roots or []),
-            len(self.base_models_roots or []), len(self.extra_checkpoints_roots or []),
-            len(self.embeddings_roots or []), len(self.extra_embeddings_roots or []),
+            len(self.loras_roots or []),
+            len(self.extra_loras_roots or []),
+            len(self.base_models_roots or []),
+            len(self.extra_checkpoints_roots or []),
+            len(self.embeddings_roots or []),
+            len(self.extra_embeddings_roots or []),
         )
 
     def get_library_registry_snapshot(self) -> Dict[str, object]:
@@ -933,5 +1269,6 @@ class Config:
             logger.debug("Failed to collect library registry snapshot: %s", exc)
             return {"active_library": "", "libraries": {}}
 
+
 # Global config instance
 config = Config()

py/lora_manager.py

@@ -5,16 +5,22 @@ import logging
 from .utils.logging_config import setup_logging
 
 # Check if we're in standalone mode
-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"
+)
 
 # Only setup logging prefix if not in standalone mode
 if not standalone_mode:
     setup_logging()
 
-from server import PromptServer # type: ignore
+from server import PromptServer  # type: ignore
 
 from .config import config
-from .services.model_service_factory import ModelServiceFactory, register_default_model_types
+from .services.model_service_factory import (
+    ModelServiceFactory,
+    register_default_model_types,
+)
 from .routes.recipe_routes import RecipeRoutes
 from .routes.stats_routes import StatsRoutes
 from .routes.update_routes import UpdateRoutes
@@ -61,9 +67,10 @@ class _SettingsProxy:
 
 settings = _SettingsProxy()
 
+
 class LoraManager:
     """Main entry point for LoRA Manager plugin"""
-    
+
     @classmethod
     def add_routes(cls):
         """Initialize and register all routes using the new refactored architecture"""
@@ -76,7 +83,8 @@ class LoraManager:
                 (
                     idx
                     for idx, middleware in enumerate(app.middlewares)
-                    if getattr(middleware, "__name__", "") == "block_external_middleware"
+                    if getattr(middleware, "__name__", "")
+                    == "block_external_middleware"
                 ),
                 None,
             )
@@ -84,7 +92,9 @@ class LoraManager:
             if block_middleware_index is None:
                 app.middlewares.append(relax_csp_for_remote_media)
             else:
-                app.middlewares.insert(block_middleware_index, relax_csp_for_remote_media)
+                app.middlewares.insert(
+                    block_middleware_index, relax_csp_for_remote_media
+                )
 
         # Increase allowed header sizes so browsers with large localhost cookie
         # jars (multiple UIs on 127.0.0.1) don't trip aiohttp's 8KB default
@@ -105,7 +115,7 @@ class LoraManager:
         app._handler_args = updated_handler_args
 
         # Configure aiohttp access logger to be less verbose
-        logging.getLogger('aiohttp.access').setLevel(logging.WARNING)
+        logging.getLogger("aiohttp.access").setLevel(logging.WARNING)
 
         # Add specific suppression for connection reset errors
         class ConnectionResetFilter(logging.Filter):
@@ -124,214 +134,291 @@ class LoraManager:
         asyncio_logger.addFilter(ConnectionResetFilter())
 
         # Add static route for example images if the path exists in settings
-        example_images_path = settings.get('example_images_path')
+        example_images_path = settings.get("example_images_path")
         logger.info(f"Example images path: {example_images_path}")
         if example_images_path and os.path.exists(example_images_path):
-            app.router.add_static('/example_images_static', example_images_path)
-            logger.info(f"Added static route for example images: /example_images_static -> {example_images_path}")
+            app.router.add_static("/example_images_static", example_images_path)
+            logger.info(
+                f"Added static route for example images: /example_images_static -> {example_images_path}"
+            )
 
         # Add static route for locales JSON files
         if os.path.exists(config.i18n_path):
-            app.router.add_static('/locales', config.i18n_path)
-            logger.info(f"Added static route for locales: /locales -> {config.i18n_path}")
+            app.router.add_static("/locales", config.i18n_path)
+            logger.info(
+                f"Added static route for locales: /locales -> {config.i18n_path}"
+            )
 
         # Add static route for plugin assets
-        app.router.add_static('/loras_static', config.static_path)
-        
+        app.router.add_static("/loras_static", config.static_path)
+
         # Register default model types with the factory
         register_default_model_types()
-        
+
         # Setup all model routes using the factory
         ModelServiceFactory.setup_all_routes(app)
-        
+
         # Setup non-model-specific routes
         stats_routes = StatsRoutes()
         stats_routes.setup_routes(app)
         RecipeRoutes.setup_routes(app)
-        UpdateRoutes.setup_routes(app)  
+        UpdateRoutes.setup_routes(app)
         MiscRoutes.setup_routes(app)
         ExampleImagesRoutes.setup_routes(app, ws_manager=ws_manager)
         PreviewRoutes.setup_routes(app)
-        
+
         # Setup WebSocket routes that are shared across all model types
-        app.router.add_get('/ws/fetch-progress', ws_manager.handle_connection)
-        app.router.add_get('/ws/download-progress', ws_manager.handle_download_connection)
-        app.router.add_get('/ws/init-progress', ws_manager.handle_init_connection)
-        
-        # Schedule service initialization 
+        app.router.add_get("/ws/fetch-progress", ws_manager.handle_connection)
+        app.router.add_get(
+            "/ws/download-progress", ws_manager.handle_download_connection
+        )
+        app.router.add_get("/ws/init-progress", ws_manager.handle_init_connection)
+
+        # Schedule service initialization
         app.on_startup.append(lambda app: cls._initialize_services())
-        
+
         # Add cleanup
         app.on_shutdown.append(cls._cleanup)
-        
+
     @classmethod
     async def _initialize_services(cls):
         """Initialize all services using the ServiceRegistry"""
         try:
+            # Apply library settings to load extra folder paths before scanning
+            # Only apply if extra paths haven't been loaded yet (preserves test mocks)
+            try:
+                from .services.settings_manager import get_settings_manager
+
+                settings_manager = get_settings_manager()
+                library_name = settings_manager.get_active_library_name()
+                libraries = settings_manager.get_libraries()
+                if library_name and library_name in libraries:
+                    library_config = libraries[library_name]
+                    # Only apply settings if extra paths are not already configured
+                    # This preserves values set by tests via monkeypatch
+                    extra_paths = library_config.get("extra_folder_paths", {})
+                    has_extra_paths = (
+                        config.extra_loras_roots
+                        or config.extra_checkpoints_roots
+                        or config.extra_unet_roots
+                        or config.extra_embeddings_roots
+                    )
+                    if not has_extra_paths and any(extra_paths.values()):
+                        config.apply_library_settings(library_config)
+                        logger.info(
+                            "Applied library settings for '%s' with extra paths: loras=%s, checkpoints=%s, embeddings=%s",
+                            library_name,
+                            extra_paths.get("loras", []),
+                            extra_paths.get("checkpoints", []),
+                            extra_paths.get("embeddings", []),
+                        )
+            except Exception as exc:
+                logger.warning(
+                    "Failed to apply library settings during initialization: %s", exc
+                )
+
             # Initialize CivitaiClient first to ensure it's ready for other services
             await ServiceRegistry.get_civitai_client()
 
             # Register DownloadManager with ServiceRegistry
             await ServiceRegistry.get_download_manager()
+            await ServiceRegistry.get_backup_service()
 
             from .services.metadata_service import initialize_metadata_providers
+
             await initialize_metadata_providers()
-            
+
             # Initialize WebSocket manager
             await ServiceRegistry.get_websocket_manager()
-            
+
             # Initialize scanners in background
             lora_scanner = await ServiceRegistry.get_lora_scanner()
             checkpoint_scanner = await ServiceRegistry.get_checkpoint_scanner()
             embedding_scanner = await ServiceRegistry.get_embedding_scanner()
-            
+
             # Initialize recipe scanner if needed
             recipe_scanner = await ServiceRegistry.get_recipe_scanner()
-            
+
             # Create low-priority initialization tasks
             init_tasks = [
-                asyncio.create_task(lora_scanner.initialize_in_background(), name='lora_cache_init'),
-                asyncio.create_task(checkpoint_scanner.initialize_in_background(), name='checkpoint_cache_init'),
-                asyncio.create_task(embedding_scanner.initialize_in_background(), name='embedding_cache_init'),
-                asyncio.create_task(recipe_scanner.initialize_in_background(), name='recipe_cache_init')
+                asyncio.create_task(
+                    lora_scanner.initialize_in_background(), name="lora_cache_init"
+                ),
+                asyncio.create_task(
+                    checkpoint_scanner.initialize_in_background(),
+                    name="checkpoint_cache_init",
+                ),
+                asyncio.create_task(
+                    embedding_scanner.initialize_in_background(),
+                    name="embedding_cache_init",
+                ),
+                asyncio.create_task(
+                    recipe_scanner.initialize_in_background(), name="recipe_cache_init"
+                ),
             ]
 
             await ExampleImagesMigration.check_and_run_migrations()
-            
+
             # Schedule post-initialization tasks to run after scanners complete
             asyncio.create_task(
-                cls._run_post_initialization_tasks(init_tasks), 
-                name='post_init_tasks'
+                cls._run_post_initialization_tasks(init_tasks), name="post_init_tasks"
             )
-            
-            logger.debug("LoRA Manager: All services initialized and background tasks scheduled")
-                
+
+            logger.debug(
+                "LoRA Manager: All services initialized and background tasks scheduled"
+            )
+
         except Exception as e:
-            logger.error(f"LoRA Manager: Error initializing services: {e}", exc_info=True)
-    
+            logger.error(
+                f"LoRA Manager: Error initializing services: {e}", exc_info=True
+            )
+
     @classmethod
     async def _run_post_initialization_tasks(cls, init_tasks):
         """Run post-initialization tasks after all scanners complete"""
         try:
-            logger.debug("LoRA Manager: Waiting for scanner initialization to complete...")
-            
+            logger.debug(
+                "LoRA Manager: Waiting for scanner initialization to complete..."
+            )
+
             # Wait for all scanner initialization tasks to complete
             await asyncio.gather(*init_tasks, return_exceptions=True)
 
-            logger.debug("LoRA Manager: Scanner initialization completed, starting post-initialization tasks...")
-            
+            logger.debug(
+                "LoRA Manager: Scanner initialization completed, starting post-initialization tasks..."
+            )
+
             # Run post-initialization tasks
             post_tasks = [
-                asyncio.create_task(cls._cleanup_backup_files(), name='cleanup_bak_files'),
+                asyncio.create_task(
+                    cls._cleanup_backup_files(), name="cleanup_bak_files"
+                ),
                 # Add more post-initialization tasks here as needed
                 # asyncio.create_task(cls._another_post_task(), name='another_task'),
             ]
-            
+
             # Run all post-initialization tasks
             results = await asyncio.gather(*post_tasks, return_exceptions=True)
-            
+
             # Log results
             for i, result in enumerate(results):
                 task_name = post_tasks[i].get_name()
                 if isinstance(result, Exception):
-                    logger.error(f"Post-initialization task '{task_name}' failed: {result}")
+                    logger.error(
+                        f"Post-initialization task '{task_name}' failed: {result}"
+                    )
                 else:
-                    logger.debug(f"Post-initialization task '{task_name}' completed successfully")
-                    
+                    logger.debug(
+                        f"Post-initialization task '{task_name}' completed successfully"
+                    )
+
             logger.debug("LoRA Manager: All post-initialization tasks completed")
-            
+
         except Exception as e:
-            logger.error(f"LoRA Manager: Error in post-initialization tasks: {e}", exc_info=True)
-    
+            logger.error(
+                f"LoRA Manager: Error in post-initialization tasks: {e}", exc_info=True
+            )
+
     @classmethod
     async def _cleanup_backup_files(cls):
         """Clean up .bak files in all model roots"""
         try:
             logger.debug("Starting cleanup of .bak files in model directories...")
-            
+
             # Collect all model roots
             all_roots = set()
             all_roots.update(config.loras_roots)
-            all_roots.update(config.base_models_roots) 
-            all_roots.update(config.embeddings_roots)
-            
+            all_roots.update(config.base_models_roots or [])
+            all_roots.update(config.embeddings_roots or [])
+
             total_deleted = 0
             total_size_freed = 0
-            
+
             for root_path in all_roots:
                 if not os.path.exists(root_path):
                     continue
-                    
+
                 try:
-                    deleted_count, size_freed = await cls._cleanup_backup_files_in_directory(root_path)
+                    (
+                        deleted_count,
+                        size_freed,
+                    ) = await cls._cleanup_backup_files_in_directory(root_path)
                     total_deleted += deleted_count
                     total_size_freed += size_freed
-                    
+
                     if deleted_count > 0:
-                        logger.debug(f"Cleaned up {deleted_count} .bak files in {root_path} (freed {size_freed / (1024*1024):.2f} MB)")
-                        
+                        logger.debug(
+                            f"Cleaned up {deleted_count} .bak files in {root_path} (freed {size_freed / (1024 * 1024):.2f} MB)"
+                        )
+
                 except Exception as e:
                     logger.error(f"Error cleaning up .bak files in {root_path}: {e}")
-                    
+
                 # Yield control periodically
                 await asyncio.sleep(0.01)
-            
+
             if total_deleted > 0:
-                logger.debug(f"Backup cleanup completed: removed {total_deleted} .bak files, freed {total_size_freed / (1024*1024):.2f} MB total")
+                logger.debug(
+                    f"Backup cleanup completed: removed {total_deleted} .bak files, freed {total_size_freed / (1024 * 1024):.2f} MB total"
+                )
             else:
                 logger.debug("Backup cleanup completed: no .bak files found")
 
         except Exception as e:
             logger.error(f"Error during backup file cleanup: {e}", exc_info=True)
-    
+
     @classmethod
     async def _cleanup_backup_files_in_directory(cls, directory_path: str):
         """Clean up .bak files in a specific directory recursively
-        
+
         Args:
             directory_path: Path to the directory to clean
-            
+
         Returns:
             Tuple[int, int]: (number of files deleted, total size freed in bytes)
         """
         deleted_count = 0
         size_freed = 0
         visited_paths = set()
-        
+
         def cleanup_recursive(path):
             nonlocal deleted_count, size_freed
-            
+
             try:
                 real_path = os.path.realpath(path)
                 if real_path in visited_paths:
                     return
                 visited_paths.add(real_path)
-                
+
                 with os.scandir(path) as it:
                     for entry in it:
                         try:
-                            if entry.is_file(follow_symlinks=True) and entry.name.endswith('.bak'):
+                            if entry.is_file(
+                                follow_symlinks=True
+                            ) and entry.name.endswith(".bak"):
                                 file_size = entry.stat().st_size
                                 os.remove(entry.path)
                                 deleted_count += 1
                                 size_freed += file_size
                                 logger.debug(f"Deleted .bak file: {entry.path}")
-                                
+
                             elif entry.is_dir(follow_symlinks=True):
                                 cleanup_recursive(entry.path)
-                                
+
                         except Exception as e:
-                            logger.warning(f"Could not delete .bak file {entry.path}: {e}")
-                            
+                            logger.warning(
+                                f"Could not delete .bak file {entry.path}: {e}"
+                            )
+
             except Exception as e:
                 logger.error(f"Error scanning directory {path} for .bak files: {e}")
-        
+
         # Run the recursive cleanup in a thread pool to avoid blocking
         loop = asyncio.get_event_loop()
         await loop.run_in_executor(None, cleanup_recursive, directory_path)
-        
+
         return deleted_count, size_freed
-    
+
     @classmethod
     async def _cleanup_example_images_folders(cls):
         """Invoke the example images cleanup service for manual execution."""
@@ -339,21 +426,21 @@ class LoraManager:
             service = ExampleImagesCleanupService()
             result = await service.cleanup_example_image_folders()
 
-            if result.get('success'):
+            if result.get("success"):
                 logger.debug(
                     "Manual example images cleanup completed: moved=%s",
-                    result.get('moved_total'),
+                    result.get("moved_total"),
                 )
-            elif result.get('partial_success'):
+            elif result.get("partial_success"):
                 logger.warning(
                     "Manual example images cleanup partially succeeded: moved=%s failures=%s",
-                    result.get('moved_total'),
-                    result.get('move_failures'),
+                    result.get("moved_total"),
+                    result.get("move_failures"),
                 )
             else:
                 logger.debug(
                     "Manual example images cleanup skipped or failed: %s",
-                    result.get('error', 'no changes'),
+                    result.get("error", "no changes"),
                 )
 
             return result
@@ -361,9 +448,9 @@ class LoraManager:
         except Exception as e:  # pragma: no cover - defensive guard
             logger.error(f"Error during example images cleanup: {e}", exc_info=True)
             return {
-                'success': False,
-                'error': str(e),
-                'error_code': 'unexpected_error',
+                "success": False,
+                "error": str(e),
+                "error_code": "unexpected_error",
             }
 
     @classmethod
@@ -371,6 +458,6 @@ class LoraManager:
         """Cleanup resources using ServiceRegistry"""
         try:
             logger.info("LoRA Manager: Cleaning up services")
-                
+
         except Exception as e:
             logger.error(f"Error during cleanup: {e}", exc_info=True)

py/metadata_collector/__init__.py

@@ -4,7 +4,10 @@ import logging
 logger = logging.getLogger(__name__)
 
 # Check if running in standalone mode
-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_hook import MetadataHook
@@ -13,13 +16,13 @@ if not standalone_mode:
     def init():
         # Install hooks to collect metadata during execution
         MetadataHook.install()
-        
+
         # Initialize registry
         registry = MetadataRegistry()
-        
+
         logger.info("ComfyUI Metadata Collector initialized")
-        
-    def get_metadata(prompt_id=None):
+
+    def get_metadata(prompt_id=None):  # type: ignore[no-redef]
         """Helper function to get metadata from the registry"""
         registry = MetadataRegistry()
         return registry.get_metadata(prompt_id)
@@ -27,7 +30,7 @@ else:
     # Standalone mode - provide dummy implementations
     def init():
         logger.info("ComfyUI Metadata Collector disabled in standalone mode")
-        
-    def get_metadata(prompt_id=None):
+
+    def get_metadata(prompt_id=None):  # type: ignore[no-redef]
         """Dummy implementation for standalone mode"""
         return {}

py/metadata_collector/metadata_hook.py

@@ -148,10 +148,13 @@ class MetadataHook:
         """Install hooks for asynchronous execution model"""
         # Store the original _async_map_node_over_list function
         original_map_node_over_list = getattr(execution, map_node_func_name)
-        
-        # Wrapped async function, compatible with both stable and nightly
-        async def async_map_node_over_list_with_metadata(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, *args, **kwargs):
-            hidden_inputs = kwargs.get('hidden_inputs', None)
+
+        # Wrapped async function - signature must exactly match _async_map_node_over_list
+        async def async_map_node_over_list_with_metadata(
+            prompt_id, unique_id, obj, input_data_all, func,
+            allow_interrupt=False, execution_block_cb=None,
+            pre_execute_cb=None, v3_data=None
+        ):
             # Only collect metadata when calling the main function of nodes
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
@@ -163,13 +166,13 @@ class MetadataHook:
                             registry.record_node_execution(node_id, class_type, input_data_all, None)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (pre-execution): {str(e)}")
-            
-            # Call original function with all args/kwargs
+
+            # Call original function with exact parameters
             results = await original_map_node_over_list(
                 prompt_id, unique_id, obj, input_data_all, func,
-                allow_interrupt, execution_block_cb, pre_execute_cb, *args, **kwargs
+                allow_interrupt, execution_block_cb, pre_execute_cb, v3_data=v3_data
             )
-            
+
             if func == obj.FUNCTION and hasattr(obj, '__class__'):
                 try:
                     registry = MetadataRegistry()
@@ -180,28 +183,28 @@ class MetadataHook:
                             registry.update_node_execution(node_id, class_type, results)
                 except Exception as e:
                     logger.error(f"Error collecting metadata (post-execution): {str(e)}")
-            
+
             return results
-        
+
         # Also hook the execute function to track the current prompt_id
         original_execute = execution.execute
-        
+
         async def async_execute_with_prompt_tracking(*args, **kwargs):
             if len(args) >= 7:  # Check if we have enough arguments
                 server, prompt, caches, node_id, extra_data, executed, prompt_id = args[:7]
                 registry = MetadataRegistry()
-                
+
                 # Start collection if this is a new prompt
                 if not registry.current_prompt_id or registry.current_prompt_id != prompt_id:
                     registry.start_collection(prompt_id)
-                    
+
                 # Store the dynprompt reference for node lookups
                 if hasattr(prompt, 'original_prompt'):
                     registry.set_current_prompt(prompt)
-            
+
             # Execute the original function
             return await original_execute(*args, **kwargs)
-            
+
         # Replace the functions with async versions
         setattr(execution, map_node_func_name, async_map_node_over_list_with_metadata)
         execution.execute = async_execute_with_prompt_tracking

py/metadata_collector/metadata_processor.py

@@ -595,6 +595,15 @@ class MetadataProcessor:
                     if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
                         params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")
                 else:
-                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
+                    # Generic guider nodes often expose separate positive/negative inputs.
+                    positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "positive", max_depth=10)
+                    if not positive_node_id:
+                        positive_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
                     if positive_node_id and positive_node_id in metadata.get(PROMPTS, {}):
                         params["prompt"] = metadata[PROMPTS][positive_node_id].get("text", "")
+
+                    negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "negative", max_depth=10)
+                    if not negative_node_id:
+                        negative_node_id = MetadataProcessor.trace_node_input(prompt, guider_node_id, "conditioning", max_depth=10)
+                    if negative_node_id and negative_node_id in metadata.get(PROMPTS, {}):
+                        params["negative_prompt"] = metadata[PROMPTS][negative_node_id].get("text", "")

py/metadata_collector/metadata_registry.py

@@ -1,50 +1,54 @@
 import time
-from nodes import NODE_CLASS_MAPPINGS
+from nodes import NODE_CLASS_MAPPINGS  # type: ignore
 from .node_extractors import NODE_EXTRACTORS, GenericNodeExtractor
 from .constants import METADATA_CATEGORIES, IMAGES
 
+
 class MetadataRegistry:
     """A singleton registry to store and retrieve workflow metadata"""
+
     _instance = None
-    
+
     def __new__(cls):
         if cls._instance is None:
             cls._instance = super().__new__(cls)
             cls._instance._reset()
         return cls._instance
-    
+
     def _reset(self):
         self.current_prompt_id = None
         self.current_prompt = None
         self.metadata = {}
         self.prompt_metadata = {}
         self.executed_nodes = set()
-        
+
         # Node-level cache for metadata
         self.node_cache = {}
-        
+
         # Limit the number of stored prompts
         self.max_prompt_history = 3
-        
+
         # Categories we want to track and retrieve from cache
         self.metadata_categories = METADATA_CATEGORIES
-    
+
     def _clean_old_prompts(self):
         """Clean up old prompt metadata, keeping only recent ones"""
         if len(self.prompt_metadata) <= self.max_prompt_history:
             return
-            
+
         # Sort all prompt_ids by timestamp
         sorted_prompts = sorted(
             self.prompt_metadata.keys(),
-            key=lambda pid: self.prompt_metadata[pid].get("timestamp", 0)
+            key=lambda pid: self.prompt_metadata[pid].get("timestamp", 0),
         )
-        
+
         # Remove oldest records
-        prompts_to_remove = sorted_prompts[:len(sorted_prompts) - self.max_prompt_history]
+        prompts_to_remove = sorted_prompts[
+            : len(sorted_prompts) - self.max_prompt_history
+        ]
         for pid in prompts_to_remove:
             del self.prompt_metadata[pid]
-    
+
     def start_collection(self, prompt_id):
         """Begin metadata collection for a new prompt"""
         self.current_prompt_id = prompt_id
@@ -53,90 +57,96 @@ class MetadataRegistry:
             category: {} for category in METADATA_CATEGORIES
         }
         # Add additional metadata fields
-        self.prompt_metadata[prompt_id].update({
-            "execution_order": [],
-            "current_prompt": None,  # Will store the prompt object
-            "timestamp": time.time()
-        })
-        
+        self.prompt_metadata[prompt_id].update(
+            {
+                "execution_order": [],
+                "current_prompt": None,  # Will store the prompt object
+                "timestamp": time.time(),
+            }
+        )
+
         # Clean up old prompt data
         self._clean_old_prompts()
-    
+
     def set_current_prompt(self, prompt):
         """Set the current prompt object reference"""
         self.current_prompt = prompt
         if self.current_prompt_id and self.current_prompt_id in self.prompt_metadata:
             # Store the prompt in the metadata for later relationship tracing
             self.prompt_metadata[self.current_prompt_id]["current_prompt"] = prompt
-        
+
     def get_metadata(self, prompt_id=None):
         """Get collected metadata for a prompt"""
         key = prompt_id if prompt_id is not None else self.current_prompt_id
         if key not in self.prompt_metadata:
             return {}
-            
+
         metadata = self.prompt_metadata[key]
-        
+
         # If we have a current prompt object, check for non-executed nodes
         prompt_obj = metadata.get("current_prompt")
         if prompt_obj and hasattr(prompt_obj, "original_prompt"):
             original_prompt = prompt_obj.original_prompt
-            
+
             # Fill in missing metadata from cache for nodes that weren't executed
             self._fill_missing_metadata(key, original_prompt)
-            
+
         return self.prompt_metadata.get(key, {})
-    
+
     def _fill_missing_metadata(self, prompt_id, original_prompt):
         """Fill missing metadata from cache for non-executed nodes"""
         if not original_prompt:
             return
-            
+
         executed_nodes = self.executed_nodes
         metadata = self.prompt_metadata[prompt_id]
-        
+
         # Iterate through nodes in the original prompt
         for node_id, node_data in original_prompt.items():
             # Skip if already executed in this run
             if node_id in executed_nodes:
                 continue
-                
+
             # Get the node type from the prompt (this is the key in NODE_CLASS_MAPPINGS)
             prompt_class_type = node_data.get("class_type")
             if not prompt_class_type:
                 continue
-                
+
             # Convert to actual class name (which is what we use in our cache)
             class_type = prompt_class_type
             if prompt_class_type in NODE_CLASS_MAPPINGS:
                 class_obj = NODE_CLASS_MAPPINGS[prompt_class_type]
                 class_type = class_obj.__name__
-            
+
             # Create cache key using the actual class name
             cache_key = f"{node_id}:{class_type}"
-            
+
             # Check if this node type is relevant for metadata collection
             if class_type in NODE_EXTRACTORS:
                 # Check if we have cached metadata for this node
                 if cache_key in self.node_cache:
                     cached_data = self.node_cache[cache_key]
-                    
+
                     # Apply cached metadata to the current metadata
                     for category in self.metadata_categories:
                         if category in cached_data and node_id in cached_data[category]:
                             if node_id not in metadata[category]:
-                                metadata[category][node_id] = cached_data[category][node_id]
-    
+                                metadata[category][node_id] = cached_data[category][
+                                    node_id
+                                ]
+
     def record_node_execution(self, node_id, class_type, inputs, outputs):
         """Record information about a node's execution"""
         if not self.current_prompt_id:
             return
-            
+
         # Add to execution order and mark as executed
         if node_id not in self.executed_nodes:
             self.executed_nodes.add(node_id)
-            self.prompt_metadata[self.current_prompt_id]["execution_order"].append(node_id)
-        
+            self.prompt_metadata[self.current_prompt_id]["execution_order"].append(
+                node_id
+            )
+
         # Process inputs to simplify working with them
         processed_inputs = {}
         for input_name, input_values in inputs.items():
@@ -145,63 +155,61 @@ class MetadataRegistry:
                 processed_inputs[input_name] = input_values[0]
             else:
                 processed_inputs[input_name] = input_values
-            
+
         # Extract node-specific metadata
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
         extractor.extract(
-            node_id, 
-            processed_inputs, 
-            outputs, 
-            self.prompt_metadata[self.current_prompt_id]
+            node_id,
+            processed_inputs,
+            outputs,
+            self.prompt_metadata[self.current_prompt_id],
         )
-        
+
         # Cache this node's metadata
         self._cache_node_metadata(node_id, class_type)
-    
+
     def update_node_execution(self, node_id, class_type, outputs):
         """Update node metadata with output information"""
         if not self.current_prompt_id:
             return
-        
+
         # Process outputs to make them more usable
         processed_outputs = outputs
-            
+
         # Use the same extractor to update with outputs
         extractor = NODE_EXTRACTORS.get(class_type, GenericNodeExtractor)
-        if hasattr(extractor, 'update'):
+        if hasattr(extractor, "update"):
             extractor.update(
-                node_id, 
-                processed_outputs, 
-                self.prompt_metadata[self.current_prompt_id]
+                node_id, processed_outputs, self.prompt_metadata[self.current_prompt_id]
             )
-            
+
         # Update the cached metadata for this node
         self._cache_node_metadata(node_id, class_type)
-            
+
     def _cache_node_metadata(self, node_id, class_type):
         """Cache the metadata for a specific node"""
         if not self.current_prompt_id or not node_id or not class_type:
             return
-            
+
         # Create a cache key combining node_id and class_type
         cache_key = f"{node_id}:{class_type}"
-        
+
         # Create a shallow copy of the node's metadata
         node_metadata = {}
         current_metadata = self.prompt_metadata[self.current_prompt_id]
-        
+
         for category in self.metadata_categories:
             if category in current_metadata and node_id in current_metadata[category]:
                 if category not in node_metadata:
                     node_metadata[category] = {}
                 node_metadata[category][node_id] = current_metadata[category][node_id]
-        
+
         # Save new metadata or clear stale cache entries when metadata is empty
         if any(node_metadata.values()):
             self.node_cache[cache_key] = node_metadata
         else:
             self.node_cache.pop(cache_key, None)
-    
+
     def clear_unused_cache(self):
         """Clean up node_cache entries that are no longer in use"""
         # Collect all node_ids currently in prompt_metadata
@@ -210,18 +218,18 @@ class MetadataRegistry:
             for category in self.metadata_categories:
                 if category in prompt_data:
                     active_node_ids.update(prompt_data[category].keys())
-        
+
         # Find cache keys that are no longer needed
         keys_to_remove = []
         for cache_key in self.node_cache:
-            node_id = cache_key.split(':')[0]
+            node_id = cache_key.split(":")[0]
             if node_id not in active_node_ids:
                 keys_to_remove.append(cache_key)
-        
+
         # Remove cache entries that are no longer needed
         for key in keys_to_remove:
             del self.node_cache[key]
-    
+
     def clear_metadata(self, prompt_id=None):
         """Clear metadata for a specific prompt or reset all data"""
         if prompt_id is not None:
@@ -232,25 +240,25 @@ class MetadataRegistry:
         else:
             # Reset all data
             self._reset()
-    
+
     def get_first_decoded_image(self, prompt_id=None):
         """Get the first decoded image result"""
         key = prompt_id if prompt_id is not None else self.current_prompt_id
         if key not in self.prompt_metadata:
             return None
-            
+
         metadata = self.prompt_metadata[key]
         if IMAGES in metadata and "first_decode" in metadata[IMAGES]:
             image_data = metadata[IMAGES]["first_decode"]["image"]
-            
+
             # If it's an image batch or tuple, handle various formats
             if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
                 # Return first element of list/tuple
                 return image_data[0]
-            
+
             # If it's a tensor, return as is for processing in the route handler
             return image_data
-        
+
         # If no image is found in the current metadata, try to find it in the cache
         # This handles the case where VAEDecode was cached by ComfyUI and not executed
         prompt_obj = metadata.get("current_prompt")
@@ -270,8 +278,11 @@ class MetadataRegistry:
                             if IMAGES in cached_data and node_id in cached_data[IMAGES]:
                                 image_data = cached_data[IMAGES][node_id]["image"]
                                 # Handle different image formats
-                                if isinstance(image_data, (list, tuple)) and len(image_data) > 0:
+                                if (
+                                    isinstance(image_data, (list, tuple))
+                                    and len(image_data) > 0
+                                ):
                                     return image_data[0]
                                 return image_data
-        
+
         return None

py/metadata_collector/node_extractors.py

@@ -1,4 +1,6 @@
+import json
 import os
+import re
 
 from .constants import MODELS, PROMPTS, SAMPLING, LORAS, SIZE, IMAGES, IS_SAMPLER
 
@@ -427,6 +429,75 @@ class ImageSizeExtractor(NodeMetadataExtractor):
             "node_id": node_id
         }
 
+class RgthreePowerLoraLoaderExtractor(NodeMetadataExtractor):
+    """Extract LoRA metadata from rgthree Power Lora Loader.
+
+    The node passes LoRAs as dynamic kwargs: LORA_1, LORA_2, ... each containing
+    {'on': bool, 'lora': filename, 'strength': float, 'strengthTwo': float}.
+    """
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs:
+            return
+
+        active_loras = []
+        for key, value in inputs.items():
+            if not key.upper().startswith('LORA_'):
+                continue
+            if not isinstance(value, dict):
+                continue
+            if not value.get('on') or not value.get('lora'):
+                continue
+            lora_name = os.path.splitext(os.path.basename(value['lora']))[0]
+            active_loras.append({
+                "name": lora_name,
+                "strength": round(float(value.get('strength', 1.0)), 2)
+            })
+
+        if active_loras:
+            metadata[LORAS][node_id] = {
+                "lora_list": active_loras,
+                "node_id": node_id
+            }
+
+
+class TensorRTLoaderExtractor(NodeMetadataExtractor):
+    """Extract checkpoint metadata from TensorRT Loader.
+
+    extract() parses the engine filename from 'unet_name' as a best-effort
+    fallback (strips profile suffix after '_$' and counter suffix).
+
+    update() checks if the output MODEL has attachments["source_model"]
+    set by the node (NubeBuster fork) and overrides with the real name.
+    Vanilla TRT doesn't set this — the filename parse stands.
+    """
+    @staticmethod
+    def extract(node_id, inputs, outputs, metadata):
+        if not inputs or "unet_name" not in inputs:
+            return
+        unet_name = inputs.get("unet_name")
+        # Strip path and extension, then drop the $_profile suffix
+        model_name = os.path.splitext(os.path.basename(unet_name))[0]
+        if "_$" in model_name:
+            model_name = model_name[:model_name.index("_$")]
+        # Strip counter suffix (e.g. _00001_) left by ComfyUI's save path
+        model_name = re.sub(r'_\d+_?$', '', model_name)
+        _store_checkpoint_metadata(metadata, node_id, model_name)
+
+    @staticmethod
+    def update(node_id, outputs, metadata):
+        if not outputs or not isinstance(outputs, list) or len(outputs) == 0:
+            return
+        first_output = outputs[0]
+        if not isinstance(first_output, tuple) or len(first_output) < 1:
+            return
+        model = first_output[0]
+        # NubeBuster fork sets attachments["source_model"] on the ModelPatcher
+        source_model = getattr(model, 'attachments', {}).get("source_model")
+        if source_model:
+            _store_checkpoint_metadata(metadata, node_id, source_model)
+
+
 class LoraLoaderManagerExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -577,8 +648,6 @@ class SamplerCustomAdvancedExtractor(BaseSamplerExtractor):
         # Extract latent dimensions
         BaseSamplerExtractor.extract_latent_dimensions(node_id, inputs, metadata)
 
-import json
-
 class CLIPTextEncodeFluxExtractor(NodeMetadataExtractor):
     @staticmethod
     def extract(node_id, inputs, outputs, metadata):
@@ -715,8 +784,11 @@ NODE_EXTRACTORS = {
     "UnetLoaderGGUF": UNETLoaderExtractor,  # Updated to use dedicated extractor
     "LoraLoader": LoraLoaderExtractor,
     "LoraLoaderLM": LoraLoaderManagerExtractor,
+    "RgthreePowerLoraLoader": RgthreePowerLoraLoaderExtractor,
+    "TensorRTLoader": TensorRTLoaderExtractor,
     # Conditioning
     "CLIPTextEncode": CLIPTextEncodeExtractor,
+    "CLIPTextEncodeAttentionBias": CLIPTextEncodeExtractor,  # From https://github.com/silveroxides/ComfyUI_PromptAttention
     "PromptLM": CLIPTextEncodeExtractor,
     "CLIPTextEncodeFlux": CLIPTextEncodeFluxExtractor,  # Add CLIPTextEncodeFlux
     "WAS_Text_to_Conditioning": CLIPTextEncodeExtractor,

py/middleware/csp_middleware.py

@@ -4,15 +4,21 @@ from typing import Awaitable, Callable, Dict, List
 
 from aiohttp import web
 
+# Use wildcard for CivitAI to support their CDN subdomains (e.g., image-b2.civitai.com)
+# Security note: This is acceptable because:
+# 1. CSP img-src only controls image/video loading, not script execution
+# 2. All *.civitai.com subdomains are controlled by Civitai
+# 3. Explicit domain list would require constant updates as Civitai adds CDN nodes
 REMOTE_MEDIA_SOURCES = (
-    "https://image.civitai.com",
+    "https://*.civitai.com",
     "https://img.genur.art",
 )
 
 
 @web.middleware
 async def relax_csp_for_remote_media(
-    request: web.Request, handler: Callable[[web.Request], Awaitable[web.StreamResponse]]
+    request: web.Request,
+    handler: Callable[[web.Request], Awaitable[web.StreamResponse]],
 ) -> web.StreamResponse:
     """Allow LoRA Manager media previews to load from trusted remote domains.
 
@@ -43,7 +49,9 @@ async def relax_csp_for_remote_media(
             directive_order.append(name)
         directives[name] = values
 
-    def merge_sources(name: str, sources: List[str], defaults: List[str] | None = None) -> None:
+    def merge_sources(
+        name: str, sources: List[str], defaults: List[str] | None = None
+    ) -> None:
         existing = directives.get(name, list(defaults or []))
 
         for source in sources:

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

@@ -8,6 +8,7 @@ and tracks the cycle progress which persists across workflow save/load.
 
 import logging
 import os
+
 from ..utils.utils import get_lora_info
 
 logger = logging.getLogger(__name__)
@@ -54,8 +55,14 @@ class LoraCyclerLM:
         current_index = cycler_config.get("current_index", 1)  # 1-based
         model_strength = float(cycler_config.get("model_strength", 1.0))
         clip_strength = float(cycler_config.get("clip_strength", 1.0))
+        use_same_clip_strength = cycler_config.get("use_same_clip_strength", True)
+        use_preset_strength = cycler_config.get("use_preset_strength", False)
+        preset_strength_scale = float(cycler_config.get("preset_strength_scale", 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 +78,10 @@ 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 +103,99 @@ 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)
+
+                if use_preset_strength:
+                    lora_metadata = await lora_service.get_lora_metadata_by_filename(
+                        current_lora["file_name"]
+                    )
+                    if lora_metadata:
+                        recommended_strength = (
+                            lora_service.get_recommended_strength_from_lora_data(
+                                lora_metadata
+                            )
+                        )
+                        if recommended_strength is not None:
+                            model_strength = round(
+                                recommended_strength * preset_strength_scale, 2
+                            )
+
+                        if use_same_clip_strength:
+                            clip_strength = model_strength
+                        else:
+                            recommended_clip_strength = (
+                                lora_service.get_recommended_clip_strength_from_lora_data(
+                                    lora_metadata
+                                )
+                            )
+                            if recommended_clip_strength is not None:
+                                clip_strength = round(
+                                    recommended_clip_strength * preset_strength_scale, 2
+                                )
+                    elif use_same_clip_strength:
+                        clip_strength = model_strength
+                elif use_same_clip_strength:
+                    clip_strength = model_strength
+
+                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_loader.py

@@ -1,22 +1,138 @@
+import importlib
 import logging
 import re
-import comfy.utils # type: ignore
-import comfy.sd # type: ignore
+
+import comfy.sd  # type: ignore
+import comfy.utils  # type: ignore
+
 from ..utils.utils import get_lora_info_absolute
-from .utils import FlexibleOptionalInputType, any_type, extract_lora_name, get_loras_list, nunchaku_load_lora
+from .utils import (
+    FlexibleOptionalInputType,
+    any_type,
+    detect_nunchaku_model_kind,
+    extract_lora_name,
+    get_loras_list,
+    nunchaku_load_lora,
+)
 
 logger = logging.getLogger(__name__)
 
+
+def _get_nunchaku_load_qwen_loras():
+    try:
+        module = importlib.import_module(".nunchaku_qwen", __package__)
+    except ImportError as exc:
+        raise RuntimeError(
+            "Qwen-Image LoRA loading requires the ComfyUI runtime with its torch dependency available."
+        ) from exc
+    return module.nunchaku_load_qwen_loras
+
+
+def _collect_stack_entries(lora_stack):
+    entries = []
+    if not lora_stack:
+        return entries
+
+    for lora_path, model_strength, clip_strength in lora_stack:
+        lora_name = extract_lora_name(lora_path)
+        absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
+        entries.append({
+            "name": lora_name,
+            "absolute_path": absolute_lora_path,
+            "input_path": lora_path,
+            "model_strength": float(model_strength),
+            "clip_strength": float(clip_strength),
+            "trigger_words": trigger_words,
+        })
+    return entries
+
+
+def _collect_widget_entries(kwargs):
+    entries = []
+    for lora in get_loras_list(kwargs):
+        if not lora.get("active", False):
+            continue
+        lora_name = lora["name"]
+        model_strength = float(lora["strength"])
+        clip_strength = float(lora.get("clipStrength", model_strength))
+        lora_path, trigger_words = get_lora_info_absolute(lora_name)
+        entries.append({
+            "name": lora_name,
+            "absolute_path": lora_path,
+            "input_path": lora_path,
+            "model_strength": model_strength,
+            "clip_strength": clip_strength,
+            "trigger_words": trigger_words,
+        })
+    return entries
+
+
+def _format_loaded_loras(loaded_loras):
+    formatted_loras = []
+    for item in loaded_loras:
+        if item["include_clip_strength"]:
+            formatted_loras.append(
+                f"<lora:{item['name']}:{item['model_strength']}:{item['clip_strength']}>"
+            )
+        else:
+            formatted_loras.append(f"<lora:{item['name']}:{item['model_strength']}>")
+    return " ".join(formatted_loras)
+
+
+def _apply_entries(model, clip, lora_entries, nunchaku_model_kind):
+    loaded_loras = []
+    all_trigger_words = []
+
+    if nunchaku_model_kind == "qwen_image":
+        nunchaku_load_qwen_loras = _get_nunchaku_load_qwen_loras()
+        qwen_lora_configs = []
+        for entry in lora_entries:
+            qwen_lora_configs.append((entry["absolute_path"], entry["model_strength"]))
+            loaded_loras.append({
+                "name": entry["name"],
+                "model_strength": entry["model_strength"],
+                "clip_strength": entry["model_strength"],
+                "include_clip_strength": False,
+            })
+            all_trigger_words.extend(entry["trigger_words"])
+        if qwen_lora_configs:
+            model = nunchaku_load_qwen_loras(model, qwen_lora_configs)
+        return model, clip, loaded_loras, all_trigger_words
+
+    for entry in lora_entries:
+        if nunchaku_model_kind == "flux":
+            model = nunchaku_load_lora(model, entry["input_path"], entry["model_strength"])
+        else:
+            lora = comfy.utils.load_torch_file(entry["absolute_path"], safe_load=True)
+            model, clip = comfy.sd.load_lora_for_models(
+                model,
+                clip,
+                lora,
+                entry["model_strength"],
+                entry["clip_strength"],
+            )
+
+        include_clip_strength = nunchaku_model_kind is None and abs(entry["model_strength"] - entry["clip_strength"]) > 0.001
+        loaded_loras.append({
+            "name": entry["name"],
+            "model_strength": entry["model_strength"],
+            "clip_strength": entry["clip_strength"],
+            "include_clip_strength": include_clip_strength,
+        })
+        all_trigger_words.extend(entry["trigger_words"])
+
+    return model, clip, loaded_loras, all_trigger_words
+
+
 class LoraLoaderLM:
     NAME = "Lora Loader (LoraManager)"
     CATEGORY = "Lora Manager/loaders"
-    
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "model": ("MODEL",),
-                # "clip": ("CLIP",),
                 "text": ("AUTOCOMPLETE_TEXT_LORAS", {
                     "placeholder": "Search LoRAs to add...",
                     "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
@@ -28,114 +144,30 @@ class LoraLoaderLM:
     RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras"
-    
+
     def load_loras(self, model, text, **kwargs):
         """Loads multiple LoRAs based on the kwargs input and lora_stack."""
-        loaded_loras = []
-        all_trigger_words = []
-        
-        clip = kwargs.get('clip', None)
-        lora_stack = kwargs.get('lora_stack', None)
-        
-        # Check if model is a Nunchaku Flux model - simplified approach
-        is_nunchaku_model = False
-        
-        try:
-            model_wrapper = model.model.diffusion_model
-            # Check if model is a Nunchaku Flux model using only class name
-            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
-                is_nunchaku_model = True
-                logger.info("Detected Nunchaku Flux model")
-        except (AttributeError, TypeError):
-            # Not a model with the expected structure
-            pass
-        
-        # First process lora_stack if available
-        if lora_stack:
-            for lora_path, model_strength, clip_strength in lora_stack:
-                # Extract lora name and convert to absolute path
-                # lora_stack stores relative paths, but load_torch_file needs absolute paths
-                lora_name = extract_lora_name(lora_path)
-                absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
-                
-                # Apply the LoRA using the appropriate loader
-                if is_nunchaku_model:
-                    # Use our custom function for Flux models
-                    model = nunchaku_load_lora(model, lora_path, model_strength)
-                    # clip remains unchanged for Nunchaku models
-                else:
-                    # Use lower-level API to load LoRA directly without folder_paths validation
-                    lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
-                    model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-                
-                all_trigger_words.extend(trigger_words)
-                # Add clip strength to output if different from model strength (except for Nunchaku models)
-                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-                else:
-                    loaded_loras.append(f"{lora_name}: {model_strength}")
-        
-        # Then process loras from kwargs with support for both old and new formats
-        loras_list = get_loras_list(kwargs)
-        for lora in loras_list:
-            if not lora.get('active', False):
-                continue
-                
-            lora_name = lora['name']
-            model_strength = float(lora['strength'])
-            # Get clip strength - use model strength as default if not specified
-            clip_strength = float(lora.get('clipStrength', model_strength))
-            
-            # Get lora path and trigger words
-            lora_path, trigger_words = get_lora_info_absolute(lora_name)
-            
-            # Apply the LoRA using the appropriate loader
-            if is_nunchaku_model:
-                # For Nunchaku models, use our custom function
-                model = nunchaku_load_lora(model, lora_path, model_strength)
-                # clip remains unchanged
-            else:
-                # Use lower-level API to load LoRA directly without folder_paths validation
-                lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
-                model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-            
-            # Include clip strength in output if different from model strength and not a Nunchaku model
-            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-            else:
-                loaded_loras.append(f"{lora_name}: {model_strength}")
-            
-            # Add trigger words to collection
-            all_trigger_words.extend(trigger_words)
-        
-        # use ',, ' to separate trigger words for group mode
-        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-        
-        # Format loaded_loras with support for both formats
-        formatted_loras = []
-        for item in loaded_loras:
-            parts = item.split(":")
-            lora_name = parts[0]
-            strength_parts = parts[1].strip().split(",")
-            
-            if len(strength_parts) > 1:
-                # Different model and clip strengths
-                model_str = strength_parts[0].strip()
-                clip_str = strength_parts[1].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
-            else:
-                # Same strength for both
-                model_str = strength_parts[0].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
-                
-        formatted_loras_text = " ".join(formatted_loras)
+        del text
+        clip = kwargs.get("clip", None)
+        lora_entries = _collect_stack_entries(kwargs.get("lora_stack", None))
+        lora_entries.extend(_collect_widget_entries(kwargs))
 
+        nunchaku_model_kind = detect_nunchaku_model_kind(model)
+        if nunchaku_model_kind == "flux":
+            logger.info("Detected Nunchaku Flux model")
+        elif nunchaku_model_kind == "qwen_image":
+            logger.info("Detected Nunchaku Qwen-Image model")
+
+        model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
+        trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
+        formatted_loras_text = _format_loaded_loras(loaded_loras)
         return (model, clip, trigger_words_text, formatted_loras_text)
 
+
 class LoraTextLoaderLM:
     NAME = "LoRA Text Loader (LoraManager)"
     CATEGORY = "Lora Manager/loaders"
-    
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
@@ -143,131 +175,55 @@ class LoraTextLoaderLM:
                 "model": ("MODEL",),
                 "lora_syntax": ("STRING", {
                     "forceInput": True,
-                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation"
+                    "tooltip": "Format: <lora:lora_name:strength> separated by spaces or punctuation",
                 }),
             },
             "optional": {
                 "clip": ("CLIP",),
                 "lora_stack": ("LORA_STACK",),
-            }
+            },
         }
 
     RETURN_TYPES = ("MODEL", "CLIP", "STRING", "STRING")
     RETURN_NAMES = ("MODEL", "CLIP", "trigger_words", "loaded_loras")
     FUNCTION = "load_loras_from_text"
-    
+
     def parse_lora_syntax(self, text):
         """Parse LoRA syntax from text input."""
-        # Pattern to match <lora:name:strength> or <lora:name:model_strength:clip_strength>
-        pattern = r'<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>'
+        pattern = r"<lora:([^:>]+):([^:>]+)(?::([^:>]+))?>"
         matches = re.findall(pattern, text, re.IGNORECASE)
-        
+
         loras = []
         for match in matches:
-            lora_name = match[0]
             model_strength = float(match[1])
-            clip_strength = float(match[2]) if match[2] else model_strength
-            
             loras.append({
-                'name': lora_name,
-                'model_strength': model_strength,
-                'clip_strength': clip_strength
+                "name": match[0],
+                "model_strength": model_strength,
+                "clip_strength": float(match[2]) if match[2] else model_strength,
             })
-        
         return loras
-    
+
     def load_loras_from_text(self, model, lora_syntax, clip=None, lora_stack=None):
         """Load LoRAs based on text syntax input."""
-        loaded_loras = []
-        all_trigger_words = []
-        
-        # Check if model is a Nunchaku Flux model - simplified approach
-        is_nunchaku_model = False
-        
-        try:
-            model_wrapper = model.model.diffusion_model
-            # Check if model is a Nunchaku Flux model using only class name
-            if model_wrapper.__class__.__name__ == "ComfyFluxWrapper":
-                is_nunchaku_model = True
-                logger.info("Detected Nunchaku Flux model")
-        except (AttributeError, TypeError):
-            # Not a model with the expected structure
-            pass
-        
-        # First process lora_stack if available
-        if lora_stack:
-            for lora_path, model_strength, clip_strength in lora_stack:
-                # Extract lora name and convert to absolute path
-                # lora_stack stores relative paths, but load_torch_file needs absolute paths
-                lora_name = extract_lora_name(lora_path)
-                absolute_lora_path, trigger_words = get_lora_info_absolute(lora_name)
-                
-                # Apply the LoRA using the appropriate loader
-                if is_nunchaku_model:
-                    # Use our custom function for Flux models
-                    model = nunchaku_load_lora(model, lora_path, model_strength)
-                    # clip remains unchanged for Nunchaku models
-                else:
-                    # Use lower-level API to load LoRA directly without folder_paths validation
-                    lora = comfy.utils.load_torch_file(absolute_lora_path, safe_load=True)
-                    model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-                
-                all_trigger_words.extend(trigger_words)
-                # Add clip strength to output if different from model strength (except for Nunchaku models)
-                if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                    loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-                else:
-                    loaded_loras.append(f"{lora_name}: {model_strength}")
-        
-        # Parse and process LoRAs from text syntax
-        parsed_loras = self.parse_lora_syntax(lora_syntax)
-        for lora in parsed_loras:
-            lora_name = lora['name']
-            model_strength = lora['model_strength']
-            clip_strength = lora['clip_strength']
-            
-            # Get lora path and trigger words
-            lora_path, trigger_words = get_lora_info_absolute(lora_name)
-            
-            # Apply the LoRA using the appropriate loader
-            if is_nunchaku_model:
-                # For Nunchaku models, use our custom function
-                model = nunchaku_load_lora(model, lora_path, model_strength)
-                # clip remains unchanged
-            else:
-                # Use lower-level API to load LoRA directly without folder_paths validation
-                lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
-                model, clip = comfy.sd.load_lora_for_models(model, clip, lora, model_strength, clip_strength)
-            
-            # Include clip strength in output if different from model strength and not a Nunchaku model
-            if not is_nunchaku_model and abs(model_strength - clip_strength) > 0.001:
-                loaded_loras.append(f"{lora_name}: {model_strength},{clip_strength}")
-            else:
-                loaded_loras.append(f"{lora_name}: {model_strength}")
-            
-            # Add trigger words to collection
-            all_trigger_words.extend(trigger_words)
-        
-        # use ',, ' to separate trigger words for group mode
+        lora_entries = _collect_stack_entries(lora_stack)
+        for lora in self.parse_lora_syntax(lora_syntax):
+            lora_path, trigger_words = get_lora_info_absolute(lora["name"])
+            lora_entries.append({
+                "name": lora["name"],
+                "absolute_path": lora_path,
+                "input_path": lora_path,
+                "model_strength": lora["model_strength"],
+                "clip_strength": lora["clip_strength"],
+                "trigger_words": trigger_words,
+            })
+
+        nunchaku_model_kind = detect_nunchaku_model_kind(model)
+        if nunchaku_model_kind == "flux":
+            logger.info("Detected Nunchaku Flux model")
+        elif nunchaku_model_kind == "qwen_image":
+            logger.info("Detected Nunchaku Qwen-Image model")
+
+        model, clip, loaded_loras, all_trigger_words = _apply_entries(model, clip, lora_entries, nunchaku_model_kind)
         trigger_words_text = ",, ".join(all_trigger_words) if all_trigger_words else ""
-
-        # Format loaded_loras with support for both formats
-        formatted_loras = []
-        for item in loaded_loras:
-            parts = item.split(":")
-            lora_name = parts[0].strip()
-            strength_parts = parts[1].strip().split(",")
-            
-            if len(strength_parts) > 1:
-                # Different model and clip strengths
-                model_str = strength_parts[0].strip()
-                clip_str = strength_parts[1].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}:{clip_str}>")
-            else:
-                # Same strength for both
-                model_str = strength_parts[0].strip()
-                formatted_loras.append(f"<lora:{lora_name}:{model_str}>")
-                
-        formatted_loras_text = " ".join(formatted_loras)
-
-        return (model, clip, trigger_words_text, formatted_loras_text)
+        formatted_loras_text = _format_loaded_loras(loaded_loras)
+        return (model, clip, trigger_words_text, formatted_loras_text)

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/lora_stack_combiner.py

@@ -0,0 +1,26 @@
+class LoraStackCombinerLM:
+    NAME = "Lora Stack Combiner (LoraManager)"
+    CATEGORY = "Lora Manager/stackers"
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "lora_stack_a": ("LORA_STACK",),
+                "lora_stack_b": ("LORA_STACK",),
+            },
+        }
+
+    RETURN_TYPES = ("LORA_STACK",)
+    RETURN_NAMES = ("LORA_STACK",)
+    FUNCTION = "combine_stacks"
+
+    def combine_stacks(self, lora_stack_a, lora_stack_b):
+        combined_stack = []
+
+        if lora_stack_a:
+            combined_stack.extend(lora_stack_a)
+        if lora_stack_b:
+            combined_stack.extend(lora_stack_b)
+
+        return (combined_stack,)

py/nodes/nunchaku_qwen.py

@@ -0,0 +1,570 @@
+from __future__ import annotations
+
+"""Qwen-Image LoRA support for Nunchaku models.
+
+Portions of the LoRA mapping/application logic in this file are adapted from
+ComfyUI-QwenImageLoraLoader by GitHub user ussoewwin:
+https://github.com/ussoewwin/ComfyUI-QwenImageLoraLoader
+
+The upstream project is licensed under Apache License 2.0.
+"""
+
+import copy
+import logging
+import os
+import re
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple, Union
+
+import comfy.utils  # type: ignore
+import folder_paths  # type: ignore
+import torch
+import torch.nn as nn
+from safetensors import safe_open
+
+from nunchaku.lora.flux.nunchaku_converter import (
+    pack_lowrank_weight,
+    unpack_lowrank_weight,
+)
+
+logger = logging.getLogger(__name__)
+
+KEY_MAPPING = [
+    (re.compile(r"^(layers)[._](\d+)[._]attention[._]to[._]([qkv])$"), r"\1.\2.attention.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(layers)[._](\d+)[._]feed_forward[._](w1|w3)$"), r"\1.\2.feed_forward.net.0.proj", "glu", lambda m: m.group(3)),
+    (re.compile(r"^(layers)[._](\d+)[._]feed_forward[._]w2$"), r"\1.\2.feed_forward.net.2", "regular", None),
+    (re.compile(r"^(layers)[._](\d+)[._](.*)$"), r"\1.\2.\3", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._](q|k|v)[._]proj$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]add[._](q|k|v)[._]proj$"), r"\1.\2.attn.add_qkv_proj", "add_qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj[._]context$"), r"\1.\2.attn.to_add_out", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]out[._]proj$"), r"\1.\2.attn.to_out.0", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out.0", "regular", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]([qkv])$"), r"\1.\2.attn.to_qkv", "qkv", lambda m: m.group(3).upper()),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]attn[._]to[._]out$"), r"\1.\2.attn.to_out", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_fc1", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff[._]net[._]2$"), r"\1.\2.mlp_fc2", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]0(?:[._]proj)?$"), r"\1.\2.mlp_context_fc1", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]ff_context[._]net[._]2$"), r"\1.\2.mlp_context_fc2", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](0)[._](proj)$"), r"\1.\2.\3.\4.\5.\6", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mlp)[._](net)[._](2)$"), r"\1.\2.\3.\4.\5", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](img_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._](txt_mod)[._](1)$"), r"\1.\2.\3.\4", "regular", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]out$"), r"\1.\2.proj_out", "single_proj_out", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]proj[._]mlp$"), r"\1.\2.mlp_fc1", "regular", None),
+    (re.compile(r"^(single_transformer_blocks)[._](\d+)[._]norm[._]linear$"), r"\1.\2.norm.linear", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1[._]linear$"), r"\1.\2.norm1.linear", "regular", None),
+    (re.compile(r"^(transformer_blocks)[._](\d+)[._]norm1_context[._]linear$"), r"\1.\2.norm1_context.linear", "regular", None),
+    (re.compile(r"^(img_in)$"), r"\1", "regular", None),
+    (re.compile(r"^(txt_in)$"), r"\1", "regular", None),
+    (re.compile(r"^(proj_out)$"), r"\1", "regular", None),
+    (re.compile(r"^(norm_out)[._](linear)$"), r"\1.\2", "regular", None),
+    (re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_1)$"), r"\1.\2.\3", "regular", None),
+    (re.compile(r"^(time_text_embed)[._](timestep_embedder)[._](linear_2)$"), r"\1.\2.\3", "regular", None),
+]
+
+_RE_LORA_SUFFIX = re.compile(r"\.(?P<tag>lora(?:[._](?:A|B|down|up)))(?:\.[^.]+)*\.weight$")
+_RE_ALPHA_SUFFIX = re.compile(r"\.(?:alpha|lora_alpha)(?:\.[^.]+)*$")
+
+
+def _rename_layer_underscore_layer_name(old_name: str) -> str:
+    rules = [
+        (r"_(\d+)_attn_to_out_(\d+)", r".\1.attn.to_out.\2"),
+        (r"_(\d+)_img_mlp_net_(\d+)_proj", r".\1.img_mlp.net.\2.proj"),
+        (r"_(\d+)_txt_mlp_net_(\d+)_proj", r".\1.txt_mlp.net.\2.proj"),
+        (r"_(\d+)_img_mlp_net_(\d+)", r".\1.img_mlp.net.\2"),
+        (r"_(\d+)_txt_mlp_net_(\d+)", r".\1.txt_mlp.net.\2"),
+        (r"_(\d+)_img_mod_(\d+)", r".\1.img_mod.\2"),
+        (r"_(\d+)_txt_mod_(\d+)", r".\1.txt_mod.\2"),
+        (r"_(\d+)_attn_", r".\1.attn."),
+    ]
+    new_name = old_name
+    for pattern, replacement in rules:
+        new_name = re.sub(pattern, replacement, new_name)
+    return new_name
+
+
+def _is_indexable_module(module):
+    return isinstance(module, (nn.ModuleList, nn.Sequential, list, tuple))
+
+
+def _get_module_by_name(model: nn.Module, name: str) -> Optional[nn.Module]:
+    if not name:
+        return model
+    module = model
+    for part in name.split("."):
+        if not part:
+            continue
+        if hasattr(module, part):
+            module = getattr(module, part)
+        elif part.isdigit() and _is_indexable_module(module):
+            try:
+                module = module[int(part)]
+            except (IndexError, TypeError):
+                return None
+        else:
+            return None
+    return module
+
+
+def _resolve_module_name(model: nn.Module, name: str) -> Tuple[str, Optional[nn.Module]]:
+    module = _get_module_by_name(model, name)
+    if module is not None:
+        return name, module
+
+    replacements = [
+        (".attn.to_out.0", ".attn.to_out"),
+        (".attention.to_qkv", ".attention.qkv"),
+        (".attention.to_out.0", ".attention.out"),
+        (".feed_forward.net.0.proj", ".feed_forward.w13"),
+        (".feed_forward.net.2", ".feed_forward.w2"),
+        (".ff.net.0.proj", ".mlp_fc1"),
+        (".ff.net.2", ".mlp_fc2"),
+        (".ff_context.net.0.proj", ".mlp_context_fc1"),
+        (".ff_context.net.2", ".mlp_context_fc2"),
+    ]
+    for src, dst in replacements:
+        if src in name:
+            alt = name.replace(src, dst)
+            module = _get_module_by_name(model, alt)
+            if module is not None:
+                return alt, module
+    return name, None
+
+
+def _classify_and_map_key(key: str) -> Optional[Tuple[str, str, Optional[str], str]]:
+    normalized = key
+    if normalized.startswith("transformer."):
+        normalized = normalized[len("transformer."):]
+    if normalized.startswith("diffusion_model."):
+        normalized = normalized[len("diffusion_model."):]
+    if normalized.startswith("lora_unet_"):
+        normalized = _rename_layer_underscore_layer_name(normalized[len("lora_unet_"):])
+
+    match = _RE_LORA_SUFFIX.search(normalized)
+    if match:
+        tag = match.group("tag")
+        base = normalized[:match.start()]
+        ab = "A" if ("lora_A" in tag or tag.endswith(".A") or "down" in tag) else "B"
+    else:
+        match = _RE_ALPHA_SUFFIX.search(normalized)
+        if not match:
+            return None
+        base = normalized[:match.start()]
+        ab = "alpha"
+
+    for pattern, template, group, comp_fn in KEY_MAPPING:
+        key_match = pattern.match(base)
+        if key_match:
+            return group, key_match.expand(template), comp_fn(key_match) if comp_fn else None, ab
+    return None
+
+
+def _detect_lora_format(lora_state_dict: Dict[str, torch.Tensor]) -> bool:
+    standard_patterns = (
+        ".lora_up.",
+        ".lora_down.",
+        ".lora_A.",
+        ".lora_B.",
+        ".lora.up.",
+        ".lora.down.",
+        ".lora.A.",
+        ".lora.B.",
+    )
+    return any(pattern in key for key in lora_state_dict for pattern in standard_patterns)
+
+
+def _load_lora_state_dict(path_or_dict: Union[str, Path, Dict[str, torch.Tensor]]) -> Dict[str, torch.Tensor]:
+    if isinstance(path_or_dict, dict):
+        return path_or_dict
+    path = Path(path_or_dict)
+    if path.suffix == ".safetensors":
+        state_dict: Dict[str, torch.Tensor] = {}
+        with safe_open(path, framework="pt", device="cpu") as handle:
+            for key in handle.keys():
+                state_dict[key] = handle.get_tensor(key)
+        return state_dict
+    return comfy.utils.load_torch_file(str(path), safe_load=True)
+
+
+def _fuse_glu_lora(glu_weights: Dict[str, torch.Tensor]) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
+    if "w1_A" not in glu_weights or "w3_A" not in glu_weights:
+        return None, None, None
+    a_w1, b_w1 = glu_weights["w1_A"], glu_weights["w1_B"]
+    a_w3, b_w3 = glu_weights["w3_A"], glu_weights["w3_B"]
+    if a_w1.shape[1] != a_w3.shape[1]:
+        return None, None, None
+    a_fused = torch.cat([a_w1, a_w3], dim=0)
+    out1, out3 = b_w1.shape[0], b_w3.shape[0]
+    rank1, rank3 = b_w1.shape[1], b_w3.shape[1]
+    b_fused = torch.zeros(out1 + out3, rank1 + rank3, dtype=b_w1.dtype, device=b_w1.device)
+    b_fused[:out1, :rank1] = b_w1
+    b_fused[out1:, rank1:] = b_w3
+    return a_fused, b_fused, glu_weights.get("w1_alpha")
+
+
+def _fuse_qkv_lora(qkv_weights: Dict[str, torch.Tensor], model: Optional[nn.Module] = None, base_key: Optional[str] = None) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
+    required_keys = ["Q_A", "Q_B", "K_A", "K_B", "V_A", "V_B"]
+    if not all(key in qkv_weights for key in required_keys):
+        return None, None, None
+    a_q, a_k, a_v = qkv_weights["Q_A"], qkv_weights["K_A"], qkv_weights["V_A"]
+    b_q, b_k, b_v = qkv_weights["Q_B"], qkv_weights["K_B"], qkv_weights["V_B"]
+    if not (a_q.shape == a_k.shape == a_v.shape):
+        return None, None, None
+    if not (b_q.shape[1] == b_k.shape[1] == b_v.shape[1]):
+        return None, None, None
+
+    out_features = None
+    if model is not None and base_key is not None:
+        _, module = _resolve_module_name(model, base_key)
+        out_features = getattr(module, "out_features", None) if module is not None else None
+
+    alpha_fused = None
+    alpha_q = qkv_weights.get("Q_alpha")
+    alpha_k = qkv_weights.get("K_alpha")
+    alpha_v = qkv_weights.get("V_alpha")
+    if alpha_q is not None and alpha_k is not None and alpha_v is not None and alpha_q.item() == alpha_k.item() == alpha_v.item():
+        alpha_fused = alpha_q
+
+    a_fused = torch.cat([a_q, a_k, a_v], dim=0)
+    rank = b_q.shape[1]
+    out_q, out_k, out_v = b_q.shape[0], b_k.shape[0], b_v.shape[0]
+    total_out = out_features if out_features is not None else out_q + out_k + out_v
+    b_fused = torch.zeros(total_out, 3 * rank, dtype=b_q.dtype, device=b_q.device)
+    b_fused[:out_q, :rank] = b_q
+    b_fused[out_q:out_q + out_k, rank:2 * rank] = b_k
+    b_fused[out_q + out_k:out_q + out_k + out_v, 2 * rank:] = b_v
+    return a_fused, b_fused, alpha_fused
+
+
+def _handle_proj_out_split(lora_dict: Dict[str, Dict[str, torch.Tensor]], base_key: str, model: nn.Module) -> Tuple[Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]], List[str]]:
+    result: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
+    consumed: List[str] = []
+    match = re.search(r"single_transformer_blocks\.(\d+)", base_key)
+    if not match or base_key not in lora_dict:
+        return result, consumed
+    block_idx = match.group(1)
+    block = _get_module_by_name(model, f"single_transformer_blocks.{block_idx}")
+    if block is None:
+        return result, consumed
+    a_full = lora_dict[base_key].get("A")
+    b_full = lora_dict[base_key].get("B")
+    alpha = lora_dict[base_key].get("alpha")
+    attn_to_out = getattr(getattr(block, "attn", None), "to_out", None)
+    mlp_fc2 = getattr(block, "mlp_fc2", None)
+    if a_full is None or b_full is None or attn_to_out is None or mlp_fc2 is None:
+        return result, consumed
+    attn_in = getattr(attn_to_out, "in_features", None)
+    mlp_in = getattr(mlp_fc2, "in_features", None)
+    if attn_in is None or mlp_in is None or a_full.shape[1] != attn_in + mlp_in:
+        return result, consumed
+    result[f"single_transformer_blocks.{block_idx}.attn.to_out"] = (a_full[:, :attn_in], b_full.clone(), alpha)
+    result[f"single_transformer_blocks.{block_idx}.mlp_fc2"] = (a_full[:, attn_in:], b_full.clone(), alpha)
+    consumed.append(base_key)
+    return result, consumed
+
+
+def _apply_lora_to_module(module: nn.Module, a_tensor: torch.Tensor, b_tensor: torch.Tensor, module_name: str, model: nn.Module) -> None:
+    if not hasattr(module, "in_features") or not hasattr(module, "out_features"):
+        raise ValueError(f"{module_name}: unsupported module without in/out features")
+    if a_tensor.shape[1] != module.in_features or b_tensor.shape[0] != module.out_features:
+        raise ValueError(f"{module_name}: LoRA shape mismatch")
+
+    if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
+        if not hasattr(module, "_lora_original_forward"):
+            module._lora_original_forward = module.forward
+        if not hasattr(module, "_nunchaku_lora_bundle"):
+            module._nunchaku_lora_bundle = []
+        module._nunchaku_lora_bundle.append((a_tensor, b_tensor))
+
+        def _awq_lora_forward(x, *args, **kwargs):
+            out = module._lora_original_forward(x, *args, **kwargs)
+            x_flat = x.reshape(-1, module.in_features)
+            for local_a, local_b in module._nunchaku_lora_bundle:
+                local_a = local_a.to(device=out.device, dtype=out.dtype)
+                local_b = local_b.to(device=out.device, dtype=out.dtype)
+                lora_term = (x_flat @ local_a.transpose(0, 1)) @ local_b.transpose(0, 1)
+                try:
+                    out = out + lora_term.reshape(out.shape)
+                except Exception:
+                    pass
+            return out
+
+        module.forward = _awq_lora_forward
+        if not hasattr(model, "_lora_slots"):
+            model._lora_slots = {}
+        model._lora_slots[module_name] = {"type": "awq_w4a16"}
+        return
+
+    if hasattr(module, "proj_down") and hasattr(module, "proj_up"):
+        proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
+        proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
+        base_rank = proj_down.shape[0] if proj_down.shape[1] == module.in_features else proj_down.shape[1]
+        if proj_down.shape[1] == module.in_features:
+            updated_down = torch.cat([proj_down, a_tensor], dim=0)
+            axis_down = 0
+        else:
+            updated_down = torch.cat([proj_down, a_tensor.T], dim=1)
+            axis_down = 1
+        updated_up = torch.cat([proj_up, b_tensor], dim=1)
+        module.proj_down.data = pack_lowrank_weight(updated_down, down=True)
+        module.proj_up.data = pack_lowrank_weight(updated_up, down=False)
+        module.rank = base_rank + a_tensor.shape[0]
+        if not hasattr(model, "_lora_slots"):
+            model._lora_slots = {}
+        model._lora_slots[module_name] = {
+            "type": "nunchaku",
+            "base_rank": base_rank,
+            "axis_down": axis_down,
+        }
+        return
+
+    if isinstance(module, nn.Linear):
+        if not hasattr(model, "_lora_slots"):
+            model._lora_slots = {}
+        if module_name not in model._lora_slots:
+            model._lora_slots[module_name] = {
+                "type": "linear",
+                "original_weight": module.weight.detach().cpu().clone(),
+            }
+        module.weight.data.add_((b_tensor @ a_tensor).to(dtype=module.weight.dtype, device=module.weight.device))
+        return
+
+    raise ValueError(f"{module_name}: unsupported module type {type(module)}")
+
+
+def reset_lora_v2(model: nn.Module) -> None:
+    slots = getattr(model, "_lora_slots", None)
+    if not slots:
+        return
+    for name, info in list(slots.items()):
+        module = _get_module_by_name(model, name)
+        if module is None:
+            continue
+        module_type = info.get("type", "nunchaku")
+        if module_type == "nunchaku":
+            base_rank = info["base_rank"]
+            proj_down = unpack_lowrank_weight(module.proj_down.data, down=True)
+            proj_up = unpack_lowrank_weight(module.proj_up.data, down=False)
+            if info.get("axis_down", 0) == 0:
+                proj_down = proj_down[:base_rank, :].clone()
+            else:
+                proj_down = proj_down[:, :base_rank].clone()
+            proj_up = proj_up[:, :base_rank].clone()
+            module.proj_down.data = pack_lowrank_weight(proj_down, down=True)
+            module.proj_up.data = pack_lowrank_weight(proj_up, down=False)
+            module.rank = base_rank
+        elif module_type == "linear" and "original_weight" in info:
+            module.weight.data.copy_(info["original_weight"].to(device=module.weight.device, dtype=module.weight.dtype))
+        elif module_type == "awq_w4a16":
+            if hasattr(module, "_lora_original_forward"):
+                module.forward = module._lora_original_forward
+            for attr in ("_lora_original_forward", "_nunchaku_lora_bundle"):
+                if hasattr(module, attr):
+                    delattr(module, attr)
+    model._lora_slots = {}
+
+
+def compose_loras_v2(model: nn.Module, lora_configs: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]], apply_awq_mod: bool = True) -> bool:
+    del apply_awq_mod  # retained for interface compatibility
+    reset_lora_v2(model)
+    aggregated_weights: Dict[str, List[Dict[str, object]]] = defaultdict(list)
+    saw_supported_format = False
+    unresolved_targets = 0
+
+    for index, (path_or_dict, strength) in enumerate(lora_configs):
+        if abs(strength) < 1e-5:
+            continue
+        lora_name = str(path_or_dict) if not isinstance(path_or_dict, dict) else f"lora_{index}"
+        lora_state_dict = _load_lora_state_dict(path_or_dict)
+        if not lora_state_dict or not _detect_lora_format(lora_state_dict):
+            logger.warning("Skipping unsupported Qwen LoRA: %s", lora_name)
+            continue
+        saw_supported_format = True
+
+        grouped_weights: Dict[str, Dict[str, torch.Tensor]] = defaultdict(dict)
+        for key, value in lora_state_dict.items():
+            parsed = _classify_and_map_key(key)
+            if parsed is None:
+                continue
+            group, base_key, component, ab = parsed
+            if component and ab:
+                grouped_weights[base_key][f"{component}_{ab}"] = value
+            else:
+                grouped_weights[base_key][ab] = value
+
+        processed_groups: Dict[str, Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]] = {}
+        handled: set[str] = set()
+        for base_key, weights in grouped_weights.items():
+            if base_key in handled:
+                continue
+            a_tensor = b_tensor = alpha = None
+            if "qkv" in base_key or "add_qkv_proj" in base_key:
+                a_tensor, b_tensor, alpha = _fuse_qkv_lora(weights, model=model, base_key=base_key)
+            elif "w1_A" in weights or "w3_A" in weights:
+                a_tensor, b_tensor, alpha = _fuse_glu_lora(weights)
+            elif ".proj_out" in base_key and "single_transformer_blocks" in base_key:
+                split_map, consumed = _handle_proj_out_split(grouped_weights, base_key, model)
+                processed_groups.update(split_map)
+                handled.update(consumed)
+                continue
+            else:
+                a_tensor, b_tensor, alpha = weights.get("A"), weights.get("B"), weights.get("alpha")
+            if a_tensor is not None and b_tensor is not None:
+                processed_groups[base_key] = (a_tensor, b_tensor, alpha)
+
+        for module_name, (a_tensor, b_tensor, alpha) in processed_groups.items():
+            aggregated_weights[module_name].append({
+                "A": a_tensor,
+                "B": b_tensor,
+                "alpha": alpha,
+                "strength": strength,
+            })
+
+    for module_name, weight_list in aggregated_weights.items():
+        resolved_name, module = _resolve_module_name(model, module_name)
+        if module is None:
+            logger.warning("Skipping unresolved Qwen LoRA target: %s", module_name)
+            unresolved_targets += 1
+            continue
+        all_a = []
+        all_b_scaled = []
+        for item in weight_list:
+            a_tensor = item["A"]
+            b_tensor = item["B"]
+            alpha = item["alpha"]
+            strength = float(item["strength"])
+            rank = a_tensor.shape[0]
+            scale = strength * ((alpha / rank) if alpha is not None else 1.0)
+            if module.__class__.__name__ == "AWQW4A16Linear" and hasattr(module, "qweight"):
+                target_dtype = torch.float16
+                target_device = module.qweight.device
+            elif hasattr(module, "proj_down"):
+                target_dtype = module.proj_down.dtype
+                target_device = module.proj_down.device
+            elif hasattr(module, "weight"):
+                target_dtype = module.weight.dtype
+                target_device = module.weight.device
+            else:
+                target_dtype = torch.float16
+                target_device = "cuda" if torch.cuda.is_available() else "cpu"
+            all_a.append(a_tensor.to(dtype=target_dtype, device=target_device))
+            all_b_scaled.append((b_tensor * scale).to(dtype=target_dtype, device=target_device))
+        if not all_a:
+            continue
+        _apply_lora_to_module(module, torch.cat(all_a, dim=0), torch.cat(all_b_scaled, dim=1), resolved_name, model)
+
+    slot_count = len(getattr(model, "_lora_slots", {}) or {})
+    logger.info(
+        "Qwen LoRA composition finished: requested=%d supported=%s applied_targets=%d unresolved=%d",
+        len(lora_configs),
+        saw_supported_format,
+        slot_count,
+        unresolved_targets,
+    )
+    return saw_supported_format
+
+
+class ComfyQwenImageWrapperLM(nn.Module):
+    def __init__(self, model: nn.Module, config=None, apply_awq_mod: bool = True):
+        super().__init__()
+        self.model = model
+        self.config = {} if config is None else config
+        self.dtype = next(model.parameters()).dtype
+        self.loras: List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]] = []
+        self._applied_loras: Optional[List[Tuple[Union[str, Path, Dict[str, torch.Tensor]], float]]] = None
+        self.apply_awq_mod = apply_awq_mod
+
+    def __getattr__(self, name):
+        try:
+            inner = object.__getattribute__(self, "_modules").get("model")
+        except (AttributeError, KeyError):
+            inner = None
+        if inner is None:
+            raise AttributeError(f"{type(self).__name__!s} has no attribute {name}")
+        if name == "model":
+            return inner
+        return getattr(inner, name)
+
+    def process_img(self, *args, **kwargs):
+        return self.model.process_img(*args, **kwargs)
+
+    def _ensure_composed(self):
+        if self._applied_loras != self.loras or (not self.loras and getattr(self.model, "_lora_slots", None)):
+            is_supported_format = compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
+            self._applied_loras = self.loras.copy()
+            has_slots = bool(getattr(self.model, "_lora_slots", None))
+            if self.loras and is_supported_format and not has_slots:
+                logger.warning("Qwen LoRA compose produced 0 target modules. Resetting and retrying once.")
+                reset_lora_v2(self.model)
+                compose_loras_v2(self.model, self.loras, apply_awq_mod=self.apply_awq_mod)
+                has_slots = bool(getattr(self.model, "_lora_slots", None))
+                logger.info("Qwen LoRA retry result: applied_targets=%d", len(getattr(self.model, "_lora_slots", {}) or {}))
+
+            offload_manager = getattr(self.model, "offload_manager", None)
+            if offload_manager is not None:
+                offload_settings = {
+                    "num_blocks_on_gpu": getattr(offload_manager, "num_blocks_on_gpu", 1),
+                    "use_pin_memory": getattr(offload_manager, "use_pin_memory", False),
+                }
+                logger.info(
+                    "Rebuilding Qwen offload manager after LoRA compose: num_blocks_on_gpu=%s use_pin_memory=%s",
+                    offload_settings["num_blocks_on_gpu"],
+                    offload_settings["use_pin_memory"],
+                )
+                self.model.set_offload(False)
+                self.model.set_offload(True, **offload_settings)
+
+    def forward(self, *args, **kwargs):
+        self._ensure_composed()
+        return self.model(*args, **kwargs)
+
+
+def _get_qwen_wrapper_and_transformer(model):
+    model_wrapper = model.model.diffusion_model
+    if hasattr(model_wrapper, "model") and hasattr(model_wrapper, "loras"):
+        transformer = model_wrapper.model
+        if transformer.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
+            return model_wrapper, transformer
+    if model_wrapper.__class__.__name__.endswith("NunchakuQwenImageTransformer2DModel"):
+        wrapped_model = ComfyQwenImageWrapperLM(model_wrapper, getattr(model_wrapper, "config", {}))
+        model.model.diffusion_model = wrapped_model
+        return wrapped_model, wrapped_model.model
+    raise TypeError(f"This LoRA loader only works with Nunchaku Qwen Image models, but got {type(model_wrapper).__name__}.")
+
+
+def nunchaku_load_qwen_loras(model, lora_configs: List[Tuple[str, float]], apply_awq_mod: bool = True):
+    model_wrapper, transformer = _get_qwen_wrapper_and_transformer(model)
+    model_wrapper.apply_awq_mod = apply_awq_mod
+
+    saved_config = None
+    if hasattr(model, "model") and hasattr(model.model, "model_config"):
+        saved_config = model.model.model_config
+        model.model.model_config = None
+
+    model_wrapper.model = None
+    try:
+        ret_model = copy.deepcopy(model)
+    finally:
+        if saved_config is not None:
+            model.model.model_config = saved_config
+        model_wrapper.model = transformer
+
+    ret_model_wrapper = ret_model.model.diffusion_model
+    if saved_config is not None:
+        ret_model.model.model_config = saved_config
+    ret_model_wrapper.model = transformer
+    ret_model_wrapper.apply_awq_mod = apply_awq_mod
+    ret_model_wrapper.loras = list(getattr(model_wrapper, "loras", []))
+
+    for lora_name, lora_strength in lora_configs:
+        lora_path = lora_name if os.path.isfile(lora_name) else folder_paths.get_full_path("loras", lora_name)
+        if not lora_path or not os.path.isfile(lora_path):
+            logger.warning("Skipping Qwen LoRA '%s' because it could not be found", lora_name)
+            continue
+        ret_model_wrapper.loras.append((lora_path, lora_strength))
+
+    return ret_model

py/nodes/prompt.py

@@ -1,15 +1,38 @@
+from __future__ import annotations
+
 from typing import Any
 import inspect
 
+from ..services.wildcard_service import (
+    contains_dynamic_syntax,
+    get_wildcard_service,
+    is_trigger_words_input,
+)
 
-class _AllContainer:
-    """Container that accepts any key for dynamic input validation."""
 
-    def __contains__(self, item):
-        return True
+class _PromptOptionalInputs:
+    """Lookup that preserves explicit optional inputs and dynamic trigger slots."""
 
-    def __getitem__(self, key):
-        return ("STRING", {"forceInput": True})
+    def __init__(self, explicit_inputs: dict[str, tuple[str, dict[str, Any]]]) -> None:
+        self._explicit_inputs = explicit_inputs
+
+    def __contains__(self, item: object) -> bool:
+        if not isinstance(item, str):
+            return False
+        return item in self._explicit_inputs or is_trigger_words_input(item)
+
+    def __getitem__(self, key: str) -> tuple[str, dict[str, Any]]:
+        if key in self._explicit_inputs:
+            return self._explicit_inputs[key]
+        if is_trigger_words_input(key):
+            return (
+                "STRING",
+                {
+                    "forceInput": True,
+                    "tooltip": "Trigger words to prepend. Connect to add more inputs.",
+                },
+            )
+        raise KeyError(key)
 
 
 class PromptLM:
@@ -20,12 +43,19 @@ class PromptLM:
     DESCRIPTION = (
         "Encodes a text prompt using a CLIP model into an embedding that can be used "
         "to guide the diffusion model towards generating specific images. "
-        "Supports dynamic trigger words inputs."
+        "Supports dynamic trigger words inputs and runtime wildcard expansion."
     )
 
     @classmethod
     def INPUT_TYPES(cls):
-        dyn_inputs = {
+        optional_inputs: dict[str, tuple[str, dict[str, Any]]] = {
+            "seed": (
+                "INT",
+                {
+                    "forceInput": True,
+                    "tooltip": "Optional seed for wildcard generation. Leave unconnected for non-deterministic wildcard expansion.",
+                },
+            ),
             "trigger_words1": (
                 "STRING",
                 {
@@ -35,10 +65,9 @@ class PromptLM:
             ),
         }
 
-        # Bypass validation for dynamic inputs during graph execution
         stack = inspect.stack()
         if len(stack) > 2 and stack[2].function == "get_input_info":
-            dyn_inputs = _AllContainer()
+            optional_inputs = _PromptOptionalInputs(optional_inputs)  # type: ignore[assignment]
 
         return {
             "required": {
@@ -46,8 +75,8 @@ class PromptLM:
                     "AUTOCOMPLETE_TEXT_PROMPT,STRING",
                     {
                         "widgetType": "AUTOCOMPLETE_TEXT_PROMPT",
-                        "placeholder": "Enter prompt... /char, /artist for quick tag search",
-                        "tooltip": "The text to be encoded.",
+                        "placeholder": "Enter prompt... /character, /artist, /wildcard for quick search",
+                        "tooltip": "The text to be encoded. Wildcard references inserted with /wildcard are expanded at runtime.",
                     },
                 ),
                 "clip": (
@@ -55,7 +84,7 @@ class PromptLM:
                     {"tooltip": "The CLIP model used for encoding the text."},
                 ),
             },
-            "optional": dyn_inputs,
+            "optional": optional_inputs,
         }
 
     RETURN_TYPES = ("CONDITIONING", "STRING")
@@ -65,20 +94,39 @@ class PromptLM:
     )
     FUNCTION = "encode"
 
-    def encode(self, text: str, clip: Any, **kwargs):
-        # Collect all trigger words from dynamic inputs
+    @classmethod
+    def IS_CHANGED(
+        cls,
+        text: str,
+        clip: Any | None = None,
+        seed: int | None = None,
+        **kwargs: Any,
+    ):
+        del clip, kwargs
+        if contains_dynamic_syntax(text) and seed is None:
+            return float("NaN")
+        return False
+
+    def encode(
+        self,
+        text: str,
+        clip: Any,
+        seed: int | None = None,
+        **kwargs: Any,
+    ):
+        expanded_text = get_wildcard_service().expand_text(text, seed=seed)
+
         trigger_words = []
         for key, value in kwargs.items():
-            if key.startswith("trigger_words") and value:
+            if is_trigger_words_input(key) and value:
                 trigger_words.append(value)
 
-        # Build final prompt
         if trigger_words:
-            prompt = ", ".join(trigger_words + [text])
+            prompt = ", ".join(trigger_words + [expanded_text])
         else:
-            prompt = text
+            prompt = expanded_text
 
         from nodes import CLIPTextEncode  # type: ignore
 
         conditioning = CLIPTextEncode().encode(clip, prompt)[0]
-        return (conditioning, prompt)
+        return (conditioning, prompt)

py/nodes/save_image.py

@@ -1,8 +1,9 @@
 import json
 import os
 import re
+from typing import Any, Dict, Optional
 import numpy as np
-import folder_paths # type: ignore
+import folder_paths  # type: ignore
 from ..services.service_registry import ServiceRegistry
 from ..metadata_collector.metadata_processor import MetadataProcessor
 from ..metadata_collector import get_metadata
@@ -12,6 +13,7 @@ import logging
 
 logger = logging.getLogger(__name__)
 
+
 class SaveImageLM:
     NAME = "Save Image (LoraManager)"
     CATEGORY = "Lora Manager/utils"
@@ -23,42 +25,67 @@ class SaveImageLM:
         self.prefix_append = ""
         self.compress_level = 4
         self.counter = 0
-    
+
     # Add pattern format regex for filename substitution
     pattern_format = re.compile(r"(%[^%]+%)")
-    
+
     @classmethod
     def INPUT_TYPES(cls):
         return {
             "required": {
                 "images": ("IMAGE",),
-                "filename_prefix": ("STRING", {
-                    "default": "ComfyUI", 
-                    "tooltip": "Base filename for saved images. Supports format patterns like %seed%, %width%, %height%, %model%, etc."
-                }),
-                "file_format": (["png", "jpeg", "webp"], {
-                    "tooltip": "Image format to save as. PNG preserves quality, JPEG is smaller, WebP balances size and quality."
-                }),
+                "filename_prefix": (
+                    "STRING",
+                    {
+                        "default": "ComfyUI",
+                        "tooltip": "Base filename for saved images. Supports format patterns like %seed%, %width%, %height%, %model%, etc.",
+                    },
+                ),
+                "file_format": (
+                    ["png", "jpeg", "webp"],
+                    {
+                        "tooltip": "Image format to save as. PNG preserves quality, JPEG is smaller, WebP balances size and quality."
+                    },
+                ),
             },
             "optional": {
-                "lossless_webp": ("BOOLEAN", {
-                    "default": False,
-                    "tooltip": "When enabled, saves WebP images with lossless compression. Results in larger files but no quality loss."
-                }),
-                "quality": ("INT", {
-                    "default": 100, 
-                    "min": 1, 
-                    "max": 100,
-                    "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files."
-                }),
-                "embed_workflow": ("BOOLEAN", {
-                    "default": False,
-                    "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats."
-                }),
-                "add_counter_to_filename": ("BOOLEAN", {
-                    "default": True,
-                    "tooltip": "Adds an incremental counter to filenames to prevent overwriting previous images."
-                }),
+                "lossless_webp": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "When enabled, saves WebP images with lossless compression. Results in larger files but no quality loss.",
+                    },
+                ),
+                "quality": (
+                    "INT",
+                    {
+                        "default": 100,
+                        "min": 1,
+                        "max": 100,
+                        "tooltip": "Compression quality for JPEG and lossy WebP formats (1-100). Higher values mean better quality but larger files.",
+                    },
+                ),
+                "embed_workflow": (
+                    "BOOLEAN",
+                    {
+                        "default": False,
+                        "tooltip": "Embeds the complete workflow data into the image metadata. Only works with PNG and WebP formats.",
+                    },
+                ),
+                "save_with_metadata": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "When enabled, embeds generation parameters into the saved image metadata. Disable to skip writing generation metadata.",
+                    },
+                ),
+                "add_counter_to_filename": (
+                    "BOOLEAN",
+                    {
+                        "default": True,
+                        "tooltip": "Adds an incremental counter to filenames to prevent overwriting previous images.",
+                    },
+                ),
             },
             "hidden": {
                 "id": "UNIQUE_ID",
@@ -75,57 +102,59 @@ class SaveImageLM:
     def get_lora_hash(self, lora_name):
         """Get the lora hash from cache"""
         scanner = ServiceRegistry.get_service_sync("lora_scanner")
-        
+
         # Use the new direct filename lookup method
-        hash_value = scanner.get_hash_by_filename(lora_name)
-        if hash_value:
-            return hash_value
-            
+        if scanner is not None:
+            hash_value = scanner.get_hash_by_filename(lora_name)
+            if hash_value:
+                return hash_value
+
         return None
 
     def get_checkpoint_hash(self, checkpoint_path):
         """Get the checkpoint hash from cache"""
         scanner = ServiceRegistry.get_service_sync("checkpoint_scanner")
-        
+
         if not checkpoint_path:
             return None
-            
+
         # Extract basename without extension
         checkpoint_name = os.path.basename(checkpoint_path)
         checkpoint_name = os.path.splitext(checkpoint_name)[0]
-        
+
         # Try direct filename lookup first
-        hash_value = scanner.get_hash_by_filename(checkpoint_name)
-        if hash_value:
-            return hash_value
-                
+        if scanner is not None:
+            hash_value = scanner.get_hash_by_filename(checkpoint_name)
+            if hash_value:
+                return hash_value
+
         return None
 
     def format_metadata(self, metadata_dict):
         """Format metadata in the requested format similar to userComment example"""
         if not metadata_dict:
             return ""
-        
+
         # Helper function to only add parameter if value is not None
         def add_param_if_not_none(param_list, label, value):
             if value is not None:
                 param_list.append(f"{label}: {value}")
-        
+
         # Extract the prompt and negative prompt
-        prompt = metadata_dict.get('prompt', '')
-        negative_prompt = metadata_dict.get('negative_prompt', '')
-        
+        prompt = metadata_dict.get("prompt", "")
+        negative_prompt = metadata_dict.get("negative_prompt", "")
+
         # Extract loras from the prompt if present
-        loras_text = metadata_dict.get('loras', '')
+        loras_text = metadata_dict.get("loras", "")
         lora_hashes = {}
-        
+
         # If loras are found, add them on a new line after the prompt
         if loras_text:
             prompt_with_loras = f"{prompt}\n{loras_text}"
-            
+
             # Extract lora names from the format <lora:name:strength>
-            lora_matches = re.findall(r'<lora:([^:]+):([^>]+)>', loras_text)
-            
+            lora_matches = re.findall(r"<lora:([^:]+):([^>]+)>", loras_text)
+
             # Get hash for each lora
             for lora_name, strength in lora_matches:
                 hash_value = self.get_lora_hash(lora_name)
@@ -133,112 +162,114 @@ class SaveImageLM:
                     lora_hashes[lora_name] = hash_value
         else:
             prompt_with_loras = prompt
-        
+
         # Format the first part (prompt and loras)
         metadata_parts = [prompt_with_loras]
-        
+
         # Add negative prompt
         if negative_prompt:
             metadata_parts.append(f"Negative prompt: {negative_prompt}")
-        
+
         # Format the second part (generation parameters)
         params = []
-        
+
         # Add standard parameters in the correct order
-        if 'steps' in metadata_dict:
-            add_param_if_not_none(params, "Steps", metadata_dict.get('steps'))
-        
+        if "steps" in metadata_dict:
+            add_param_if_not_none(params, "Steps", metadata_dict.get("steps"))
+
         # Combine sampler and scheduler information
         sampler_name = None
         scheduler_name = None
-        
-        if 'sampler' in metadata_dict:
-            sampler = metadata_dict.get('sampler')
+
+        if "sampler" in metadata_dict:
+            sampler = metadata_dict.get("sampler")
             # Convert ComfyUI sampler names to user-friendly names
             sampler_mapping = {
-                'euler': 'Euler',
-                'euler_ancestral': 'Euler a',
-                'dpm_2': 'DPM2',
-                'dpm_2_ancestral': 'DPM2 a',
-                'heun': 'Heun',
-                'dpm_fast': 'DPM fast',
-                'dpm_adaptive': 'DPM adaptive',
-                'lms': 'LMS',
-                'dpmpp_2s_ancestral': 'DPM++ 2S a',
-                'dpmpp_sde': 'DPM++ SDE',
-                'dpmpp_sde_gpu': 'DPM++ SDE',
-                'dpmpp_2m': 'DPM++ 2M',
-                'dpmpp_2m_sde': 'DPM++ 2M SDE',
-                'dpmpp_2m_sde_gpu': 'DPM++ 2M SDE',
-                'ddim': 'DDIM'
+                "euler": "Euler",
+                "euler_ancestral": "Euler a",
+                "dpm_2": "DPM2",
+                "dpm_2_ancestral": "DPM2 a",
+                "heun": "Heun",
+                "dpm_fast": "DPM fast",
+                "dpm_adaptive": "DPM adaptive",
+                "lms": "LMS",
+                "dpmpp_2s_ancestral": "DPM++ 2S a",
+                "dpmpp_sde": "DPM++ SDE",
+                "dpmpp_sde_gpu": "DPM++ SDE",
+                "dpmpp_2m": "DPM++ 2M",
+                "dpmpp_2m_sde": "DPM++ 2M SDE",
+                "dpmpp_2m_sde_gpu": "DPM++ 2M SDE",
+                "ddim": "DDIM",
             }
             sampler_name = sampler_mapping.get(sampler, sampler)
-        
-        if 'scheduler' in metadata_dict:
-            scheduler = metadata_dict.get('scheduler')
+
+        if "scheduler" in metadata_dict:
+            scheduler = metadata_dict.get("scheduler")
             scheduler_mapping = {
-                'normal': 'Simple',
-                'karras': 'Karras',
-                'exponential': 'Exponential',
-                'sgm_uniform': 'SGM Uniform',
-                'sgm_quadratic': 'SGM Quadratic'
+                "normal": "Simple",
+                "karras": "Karras",
+                "exponential": "Exponential",
+                "sgm_uniform": "SGM Uniform",
+                "sgm_quadratic": "SGM Quadratic",
             }
             scheduler_name = scheduler_mapping.get(scheduler, scheduler)
-        
+
         # Add combined sampler and scheduler information
         if sampler_name:
             if scheduler_name:
                 params.append(f"Sampler: {sampler_name} {scheduler_name}")
             else:
                 params.append(f"Sampler: {sampler_name}")
-        
+
         # CFG scale (Use guidance if available, otherwise fall back to cfg_scale or cfg)
-        if 'guidance' in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get('guidance'))
-        elif 'cfg_scale' in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg_scale'))
-        elif 'cfg' in metadata_dict:
-            add_param_if_not_none(params, "CFG scale", metadata_dict.get('cfg'))
-        
+        if "guidance" in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get("guidance"))
+        elif "cfg_scale" in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg_scale"))
+        elif "cfg" in metadata_dict:
+            add_param_if_not_none(params, "CFG scale", metadata_dict.get("cfg"))
+
         # Seed
-        if 'seed' in metadata_dict:
-            add_param_if_not_none(params, "Seed", metadata_dict.get('seed'))
-        
+        if "seed" in metadata_dict:
+            add_param_if_not_none(params, "Seed", metadata_dict.get("seed"))
+
         # Size
-        if 'size' in metadata_dict:
-            add_param_if_not_none(params, "Size", metadata_dict.get('size'))
-        
+        if "size" in metadata_dict:
+            add_param_if_not_none(params, "Size", metadata_dict.get("size"))
+
         # Model info
-        if 'checkpoint' in metadata_dict:
+        if "checkpoint" in metadata_dict:
             # Ensure checkpoint is a string before processing
-            checkpoint = metadata_dict.get('checkpoint')
+            checkpoint = metadata_dict.get("checkpoint")
             if checkpoint is not None:
                 # Get model hash
                 model_hash = self.get_checkpoint_hash(checkpoint)
-                
+
                 # Extract basename without path
                 checkpoint_name = os.path.basename(checkpoint)
                 # Remove extension if present
                 checkpoint_name = os.path.splitext(checkpoint_name)[0]
-                
+
                 # Add model hash if available
                 if model_hash:
-                    params.append(f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}")
+                    params.append(
+                        f"Model hash: {model_hash[:10]}, Model: {checkpoint_name}"
+                    )
                 else:
                     params.append(f"Model: {checkpoint_name}")
-        
+
         # Add LoRA hashes if available
         if lora_hashes:
             lora_hash_parts = []
             for lora_name, hash_value in lora_hashes.items():
                 lora_hash_parts.append(f"{lora_name}: {hash_value[:10]}")
-            
+
             if lora_hash_parts:
-                params.append(f"Lora hashes: \"{', '.join(lora_hash_parts)}\"")
-        
+                params.append(f'Lora hashes: "{", ".join(lora_hash_parts)}"')
+
         # Combine all parameters with commas
         metadata_parts.append(", ".join(params))
-        
+
         # Join all parts with a new line
         return "\n".join(metadata_parts)
 
@@ -248,36 +279,36 @@ class SaveImageLM:
         """Format filename with metadata values"""
         if not metadata_dict:
             return filename
-            
+
         result = re.findall(self.pattern_format, filename)
         for segment in result:
             parts = segment.replace("%", "").split(":")
             key = parts[0]
-            
-            if key == "seed" and 'seed' in metadata_dict:
-                filename = filename.replace(segment, str(metadata_dict.get('seed', '')))
-            elif key == "width" and 'size' in metadata_dict:
-                size = metadata_dict.get('size', 'x')
-                w = size.split('x')[0] if isinstance(size, str) else size[0]
+
+            if key == "seed" and "seed" in metadata_dict:
+                filename = filename.replace(segment, str(metadata_dict.get("seed", "")))
+            elif key == "width" and "size" in metadata_dict:
+                size = metadata_dict.get("size", "x")
+                w = size.split("x")[0] if isinstance(size, str) else size[0]
                 filename = filename.replace(segment, str(w))
-            elif key == "height" and 'size' in metadata_dict:
-                size = metadata_dict.get('size', 'x')
-                h = size.split('x')[1] if isinstance(size, str) else size[1]
+            elif key == "height" and "size" in metadata_dict:
+                size = metadata_dict.get("size", "x")
+                h = size.split("x")[1] if isinstance(size, str) else size[1]
                 filename = filename.replace(segment, str(h))
-            elif key == "pprompt" and 'prompt' in metadata_dict:
-                prompt = metadata_dict.get('prompt', '').replace("\n", " ")
+            elif key == "pprompt" and "prompt" in metadata_dict:
+                prompt = metadata_dict.get("prompt", "").replace("\n", " ")
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
-            elif key == "nprompt" and 'negative_prompt' in metadata_dict:
-                prompt = metadata_dict.get('negative_prompt', '').replace("\n", " ")
+            elif key == "nprompt" and "negative_prompt" in metadata_dict:
+                prompt = metadata_dict.get("negative_prompt", "").replace("\n", " ")
                 if len(parts) >= 2:
                     length = int(parts[1])
                     prompt = prompt[:length]
                 filename = filename.replace(segment, prompt.strip())
             elif key == "model":
-                model_value = metadata_dict.get('checkpoint')
+                model_value = metadata_dict.get("checkpoint")
                 if isinstance(model_value, (bytes, os.PathLike)):
                     model_value = str(model_value)
 
@@ -291,6 +322,7 @@ class SaveImageLM:
                 filename = filename.replace(segment, model)
             elif key == "date":
                 from datetime import datetime
+
                 now = datetime.now()
                 date_table = {
                     "yyyy": f"{now.year:04d}",
@@ -311,46 +343,63 @@ class SaveImageLM:
                     for k, v in date_table.items():
                         date_format = date_format.replace(k, v)
                     filename = filename.replace(segment, date_format)
-                    
+
         return filename
 
-    def save_images(self, images, filename_prefix, file_format, id, prompt=None, extra_pnginfo=None, 
-                   lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
+    def save_images(
+        self,
+        images,
+        filename_prefix,
+        file_format,
+        id,
+        prompt=None,
+        extra_pnginfo=None,
+        lossless_webp=True,
+        quality=100,
+        embed_workflow=False,
+        save_with_metadata=True,
+        add_counter_to_filename=True,
+    ):
         """Save images with metadata"""
         results = []
 
         # Get metadata using the metadata collector
         raw_metadata = get_metadata()
         metadata_dict = MetadataProcessor.to_dict(raw_metadata, id)
-            
+
         metadata = self.format_metadata(metadata_dict)
-        
+
         # Process filename_prefix with pattern substitution
         filename_prefix = self.format_filename(filename_prefix, metadata_dict)
-        
+
         # Get initial save path info once for the batch
-        full_output_folder, filename, counter, subfolder, processed_prefix = folder_paths.get_save_image_path(
-            filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
+        full_output_folder, filename, counter, subfolder, processed_prefix = (
+            folder_paths.get_save_image_path(
+                filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0]
+            )
         )
-        
+
         # Create directory if it doesn't exist
         if not os.path.exists(full_output_folder):
             os.makedirs(full_output_folder, exist_ok=True)
-        
+
         # Process each image with incrementing counter
         for i, image in enumerate(images):
             # Convert the tensor image to numpy array
-            img = 255. * image.cpu().numpy()
+            img = 255.0 * image.cpu().numpy()
             img = Image.fromarray(np.clip(img, 0, 255).astype(np.uint8))
-            
+
             # Generate filename with counter if needed
             base_filename = filename
             if add_counter_to_filename:
                 # Use counter + i to ensure unique filenames for all images in batch
                 current_counter = counter + i
                 base_filename += f"_{current_counter:05}_"
-                
+
             # Set file extension and prepare saving parameters
+            file: str
+            save_kwargs: Dict[str, Any]
+            pnginfo: Optional[PngImagePlugin.PngInfo] = None
             if file_format == "png":
                 file = base_filename + ".png"
                 file_extension = ".png"
@@ -362,18 +411,25 @@ class SaveImageLM:
                 file_extension = ".jpg"
                 save_kwargs = {"quality": quality, "optimize": True}
             elif file_format == "webp":
-                file = base_filename + ".webp" 
+                file = base_filename + ".webp"
                 file_extension = ".webp"
                 # Add optimization param to control performance
-                save_kwargs = {"quality": quality, "lossless": lossless_webp, "method": 0}
-            
+                save_kwargs = {
+                    "quality": quality,
+                    "lossless": lossless_webp,
+                    "method": 0,
+                }
+            else:
+                raise ValueError(f"Unsupported file format: {file_format}")
+
             # Full save path
             file_path = os.path.join(full_output_folder, file)
-            
+
             # Save the image with metadata
             try:
                 if file_format == "png":
-                    if metadata:
+                    assert pnginfo is not None
+                    if save_with_metadata and metadata:
                         pnginfo.add_text("parameters", metadata)
                     if embed_workflow and extra_pnginfo is not None:
                         workflow_json = json.dumps(extra_pnginfo["workflow"])
@@ -382,9 +438,14 @@ class SaveImageLM:
                     img.save(file_path, format="PNG", **save_kwargs)
                 elif file_format == "jpeg":
                     # For JPEG, use piexif
-                    if metadata:
+                    if save_with_metadata and metadata:
                         try:
-                            exif_dict = {'Exif': {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}}
+                            exif_dict = {
+                                "Exif": {
+                                    piexif.ExifIFD.UserComment: b"UNICODE\0"
+                                    + metadata.encode("utf-16be")
+                                }
+                            }
                             exif_bytes = piexif.dump(exif_dict)
                             save_kwargs["exif"] = exif_bytes
                         except Exception as e:
@@ -395,38 +456,54 @@ class SaveImageLM:
                         # For WebP, use piexif for metadata
                         exif_dict = {}
 
-                        if metadata:
-                            exif_dict['Exif'] = {piexif.ExifIFD.UserComment: b'UNICODE\0' + metadata.encode('utf-16be')}
-                        
+                        if save_with_metadata and metadata:
+                            exif_dict["Exif"] = {
+                                piexif.ExifIFD.UserComment: b"UNICODE\0"
+                                + metadata.encode("utf-16be")
+                            }
+
                         # Add workflow if needed
                         if embed_workflow and extra_pnginfo is not None:
-                            workflow_json = json.dumps(extra_pnginfo["workflow"]) 
-                            exif_dict['0th'] = {piexif.ImageIFD.ImageDescription: "Workflow:" + workflow_json}
-                            
+                            workflow_json = json.dumps(extra_pnginfo["workflow"])
+                            exif_dict["0th"] = {
+                                piexif.ImageIFD.ImageDescription: "Workflow:"
+                                + workflow_json
+                            }
+
                         exif_bytes = piexif.dump(exif_dict)
                         save_kwargs["exif"] = exif_bytes
                     except Exception as e:
                         logger.error(f"Error adding EXIF data: {e}")
-                    
+
                     img.save(file_path, format="WEBP", **save_kwargs)
-                
-                results.append({
-                    "filename": file,
-                    "subfolder": subfolder,
-                    "type": self.type
-                })
-                
+
+                results.append(
+                    {"filename": file, "subfolder": subfolder, "type": self.type}
+                )
+
             except Exception as e:
                 logger.error(f"Error saving image: {e}")
-        
+
         return results
 
-    def process_image(self, images, id, filename_prefix="ComfyUI", file_format="png", prompt=None, extra_pnginfo=None,
-                     lossless_webp=True, quality=100, embed_workflow=False, add_counter_to_filename=True):
+    def process_image(
+        self,
+        images,
+        id,
+        filename_prefix="ComfyUI",
+        file_format="png",
+        prompt=None,
+        extra_pnginfo=None,
+        lossless_webp=True,
+        quality=100,
+        embed_workflow=False,
+        save_with_metadata=True,
+        add_counter_to_filename=True,
+    ):
         """Process and save image with metadata"""
         # Make sure the output directory exists
         os.makedirs(self.output_dir, exist_ok=True)
-        
+
         # If images is already a list or array of images, do nothing; otherwise, convert to list
         if isinstance(images, (list, np.ndarray)):
             pass
@@ -436,19 +513,23 @@ class SaveImageLM:
                 images = [images]
             else:  # Multiple images (batch, height, width, channels)
                 images = [img for img in images]
-        
+
         # Save all images
         results = self.save_images(
-            images, 
-            filename_prefix, 
-            file_format, 
+            images,
+            filename_prefix,
+            file_format,
             id,
-            prompt, 
+            prompt,
             extra_pnginfo,
             lossless_webp,
             quality,
             embed_workflow,
-            add_counter_to_filename
+            save_with_metadata,
+            add_counter_to_filename,
         )
-        
-        return (images,)
+
+        return {
+            "result": (images,),
+            "ui": {"images": results},
+        }

py/nodes/text.py

@@ -1,10 +1,15 @@
+from __future__ import annotations
+
+from ..services.wildcard_service import contains_dynamic_syntax, get_wildcard_service
+
+
 class TextLM:
     """A simple text node with autocomplete support."""
 
     NAME = "Text (LoraManager)"
     CATEGORY = "Lora Manager/utils"
     DESCRIPTION = (
-        "A simple text input node with autocomplete support for tags and styles."
+        "A simple text input node with autocomplete support for tags, styles, and wildcard expansion."
     )
 
     @classmethod
@@ -15,8 +20,17 @@ class TextLM:
                     "AUTOCOMPLETE_TEXT_PROMPT,STRING",
                     {
                         "widgetType": "AUTOCOMPLETE_TEXT_PROMPT",
-                        "placeholder": "Enter text... /char, /artist for quick tag search",
-                        "tooltip": "The text output.",
+                        "placeholder": "Enter text... /character, /artist, /wildcard for quick search",
+                        "tooltip": "The text output. Wildcard references inserted with /wildcard are expanded at runtime.",
+                    },
+                ),
+            },
+            "optional": {
+                "seed": (
+                    "INT",
+                    {
+                        "forceInput": True,
+                        "tooltip": "Optional seed for wildcard generation. Leave unconnected for non-deterministic wildcard expansion.",
                     },
                 ),
             },
@@ -24,10 +38,14 @@ class TextLM:
 
     RETURN_TYPES = ("STRING",)
     RETURN_NAMES = ("STRING",)
-    OUTPUT_TOOLTIPS = (
-        "The text output.",
-    )
+    OUTPUT_TOOLTIPS = ("The text output.",)
     FUNCTION = "process"
 
-    def process(self, text: str):
-        return (text,)
+    @classmethod
+    def IS_CHANGED(cls, text: str, seed: int | None = None):
+        if contains_dynamic_syntax(text) and seed is None:
+            return float("NaN")
+        return False
+
+    def process(self, text: str, seed: int | None = None):
+        return (get_wildcard_service().expand_text(text, seed=seed),)

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/nodes/utils.py

@@ -1,33 +1,35 @@
 class AnyType(str):
-  """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+    """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+    def __ne__(self, __value: object) -> bool:
+        return False
 
-  def __ne__(self, __value: object) -> bool:
-    return False
 
 # Credit to Regis Gaughan, III (rgthree)
 class FlexibleOptionalInputType(dict):
-  """A special class to make flexible nodes that pass data to our python handlers.
+    """A special class to make flexible nodes that pass data to our python handlers.
 
-  Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs
-  (like for Any Switch, Context Switch, Context Merge, Power Lora Loader, etc).
+    Enables both flexible/dynamic input types (like for Any Switch) or a dynamic number of inputs
+    (like for Any Switch, Context Switch, Context Merge, Power Lora Loader, etc).
 
-  Note, for ComfyUI, all that's needed is the `__contains__` override below, which tells ComfyUI
-  that our node will handle the input, regardless of what it is.
+    Note, for ComfyUI, all that's needed is the `__contains__` override below, which tells ComfyUI
+    that our node will handle the input, regardless of what it is.
 
-  However, with https://github.com/comfyanonymous/ComfyUI/pull/2666 a large change would occur
-  requiring more details on the input itself. There, we need to return a list/tuple where the first
-  item is the type. This can be a real type, or use the AnyType for additional flexibility.
+    However, with https://github.com/comfyanonymous/ComfyUI/pull/2666 a large change would occur
+    requiring more details on the input itself. There, we need to return a list/tuple where the first
+    item is the type. This can be a real type, or use the AnyType for additional flexibility.
 
-  This should be forwards compatible unless more changes occur in the PR.
-  """
-  def __init__(self, type):
-    self.type = type
+    This should be forwards compatible unless more changes occur in the PR.
+    """
 
-  def __getitem__(self, key):
-    return (self.type, )
+    def __init__(self, type):
+        self.type = type
 
-  def __contains__(self, key):
-    return True
+    def __getitem__(self, key):
+        return (self.type,)
+
+    def __contains__(self, key):
+        return True
 
 
 any_type = AnyType("*")
@@ -37,25 +39,27 @@ import os
 import logging
 import copy
 import sys
-import folder_paths
+import folder_paths  # type: ignore
 
 logger = logging.getLogger(__name__)
 
+
 def extract_lora_name(lora_path):
     """Extract the lora name from a lora path (e.g., 'IL\\aorunIllstrious.safetensors' -> 'aorunIllstrious')"""
     # Get the basename without extension
     basename = os.path.basename(lora_path)
     return os.path.splitext(basename)[0]
 
+
 def get_loras_list(kwargs):
     """Helper to extract loras list from either old or new kwargs format"""
-    if 'loras' not in kwargs:
+    if "loras" not in kwargs:
         return []
-        
-    loras_data = kwargs['loras']
+
+    loras_data = kwargs["loras"]
     # Handle new format: {'loras': {'__value__': [...]}}
-    if isinstance(loras_data, dict) and '__value__' in loras_data:
-        return loras_data['__value__']
+    if isinstance(loras_data, dict) and "__value__" in loras_data:
+        return loras_data["__value__"]
     # Handle old format: {'loras': [...]}
     elif isinstance(loras_data, list):
         return loras_data
@@ -64,24 +68,26 @@ def get_loras_list(kwargs):
         logger.warning(f"Unexpected loras format: {type(loras_data)}")
         return []
 
+
 def load_state_dict_in_safetensors(path, device="cpu", filter_prefix=""):
-    """Simplified version of load_state_dict_in_safetensors that just loads from a local path"""  
+    """Simplified version of load_state_dict_in_safetensors that just loads from a local path"""
     import safetensors.torch
-    
+
     state_dict = {}
-    with safetensors.torch.safe_open(path, framework="pt", device=device) as f:
+    with safetensors.torch.safe_open(path, framework="pt", device=device) as f:  # type: ignore[attr-defined]
         for k in f.keys():
             if filter_prefix and not k.startswith(filter_prefix):
                 continue
             state_dict[k.removeprefix(filter_prefix)] = f.get_tensor(k)
     return state_dict
 
+
 def to_diffusers(input_lora):
     """Simplified version of to_diffusers for Flux LoRA conversion"""
     import torch
     from diffusers.utils.state_dict_utils import convert_unet_state_dict_to_peft
-    from diffusers.loaders import FluxLoraLoaderMixin
-    
+    from diffusers.loaders import FluxLoraLoaderMixin  # type: ignore[attr-defined]
+
     if isinstance(input_lora, str):
         tensors = load_state_dict_in_safetensors(input_lora, device="cpu")
     else:
@@ -91,22 +97,27 @@ def to_diffusers(input_lora):
     for k, v in tensors.items():
         if v.dtype not in [torch.float64, torch.float32, torch.bfloat16, torch.float16]:
             tensors[k] = v.to(torch.bfloat16)
-    
+
     new_tensors = FluxLoraLoaderMixin.lora_state_dict(tensors)
     new_tensors = convert_unet_state_dict_to_peft(new_tensors)
 
     return new_tensors
 
+
 def nunchaku_load_lora(model, lora_name, lora_strength):
-    """Load a Flux LoRA for Nunchaku model"""   
+    """Load a Flux LoRA for Nunchaku model"""
     # Get full path to the LoRA file. Allow both direct paths and registered LoRA names.
-    lora_path = lora_name if os.path.isfile(lora_name) else folder_paths.get_full_path("loras", lora_name)
+    lora_path = (
+        lora_name
+        if os.path.isfile(lora_name)
+        else folder_paths.get_full_path("loras", lora_name)
+    )
     if not lora_path or not os.path.isfile(lora_path):
         logger.warning("Skipping LoRA '%s' because it could not be found", lora_name)
         return model
 
     model_wrapper = model.model.diffusion_model
-    
+
     # Try to find copy_with_ctx in the same module as ComfyFluxWrapper
     module_name = model_wrapper.__class__.__module__
     module = sys.modules.get(module_name)
@@ -118,14 +129,16 @@ def nunchaku_load_lora(model, lora_name, lora_strength):
         ret_model_wrapper.loras = [*model_wrapper.loras, (lora_path, lora_strength)]
     else:
         # Fallback to legacy logic
-        logger.warning("Please upgrade ComfyUI-nunchaku to 1.1.0 or above for better LoRA support. Falling back to legacy loading logic.")
+        logger.warning(
+            "Please upgrade ComfyUI-nunchaku to 1.1.0 or above for better LoRA support. Falling back to legacy loading logic."
+        )
         transformer = model_wrapper.model
-        
+
         # Save the transformer temporarily
         model_wrapper.model = None
         ret_model = copy.deepcopy(model)  # copy everything except the model
         ret_model_wrapper = ret_model.model.diffusion_model
-        
+
         # Restore the model and set it for the copy
         model_wrapper.model = transformer
         ret_model_wrapper.model = transformer
@@ -133,15 +146,36 @@ def nunchaku_load_lora(model, lora_name, lora_strength):
 
     # Convert the LoRA to diffusers format
     sd = to_diffusers(lora_path)
-    
+
     # Handle embedding adjustment if needed
     if "transformer.x_embedder.lora_A.weight" in sd:
         new_in_channels = sd["transformer.x_embedder.lora_A.weight"].shape[1]
         assert new_in_channels % 4 == 0
         new_in_channels = new_in_channels // 4
-        
+
         old_in_channels = ret_model.model.model_config.unet_config["in_channels"]
         if old_in_channels < new_in_channels:
             ret_model.model.model_config.unet_config["in_channels"] = new_in_channels
-    
-    return ret_model
+
+    return ret_model
+
+
+def detect_nunchaku_model_kind(model):
+    """Return the supported Nunchaku model kind for a Comfy model, if any."""
+    try:
+        model_wrapper = model.model.diffusion_model
+    except (AttributeError, TypeError):
+        return None
+
+    wrapper_name = model_wrapper.__class__.__name__
+    if wrapper_name == "ComfyFluxWrapper":
+        return "flux"
+
+    inner_model = getattr(model_wrapper, "model", None)
+    inner_name = inner_model.__class__.__name__ if inner_model is not None else ""
+    if wrapper_name.endswith("NunchakuQwenImageTransformer2DModel"):
+        return "qwen_image"
+    if inner_name.endswith("NunchakuQwenImageTransformer2DModel"):
+        return "qwen_image"
+
+    return None

py/recipes/constants.py

@@ -13,4 +13,5 @@ GEN_PARAM_KEYS = [
     'seed',
     'size',
     'clip_skip',
+    'denoising_strength',
 ]

py/recipes/enrichment.py

@@ -1,11 +1,11 @@
 import logging
 import json
-import re
 import os
 from typing import Any, Dict, Optional
 from .merger import GenParamsMerger
 from .base import RecipeMetadataParser
 from ..services.metadata_service import get_default_metadata_provider
+from ..utils.civitai_utils import extract_civitai_image_id
 
 logger = logging.getLogger(__name__)
 
@@ -39,11 +39,12 @@ class RecipeEnricher:
         source_url = recipe.get("source_url") or recipe.get("source_path", "")
         
         # Check if it's a Civitai image URL
-        image_id_match = re.search(r'civitai\.com/images/(\d+)', str(source_url))
-        if image_id_match:
-            image_id = image_id_match.group(1)
+        image_id = extract_civitai_image_id(str(source_url))
+        if image_id:
             try:
-                image_info = await civitai_client.get_image_info(image_id)
+                image_info = await civitai_client.get_image_info(
+                    image_id, source_url=str(source_url)
+                )
                 if image_info:
                     # Handle nested meta often found in Civitai API responses
                     raw_meta = image_info.get("meta")

py/recipes/factory.py

@@ -6,23 +6,25 @@ from .parsers import (
     ComfyMetadataParser,
     MetaFormatParser,
     AutomaticMetadataParser,
-    CivitaiApiMetadataParser
+    CivitaiApiMetadataParser,
+    SuiImageParamsParser,
 )
 from .base import RecipeMetadataParser
 
 logger = logging.getLogger(__name__)
 
+
 class RecipeParserFactory:
     """Factory for creating recipe metadata parsers"""
-    
+
     @staticmethod
-    def create_parser(metadata) -> RecipeMetadataParser:
+    def create_parser(metadata) -> RecipeMetadataParser | None:
         """
         Create appropriate parser based on the metadata content
-        
+
         Args:
             metadata: The metadata from the image (dict or str)
-            
+
         Returns:
             Appropriate RecipeMetadataParser implementation
         """
@@ -34,17 +36,18 @@ class RecipeParserFactory:
             except Exception as e:
                 logger.debug(f"CivitaiApiMetadataParser check failed: {e}")
                 pass
-            
+
             # Convert dict to string for other parsers that expect string input
             try:
                 import json
+
                 metadata_str = json.dumps(metadata)
             except Exception as e:
                 logger.debug(f"Failed to convert dict to JSON string: {e}")
                 return None
         else:
             metadata_str = metadata
-        
+
         # Try ComfyMetadataParser which requires valid JSON
         try:
             if ComfyMetadataParser().is_metadata_matching(metadata_str):
@@ -52,7 +55,14 @@ class RecipeParserFactory:
         except Exception:
             # If JSON parsing fails, move on to other parsers
             pass
-        
+
+        # Try SuiImageParamsParser for SuiImage metadata format
+        try:
+            if SuiImageParamsParser().is_metadata_matching(metadata_str):
+                return SuiImageParamsParser()
+        except Exception:
+            pass
+
         # Check other parsers that expect string input
         if RecipeFormatParser().is_metadata_matching(metadata_str):
             return RecipeFormatParser()

py/recipes/merger.py

@@ -1,27 +1,33 @@
 from typing import Any, Dict, Optional
 import logging
 
+from .constants import GEN_PARAM_KEYS
+
 logger = logging.getLogger(__name__)
 
+
 class GenParamsMerger:
     """Utility to merge generation parameters from multiple sources with priority."""
 
+    ALLOWED_KEYS = set(GEN_PARAM_KEYS)
+
     BLACKLISTED_KEYS = {
         "id", "url", "userId", "username", "createdAt", "updatedAt", "hash", "meta",
         "draft", "extra", "width", "height", "process", "quantity", "workflow",
         "baseModel", "resources", "disablePoi", "aspectRatio", "Created Date",
         "experimental", "civitaiResources", "civitai_resources", "Civitai resources",
         "modelVersionId", "modelId", "hashes", "Model", "Model hash", "checkpoint_hash",
-        "checkpoint", "checksum", "model_checksum"
+        "checkpoint", "checksum", "model_checksum", "raw_metadata",
     }
-    
+
     NORMALIZATION_MAPPING = {
-        # Civitai specific
+        "cfg": "cfg_scale",
         "cfgScale": "cfg_scale",
         "clipSkip": "clip_skip",
         "negativePrompt": "negative_prompt",
-        # Case variations
         "Sampler": "sampler",
+        "sampler_name": "sampler",
+        "scheduler": "sampler",
         "Steps": "steps",
         "Seed": "seed",
         "Size": "size",
@@ -36,63 +42,40 @@ class GenParamsMerger:
     def merge(
         request_params: Optional[Dict[str, Any]] = None,
         civitai_meta: Optional[Dict[str, Any]] = None,
-        embedded_metadata: Optional[Dict[str, Any]] = None
+        embedded_metadata: Optional[Dict[str, Any]] = None,
     ) -> Dict[str, Any]:
         """
         Merge generation parameters from three sources.
-        
-        Priority: request_params > civitai_meta > embedded_metadata
-        
-        Args:
-            request_params: Params provided directly in the import request
-            civitai_meta: Params from Civitai Image API 'meta' field
-            embedded_metadata: Params extracted from image EXIF/embedded metadata
-            
-        Returns:
-            Merged parameters dictionary
-        """
-        result = {}
 
-        # 1. Start with embedded metadata (lowest priority)
+        Priority: request_params > civitai_meta > embedded_metadata
+        """
+        result: Dict[str, Any] = {}
+
         if embedded_metadata:
-            # If it's a full recipe metadata, we use its gen_params
-            if "gen_params" in embedded_metadata and isinstance(embedded_metadata["gen_params"], dict):
+            if "gen_params" in embedded_metadata and isinstance(
+                embedded_metadata["gen_params"], dict
+            ):
                 GenParamsMerger._update_normalized(result, embedded_metadata["gen_params"])
             else:
-                # Otherwise assume the dict itself contains gen_params
                 GenParamsMerger._update_normalized(result, embedded_metadata)
 
-        # 2. Layer Civitai meta (medium priority)
         if civitai_meta:
             GenParamsMerger._update_normalized(result, civitai_meta)
 
-        # 3. Layer request params (highest priority)
         if request_params:
             GenParamsMerger._update_normalized(result, request_params)
 
-        # Filter out blacklisted keys and also the original camelCase keys if they were normalized
-        final_result = {}
-        for k, v in result.items():
-            if k in GenParamsMerger.BLACKLISTED_KEYS:
-                continue
-            if k in GenParamsMerger.NORMALIZATION_MAPPING:
-                continue
-            final_result[k] = v
-            
-        return final_result
+        return result
 
     @staticmethod
     def _update_normalized(target: Dict[str, Any], source: Dict[str, Any]) -> None:
-        """Update target dict with normalized keys from source."""
-        for k, v in source.items():
-            normalized_key = GenParamsMerger.NORMALIZATION_MAPPING.get(k, k)
-            target[normalized_key] = v
-            # Also keep the original key for now if it's not the same, 
-            # so we can filter at the end or avoid losing it if it wasn't supposed to be renamed?
-            # Actually, if we rename it, we should probably NOT keep both in 'target' 
-            # because we want to filter them out at the end anyway.
-            if normalized_key != k:
-                # If we are overwriting an existing snake_case key with a camelCase one's value,
-                # that's fine because of the priority order of calls to _update_normalized.
-                pass
-            target[k] = v
+        """Update target dict with normalized, persistence-safe keys from source."""
+        for key, value in source.items():
+            if key in GenParamsMerger.BLACKLISTED_KEYS:
+                continue
+
+            normalized_key = GenParamsMerger.NORMALIZATION_MAPPING.get(key, key)
+            if normalized_key not in GenParamsMerger.ALLOWED_KEYS:
+                continue
+
+            target[normalized_key] = value

py/recipes/parsers/__init__.py

@@ -5,6 +5,7 @@ from .comfy import ComfyMetadataParser
 from .meta_format import MetaFormatParser
 from .automatic import AutomaticMetadataParser
 from .civitai_image import CivitaiApiMetadataParser
+from .sui_image_params import SuiImageParamsParser
 
 __all__ = [
     'RecipeFormatParser',
@@ -12,4 +13,5 @@ __all__ = [
     'MetaFormatParser',
     'AutomaticMetadataParser',
     'CivitaiApiMetadataParser',
+    'SuiImageParamsParser',
 ]

py/recipes/parsers/civitai_image.py

@@ -9,15 +9,16 @@ from ...services.metadata_service import get_default_metadata_provider
 
 logger = logging.getLogger(__name__)
 
+
 class CivitaiApiMetadataParser(RecipeMetadataParser):
     """Parser for Civitai image metadata format"""
-    
+
     def is_metadata_matching(self, metadata) -> bool:
         """Check if the metadata matches the Civitai image metadata format
-        
+
         Args:
             metadata: The metadata from the image (dict)
-            
+
         Returns:
             bool: True if this parser can handle the metadata
         """
@@ -28,7 +29,7 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
             # Check for common CivitAI image metadata fields
             civitai_image_fields = (
                 "resources",
-                "civitaiResources", 
+                "civitaiResources",
                 "additionalResources",
                 "hashes",
                 "prompt",
@@ -40,7 +41,8 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 "width",
                 "height",
                 "Model",
-                "Model hash"
+                "Model hash",
+                "modelVersionIds",
             )
             return any(key in payload for key in civitai_image_fields)
 
@@ -50,7 +52,9 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
         # Check for LoRA hash patterns
         hashes = metadata.get("hashes")
-        if isinstance(hashes, dict) and any(str(key).lower().startswith("lora:") for key in hashes):
+        if isinstance(hashes, dict) and any(
+            str(key).lower().startswith("lora:") for key in hashes
+        ):
             return True
 
         # Check nested meta object (common in CivitAI image responses)
@@ -61,22 +65,28 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
             # Also check for LoRA hash patterns in nested meta
             hashes = nested_meta.get("hashes")
-            if isinstance(hashes, dict) and any(str(key).lower().startswith("lora:") for key in hashes):
+            if isinstance(hashes, dict) and any(
+                str(key).lower().startswith("lora:") for key in hashes
+            ):
                 return True
 
         return False
-    
-    async def parse_metadata(self, metadata, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+
+    async def parse_metadata(  # type: ignore[override]
+        self, user_comment, recipe_scanner=None, civitai_client=None
+    ) -> Dict[str, Any]:
         """Parse metadata from Civitai image format
-        
+
         Args:
-            metadata: The metadata from the image (dict)
+            user_comment: The metadata from the image (dict)
             recipe_scanner: Optional recipe scanner service
             civitai_client: Optional Civitai API client (deprecated, use metadata_provider instead)
-            
+
         Returns:
             Dict containing parsed recipe data
         """
+        metadata: Dict[str, Any] = user_comment  # type: ignore[assignment]
+        metadata = user_comment
         try:
             # Get metadata provider instead of using civitai_client directly
             metadata_provider = await get_default_metadata_provider()
@@ -100,19 +110,19 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     )
                 ):
                     metadata = inner_meta
-            
+
             # Initialize result structure
             result = {
-                'base_model': None,
-                'loras': [],
-                'model': None,
-                'gen_params': {},
-                'from_civitai_image': True
+                "base_model": None,
+                "loras": [],
+                "model": None,
+                "gen_params": {},
+                "from_civitai_image": True,
             }
-            
+
             # Track already added LoRAs to prevent duplicates
             added_loras = {}  # key: model_version_id or hash, value: index in result["loras"]
-            
+
             # Extract hash information from hashes field for LoRA matching
             lora_hashes = {}
             if "hashes" in metadata and isinstance(metadata["hashes"], dict):
@@ -121,14 +131,14 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     if key_str.lower().startswith("lora:"):
                         lora_name = key_str.split(":", 1)[1]
                         lora_hashes[lora_name] = hash_value
-            
+
             # Extract prompt and negative prompt
             if "prompt" in metadata:
                 result["gen_params"]["prompt"] = metadata["prompt"]
-            
+
             if "negativePrompt" in metadata:
                 result["gen_params"]["negative_prompt"] = metadata["negativePrompt"]
-            
+
             # Extract other generation parameters
             param_mapping = {
                 "steps": "steps",
@@ -138,98 +148,117 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                 "Size": "size",
                 "clipSkip": "clip_skip",
             }
-            
+
             for civitai_key, our_key in param_mapping.items():
                 if civitai_key in metadata and our_key in GEN_PARAM_KEYS:
                     result["gen_params"][our_key] = metadata[civitai_key]
-            
+
             # Extract base model information - directly if available
             if "baseModel" in metadata:
                 result["base_model"] = metadata["baseModel"]
             elif "Model hash" in metadata and metadata_provider:
                 model_hash = metadata["Model hash"]
-                model_info, error = await metadata_provider.get_model_by_hash(model_hash)
+                model_info, error = await metadata_provider.get_model_by_hash(
+                    model_hash
+                )
                 if model_info:
                     result["base_model"] = model_info.get("baseModel", "")
             elif "Model" in metadata and isinstance(metadata.get("resources"), list):
                 # Try to find base model in resources
                 for resource in metadata.get("resources", []):
-                    if resource.get("type") == "model" and resource.get("name") == metadata.get("Model"):
+                    if resource.get("type") == "model" and resource.get(
+                        "name"
+                    ) == metadata.get("Model"):
                         # This is likely the checkpoint model
                         if metadata_provider and resource.get("hash"):
-                            model_info, error = await metadata_provider.get_model_by_hash(resource.get("hash"))
+                            (
+                                model_info,
+                                error,
+                            ) = await metadata_provider.get_model_by_hash(
+                                resource.get("hash")
+                            )
                             if model_info:
                                 result["base_model"] = model_info.get("baseModel", "")
-            
+
             base_model_counts = {}
-            
+
             # Process standard resources array
             if "resources" in metadata and isinstance(metadata["resources"], list):
                 for resource in metadata["resources"]:
                     # Modified to process resources without a type field as potential LoRAs
                     if resource.get("type", "lora") == "lora":
                         lora_hash = resource.get("hash", "")
-                        
+
                         # Try to get hash from the hashes field if not present in resource
                         if not lora_hash and resource.get("name"):
                             lora_hash = lora_hashes.get(resource["name"], "")
-                        
+
                         # Skip LoRAs without proper identification (hash or modelVersionId)
                         if not lora_hash and not resource.get("modelVersionId"):
-                            logger.debug(f"Skipping LoRA resource '{resource.get('name', 'Unknown')}' - no hash or modelVersionId")
+                            logger.debug(
+                                f"Skipping LoRA resource '{resource.get('name', 'Unknown')}' - no hash or modelVersionId"
+                            )
                             continue
-                        
+
                         # Skip if we've already added this LoRA by hash
                         if lora_hash and lora_hash in added_loras:
                             continue
-                        
+
                         lora_entry = {
-                            'name': resource.get("name", "Unknown LoRA"),
-                            'type': "lora",
-                            'weight': float(resource.get("weight", 1.0)),
-                            'hash': lora_hash,
-                            'existsLocally': False,
-                            'localPath': None,
-                            'file_name': resource.get("name", "Unknown"),
-                            'thumbnailUrl': '/loras_static/images/no-preview.png',
-                            'baseModel': '',
-                            'size': 0,
-                            'downloadUrl': '',
-                            'isDeleted': False
+                            "name": resource.get("name", "Unknown LoRA"),
+                            "type": "lora",
+                            "weight": float(resource.get("weight", 1.0)),
+                            "hash": lora_hash,
+                            "existsLocally": False,
+                            "localPath": None,
+                            "file_name": resource.get("name", "Unknown"),
+                            "thumbnailUrl": "/loras_static/images/no-preview.png",
+                            "baseModel": "",
+                            "size": 0,
+                            "downloadUrl": "",
+                            "isDeleted": False,
                         }
-                        
+
                         # Try to get info from Civitai if hash is available
-                        if lora_entry['hash'] and metadata_provider:
+                        if lora_entry["hash"] and metadata_provider:
                             try:
-                                civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
-                                
+                                civitai_info = (
+                                    await metadata_provider.get_model_by_hash(lora_hash)
+                                )
+
                                 populated_entry = await self.populate_lora_from_civitai(
                                     lora_entry,
                                     civitai_info,
                                     recipe_scanner,
                                     base_model_counts,
-                                    lora_hash
+                                    lora_hash,
                                 )
-                                
+
                                 if populated_entry is None:
                                     continue  # Skip invalid LoRA types
-                                    
+
                                 lora_entry = populated_entry
-                                
+
                                 # If we have a version ID from Civitai, track it for deduplication
-                                if 'id' in lora_entry and lora_entry['id']:
-                                    added_loras[str(lora_entry['id'])] = len(result["loras"])
+                                if "id" in lora_entry and lora_entry["id"]:
+                                    added_loras[str(lora_entry["id"])] = len(
+                                        result["loras"]
+                                    )
                             except Exception as e:
-                                logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
-                        
+                                logger.error(
+                                    f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
+                                )
+
                         # Track by hash if we have it
                         if lora_hash:
                             added_loras[lora_hash] = len(result["loras"])
-                            
+
                         result["loras"].append(lora_entry)
-            
+
             # Process civitaiResources array
-            if "civitaiResources" in metadata and isinstance(metadata["civitaiResources"], list):
+            if "civitaiResources" in metadata and isinstance(
+                metadata["civitaiResources"], list
+            ):
                 for resource in metadata["civitaiResources"]:
                     # Get resource type and identifier
                     resource_type = str(resource.get("type") or "").lower()
@@ -237,32 +266,39 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                     if resource_type == "checkpoint":
                         checkpoint_entry = {
-                            'id': resource.get("modelVersionId", 0),
-                            'modelId': resource.get("modelId", 0),
-                            'name': resource.get("modelName", "Unknown Checkpoint"),
-                            'version': resource.get("modelVersionName", ""),
-                            'type': resource.get("type", "checkpoint"),
-                            'existsLocally': False,
-                            'localPath': None,
-                            'file_name': resource.get("modelName", ""),
-                            'hash': resource.get("hash", "") or "",
-                            'thumbnailUrl': '/loras_static/images/no-preview.png',
-                            'baseModel': '',
-                            'size': 0,
-                            'downloadUrl': '',
-                            'isDeleted': False
+                            "id": resource.get("modelVersionId", 0),
+                            "modelId": resource.get("modelId", 0),
+                            "name": resource.get("modelName", "Unknown Checkpoint"),
+                            "version": resource.get("modelVersionName", ""),
+                            "type": resource.get("type", "checkpoint"),
+                            "existsLocally": False,
+                            "localPath": None,
+                            "file_name": resource.get("modelName", ""),
+                            "hash": resource.get("hash", "") or "",
+                            "thumbnailUrl": "/loras_static/images/no-preview.png",
+                            "baseModel": "",
+                            "size": 0,
+                            "downloadUrl": "",
+                            "isDeleted": False,
                         }
 
                         if version_id and metadata_provider:
                             try:
-                                civitai_info = await metadata_provider.get_model_version_info(version_id)
+                                civitai_info = (
+                                    await metadata_provider.get_model_version_info(
+                                        version_id
+                                    )
+                                )
 
-                                checkpoint_entry = await self.populate_checkpoint_from_civitai(
-                                    checkpoint_entry,
-                                    civitai_info
+                                checkpoint_entry = (
+                                    await self.populate_checkpoint_from_civitai(
+                                        checkpoint_entry, civitai_info
+                                    )
                                 )
                             except Exception as e:
-                                logger.error(f"Error fetching Civitai info for checkpoint version {version_id}: {e}")
+                                logger.error(
+                                    f"Error fetching Civitai info for checkpoint version {version_id}: {e}"
+                                )
 
                         if result["model"] is None:
                             result["model"] = checkpoint_entry
@@ -275,31 +311,35 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                     # Initialize lora entry
                     lora_entry = {
-                        'id': resource.get("modelVersionId", 0),
-                        'modelId': resource.get("modelId", 0),
-                        'name': resource.get("modelName", "Unknown LoRA"),
-                        'version': resource.get("modelVersionName", ""),
-                        'type': resource.get("type", "lora"),
-                        'weight': round(float(resource.get("weight", 1.0)), 2),
-                        'existsLocally': False,
-                        'thumbnailUrl': '/loras_static/images/no-preview.png',
-                        'baseModel': '',
-                        'size': 0,
-                        'downloadUrl': '',
-                        'isDeleted': False
+                        "id": resource.get("modelVersionId", 0),
+                        "modelId": resource.get("modelId", 0),
+                        "name": resource.get("modelName", "Unknown LoRA"),
+                        "version": resource.get("modelVersionName", ""),
+                        "type": resource.get("type", "lora"),
+                        "weight": round(float(resource.get("weight", 1.0)), 2),
+                        "existsLocally": False,
+                        "thumbnailUrl": "/loras_static/images/no-preview.png",
+                        "baseModel": "",
+                        "size": 0,
+                        "downloadUrl": "",
+                        "isDeleted": False,
                     }
 
                     # Try to get info from Civitai if modelVersionId is available
                     if version_id and metadata_provider:
                         try:
                             # Use get_model_version_info instead of get_model_version
-                            civitai_info = await metadata_provider.get_model_version_info(version_id)
+                            civitai_info = (
+                                await metadata_provider.get_model_version_info(
+                                    version_id
+                                )
+                            )
 
                             populated_entry = await self.populate_lora_from_civitai(
                                 lora_entry,
                                 civitai_info,
                                 recipe_scanner,
-                                base_model_counts
+                                base_model_counts,
                             )
 
                             if populated_entry is None:
@@ -307,76 +347,148 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                             lora_entry = populated_entry
                         except Exception as e:
-                            logger.error(f"Error fetching Civitai info for model version {version_id}: {e}")
+                            logger.error(
+                                f"Error fetching Civitai info for model version {version_id}: {e}"
+                            )
 
                     # Track this LoRA in our deduplication dict
                     if version_id:
                         added_loras[version_id] = len(result["loras"])
 
                     result["loras"].append(lora_entry)
-            
+
             # Process additionalResources array
-            if "additionalResources" in metadata and isinstance(metadata["additionalResources"], list):
+            if "additionalResources" in metadata and isinstance(
+                metadata["additionalResources"], list
+            ):
                 for resource in metadata["additionalResources"]:
                     # Skip resources that aren't LoRAs or LyCORIS
-                    if resource.get("type") not in ["lora", "lycoris"] and "type" not in resource:
+                    if (
+                        resource.get("type") not in ["lora", "lycoris"]
+                        and "type" not in resource
+                    ):
                         continue
-                        
+
                     lora_type = resource.get("type", "lora")
                     name = resource.get("name", "")
-                    
+
                     # Extract ID from URN format if available
                     version_id = None
                     if name and "civitai:" in name:
                         parts = name.split("@")
                         if len(parts) > 1:
                             version_id = parts[1]
-                            
+
                             # Skip if we've already added this LoRA
                             if version_id in added_loras:
                                 continue
-                    
+
                     lora_entry = {
-                        'name': name,
-                        'type': lora_type,
-                        'weight': float(resource.get("strength", 1.0)),
-                        'hash': "",
-                        'existsLocally': False,
-                        'localPath': None,
-                        'file_name': name,
-                        'thumbnailUrl': '/loras_static/images/no-preview.png',
-                        'baseModel': '',
-                        'size': 0,
-                        'downloadUrl': '',
-                        'isDeleted': False
+                        "name": name,
+                        "type": lora_type,
+                        "weight": float(resource.get("strength", 1.0)),
+                        "hash": "",
+                        "existsLocally": False,
+                        "localPath": None,
+                        "file_name": name,
+                        "thumbnailUrl": "/loras_static/images/no-preview.png",
+                        "baseModel": "",
+                        "size": 0,
+                        "downloadUrl": "",
+                        "isDeleted": False,
                     }
-                    
+
                     # If we have a version ID and metadata provider, try to get more info
                     if version_id and metadata_provider:
                         try:
                             # Use get_model_version_info with the version ID
-                            civitai_info = await metadata_provider.get_model_version_info(version_id)
-                            
+                            civitai_info = (
+                                await metadata_provider.get_model_version_info(
+                                    version_id
+                                )
+                            )
+
                             populated_entry = await self.populate_lora_from_civitai(
                                 lora_entry,
                                 civitai_info,
                                 recipe_scanner,
-                                base_model_counts
+                                base_model_counts,
                             )
-                            
+
                             if populated_entry is None:
                                 continue  # Skip invalid LoRA types
-                                
+
                             lora_entry = populated_entry
-                            
+
                             # Track this LoRA for deduplication
                             if version_id:
                                 added_loras[version_id] = len(result["loras"])
                         except Exception as e:
-                            logger.error(f"Error fetching Civitai info for model ID {version_id}: {e}")
-                    
+                            logger.error(
+                                f"Error fetching Civitai info for model ID {version_id}: {e}"
+                            )
+
                         result["loras"].append(lora_entry)
 
+            # Process modelVersionIds from Civitai image API
+            # These are model version IDs returned at root level when meta doesn't contain resources
+            if "modelVersionIds" in metadata and isinstance(
+                metadata["modelVersionIds"], list
+            ):
+                for version_id in metadata["modelVersionIds"]:
+                    version_id_str = str(version_id)
+
+                    # Skip if we've already added this LoRA by version ID
+                    if version_id_str in added_loras:
+                        continue
+
+                    # Initialize lora entry with version ID
+                    lora_entry = {
+                        "id": version_id,
+                        "modelId": 0,
+                        "name": "Unknown LoRA",
+                        "version": "",
+                        "type": "lora",
+                        "weight": 1.0,
+                        "existsLocally": False,
+                        "thumbnailUrl": "/loras_static/images/no-preview.png",
+                        "baseModel": "",
+                        "size": 0,
+                        "downloadUrl": "",
+                        "isDeleted": False,
+                    }
+
+                    # Fetch model info from Civitai
+                    if metadata_provider and version_id_str:
+                        try:
+                            civitai_info = (
+                                await metadata_provider.get_model_version_info(
+                                    version_id_str
+                                )
+                            )
+
+                            populated_entry = await self.populate_lora_from_civitai(
+                                lora_entry,
+                                civitai_info,
+                                recipe_scanner,
+                                base_model_counts,
+                            )
+
+                            if populated_entry is None:
+                                continue  # Skip invalid LoRA types
+
+                            lora_entry = populated_entry
+                        except Exception as e:
+                            logger.error(
+                                f"Error fetching Civitai info for model version {version_id}: {e}"
+                            )
+
+                    # Track this LoRA for deduplication
+                    if version_id_str:
+                        added_loras[version_id_str] = len(result["loras"])
+
+                    result["loras"].append(lora_entry)
+
             # If we found LoRA hashes in the metadata but haven't already
             # populated entries for them, fall back to creating LoRAs from
             # the hashes section. Some Civitai image responses only include
@@ -390,30 +502,32 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
                     continue
 
                 lora_entry = {
-                    'name': lora_name,
-                    'type': "lora",
-                    'weight': 1.0,
-                    'hash': lora_hash,
-                    'existsLocally': False,
-                    'localPath': None,
-                    'file_name': lora_name,
-                    'thumbnailUrl': '/loras_static/images/no-preview.png',
-                    'baseModel': '',
-                    'size': 0,
-                    'downloadUrl': '',
-                    'isDeleted': False
+                    "name": lora_name,
+                    "type": "lora",
+                    "weight": 1.0,
+                    "hash": lora_hash,
+                    "existsLocally": False,
+                    "localPath": None,
+                    "file_name": lora_name,
+                    "thumbnailUrl": "/loras_static/images/no-preview.png",
+                    "baseModel": "",
+                    "size": 0,
+                    "downloadUrl": "",
+                    "isDeleted": False,
                 }
 
                 if metadata_provider:
                     try:
-                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
+                        civitai_info = await metadata_provider.get_model_by_hash(
+                            lora_hash
+                        )
 
                         populated_entry = await self.populate_lora_from_civitai(
                             lora_entry,
                             civitai_info,
                             recipe_scanner,
                             base_model_counts,
-                            lora_hash
+                            lora_hash,
                         )
 
                         if populated_entry is None:
@@ -421,80 +535,93 @@ class CivitaiApiMetadataParser(RecipeMetadataParser):
 
                         lora_entry = populated_entry
 
-                        if 'id' in lora_entry and lora_entry['id']:
-                            added_loras[str(lora_entry['id'])] = len(result["loras"])
+                        if "id" in lora_entry and lora_entry["id"]:
+                            added_loras[str(lora_entry["id"])] = len(result["loras"])
                     except Exception as e:
-                        logger.error(f"Error fetching Civitai info for LoRA hash {lora_hash}: {e}")
+                        logger.error(
+                            f"Error fetching Civitai info for LoRA hash {lora_hash}: {e}"
+                        )
 
                 added_loras[lora_hash] = len(result["loras"])
                 result["loras"].append(lora_entry)
 
             # Check for LoRA info in the format "Lora_0 Model hash", "Lora_0 Model name", etc.
             lora_index = 0
-            while f"Lora_{lora_index} Model hash" in metadata and f"Lora_{lora_index} Model name" in metadata:
+            while (
+                f"Lora_{lora_index} Model hash" in metadata
+                and f"Lora_{lora_index} Model name" in metadata
+            ):
                 lora_hash = metadata[f"Lora_{lora_index} Model hash"]
                 lora_name = metadata[f"Lora_{lora_index} Model name"]
-                lora_strength_model = float(metadata.get(f"Lora_{lora_index} Strength model", 1.0))
-                
+                lora_strength_model = float(
+                    metadata.get(f"Lora_{lora_index} Strength model", 1.0)
+                )
+
                 # Skip if we've already added this LoRA by hash
                 if lora_hash and lora_hash in added_loras:
                     lora_index += 1
                     continue
-                
+
                 lora_entry = {
-                    'name': lora_name,
-                    'type': "lora",
-                    'weight': lora_strength_model,
-                    'hash': lora_hash,
-                    'existsLocally': False,
-                    'localPath': None,
-                    'file_name': lora_name,
-                    'thumbnailUrl': '/loras_static/images/no-preview.png',
-                    'baseModel': '',
-                    'size': 0,
-                    'downloadUrl': '',
-                    'isDeleted': False
+                    "name": lora_name,
+                    "type": "lora",
+                    "weight": lora_strength_model,
+                    "hash": lora_hash,
+                    "existsLocally": False,
+                    "localPath": None,
+                    "file_name": lora_name,
+                    "thumbnailUrl": "/loras_static/images/no-preview.png",
+                    "baseModel": "",
+                    "size": 0,
+                    "downloadUrl": "",
+                    "isDeleted": False,
                 }
-                
+
                 # Try to get info from Civitai if hash is available
-                if lora_entry['hash'] and metadata_provider:
+                if lora_entry["hash"] and metadata_provider:
                     try:
-                        civitai_info = await metadata_provider.get_model_by_hash(lora_hash)
-                        
+                        civitai_info = await metadata_provider.get_model_by_hash(
+                            lora_hash
+                        )
+
                         populated_entry = await self.populate_lora_from_civitai(
                             lora_entry,
                             civitai_info,
                             recipe_scanner,
                             base_model_counts,
-                            lora_hash
+                            lora_hash,
                         )
-                        
+
                         if populated_entry is None:
                             lora_index += 1
                             continue  # Skip invalid LoRA types
-                            
+
                         lora_entry = populated_entry
-                        
+
                         # If we have a version ID from Civitai, track it for deduplication
-                        if 'id' in lora_entry and lora_entry['id']:
-                            added_loras[str(lora_entry['id'])] = len(result["loras"])
+                        if "id" in lora_entry and lora_entry["id"]:
+                            added_loras[str(lora_entry["id"])] = len(result["loras"])
                     except Exception as e:
-                        logger.error(f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}")
-                
+                        logger.error(
+                            f"Error fetching Civitai info for LoRA hash {lora_entry['hash']}: {e}"
+                        )
+
                 # Track by hash if we have it
                 if lora_hash:
                     added_loras[lora_hash] = len(result["loras"])
-                    
+
                 result["loras"].append(lora_entry)
-                
+
                 lora_index += 1
-            
+
             # If base model wasn't found earlier, use the most common one from LoRAs
             if not result["base_model"] and base_model_counts:
-                result["base_model"] = max(base_model_counts.items(), key=lambda x: x[1])[0]
-            
+                result["base_model"] = max(
+                    base_model_counts.items(), key=lambda x: x[1]
+                )[0]
+
             return result
-            
+
         except Exception as e:
             logger.error(f"Error parsing Civitai image metadata: {e}", exc_info=True)
             return {"error": str(e), "loras": []}

py/recipes/parsers/sui_image_params.py

@@ -0,0 +1,188 @@
+"""Parser for SuiImage (Stable Diffusion WebUI) metadata format."""
+
+import json
+import logging
+from typing import Dict, Any, Optional, List
+from ..base import RecipeMetadataParser
+from ...services.metadata_service import get_default_metadata_provider
+
+logger = logging.getLogger(__name__)
+
+
+class SuiImageParamsParser(RecipeMetadataParser):
+    """Parser for SuiImage metadata JSON format.
+
+    This format is used by some Stable Diffusion WebUI variants.
+    Structure:
+    {
+        "sui_image_params": {
+            "prompt": "...",
+            "negativeprompt": "...",
+            "model": "...",
+            "seed": ...,
+            "steps": ...,
+            ...
+        },
+        "sui_models": [
+            {"name": "...", "param": "model", "hash": "..."},
+            ...
+        ],
+        "sui_extra_data": {...}
+    }
+    """
+
+    def is_metadata_matching(self, user_comment: str) -> bool:
+        """Check if the user comment matches the SuiImage metadata format"""
+        try:
+            data = json.loads(user_comment)
+            return isinstance(data, dict) and 'sui_image_params' in data
+        except (json.JSONDecodeError, TypeError):
+            return False
+
+    async def parse_metadata(self, user_comment: str, recipe_scanner=None, civitai_client=None) -> Dict[str, Any]:
+        """Parse metadata from SuiImage metadata format"""
+        try:
+            metadata_provider = await get_default_metadata_provider()
+
+            data = json.loads(user_comment)
+            params = data.get('sui_image_params', {})
+            models = data.get('sui_models', [])
+
+            # Extract prompt and negative prompt
+            prompt = params.get('prompt', '')
+            negative_prompt = params.get('negativeprompt', '') or params.get('negative_prompt', '')
+
+            # Extract generation parameters
+            gen_params = {}
+            if prompt:
+                gen_params['prompt'] = prompt
+            if negative_prompt:
+                gen_params['negative_prompt'] = negative_prompt
+
+            # Map standard parameters
+            param_mapping = {
+                'steps': 'steps',
+                'seed': 'seed',
+                'cfgscale': 'cfg_scale',
+                'cfg_scale': 'cfg_scale',
+                'width': 'width',
+                'height': 'height',
+                'sampler': 'sampler',
+                'scheduler': 'scheduler',
+                'model': 'model',
+                'vae': 'vae',
+            }
+
+            for src_key, dest_key in param_mapping.items():
+                if src_key in params and params[src_key] is not None:
+                    gen_params[dest_key] = params[src_key]
+
+            # Add size info if available
+            if 'width' in gen_params and 'height' in gen_params:
+                gen_params['size'] = f"{gen_params['width']}x{gen_params['height']}"
+
+            # Process models - extract checkpoint and loras
+            loras: List[Dict[str, Any]] = []
+            checkpoint: Optional[Dict[str, Any]] = None
+
+            for model in models:
+                model_name = model.get('name', '')
+                param_type = model.get('param', '')
+                model_hash = model.get('hash', '')
+
+                # Remove .safetensors extension for cleaner name
+                clean_name = model_name.replace('.safetensors', '') if model_name else ''
+
+                # Check if this is a LoRA by looking at the name or param type
+                is_lora = 'lora' in model_name.lower() or param_type.lower().startswith('lora')
+
+                if is_lora:
+                    lora_entry = {
+                        'id': 0,
+                        'modelId': 0,
+                        'name': clean_name,
+                        'version': '',
+                        'type': 'lora',
+                        'weight': 1.0,
+                        'existsLocally': False,
+                        'localPath': None,
+                        'file_name': model_name,
+                        'hash': model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash,
+                        'thumbnailUrl': '/loras_static/images/no-preview.png',
+                        'baseModel': '',
+                        'size': 0,
+                        'downloadUrl': '',
+                        'isDeleted': False
+                    }
+
+                    # Try to get additional info from metadata provider
+                    if metadata_provider and model_hash:
+                        try:
+                            civitai_info = await metadata_provider.get_model_by_hash(
+                                model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash
+                            )
+                            if civitai_info:
+                                lora_entry = await self.populate_lora_from_civitai(
+                                    lora_entry, civitai_info, recipe_scanner
+                                )
+                        except Exception as e:
+                            logger.debug(f"Error fetching info for LoRA {clean_name}: {e}")
+
+                    if lora_entry:
+                        loras.append(lora_entry)
+                elif param_type == 'model' or 'lora' not in model_name.lower():
+                    # This is likely a checkpoint
+                    checkpoint_entry = {
+                        'id': 0,
+                        'modelId': 0,
+                        'name': clean_name,
+                        'version': '',
+                        'type': 'checkpoint',
+                        'hash': model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash,
+                        'existsLocally': False,
+                        'localPath': None,
+                        'file_name': model_name,
+                        'thumbnailUrl': '/loras_static/images/no-preview.png',
+                        'baseModel': '',
+                        'size': 0,
+                        'downloadUrl': '',
+                        'isDeleted': False
+                    }
+
+                    # Try to get additional info from metadata provider
+                    if metadata_provider and model_hash:
+                        try:
+                            civitai_info = await metadata_provider.get_model_by_hash(
+                                model_hash.replace('0x', '') if model_hash.startswith('0x') else model_hash
+                            )
+                            if civitai_info:
+                                checkpoint_entry = await self.populate_checkpoint_from_civitai(
+                                    checkpoint_entry, civitai_info
+                                )
+                        except Exception as e:
+                            logger.debug(f"Error fetching info for checkpoint {clean_name}: {e}")
+
+                    checkpoint = checkpoint_entry
+
+            # Determine base model from loras or checkpoint
+            base_model = None
+            if loras:
+                base_models = [lora.get('baseModel') for lora in loras if lora.get('baseModel')]
+                if base_models:
+                    from collections import Counter
+                    base_model_counts = Counter(base_models)
+                    base_model = base_model_counts.most_common(1)[0][0]
+            elif checkpoint and checkpoint.get('baseModel'):
+                base_model = checkpoint['baseModel']
+
+            return {
+                'base_model': base_model,
+                'loras': loras,
+                'checkpoint': checkpoint,
+                'gen_params': gen_params,
+                'from_sui_image_params': True
+            }
+
+        except Exception as e:
+            logger.error(f"Error parsing SuiImage metadata: {e}", exc_info=True)
+            return {"error": str(e), "loras": []}

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/checkpoint_routes.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Dict
+from typing import Dict, List, Set
 from aiohttp import web
 
 from .base_model_routes import BaseModelRoutes
@@ -82,12 +82,22 @@ class CheckpointRoutes(BaseModelRoutes):
             return web.json_response({"error": str(e)}, status=500)
     
     async def get_checkpoints_roots(self, request: web.Request) -> web.Response:
-        """Return the list of checkpoint roots from config"""
+        """Return the list of checkpoint roots from config (including extra paths)"""
         try:
-            roots = config.checkpoints_roots
+            # Merge checkpoints_roots with extra_checkpoints_roots, preserving order and removing duplicates
+            roots: List[str] = []
+            roots.extend(config.checkpoints_roots or [])
+            roots.extend(config.extra_checkpoints_roots or [])
+            # Remove duplicates while preserving order
+            seen: set = set()
+            unique_roots: List[str] = []
+            for root in roots:
+                if root and root not in seen:
+                    seen.add(root)
+                    unique_roots.append(root)
             return web.json_response({
                 "success": True,
-                "roots": roots
+                "roots": unique_roots
             })
         except Exception as e:
             logger.error(f"Error getting checkpoint roots: {e}", exc_info=True)
@@ -97,12 +107,22 @@ class CheckpointRoutes(BaseModelRoutes):
             }, status=500)
 
     async def get_unet_roots(self, request: web.Request) -> web.Response:
-        """Return the list of unet roots from config"""
+        """Return the list of unet roots from config (including extra paths)"""
         try:
-            roots = config.unet_roots
+            # Merge unet_roots with extra_unet_roots, preserving order and removing duplicates
+            roots: List[str] = []
+            roots.extend(config.unet_roots or [])
+            roots.extend(config.extra_unet_roots or [])
+            # Remove duplicates while preserving order
+            seen: set = set()
+            unique_roots: List[str] = []
+            for root in roots:
+                if root and root not in seen:
+                    seen.add(root)
+                    unique_roots.append(root)
             return web.json_response({
                 "success": True,
-                "roots": roots
+                "roots": unique_roots
             })
         except Exception as e:
             logger.error(f"Error getting unet roots: {e}", exc_info=True)

py/routes/handlers/base_model_handlers.py

@@ -0,0 +1,141 @@
+"""Handlers for base model related endpoints."""
+
+from __future__ import annotations
+
+import logging
+from typing import Any, Awaitable, Callable, Dict
+
+from aiohttp import web
+
+from ...services.civitai_base_model_service import get_civitai_base_model_service
+
+logger = logging.getLogger(__name__)
+
+
+class BaseModelHandlerSet:
+    """Collection of handlers for base model operations."""
+
+    def __init__(
+        self,
+        base_model_service_factory: Callable[[], Any] = get_civitai_base_model_service,
+    ) -> None:
+        self._base_model_service_factory = base_model_service_factory
+
+    def to_route_mapping(
+        self,
+    ) -> Dict[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
+        """Return mapping of route names to handler methods."""
+        return {
+            "get_base_models": self.get_base_models,
+            "refresh_base_models": self.refresh_base_models,
+            "get_base_model_categories": self.get_base_model_categories,
+            "get_base_model_cache_status": self.get_base_model_cache_status,
+        }
+
+    async def get_base_models(self, request: web.Request) -> web.Response:
+        """Get merged base models (hardcoded + remote from Civitai).
+
+        Query Parameters:
+            refresh: If 'true', force refresh from API
+
+        Returns:
+            JSON response with:
+            - models: List of base model names
+            - source: 'cache', 'api', or 'fallback'
+            - last_updated: ISO timestamp
+            - counts: hardcoded_count, remote_count, merged_count
+        """
+        try:
+            service = await self._base_model_service_factory()
+
+            # Check for refresh parameter
+            force_refresh = request.query.get("refresh", "").lower() == "true"
+
+            result = await service.get_base_models(force_refresh=force_refresh)
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": result,
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in get_base_models: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )
+
+    async def refresh_base_models(self, request: web.Request) -> web.Response:
+        """Force refresh base models from Civitai API.
+
+        Returns:
+            JSON response with refreshed data
+        """
+        try:
+            service = await self._base_model_service_factory()
+            result = await service.refresh_cache()
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": result,
+                    "message": "Base models cache refreshed successfully",
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in refresh_base_models: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )
+
+    async def get_base_model_categories(self, request: web.Request) -> web.Response:
+        """Get categorized base models.
+
+        Returns:
+            JSON response with categorized models
+        """
+        try:
+            service = await self._base_model_service_factory()
+            categories = service.get_model_categories()
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": categories,
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in get_base_model_categories: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )
+
+    async def get_base_model_cache_status(self, request: web.Request) -> web.Response:
+        """Get cache status for base models.
+
+        Returns:
+            JSON response with cache status
+        """
+        try:
+            service = await self._base_model_service_factory()
+            status = service.get_cache_status()
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "data": status,
+                }
+            )
+
+        except Exception as e:
+            logger.error(f"Error in get_base_model_cache_status: {e}")
+            return web.json_response(
+                {"success": False, "error": str(e)},
+                status=500,
+            )

py/routes/handlers/model_handlers.py

@@ -16,9 +16,14 @@ import jinja2
 
 from ...config import config
 from ...services.download_coordinator import DownloadCoordinator
+from ...services.connectivity_guard import (
+    OFFLINE_FRIENDLY_MESSAGE,
+    is_expected_offline_error,
+)
 from ...services.metadata_sync_service import MetadataSyncService
 from ...services.model_file_service import ModelMoveService
 from ...services.preview_asset_service import PreviewAssetService
+from ...services.service_registry import ServiceRegistry
 from ...services.settings_manager import SettingsManager, get_settings_manager
 from ...services.tag_update_service import TagUpdateService
 from ...services.use_cases import (
@@ -64,7 +69,23 @@ class ModelPageView:
         self._settings = settings_service
         self._server_i18n = server_i18n
         self._logger = logger
-        self._app_version = self._get_app_version()
+
+    def _load_supporters(self) -> dict:
+        """Load supporters data from JSON file."""
+        try:
+            current_file = os.path.abspath(__file__)
+            root_dir = os.path.dirname(
+                os.path.dirname(os.path.dirname(os.path.dirname(current_file)))
+            )
+            supporters_path = os.path.join(root_dir, "data", "supporters.json")
+
+            if os.path.exists(supporters_path):
+                with open(supporters_path, "r", encoding="utf-8") as f:
+                    return json.load(f)
+        except Exception as e:
+            self._logger.debug(f"Failed to load supporters data: {e}")
+
+        return {"specialThanks": [], "allSupporters": [], "totalCount": 0}
 
     def _get_app_version(self) -> str:
         version = "1.0.0"
@@ -138,7 +159,7 @@ class ModelPageView:
                 "request": request,
                 "folders": [],
                 "t": self._server_i18n.get_translation,
-                "version": self._app_version,
+                "version": self._get_app_version(),
             }
 
             if not is_initializing:
@@ -207,6 +228,42 @@ class ModelListingHandler:
             )
             return web.json_response({"error": str(exc)}, status=500)
 
+    async def get_excluded_models(self, request: web.Request) -> web.Response:
+        start_time = time.perf_counter()
+        try:
+            params = self._parse_common_params(request)
+            result = await self._service.get_excluded_paginated_data(**params)
+
+            format_start = time.perf_counter()
+            formatted_result = {
+                "items": [
+                    await self._service.format_response(item)
+                    for item in result["items"]
+                ],
+                "total": result["total"],
+                "page": result["page"],
+                "page_size": result["page_size"],
+                "total_pages": result["total_pages"],
+            }
+            format_duration = time.perf_counter() - format_start
+
+            duration = time.perf_counter() - start_time
+            self._logger.debug(
+                "Request for %s/excluded took %.3fs (formatting: %.3fs)",
+                self._service.model_type,
+                duration,
+                format_duration,
+            )
+            return web.json_response(formatted_result)
+        except Exception as exc:
+            self._logger.error(
+                "Error retrieving excluded %ss: %s",
+                self._service.model_type,
+                exc,
+                exc_info=True,
+            )
+            return web.json_response({"error": str(exc)}, status=500)
+
     def _parse_common_params(self, request: web.Request) -> Dict:
         page = int(request.query.get("page", "1"))
         page_size = min(int(request.query.get("page_size", "20")), 100)
@@ -292,6 +349,13 @@ 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,
@@ -311,6 +375,9 @@ 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),
         }
 
@@ -365,6 +432,21 @@ class ModelManagementHandler:
             self._logger.error("Error excluding model: %s", exc, exc_info=True)
             return web.Response(text=str(exc), status=500)
 
+    async def unexclude_model(self, request: web.Request) -> web.Response:
+        try:
+            data = await request.json()
+            file_path = data.get("file_path")
+            if not file_path:
+                return web.Response(text="Model path is required", status=400)
+
+            result = await self._lifecycle_service.unexclude_model(file_path)
+            return web.json_response(result)
+        except ValueError as exc:
+            return web.json_response({"success": False, "error": str(exc)}, status=400)
+        except Exception as exc:
+            self._logger.error("Error restoring model: %s", exc, exc_info=True)
+            return web.Response(text=str(exc), status=500)
+
     async def fetch_civitai(self, request: web.Request) -> web.Response:
         try:
             data = await request.json()
@@ -383,20 +465,26 @@ class ModelManagementHandler:
                 return web.json_response(
                     {"success": False, "error": "Model not found in cache"}, status=404
                 )
-            
+
             # Check if hash needs to be calculated (lazy hash for checkpoints)
             sha256 = model_data.get("sha256")
             hash_status = model_data.get("hash_status", "completed")
-            
+
             if not sha256 or hash_status != "completed":
                 # For checkpoints, calculate hash on-demand
                 scanner = self._service.scanner
-                if hasattr(scanner, 'calculate_hash_for_model'):
-                    self._logger.info(f"Lazy hash calculation triggered for {file_path}")
+                if hasattr(scanner, "calculate_hash_for_model"):
+                    self._logger.info(
+                        f"Lazy hash calculation triggered for {file_path}"
+                    )
                     sha256 = await scanner.calculate_hash_for_model(file_path)
                     if not sha256:
                         return web.json_response(
-                            {"success": False, "error": "Failed to calculate SHA256 hash"}, status=500
+                            {
+                                "success": False,
+                                "error": "Failed to calculate SHA256 hash",
+                            },
+                            status=500,
                         )
                     # Update model_data with new hash
                     model_data["sha256"] = sha256
@@ -420,6 +508,11 @@ class ModelManagementHandler:
             formatted_metadata = await self._service.format_response(model_data)
             return web.json_response({"success": True, "metadata": formatted_metadata})
         except Exception as exc:
+            if is_expected_offline_error(str(exc)):
+                return web.json_response(
+                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
+                    status=503,
+                )
             self._logger.error("Error fetching from CivitAI: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
@@ -466,6 +559,11 @@ class ModelManagementHandler:
                 }
             )
         except Exception as exc:
+            if is_expected_offline_error(str(exc)):
+                return web.json_response(
+                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
+                    status=503,
+                )
             self._logger.error("Error re-linking to CivitAI: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
@@ -524,6 +622,153 @@ class ModelManagementHandler:
             self._logger.error("Error replacing preview: %s", exc, exc_info=True)
             return web.Response(text=str(exc), status=500)
 
+    async def set_preview_from_url(self, request: web.Request) -> web.Response:
+        """Set a preview image from a remote URL (e.g., CivitAI)."""
+        try:
+            from ...utils.civitai_utils import rewrite_preview_url
+            from ...services.downloader import get_downloader
+
+            data = await request.json()
+            model_path = data.get("model_path")
+            image_url = data.get("image_url")
+            nsfw_level = data.get("nsfw_level", 0)
+
+            if not model_path:
+                return web.json_response(
+                    {"success": False, "error": "Model path is required"}, status=400
+                )
+
+            if not image_url:
+                return web.json_response(
+                    {"success": False, "error": "Image URL is required"}, status=400
+                )
+
+            # Rewrite URL to use optimized rendition if it's a Civitai URL
+            optimized_url, was_rewritten = rewrite_preview_url(
+                image_url, media_type="image"
+            )
+            if was_rewritten and optimized_url:
+                self._logger.info(
+                    f"Rewritten preview URL to optimized version: {optimized_url}"
+                )
+            else:
+                optimized_url = image_url
+
+            # Download the image using the Downloader service
+            self._logger.info(
+                f"Downloading preview from {optimized_url} for {model_path}"
+            )
+            downloader = await get_downloader()
+            success, preview_data, headers = await downloader.download_to_memory(
+                optimized_url, use_auth=False, return_headers=True
+            )
+
+            if not success:
+                return web.json_response(
+                    {
+                        "success": False,
+                        "error": f"Failed to download image: {preview_data}",
+                    },
+                    status=502,
+                )
+
+            # preview_data is bytes when success is True
+            preview_bytes = (
+                preview_data
+                if isinstance(preview_data, bytes)
+                else preview_data.encode("utf-8")
+            )
+
+            # Determine content type from response headers
+            content_type = (
+                headers.get("Content-Type", "image/jpeg") if headers else "image/jpeg"
+            )
+
+            # Extract original filename from URL
+            original_filename = None
+            if "?" in image_url:
+                url_path = image_url.split("?")[0]
+            else:
+                url_path = image_url
+            original_filename = url_path.split("/")[-1] if "/" in url_path else None
+
+            result = await self._preview_service.replace_preview(
+                model_path=model_path,
+                preview_data=preview_data,
+                content_type=content_type,
+                original_filename=original_filename,
+                nsfw_level=nsfw_level,
+                update_preview_in_cache=self._service.scanner.update_preview_in_cache,
+                metadata_loader=self._metadata_sync.load_local_metadata,
+            )
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "preview_url": config.get_preview_static_url(
+                        result["preview_path"]
+                    ),
+                    "preview_nsfw_level": result["preview_nsfw_level"],
+                }
+            )
+        except Exception as exc:
+            self._logger.error("Error setting preview from URL: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
+            if not image_url:
+                return web.json_response(
+                    {"success": False, "error": "Image URL is required"}, status=400
+                )
+
+            # Download the image from the remote URL
+            self._logger.info(f"Downloading preview from {image_url} for {model_path}")
+            async with aiohttp.ClientSession() as session:
+                async with session.get(image_url) as response:
+                    if response.status != 200:
+                        return web.json_response(
+                            {
+                                "success": False,
+                                "error": f"Failed to download image: HTTP {response.status}",
+                            },
+                            status=502,
+                        )
+
+                    content_type = response.headers.get("Content-Type", "image/jpeg")
+                    preview_data = await response.read()
+
+                    # Extract original filename from URL
+                    original_filename = None
+                    if "?" in image_url:
+                        url_path = image_url.split("?")[0]
+                    else:
+                        url_path = image_url
+                    original_filename = (
+                        url_path.split("/")[-1] if "/" in url_path else None
+                    )
+
+            result = await self._preview_service.replace_preview(
+                model_path=model_path,
+                preview_data=preview_bytes,
+                content_type=content_type,
+                original_filename=original_filename,
+                nsfw_level=nsfw_level,
+                update_preview_in_cache=self._service.scanner.update_preview_in_cache,
+                metadata_loader=self._metadata_sync.load_local_metadata,
+            )
+
+            return web.json_response(
+                {
+                    "success": True,
+                    "preview_url": config.get_preview_static_url(
+                        result["preview_path"]
+                    ),
+                    "preview_nsfw_level": result["preview_nsfw_level"],
+                }
+            )
+        except Exception as exc:
+            self._logger.error("Error setting preview from URL: %s", exc, exc_info=True)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def save_metadata(self, request: web.Request) -> web.Response:
         try:
             data = await request.json()
@@ -679,7 +924,7 @@ class ModelQueryHandler:
     async def get_base_models(self, request: web.Request) -> web.Response:
         try:
             limit = int(request.query.get("limit", "20"))
-            if limit < 1 or limit > 100:
+            if limit < 0 or limit > 100:
                 limit = 20
             base_models = await self._service.get_base_models(limit)
             return web.json_response({"success": True, "base_models": base_models})
@@ -814,9 +1059,7 @@ class ModelQueryHandler:
                 # Format response
                 group = {"hash": sha256, "models": []}
                 for model in sorted_models:
-                    group["models"].append(
-                        await self._service.format_response(model)
-                    )
+                    group["models"].append(await self._service.format_response(model))
 
                 # Only include groups with 2+ models after filtering
                 if len(group["models"]) > 1:
@@ -845,7 +1088,9 @@ class ModelQueryHandler:
             "favorites_only": request.query.get("favorites_only", "").lower() == "true",
         }
 
-    def _apply_duplicate_filters(self, models: List[Dict[str, Any]], filters: Dict[str, Any]) -> List[Dict[str, Any]]:
+    def _apply_duplicate_filters(
+        self, models: List[Dict[str, Any]], filters: Dict[str, Any]
+    ) -> List[Dict[str, Any]]:
         """Apply filters to a list of models within a duplicate group."""
         result = models
 
@@ -886,7 +1131,9 @@ class ModelQueryHandler:
 
         return result
 
-    def _sort_duplicate_group(self, models: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    def _sort_duplicate_group(
+        self, models: List[Dict[str, Any]]
+    ) -> List[Dict[str, Any]]:
         """Sort models: originals first (left), copies (with -????. pattern) last (right)."""
         if len(models) <= 1:
             return models
@@ -1096,8 +1343,11 @@ class ModelQueryHandler:
     async def get_relative_paths(self, request: web.Request) -> web.Response:
         try:
             search = request.query.get("search", "").strip()
-            limit = min(int(request.query.get("limit", "15")), 50)
-            matching_paths = await self._service.search_relative_paths(search, limit)
+            limit = min(int(request.query.get("limit", "15")), 100)
+            offset = max(0, int(request.query.get("offset", "0")))
+            matching_paths = await self._service.search_relative_paths(
+                search, limit, offset
+            )
             return web.json_response(
                 {"success": True, "relative_paths": matching_paths}
             )
@@ -1171,10 +1421,13 @@ class ModelDownloadHandler:
                 data["source"] = source
             if file_params_json:
                 import json
+
                 try:
                     data["file_params"] = json.loads(file_params_json)
                 except json.JSONDecodeError:
-                    self._logger.warning("Invalid file_params JSON: %s", file_params_json)
+                    self._logger.warning(
+                        "Invalid file_params JSON: %s", file_params_json
+                    )
 
             loop = asyncio.get_event_loop()
             future = loop.create_future()
@@ -1344,6 +1597,20 @@ class ModelCivitaiHandler:
 
             cache = await self._service.scanner.get_cached_data()
             version_index = cache.version_index
+            downloaded_version_ids: set[int] = set()
+            try:
+                history_service = await ServiceRegistry.get_downloaded_version_history_service()
+                downloaded_version_ids = set(
+                    await history_service.get_downloaded_version_ids(
+                        self._service.model_type,
+                        model_id,
+                    )
+                )
+            except Exception as exc:  # pragma: no cover - defensive logging
+                self._logger.debug(
+                    "Failed to load download history for CivitAI versions: %s",
+                    exc,
+                )
 
             for version in versions:
                 version_id = None
@@ -1360,6 +1627,9 @@ class ModelCivitaiHandler:
                     else None
                 )
                 version["existsLocally"] = cache_entry is not None
+                version["hasBeenDownloaded"] = (
+                    version_id in downloaded_version_ids if version_id is not None else False
+                )
                 if cache_entry and isinstance(cache_entry, Mapping):
                     local_path = cache_entry.get("file_path")
                     if local_path:
@@ -1602,6 +1872,11 @@ class ModelUpdateHandler:
                 status=429,
             )
         except Exception as exc:  # pragma: no cover - defensive log
+            if is_expected_offline_error(str(exc)):
+                return web.json_response(
+                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
+                    status=503,
+                )
             self._logger.error("Failed to fetch license info: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
@@ -1690,9 +1965,12 @@ class ModelUpdateHandler:
                 {"success": False, "error": str(exc) or "Rate limited"}, status=429
             )
         except Exception as exc:  # pragma: no cover - defensive logging
-            self._logger.error(
-                "Failed to refresh model updates: %s", exc, exc_info=True
-            )
+            if is_expected_offline_error(str(exc)):
+                return web.json_response(
+                    {"success": False, "error": OFFLINE_FRIENDLY_MESSAGE},
+                    status=503,
+                )
+            self._logger.error("Failed to refresh model updates: %s", exc, exc_info=True)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
         serialized_records = []
@@ -1905,7 +2183,8 @@ class ModelUpdateHandler:
         from dataclasses import replace
 
         new_record = replace(
-            record, versions=list(version_map.values()),
+            record,
+            versions=list(version_map.values()),
         )
 
         # Optionally persist to database for caching
@@ -2077,7 +2356,7 @@ class ModelUpdateHandler:
         self,
         record,
         *,
-        version_context: Optional[Dict[int, Dict[str, Optional[str]]]] = None,
+        version_context: Optional[Dict[int, Dict[str, Any]]] = None,
     ) -> Dict:
         context = version_context or {}
         # Check user setting for hiding early access versions
@@ -2106,7 +2385,7 @@ class ModelUpdateHandler:
 
     @staticmethod
     def _serialize_version(
-        version, context: Optional[Dict[str, Optional[str]]]
+        version, context: Optional[Dict[str, Any]]
     ) -> Dict:
         context = context or {}
         preview_override = context.get("preview_override")
@@ -2120,6 +2399,7 @@ class ModelUpdateHandler:
         if version.early_access_ends_at:
             try:
                 from datetime import datetime, timezone
+
                 ea_date = datetime.fromisoformat(
                     version.early_access_ends_at.replace("Z", "+00:00")
                 )
@@ -2127,7 +2407,7 @@ class ModelUpdateHandler:
             except (ValueError, AttributeError):
                 # If date parsing fails, treat as active EA (conservative)
                 is_early_access = True
-        elif getattr(version, 'is_early_access', False):
+        elif getattr(version, "is_early_access", False):
             # Fallback to basic EA flag from bulk API
             is_early_access = True
 
@@ -2139,6 +2419,7 @@ class ModelUpdateHandler:
             "sizeBytes": version.size_bytes,
             "previewUrl": preview_url,
             "isInLibrary": version.is_in_library,
+            "hasBeenDownloaded": bool(context.get("has_been_downloaded", False)),
             "shouldIgnore": version.should_ignore,
             "earlyAccessEndsAt": version.early_access_ends_at,
             "isEarlyAccess": is_early_access,
@@ -2148,8 +2429,31 @@ class ModelUpdateHandler:
 
     async def _build_version_context(
         self, record
-    ) -> Dict[int, Dict[str, Optional[str]]]:
-        context: Dict[int, Dict[str, Optional[str]]] = {}
+    ) -> Dict[int, Dict[str, Any]]:
+        context: Dict[int, Dict[str, Any]] = {}
+        downloaded_version_ids: set[int] = set()
+        try:
+            history_service = await ServiceRegistry.get_downloaded_version_history_service()
+            downloaded_version_ids = set(
+                await history_service.get_downloaded_version_ids(
+                    record.model_type,
+                    record.model_id,
+                )
+            )
+        except Exception as exc:  # pragma: no cover - defensive logging
+            self._logger.debug(
+                "Failed to load download history while building version context: %s",
+                exc,
+            )
+
+        for version in record.versions:
+            context[version.version_id] = {
+                "file_path": None,
+                "file_name": None,
+                "preview_override": None,
+                "has_been_downloaded": version.version_id in downloaded_version_ids,
+            }
+
         try:
             cache = await self._service.scanner.get_cached_data()
         except Exception as exc:  # pragma: no cover - defensive logging
@@ -2168,16 +2472,21 @@ class ModelUpdateHandler:
             cache_entry = version_index.get(version.version_id)
             if isinstance(cache_entry, Mapping):
                 preview = cache_entry.get("preview_url")
-                context_entry: Dict[str, Optional[str]] = {
-                    "file_path": cache_entry.get("file_path"),
-                    "file_name": cache_entry.get("file_name"),
-                    "preview_override": None,
-                }
+                context_entry = context.setdefault(
+                    version.version_id,
+                    {
+                        "file_path": None,
+                        "file_name": None,
+                        "preview_override": None,
+                        "has_been_downloaded": version.version_id in downloaded_version_ids,
+                    },
+                )
+                context_entry["file_path"] = cache_entry.get("file_path")
+                context_entry["file_name"] = cache_entry.get("file_name")
                 if isinstance(preview, str) and preview:
                     context_entry["preview_override"] = config.get_preview_static_url(
                         preview
                     )
-                context[version.version_id] = context_entry
         return context
 
 
@@ -2201,12 +2510,15 @@ class ModelHandlerSet:
         return {
             "handle_models_page": self.page_view.handle,
             "get_models": self.listing.get_models,
+            "get_excluded_models": self.listing.get_excluded_models,
             "delete_model": self.management.delete_model,
             "exclude_model": self.management.exclude_model,
+            "unexclude_model": self.management.unexclude_model,
             "fetch_civitai": self.management.fetch_civitai,
             "fetch_all_civitai": self.civitai.fetch_all_civitai,
             "relink_civitai": self.management.relink_civitai,
             "replace_preview": self.management.replace_preview,
+            "set_preview_from_url": self.management.set_preview_from_url,
             "save_metadata": self.management.save_metadata,
             "add_tags": self.management.add_tags,
             "rename_model": self.management.rename_model,

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
@@ -24,11 +26,12 @@ from ...services.recipes import (
     RecipeValidationError,
 )
 from ...services.metadata_service import get_default_metadata_provider
-from ...utils.civitai_utils import rewrite_preview_url
+from ...utils.civitai_utils import extract_civitai_image_id, rewrite_preview_url
 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 {
@@ -75,12 +81,18 @@ class RecipeHandlerSet:
             "bulk_delete": self.management.bulk_delete,
             "save_recipe_from_widget": self.management.save_recipe_from_widget,
             "get_recipes_for_lora": self.query.get_recipes_for_lora,
+            "get_recipes_for_checkpoint": self.query.get_recipes_for_checkpoint,
             "scan_recipes": self.query.scan_recipes,
             "move_recipe": self.management.move_recipe,
             "repair_recipes": self.management.repair_recipes,
             "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 +182,10 @@ 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",
             }
 
@@ -205,6 +219,7 @@ class RecipeListingHandler:
                 filters["tags"] = tag_filters
 
             lora_hash = request.query.get("lora_hash")
+            checkpoint_hash = request.query.get("checkpoint_hash")
 
             result = await recipe_scanner.get_paginated_data(
                 page=page,
@@ -214,6 +229,7 @@ class RecipeListingHandler:
                 filters=filters,
                 search_options=search_options,
                 lora_hash=lora_hash,
+                checkpoint_hash=checkpoint_hash,
                 folder=folder,
                 recursive=recursive,
             )
@@ -246,7 +262,9 @@ 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 +274,9 @@ 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 +313,9 @@ 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:
@@ -307,16 +329,24 @@ class RecipeQueryHandler:
             if recipe_scanner is None:
                 raise RuntimeError("Recipe scanner unavailable")
 
+            limit = int(request.query.get("limit", "20"))
             cache = await recipe_scanner.get_cached_data()
 
             base_model_counts: Dict[str, int] = {}
             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)
+            if limit > 0:
+                sorted_models = sorted_models[:limit]
             return web.json_response({"success": True, "base_models": sorted_models})
         except Exception as exc:
             self._logger.error("Error retrieving base models: %s", exc, exc_info=True)
@@ -345,7 +375,9 @@ 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 +390,9 @@ 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 +405,9 @@ 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 +419,9 @@ 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})
@@ -391,6 +429,28 @@ class RecipeQueryHandler:
             self._logger.error("Error getting recipes for Lora: %s", exc)
             return web.json_response({"success": False, "error": str(exc)}, status=500)
 
+    async def get_recipes_for_checkpoint(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")
+
+            checkpoint_hash = request.query.get("hash")
+            if not checkpoint_hash:
+                return web.json_response(
+                    {"success": False, "error": "Checkpoint hash is required"},
+                    status=400,
+                )
+
+            matching_recipes = await recipe_scanner.get_recipes_for_checkpoint(
+                checkpoint_hash
+            )
+            return web.json_response({"success": True, "recipes": matching_recipes})
+        except Exception as exc:
+            self._logger.error("Error getting recipes for checkpoint: %s", exc)
+            return web.json_response({"success": False, "error": str(exc)}, status=500)
+
     async def scan_recipes(self, request: web.Request) -> web.Response:
         try:
             await self._ensure_dependencies_ready()
@@ -400,7 +460,9 @@ 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 +491,9 @@ 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 +501,9 @@ 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 +526,9 @@ 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 +536,9 @@ 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 +549,13 @@ 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 +572,13 @@ 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 +639,17 @@ 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 +663,12 @@ 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 +678,9 @@ 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 +690,15 @@ 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 +708,10 @@ 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 +727,26 @@ 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 +754,88 @@ 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"))
-            
+
+            self._logger.info(
+                "Remote recipe import received: url=%s, request_gen_params_keys=%s, lora_count=%d, checkpoint_keys=%s",
+                image_url,
+                sorted(gen_params_request.keys()) if gen_params_request else [],
+                len(lora_entries),
+                sorted(checkpoint_entry.keys()) if isinstance(checkpoint_entry, dict) else [],
+            )
+
             # 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 +844,9 @@ 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 +854,18 @@ 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 +884,9 @@ 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 +940,11 @@ 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 +973,11 @@ 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 +1066,9 @@ 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 +1140,9 @@ 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:
@@ -1063,18 +1199,24 @@ class RecipeManagementHandler:
                 temp_path = temp_file.name
             download_url = image_url
             image_info = None
-            civitai_match = re.match(r"https://civitai\.com/images/(\d+)", image_url)
-            if civitai_match:
+            civitai_image_id = extract_civitai_image_id(image_url)
+            if civitai_image_id:
                 if civitai_client is None:
-                    raise RecipeDownloadError("Civitai client unavailable for image download")
-                image_info = await civitai_client.get_image_info(civitai_match.group(1))
+                    raise RecipeDownloadError(
+                        "Civitai client unavailable for image download"
+                    )
+                image_info = await civitai_client.get_image_info(
+                    civitai_image_id, source_url=image_url
+                )
                 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 +1225,24 @@ 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_image_id and image_info else None,
+                )
         except RecipeDownloadError:
             raise
         except RecipeValidationError:
@@ -1108,14 +1256,15 @@ 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 +1283,9 @@ 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 +1430,311 @@ 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/misc_route_registrar.py

@@ -22,10 +22,16 @@ class RouteDefinition:
 MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/settings", "get_settings"),
     RouteDefinition("POST", "/api/lm/settings", "update_settings"),
+    RouteDefinition("GET", "/api/lm/doctor/diagnostics", "get_doctor_diagnostics"),
+    RouteDefinition("POST", "/api/lm/doctor/repair-cache", "repair_doctor_cache"),
+    RouteDefinition("POST", "/api/lm/doctor/export-bundle", "export_doctor_bundle"),
     RouteDefinition("GET", "/api/lm/priority-tags", "get_priority_tags"),
     RouteDefinition("GET", "/api/lm/settings/libraries", "get_settings_libraries"),
     RouteDefinition("POST", "/api/lm/settings/libraries/activate", "activate_library"),
     RouteDefinition("GET", "/api/lm/health-check", "health_check"),
+    RouteDefinition("GET", "/api/lm/supporters", "get_supporters"),
+    RouteDefinition("GET", "/api/lm/wildcards/search", "search_wildcards"),
+    RouteDefinition("POST", "/api/lm/wildcards/open-location", "open_wildcards_location"),
     RouteDefinition("POST", "/api/lm/open-file-location", "open_file_location"),
     RouteDefinition("POST", "/api/lm/update-usage-stats", "update_usage_stats"),
     RouteDefinition("GET", "/api/lm/get-usage-stats", "get_usage_stats"),
@@ -36,13 +42,53 @@ MISC_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/update-node-widget", "update_node_widget"),
     RouteDefinition("GET", "/api/lm/get-registry", "get_registry"),
     RouteDefinition("GET", "/api/lm/check-model-exists", "check_model_exists"),
+    RouteDefinition(
+        "GET",
+        "/api/lm/model-version-download-status",
+        "get_model_version_download_status",
+    ),
+    RouteDefinition(
+        "POST",
+        "/api/lm/model-version-download-status",
+        "set_model_version_download_status",
+    ),
+    RouteDefinition(
+        "GET",
+        "/api/lm/set-model-version-download-status",
+        "set_model_version_download_status",
+    ),
     RouteDefinition("GET", "/api/lm/civitai/user-models", "get_civitai_user_models"),
-    RouteDefinition("POST", "/api/lm/download-metadata-archive", "download_metadata_archive"),
-    RouteDefinition("POST", "/api/lm/remove-metadata-archive", "remove_metadata_archive"),
-    RouteDefinition("GET", "/api/lm/metadata-archive-status", "get_metadata_archive_status"),
-    RouteDefinition("GET", "/api/lm/model-versions-status", "get_model_versions_status"),
+    RouteDefinition(
+        "POST", "/api/lm/download-metadata-archive", "download_metadata_archive"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/remove-metadata-archive", "remove_metadata_archive"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/metadata-archive-status", "get_metadata_archive_status"
+    ),
+    RouteDefinition("GET", "/api/lm/backup/status", "get_backup_status"),
+    RouteDefinition("POST", "/api/lm/backup/export", "export_backup"),
+    RouteDefinition("POST", "/api/lm/backup/import", "import_backup"),
+    RouteDefinition("POST", "/api/lm/backup/open-location", "open_backup_location"),
+    RouteDefinition(
+        "GET", "/api/lm/model-versions-status", "get_model_versions_status"
+    ),
     RouteDefinition("POST", "/api/lm/settings/open-location", "open_settings_location"),
     RouteDefinition("GET", "/api/lm/custom-words/search", "search_custom_words"),
+    RouteDefinition("GET", "/api/lm/example-workflows", "get_example_workflows"),
+    RouteDefinition(
+        "GET", "/api/lm/example-workflows/{filename}", "get_example_workflow"
+    ),
+    # Base model management routes
+    RouteDefinition("GET", "/api/lm/base-models", "get_base_models"),
+    RouteDefinition("POST", "/api/lm/base-models/refresh", "refresh_base_models"),
+    RouteDefinition(
+        "GET", "/api/lm/base-models/categories", "get_base_model_categories"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/base-models/cache-status", "get_base_model_cache_status"
+    ),
 )
 
 
@@ -66,7 +112,11 @@ class MiscRouteRegistrar:
         definitions: Iterable[RouteDefinition] = MISC_ROUTE_DEFINITIONS,
     ) -> None:
         for definition in definitions:
-            self._bind(definition.method, definition.path, handler_lookup[definition.handler_name])
+            self._bind(
+                definition.method,
+                definition.path,
+                handler_lookup[definition.handler_name],
+            )
 
     def _bind(self, method: str, path: str, handler: Callable) -> None:
         add_method_name = self._METHOD_MAP[method.upper()]

py/routes/misc_routes.py

@@ -19,9 +19,12 @@ from ..services.downloader import get_downloader
 from ..utils.usage_stats import UsageStats
 from .handlers.misc_handlers import (
     CustomWordsHandler,
+    DoctorHandler,
+    ExampleWorkflowsHandler,
     FileSystemHandler,
     HealthCheckHandler,
     LoraCodeHandler,
+    BackupHandler,
     MetadataArchiveHandler,
     MiscHandlerSet,
     ModelExampleFilesHandler,
@@ -29,17 +32,21 @@ from .handlers.misc_handlers import (
     NodeRegistry,
     NodeRegistryHandler,
     SettingsHandler,
+    SupportersHandler,
     TrainedWordsHandler,
     UsageStatsHandler,
+    WildcardsHandler,
     build_service_registry_adapter,
 )
+from .handlers.base_model_handlers import BaseModelHandlerSet
 from .misc_route_registrar import MiscRouteRegistrar
 
 logger = logging.getLogger(__name__)
 
-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"
+)
 
 
 class MiscRoutes:
@@ -74,7 +81,9 @@ class MiscRoutes:
         self._node_registry = node_registry or NodeRegistry()
         self._standalone_mode = standalone_mode_flag
 
-        self._handler_mapping: Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]] | None = None
+        self._handler_mapping: (
+            Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]] | None
+        ) = None
 
     @staticmethod
     def setup_routes(app: web.Application) -> None:
@@ -86,7 +95,9 @@ class MiscRoutes:
         registrar = self._registrar_factory(app)
         registrar.register_routes(self._ensure_handler_mapping())
 
-    def _ensure_handler_mapping(self) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
+    def _ensure_handler_mapping(
+        self,
+    ) -> Mapping[str, Callable[[web.Request], Awaitable[web.StreamResponse]]]:
         if self._handler_mapping is None:
             handler_set = self._create_handler_set()
             self._handler_mapping = handler_set.to_route_mapping()
@@ -108,6 +119,7 @@ class MiscRoutes:
             settings_service=self._settings,
             metadata_provider_updater=self._metadata_provider_updater,
         )
+        backup = BackupHandler()
         filesystem = FileSystemHandler(settings_service=self._settings)
         node_registry_handler = NodeRegistryHandler(
             node_registry=self._node_registry,
@@ -119,6 +131,11 @@ class MiscRoutes:
             metadata_provider_factory=self._metadata_provider_factory,
         )
         custom_words = CustomWordsHandler()
+        wildcards = WildcardsHandler()
+        supporters = SupportersHandler()
+        doctor = DoctorHandler(settings_service=self._settings)
+        example_workflows = ExampleWorkflowsHandler()
+        base_model = BaseModelHandlerSet()
 
         return self._handler_set_factory(
             health=health,
@@ -130,8 +147,14 @@ class MiscRoutes:
             node_registry=node_registry_handler,
             model_library=model_library,
             metadata_archive=metadata_archive,
+            backup=backup,
             filesystem=filesystem,
             custom_words=custom_words,
+            wildcards=wildcards,
+            supporters=supporters,
+            doctor=doctor,
+            example_workflows=example_workflows,
+            base_model=base_model,
         )
 
 

py/routes/model_route_registrar.py

@@ -1,4 +1,5 @@
 """Route registrar for model endpoints."""
+
 from __future__ import annotations
 
 from dataclasses import dataclass
@@ -21,12 +22,17 @@ class RouteDefinition:
 
 COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/{prefix}/list", "get_models"),
+    RouteDefinition("GET", "/api/lm/{prefix}/excluded", "get_excluded_models"),
     RouteDefinition("POST", "/api/lm/{prefix}/delete", "delete_model"),
     RouteDefinition("POST", "/api/lm/{prefix}/exclude", "exclude_model"),
+    RouteDefinition("POST", "/api/lm/{prefix}/unexclude", "unexclude_model"),
     RouteDefinition("POST", "/api/lm/{prefix}/fetch-civitai", "fetch_civitai"),
     RouteDefinition("POST", "/api/lm/{prefix}/fetch-all-civitai", "fetch_all_civitai"),
     RouteDefinition("POST", "/api/lm/{prefix}/relink-civitai", "relink_civitai"),
     RouteDefinition("POST", "/api/lm/{prefix}/replace-preview", "replace_preview"),
+    RouteDefinition(
+        "POST", "/api/lm/{prefix}/set-preview-from-url", "set_preview_from_url"
+    ),
     RouteDefinition("POST", "/api/lm/{prefix}/save-metadata", "save_metadata"),
     RouteDefinition("POST", "/api/lm/{prefix}/add-tags", "add_tags"),
     RouteDefinition("POST", "/api/lm/{prefix}/rename", "rename_model"),
@@ -36,7 +42,9 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("POST", "/api/lm/{prefix}/move_models_bulk", "move_models_bulk"),
     RouteDefinition("GET", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
     RouteDefinition("POST", "/api/lm/{prefix}/auto-organize", "auto_organize_models"),
-    RouteDefinition("GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/auto-organize-progress", "get_auto_organize_progress"
+    ),
     RouteDefinition("GET", "/api/lm/{prefix}/top-tags", "get_top_tags"),
     RouteDefinition("GET", "/api/lm/{prefix}/base-models", "get_base_models"),
     RouteDefinition("GET", "/api/lm/{prefix}/model-types", "get_model_types"),
@@ -44,30 +52,60 @@ COMMON_ROUTE_DEFINITIONS: tuple[RouteDefinition, ...] = (
     RouteDefinition("GET", "/api/lm/{prefix}/roots", "get_model_roots"),
     RouteDefinition("GET", "/api/lm/{prefix}/folders", "get_folders"),
     RouteDefinition("GET", "/api/lm/{prefix}/folder-tree", "get_folder_tree"),
-    RouteDefinition("GET", "/api/lm/{prefix}/unified-folder-tree", "get_unified_folder_tree"),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/unified-folder-tree", "get_unified_folder_tree"
+    ),
     RouteDefinition("GET", "/api/lm/{prefix}/find-duplicates", "find_duplicate_models"),
-    RouteDefinition("GET", "/api/lm/{prefix}/find-filename-conflicts", "find_filename_conflicts"),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/find-filename-conflicts", "find_filename_conflicts"
+    ),
     RouteDefinition("GET", "/api/lm/{prefix}/get-notes", "get_model_notes"),
     RouteDefinition("GET", "/api/lm/{prefix}/preview-url", "get_model_preview_url"),
     RouteDefinition("GET", "/api/lm/{prefix}/civitai-url", "get_model_civitai_url"),
     RouteDefinition("GET", "/api/lm/{prefix}/metadata", "get_model_metadata"),
-    RouteDefinition("GET", "/api/lm/{prefix}/model-description", "get_model_description"),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/model-description", "get_model_description"
+    ),
     RouteDefinition("GET", "/api/lm/{prefix}/relative-paths", "get_relative_paths"),
-    RouteDefinition("GET", "/api/lm/{prefix}/civitai/versions/{model_id}", "get_civitai_versions"),
-    RouteDefinition("GET", "/api/lm/{prefix}/civitai/model/version/{modelVersionId}", "get_civitai_model_by_version"),
-    RouteDefinition("GET", "/api/lm/{prefix}/civitai/model/hash/{hash}", "get_civitai_model_by_hash"),
-    RouteDefinition("POST", "/api/lm/{prefix}/updates/refresh", "refresh_model_updates"),
-    RouteDefinition("POST", "/api/lm/{prefix}/updates/fetch-missing-license", "fetch_missing_civitai_license_data"),
-    RouteDefinition("POST", "/api/lm/{prefix}/updates/ignore", "set_model_update_ignore"),
-    RouteDefinition("POST", "/api/lm/{prefix}/updates/ignore-version", "set_version_update_ignore"),
-    RouteDefinition("GET", "/api/lm/{prefix}/updates/status/{model_id}", "get_model_update_status"),
-    RouteDefinition("GET", "/api/lm/{prefix}/updates/versions/{model_id}", "get_model_versions"),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/civitai/versions/{model_id}", "get_civitai_versions"
+    ),
+    RouteDefinition(
+        "GET",
+        "/api/lm/{prefix}/civitai/model/version/{modelVersionId}",
+        "get_civitai_model_by_version",
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/civitai/model/hash/{hash}", "get_civitai_model_by_hash"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/{prefix}/updates/refresh", "refresh_model_updates"
+    ),
+    RouteDefinition(
+        "POST",
+        "/api/lm/{prefix}/updates/fetch-missing-license",
+        "fetch_missing_civitai_license_data",
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/{prefix}/updates/ignore", "set_model_update_ignore"
+    ),
+    RouteDefinition(
+        "POST", "/api/lm/{prefix}/updates/ignore-version", "set_version_update_ignore"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/updates/status/{model_id}", "get_model_update_status"
+    ),
+    RouteDefinition(
+        "GET", "/api/lm/{prefix}/updates/versions/{model_id}", "get_model_versions"
+    ),
     RouteDefinition("POST", "/api/lm/download-model", "download_model"),
     RouteDefinition("GET", "/api/lm/download-model-get", "download_model_get"),
     RouteDefinition("GET", "/api/lm/cancel-download-get", "cancel_download_get"),
     RouteDefinition("GET", "/api/lm/pause-download", "pause_download_get"),
     RouteDefinition("GET", "/api/lm/resume-download", "resume_download_get"),
-    RouteDefinition("GET", "/api/lm/download-progress/{download_id}", "get_download_progress"),
+    RouteDefinition(
+        "GET", "/api/lm/download-progress/{download_id}", "get_download_progress"
+    ),
     RouteDefinition("POST", "/api/lm/{prefix}/cancel-task", "cancel_task"),
     RouteDefinition("GET", "/{prefix}", "handle_models_page"),
 )
@@ -94,12 +132,18 @@ class ModelRouteRegistrar:
         definitions: Iterable[RouteDefinition] = COMMON_ROUTE_DEFINITIONS,
     ) -> None:
         for definition in definitions:
-            self._bind_route(definition.method, definition.build_path(prefix), handler_lookup[definition.handler_name])
+            self._bind_route(
+                definition.method,
+                definition.build_path(prefix),
+                handler_lookup[definition.handler_name],
+            )
 
     def add_route(self, method: str, path: str, handler: Callable) -> None:
         self._bind_route(method, path, handler)
 
-    def add_prefixed_route(self, method: str, path_template: str, prefix: str, handler: Callable) -> None:
+    def add_prefixed_route(
+        self, method: str, path_template: str, prefix: str, handler: Callable
+    ) -> None:
         self._bind_route(method, path_template.replace("{prefix}", prefix), handler)
 
     def _bind_route(self, method: str, path: str, handler: Callable) -> None:

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 +47,29 @@ 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/for-checkpoint", "get_recipes_for_checkpoint"
+    ),
     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 +86,9 @@ 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/routes/stats_routes.py

@@ -209,6 +209,80 @@ class StatsRoutes:
                 'error': str(e)
             }, status=500)
 
+    async def get_model_usage_list(self, request: web.Request) -> web.Response:
+        """Get paginated model usage list for infinite scrolling"""
+        try:
+            await self.init_services()
+            
+            model_type = request.query.get('type', 'lora')
+            sort_order = request.query.get('sort', 'desc')
+            
+            try:
+                limit = int(request.query.get('limit', '50'))
+                offset = int(request.query.get('offset', '0'))
+            except ValueError:
+                limit = 50
+                offset = 0
+
+            # Get usage statistics
+            usage_data = await self.usage_stats.get_stats()
+            
+            # Select proper cache and usage dict based on type
+            if model_type == 'lora':
+                cache = await self.lora_scanner.get_cached_data()
+                type_usage_data = usage_data.get('loras', {})
+            elif model_type == 'checkpoint':
+                cache = await self.checkpoint_scanner.get_cached_data()
+                type_usage_data = usage_data.get('checkpoints', {})
+            elif model_type == 'embedding':
+                cache = await self.embedding_scanner.get_cached_data()
+                type_usage_data = usage_data.get('embeddings', {})
+            else:
+                return web.json_response({'success': False, 'error': f"Invalid model type: {model_type}"}, status=400)
+
+            # Create list of all models
+            all_models = []
+            for item in cache.raw_data:
+                sha256 = item.get('sha256')
+                usage_info = type_usage_data.get(sha256, {}) if sha256 else {}
+                usage_count = usage_info.get('total', 0) if isinstance(usage_info, dict) else 0
+                
+                all_models.append({
+                    'name': item.get('model_name', 'Unknown'),
+                    'usage_count': usage_count,
+                    'base_model': item.get('base_model', 'Unknown'),
+                    'preview_url': config.get_preview_static_url(item.get('preview_url', '')),
+                    'folder': item.get('folder', '')
+                })
+
+            # Sort the models
+            reverse = (sort_order == 'desc')
+            all_models.sort(key=lambda x: (x['usage_count'], x['name'].lower()), reverse=reverse)
+            if not reverse:
+                # If asc, sort by usage_count ascending, but keep name ascending
+                all_models.sort(key=lambda x: (x['usage_count'], x['name'].lower()))
+            else:
+                all_models.sort(key=lambda x: (-x['usage_count'], x['name'].lower()))
+
+            # Slice for pagination
+            paginated_models = all_models[offset:offset + limit]
+
+            return web.json_response({
+                'success': True,
+                'data': {
+                    'items': paginated_models,
+                    'total': len(all_models),
+                    'type': model_type
+                }
+            })
+
+        except Exception as e:
+            logger.error(f"Error getting model usage list: {e}", exc_info=True)
+            return web.json_response({
+                'success': False,
+                'error': str(e)
+            }, status=500)
+
     async def get_base_model_distribution(self, request: web.Request) -> web.Response:
         """Get base model distribution statistics"""
         try:
@@ -530,6 +604,7 @@ class StatsRoutes:
         # Register API routes
         app.router.add_get('/api/lm/stats/collection-overview', self.get_collection_overview)
         app.router.add_get('/api/lm/stats/usage-analytics', self.get_usage_analytics)
+        app.router.add_get('/api/lm/stats/model-usage-list', self.get_model_usage_list)
         app.router.add_get('/api/lm/stats/base-model-distribution', self.get_base_model_distribution)
         app.router.add_get('/api/lm/stats/tag-analytics', self.get_tag_analytics)
         app.router.add_get('/api/lm/stats/storage-analytics', self.get_storage_analytics)

py/services/aria2_downloader.py

@@ -0,0 +1,570 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import os
+import secrets
+import shutil
+import socket
+from dataclasses import dataclass
+from datetime import datetime
+from pathlib import Path
+from typing import Any, Dict, Optional, Tuple
+
+import aiohttp
+
+from .downloader import DownloadProgress, get_downloader
+from .aria2_transfer_state import Aria2TransferStateStore
+from .settings_manager import get_settings_manager
+
+logger = logging.getLogger(__name__)
+
+CIVITAI_DOWNLOAD_URL_PREFIXES = (
+    "https://civitai.com/api/download/",
+    "https://civitai.red/api/download/",
+)
+
+
+class Aria2Error(RuntimeError):
+    """Raised when aria2 integration fails."""
+
+
+@dataclass
+class Aria2Transfer:
+    """Track an aria2 download registered by the Python coordinator."""
+
+    gid: str
+    save_path: str
+
+
+class Aria2Downloader:
+    """Manage an aria2 RPC daemon for experimental model downloads."""
+
+    _instance = None
+    _lock = asyncio.Lock()
+
+    @classmethod
+    async def get_instance(cls) -> "Aria2Downloader":
+        async with cls._lock:
+            if cls._instance is None:
+                cls._instance = cls()
+            return cls._instance
+
+    def __init__(self) -> None:
+        if hasattr(self, "_initialized"):
+            return
+
+        self._initialized = True
+        self._process: Optional[asyncio.subprocess.Process] = None
+        self._rpc_port: Optional[int] = None
+        self._rpc_secret = ""
+        self._rpc_url = ""
+        self._rpc_session: Optional[aiohttp.ClientSession] = None
+        self._rpc_session_lock = asyncio.Lock()
+        self._process_lock = asyncio.Lock()
+        self._transfers: Dict[str, Aria2Transfer] = {}
+        self._poll_interval = 0.5
+        self._state_store = Aria2TransferStateStore()
+
+    @property
+    def is_running(self) -> bool:
+        return self._process is not None and self._process.returncode is None
+
+    async def download_file(
+        self,
+        url: str,
+        save_path: str,
+        *,
+        download_id: str,
+        progress_callback=None,
+        headers: Optional[Dict[str, str]] = None,
+    ) -> Tuple[bool, str]:
+        """Download a file using aria2 RPC and wait for completion."""
+
+        await self._ensure_process()
+        save_path = os.path.abspath(save_path)
+        transfer = self._transfers.get(download_id)
+        if transfer is None or os.path.abspath(transfer.save_path) != save_path:
+            gid = await self._schedule_download(
+                url,
+                save_path,
+                download_id=download_id,
+                headers=headers,
+            )
+            transfer = Aria2Transfer(gid=gid, save_path=save_path)
+            self._transfers[download_id] = transfer
+
+        try:
+            while True:
+                status = await self.get_status(download_id)
+                if status is None:
+                    return False, "aria2 download not found"
+
+                snapshot = self._build_progress_snapshot(status)
+                if progress_callback is not None:
+                    await self._dispatch_progress(progress_callback, snapshot)
+
+                state = status.get("status", "")
+                if state == "complete":
+                    completed_path = self._resolve_completed_path(status, save_path)
+                    return True, completed_path
+                if state == "error":
+                    return False, status.get("errorMessage") or "aria2 download failed"
+                if state == "removed":
+                    return False, "Download was cancelled"
+
+                await asyncio.sleep(self._poll_interval)
+        finally:
+            self._transfers.pop(download_id, None)
+
+    async def _schedule_download(
+        self,
+        url: str,
+        save_path: str,
+        *,
+        download_id: str,
+        headers: Optional[Dict[str, str]] = None,
+    ) -> str:
+        save_dir = os.path.dirname(save_path)
+        out_name = os.path.basename(save_path)
+
+        Path(save_dir).mkdir(parents=True, exist_ok=True)
+
+        resolved_url = url
+        request_headers = headers
+        if headers and url.startswith(CIVITAI_DOWNLOAD_URL_PREFIXES):
+            resolved_url = await self._resolve_authenticated_redirect_url(url, headers)
+            if resolved_url != url:
+                request_headers = None
+                logger.debug(
+                    "Resolved Civitai download %s to signed URL for aria2",
+                    download_id,
+                )
+
+        options: Dict[str, str] = {
+            "dir": save_dir,
+            "out": out_name,
+            "continue": "true",
+            "max-connection-per-server": "4",
+            "split": "4",
+            "min-split-size": "1M",
+            "allow-overwrite": "true",
+            "auto-file-renaming": "false",
+            "file-allocation": "none",
+        }
+        if request_headers:
+            options["header"] = [
+                f"{key}: {value}" for key, value in request_headers.items()
+            ]
+
+        logger.debug(
+            "Submitting aria2 download %s -> %s (auth=%s, civitai_signed=%s)",
+            download_id,
+            save_path,
+            bool(request_headers),
+            resolved_url != url,
+        )
+
+        try:
+            gid = await self._rpc_call("aria2.addUri", [[resolved_url], options])
+        except Exception as exc:
+            raise Aria2Error(f"Failed to schedule aria2 download: {exc}") from exc
+
+        logger.debug("aria2 accepted download %s with gid %s", download_id, gid)
+        await self._state_store.upsert(
+            download_id,
+            {
+                "gid": gid,
+                "save_path": save_path,
+                "status": "downloading",
+                "url": url,
+            },
+        )
+        return gid
+
+    async def get_status(self, download_id: str) -> Optional[Dict[str, Any]]:
+        """Return the raw aria2 status payload for a known download."""
+
+        transfer = self._transfers.get(download_id)
+        if transfer is None:
+            return None
+
+        keys = [
+            "gid",
+            "status",
+            "totalLength",
+            "completedLength",
+            "downloadSpeed",
+            "errorMessage",
+            "files",
+        ]
+        try:
+            status = await self._rpc_call("aria2.tellStatus", [transfer.gid, keys])
+        except Exception as exc:
+            raise Aria2Error(f"Failed to query aria2 download status: {exc}") from exc
+
+        if isinstance(status, dict):
+            return status
+        return None
+
+    async def get_status_by_gid(self, gid: str) -> Optional[Dict[str, Any]]:
+        keys = [
+            "gid",
+            "status",
+            "totalLength",
+            "completedLength",
+            "downloadSpeed",
+            "errorMessage",
+            "files",
+        ]
+        try:
+            status = await self._rpc_call("aria2.tellStatus", [gid, keys])
+        except Exception as exc:
+            message = str(exc)
+            if "cannot be found" in message.lower() or "not found" in message.lower():
+                return None
+            raise Aria2Error(f"Failed to query aria2 download status: {exc}") from exc
+
+        if isinstance(status, dict):
+            return status
+        return None
+
+    async def restore_transfer(self, download_id: str, gid: str, save_path: str) -> None:
+        await self._ensure_process()
+        self._transfers[download_id] = Aria2Transfer(
+            gid=gid,
+            save_path=os.path.abspath(save_path),
+        )
+
+    async def reassign_transfer(
+        self, from_download_id: str, to_download_id: str
+    ) -> Optional[Aria2Transfer]:
+        transfer = self._transfers.get(from_download_id)
+        if transfer is None:
+            return None
+
+        self._transfers[to_download_id] = transfer
+        if from_download_id != to_download_id:
+            self._transfers.pop(from_download_id, None)
+        return transfer
+
+    async def has_transfer(self, download_id: str) -> bool:
+        return download_id in self._transfers
+
+    async def pause_download(self, download_id: str) -> Dict[str, Any]:
+        transfer = self._transfers.get(download_id)
+        if transfer is None:
+            return {"success": False, "error": "Download task not found"}
+
+        try:
+            await self._rpc_call("aria2.forcePause", [transfer.gid])
+        except Exception as exc:
+            return {"success": False, "error": str(exc)}
+
+        await self._state_store.upsert(download_id, {"status": "paused"})
+        return {"success": True, "message": "Download paused successfully"}
+
+    async def resume_download(self, download_id: str) -> Dict[str, Any]:
+        transfer = self._transfers.get(download_id)
+        if transfer is None:
+            return {"success": False, "error": "Download task not found"}
+
+        try:
+            await self._rpc_call("aria2.unpause", [transfer.gid])
+        except Exception as exc:
+            return {"success": False, "error": str(exc)}
+
+        await self._state_store.upsert(download_id, {"status": "downloading"})
+        return {"success": True, "message": "Download resumed successfully"}
+
+    async def cancel_download(self, download_id: str) -> Dict[str, Any]:
+        transfer = self._transfers.get(download_id)
+        if transfer is None:
+            return {"success": False, "error": "Download task not found"}
+
+        try:
+            await self._rpc_call("aria2.forceRemove", [transfer.gid])
+        except Exception as exc:
+            return {"success": False, "error": str(exc)}
+
+        await self._state_store.remove(download_id)
+        return {"success": True, "message": "Download cancelled successfully"}
+
+    async def close(self) -> None:
+        """Shut down the RPC process and session."""
+
+        if self._rpc_session is not None:
+            await self._rpc_session.close()
+            self._rpc_session = None
+
+        process = self._process
+        self._process = None
+        self._transfers.clear()
+
+        if process is None:
+            return
+
+        if process.returncode is None:
+            process.terminate()
+            try:
+                await asyncio.wait_for(process.wait(), timeout=5.0)
+            except asyncio.TimeoutError:
+                process.kill()
+                await process.wait()
+
+    async def _dispatch_progress(self, callback, snapshot: DownloadProgress) -> None:
+        try:
+            result = callback(snapshot, snapshot)
+        except TypeError:
+            result = callback(snapshot.percent_complete)
+
+        if asyncio.iscoroutine(result):
+            await result
+        elif hasattr(result, "__await__"):
+            await result
+
+    def _build_progress_snapshot(self, status: Dict[str, Any]) -> DownloadProgress:
+        completed = self._parse_int(status.get("completedLength"))
+        total = self._parse_int(status.get("totalLength"))
+        speed = float(self._parse_int(status.get("downloadSpeed")))
+        percent = 0.0
+        if total > 0:
+            percent = (completed / total) * 100.0
+
+        return DownloadProgress(
+            percent_complete=max(0.0, min(percent, 100.0)),
+            bytes_downloaded=completed,
+            total_bytes=total or None,
+            bytes_per_second=speed,
+            timestamp=datetime.now().timestamp(),
+        )
+
+    def _resolve_completed_path(self, status: Dict[str, Any], default_path: str) -> str:
+        files = status.get("files")
+        if isinstance(files, list) and files:
+            first = files[0]
+            if isinstance(first, dict):
+                candidate = first.get("path")
+                if isinstance(candidate, str) and candidate:
+                    return candidate
+        return default_path
+
+    @staticmethod
+    def _parse_int(value: Any) -> int:
+        try:
+            return int(value)
+        except (TypeError, ValueError):
+            return 0
+
+    async def _resolve_authenticated_redirect_url(
+        self,
+        url: str,
+        headers: Dict[str, str],
+    ) -> str:
+        downloader = await get_downloader()
+        session = await downloader.session
+        request_headers = dict(downloader.default_headers)
+        request_headers.update(headers)
+        request_headers["Accept-Encoding"] = "identity"
+
+        try:
+            async with session.get(
+                url,
+                headers=request_headers,
+                allow_redirects=False,
+                proxy=downloader.proxy_url,
+            ) as response:
+                if response.status in {301, 302, 303, 307, 308}:
+                    location = response.headers.get("Location")
+                    if location:
+                        return location
+                    raise Aria2Error(
+                        "Authenticated Civitai redirect did not include a Location header"
+                    )
+
+                if response.status == 200:
+                    return url
+
+                body = await response.text()
+                raise Aria2Error(
+                    f"Failed to resolve authenticated Civitai redirect: status={response.status} body={body[:300]}"
+                )
+        except aiohttp.ClientError as exc:
+            raise Aria2Error(
+                f"Failed to resolve authenticated Civitai redirect: {exc}"
+            ) from exc
+
+    async def _ensure_process(self) -> None:
+        async with self._process_lock:
+            if self.is_running and await self._ping():
+                return
+
+            await self.close()
+
+            executable = self._resolve_executable()
+            self._rpc_port = self._find_free_port()
+            self._rpc_secret = secrets.token_hex(16)
+            self._rpc_url = f"http://127.0.0.1:{self._rpc_port}/jsonrpc"
+
+            command = [
+                executable,
+                "--enable-rpc=true",
+                "--rpc-listen-all=false",
+                f"--rpc-listen-port={self._rpc_port}",
+                f"--rpc-secret={self._rpc_secret}",
+                "--check-certificate=true",
+                "--allow-overwrite=true",
+                "--auto-file-renaming=false",
+                "--file-allocation=none",
+                "--max-concurrent-downloads=5",
+                "--continue=true",
+                "--daemon=false",
+                "--quiet=true",
+                f"--stop-with-process={os.getpid()}",
+            ]
+
+            logger.info("Starting aria2 RPC daemon from %s", executable)
+            self._process = await asyncio.create_subprocess_exec(
+                *command,
+                stdout=asyncio.subprocess.DEVNULL,
+                stderr=asyncio.subprocess.PIPE,
+            )
+
+            await self._wait_until_ready()
+
+    def _resolve_executable(self) -> str:
+        settings = get_settings_manager()
+        configured_path = (settings.get("aria2c_path") or "").strip()
+        candidate = configured_path or "aria2c"
+
+        resolved = shutil.which(candidate)
+        if resolved:
+            return resolved
+
+        if configured_path and os.path.isfile(configured_path) and os.access(
+            configured_path, os.X_OK
+        ):
+            return configured_path
+
+        raise Aria2Error(
+            "aria2c executable was not found. Install aria2 or configure aria2c_path."
+        )
+
+    async def _wait_until_ready(self) -> None:
+        assert self._process is not None
+
+        start_time = asyncio.get_running_loop().time()
+        last_error = ""
+        while asyncio.get_running_loop().time() - start_time < 10.0:
+            if self._process.returncode is not None:
+                stderr_output = ""
+                if self._process.stderr is not None:
+                    try:
+                        stderr_output = (
+                            await asyncio.wait_for(self._process.stderr.read(), timeout=0.2)
+                        ).decode("utf-8", errors="replace")
+                    except Exception:
+                        stderr_output = ""
+                raise Aria2Error(
+                    f"aria2 RPC process exited early with code {self._process.returncode}: {stderr_output.strip()}"
+                )
+
+            try:
+                if await self._ping():
+                    return
+            except Exception as exc:  # pragma: no cover - startup race
+                last_error = str(exc)
+
+            await asyncio.sleep(0.2)
+
+        raise Aria2Error(
+            f"Timed out waiting for aria2 RPC to become ready{': ' + last_error if last_error else ''}"
+        )
+
+    async def _ping(self) -> bool:
+        try:
+            result = await self._rpc_call("aria2.getVersion", [])
+        except Exception:
+            return False
+
+        return isinstance(result, dict)
+
+    async def _rpc_call(self, method: str, params: list[Any]) -> Any:
+        if not self._rpc_url:
+            raise Aria2Error("aria2 RPC endpoint is not initialized")
+
+        session = await self._get_rpc_session()
+        payload = {
+            "jsonrpc": "2.0",
+            "id": secrets.token_hex(8),
+            "method": method,
+            "params": [f"token:{self._rpc_secret}", *params],
+        }
+
+        async with session.post(self._rpc_url, json=payload) as response:
+            text = await response.text()
+
+        try:
+            body = json.loads(text)
+        except json.JSONDecodeError:
+            body = None
+
+        if body is None:
+            if response.status != 200:
+                raise Aria2Error(
+                    f"aria2 RPC returned status {response.status} with non-JSON body: {text}"
+                )
+            raise Aria2Error(f"Invalid aria2 RPC response: {text}")
+
+        if "error" in body:
+            error = body["error"] or {}
+            code = error.get("code") if isinstance(error, dict) else None
+            message = error.get("message") if isinstance(error, dict) else str(error)
+            logger.error(
+                "aria2 RPC %s failed with HTTP %s, code=%s, message=%s",
+                method,
+                response.status,
+                code,
+                message,
+            )
+            status_message = (
+                f"aria2 RPC {method} failed with status {response.status}: {message}"
+                if response.status != 200
+                else message
+            )
+            raise Aria2Error(status_message or "Unknown aria2 RPC error")
+
+        if response.status != 200:
+            logger.error(
+                "aria2 RPC %s returned unexpected HTTP status %s without error payload: %s",
+                method,
+                response.status,
+                body,
+            )
+            raise Aria2Error(
+                f"aria2 RPC {method} returned unexpected status {response.status}"
+            )
+
+        return body.get("result")
+
+    async def _get_rpc_session(self) -> aiohttp.ClientSession:
+        if self._rpc_session is None or self._rpc_session.closed:
+            async with self._rpc_session_lock:
+                if self._rpc_session is None or self._rpc_session.closed:
+                    timeout = aiohttp.ClientTimeout(total=30)
+                    self._rpc_session = aiohttp.ClientSession(timeout=timeout)
+        return self._rpc_session
+
+    @staticmethod
+    def _find_free_port() -> int:
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+            sock.bind(("127.0.0.1", 0))
+            sock.listen(1)
+            return int(sock.getsockname()[1])
+
+
+async def get_aria2_downloader() -> Aria2Downloader:
+    """Get the singleton aria2 downloader."""
+
+    return await Aria2Downloader.get_instance()

py/services/aria2_transfer_state.py

@@ -0,0 +1,108 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import os
+from copy import deepcopy
+from typing import Any, Dict, Optional
+
+from ..utils.cache_paths import get_cache_base_dir
+
+
+def get_aria2_state_path() -> str:
+    base_dir = get_cache_base_dir(create=True)
+    state_dir = os.path.join(base_dir, "aria2")
+    os.makedirs(state_dir, exist_ok=True)
+    return os.path.join(state_dir, "downloads.json")
+
+
+class Aria2TransferStateStore:
+    """Persist aria2 transfer metadata needed for restart recovery."""
+
+    _locks_by_path: Dict[str, asyncio.Lock] = {}
+
+    def __init__(self, state_path: Optional[str] = None) -> None:
+        self._state_path = os.path.abspath(state_path or get_aria2_state_path())
+        self._lock = self._locks_by_path.setdefault(self._state_path, asyncio.Lock())
+
+    def _read_all_unlocked(self) -> Dict[str, Dict[str, Any]]:
+        try:
+            with open(self._state_path, "r", encoding="utf-8") as handle:
+                data = json.load(handle)
+        except FileNotFoundError:
+            return {}
+        except json.JSONDecodeError:
+            return {}
+
+        if not isinstance(data, dict):
+            return {}
+
+        normalized: Dict[str, Dict[str, Any]] = {}
+        for download_id, entry in data.items():
+            if isinstance(download_id, str) and isinstance(entry, dict):
+                normalized[download_id] = entry
+        return normalized
+
+    def _write_all_unlocked(self, data: Dict[str, Dict[str, Any]]) -> None:
+        directory = os.path.dirname(self._state_path)
+        if directory:
+            os.makedirs(directory, exist_ok=True)
+
+        temp_path = f"{self._state_path}.tmp"
+        with open(temp_path, "w", encoding="utf-8") as handle:
+            json.dump(data, handle, ensure_ascii=True, indent=2, sort_keys=True)
+        os.replace(temp_path, self._state_path)
+
+    async def load_all(self) -> Dict[str, Dict[str, Any]]:
+        async with self._lock:
+            return deepcopy(self._read_all_unlocked())
+
+    async def get(self, download_id: str) -> Optional[Dict[str, Any]]:
+        async with self._lock:
+            return deepcopy(self._read_all_unlocked().get(download_id))
+
+    async def upsert(self, download_id: str, payload: Dict[str, Any]) -> Dict[str, Any]:
+        async with self._lock:
+            data = self._read_all_unlocked()
+            current = data.get(download_id, {})
+            current.update(payload)
+            data[download_id] = current
+            self._write_all_unlocked(data)
+            return deepcopy(current)
+
+    async def remove(self, download_id: str) -> None:
+        async with self._lock:
+            data = self._read_all_unlocked()
+            if download_id in data:
+                del data[download_id]
+                self._write_all_unlocked(data)
+
+    async def find_by_save_path(
+        self, save_path: str, *, exclude_download_id: Optional[str] = None
+    ) -> Optional[Dict[str, Any]]:
+        normalized_target = os.path.abspath(save_path)
+        async with self._lock:
+            data = self._read_all_unlocked()
+            for download_id, entry in data.items():
+                if exclude_download_id and download_id == exclude_download_id:
+                    continue
+                candidate = entry.get("save_path")
+                if isinstance(candidate, str) and os.path.abspath(candidate) == normalized_target:
+                    result = dict(entry)
+                    result["download_id"] = download_id
+                    return result
+        return None
+
+    async def reassign(self, from_download_id: str, to_download_id: str) -> Optional[Dict[str, Any]]:
+        async with self._lock:
+            data = self._read_all_unlocked()
+            existing = data.get(from_download_id)
+            if existing is None:
+                return None
+            updated = dict(existing)
+            updated["download_id"] = to_download_id
+            data[to_download_id] = updated
+            if from_download_id != to_download_id:
+                data.pop(from_download_id, None)
+            self._write_all_unlocked(data)
+            return deepcopy(updated)

py/services/backup_service.py

@@ -0,0 +1,411 @@
+from __future__ import annotations
+
+import asyncio
+import contextlib
+import hashlib
+import json
+import logging
+import os
+import shutil
+import tempfile
+import time
+import zipfile
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Iterable, Optional
+
+from ..utils.cache_paths import CacheType, get_cache_base_dir, get_cache_file_path
+from ..utils.settings_paths import get_settings_dir
+from .settings_manager import get_settings_manager
+
+logger = logging.getLogger(__name__)
+
+
+BACKUP_MANIFEST_VERSION = 1
+DEFAULT_BACKUP_RETENTION_COUNT = 5
+DEFAULT_BACKUP_INTERVAL_SECONDS = 24 * 60 * 60
+
+
+@dataclass(frozen=True)
+class BackupEntry:
+    kind: str
+    archive_path: str
+    target_path: str
+    sha256: str
+    size: int
+    mtime: float
+
+
+class BackupService:
+    """Create and restore user-state backup archives."""
+
+    _instance: "BackupService | None" = None
+    _instance_lock = asyncio.Lock()
+
+    def __init__(self, *, settings_manager=None, backup_dir: str | None = None) -> None:
+        self._settings = settings_manager or get_settings_manager()
+        self._backup_dir = Path(backup_dir or self._resolve_backup_dir())
+        self._backup_dir.mkdir(parents=True, exist_ok=True)
+        self._lock = asyncio.Lock()
+        self._auto_task: asyncio.Task[None] | None = None
+
+    @classmethod
+    async def get_instance(cls) -> "BackupService":
+        async with cls._instance_lock:
+            if cls._instance is None:
+                cls._instance = cls()
+            cls._instance._ensure_auto_snapshot_task()
+            return cls._instance
+
+    @staticmethod
+    def _resolve_backup_dir() -> str:
+        return os.path.join(get_settings_dir(create=True), "backups")
+
+    def get_backup_dir(self) -> str:
+        return str(self._backup_dir)
+
+    def _ensure_auto_snapshot_task(self) -> None:
+        if self._auto_task is not None and not self._auto_task.done():
+            return
+
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            return
+
+        self._auto_task = loop.create_task(self._auto_backup_loop())
+
+    def _get_setting_bool(self, key: str, default: bool) -> bool:
+        try:
+            return bool(self._settings.get(key, default))
+        except Exception:
+            return default
+
+    def _get_setting_int(self, key: str, default: int) -> int:
+        try:
+            value = self._settings.get(key, default)
+            return max(1, int(value))
+        except Exception:
+            return default
+
+    def _settings_file_path(self) -> str:
+        settings_file = getattr(self._settings, "settings_file", None)
+        if settings_file:
+            return str(settings_file)
+        return os.path.join(get_settings_dir(create=True), "settings.json")
+
+    def _download_history_path(self) -> str:
+        base_dir = get_cache_base_dir(create=True)
+        history_dir = os.path.join(base_dir, "download_history")
+        os.makedirs(history_dir, exist_ok=True)
+        return os.path.join(history_dir, "downloaded_versions.sqlite")
+
+    def _model_update_dir(self) -> str:
+        return str(Path(get_cache_file_path(CacheType.MODEL_UPDATE, create_dir=True)).parent)
+
+    def _model_update_targets(self) -> list[tuple[str, str, str]]:
+        """Return (kind, archive_path, target_path) tuples for backup."""
+
+        targets: list[tuple[str, str, str]] = []
+
+        settings_path = self._settings_file_path()
+        targets.append(("settings", "settings/settings.json", settings_path))
+
+        history_path = self._download_history_path()
+        targets.append(
+            (
+                "download_history",
+                "cache/download_history/downloaded_versions.sqlite",
+                history_path,
+            )
+        )
+
+        symlink_path = get_cache_file_path(CacheType.SYMLINK, create_dir=True)
+        targets.append(
+            (
+                "symlink_map",
+                "cache/symlink/symlink_map.json",
+                symlink_path,
+            )
+        )
+
+        model_update_dir = Path(self._model_update_dir())
+        if model_update_dir.exists():
+            for sqlite_file in sorted(model_update_dir.glob("*.sqlite")):
+                targets.append(
+                    (
+                        "model_update",
+                        f"cache/model_update/{sqlite_file.name}",
+                        str(sqlite_file),
+                    )
+                )
+
+        return targets
+
+    @staticmethod
+    def _hash_file(path: str) -> tuple[str, int, float]:
+        digest = hashlib.sha256()
+        total = 0
+        with open(path, "rb") as handle:
+            for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+                total += len(chunk)
+                digest.update(chunk)
+        mtime = os.path.getmtime(path)
+        return digest.hexdigest(), total, mtime
+
+    def _build_manifest(self, entries: Iterable[BackupEntry], *, snapshot_type: str) -> dict[str, Any]:
+        created_at = datetime.now(timezone.utc).isoformat()
+        active_library = None
+        try:
+            active_library = self._settings.get_active_library_name()
+        except Exception:
+            active_library = None
+
+        return {
+            "manifest_version": BACKUP_MANIFEST_VERSION,
+            "created_at": created_at,
+            "snapshot_type": snapshot_type,
+            "active_library": active_library,
+            "files": [
+                {
+                    "kind": entry.kind,
+                    "archive_path": entry.archive_path,
+                    "target_path": entry.target_path,
+                    "sha256": entry.sha256,
+                    "size": entry.size,
+                    "mtime": entry.mtime,
+                }
+                for entry in entries
+            ],
+        }
+
+    def _write_archive(self, archive_path: str, entries: list[BackupEntry], manifest: dict[str, Any]) -> None:
+        with zipfile.ZipFile(
+            archive_path,
+            mode="w",
+            compression=zipfile.ZIP_DEFLATED,
+            compresslevel=6,
+        ) as zf:
+            zf.writestr(
+                "manifest.json",
+                json.dumps(manifest, indent=2, ensure_ascii=False).encode("utf-8"),
+            )
+            for entry in entries:
+                zf.write(entry.target_path, arcname=entry.archive_path)
+
+    async def create_snapshot(self, *, snapshot_type: str = "manual", persist: bool = False) -> dict[str, Any]:
+        """Create a backup archive.
+
+        If ``persist`` is true, the archive is stored in the backup directory
+        and retained according to the configured retention policy.
+        """
+
+        async with self._lock:
+            raw_targets = self._model_update_targets()
+            entries: list[BackupEntry] = []
+            for kind, archive_path, target_path in raw_targets:
+                if not os.path.exists(target_path):
+                    continue
+                sha256, size, mtime = self._hash_file(target_path)
+                entries.append(
+                    BackupEntry(
+                        kind=kind,
+                        archive_path=archive_path,
+                        target_path=target_path,
+                        sha256=sha256,
+                        size=size,
+                        mtime=mtime,
+                    )
+                )
+
+            if not entries:
+                raise FileNotFoundError("No backupable files were found")
+
+            manifest = self._build_manifest(entries, snapshot_type=snapshot_type)
+            archive_name = self._build_archive_name(snapshot_type=snapshot_type)
+            fd, temp_path = tempfile.mkstemp(suffix=".zip", dir=str(self._backup_dir))
+            os.close(fd)
+
+            try:
+                self._write_archive(temp_path, entries, manifest)
+                if persist:
+                    final_path = self._backup_dir / archive_name
+                    os.replace(temp_path, final_path)
+                    self._prune_snapshots()
+                    return {
+                        "archive_path": str(final_path),
+                        "archive_name": final_path.name,
+                        "manifest": manifest,
+                    }
+
+                with open(temp_path, "rb") as handle:
+                    data = handle.read()
+                return {
+                    "archive_name": archive_name,
+                    "archive_bytes": data,
+                    "manifest": manifest,
+                }
+            finally:
+                with contextlib.suppress(FileNotFoundError):
+                    os.remove(temp_path)
+
+    def _build_archive_name(self, *, snapshot_type: str) -> str:
+        timestamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ")
+        return f"lora-manager-backup-{timestamp}-{snapshot_type}.zip"
+
+    def _prune_snapshots(self) -> None:
+        retention = self._get_setting_int(
+            "backup_retention_count", DEFAULT_BACKUP_RETENTION_COUNT
+        )
+        archives = sorted(
+            self._backup_dir.glob("lora-manager-backup-*-auto.zip"),
+            key=lambda path: path.stat().st_mtime,
+            reverse=True,
+        )
+        for path in archives[retention:]:
+            with contextlib.suppress(OSError):
+                path.unlink()
+
+    async def restore_snapshot(self, archive_path: str) -> dict[str, Any]:
+        """Restore backup contents from a ZIP archive."""
+
+        async with self._lock:
+            try:
+                zf = zipfile.ZipFile(archive_path, mode="r")
+            except zipfile.BadZipFile as exc:
+                raise ValueError("Backup archive is not a valid ZIP file") from exc
+
+            with zf:
+                try:
+                    manifest = json.loads(zf.read("manifest.json").decode("utf-8"))
+                except KeyError as exc:
+                    raise ValueError("Backup archive is missing manifest.json") from exc
+
+                if not isinstance(manifest, dict):
+                    raise ValueError("Backup manifest is invalid")
+                if manifest.get("manifest_version") != BACKUP_MANIFEST_VERSION:
+                    raise ValueError("Backup manifest version is not supported")
+
+                files = manifest.get("files", [])
+                if not isinstance(files, list):
+                    raise ValueError("Backup manifest file list is invalid")
+
+                extracted_paths: list[tuple[str, str]] = []
+                temp_dir = Path(tempfile.mkdtemp(prefix="lora-manager-restore-"))
+                try:
+                    for item in files:
+                        if not isinstance(item, dict):
+                            continue
+                        archive_member = item.get("archive_path")
+                        if not isinstance(archive_member, str) or not archive_member:
+                            continue
+                        archive_member_path = Path(archive_member)
+                        if archive_member_path.is_absolute() or ".." in archive_member_path.parts:
+                            raise ValueError(f"Invalid archive member path: {archive_member}")
+
+                        kind = item.get("kind")
+                        target_path = self._resolve_restore_target(kind, archive_member)
+                        if target_path is None:
+                            continue
+
+                        extracted_path = temp_dir / archive_member_path
+                        extracted_path.parent.mkdir(parents=True, exist_ok=True)
+                        with zf.open(archive_member) as source, open(
+                            extracted_path, "wb"
+                        ) as destination:
+                            shutil.copyfileobj(source, destination)
+
+                        expected_hash = item.get("sha256")
+                        if isinstance(expected_hash, str) and expected_hash:
+                            actual_hash, _, _ = self._hash_file(str(extracted_path))
+                            if actual_hash != expected_hash:
+                                raise ValueError(
+                                    f"Checksum mismatch for {archive_member}"
+                                )
+
+                        extracted_paths.append((str(extracted_path), target_path))
+
+                    for extracted_path, target_path in extracted_paths:
+                        os.makedirs(os.path.dirname(target_path), exist_ok=True)
+                        os.replace(extracted_path, target_path)
+                finally:
+                    shutil.rmtree(temp_dir, ignore_errors=True)
+
+            return {
+                "success": True,
+                "restored_files": len(extracted_paths),
+                "snapshot_type": manifest.get("snapshot_type"),
+            }
+
+    def _resolve_restore_target(self, kind: Any, archive_member: str) -> str | None:
+        if kind == "settings":
+            return self._settings_file_path()
+        if kind == "download_history":
+            return self._download_history_path()
+        if kind == "symlink_map":
+            return get_cache_file_path(CacheType.SYMLINK, create_dir=True)
+        if kind == "model_update":
+            filename = os.path.basename(archive_member)
+            return str(Path(get_cache_file_path(CacheType.MODEL_UPDATE, create_dir=True)).parent / filename)
+        return None
+
+    async def create_auto_snapshot_if_due(self) -> Optional[dict[str, Any]]:
+        if not self._get_setting_bool("backup_auto_enabled", True):
+            return None
+
+        latest = self.get_latest_auto_snapshot()
+        now = time.time()
+        if latest and now - latest["mtime"] < DEFAULT_BACKUP_INTERVAL_SECONDS:
+            return None
+
+        return await self.create_snapshot(snapshot_type="auto", persist=True)
+
+    async def _auto_backup_loop(self) -> None:
+        while True:
+            try:
+                await self.create_auto_snapshot_if_due()
+                await asyncio.sleep(DEFAULT_BACKUP_INTERVAL_SECONDS)
+            except asyncio.CancelledError:
+                raise
+            except Exception as exc:  # pragma: no cover - defensive guard
+                logger.warning("Automatic backup snapshot failed: %s", exc, exc_info=True)
+                await asyncio.sleep(60)
+
+    def get_available_snapshots(self) -> list[dict[str, Any]]:
+        snapshots: list[dict[str, Any]] = []
+        for path in sorted(self._backup_dir.glob("lora-manager-backup-*.zip")):
+            try:
+                stat = path.stat()
+            except OSError:
+                continue
+            snapshots.append(
+                {
+                    "name": path.name,
+                    "path": str(path),
+                    "size": stat.st_size,
+                    "mtime": stat.st_mtime,
+                    "is_auto": path.name.endswith("-auto.zip"),
+                }
+            )
+        snapshots.sort(key=lambda item: item["mtime"], reverse=True)
+        return snapshots
+
+    def get_latest_auto_snapshot(self) -> Optional[dict[str, Any]]:
+        autos = [snapshot for snapshot in self.get_available_snapshots() if snapshot["is_auto"]]
+        if not autos:
+            return None
+        return autos[0]
+
+    def get_status(self) -> dict[str, Any]:
+        snapshots = self.get_available_snapshots()
+        return {
+            "backupDir": self.get_backup_dir(),
+            "enabled": self._get_setting_bool("backup_auto_enabled", True),
+            "retentionCount": self._get_setting_int(
+                "backup_retention_count", DEFAULT_BACKUP_RETENTION_COUNT
+            ),
+            "snapshotCount": len(snapshots),
+            "latestSnapshot": snapshots[0] if snapshots else None,
+            "latestAutoSnapshot": self.get_latest_auto_snapshot(),
+        }

py/services/base_model_service.py

@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 import asyncio
+import re
 from typing import Any, Dict, List, Optional, Type, TYPE_CHECKING
 import logging
 import os
@@ -19,6 +20,7 @@ from .model_query import (
     resolve_sub_type,
 )
 from .settings_manager import get_settings_manager
+from ..utils.civitai_utils import build_civitai_model_page_url
 
 logger = logging.getLogger(__name__)
 
@@ -177,6 +179,57 @@ class BaseModelService(ABC):
         )
         return paginated
 
+    async def get_excluded_paginated_data(
+        self,
+        page: int,
+        page_size: int,
+        sort_by: str = "name",
+        search: str = None,
+        fuzzy_search: bool = False,
+        search_options: dict = None,
+        **kwargs,
+    ) -> Dict:
+        """Get paginated excluded model data."""
+        excluded_paths = list(self.scanner.get_excluded_models())
+        excluded_entries: List[Dict[str, Any]] = []
+        stale_paths: List[str] = []
+
+        for file_path in excluded_paths:
+            if not file_path or not os.path.exists(file_path):
+                stale_paths.append(file_path)
+                continue
+
+            entry = await self._build_excluded_entry(file_path)
+            if entry:
+                excluded_entries.append(entry)
+            else:
+                stale_paths.append(file_path)
+
+        if stale_paths:
+            current_excluded = getattr(self.scanner, "_excluded_models", None)
+            if isinstance(current_excluded, list):
+                stale_set = set(stale_paths)
+                self.scanner._excluded_models = [
+                    path for path in current_excluded if path not in stale_set
+                ]
+                persist_current_cache = getattr(self.scanner, "_persist_current_cache", None)
+                if callable(persist_current_cache):
+                    await persist_current_cache()
+
+        excluded_entries = self._sort_entries(excluded_entries, sort_by)
+
+        if search:
+            excluded_entries = await self._apply_search_filters(
+                excluded_entries,
+                search,
+                fuzzy_search,
+                search_options,
+            )
+
+        paginated = self._paginate(excluded_entries, page, page_size)
+        paginated["items"] = await self._annotate_update_flags(paginated["items"])
+        return paginated
+
     async def _fetch_with_usage_sort(self, sort_params):
         """Fetch data sorted by usage count (desc/asc)."""
         cache = await self.cache_repository.get_cache()
@@ -207,11 +260,71 @@ 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
 
+    def _sort_entries(self, data: List[Dict[str, Any]], sort_by: str) -> List[Dict[str, Any]]:
+        sort_params = self.cache_repository.parse_sort(sort_by)
+        key_name = sort_params.key
+
+        if key_name == "date":
+            key_fn = lambda item: (
+                float(item.get("modified", 0.0) or 0.0),
+                (item.get("model_name") or item.get("file_name") or "").lower(),
+                item.get("file_path", "").lower(),
+            )
+        elif key_name == "size":
+            key_fn = lambda item: (
+                int(item.get("size", 0) or 0),
+                (item.get("model_name") or item.get("file_name") or "").lower(),
+                item.get("file_path", "").lower(),
+            )
+        elif key_name == "usage":
+            key_fn = lambda item: (
+                int(item.get("usage_count", 0) or 0),
+                (item.get("model_name") or item.get("file_name") or "").lower(),
+                item.get("file_path", "").lower(),
+            )
+        else:
+            key_fn = lambda item: (
+                (item.get("model_name") or item.get("file_name") or "").lower(),
+                item.get("file_path", "").lower(),
+            )
+
+        return sorted(data, key=key_fn, reverse=sort_params.order == "desc")
+
+    async def _build_excluded_entry(self, file_path: str) -> Optional[Dict[str, Any]]:
+        root_path = self.scanner._find_root_for_file(file_path)
+        if not root_path:
+            return None
+
+        metadata, should_skip = await MetadataManager.load_metadata(
+            file_path,
+            self.metadata_class,
+        )
+        if should_skip:
+            return None
+
+        if metadata is None:
+            metadata = await self.scanner._create_default_metadata(file_path)
+            if metadata is None:
+                return None
+
+        metadata = self.scanner.adjust_metadata(metadata, file_path, root_path)
+        folder = os.path.dirname(os.path.relpath(file_path, root_path)).replace(
+            os.path.sep, "/"
+        )
+        entry = self.scanner._build_cache_entry(metadata, folder=folder)
+        entry = self.scanner.adjust_cached_entry(entry)
+        entry["exclude"] = True
+        return entry
+
     async def _apply_hash_filters(
         self, data: List[Dict], hash_filters: Dict
     ) -> List[Dict]:
@@ -383,7 +496,9 @@ class BaseModelService(ABC):
         # Check user setting for hiding early access updates
         hide_early_access = False
         try:
-            hide_early_access = bool(self.settings.get("hide_early_access_updates", False))
+            hide_early_access = bool(
+                self.settings.get("hide_early_access_updates", False)
+            )
         except Exception:
             hide_early_access = False
 
@@ -413,7 +528,11 @@ class BaseModelService(ABC):
             bulk_method = getattr(self.update_service, "has_updates_bulk", None)
             if callable(bulk_method):
                 try:
-                    resolved = await bulk_method(self.model_type, ordered_ids, hide_early_access=hide_early_access)
+                    resolved = await bulk_method(
+                        self.model_type,
+                        ordered_ids,
+                        hide_early_access=hide_early_access,
+                    )
                 except Exception as exc:
                     logger.error(
                         "Failed to resolve update status in bulk for %s models (%s): %s",
@@ -426,7 +545,9 @@ class BaseModelService(ABC):
 
         if resolved is None:
             tasks = [
-                self.update_service.has_update(self.model_type, model_id, hide_early_access=hide_early_access)
+                self.update_service.has_update(
+                    self.model_type, model_id, hide_early_access=hide_early_access
+                )
                 for model_id in ordered_ids
             ]
             results = await asyncio.gather(*tasks, return_exceptions=True)
@@ -588,13 +709,19 @@ class BaseModelService(ABC):
             normalized_type = normalize_sub_type(resolve_sub_type(entry))
             if not normalized_type:
                 continue
-            
+
             # Filter by valid sub-types based on scanner type
-            if self.model_type == "lora" and normalized_type not in VALID_LORA_SUB_TYPES:
+            if (
+                self.model_type == "lora"
+                and normalized_type not in VALID_LORA_SUB_TYPES
+            ):
                 continue
-            if self.model_type == "checkpoint" and normalized_type not in VALID_CHECKPOINT_SUB_TYPES:
+            if (
+                self.model_type == "checkpoint"
+                and normalized_type not in VALID_CHECKPOINT_SUB_TYPES
+            ):
                 continue
-            
+
             type_counts[normalized_type] = type_counts.get(normalized_type, 0) + 1
 
         sorted_types = sorted(
@@ -755,9 +882,12 @@ class BaseModelService(ABC):
                 version_id = civitai_data.get("id")
 
                 if model_id:
-                    civitai_url = f"https://civitai.com/models/{model_id}"
-                    if version_id:
-                        civitai_url += f"?modelVersionId={version_id}"
+                    civitai_host = self.settings.get("civitai_host", "civitai.com")
+                    civitai_url = build_civitai_model_page_url(
+                        model_id,
+                        version_id,
+                        host=civitai_host,
+                    )
 
                     return {
                         "civitai_url": civitai_url,
@@ -807,38 +937,61 @@ class BaseModelService(ABC):
 
         return include_terms, exclude_terms
 
+    @staticmethod
+    def _remove_model_extension(path: str) -> str:
+        """Remove model file extension (.safetensors, .ckpt, .pt, .bin) for cleaner matching."""
+        return re.sub(r"\.(safetensors|ckpt|pt|bin)$", "", path, flags=re.IGNORECASE)
+
     @staticmethod
     def _relative_path_matches_tokens(
         path_lower: str, include_terms: List[str], exclude_terms: List[str]
     ) -> bool:
-        """Determine whether a relative path string satisfies include/exclude tokens."""
-        if any(term and term in path_lower for term in exclude_terms):
+        """Determine whether a relative path string satisfies include/exclude tokens.
+
+        Matches against the path without extension to avoid matching .safetensors
+        when searching for 's'.
+        """
+        # Use path without extension for matching
+        path_for_matching = BaseModelService._remove_model_extension(path_lower)
+
+        if any(term and term in path_for_matching for term in exclude_terms):
             return False
 
         for term in include_terms:
-            if term and term not in path_lower:
+            if term and term not in path_for_matching:
                 return False
 
         return True
 
     @staticmethod
     def _relative_path_sort_key(relative_path: str, include_terms: List[str]) -> tuple:
-        """Sort paths by how well they satisfy the include tokens."""
-        path_lower = relative_path.lower()
+        """Sort paths by how well they satisfy the include tokens.
+
+        Sorts based on path without extension for consistent ordering.
+        """
+        # Use path without extension for sorting
+        path_for_sorting = BaseModelService._remove_model_extension(
+            relative_path.lower()
+        )
         prefix_hits = sum(
-            1 for term in include_terms if term and path_lower.startswith(term)
+            1 for term in include_terms if term and path_for_sorting.startswith(term)
         )
         match_positions = [
-            path_lower.find(term)
+            path_for_sorting.find(term)
             for term in include_terms
-            if term and term in path_lower
+            if term and term in path_for_sorting
         ]
         first_match_index = min(match_positions) if match_positions else 0
 
-        return (-prefix_hits, first_match_index, len(relative_path), path_lower)
+        return (
+            -prefix_hits,
+            first_match_index,
+            len(path_for_sorting),
+            path_for_sorting,
+        )
 
     async def search_relative_paths(
-        self, search_term: str, limit: int = 15
+        self, search_term: str, limit: int = 15, offset: int = 0
     ) -> List[str]:
         """Search model relative file paths for autocomplete functionality"""
         cache = await self.scanner.get_cached_data()
@@ -849,6 +1002,7 @@ class BaseModelService(ABC):
         # Get model roots for path calculation
         model_roots = self.scanner.get_model_roots()
 
+        # Collect all matching paths first (needed for proper sorting and offset)
         for model in cache.raw_data:
             file_path = model.get("file_path", "")
             if not file_path:
@@ -877,12 +1031,12 @@ class BaseModelService(ABC):
             ):
                 matching_paths.append(relative_path)
 
-                if len(matching_paths) >= limit * 2:  # Get more for better sorting
-                    break
-
         # Sort by relevance (prefix and earliest hits first, then by length and alphabetically)
         matching_paths.sort(
             key=lambda relative: self._relative_path_sort_key(relative, include_terms)
         )
 
-        return matching_paths[:limit]
+        # Apply offset and limit
+        start = min(offset, len(matching_paths))
+        end = min(start + limit, len(matching_paths))
+        return matching_paths[start:end]

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)
@@ -125,23 +148,32 @@ 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', '')
+        # Use the effective hash_status we determined earlier
         if not sha256 or (isinstance(sha256, str) and not sha256.strip()):
-            errors.append("Required field 'sha256' is empty")
-            # Cannot repair empty sha256 - entry is invalid
-            return ValidationResult(
-                is_valid=False,
-                repaired=repaired,
-                errors=errors,
-                entry=working_entry if auto_repair else None
-            )
+            # Allow empty sha256 for lazy hash calculation (checkpoints)
+            if hash_status != 'pending':
+                errors.append("Required field 'sha256' is empty")
+                # Cannot repair empty sha256 - entry is invalid
+                return ValidationResult(
+                    is_valid=False,
+                    repaired=repaired,
+                    errors=errors,
+                    entry=working_entry if auto_repair else None
+                )
 
         # Normalize sha256 to lowercase if needed
         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 +72,82 @@ 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, should_skip = await MetadataManager.load_metadata(
+                file_path, self.model_class
+            )
             if metadata is None:
-                logger.error(f"No metadata found for {file_path}")
-                return None
-            
+                if should_skip:
+                    logger.error(f"Invalid metadata found for {file_path}")
+                    return None
+                created_metadata = await self._create_default_metadata(file_path)
+                if created_metadata is None:
+                    logger.error(f"No metadata found for {file_path}")
+                    return None
+                metadata = created_metadata
+
             # 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 +155,46 @@ 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 +204,102 @@ 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/checkpoint_service.py

@@ -42,6 +42,7 @@ class CheckpointService(BaseModelService):
             "notes": checkpoint_data.get("notes", ""),
             "sub_type": sub_type,
             "favorite": checkpoint_data.get("favorite", False),
+            "exclude": bool(checkpoint_data.get("exclude", False)),
             "update_available": bool(checkpoint_data.get("update_available", False)),
             "skip_metadata_refresh": bool(checkpoint_data.get("skip_metadata_refresh", False)),
             "civitai": self.filter_civitai_data(checkpoint_data.get("civitai", {}), minimal=True)

py/services/civitai_base_model_service.py

@@ -0,0 +1,430 @@
+from __future__ import annotations
+
+import asyncio
+import json
+import logging
+import re
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional, Set, Tuple
+
+from ..utils.constants import SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS
+from .downloader import get_downloader
+
+logger = logging.getLogger(__name__)
+
+
+class CivitaiBaseModelService:
+    """Service for fetching and managing Civitai base models.
+
+    This service provides:
+    - Fetching base models from Civitai API
+    - Caching with TTL (7 days default)
+    - Merging hardcoded and remote base models
+    - Generating abbreviations for new/unknown models
+    """
+
+    _instance: Optional[CivitaiBaseModelService] = None
+    _lock = asyncio.Lock()
+
+    # Default TTL for cache in seconds (7 days)
+    DEFAULT_CACHE_TTL = 7 * 24 * 60 * 60
+
+    # Civitai API endpoint for enums
+    CIVITAI_ENUMS_URL = "https://civitai.red/api/v1/enums"
+
+    @classmethod
+    async def get_instance(cls) -> CivitaiBaseModelService:
+        """Get singleton instance of the service."""
+        async with cls._lock:
+            if cls._instance is None:
+                cls._instance = cls()
+            return cls._instance
+
+    def __init__(self):
+        """Initialize the service."""
+        if hasattr(self, "_initialized"):
+            return
+        self._initialized = True
+
+        # Cache storage
+        self._cache: Optional[Dict[str, Any]] = None
+        self._cache_timestamp: Optional[datetime] = None
+        self._cache_ttl = self.DEFAULT_CACHE_TTL
+
+        # Hardcoded models for fallback
+        self._hardcoded_models = set(SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS)
+
+        logger.info("CivitaiBaseModelService initialized")
+
+    async def get_base_models(self, force_refresh: bool = False) -> Dict[str, Any]:
+        """Get merged base models (hardcoded + remote).
+
+        Args:
+            force_refresh: If True, fetch from API regardless of cache state.
+
+        Returns:
+            Dictionary containing:
+            - models: List of merged base model names
+            - source: 'cache', 'api', or 'fallback'
+            - last_updated: ISO timestamp of last successful API fetch
+            - hardcoded_count: Number of hardcoded models
+            - remote_count: Number of remote models
+            - merged_count: Total unique models
+        """
+        # Check if cache is valid
+        if not force_refresh and self._is_cache_valid():
+            logger.debug("Returning cached base models")
+            return self._build_response("cache")
+
+        # Try to fetch from API
+        try:
+            remote_models = await self._fetch_from_civitai()
+            if remote_models:
+                self._update_cache(remote_models)
+                return self._build_response("api")
+        except Exception as e:
+            logger.error(f"Failed to fetch base models from Civitai: {e}")
+
+        # Fallback to hardcoded models
+        return self._build_response("fallback")
+
+    async def refresh_cache(self) -> Dict[str, Any]:
+        """Force refresh the cache from Civitai API.
+
+        Returns:
+            Response dict same as get_base_models()
+        """
+        return await self.get_base_models(force_refresh=True)
+
+    def get_cache_status(self) -> Dict[str, Any]:
+        """Get current cache status.
+
+        Returns:
+            Dictionary containing:
+            - has_cache: Whether cache exists
+            - last_updated: ISO timestamp or None
+            - is_expired: Whether cache is expired
+            - ttl_seconds: TTL in seconds
+            - age_seconds: Age of cache in seconds (if exists)
+        """
+        if self._cache is None or self._cache_timestamp is None:
+            return {
+                "has_cache": False,
+                "last_updated": None,
+                "is_expired": True,
+                "ttl_seconds": self._cache_ttl,
+                "age_seconds": None,
+            }
+
+        age = (datetime.now(timezone.utc) - self._cache_timestamp).total_seconds()
+        return {
+            "has_cache": True,
+            "last_updated": self._cache_timestamp.isoformat(),
+            "is_expired": age > self._cache_ttl,
+            "ttl_seconds": self._cache_ttl,
+            "age_seconds": int(age),
+        }
+
+    def generate_abbreviation(self, model_name: str) -> str:
+        """Generate abbreviation for a base model name.
+
+        Algorithm:
+        1. Extract version patterns (e.g., "2.5" from "Wan Video 2.5")
+        2. Extract main acronym (e.g., "SD" from "SD 1.5")
+        3. Handle special cases (Flux, Wan, etc.)
+        4. Fallback to first letters of words (max 4 chars)
+
+        Args:
+            model_name: Full base model name
+
+        Returns:
+            Generated abbreviation (max 4 characters)
+        """
+        if not model_name or not isinstance(model_name, str):
+            return "OTH"
+
+        name = model_name.strip()
+        if not name:
+            return "OTH"
+
+        # Check if it's already in hardcoded abbreviations
+        # This is a simplified check - in practice you'd have a mapping
+        lower_name = name.lower()
+
+        # Special cases
+        special_cases = {
+            "sd 1.4": "SD1",
+            "sd 1.5": "SD1",
+            "sd 1.5 lcm": "SD1",
+            "sd 1.5 hyper": "SD1",
+            "sd 2.0": "SD2",
+            "sd 2.1": "SD2",
+            "sd 3": "SD3",
+            "sd 3.5": "SD3",
+            "sd 3.5 medium": "SD3",
+            "sd 3.5 large": "SD3",
+            "sd 3.5 large turbo": "SD3",
+            "sdxl 1.0": "XL",
+            "sdxl lightning": "XL",
+            "sdxl hyper": "XL",
+            "flux.1 d": "F1D",
+            "flux.1 s": "F1S",
+            "flux.1 krea": "F1KR",
+            "flux.1 kontext": "F1KX",
+            "flux.2 d": "F2D",
+            "flux.2 klein 9b": "FK9",
+            "flux.2 klein 9b-base": "FK9B",
+            "flux.2 klein 4b": "FK4",
+            "flux.2 klein 4b-base": "FK4B",
+            "auraflow": "AF",
+            "chroma": "CHR",
+            "pixart a": "PXA",
+            "pixart e": "PXE",
+            "hunyuan 1": "HY",
+            "hunyuan video": "HYV",
+            "lumina": "L",
+            "kolors": "KLR",
+            "noobai": "NAI",
+            "illustrious": "IL",
+            "pony": "PONY",
+            "pony v7": "PNY7",
+            "hidream": "HID",
+            "qwen": "QWEN",
+            "zimageturbo": "ZIT",
+            "zimagebase": "ZIB",
+            "anima": "ANI",
+            "svd": "SVD",
+            "ltxv": "LTXV",
+            "ltxv2": "LTV2",
+            "ltxv 2.3": "LTX",
+            "cogvideox": "CVX",
+            "mochi": "MCHI",
+            "wan video": "WAN",
+            "wan video 1.3b t2v": "WAN",
+            "wan video 14b t2v": "WAN",
+            "wan video 14b i2v 480p": "WAN",
+            "wan video 14b i2v 720p": "WAN",
+            "wan video 2.2 ti2v-5b": "WAN",
+            "wan video 2.2 t2v-a14b": "WAN",
+            "wan video 2.2 i2v-a14b": "WAN",
+            "wan video 2.5 t2v": "WAN",
+            "wan video 2.5 i2v": "WAN",
+        }
+
+        if lower_name in special_cases:
+            return special_cases[lower_name]
+
+        # Try to extract acronym from version pattern
+        # e.g., "Model Name 2.5" -> "MN25"
+        version_match = re.search(r"(\d+(?:\.\d+)?)", name)
+        version = version_match.group(1) if version_match else ""
+
+        # Remove version and common words
+        words = re.sub(r"\d+(?:\.\d+)?", "", name)
+        words = re.sub(
+            r"\b(model|video|diffusion|checkpoint|textualinversion)\b",
+            "",
+            words,
+            flags=re.I,
+        )
+        words = words.strip()
+
+        # Get first letters of remaining words
+        tokens = re.findall(r"[A-Za-z]+", words)
+        if tokens:
+            # Build abbreviation from first letters
+            abbrev = "".join(token[0].upper() for token in tokens)
+            # Add version if present
+            if version:
+                # Clean version (remove dots for abbreviation)
+                version_clean = version.replace(".", "")
+                abbrev = abbrev[: 4 - len(version_clean)] + version_clean
+            return abbrev[:4]
+
+        # Final fallback: just take first 4 alphanumeric chars
+        alphanumeric = re.sub(r"[^A-Za-z0-9]", "", name)
+        if alphanumeric:
+            return alphanumeric[:4].upper()
+
+        return "OTH"
+
+    async def _fetch_from_civitai(self) -> Optional[Set[str]]:
+        """Fetch base models from Civitai API.
+
+        Returns:
+            Set of base model names, or None if failed
+        """
+        try:
+            downloader = await get_downloader()
+            success, result = await downloader.make_request(
+                "GET",
+                self.CIVITAI_ENUMS_URL,
+                use_auth=False,  # enums endpoint doesn't require auth
+            )
+
+            if not success:
+                logger.warning(f"Failed to fetch enums from Civitai: {result}")
+                return None
+
+            if isinstance(result, str):
+                data = json.loads(result)
+            else:
+                data = result
+
+            # Extract base models from response
+            base_models = set()
+
+            # Use ActiveBaseModel if available (recommended active models)
+            if "ActiveBaseModel" in data:
+                base_models.update(data["ActiveBaseModel"])
+                logger.info(f"Fetched {len(base_models)} models from ActiveBaseModel")
+            # Fallback to full BaseModel list
+            elif "BaseModel" in data:
+                base_models.update(data["BaseModel"])
+                logger.info(f"Fetched {len(base_models)} models from BaseModel")
+            else:
+                logger.warning("No base model data found in Civitai response")
+                return None
+
+            return base_models
+
+        except Exception as e:
+            logger.error(f"Error fetching from Civitai: {e}")
+            return None
+
+    def _update_cache(self, remote_models: Set[str]) -> None:
+        """Update internal cache with fetched models.
+
+        Args:
+            remote_models: Set of base model names from API
+        """
+        self._cache = {
+            "remote_models": sorted(remote_models),
+            "hardcoded_models": sorted(self._hardcoded_models),
+        }
+        self._cache_timestamp = datetime.now(timezone.utc)
+        logger.info(f"Cache updated with {len(remote_models)} remote models")
+
+    def _is_cache_valid(self) -> bool:
+        """Check if current cache is valid (not expired).
+
+        Returns:
+            True if cache exists and is not expired
+        """
+        if self._cache is None or self._cache_timestamp is None:
+            return False
+
+        age = (datetime.now(timezone.utc) - self._cache_timestamp).total_seconds()
+        return age <= self._cache_ttl
+
+    def _build_response(self, source: str) -> Dict[str, Any]:
+        """Build response dictionary.
+
+        Args:
+            source: 'cache', 'api', or 'fallback'
+
+        Returns:
+            Response dictionary
+        """
+        if source == "fallback" or self._cache is None:
+            # Use only hardcoded models
+            merged = sorted(self._hardcoded_models)
+            return {
+                "models": merged,
+                "source": source,
+                "last_updated": None,
+                "hardcoded_count": len(self._hardcoded_models),
+                "remote_count": 0,
+                "merged_count": len(merged),
+            }
+
+        # Merge hardcoded and remote models
+        remote_set = set(self._cache.get("remote_models", []))
+        merged = sorted(self._hardcoded_models | remote_set)
+
+        return {
+            "models": merged,
+            "source": source,
+            "last_updated": self._cache_timestamp.isoformat()
+            if self._cache_timestamp
+            else None,
+            "hardcoded_count": len(self._hardcoded_models),
+            "remote_count": len(remote_set),
+            "merged_count": len(merged),
+        }
+
+    def get_model_categories(self) -> Dict[str, List[str]]:
+        """Get categorized base models.
+
+        Returns:
+            Dictionary mapping category names to lists of model names
+        """
+        # Define category patterns
+        categories = {
+            "Stable Diffusion 1.x": ["SD 1.4", "SD 1.5", "SD 1.5 LCM", "SD 1.5 Hyper"],
+            "Stable Diffusion 2.x": ["SD 2.0", "SD 2.1"],
+            "Stable Diffusion 3.x": [
+                "SD 3",
+                "SD 3.5",
+                "SD 3.5 Medium",
+                "SD 3.5 Large",
+                "SD 3.5 Large Turbo",
+            ],
+            "SDXL": ["SDXL 1.0", "SDXL Lightning", "SDXL Hyper"],
+            "Flux Models": [
+                "Flux.1 D",
+                "Flux.1 S",
+                "Flux.1 Krea",
+                "Flux.1 Kontext",
+                "Flux.2 D",
+                "Flux.2 Klein 9B",
+                "Flux.2 Klein 9B-base",
+                "Flux.2 Klein 4B",
+                "Flux.2 Klein 4B-base",
+            ],
+            "Video Models": [
+                "SVD",
+                "LTXV",
+                "LTXV2",
+                "LTXV 2.3",
+                "CogVideoX",
+                "Mochi",
+                "Hunyuan Video",
+                "Wan Video",
+                "Wan Video 1.3B t2v",
+                "Wan Video 14B t2v",
+                "Wan Video 14B i2v 480p",
+                "Wan Video 14B i2v 720p",
+                "Wan Video 2.2 TI2V-5B",
+                "Wan Video 2.2 T2V-A14B",
+                "Wan Video 2.2 I2V-A14B",
+                "Wan Video 2.5 T2V",
+                "Wan Video 2.5 I2V",
+            ],
+            "Other Models": [
+                "Illustrious",
+                "Pony",
+                "Pony V7",
+                "HiDream",
+                "Qwen",
+                "AuraFlow",
+                "Chroma",
+                "ZImageTurbo",
+                "ZImageBase",
+                "PixArt a",
+                "PixArt E",
+                "Hunyuan 1",
+                "Lumina",
+                "Kolors",
+                "NoobAI",
+                "Anima",
+            ],
+        }
+
+        return categories
+
+
+# Convenience function for getting the singleton instance
+async def get_civitai_base_model_service() -> CivitaiBaseModelService:
+    """Get the singleton instance of CivitaiBaseModelService."""
+    return await CivitaiBaseModelService.get_instance()

py/services/civitai_client.py

@@ -3,37 +3,51 @@ import copy
 import logging
 import os
 from typing import Any, Optional, Dict, Tuple, List, Sequence
-from .model_metadata_provider import CivitaiModelMetadataProvider, ModelMetadataProviderManager
+from .connectivity_guard import (
+    OFFLINE_FRIENDLY_MESSAGE,
+    is_expected_offline_error,
+    is_offline_cooldown_error,
+)
+from .model_metadata_provider import (
+    CivitaiModelMetadataProvider,
+    ModelMetadataProviderManager,
+)
 from .downloader import get_downloader
 from .errors import RateLimitError, ResourceNotFoundError
 from ..utils.civitai_utils import resolve_license_payload
 
 logger = logging.getLogger(__name__)
 
+
 class CivitaiClient:
     _instance = None
     _lock = asyncio.Lock()
-    
+
     @classmethod
     async def get_instance(cls):
         """Get singleton instance of CivitaiClient"""
         async with cls._lock:
             if cls._instance is None:
                 cls._instance = cls()
-                
+
                 # Register this client as a metadata provider
                 provider_manager = await ModelMetadataProviderManager.get_instance()
-                provider_manager.register_provider('civitai', CivitaiModelMetadataProvider(cls._instance), True)
-                
+                provider_manager.register_provider(
+                    "civitai", CivitaiModelMetadataProvider(cls._instance), True
+                )
+
             return cls._instance
 
     def __init__(self):
         # Check if already initialized for singleton pattern
-        if hasattr(self, '_initialized'):
+        if hasattr(self, "_initialized"):
             return
         self._initialized = True
-        
-        self.base_url = "https://civitai.com/api/v1"
+
+        self.base_url = "https://civitai.red/api/v1"
+
+    def _build_image_info_url(self, image_id: str) -> str:
+        return f"{self.base_url}/images?imageId={image_id}&nsfw=X"
 
     async def _make_request(
         self,
@@ -56,6 +70,8 @@ class CivitaiClient:
             if result.provider is None:
                 result.provider = "civitai_api"
             raise result
+        if not success and is_offline_cooldown_error(result):
+            return False, OFFLINE_FRIENDLY_MESSAGE
         return success, result
 
     @staticmethod
@@ -75,8 +91,10 @@ class CivitaiClient:
             meta = image.get("meta")
             if isinstance(meta, dict) and "comfy" in meta:
                 meta.pop("comfy", None)
-    
-    async def download_file(self, url: str, save_dir: str, default_filename: str, progress_callback=None) -> Tuple[bool, str]:
+
+    async def download_file(
+        self, url: str, save_dir: str, default_filename: str, progress_callback=None
+    ) -> Tuple[bool, str]:
         """Download file with resumable downloads and retry mechanism
 
         Args:
@@ -90,41 +108,50 @@ class CivitaiClient:
         """
         downloader = await get_downloader()
         save_path = os.path.join(save_dir, default_filename)
-        
+
         # Use unified downloader with CivitAI authentication
         success, result = await downloader.download_file(
             url=url,
             save_path=save_path,
             progress_callback=progress_callback,
             use_auth=True,  # Enable CivitAI authentication
-            allow_resume=True
+            allow_resume=True,
         )
-        
+
         return success, result
 
-    async def get_model_by_hash(self, model_hash: str) -> Tuple[Optional[Dict], Optional[str]]:
+    async def get_model_by_hash(
+        self, model_hash: str
+    ) -> Tuple[Optional[Dict], Optional[str]]:
         try:
             success, version = await self._make_request(
-                'GET',
+                "GET",
                 f"{self.base_url}/model-versions/by-hash/{model_hash}",
-                use_auth=True
+                use_auth=True,
             )
             if not success:
                 message = str(version)
+                if is_expected_offline_error(message):
+                    return None, OFFLINE_FRIENDLY_MESSAGE
                 if "not found" in message.lower():
                     return None, "Model not found"
 
-                logger.error("Failed to fetch model info for %s: %s", model_hash[:10], message)
+                logger.error(
+                    "Failed to fetch model info for %s: %s", model_hash[:10], message
+                )
                 return None, message
 
-            model_id = version.get('modelId')
-            if model_id:
-                model_data = await self._fetch_model_data(model_id)
-                if model_data:
-                    self._enrich_version_with_model_data(version, model_data)
+            if isinstance(version, dict):
+                model_id = version.get("modelId")
+                if model_id:
+                    model_data = await self._fetch_model_data(model_id)
+                    if model_data:
+                        self._enrich_version_with_model_data(version, model_data)
 
-            self._remove_comfy_metadata(version)
-            return version, None
+                self._remove_comfy_metadata(version)
+                return version, None
+            else:
+                return None, "Invalid response format"
         except RateLimitError:
             raise
         except Exception as exc:
@@ -136,19 +163,22 @@ class CivitaiClient:
             downloader = await get_downloader()
             success, content, headers = await downloader.download_to_memory(
                 image_url,
-                use_auth=False  # Preview images don't need auth
+                use_auth=False,  # Preview images don't need auth
             )
             if success:
                 # Ensure directory exists
                 os.makedirs(os.path.dirname(save_path), exist_ok=True)
-                with open(save_path, 'wb') as f:
+                with open(save_path, "wb") as f:
                     f.write(content)
                 return True
             return False
         except Exception as e:
+            if is_expected_offline_error(str(e)):
+                logger.debug("Preview download skipped due to offline state.")
+                return False
             logger.error(f"Download Error: {str(e)}")
             return False
-            
+
     @staticmethod
     def _extract_error_message(payload: Any) -> str:
         """Return a human-readable error message from an API payload."""
@@ -175,20 +205,23 @@ class CivitaiClient:
         """Get all versions of a model with local availability info"""
         try:
             success, result = await self._make_request(
-                'GET',
+                "GET",
                 f"{self.base_url}/models/{model_id}",
-                use_auth=True
+                use_auth=True,
             )
             if success:
                 # Also return model type along with versions
                 return {
-                    'modelVersions': result.get('modelVersions', []),
-                    'type': result.get('type', ''),
-                    'name': result.get('name', '')
+                    "modelVersions": result.get("modelVersions", []),
+                    "type": result.get("type", ""),
+                    "name": result.get("name", ""),
                 }
             message = self._extract_error_message(result)
-            if message and 'not found' in message.lower():
+            if message and "not found" in message.lower():
                 raise ResourceNotFoundError(f"Resource not found for model {model_id}")
+            if is_expected_offline_error(message):
+                logger.info("Civitai request skipped: %s", OFFLINE_FRIENDLY_MESSAGE)
+                return None
             if message:
                 raise RuntimeError(message)
             return None
@@ -221,15 +254,15 @@ class CivitaiClient:
         try:
             query = ",".join(normalized_ids)
             success, result = await self._make_request(
-                'GET',
+                "GET",
                 f"{self.base_url}/models",
                 use_auth=True,
-                params={'ids': query},
+                params={"ids": query},
             )
             if not success:
                 return None
 
-            items = result.get('items') if isinstance(result, dict) else None
+            items = result.get("items") if isinstance(result, dict) else None
             if not isinstance(items, list):
                 return {}
 
@@ -237,19 +270,19 @@ class CivitaiClient:
             for item in items:
                 if not isinstance(item, dict):
                     continue
-                model_id = item.get('id')
+                model_id = item.get("id")
                 try:
                     normalized_id = int(model_id)
                 except (TypeError, ValueError):
                     continue
                 payload[normalized_id] = {
-                    'modelVersions': item.get('modelVersions', []),
-                    'type': item.get('type', ''),
-                    'name': item.get('name', ''),
-                    'allowNoCredit': item.get('allowNoCredit'),
-                    'allowCommercialUse': item.get('allowCommercialUse'),
-                    'allowDerivatives': item.get('allowDerivatives'),
-                    'allowDifferentLicense': item.get('allowDifferentLicense'),
+                    "modelVersions": item.get("modelVersions", []),
+                    "type": item.get("type", ""),
+                    "name": item.get("name", ""),
+                    "allowNoCredit": item.get("allowNoCredit"),
+                    "allowCommercialUse": item.get("allowCommercialUse"),
+                    "allowDerivatives": item.get("allowDerivatives"),
+                    "allowDifferentLicense": item.get("allowDifferentLicense"),
                 }
             return payload
         except RateLimitError:
@@ -257,8 +290,10 @@ class CivitaiClient:
         except Exception as exc:
             logger.error(f"Error fetching model versions in bulk: {exc}")
             return None
-            
-    async def get_model_version(self, model_id: int = None, version_id: int = None) -> Optional[Dict]:
+
+    async def get_model_version(
+        self, model_id: int = None, version_id: int = None
+    ) -> Optional[Dict]:
         """Get specific model version with additional metadata."""
         try:
             if model_id is None and version_id is not None:
@@ -281,7 +316,7 @@ class CivitaiClient:
         if version is None:
             return None
 
-        model_id = version.get('modelId')
+        model_id = version.get("modelId")
         if not model_id:
             logger.error(f"No modelId found in version {version_id}")
             return None
@@ -293,7 +328,9 @@ class CivitaiClient:
         self._remove_comfy_metadata(version)
         return version
 
-    async def _get_version_with_model_id(self, model_id: int, version_id: Optional[int]) -> Optional[Dict]:
+    async def _get_version_with_model_id(
+        self, model_id: int, version_id: Optional[int]
+    ) -> Optional[Dict]:
         model_data = await self._fetch_model_data(model_id)
         if not model_data:
             return None
@@ -302,8 +339,12 @@ class CivitaiClient:
         if target_version is None:
             return None
 
-        target_version_id = target_version.get('id')
-        version = await self._fetch_version_by_id(target_version_id) if target_version_id else None
+        target_version_id = target_version.get("id")
+        version = (
+            await self._fetch_version_by_id(target_version_id)
+            if target_version_id
+            else None
+        )
 
         if version is None:
             model_hash = self._extract_primary_model_hash(target_version)
@@ -315,7 +356,9 @@ class CivitaiClient:
                 )
 
         if version is None:
-            version = self._build_version_from_model_data(target_version, model_id, model_data)
+            version = self._build_version_from_model_data(
+                target_version, model_id, model_data
+            )
 
         self._enrich_version_with_model_data(version, model_data)
         self._remove_comfy_metadata(version)
@@ -323,12 +366,14 @@ class CivitaiClient:
 
     async def _fetch_model_data(self, model_id: int) -> Optional[Dict]:
         success, data = await self._make_request(
-            'GET',
+            "GET",
             f"{self.base_url}/models/{model_id}",
-            use_auth=True
+            use_auth=True,
         )
         if success:
             return data
+        if is_expected_offline_error(data):
+            return None
         logger.warning(f"Failed to fetch model data for model {model_id}")
         return None
 
@@ -337,12 +382,14 @@ class CivitaiClient:
             return None
 
         success, version = await self._make_request(
-            'GET',
+            "GET",
             f"{self.base_url}/model-versions/{version_id}",
-            use_auth=True
+            use_auth=True,
         )
         if success:
             return version
+        if is_expected_offline_error(version):
+            return None
 
         logger.warning(f"Failed to fetch version by id {version_id}")
         return None
@@ -352,26 +399,29 @@ class CivitaiClient:
             return None
 
         success, version = await self._make_request(
-            'GET',
+            "GET",
             f"{self.base_url}/model-versions/by-hash/{model_hash}",
-            use_auth=True
+            use_auth=True,
         )
         if success:
             return version
+        if is_expected_offline_error(version):
+            return None
 
         logger.warning(f"Failed to fetch version by hash {model_hash}")
         return None
 
-    def _select_target_version(self, model_data: Dict, model_id: int, version_id: Optional[int]) -> Optional[Dict]:
-        model_versions = model_data.get('modelVersions', [])
+    def _select_target_version(
+        self, model_data: Dict, model_id: int, version_id: Optional[int]
+    ) -> Optional[Dict]:
+        model_versions = model_data.get("modelVersions", [])
         if not model_versions:
             logger.warning(f"No model versions found for model {model_id}")
             return None
 
         if version_id is not None:
             target_version = next(
-                (item for item in model_versions if item.get('id') == version_id),
-                None
+                (item for item in model_versions if item.get("id") == version_id), None
             )
             if target_version is None:
                 logger.warning(
@@ -383,46 +433,50 @@ class CivitaiClient:
         return model_versions[0]
 
     def _extract_primary_model_hash(self, version_entry: Dict) -> Optional[str]:
-        for file_info in version_entry.get('files', []):
-            if file_info.get('type') == 'Model' and file_info.get('primary'):
-                hashes = file_info.get('hashes', {})
-                model_hash = hashes.get('SHA256')
+        for file_info in version_entry.get("files", []):
+            if file_info.get("type") == "Model" and file_info.get("primary"):
+                hashes = file_info.get("hashes", {})
+                model_hash = hashes.get("SHA256")
                 if model_hash:
                     return model_hash
         return None
 
-    def _build_version_from_model_data(self, version_entry: Dict, model_id: int, model_data: Dict) -> Dict:
+    def _build_version_from_model_data(
+        self, version_entry: Dict, model_id: int, model_data: Dict
+    ) -> Dict:
         version = copy.deepcopy(version_entry)
-        version.pop('index', None)
-        version['modelId'] = model_id
-        version['model'] = {
-            'name': model_data.get('name'),
-            'type': model_data.get('type'),
-            'nsfw': model_data.get('nsfw'),
-            'poi': model_data.get('poi')
+        version.pop("index", None)
+        version["modelId"] = model_id
+        version["model"] = {
+            "name": model_data.get("name"),
+            "type": model_data.get("type"),
+            "nsfw": model_data.get("nsfw"),
+            "poi": model_data.get("poi"),
         }
         return version
 
     def _enrich_version_with_model_data(self, version: Dict, model_data: Dict) -> None:
-        model_info = version.get('model')
+        model_info = version.get("model")
         if not isinstance(model_info, dict):
             model_info = {}
-            version['model'] = model_info
+            version["model"] = model_info
 
-        model_info['description'] = model_data.get("description")
-        model_info['tags'] = model_data.get("tags", [])
-        version['creator'] = model_data.get("creator")
+        model_info["description"] = model_data.get("description")
+        model_info["tags"] = model_data.get("tags", [])
+        version["creator"] = model_data.get("creator")
 
         license_payload = resolve_license_payload(model_data)
         for field, value in license_payload.items():
             model_info[field] = value
 
-    async def get_model_version_info(self, version_id: str) -> Tuple[Optional[Dict], Optional[str]]:
+    async def get_model_version_info(
+        self, version_id: str
+    ) -> Tuple[Optional[Dict], Optional[str]]:
         """Fetch model version metadata from Civitai
-        
+
         Args:
             version_id: The Civitai model version ID
-            
+
         Returns:
             Tuple[Optional[Dict], Optional[str]]: A tuple containing:
                 - The model version data or None if not found
@@ -430,25 +484,23 @@ class CivitaiClient:
         """
         try:
             url = f"{self.base_url}/model-versions/{version_id}"
-            
-            logger.debug(f"Resolving DNS for model version info: {url}")
-            success, result = await self._make_request(
-                'GET',
-                url,
-                use_auth=True
-            )
-            
+
+            logger.debug("Resolving Civitai model version info: %s", url)
+            success, result = await self._make_request("GET", url, use_auth=True)
+
             if success:
-                logger.debug(f"Successfully fetched model version info for: {version_id}")
+                logger.debug("Successfully fetched model version info for: %s", version_id)
                 self._remove_comfy_metadata(result)
                 return result, None
-            
+
             # Handle specific error cases
+            if is_expected_offline_error(result):
+                return None, OFFLINE_FRIENDLY_MESSAGE
             if "not found" in str(result):
                 error_msg = f"Model not found"
                 logger.warning(f"Model version not found: {version_id} - {error_msg}")
                 return None, error_msg
-            
+
             # Other error cases
             logger.error(f"Failed to fetch model info for {version_id}: {result}")
             return None, str(result)
@@ -459,36 +511,67 @@ class CivitaiClient:
             logger.error(error_msg)
             return None, error_msg
 
-    async def get_image_info(self, image_id: str) -> Optional[Dict]:
+    async def get_image_info(
+        self, image_id: str, source_url: str | None = None
+    ) -> Optional[Dict]:
         """Fetch image information from Civitai API
 
         Args:
             image_id: The Civitai image ID
-            
+            source_url: Original image page URL. Accepted for caller compatibility;
+                API requests always target ``civitai.red``.
+
         Returns:
             Optional[Dict]: The image data or None if not found
         """
         try:
-            url = f"{self.base_url}/images?imageId={image_id}&nsfw=X"
-            
-            logger.debug(f"Fetching image info for ID: {image_id}")
-            success, result = await self._make_request(
-                'GET',
-                url,
-                use_auth=True
-            )
-            
-            if success:
-                if result and "items" in result and len(result["items"]) > 0:
-                    logger.debug(f"Successfully fetched image info for ID: {image_id}")
-                    return result["items"][0]
-                logger.warning(f"No image found with ID: {image_id}")
+            requested_id = int(image_id)
+            url = self._build_image_info_url(image_id)
+            success, result = await self._make_request("GET", url, use_auth=True)
+
+            if not success:
+                if is_expected_offline_error(result):
+                    return None
+                logger.error(
+                    "Failed to fetch image info for ID %s from civitai.red: %s",
+                    image_id,
+                    result,
+                )
                 return None
-            
-            logger.error(f"Failed to fetch image info for ID: {image_id}: {result}")
+
+            if result and "items" in result and isinstance(result["items"], list):
+                items = result["items"]
+
+                for item in items:
+                    if isinstance(item, dict) and item.get("id") == requested_id:
+                        logger.debug(
+                            "Successfully fetched image info for ID %s from civitai.red",
+                            image_id,
+                        )
+                        return item
+
+                returned_ids = [
+                    item.get("id")
+                    for item in items
+                    if isinstance(item, dict) and "id" in item
+                ]
+
+                logger.warning(
+                    "CivitAI API returned no matching image for requested ID %s from civitai.red. Returned %d item(s) with IDs: %s. This may indicate the image was deleted, hidden, or there is a database lag.",
+                    image_id,
+                    len(items),
+                    returned_ids,
+                )
+                return None
+
+            logger.warning("No image found with ID: %s", image_id)
             return None
         except RateLimitError:
             raise
+        except ValueError as e:
+            error_msg = f"Invalid image ID format: {image_id}"
+            logger.error(error_msg)
+            return None
         except Exception as e:
             error_msg = f"Error fetching image info: {e}"
             logger.error(error_msg)
@@ -500,14 +583,17 @@ class CivitaiClient:
             return None
 
         try:
-            url = f"{self.base_url}/models?username={username}"
             success, result = await self._make_request(
-                'GET',
-                url,
-                use_auth=True
+                "GET",
+                f"{self.base_url}/models",
+                use_auth=True,
+                params={"username": username},
             )
 
             if not success:
+                if is_expected_offline_error(result):
+                    logger.info("User model fetch skipped: %s", OFFLINE_FRIENDLY_MESSAGE)
+                    return None
                 logger.error("Failed to fetch models for %s: %s", username, result)
                 return None
 

py/services/connectivity_guard.py

@@ -0,0 +1,204 @@
+"""In-memory connectivity guard to suppress repeated network retries when offline."""
+
+from __future__ import annotations
+
+import asyncio
+import errno
+import logging
+import socket
+from dataclasses import dataclass
+from datetime import datetime, timedelta
+from typing import Any
+
+import aiohttp
+
+logger = logging.getLogger(__name__)
+
+OFFLINE_COOLDOWN_ERROR = "offline_cooldown"
+OFFLINE_FRIENDLY_MESSAGE = "Network offline, will retry automatically later"
+
+
+def is_offline_cooldown_error(value: Any) -> bool:
+    """Return True when a response payload represents guard short-circuit."""
+    return isinstance(value, str) and value == OFFLINE_COOLDOWN_ERROR
+
+
+def is_expected_offline_error(value: Any) -> bool:
+    """Return True when payload is an expected offline-related result."""
+    if is_offline_cooldown_error(value):
+        return True
+    if not isinstance(value, str):
+        return False
+    normalized = value.lower()
+    return "network offline" in normalized or "offline" in normalized
+
+
+class ConnectivityGuard:
+    """Tracks network failures and gates outbound requests during cooldown."""
+
+    _instance: "ConnectivityGuard | None" = None
+    _instance_lock = asyncio.Lock()
+
+    @classmethod
+    async def get_instance(cls) -> "ConnectivityGuard":
+        async with cls._instance_lock:
+            if cls._instance is None:
+                cls._instance = cls()
+            return cls._instance
+
+    def __init__(self) -> None:
+        if hasattr(self, "_initialized"):
+            return
+        self._initialized = True
+        self._default_destination = "__global__"
+        self._destination_states: dict[str, _DestinationState] = {
+            self._default_destination: _DestinationState()
+        }
+        self.base_backoff_seconds = 30
+        self.max_backoff_seconds = 300
+        self.failure_threshold = 3
+
+    @property
+    def online(self) -> bool:
+        return self._state_for_destination(None).online
+
+    @online.setter
+    def online(self, value: bool) -> None:
+        self._state_for_destination(None).online = value
+
+    @property
+    def failure_count(self) -> int:
+        return self._state_for_destination(None).failure_count
+
+    @failure_count.setter
+    def failure_count(self, value: int) -> None:
+        self._state_for_destination(None).failure_count = value
+
+    @property
+    def cooldown_until(self) -> datetime | None:
+        return self._state_for_destination(None).cooldown_until
+
+    @cooldown_until.setter
+    def cooldown_until(self, value: datetime | None) -> None:
+        self._state_for_destination(None).cooldown_until = value
+
+    def _now(self) -> datetime:
+        return datetime.now()
+
+    def _normalize_destination(self, destination: str | None) -> str:
+        if destination is None or not destination.strip():
+            return self._default_destination
+        return destination.lower().strip()
+
+    def _state_for_destination(self, destination: str | None) -> "_DestinationState":
+        destination_key = self._normalize_destination(destination)
+        if destination_key not in self._destination_states:
+            self._destination_states[destination_key] = _DestinationState()
+        return self._destination_states[destination_key]
+
+    def in_cooldown(self, destination: str | None = None) -> bool:
+        state = self._state_for_destination(destination)
+        if state.cooldown_until is None:
+            return False
+        return self._now() < state.cooldown_until
+
+    def cooldown_remaining_seconds(self, destination: str | None = None) -> float:
+        state = self._state_for_destination(destination)
+        if state.cooldown_until is None:
+            return 0.0
+        return max(0.0, (state.cooldown_until - self._now()).total_seconds())
+
+    def should_block_request(self, destination: str | None = None) -> bool:
+        return self.in_cooldown(destination)
+
+    def register_success(self, destination: str | None = None) -> None:
+        destination_key = self._normalize_destination(destination)
+        state = self._state_for_destination(destination_key)
+        was_offline = (not state.online) or state.cooldown_until is not None
+        state.online = True
+        state.failure_count = 0
+        state.cooldown_until = None
+        if was_offline:
+            logger.info(
+                "Connectivity restored for destination '%s'; requests resumed.",
+                destination_key,
+            )
+
+    def register_network_failure(
+        self, exc: Exception, destination: str | None = None
+    ) -> None:
+        destination_key = self._normalize_destination(destination)
+        state = self._state_for_destination(destination_key)
+        state.online = False
+        state.failure_count += 1
+
+        if state.failure_count < self.failure_threshold:
+            logger.debug(
+                "Network failure tracked for destination '%s' (%d/%d): %s",
+                destination_key,
+                state.failure_count,
+                self.failure_threshold,
+                exc,
+            )
+            return
+
+        retry_step = state.failure_count - self.failure_threshold
+        backoff = min(
+            self.max_backoff_seconds,
+            self.base_backoff_seconds * (2**retry_step),
+        )
+        should_log_warning = not self.in_cooldown(destination_key)
+        state.cooldown_until = self._now() + timedelta(seconds=backoff)
+
+        if should_log_warning:
+            logger.warning(
+                "Connectivity offline for destination '%s'; enter cooldown for %ss after %d network failures.",
+                destination_key,
+                int(backoff),
+                state.failure_count,
+            )
+        else:
+            logger.debug(
+                "Cooldown still active for destination '%s'; failure_count=%d, backoff=%ss.",
+                destination_key,
+                state.failure_count,
+                int(backoff),
+            )
+
+    @staticmethod
+    def is_network_unreachable_error(exc: Exception) -> bool:
+        """Return whether the exception should count as connectivity failure."""
+        if isinstance(exc, asyncio.CancelledError):
+            return False
+
+        if isinstance(
+            exc,
+            (
+                asyncio.TimeoutError,
+                TimeoutError,
+                ConnectionRefusedError,
+                socket.gaierror,
+                aiohttp.ServerTimeoutError,
+                aiohttp.ConnectionTimeoutError,
+                aiohttp.ClientConnectorError,
+                aiohttp.ClientConnectionError,
+            ),
+        ):
+            return True
+
+        if isinstance(exc, OSError) and exc.errno in {
+            errno.ENETUNREACH,
+            errno.EHOSTUNREACH,
+            errno.ETIMEDOUT,
+            errno.ECONNREFUSED,
+        }:
+            return True
+
+        return False
+
+
+@dataclass
+class _DestinationState:
+    online: bool = True
+    failure_count: int = 0
+    cooldown_until: datetime | None = None

py/services/custom_words_service.py

@@ -7,11 +7,13 @@ with category filtering and enriched results including post counts.
 from __future__ import annotations
 
 import logging
+import re
 from typing import List, Dict, Any, Optional
 
 logger = logging.getLogger(__name__)
 
 
+_EMBEDDED_COMMAND_PATTERN = re.compile(r"\s/\w")
 class CustomWordsService:
     """Service for autocomplete via TagFTSIndex.
 
@@ -49,6 +51,7 @@ class CustomWordsService:
         if self._tag_index is None:
             try:
                 from .tag_fts_index import get_tag_fts_index
+
                 self._tag_index = get_tag_fts_index()
             except Exception as e:
                 logger.warning(f"Failed to initialize TagFTSIndex: {e}")
@@ -59,14 +62,16 @@ class CustomWordsService:
         self,
         search_term: str,
         limit: int = 20,
+        offset: int = 0,
         categories: Optional[List[int]] = None,
-        enriched: bool = False
+        enriched: bool = False,
     ) -> List[Dict[str, Any]]:
         """Search tags using TagFTSIndex with category filtering.
 
         Args:
             search_term: The search term to match against.
             limit: Maximum number of results to return.
+            offset: Number of results to skip.
             categories: Optional list of category IDs to filter by.
             enriched: If True, always return enriched results with category
                        and post_count (default behavior now).
@@ -74,10 +79,28 @@ class CustomWordsService:
         Returns:
             List of dicts with tag_name, category, and post_count.
         """
+        normalized_search = search_term.strip()
+        if not normalized_search:
+            return []
+
+        # Prompt widgets should only send the active token, but guard against
+        # accidental full-prompt queries reaching the FTS path.
+        if (
+            "__" in normalized_search
+            or "," in normalized_search
+            or ">" in normalized_search
+            or "\n" in normalized_search
+            or "\r" in normalized_search
+            or _EMBEDDED_COMMAND_PATTERN.search(normalized_search)
+        ):
+            logger.debug("Skipping prompt-like custom words query: %s", normalized_search)
+            return []
+
         tag_index = self._get_tag_index()
         if tag_index is not None:
-            results = tag_index.search(search_term, categories=categories, limit=limit)
-            return results
+            return tag_index.search(
+                normalized_search, categories=categories, limit=limit, offset=offset
+            )
 
         logger.debug("TagFTSIndex not available, returning empty results")
         return []

py/services/downloaded_version_history_service.py

@@ -0,0 +1,313 @@
+from __future__ import annotations
+
+import asyncio
+import logging
+import os
+import sqlite3
+import time
+from typing import Iterable, Mapping, Optional, Sequence
+
+from ..utils.cache_paths import get_cache_base_dir
+from .settings_manager import get_settings_manager
+
+logger = logging.getLogger(__name__)
+
+
+def _normalize_model_type(model_type: str | None) -> Optional[str]:
+    if not isinstance(model_type, str):
+        return None
+    normalized = model_type.strip().lower()
+    if normalized in {"lora", "locon", "dora"}:
+        return "lora"
+    if normalized == "checkpoint":
+        return "checkpoint"
+    if normalized in {"embedding", "textualinversion"}:
+        return "embedding"
+    return None
+
+
+def _normalize_int(value) -> Optional[int]:
+    try:
+        if value is None:
+            return None
+        return int(value)
+    except (TypeError, ValueError):
+        return None
+
+
+def _resolve_database_path() -> str:
+    base_dir = get_cache_base_dir(create=True)
+    history_dir = os.path.join(base_dir, "download_history")
+    os.makedirs(history_dir, exist_ok=True)
+    return os.path.join(history_dir, "downloaded_versions.sqlite")
+
+
+class DownloadedVersionHistoryService:
+    _SCHEMA = """
+        CREATE TABLE IF NOT EXISTS downloaded_model_versions (
+            model_type TEXT NOT NULL,
+            version_id INTEGER NOT NULL,
+            model_id INTEGER,
+            first_seen_at REAL NOT NULL,
+            last_seen_at REAL NOT NULL,
+            source TEXT NOT NULL,
+            last_file_path TEXT,
+            last_library_name TEXT,
+            is_deleted_override INTEGER NOT NULL DEFAULT 0,
+            PRIMARY KEY (model_type, version_id)
+        );
+        CREATE INDEX IF NOT EXISTS idx_downloaded_model_versions_model
+            ON downloaded_model_versions(model_type, model_id);
+    """
+
+    def __init__(self, db_path: str | None = None, *, settings_manager=None) -> None:
+        self._db_path = db_path or _resolve_database_path()
+        self._settings = settings_manager or get_settings_manager()
+        self._lock = asyncio.Lock()
+        self._schema_initialized = False
+        self._ensure_directory()
+        self._initialize_schema()
+
+    def _ensure_directory(self) -> None:
+        directory = os.path.dirname(self._db_path)
+        if directory:
+            os.makedirs(directory, exist_ok=True)
+
+    def _connect(self) -> sqlite3.Connection:
+        conn = sqlite3.connect(self._db_path, check_same_thread=False)
+        conn.row_factory = sqlite3.Row
+        return conn
+
+    def _initialize_schema(self) -> None:
+        if self._schema_initialized:
+            return
+        with self._connect() as conn:
+            conn.executescript(self._SCHEMA)
+            conn.commit()
+        self._schema_initialized = True
+
+    def get_database_path(self) -> str:
+        return self._db_path
+
+    def _get_active_library_name(self) -> str | None:
+        try:
+            value = self._settings.get_active_library_name()
+        except Exception:
+            return None
+        return value or None
+
+    async def mark_downloaded(
+        self,
+        model_type: str,
+        version_id: int,
+        *,
+        model_id: int | None = None,
+        source: str = "manual",
+        file_path: str | None = None,
+        library_name: str | None = None,
+    ) -> None:
+        normalized_type = _normalize_model_type(model_type)
+        normalized_version_id = _normalize_int(version_id)
+        normalized_model_id = _normalize_int(model_id)
+        if normalized_type is None or normalized_version_id is None:
+            return
+
+        active_library_name = library_name or self._get_active_library_name()
+        timestamp = time.time()
+
+        async with self._lock:
+            with self._connect() as conn:
+                conn.execute(
+                    """
+                    INSERT INTO downloaded_model_versions (
+                        model_type, version_id, model_id, first_seen_at, last_seen_at,
+                        source, last_file_path, last_library_name, is_deleted_override
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0)
+                    ON CONFLICT(model_type, version_id) DO UPDATE SET
+                        model_id = COALESCE(excluded.model_id, downloaded_model_versions.model_id),
+                        last_seen_at = excluded.last_seen_at,
+                        source = excluded.source,
+                        last_file_path = COALESCE(excluded.last_file_path, downloaded_model_versions.last_file_path),
+                        last_library_name = COALESCE(excluded.last_library_name, downloaded_model_versions.last_library_name),
+                        is_deleted_override = 0
+                    """,
+                    (
+                        normalized_type,
+                        normalized_version_id,
+                        normalized_model_id,
+                        timestamp,
+                        timestamp,
+                        source,
+                        file_path,
+                        active_library_name,
+                    ),
+                )
+                conn.commit()
+
+    async def mark_downloaded_bulk(
+        self,
+        model_type: str,
+        records: Sequence[Mapping[str, object]],
+        *,
+        source: str = "scan",
+        library_name: str | None = None,
+    ) -> None:
+        normalized_type = _normalize_model_type(model_type)
+        if normalized_type is None or not records:
+            return
+
+        timestamp = time.time()
+        active_library_name = library_name or self._get_active_library_name()
+        payload: list[tuple[object, ...]] = []
+        for record in records:
+            version_id = _normalize_int(record.get("version_id"))
+            if version_id is None:
+                continue
+            payload.append(
+                (
+                    normalized_type,
+                    version_id,
+                    _normalize_int(record.get("model_id")),
+                    timestamp,
+                    timestamp,
+                    source,
+                    record.get("file_path"),
+                    active_library_name,
+                )
+            )
+
+        if not payload:
+            return
+
+        async with self._lock:
+            with self._connect() as conn:
+                conn.executemany(
+                    """
+                    INSERT INTO downloaded_model_versions (
+                        model_type, version_id, model_id, first_seen_at, last_seen_at,
+                        source, last_file_path, last_library_name, is_deleted_override
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0)
+                    ON CONFLICT(model_type, version_id) DO UPDATE SET
+                        model_id = COALESCE(excluded.model_id, downloaded_model_versions.model_id),
+                        last_seen_at = excluded.last_seen_at,
+                        source = excluded.source,
+                        last_file_path = COALESCE(excluded.last_file_path, downloaded_model_versions.last_file_path),
+                        last_library_name = COALESCE(excluded.last_library_name, downloaded_model_versions.last_library_name),
+                        is_deleted_override = 0
+                    """,
+                    payload,
+                )
+                conn.commit()
+
+    async def mark_not_downloaded(self, model_type: str, version_id: int) -> None:
+        normalized_type = _normalize_model_type(model_type)
+        normalized_version_id = _normalize_int(version_id)
+        if normalized_type is None or normalized_version_id is None:
+            return
+
+        timestamp = time.time()
+
+        async with self._lock:
+            with self._connect() as conn:
+                conn.execute(
+                    """
+                    INSERT INTO downloaded_model_versions (
+                        model_type, version_id, model_id, first_seen_at, last_seen_at,
+                        source, last_file_path, last_library_name, is_deleted_override
+                    ) VALUES (?, ?, NULL, ?, ?, 'manual', NULL, ?, 1)
+                    ON CONFLICT(model_type, version_id) DO UPDATE SET
+                        last_seen_at = excluded.last_seen_at,
+                        source = excluded.source,
+                        last_library_name = COALESCE(excluded.last_library_name, downloaded_model_versions.last_library_name),
+                        is_deleted_override = 1
+                    """,
+                    (
+                        normalized_type,
+                        normalized_version_id,
+                        timestamp,
+                        timestamp,
+                        self._get_active_library_name(),
+                    ),
+                )
+                conn.commit()
+
+    async def has_been_downloaded(self, model_type: str, version_id: int) -> bool:
+        normalized_type = _normalize_model_type(model_type)
+        normalized_version_id = _normalize_int(version_id)
+        if normalized_type is None or normalized_version_id is None:
+            return False
+
+        async with self._lock:
+            with self._connect() as conn:
+                row = conn.execute(
+                    """
+                    SELECT is_deleted_override
+                    FROM downloaded_model_versions
+                    WHERE model_type = ? AND version_id = ?
+                    """,
+                    (normalized_type, normalized_version_id),
+                ).fetchone()
+        return bool(row) and not bool(row["is_deleted_override"])
+
+    async def get_downloaded_version_ids(
+        self, model_type: str, model_id: int
+    ) -> list[int]:
+        normalized_type = _normalize_model_type(model_type)
+        normalized_model_id = _normalize_int(model_id)
+        if normalized_type is None or normalized_model_id is None:
+            return []
+
+        async with self._lock:
+            with self._connect() as conn:
+                rows = conn.execute(
+                    """
+                    SELECT version_id
+                    FROM downloaded_model_versions
+                    WHERE model_type = ? AND model_id = ? AND is_deleted_override = 0
+                    ORDER BY version_id ASC
+                    """,
+                    (normalized_type, normalized_model_id),
+                ).fetchall()
+        return [int(row["version_id"]) for row in rows]
+
+    async def get_downloaded_version_ids_bulk(
+        self, model_type: str, model_ids: Iterable[int]
+    ) -> dict[int, set[int]]:
+        normalized_type = _normalize_model_type(model_type)
+        if normalized_type is None:
+            return {}
+
+        normalized_model_ids = sorted(
+            {
+                value
+                for value in (_normalize_int(model_id) for model_id in model_ids)
+                if value is not None
+            }
+        )
+        if not normalized_model_ids:
+            return {}
+
+        placeholders = ", ".join(["?"] * len(normalized_model_ids))
+        params: list[object] = [normalized_type, *normalized_model_ids]
+
+        async with self._lock:
+            with self._connect() as conn:
+                rows = conn.execute(
+                    f"""
+                    SELECT model_id, version_id
+                    FROM downloaded_model_versions
+                    WHERE model_type = ?
+                      AND model_id IN ({placeholders})
+                      AND is_deleted_override = 0
+                    """,
+                    params,
+                ).fetchall()
+
+        result: dict[int, set[int]] = {}
+        for row in rows:
+            model_id = _normalize_int(row["model_id"])
+            version_id = _normalize_int(row["version_id"])
+            if model_id is None or version_id is None:
+                continue
+            result.setdefault(model_id, set()).add(version_id)
+        return result

py/services/downloader.py

@@ -18,8 +18,14 @@ from collections import deque
 from dataclasses import dataclass
 from datetime import datetime, timedelta
 from email.utils import parsedate_to_datetime
+from urllib.parse import urlparse
 from typing import Optional, Dict, Tuple, Callable, Union, Awaitable
 from ..services.settings_manager import get_settings_manager
+from .connectivity_guard import (
+    OFFLINE_COOLDOWN_ERROR,
+    OFFLINE_FRIENDLY_MESSAGE,
+    ConnectivityGuard,
+)
 from .errors import RateLimitError
 
 logger = logging.getLogger(__name__)
@@ -44,7 +50,9 @@ class DownloadStreamControl:
         self._event.set()
         self._reconnect_requested = False
         self.last_progress_timestamp: Optional[float] = None
-        self.stall_timeout: float = float(stall_timeout) if stall_timeout is not None else 120.0
+        self.stall_timeout: float = (
+            float(stall_timeout) if stall_timeout is not None else 120.0
+        )
 
     def is_set(self) -> bool:
         return self._event.is_set()
@@ -85,7 +93,9 @@ class DownloadStreamControl:
         self.last_progress_timestamp = timestamp or datetime.now().timestamp()
         self._reconnect_requested = False
 
-    def time_since_last_progress(self, *, now: Optional[float] = None) -> Optional[float]:
+    def time_since_last_progress(
+        self, *, now: Optional[float] = None
+    ) -> Optional[float]:
         if self.last_progress_timestamp is None:
             return None
         reference = now if now is not None else datetime.now().timestamp()
@@ -105,10 +115,10 @@ class DownloadStalledError(Exception):
 
 class Downloader:
     """Unified downloader for all HTTP/HTTPS downloads in the application."""
-    
+
     _instance = None
     _lock = asyncio.Lock()
-    
+
     @classmethod
     async def get_instance(cls):
         """Get singleton instance of Downloader"""
@@ -116,35 +126,37 @@ class Downloader:
             if cls._instance is None:
                 cls._instance = cls()
             return cls._instance
-    
+
     def __init__(self):
         """Initialize the downloader with optimal settings"""
         # Check if already initialized for singleton pattern
-        if hasattr(self, '_initialized'):
+        if hasattr(self, "_initialized"):
             return
         self._initialized = True
-        
+
         # Session management
         self._session = None
         self._session_created_at = None
         self._proxy_url = None  # Store proxy URL for current session
         self._session_lock = asyncio.Lock()
-        
+
         # Configuration
-        self.chunk_size = 4 * 1024 * 1024  # 4MB chunks for better throughput
-        self.max_retries = 5
+        self.chunk_size = (
+            16 * 1024 * 1024
+        )  # 16MB chunks to balance I/O reduction and memory usage
+        self.max_retries = self._resolve_max_retries()
         self.base_delay = 2.0  # Base delay for exponential backoff
         self.session_timeout = 300  # 5 minutes
         self.stall_timeout = self._resolve_stall_timeout()
-        
+
         # Default headers
         self.default_headers = {
-            'User-Agent': 'ComfyUI-LoRA-Manager/1.0',
+            "User-Agent": "ComfyUI-LoRA-Manager/1.0",
             # Explicitly request uncompressed payloads so aiohttp doesn't need optional
             # decoders (e.g. zstandard) that may be missing in runtime environments.
-            'Accept-Encoding': 'identity',
+            "Accept-Encoding": "identity",
         }
-    
+
     @property
     async def session(self) -> aiohttp.ClientSession:
         """Get or create the global aiohttp session with optimized settings"""
@@ -158,7 +170,7 @@ class Downloader:
     @property
     def proxy_url(self) -> Optional[str]:
         """Get the current proxy URL (initialize if needed)"""
-        if not hasattr(self, '_proxy_url'):
+        if not hasattr(self, "_proxy_url"):
             self._proxy_url = None
         return self._proxy_url
 
@@ -169,14 +181,14 @@ class Downloader:
 
         try:
             settings_manager = get_settings_manager()
-            settings_timeout = settings_manager.get('download_stall_timeout_seconds')
+            settings_timeout = settings_manager.get("download_stall_timeout_seconds")
         except Exception as exc:  # pragma: no cover - defensive guard
             logger.debug("Failed to read stall timeout from settings: %s", exc)
 
         raw_value = (
             settings_timeout
             if settings_timeout not in (None, "")
-            else os.environ.get('COMFYUI_DOWNLOAD_STALL_TIMEOUT')
+            else os.environ.get("COMFYUI_DOWNLOAD_STALL_TIMEOUT")
         )
 
         try:
@@ -186,97 +198,120 @@ class Downloader:
 
         return max(30.0, timeout_value)
 
+    def _resolve_max_retries(self) -> int:
+        """Determine max retry count from environment while preserving defaults."""
+        default_retries = 5
+        raw_value = os.environ.get("COMFYUI_DOWNLOAD_MAX_RETRIES")
+
+        try:
+            retries = int(raw_value)
+        except (TypeError, ValueError):
+            retries = default_retries
+
+        return max(0, retries)
+
     def _should_refresh_session(self) -> bool:
         """Check if session should be refreshed"""
         if self._session is None:
             return True
-        
-        if not hasattr(self, '_session_created_at') or self._session_created_at is None:
+
+        if not hasattr(self, "_session_created_at") or self._session_created_at is None:
             return True
-        
+
         # Refresh if session is older than timeout
-        if (datetime.now() - self._session_created_at).total_seconds() > self.session_timeout:
+        if (
+            datetime.now() - self._session_created_at
+        ).total_seconds() > self.session_timeout:
             return True
-        
+
         return False
-    
+
     async def _create_session(self):
         """Create a new aiohttp session with optimized settings.
-        
+
         Note: This is private and caller MUST hold self._session_lock.
         """
         # Close existing session if any
         if self._session is not None:
             try:
                 await self._session.close()
-            except Exception as e: # pragma: no cover
+            except Exception as e:  # pragma: no cover
                 logger.warning(f"Error closing previous session: {e}")
             finally:
                 self._session = None
-        
+
         # Check for app-level proxy settings
         proxy_url = None
         settings_manager = get_settings_manager()
-        if settings_manager.get('proxy_enabled', False):
-            proxy_host = settings_manager.get('proxy_host', '').strip()
-            proxy_port = settings_manager.get('proxy_port', '').strip()
-            proxy_type = settings_manager.get('proxy_type', 'http').lower()
-            proxy_username = settings_manager.get('proxy_username', '').strip()
-            proxy_password = settings_manager.get('proxy_password', '').strip()
-            
+        if settings_manager.get("proxy_enabled", False):
+            proxy_host = settings_manager.get("proxy_host", "").strip()
+            proxy_port = settings_manager.get("proxy_port", "").strip()
+            proxy_type = settings_manager.get("proxy_type", "http").lower()
+            proxy_username = settings_manager.get("proxy_username", "").strip()
+            proxy_password = settings_manager.get("proxy_password", "").strip()
+
             if proxy_host and proxy_port:
                 # Build proxy URL
                 if proxy_username and proxy_password:
                     proxy_url = f"{proxy_type}://{proxy_username}:{proxy_password}@{proxy_host}:{proxy_port}"
                 else:
                     proxy_url = f"{proxy_type}://{proxy_host}:{proxy_port}"
-                
-                logger.debug(f"Using app-level proxy: {proxy_type}://{proxy_host}:{proxy_port}")
+
+                logger.debug(
+                    f"Using app-level proxy: {proxy_type}://{proxy_host}:{proxy_port}"
+                )
                 logger.debug("Proxy mode: app-level proxy is active.")
         else:
-            logger.debug("Proxy mode: system-level proxy (trust_env) will be used if configured in environment.")
+            logger.debug(
+                "Proxy mode: system-level proxy (trust_env) will be used if configured in environment."
+            )
         # Optimize TCP connection parameters
         connector = aiohttp.TCPConnector(
             ssl=True,
             limit=8,  # Concurrent connections
             ttl_dns_cache=300,  # DNS cache timeout
             force_close=False,  # Keep connections for reuse
-            enable_cleanup_closed=True
+            enable_cleanup_closed=True,
         )
-        
+
         # Configure timeout parameters
         timeout = aiohttp.ClientTimeout(
             total=None,  # No total timeout for large downloads
             connect=60,  # Connection timeout
-            sock_read=300  # 5 minute socket read timeout
+            sock_read=300,  # 5 minute socket read timeout
         )
-        
+
         self._session = aiohttp.ClientSession(
             connector=connector,
-            trust_env=proxy_url is None,  # Only use system proxy if no app-level proxy is set
-            timeout=timeout
+            trust_env=proxy_url
+            is None,  # Only use system proxy if no app-level proxy is set
+            timeout=timeout,
         )
-        
+
         # Store proxy URL for use in requests
         self._proxy_url = proxy_url
         self._session_created_at = datetime.now()
-        
-        logger.debug("Created new HTTP session with proxy settings. App-level proxy: %s, System-level proxy (trust_env): %s", bool(proxy_url), proxy_url is None)
-    
+
+        logger.debug(
+            "Created new HTTP session with proxy settings. App-level proxy: %s, System-level proxy (trust_env): %s",
+            bool(proxy_url),
+            proxy_url is None,
+        )
+
     def _get_auth_headers(self, use_auth: bool = False) -> Dict[str, str]:
         """Get headers with optional authentication"""
         headers = self.default_headers.copy()
-        
+
         if use_auth:
             # Add CivitAI API key if available
             settings_manager = get_settings_manager()
-            api_key = settings_manager.get('civitai_api_key')
+            api_key = settings_manager.get("civitai_api_key")
             if api_key:
-                headers['Authorization'] = f'Bearer {api_key}'
-                headers['Content-Type'] = 'application/json'
-        
+                headers["Authorization"] = f"Bearer {api_key}"
+                headers["Content-Type"] = "application/json"
+
         return headers
-    
+
     async def download_file(
         self,
         url: str,
@@ -289,7 +324,7 @@ class Downloader:
     ) -> Tuple[bool, str]:
         """
         Download a file with resumable downloads and retry mechanism
-        
+
         Args:
             url: Download URL
             save_path: Full path where the file should be saved
@@ -298,75 +333,114 @@ class Downloader:
             custom_headers: Additional headers to include in request
             allow_resume: Whether to support resumable downloads
             pause_event: Optional stream control used to pause/resume and request reconnects
-            
+
         Returns:
             Tuple[bool, str]: (success, save_path or error message)
         """
         retry_count = 0
-        part_path = save_path + '.part' if allow_resume else save_path
-        
+        part_path = save_path + ".part" if allow_resume else save_path
+
         # Prepare headers
         headers = self._get_auth_headers(use_auth)
         if custom_headers:
             headers.update(custom_headers)
-        
+
         # Get existing file size for resume
         resume_offset = 0
         if allow_resume and os.path.exists(part_path):
             resume_offset = os.path.getsize(part_path)
             logger.info(f"Resuming download from offset {resume_offset} bytes")
-        
+
         total_size = 0
-        
+        range_redirect_retry_urls: set[str] = set()
+
         while retry_count <= self.max_retries:
             try:
                 session = await self.session
                 # Debug log for proxy mode at request time
                 if self.proxy_url:
-                    logger.debug(f"[download_file] Using app-level proxy: {self.proxy_url}")
+                    logger.debug(
+                        f"[download_file] Using app-level proxy: {self.proxy_url}"
+                    )
                 else:
-                    logger.debug("[download_file] Using system-level proxy (trust_env) if configured.")
-                
+                    logger.debug(
+                        "[download_file] Using system-level proxy (trust_env) if configured."
+                    )
+
                 # Add Range header for resume if we have partial data
                 request_headers = headers.copy()
                 if allow_resume and resume_offset > 0:
-                    request_headers['Range'] = f'bytes={resume_offset}-'
-                
+                    request_headers["Range"] = f"bytes={resume_offset}-"
+
                 # Disable compression for better chunked downloads
-                request_headers['Accept-Encoding'] = 'identity'
-                
-                logger.debug(f"Download attempt {retry_count + 1}/{self.max_retries + 1} from: {url}")
+                request_headers["Accept-Encoding"] = "identity"
+
+                logger.debug(
+                    f"Download attempt {retry_count + 1}/{self.max_retries + 1} from: {url}"
+                )
                 if resume_offset > 0:
                     logger.debug(f"Requesting range from byte {resume_offset}")
-                
-                async with session.get(url, headers=request_headers, allow_redirects=True, proxy=self.proxy_url) as response:
+
+                async with session.get(
+                    url,
+                    headers=request_headers,
+                    allow_redirects=True,
+                    proxy=self.proxy_url,
+                ) as response:
                     # Handle different response codes
                     if response.status == 200:
                         # Full content response
                         if resume_offset > 0:
+                            redirected_url = str(response.url)
+                            if (
+                                allow_resume
+                                and response.history
+                                and redirected_url
+                                and redirected_url != url
+                                and redirected_url not in range_redirect_retry_urls
+                            ):
+                                range_redirect_retry_urls.add(redirected_url)
+                                logger.info(
+                                    "Range request was not honored after redirect; retrying final URL directly: %s",
+                                    redirected_url,
+                                )
+                                url = redirected_url
+                                response.release()
+                                continue
+
                             # Server doesn't support ranges, restart from beginning
-                            logger.warning("Server doesn't support range requests, restarting download")
+                            logger.warning(
+                                "Server doesn't support range requests, restarting download"
+                            )
                             resume_offset = 0
                             if os.path.exists(part_path):
                                 os.remove(part_path)
                     elif response.status == 206:
                         # Partial content response (resume successful)
-                        content_range = response.headers.get('Content-Range')
+                        content_range = response.headers.get("Content-Range")
                         if content_range:
                             # Parse total size from Content-Range header (e.g., "bytes 1024-2047/2048")
-                            range_parts = content_range.split('/')
+                            range_parts = content_range.split("/")
                             if len(range_parts) == 2:
                                 total_size = int(range_parts[1])
-                        logger.info(f"Successfully resumed download from byte {resume_offset}")
+                        logger.info(
+                            f"Successfully resumed download from byte {resume_offset}"
+                        )
                     elif response.status == 416:
                         # Range not satisfiable - file might be complete or corrupted
                         if allow_resume and os.path.exists(part_path):
                             part_size = os.path.getsize(part_path)
-                            logger.warning(f"Range not satisfiable. Part file size: {part_size}")
+                            logger.warning(
+                                f"Range not satisfiable. Part file size: {part_size}"
+                            )
                             # Try to get actual file size
-                            head_response = await session.head(url, headers=headers, proxy=self.proxy_url)
+                            head_response = await session.head(
+                                url, headers=headers, proxy=self.proxy_url
+                            )
                             if head_response.status == 200:
-                                actual_size = int(head_response.headers.get('content-length', 0))
+                                actual_size = int(
+                                    head_response.headers.get("content-length", 0)
+                                )
                                 if part_size == actual_size:
                                     # File is complete, just rename it
                                     if allow_resume:
@@ -388,25 +462,40 @@ class Downloader:
                             resume_offset = 0
                             continue
                     elif response.status == 401:
-                        logger.warning(f"Unauthorized access to resource: {url} (Status 401)")
-                        return False, "Invalid or missing API key, or early access restriction."
+                        logger.warning(
+                            f"Unauthorized access to resource: {url} (Status 401)"
+                        )
+                        return (
+                            False,
+                            "Invalid or missing API key, or early access restriction.",
+                        )
                     elif response.status == 403:
-                        logger.warning(f"Forbidden access to resource: {url} (Status 403)")
-                        return False, "Access forbidden: You don't have permission to download this file."
+                        logger.warning(
+                            f"Forbidden access to resource: {url} (Status 403)"
+                        )
+                        return (
+                            False,
+                            "Access forbidden: You don't have permission to download this file.",
+                        )
                     elif response.status == 404:
                         logger.warning(f"Resource not found: {url} (Status 404)")
-                        return False, "File not found - the download link may be invalid or expired."
+                        return (
+                            False,
+                            "File not found - the download link may be invalid or expired.",
+                        )
                     else:
-                        logger.error(f"Download failed for {url} with status {response.status}")
+                        logger.error(
+                            f"Download failed for {url} with status {response.status}"
+                        )
                         return False, f"Download failed with status {response.status}"
-                    
+
                     # Get total file size for progress calculation (if not set from Content-Range)
                     if total_size == 0:
-                        total_size = int(response.headers.get('content-length', 0))
+                        total_size = int(response.headers.get("content-length", 0))
                         if response.status == 206:
                             # For partial content, add the offset to get total file size
                             total_size += resume_offset
-                    
+
                     current_size = resume_offset
                     last_progress_report_time = datetime.now()
                     progress_samples: deque[tuple[datetime, int]] = deque()
@@ -417,7 +506,7 @@ class Downloader:
 
                     # Stream download to file with progress updates
                     loop = asyncio.get_running_loop()
-                    mode = 'ab' if (allow_resume and resume_offset > 0) else 'wb'
+                    mode = "ab" if (allow_resume and resume_offset > 0) else "wb"
                     control = pause_event
 
                     if control is not None:
@@ -425,7 +514,9 @@ class Downloader:
 
                     with open(part_path, mode) as f:
                         while True:
-                            active_stall_timeout = control.stall_timeout if control else self.stall_timeout
+                            active_stall_timeout = (
+                                control.stall_timeout if control else self.stall_timeout
+                            )
 
                             if control is not None:
                                 if control.is_paused():
@@ -437,7 +528,9 @@ class Downloader:
                                             "Reconnect requested after resume"
                                         )
                                 elif control.consume_reconnect_request():
-                                    raise DownloadRestartRequested("Reconnect requested")
+                                    raise DownloadRestartRequested(
+                                        "Reconnect requested"
+                                    )
 
                             try:
                                 chunk = await asyncio.wait_for(
@@ -466,22 +559,32 @@ class Downloader:
                                 control.mark_progress(timestamp=now.timestamp())
 
                             # Limit progress update frequency to reduce overhead
-                            time_diff = (now - last_progress_report_time).total_seconds()
+                            time_diff = (
+                                now - last_progress_report_time
+                            ).total_seconds()
 
                             if progress_callback and time_diff >= 1.0:
                                 progress_samples.append((now, current_size))
                                 cutoff = now - timedelta(seconds=5)
-                                while progress_samples and progress_samples[0][0] < cutoff:
+                                while (
+                                    progress_samples and progress_samples[0][0] < cutoff
+                                ):
                                     progress_samples.popleft()
 
-                                percent = (current_size / total_size) * 100 if total_size else 0.0
+                                percent = (
+                                    (current_size / total_size) * 100
+                                    if total_size
+                                    else 0.0
+                                )
                                 bytes_per_second = 0.0
                                 if len(progress_samples) >= 2:
                                     first_time, first_bytes = progress_samples[0]
                                     last_time, last_bytes = progress_samples[-1]
                                     elapsed = (last_time - first_time).total_seconds()
                                     if elapsed > 0:
-                                        bytes_per_second = (last_bytes - first_bytes) / elapsed
+                                        bytes_per_second = (
+                                            last_bytes - first_bytes
+                                        ) / elapsed
 
                                 progress_snapshot = DownloadProgress(
                                     percent_complete=percent,
@@ -491,48 +594,66 @@ class Downloader:
                                     timestamp=now.timestamp(),
                                 )
 
-                                await self._dispatch_progress_callback(progress_callback, progress_snapshot)
+                                await self._dispatch_progress_callback(
+                                    progress_callback, progress_snapshot
+                                )
                                 last_progress_report_time = now
-                    
+
                     # Download completed successfully
                     # Verify file size integrity before finalizing
-                    final_size = os.path.getsize(part_path) if os.path.exists(part_path) else 0
+                    final_size = (
+                        os.path.getsize(part_path) if os.path.exists(part_path) else 0
+                    )
                     expected_size = total_size if total_size > 0 else None
 
                     integrity_error: Optional[str] = None
+                    resumable_incomplete = False
                     if final_size <= 0:
                         integrity_error = "Downloaded file is empty"
                     elif expected_size is not None and final_size != expected_size:
-                        integrity_error = (
-                            f"File size mismatch. Expected: {expected_size}, Got: {final_size}"
+                        integrity_error = f"File size mismatch. Expected: {expected_size}, Got: {final_size}"
+                        resumable_incomplete = (
+                            allow_resume
+                            and part_path != save_path
+                            and final_size > 0
+                            and final_size < expected_size
                         )
 
                     if integrity_error is not None:
-                        logger.error(
+                        log_fn = logger.warning if resumable_incomplete else logger.error
+                        log_fn(
                             "Download integrity check failed for %s: %s",
                             save_path,
                             integrity_error,
                         )
 
-                        # Remove the corrupted payload so future attempts start fresh
-                        if os.path.exists(part_path):
-                            try:
-                                os.remove(part_path)
-                            except OSError as remove_error:
-                                logger.warning(
-                                    "Failed to delete corrupted download %s: %s",
-                                    part_path,
-                                    remove_error,
-                                )
-                        if part_path != save_path and os.path.exists(save_path):
-                            try:
-                                os.remove(save_path)
-                            except OSError as remove_error:
-                                logger.warning(
-                                    "Failed to delete target file %s after integrity error: %s",
-                                    save_path,
-                                    remove_error,
-                                )
+                        if resumable_incomplete:
+                            logger.info(
+                                "Preserving incomplete download for resume: %s (%s/%s bytes)",
+                                part_path,
+                                final_size,
+                                expected_size,
+                            )
+                        else:
+                            # Remove corrupted payloads that cannot be safely resumed.
+                            if os.path.exists(part_path):
+                                try:
+                                    os.remove(part_path)
+                                except OSError as remove_error:
+                                    logger.warning(
+                                        "Failed to delete corrupted download %s: %s",
+                                        part_path,
+                                        remove_error,
+                                    )
+                            if part_path != save_path and os.path.exists(save_path):
+                                try:
+                                    os.remove(save_path)
+                                except OSError as remove_error:
+                                    logger.warning(
+                                        "Failed to delete target file %s after integrity error: %s",
+                                        save_path,
+                                        remove_error,
+                                    )
 
                         retry_count += 1
                         if retry_count <= self.max_retries:
@@ -542,8 +663,16 @@ class Downloader:
                                 delay,
                             )
                             await asyncio.sleep(delay)
-                            resume_offset = 0
-                            total_size = 0
+                            if resumable_incomplete and os.path.exists(part_path):
+                                resume_offset = os.path.getsize(part_path)
+                                total_size = expected_size or 0
+                                logger.info(
+                                    "Will resume incomplete download from byte %s",
+                                    resume_offset,
+                                )
+                            else:
+                                resume_offset = 0
+                                total_size = 0
                             await self._create_session()
                             continue
 
@@ -554,8 +683,10 @@ class Downloader:
                         max_rename_attempts = 5
                         rename_attempt = 0
                         rename_success = False
-                        
-                        while rename_attempt < max_rename_attempts and not rename_success:
+
+                        while (
+                            rename_attempt < max_rename_attempts and not rename_success
+                        ):
                             try:
                                 # If the destination file exists, remove it first (Windows safe)
                                 if os.path.exists(save_path):
@@ -566,11 +697,18 @@ class Downloader:
                             except PermissionError as e:
                                 rename_attempt += 1
                                 if rename_attempt < max_rename_attempts:
-                                    logger.info(f"File still in use, retrying rename in 2 seconds (attempt {rename_attempt}/{max_rename_attempts})")
+                                    logger.info(
+                                        f"File still in use, retrying rename in 2 seconds (attempt {rename_attempt}/{max_rename_attempts})"
+                                    )
                                     await asyncio.sleep(2)
                                 else:
-                                    logger.error(f"Failed to rename file after {max_rename_attempts} attempts: {e}")
-                                    return False, f"Failed to finalize download: {str(e)}"
+                                    logger.error(
+                                        f"Failed to rename file after {max_rename_attempts} attempts: {e}"
+                                    )
+                                    return (
+                                        False,
+                                        f"Failed to finalize download: {str(e)}",
+                                    )
 
                         final_size = os.path.getsize(save_path)
 
@@ -583,11 +721,12 @@ class Downloader:
                             bytes_per_second=0.0,
                             timestamp=datetime.now().timestamp(),
                         )
-                        await self._dispatch_progress_callback(progress_callback, final_snapshot)
+                        await self._dispatch_progress_callback(
+                            progress_callback, final_snapshot
+                        )
 
-                    
                     return True, save_path
-                    
+
             except (
                 aiohttp.ClientError,
                 aiohttp.ClientPayloadError,
@@ -597,30 +736,35 @@ class Downloader:
                 DownloadRestartRequested,
             ) as e:
                 retry_count += 1
-                logger.warning(f"Network error during download (attempt {retry_count}/{self.max_retries + 1}): {e}")
+                logger.warning(
+                    f"Network error during download (attempt {retry_count}/{self.max_retries + 1}): {e}"
+                )
 
                 if retry_count <= self.max_retries:
                     # Calculate delay with exponential backoff
                     delay = self.base_delay * (2 ** (retry_count - 1))
                     logger.info(f"Retrying in {delay} seconds...")
                     await asyncio.sleep(delay)
-                    
+
                     # Update resume offset for next attempt
                     if allow_resume and os.path.exists(part_path):
                         resume_offset = os.path.getsize(part_path)
                         logger.info(f"Will resume from byte {resume_offset}")
-                    
+
                     # Refresh session to get new connection
                     await self._create_session()
                     continue
                 else:
                     logger.error(f"Max retries exceeded for download: {e}")
-                    return False, f"Network error after {self.max_retries + 1} attempts: {str(e)}"
-                    
+                    return (
+                        False,
+                        f"Network error after {self.max_retries + 1} attempts: {str(e)}",
+                    )
+
             except Exception as e:
                 logger.error(f"Unexpected download error: {e}")
                 return False, str(e)
-        
+
         return False, f"Download failed after {self.max_retries + 1} attempts"
 
     async def _dispatch_progress_callback(
@@ -645,36 +789,48 @@ class Downloader:
         url: str,
         use_auth: bool = False,
         custom_headers: Optional[Dict[str, str]] = None,
-        return_headers: bool = False
+        return_headers: bool = False,
     ) -> Tuple[bool, Union[bytes, str], Optional[Dict]]:
         """
         Download a file to memory (for small files like preview images)
-        
+
         Args:
             url: Download URL
             use_auth: Whether to include authentication headers
             custom_headers: Additional headers to include in request
             return_headers: Whether to return response headers along with content
-            
+
         Returns:
             Tuple[bool, Union[bytes, str], Optional[Dict]]: (success, content or error message, response headers if requested)
         """
+        guard = await ConnectivityGuard.get_instance()
+        destination = self._guard_destination(url)
+        if guard.should_block_request(destination):
+            return False, OFFLINE_FRIENDLY_MESSAGE, None
+
         try:
             session = await self.session
             # Debug log for proxy mode at request time
             if self.proxy_url:
-                logger.debug(f"[download_to_memory] Using app-level proxy: {self.proxy_url}")
+                logger.debug(
+                    f"[download_to_memory] Using app-level proxy: {self.proxy_url}"
+                )
             else:
-                logger.debug("[download_to_memory] Using system-level proxy (trust_env) if configured.")
-            
+                logger.debug(
+                    "[download_to_memory] Using system-level proxy (trust_env) if configured."
+                )
+
             # Prepare headers
             headers = self._get_auth_headers(use_auth)
             if custom_headers:
                 headers.update(custom_headers)
-            
-            async with session.get(url, headers=headers, proxy=self.proxy_url) as response:
+
+            async with session.get(
+                url, headers=headers, proxy=self.proxy_url
+            ) as response:
                 if response.status == 200:
                     content = await response.read()
+                    guard.register_success(destination)
                     if return_headers:
                         return True, content, dict(response.headers)
                     else:
@@ -691,91 +847,125 @@ class Downloader:
                 else:
                     error_msg = f"Download failed with status {response.status}"
                     return False, error_msg, None
-                    
+
         except Exception as e:
+            if guard.is_network_unreachable_error(e):
+                guard.register_network_failure(e, destination)
+                if guard.should_block_request(destination):
+                    return False, OFFLINE_FRIENDLY_MESSAGE, None
+                logger.debug("Network unavailable during memory download: %s", e)
+                return False, str(e), None
             logger.error(f"Error downloading to memory from {url}: {e}")
             return False, str(e), None
-    
+
     async def get_response_headers(
         self,
         url: str,
         use_auth: bool = False,
-        custom_headers: Optional[Dict[str, str]] = None
+        custom_headers: Optional[Dict[str, str]] = None,
     ) -> Tuple[bool, Union[Dict, str]]:
         """
         Get response headers without downloading the full content
-        
+
         Args:
             url: URL to check
             use_auth: Whether to include authentication headers
             custom_headers: Additional headers to include in request
-            
+
         Returns:
             Tuple[bool, Union[Dict, str]]: (success, headers dict or error message)
         """
+        guard = await ConnectivityGuard.get_instance()
+        destination = self._guard_destination(url)
+        if guard.should_block_request(destination):
+            return False, OFFLINE_COOLDOWN_ERROR
+
         try:
             session = await self.session
             # Debug log for proxy mode at request time
             if self.proxy_url:
-                logger.debug(f"[get_response_headers] Using app-level proxy: {self.proxy_url}")
+                logger.debug(
+                    f"[get_response_headers] Using app-level proxy: {self.proxy_url}"
+                )
             else:
-                logger.debug("[get_response_headers] Using system-level proxy (trust_env) if configured.")
-            
+                logger.debug(
+                    "[get_response_headers] Using system-level proxy (trust_env) if configured."
+                )
+
             # Prepare headers
             headers = self._get_auth_headers(use_auth)
             if custom_headers:
                 headers.update(custom_headers)
-            
-            async with session.head(url, headers=headers, proxy=self.proxy_url) as response:
+
+            async with session.head(
+                url, headers=headers, proxy=self.proxy_url
+            ) as response:
                 if response.status == 200:
+                    guard.register_success(destination)
                     return True, dict(response.headers)
                 else:
                     return False, f"Head request failed with status {response.status}"
-                    
+
         except Exception as e:
+            if guard.is_network_unreachable_error(e):
+                guard.register_network_failure(e, destination)
+                if guard.should_block_request(destination):
+                    return False, OFFLINE_COOLDOWN_ERROR
+                logger.debug("Network unavailable during header probe: %s", e)
+                return False, str(e)
             logger.error(f"Error getting headers from {url}: {e}")
             return False, str(e)
-    
+
     async def make_request(
         self,
         method: str,
         url: str,
         use_auth: bool = False,
         custom_headers: Optional[Dict[str, str]] = None,
-        **kwargs
+        **kwargs,
     ) -> Tuple[bool, Union[Dict, str]]:
         """
         Make a generic HTTP request and return JSON response
-        
+
         Args:
             method: HTTP method (GET, POST, etc.)
             url: Request URL
             use_auth: Whether to include authentication headers
             custom_headers: Additional headers to include in request
             **kwargs: Additional arguments for aiohttp request
-            
+
         Returns:
             Tuple[bool, Union[Dict, str]]: (success, response data or error message)
         """
+        guard = await ConnectivityGuard.get_instance()
+        destination = self._guard_destination(url)
+        if guard.should_block_request(destination):
+            return False, OFFLINE_COOLDOWN_ERROR
+
         try:
             session = await self.session
             # Debug log for proxy mode at request time
             if self.proxy_url:
                 logger.debug(f"[make_request] Using app-level proxy: {self.proxy_url}")
             else:
-                logger.debug("[make_request] Using system-level proxy (trust_env) if configured.")
-            
+                logger.debug(
+                    "[make_request] Using system-level proxy (trust_env) if configured."
+                )
+
             # Prepare headers
             headers = self._get_auth_headers(use_auth)
             if custom_headers:
                 headers.update(custom_headers)
-            
+
             # Add proxy to kwargs if not already present
-            if 'proxy' not in kwargs:
-                kwargs['proxy'] = self.proxy_url
-            
-            async with session.request(method, url, headers=headers, **kwargs) as response:
+            if "proxy" not in kwargs:
+                kwargs["proxy"] = self.proxy_url
+
+            async with session.request(
+                method, url, headers=headers, **kwargs
+            ) as response:
                 if response.status == 200:
+                    guard.register_success(destination)
                     # Try to parse as JSON, fall back to text
                     try:
                         data = await response.json()
@@ -804,11 +994,17 @@ class Downloader:
                     )
                 else:
                     return False, f"Request failed with status {response.status}"
-                    
+
         except Exception as e:
+            if guard.is_network_unreachable_error(e):
+                guard.register_network_failure(e, destination)
+                if guard.should_block_request(destination):
+                    return False, OFFLINE_COOLDOWN_ERROR
+                logger.debug("Network unavailable for %s %s: %s", method, url, e)
+                return False, str(e)
             logger.error(f"Error making {method} request to {url}: {e}")
             return False, str(e)
-    
+
     async def close(self):
         """Close the HTTP session"""
         if self._session is not None:
@@ -817,7 +1013,7 @@ class Downloader:
             self._session_created_at = None
             self._proxy_url = None
             logger.debug("Closed HTTP session")
-    
+
     async def refresh_session(self):
         """Force refresh the HTTP session (useful when proxy settings change)"""
         async with self._session_lock:
@@ -856,6 +1052,14 @@ class Downloader:
         delta = retry_datetime - datetime.now(tz=retry_datetime.tzinfo)
         return max(0.0, delta.total_seconds())
 
+    @staticmethod
+    def _guard_destination(url: str) -> str:
+        """Build per-destination connectivity guard scope from request URL."""
+        parsed_url = urlparse(url)
+        if parsed_url.hostname:
+            return parsed_url.hostname.lower()
+        return "unknown"
+
 
 # Global instance accessor
 async def get_downloader() -> Downloader:

py/services/embedding_service.py

@@ -42,6 +42,7 @@ class EmbeddingService(BaseModelService):
             "notes": embedding_data.get("notes", ""),
             "sub_type": sub_type,
             "favorite": embedding_data.get("favorite", False),
+            "exclude": bool(embedding_data.get("exclude", False)),
             "update_available": bool(embedding_data.get("update_available", False)),
             "skip_metadata_refresh": bool(embedding_data.get("skip_metadata_refresh", False)),
             "civitai": self.filter_civitai_data(embedding_data.get("civitai", {}), minimal=True)

py/services/lora_service.py

@@ -1,5 +1,6 @@
-import os
 import logging
+import json
+import os
 from typing import Dict, List, Optional
 
 from .base_model_service import BaseModelService
@@ -27,7 +28,7 @@ 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"],
@@ -47,8 +48,11 @@ class LoraService(BaseModelService):
             "usage_tips": lora_data.get("usage_tips", ""),
             "notes": lora_data.get("notes", ""),
             "favorite": lora_data.get("favorite", False),
+            "exclude": bool(lora_data.get("exclude", 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 +66,68 @@ 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]:
@@ -214,6 +280,42 @@ class LoraService(BaseModelService):
 
         return None
 
+    @staticmethod
+    def get_recommended_strength_from_lora_data(lora_data: Dict) -> Optional[float]:
+        """Parse usage_tips JSON and extract recommended model strength."""
+        try:
+            usage_tips = lora_data.get("usage_tips", "")
+            if not usage_tips:
+                return None
+            tips_data = json.loads(usage_tips)
+            return tips_data.get("strength")
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            return None
+
+    @staticmethod
+    def get_recommended_clip_strength_from_lora_data(
+        lora_data: Dict,
+    ) -> Optional[float]:
+        """Parse usage_tips JSON and extract recommended clip strength."""
+        try:
+            usage_tips = lora_data.get("usage_tips", "")
+            if not usage_tips:
+                return None
+            tips_data = json.loads(usage_tips)
+            return tips_data.get("clipStrength")
+        except (json.JSONDecodeError, TypeError, AttributeError):
+            return None
+
+    async def get_lora_metadata_by_filename(self, filename: str) -> Optional[Dict]:
+        """Return cached raw metadata for a LoRA matching the given filename."""
+        cache = await self.scanner.get_cached_data(force_refresh=False)
+
+        for lora in cache.raw_data if cache else []:
+            if lora.get("file_name") == filename:
+                return lora
+
+        return None
+
     def find_duplicate_hashes(self) -> Dict:
         """Find LoRAs with duplicate SHA256 hashes"""
         return self.scanner._hash_index.get_duplicate_hashes()
@@ -264,34 +366,10 @@ class LoraService(BaseModelService):
             List of LoRA dicts with randomized strengths
         """
         import random
-        import json
-
         # Use a local Random instance to avoid affecting global random state
         # This ensures each execution with a different seed produces different results
         rng = random.Random(seed)
 
-        def get_recommended_strength(lora_data: Dict) -> Optional[float]:
-            """Parse usage_tips JSON and extract recommended strength"""
-            try:
-                usage_tips = lora_data.get("usage_tips", "")
-                if not usage_tips:
-                    return None
-                tips_data = json.loads(usage_tips)
-                return tips_data.get("strength")
-            except (json.JSONDecodeError, TypeError, AttributeError):
-                return None
-
-        def get_recommended_clip_strength(lora_data: Dict) -> Optional[float]:
-            """Parse usage_tips JSON and extract recommended clip strength"""
-            try:
-                usage_tips = lora_data.get("usage_tips", "")
-                if not usage_tips:
-                    return None
-                tips_data = json.loads(usage_tips)
-                return tips_data.get("clipStrength")
-            except (json.JSONDecodeError, TypeError, AttributeError):
-                return None
-
         if locked_loras is None:
             locked_loras = []
 
@@ -339,7 +417,9 @@ class LoraService(BaseModelService):
         result_loras = []
         for lora in selected:
             if use_recommended_strength:
-                recommended_strength = get_recommended_strength(lora)
+                recommended_strength = self.get_recommended_strength_from_lora_data(
+                    lora
+                )
                 if recommended_strength is not None:
                     scale = rng.uniform(
                         recommended_strength_scale_min, recommended_strength_scale_max
@@ -357,7 +437,9 @@ class LoraService(BaseModelService):
             if use_same_clip_strength:
                 clip_str = model_str
             elif use_recommended_strength:
-                recommended_clip_strength = get_recommended_clip_strength(lora)
+                recommended_clip_strength = (
+                    self.get_recommended_clip_strength_from_lora_data(lora)
+                )
                 if recommended_clip_strength is not None:
                     scale = rng.uniform(
                         recommended_strength_scale_min, recommended_strength_scale_max
@@ -368,9 +450,7 @@ 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 +565,69 @@ 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 +653,18 @@ 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/metadata_service.py

@@ -122,11 +122,25 @@ async def get_metadata_provider(provider_name: str = None):
 
     provider_manager = await ModelMetadataProviderManager.get_instance()
 
-    provider = (
-        provider_manager._get_provider(provider_name)
-        if provider_name
-        else provider_manager._get_provider()
-    )
+    try:
+        provider = (
+            provider_manager._get_provider(provider_name)
+            if provider_name
+            else provider_manager._get_provider()
+        )
+    except ValueError as e:
+        # Provider not initialized, attempt to initialize
+        if "No default provider set" in str(e) or "not registered" in str(e):
+            logger.warning(f"Metadata provider not initialized ({e}), initializing now...")
+            await initialize_metadata_providers()
+            provider_manager = await ModelMetadataProviderManager.get_instance()
+            provider = (
+                provider_manager._get_provider(provider_name)
+                if provider_name
+                else provider_manager._get_provider()
+            )
+        else:
+            raise
 
     return _wrap_provider_with_rate_limit(provider_name, provider)
 

py/services/metadata_sync_service.py

@@ -11,6 +11,7 @@ from typing import Any, Awaitable, Callable, Dict, Iterable, Optional
 from ..services.settings_manager import SettingsManager
 from ..utils.civitai_utils import resolve_license_payload
 from ..utils.model_utils import determine_base_model
+from .connectivity_guard import OFFLINE_FRIENDLY_MESSAGE, is_expected_offline_error
 from .errors import RateLimitError
 
 logger = logging.getLogger(__name__)
@@ -274,11 +275,18 @@ class MetadataSyncService:
                     else "No provider returned metadata"
                 )
 
+                resolved_error = last_error or default_error
+                if is_expected_offline_error(resolved_error):
+                    resolved_error = OFFLINE_FRIENDLY_MESSAGE
+
                 error_msg = (
-                    f"Error fetching metadata: {last_error or default_error} "
+                    f"Error fetching metadata: {resolved_error} "
                     f"(model_name={model_data.get('model_name', '')})"
                 )
-                logger.error(error_msg)
+                if is_expected_offline_error(resolved_error):
+                    logger.info(error_msg)
+                else:
+                    logger.error(error_msg)
                 return False, error_msg
 
             model_data["from_civitai"] = True
@@ -347,6 +355,9 @@ class MetadataSyncService:
             return False, error_msg
         except Exception as exc:  # pragma: no cover - error path
             error_msg = f"Error fetching metadata: {exc}"
+            if is_expected_offline_error(str(exc)):
+                logger.info(OFFLINE_FRIENDLY_MESSAGE)
+                return False, OFFLINE_FRIENDLY_MESSAGE
             logger.error(error_msg, exc_info=True)
             return False, error_msg
 

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_lifecycle_service.py

@@ -8,6 +8,7 @@ from typing import Any, Awaitable, Callable, Dict, Iterable, List, Mapping, Opti
 
 from ..services.service_registry import ServiceRegistry
 from ..utils.constants import PREVIEW_EXTENSIONS
+from ..utils.metadata_manager import MetadataManager
 
 logger = logging.getLogger(__name__)
 
@@ -207,11 +208,56 @@ class ModelLifecycleService:
 
         excluded = getattr(self._scanner, "_excluded_models", None)
         if isinstance(excluded, list):
-            excluded.append(file_path)
+            if file_path not in excluded:
+                excluded.append(file_path)
+
+        persist_current_cache = getattr(self._scanner, "_persist_current_cache", None)
+        if callable(persist_current_cache):
+            await persist_current_cache()
 
         message = f"Model {os.path.basename(file_path)} excluded"
         return {"success": True, "message": message}
 
+    async def unexclude_model(self, file_path: str) -> Dict[str, object]:
+        """Restore a previously excluded model to the active cache."""
+
+        if not file_path:
+            raise ValueError("Model path is required")
+
+        if not os.path.exists(file_path):
+            raise ValueError("Model file does not exist")
+
+        metadata_path = os.path.splitext(file_path)[0] + ".metadata.json"
+        metadata_payload = await self._metadata_loader(metadata_path)
+        metadata_payload["exclude"] = False
+
+        await self._metadata_manager.save_metadata(file_path, metadata_payload)
+
+        metadata, should_skip = await MetadataManager.load_metadata(
+            file_path,
+            self._scanner.model_class,
+        )
+        if should_skip:
+            metadata = None
+        if metadata is None:
+            metadata = metadata_payload
+
+        excluded = getattr(self._scanner, "_excluded_models", None)
+        if isinstance(excluded, list):
+            self._scanner._excluded_models = [
+                path for path in excluded if path != file_path
+            ]
+
+        await self._scanner.update_single_model_cache(
+            file_path,
+            file_path,
+            metadata,
+            recalculate_type=True,
+        )
+
+        message = f"Model {os.path.basename(file_path)} restored"
+        return {"success": True, "message": message}
+
     async def bulk_delete_models(self, file_paths: Iterable[str]) -> Dict[str, object]:
         """Delete a collection of models via the scanner bulk operation."""
 

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
@@ -412,6 +411,7 @@ class ModelScanner:
             if scan_result:
                 await self._apply_scan_result(scan_result)
                 await self._save_persistent_cache(scan_result)
+                await self._sync_download_history(scan_result.raw_data, source='scan')
 
             # Send final progress update
             await ws_manager.broadcast_init_progress({
@@ -517,6 +517,7 @@ class ModelScanner:
         )
 
         await self._apply_scan_result(scan_result)
+        await self._sync_download_history(adjusted_raw_data, source='scan')
 
         await ws_manager.broadcast_init_progress({
             'stage': 'loading_cache',
@@ -577,6 +578,7 @@ class ModelScanner:
             excluded_models=list(self._excluded_models)
         )
         await self._save_persistent_cache(snapshot)
+        await self._sync_download_history(snapshot.raw_data, source='scan')
     def _count_model_files(self) -> int:
         """Count all model files with supported extensions in all roots
         
@@ -705,6 +707,7 @@ class ModelScanner:
             scan_result = await self._gather_model_data()
             await self._apply_scan_result(scan_result)
             await self._save_persistent_cache(scan_result)
+            await self._sync_download_history(scan_result.raw_data, source='scan')
 
             logger.info(
                 f"{self.model_type.capitalize()} Scanner: Cache initialization completed in {time.time() - start_time:.2f} seconds, "
@@ -733,18 +736,23 @@ class ModelScanner:
             # Get current cached file paths
             cached_paths = {item['file_path'] for item in self._cache.raw_data}
             path_to_item = {item['file_path']: item for item in self._cache.raw_data}
+            cached_real_paths = {}
+            for cached_path in cached_paths:
+                try:
+                    cached_real_paths.setdefault(os.path.realpath(cached_path), cached_path)
+                except Exception:
+                    continue
             
             # Track found files and new files
             found_paths = set()
             new_files = []
+            visited_real_paths = set()
+            discovered_real_files = set()
             
             # Scan all model roots
             for root_path in self.get_model_roots():
                 if not os.path.exists(root_path):
                     continue
-                    
-                # Track visited real paths to avoid symlink loops
-                visited_real_paths = set()
                 
                 # Recursively scan directory
                 for root, _, files in os.walk(root_path, followlinks=True):
@@ -758,12 +766,18 @@ class ModelScanner:
                         if ext in self.file_extensions:
                             # Construct paths exactly as they would be in cache
                             file_path = os.path.join(root, file).replace(os.sep, '/')
+                            real_file_path = os.path.realpath(os.path.join(root, file))
                             
                             # Check if this file is already in cache
                             if file_path in cached_paths:
                                 found_paths.add(file_path)
                                 continue
 
+                            cached_real_match = cached_real_paths.get(real_file_path)
+                            if cached_real_match:
+                                found_paths.add(cached_real_match)
+                                continue
+
                             if file_path in self._excluded_models:
                                 continue
                                 
@@ -779,6 +793,10 @@ class ModelScanner:
                                 if matched:
                                     continue
                                 
+                            if real_file_path in discovered_real_files:
+                                continue
+
+                            discovered_real_files.add(real_file_path)
                             # This is a new file to process
                             new_files.append(file_path)
                     
@@ -1087,6 +1105,49 @@ class ModelScanner:
 
         await self._cache.resort()
 
+    async def _sync_download_history(
+        self,
+        raw_data: List[Mapping[str, Any]],
+        *,
+        source: str,
+    ) -> None:
+        records: List[Dict[str, Any]] = []
+        for item in raw_data or []:
+            if not isinstance(item, Mapping):
+                continue
+            civitai = item.get('civitai')
+            if not isinstance(civitai, Mapping):
+                continue
+
+            version_id = civitai.get('id')
+            if version_id in (None, ''):
+                continue
+
+            records.append(
+                {
+                    'version_id': version_id,
+                    'model_id': civitai.get('modelId'),
+                    'file_path': item.get('file_path'),
+                }
+            )
+
+        if not records:
+            return
+
+        try:
+            history_service = await ServiceRegistry.get_downloaded_version_history_service()
+            await history_service.mark_downloaded_bulk(
+                self.model_type,
+                records,
+                source=source,
+            )
+        except Exception as exc:
+            logger.debug(
+                "%s Scanner: Failed to sync download history: %s",
+                self.model_type.capitalize(),
+                exc,
+            )
+
     async def _gather_model_data(
         self,
         *,
@@ -1100,6 +1161,8 @@ class ModelScanner:
         tags_count: Dict[str, int] = {}
         excluded_models: List[str] = []
         processed_files = 0
+        processed_real_files: Set[str] = set()
+        visited_real_dirs: Set[str] = set()
 
         async def handle_progress() -> None:
             if progress_callback is None:
@@ -1116,9 +1179,10 @@ class ModelScanner:
 
             try:
                 real_path = os.path.realpath(current_path)
-                if real_path in visited_paths:
+                if real_path in visited_paths or real_path in visited_real_dirs:
                     return
                 visited_paths.add(real_path)
+                visited_real_dirs.add(real_path)
 
                 with os.scandir(current_path) as iterator:
                     entries = list(iterator)
@@ -1131,6 +1195,11 @@ class ModelScanner:
                                 continue
 
                             file_path = entry.path.replace(os.sep, "/")
+                            real_file_path = os.path.realpath(entry.path)
+                            if real_file_path in processed_real_files:
+                                continue
+
+                            processed_real_files.add(real_file_path)
                             result = await self._process_model_file(
                                 file_path,
                                 root_path,
@@ -1442,14 +1511,13 @@ 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]]:
@@ -1467,7 +1535,7 @@ class ModelScanner:
         return sorted_tags[:limit]
         
     async def get_base_models(self, limit: int = 20) -> List[Dict[str, any]]:
-        """Get base models sorted by frequency"""
+        """Get base models sorted by count. If limit is 0, return all."""
         cache = await self.get_cached_data()
         
         base_model_counts = {}
@@ -1478,7 +1546,9 @@ class ModelScanner:
         
         sorted_models = [{'name': model, 'count': count} for model, count in base_model_counts.items()]
         sorted_models.sort(key=lambda x: x['count'], reverse=True)
-        
+
+        if limit == 0:
+            return sorted_models
         return sorted_models[:limit]
         
     async def get_model_info_by_name(self, name):

py/services/model_update_service.py

@@ -12,8 +12,9 @@ from typing import Any, Dict, Iterable, List, Mapping, Optional, Sequence
 
 from .errors import RateLimitError, ResourceNotFoundError
 from .settings_manager import get_settings_manager
+from ..utils.cache_paths import CacheType, resolve_cache_path_with_migration
 from ..utils.civitai_utils import rewrite_preview_url
-from ..utils.preview_selection import select_preview_media
+from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
 
 logger = logging.getLogger(__name__)
 
@@ -234,12 +235,52 @@ class ModelUpdateService:
             ON model_update_versions(model_id);
     """
 
-    def __init__(self, db_path: str, *, ttl_seconds: int = 24 * 60 * 60, settings_manager=None) -> None:
-        self._db_path = db_path
+    def __init__(
+        self,
+        db_path: str | None = None,
+        *,
+        ttl_seconds: int = 24 * 60 * 60,
+        settings_manager=None,
+    ) -> None:
+        self._settings = settings_manager or get_settings_manager()
+        self._library_name = self._get_active_library_name()
+        self._db_path = db_path or self._resolve_default_path(self._library_name)
         self._ttl_seconds = ttl_seconds
         self._lock = asyncio.Lock()
         self._schema_initialized = False
-        self._settings = settings_manager or get_settings_manager()
+        self._custom_db_path = db_path is not None
+        self._ensure_directory()
+        self._initialize_schema()
+
+    def _get_active_library_name(self) -> str:
+        try:
+            value = self._settings.get_active_library_name()
+        except Exception:
+            value = None
+        return value or "default"
+
+    def _resolve_default_path(self, library_name: str) -> str:
+        env_override = os.environ.get("LORA_MANAGER_MODEL_UPDATE_DB")
+        return resolve_cache_path_with_migration(
+            CacheType.MODEL_UPDATE,
+            library_name=library_name,
+            env_override=env_override,
+        )
+
+    def on_library_changed(self) -> None:
+        """Switch to the database for the active library."""
+
+        if self._custom_db_path:
+            return
+
+        library_name = self._get_active_library_name()
+        new_path = self._resolve_default_path(library_name)
+        if new_path == self._db_path:
+            return
+
+        self._library_name = library_name
+        self._db_path = new_path
+        self._schema_initialized = False
         self._ensure_directory()
         self._initialize_schema()
 
@@ -262,11 +303,114 @@ class ModelUpdateService:
                 conn.execute("PRAGMA foreign_keys = ON")
                 conn.executescript(self._SCHEMA)
                 self._apply_migrations(conn)
+                self._migrate_from_legacy_snapshot(conn)
             self._schema_initialized = True
         except Exception as exc:  # pragma: no cover - defensive guard
             logger.error("Failed to initialize update schema: %s", exc, exc_info=True)
             raise
 
+    def _migrate_from_legacy_snapshot(self, conn: sqlite3.Connection) -> None:
+        """Copy update tracking data out of the legacy model snapshot database."""
+
+        if self._custom_db_path:
+            return
+
+        try:
+            from .persistent_model_cache import get_persistent_cache
+
+            legacy_path = get_persistent_cache(self._library_name).get_database_path()
+        except Exception:
+            return
+
+        if not legacy_path or os.path.abspath(legacy_path) == os.path.abspath(self._db_path):
+            return
+        if not os.path.exists(legacy_path):
+            return
+
+        try:
+            existing_row = conn.execute(
+                "SELECT 1 FROM model_update_status LIMIT 1"
+            ).fetchone()
+            if existing_row:
+                return
+        except Exception:
+            return
+
+        try:
+            with sqlite3.connect(legacy_path, check_same_thread=False) as legacy_conn:
+                legacy_conn.row_factory = sqlite3.Row
+                status_rows = legacy_conn.execute(
+                    """
+                    SELECT model_id, model_type, last_checked_at, should_ignore_model
+                    FROM model_update_status
+                    """
+                ).fetchall()
+                if not status_rows:
+                    return
+
+                version_rows = legacy_conn.execute(
+                    """
+                    SELECT model_id, version_id, sort_index, name, base_model, released_at,
+                           size_bytes, preview_url, is_in_library, should_ignore,
+                           early_access_ends_at, is_early_access
+                    FROM model_update_versions
+                    ORDER BY model_id ASC, sort_index ASC, version_id ASC
+                    """
+                ).fetchall()
+
+                conn.execute("BEGIN")
+                conn.executemany(
+                    """
+                    INSERT OR REPLACE INTO model_update_status (
+                        model_id, model_type, last_checked_at, should_ignore_model
+                    ) VALUES (?, ?, ?, ?)
+                    """,
+                    [
+                        (
+                            int(row["model_id"]),
+                            row["model_type"],
+                            row["last_checked_at"],
+                            int(row["should_ignore_model"] or 0),
+                        )
+                        for row in status_rows
+                    ],
+                )
+                conn.executemany(
+                    """
+                    INSERT OR REPLACE INTO model_update_versions (
+                        model_id, version_id, sort_index, name, base_model, released_at,
+                        size_bytes, preview_url, is_in_library, should_ignore,
+                        early_access_ends_at, is_early_access
+                    ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                    """,
+                    [
+                        (
+                            int(row["model_id"]),
+                            int(row["version_id"]),
+                            int(row["sort_index"] or 0),
+                            row["name"],
+                            row["base_model"],
+                            row["released_at"],
+                            row["size_bytes"],
+                            row["preview_url"],
+                            int(row["is_in_library"] or 0),
+                            int(row["should_ignore"] or 0),
+                            row["early_access_ends_at"],
+                            int(row["is_early_access"] or 0),
+                        )
+                        for row in version_rows
+                    ],
+                )
+                conn.commit()
+                logger.info(
+                    "Migrated model update tracking data from legacy snapshot DB for %s",
+                    self._library_name,
+                )
+        except sqlite3.OperationalError as exc:
+            logger.debug("Legacy model update migration skipped: %s", exc)
+        except Exception as exc:  # pragma: no cover - defensive guard
+            logger.warning("Failed to migrate model update data: %s", exc, exc_info=True)
+
     def _apply_migrations(self, conn: sqlite3.Connection) -> None:
         """Ensure legacy databases match the current schema without dropping data."""
 
@@ -1252,14 +1396,23 @@ class ModelUpdateService:
             return None
 
         blur_mature_content = True
+        mature_threshold = resolve_mature_threshold({"mature_blur_level": "R"})
         settings = getattr(self, "_settings", None)
         if settings is not None and hasattr(settings, "get"):
             try:
                 blur_mature_content = bool(settings.get("blur_mature_content", True))
+                mature_threshold = resolve_mature_threshold(
+                    {"mature_blur_level": settings.get("mature_blur_level", "R")}
+                )
             except Exception:  # pragma: no cover - defensive guard
                 blur_mature_content = True
+                mature_threshold = resolve_mature_threshold({"mature_blur_level": "R"})
 
-        selected, _ = select_preview_media(candidates, blur_mature_content=blur_mature_content)
+        selected, _ = select_preview_media(
+            candidates,
+            blur_mature_content=blur_mature_content,
+            mature_threshold=mature_threshold,
+        )
         if not selected:
             return None
 

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/preview_asset_service.py

@@ -9,7 +9,7 @@ from urllib.parse import urlparse
 
 from ..utils.constants import CARD_PREVIEW_WIDTH, PREVIEW_EXTENSIONS
 from ..utils.civitai_utils import rewrite_preview_url
-from ..utils.preview_selection import select_preview_media
+from ..utils.preview_selection import resolve_mature_threshold, select_preview_media
 from .settings_manager import get_settings_manager
 
 logger = logging.getLogger(__name__)
@@ -49,9 +49,13 @@ class PreviewAssetService:
         blur_mature_content = bool(
             settings_manager.get("blur_mature_content", True)
         )
+        mature_threshold = resolve_mature_threshold(
+            {"mature_blur_level": settings_manager.get("mature_blur_level", "R")}
+        )
         first_preview, nsfw_level = select_preview_media(
             images,
             blur_mature_content=blur_mature_content,
+            mature_threshold=mature_threshold,
         )
 
         if not first_preview:
@@ -216,4 +220,3 @@ class PreviewAssetService:
         if "webm" in content_type:
             return ".webm"
         return ".mp4"
-

py/services/recipe_cache.py

@@ -4,6 +4,7 @@ from dataclasses import dataclass
 from operator import itemgetter
 from natsort import natsorted
 
+
 @dataclass
 class RecipeCache:
     """Cache structure for Recipe data"""
@@ -21,11 +22,18 @@ class RecipeCache:
         self.folder_tree = self.folder_tree or {}
 
     async def resort(self, name_only: bool = False):
-        """Resort all cached data views"""
+        """Resort all cached data views in a thread pool to avoid blocking the event loop."""
         async with self._lock:
-            self._resort_locked(name_only=name_only)
+            loop = asyncio.get_event_loop()
+            await loop.run_in_executor(
+                None,
+                self._resort_locked,
+                name_only,
+            )
 
-    async def update_recipe_metadata(self, recipe_id: str, metadata: Dict, *, resort: bool = True) -> bool:
+    async def update_recipe_metadata(
+        self, recipe_id: str, metadata: Dict, *, resort: bool = True
+    ) -> bool:
         """Update metadata for a specific recipe in all cached data
 
         Args:
@@ -37,7 +45,7 @@ class RecipeCache:
         """
         async with self._lock:
             for item in self.raw_data:
-                if str(item.get('id')) == str(recipe_id):
+                if str(item.get("id")) == str(recipe_id):
                     item.update(metadata)
                     if resort:
                         self._resort_locked()
@@ -52,7 +60,9 @@ class RecipeCache:
             if resort:
                 self._resort_locked()
 
-    async def remove_recipe(self, recipe_id: str, *, resort: bool = False) -> Optional[Dict]:
+    async def remove_recipe(
+        self, recipe_id: str, *, resort: bool = False
+    ) -> Optional[Dict]:
         """Remove a recipe from the cache by ID.
 
         Args:
@@ -64,14 +74,16 @@ class RecipeCache:
 
         async with self._lock:
             for index, recipe in enumerate(self.raw_data):
-                if str(recipe.get('id')) == str(recipe_id):
+                if str(recipe.get("id")) == str(recipe_id):
                     removed = self.raw_data.pop(index)
                     if resort:
                         self._resort_locked()
                     return removed
         return None
 
-    async def bulk_remove(self, recipe_ids: Iterable[str], *, resort: bool = False) -> List[Dict]:
+    async def bulk_remove(
+        self, recipe_ids: Iterable[str], *, resort: bool = False
+    ) -> List[Dict]:
         """Remove multiple recipes from the cache."""
 
         id_set = {str(recipe_id) for recipe_id in recipe_ids}
@@ -79,21 +91,25 @@ class RecipeCache:
             return []
 
         async with self._lock:
-            removed = [item for item in self.raw_data if str(item.get('id')) in id_set]
+            removed = [item for item in self.raw_data if str(item.get("id")) in id_set]
             if not removed:
                 return []
 
-            self.raw_data = [item for item in self.raw_data if str(item.get('id')) not in id_set]
+            self.raw_data = [
+                item for item in self.raw_data if str(item.get("id")) not in id_set
+            ]
             if resort:
                 self._resort_locked()
             return removed
 
-    async def replace_recipe(self, recipe_id: str, new_data: Dict, *, resort: bool = False) -> bool:
+    async def replace_recipe(
+        self, recipe_id: str, new_data: Dict, *, resort: bool = False
+    ) -> bool:
         """Replace cached data for a recipe."""
 
         async with self._lock:
             for index, recipe in enumerate(self.raw_data):
-                if str(recipe.get('id')) == str(recipe_id):
+                if str(recipe.get("id")) == str(recipe_id):
                     self.raw_data[index] = new_data
                     if resort:
                         self._resort_locked()
@@ -105,7 +121,7 @@ class RecipeCache:
 
         async with self._lock:
             for recipe in self.raw_data:
-                if str(recipe.get('id')) == str(recipe_id):
+                if str(recipe.get("id")) == str(recipe_id):
                     return dict(recipe)
         return None
 
@@ -115,16 +131,14 @@ class RecipeCache:
         async with self._lock:
             return [dict(item) for item in self.raw_data]
 
-    def _resort_locked(self, *, name_only: bool = False) -> None:
+    def _resort_locked(self, name_only: bool = False) -> None:
         """Sort cached views. Caller must hold ``_lock``."""
 
         self.sorted_by_name = natsorted(
             self.raw_data,
-            key=lambda x: x.get('title', '').lower()
+            key=lambda x: (x.get("title", "").lower(), x.get("file_path", "").lower()),
         )
         if not name_only:
             self.sorted_by_date = sorted(
-                self.raw_data,
-                key=itemgetter('created_date', 'file_path'),
-                reverse=True
-            )
+                self.raw_data, key=itemgetter("created_date", "file_path"), reverse=True
+            )

py/services/recipes/analysis_service.py

@@ -1,10 +1,10 @@
 """Services responsible for recipe metadata analysis."""
+
 from __future__ import annotations
 
 import base64
 import io
 import os
-import re
 import tempfile
 from dataclasses import dataclass
 from typing import Any, Callable, Optional
@@ -13,7 +13,7 @@ import numpy as np
 from PIL import Image
 
 from ...utils.utils import calculate_recipe_fingerprint
-from ...utils.civitai_utils import rewrite_preview_url
+from ...utils.civitai_utils import extract_civitai_image_id, rewrite_preview_url
 from .errors import (
     RecipeDownloadError,
     RecipeNotFoundError,
@@ -69,7 +69,9 @@ 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,
@@ -101,33 +103,39 @@ class RecipeAnalysisService:
         extension = ".jpg"  # Default
 
         try:
-            civitai_match = re.match(r"https://civitai\.com/images/(\d+)", url)
-            if civitai_match:
-                image_info = await civitai_client.get_image_info(civitai_match.group(1))
+            civitai_image_id = extract_civitai_image_id(url)
+            if civitai_image_id:
+                image_info = await civitai_client.get_image_info(
+                    civitai_image_id, source_url=url
+                )
                 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 +143,29 @@ class RecipeAnalysisService:
                     and isinstance(metadata["meta"], dict)
                 ):
                     metadata = metadata["meta"]
+
+                # Include modelVersionIds from root level if available
+                # Civitai API returns modelVersionIds at root level, not in meta
+                model_version_ids = image_info.get("modelVersionIds")
+                if model_version_ids and isinstance(metadata, dict):
+                    metadata["modelVersionIds"] = model_version_ids
+
+                # 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 +233,9 @@ 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 +246,22 @@ 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 +274,12 @@ 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 +302,9 @@ 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 +316,10 @@ 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 +355,9 @@ 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/recipes/persistence_service.py

@@ -12,6 +12,7 @@ from dataclasses import dataclass
 from typing import Any, Dict, Iterable, Optional
 
 from ...config import config
+from ...recipes.constants import GEN_PARAM_KEYS
 from ...utils.utils import calculate_recipe_fingerprint
 from .errors import RecipeNotFoundError, RecipeValidationError
 
@@ -90,23 +91,7 @@ class RecipePersistenceService:
         current_time = time.time()
         loras_data = [self._normalise_lora_entry(lora) for lora in (metadata.get("loras") or [])]
         checkpoint_entry = self._sanitize_checkpoint_entry(self._extract_checkpoint_entry(metadata))
-
-        gen_params = metadata.get("gen_params") or {}
-        if not gen_params and "raw_metadata" in metadata:
-            raw_metadata = metadata.get("raw_metadata", {})
-            gen_params = {
-                "prompt": raw_metadata.get("prompt", ""),
-                "negative_prompt": raw_metadata.get("negative_prompt", ""),
-                "steps": raw_metadata.get("steps", ""),
-                "sampler": raw_metadata.get("sampler", ""),
-                "cfg_scale": raw_metadata.get("cfg_scale", ""),
-                "seed": raw_metadata.get("seed", ""),
-                "size": raw_metadata.get("size", ""),
-                "clip_skip": raw_metadata.get("clip_skip", ""),
-            }
-
-        # Drop checkpoint duplication from generation parameters to store it only at top level
-        gen_params.pop("checkpoint", None)
+        gen_params = self._sanitize_gen_params_for_storage(metadata)
 
         fingerprint = calculate_recipe_fingerprint(loras_data)
         recipe_data: Dict[str, Any] = {
@@ -133,6 +118,7 @@ class RecipePersistenceService:
         json_filename = f"{recipe_id}.recipe.json"
         json_path = os.path.join(recipes_dir, json_filename)
         json_path = os.path.normpath(json_path)
+
         with open(json_path, "w", encoding="utf-8") as file_obj:
             json.dump(recipe_data, file_obj, indent=4, ensure_ascii=False)
 
@@ -152,6 +138,30 @@ class RecipePersistenceService:
             }
         )
 
+    @staticmethod
+    def _sanitize_gen_params_for_storage(metadata: dict[str, Any]) -> dict[str, Any]:
+        gen_params = metadata.get("gen_params")
+        if isinstance(gen_params, dict) and gen_params:
+            source = gen_params
+        else:
+            source = metadata.get("raw_metadata")
+
+        if not isinstance(source, dict):
+            return {}
+
+        allowed_keys = set(GEN_PARAM_KEYS)
+        sanitized: dict[str, Any] = {}
+        for key in allowed_keys:
+            if key not in source:
+                continue
+            value = source.get(key)
+            if value in (None, ""):
+                continue
+            sanitized[key] = value
+
+        sanitized.pop("checkpoint", None)
+        return sanitized
+
     async def delete_recipe(self, *, recipe_scanner, recipe_id: str) -> PersistenceResult:
         """Delete an existing recipe."""
 
@@ -173,11 +183,23 @@ class RecipePersistenceService:
     async def update_recipe(self, *, recipe_scanner, recipe_id: str, updates: dict[str, Any]) -> PersistenceResult:
         """Update persisted metadata for a recipe."""
 
-        if not any(key in updates for key in ("title", "tags", "source_path", "preview_nsfw_level", "favorite")):
+        allowed_fields = (
+            "title",
+            "tags",
+            "source_path",
+            "preview_nsfw_level",
+            "favorite",
+            "gen_params",
+        )
+
+        if not any(key in updates for key in allowed_fields):
             raise RecipeValidationError(
-                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite)"
+                "At least one field to update must be provided (title or tags or source_path or preview_nsfw_level or favorite or gen_params)"
             )
 
+        if "gen_params" in updates and not isinstance(updates["gen_params"], dict):
+            raise RecipeValidationError("gen_params must be an object")
+
         success = await recipe_scanner.update_recipe_metadata(recipe_id, updates)
         if not success:
             raise RecipeNotFoundError("Recipe not found or update failed")

py/services/service_registry.py

@@ -159,10 +159,51 @@ class ServiceRegistry:
                 return cls._services[service_name]
 
             from .model_update_service import ModelUpdateService
-            from .persistent_model_cache import get_persistent_cache
+            from .settings_manager import get_settings_manager
 
-            cache = get_persistent_cache()
-            service = ModelUpdateService(cache.get_database_path())
+            service = ModelUpdateService(settings_manager=get_settings_manager())
+            cls._services[service_name] = service
+            logger.debug(f"Created and registered {service_name}")
+            return service
+
+    @classmethod
+    async def get_downloaded_version_history_service(cls):
+        """Get or create the downloaded-version history service."""
+
+        service_name = "downloaded_version_history_service"
+
+        if service_name in cls._services:
+            return cls._services[service_name]
+
+        async with cls._get_lock(service_name):
+            if service_name in cls._services:
+                return cls._services[service_name]
+
+            from .downloaded_version_history_service import (
+                DownloadedVersionHistoryService,
+            )
+
+            service = DownloadedVersionHistoryService()
+            cls._services[service_name] = service
+            logger.debug(f"Created and registered {service_name}")
+            return service
+
+    @classmethod
+    async def get_backup_service(cls):
+        """Get or create the backup service."""
+
+        service_name = "backup_service"
+
+        if service_name in cls._services:
+            return cls._services[service_name]
+
+        async with cls._get_lock(service_name):
+            if service_name in cls._services:
+                return cls._services[service_name]
+
+            from .backup_service import BackupService
+
+            service = await BackupService.get_instance()
             cls._services[service_name] = service
             logger.debug(f"Created and registered {service_name}")
             return service
@@ -255,4 +296,4 @@ class ServiceRegistry:
         """Clear all registered services - mainly for testing"""
         cls._services.clear()
         cls._locks.clear()
-        logger.info("Cleared all registered services")
+        logger.info("Cleared all registered services")

py/services/tag_fts_index.py

@@ -69,7 +69,9 @@ class TagFTSIndex:
     _DEFAULT_FILENAME = "tag_fts.sqlite"
     _CSV_FILENAME = "danbooru_e621_merged.csv"
 
-    def __init__(self, db_path: Optional[str] = None, csv_path: Optional[str] = None) -> None:
+    def __init__(
+        self, db_path: Optional[str] = None, csv_path: Optional[str] = None
+    ) -> None:
         """Initialize the FTS index.
 
         Args:
@@ -92,7 +94,9 @@ class TagFTSIndex:
             if directory:
                 os.makedirs(directory, exist_ok=True)
         except Exception as exc:
-            logger.warning("Could not create FTS index directory %s: %s", directory, exc)
+            logger.warning(
+                "Could not create FTS index directory %s: %s", directory, exc
+            )
 
     def _resolve_default_db_path(self) -> str:
         """Resolve the default database path."""
@@ -173,13 +177,15 @@ class TagFTSIndex:
                     # Set schema version
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("schema_version", str(SCHEMA_VERSION))
+                        ("schema_version", str(SCHEMA_VERSION)),
                     )
                     conn.commit()
 
                     self._schema_initialized = True
                     self._needs_rebuild = needs_rebuild
-                    logger.debug("Tag FTS index schema initialized at %s", self._db_path)
+                    logger.debug(
+                        "Tag FTS index schema initialized at %s", self._db_path
+                    )
                 finally:
                     conn.close()
             except Exception as exc:
@@ -206,13 +212,20 @@ class TagFTSIndex:
             row = cursor.fetchone()
             if not row:
                 # Old schema without version, needs rebuild
-                logger.info("Migrating tag FTS index to schema version %d (adding alias support)", SCHEMA_VERSION)
+                logger.info(
+                    "Migrating tag FTS index to schema version %d (adding alias support)",
+                    SCHEMA_VERSION,
+                )
                 self._drop_old_tables(conn)
                 return True
 
             current_version = int(row[0])
             if current_version < SCHEMA_VERSION:
-                logger.info("Migrating tag FTS index from version %d to %d", current_version, SCHEMA_VERSION)
+                logger.info(
+                    "Migrating tag FTS index from version %d to %d",
+                    current_version,
+                    SCHEMA_VERSION,
+                )
                 self._drop_old_tables(conn)
                 return True
 
@@ -246,7 +259,9 @@ class TagFTSIndex:
             return
 
         if not os.path.exists(self._csv_path):
-            logger.warning("CSV file not found at %s, cannot build tag index", self._csv_path)
+            logger.warning(
+                "CSV file not found at %s, cannot build tag index", self._csv_path
+            )
             return
 
         self._indexing_in_progress = True
@@ -314,22 +329,24 @@ class TagFTSIndex:
                     # Update metadata
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("last_build_time", str(time.time()))
+                        ("last_build_time", str(time.time())),
                     )
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("tag_count", str(total_inserted))
+                        ("tag_count", str(total_inserted)),
                     )
                     conn.execute(
                         "INSERT OR REPLACE INTO fts_metadata (key, value) VALUES (?, ?)",
-                        ("schema_version", str(SCHEMA_VERSION))
+                        ("schema_version", str(SCHEMA_VERSION)),
                     )
 
                     conn.commit()
                     elapsed = time.time() - start_time
                     logger.info(
                         "Tag FTS index built: %d tags indexed (%d with aliases) in %.2fs",
-                        total_inserted, tags_with_aliases, elapsed
+                        total_inserted,
+                        tags_with_aliases,
+                        elapsed,
                     )
                 finally:
                     conn.close()
@@ -350,7 +367,7 @@ class TagFTSIndex:
         # Insert into tags table (with aliases)
         conn.executemany(
             "INSERT OR IGNORE INTO tags (tag_name, category, post_count, aliases) VALUES (?, ?, ?, ?)",
-            rows
+            rows,
         )
 
         # Build a map of tag_name -> aliases for FTS insertion
@@ -362,7 +379,7 @@ class TagFTSIndex:
         placeholders = ",".join("?" * len(tag_names))
         cursor = conn.execute(
             f"SELECT rowid, tag_name FROM tags WHERE tag_name IN ({placeholders})",
-            tag_names
+            tag_names,
         )
 
         # Build FTS rows with (rowid, searchable_text) = (tags.rowid, "tag_name alias1 alias2 ...")
@@ -379,13 +396,17 @@ class TagFTSIndex:
                         alias = alias[1:]  # Remove leading slash
                     if alias:
                         alias_parts.append(alias)
-                searchable_text = f"{tag_name} {' '.join(alias_parts)}" if alias_parts else tag_name
+                searchable_text = (
+                    f"{tag_name} {' '.join(alias_parts)}" if alias_parts else tag_name
+                )
             else:
                 searchable_text = tag_name
             fts_rows.append((rowid, searchable_text))
 
         if fts_rows:
-            conn.executemany("INSERT INTO tag_fts (rowid, searchable_text) VALUES (?, ?)", fts_rows)
+            conn.executemany(
+                "INSERT INTO tag_fts (rowid, searchable_text) VALUES (?, ?)", fts_rows
+            )
 
     def ensure_ready(self) -> bool:
         """Ensure the index is ready, building if necessary.
@@ -420,21 +441,28 @@ class TagFTSIndex:
         self,
         query: str,
         categories: Optional[List[int]] = None,
-        limit: int = 20
+        limit: int = 20,
+        offset: int = 0,
     ) -> List[Dict]:
         """Search tags using FTS5 with prefix matching.
 
         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. Exact prefix match boost (tag_name starts with query)
+        2. Post count to preserve expected autocomplete ordering
+        3. FTS5 bm25 relevance score as a deterministic tie-breaker
+
         Args:
             query: The search query string.
             categories: Optional list of category IDs to filter by.
             limit: Maximum number of results to return.
+            offset: Number of results to skip.
 
         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():
@@ -450,45 +478,38 @@ 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
-                    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 ?
-                            )
-                            AND t.category IN ({placeholders})
-                            ORDER BY t.post_count DESC
-                            LIMIT ?
-                        """
-                        params = [fts_query] + categories + [limit]
-                    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
-                            LIMIT ?
-                        """
-                        params = [fts_query, limit]
-
+                    sql, params = self._build_search_statement(
+                        query_lower=query_lower,
+                        fts_query=fts_query,
+                        categories=categories,
+                        limit=limit,
+                        offset=offset,
+                    )
                     cursor = conn.execute(sql, params)
+                    rows = cursor.fetchall()
                     results = []
-                    for row in cursor.fetchall():
+                    for row in rows:
                         result = {
                             "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:
@@ -502,7 +523,65 @@ class TagFTSIndex:
             logger.debug("Tag FTS search error for query '%s': %s", query, exc)
             return []
 
-    def _find_matched_alias(self, query: str, tag_name: str, aliases_str: str) -> Optional[str]:
+    def _build_search_statement(
+        self,
+        query_lower: str,
+        fts_query: str,
+        categories: Optional[List[int]],
+        limit: int,
+        offset: int,
+    ) -> tuple[str, list[object]]:
+        """Build the SQL statement and params for a tag search."""
+        # Escape special LIKE characters and add wildcard
+        query_escaped = (
+            query_lower.lstrip("/")
+            .replace("\\", "\\\\")
+            .replace("%", "\\%")
+            .replace("_", "\\_")
+        )
+
+        # FTS5 bm25() returns negative scores, lower is better.
+        # We use -bm25() to get higher=better scores, but keep post_count as the
+        # primary sort within tag-name prefix matches so autocomplete ordering
+        # remains aligned with the existing popularity-first behavior.
+        if categories:
+            placeholders = ",".join("?" * len(categories))
+            sql = f"""
+                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) AS rank_score
+                FROM tag_fts
+                CROSS JOIN tags t ON t.rowid = tag_fts.rowid
+                WHERE tag_fts.searchable_text MATCH ?
+                  AND t.category IN ({placeholders})
+                ORDER BY is_tag_name_match DESC, t.post_count DESC, rank_score DESC
+                LIMIT ? OFFSET ?
+            """
+            params = [query_escaped + "%", fts_query] + categories + [limit, offset]
+        else:
+            sql = """
+                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) 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, t.post_count DESC, rank_score DESC
+                LIMIT ? OFFSET ?
+            """
+            params = [query_escaped + "%", fts_query, limit, offset]
+
+        return sql, params
+
+    def _find_matched_alias(
+        self, query: str, tag_name: str, aliases_str: str
+    ) -> Optional[str]:
         """Find which alias matched the query, if any.
 
         Args:

py/services/use_cases/bulk_metadata_refresh_use_case.py

@@ -47,8 +47,7 @@ class BulkMetadataRefreshUseCase:
         to_process: Sequence[Dict[str, Any]] = [
             model
             for model in cache.raw_data
-            if model.get("sha256")
-            and not model.get("skip_metadata_refresh", False)
+            if not model.get("skip_metadata_refresh", False)
             and not self._is_in_skip_path(model.get("folder", ""), skip_paths)
             and (not model.get("civitai") or not model["civitai"].get("id"))
             and not (
@@ -85,6 +84,36 @@ class BulkMetadataRefreshUseCase:
                 return {"success": False, "message": "Operation cancelled", "processed": processed, "updated": success, "total": total_models}
             try:
                 original_name = model.get("model_name")
+
+                # Handle lazy hash calculation for models with pending hash status
+                sha256 = model.get("sha256", "")
+                hash_status = model.get("hash_status", "completed")
+                file_path = model.get("file_path")
+
+                if not sha256 and hash_status == "pending" and file_path:
+                    self._logger.info(f"Calculating pending hash for {file_path}")
+                    # Check if scanner has calculate_hash_for_model method (CheckpointScanner)
+                    calculate_hash_method = getattr(self._service.scanner, "calculate_hash_for_model", None)
+                    if calculate_hash_method:
+                        sha256 = await calculate_hash_method(file_path)
+                        if sha256:
+                            model["sha256"] = sha256
+                            model["hash_status"] = "completed"
+                        else:
+                            self._logger.error(f"Failed to calculate hash for {file_path}")
+                            processed += 1
+                            continue
+                    else:
+                        self._logger.warning(f"Scanner does not support lazy hash calculation for {file_path}")
+                        processed += 1
+                        continue
+
+                # Skip models without valid hash
+                if not model.get("sha256"):
+                    self._logger.warning(f"Skipping model without hash: {file_path}")
+                    processed += 1
+                    continue
+
                 await MetadataManager.hydrate_model_data(model)
                 result, _ = await self._metadata_sync.fetch_and_update_model(
                     sha256=model["sha256"],

py/services/wildcard_service.py

@@ -0,0 +1,428 @@
+"""Managed wildcard loading, search, and text expansion."""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import random
+import re
+from dataclasses import dataclass
+from typing import Any, Optional
+
+import yaml
+
+from ..utils.settings_paths import get_settings_dir
+
+logger = logging.getLogger(__name__)
+
+_WILDCARD_PATTERN = re.compile(r"__([\w\s.\-+/*\\]+?)__")
+_OPTION_PATTERN = re.compile(r"{([^{}]*?)}")
+_TRIGGER_WORD_PATTERN = re.compile(r"^trigger_words\d+$")
+_WEIGHTED_OPTION_PATTERN = re.compile(r"^\s*([0-9.]+)::")
+_NUMERIC_PATTERN = re.compile(r"^-?\d+(\.\d+)?$")
+
+
+def _normalize_wildcard_key(value: str) -> str:
+    return value.replace("\\", "/").strip("/").lower()
+
+
+def _is_numeric_string(value: str) -> bool:
+    return bool(_NUMERIC_PATTERN.match(value))
+
+
+def contains_dynamic_syntax(text: str) -> bool:
+    """Return True when text contains supported wildcard or option syntax."""
+
+    return isinstance(text, str) and bool(
+        _WILDCARD_PATTERN.search(text) or _OPTION_PATTERN.search(text)
+    )
+
+
+def get_wildcards_dir(create: bool = False) -> str:
+    """Return the managed wildcard directory inside the settings folder."""
+
+    settings_dir = get_settings_dir(create=create)
+    wildcards_dir = os.path.join(settings_dir, "wildcards")
+    if create:
+        os.makedirs(wildcards_dir, exist_ok=True)
+    return wildcards_dir
+
+
+@dataclass(frozen=True)
+class WildcardEntry:
+    key: str
+    values_count: int
+
+
+@dataclass(frozen=True)
+class WildcardMetadata:
+    has_wildcards: bool
+    wildcards_dir: str
+    supported_formats: tuple[str, ...]
+
+
+class WildcardService:
+    """Discover wildcard keys and expand wildcard syntax."""
+
+    _instance: Optional["WildcardService"] = None
+
+    def __new__(cls) -> "WildcardService":
+        if cls._instance is None:
+            cls._instance = super().__new__(cls)
+        return cls._instance
+
+    def __init__(self) -> None:
+        if getattr(self, "_initialized", False):
+            return
+        self._initialized = True
+        self._cached_signature: tuple[tuple[str, int, int], ...] | None = None
+        self._wildcard_dict: dict[str, list[str]] = {}
+
+    @classmethod
+    def get_instance(cls) -> "WildcardService":
+        return cls()
+
+    def search_keys(
+        self, search_term: str, limit: int = 20, offset: int = 0
+    ) -> list[str]:
+        """Search wildcard keys for autocomplete."""
+
+        normalized_term = _normalize_wildcard_key(search_term).strip()
+        if not normalized_term:
+            return []
+
+        ranked: list[tuple[int, str]] = []
+        compact_term = normalized_term.replace("/", "")
+        for key in self.get_wildcard_dict().keys():
+            score = self._score_entry(key, normalized_term, compact_term)
+            if score is not None:
+                ranked.append((score, key))
+
+        ranked.sort(key=lambda item: (-item[0], item[1]))
+        keys = [key for _, key in ranked]
+        return keys[offset : offset + limit]
+
+    def expand_text(self, text: str, seed: int | None = None) -> str:
+        """Expand wildcard and dynamic prompt syntax for a text value."""
+
+        if not isinstance(text, str) or not text:
+            return text
+
+        rng = random.Random(seed) if seed is not None else random.Random()
+        wildcard_dict = self.get_wildcard_dict()
+        if not wildcard_dict:
+            return self._expand_options_only(text, rng)
+
+        current = text
+        remaining_depth = 100
+
+        while remaining_depth > 0:
+            remaining_depth -= 1
+            after_options, options_replaced = self._replace_options(current, rng)
+            current, wildcards_replaced = self._replace_wildcards(
+                after_options, rng, wildcard_dict
+            )
+            if not options_replaced and not wildcards_replaced:
+                break
+
+        return current
+
+    def get_wildcard_dict(self) -> dict[str, list[str]]:
+        signature = self._build_signature()
+        if signature != self._cached_signature:
+            self._wildcard_dict = self._scan_wildcard_dict()
+            self._cached_signature = signature
+        return self._wildcard_dict
+
+    def get_entries(self) -> list[WildcardEntry]:
+        return [
+            WildcardEntry(key=key, values_count=len(values))
+            for key, values in sorted(self.get_wildcard_dict().items())
+        ]
+
+    def get_metadata(self, *, create_dir: bool = False) -> WildcardMetadata:
+        wildcards_dir = get_wildcards_dir(create=create_dir)
+        return WildcardMetadata(
+            has_wildcards=bool(self.get_wildcard_dict()),
+            wildcards_dir=wildcards_dir,
+            supported_formats=(".txt", ".yaml", ".yml", ".json"),
+        )
+
+    def _build_signature(self) -> tuple[tuple[str, int, int], ...]:
+        root = get_wildcards_dir(create=False)
+        if not os.path.isdir(root):
+            return ()
+
+        signature: list[tuple[str, int, int]] = []
+        for current_root, _dirs, files in os.walk(root, followlinks=True):
+            for file_name in sorted(files):
+                if not file_name.lower().endswith((".txt", ".yaml", ".yml", ".json")):
+                    continue
+                file_path = os.path.join(current_root, file_name)
+                try:
+                    stat = os.stat(file_path)
+                except OSError:
+                    continue
+                rel_path = os.path.relpath(file_path, root).replace("\\", "/")
+                signature.append((rel_path, int(stat.st_mtime_ns), int(stat.st_size)))
+        signature.sort()
+        return tuple(signature)
+
+    def _scan_wildcard_dict(self) -> dict[str, list[str]]:
+        root = get_wildcards_dir(create=False)
+        if not os.path.isdir(root):
+            return {}
+
+        collected: dict[str, list[str]] = {}
+        for current_root, _dirs, files in os.walk(root, followlinks=True):
+            for file_name in sorted(files):
+                file_path = os.path.join(current_root, file_name)
+                lower_name = file_name.lower()
+                try:
+                    if lower_name.endswith(".txt"):
+                        rel_path = os.path.relpath(file_path, root)
+                        key = _normalize_wildcard_key(os.path.splitext(rel_path)[0])
+                        values = self._read_txt(file_path)
+                        if values:
+                            collected[key] = values
+                    elif lower_name.endswith((".yaml", ".yml")):
+                        payload = self._read_yaml(file_path)
+                        self._merge_nested_entries(collected, payload)
+                    elif lower_name.endswith(".json"):
+                        payload = self._read_json(file_path)
+                        self._merge_nested_entries(collected, payload)
+                except Exception as exc:  # pragma: no cover - defensive logging
+                    logger.warning("Failed to load wildcard file %s: %s", file_path, exc)
+
+        return collected
+
+    def _read_txt(self, file_path: str) -> list[str]:
+        try:
+            with open(file_path, "r", encoding="utf-8", errors="ignore") as handle:
+                return [line.strip() for line in handle.read().splitlines() if line.strip()]
+        except OSError as exc:
+            logger.warning("Failed to read wildcard txt file %s: %s", file_path, exc)
+            return []
+
+    def _read_yaml(self, file_path: str) -> Any:
+        with open(file_path, "r", encoding="utf-8") as handle:
+            return yaml.safe_load(handle) or {}
+
+    def _read_json(self, file_path: str) -> Any:
+        with open(file_path, "r", encoding="utf-8") as handle:
+            return json.load(handle)
+
+    def _merge_nested_entries(
+        self, collected: dict[str, list[str]], payload: Any
+    ) -> None:
+        for key, values in self._flatten_payload(payload):
+            collected[key] = values
+
+    def _flatten_payload(
+        self, payload: Any, prefix: str = ""
+    ) -> list[tuple[str, list[str]]]:
+        entries: list[tuple[str, list[str]]] = []
+
+        if isinstance(payload, dict):
+            for key, value in payload.items():
+                next_prefix = f"{prefix}/{key}" if prefix else str(key)
+                entries.extend(self._flatten_payload(value, next_prefix))
+            return entries
+
+        if isinstance(payload, list):
+            normalized_prefix = _normalize_wildcard_key(prefix)
+            values = [value.strip() for value in payload if isinstance(value, str) and value.strip()]
+            if normalized_prefix and values:
+                entries.append((normalized_prefix, values))
+            return entries
+
+        return entries
+
+    def _score_entry(
+        self, key: str, normalized_term: str, compact_term: str
+    ) -> int | None:
+        key_compact = key.replace("/", "")
+        if key == normalized_term:
+            return 5000
+        if key.startswith(normalized_term):
+            return 4000
+        if f"/{normalized_term}" in key:
+            return 3500
+        if normalized_term in key:
+            return 3000
+        if compact_term and key_compact.startswith(compact_term):
+            return 2500
+        if compact_term and compact_term in key_compact:
+            return 2000
+        return None
+
+    def _expand_options_only(self, text: str, rng: random.Random) -> str:
+        current = text
+        remaining_depth = 100
+        while remaining_depth > 0:
+            remaining_depth -= 1
+            current, replaced = self._replace_options(current, rng)
+            if not replaced:
+                break
+        return current
+
+    def _replace_options(
+        self, text: str, rng: random.Random
+    ) -> tuple[str, bool]:
+        replaced_any = False
+
+        def replace_option(match: re.Match[str]) -> str:
+            nonlocal replaced_any
+            replacement = self._resolve_option_group(match.group(1), rng)
+            replaced_any = True
+            return replacement
+
+        return _OPTION_PATTERN.sub(replace_option, text), replaced_any
+
+    def _resolve_option_group(self, group_text: str, rng: random.Random) -> str:
+        options = group_text.split("|")
+        multi_select_pattern = options[0].split("$$")
+        select_range: tuple[int, int] | None = None
+        select_separator = " "
+
+        if len(multi_select_pattern) > 1:
+            count_spec = multi_select_pattern[0]
+            range_match = re.match(r"(\d+)(-(\d+))?$", count_spec)
+            shorthand_match = re.match(r"-(\d+)$", count_spec)
+            if range_match:
+                start_text = range_match.group(1)
+                end_text = range_match.group(3)
+                if end_text is not None and _is_numeric_string(start_text) and _is_numeric_string(end_text):
+                    select_range = (int(start_text), int(end_text))
+                elif _is_numeric_string(start_text):
+                    value = int(start_text)
+                    select_range = (value, value)
+            elif shorthand_match:
+                end_text = shorthand_match.group(1)
+                if _is_numeric_string(end_text):
+                    select_range = (1, int(end_text))
+
+            if select_range is not None and len(multi_select_pattern) == 2:
+                options[0] = multi_select_pattern[1]
+            elif select_range is not None and len(multi_select_pattern) >= 3:
+                select_separator = multi_select_pattern[1]
+                options[0] = multi_select_pattern[2]
+
+        weighted_options: list[tuple[float, str]] = []
+        for option in options:
+            weight = 1.0
+            parts = option.split("::", 1)
+            if len(parts) == 2 and _is_numeric_string(parts[0].strip()):
+                weight = float(parts[0].strip())
+            weighted_options.append((weight, option))
+
+        if select_range is None:
+            selection_count = 1
+        else:
+            selection_count = rng.randint(select_range[0], select_range[1])
+
+        if selection_count <= 1:
+            return self._strip_weight_prefix(self._weighted_choice(weighted_options, rng))
+
+        selection_count = min(selection_count, len(weighted_options))
+        selected: list[str] = []
+        used_indexes: set[int] = set()
+        while len(selected) < selection_count:
+            picked_index = self._weighted_choice_index(weighted_options, rng)
+            if picked_index in used_indexes:
+                if len(used_indexes) == len(weighted_options):
+                    break
+                continue
+            used_indexes.add(picked_index)
+            selected.append(
+                self._strip_weight_prefix(weighted_options[picked_index][1])
+            )
+
+        return select_separator.join(selected)
+
+    def _weighted_choice(
+        self, weighted_options: list[tuple[float, str]], rng: random.Random
+    ) -> str:
+        return weighted_options[self._weighted_choice_index(weighted_options, rng)][1]
+
+    def _weighted_choice_index(
+        self, weighted_options: list[tuple[float, str]], rng: random.Random
+    ) -> int:
+        total_weight = sum(max(weight, 0.0) for weight, _value in weighted_options)
+        if total_weight <= 0:
+            return rng.randrange(len(weighted_options))
+
+        threshold = rng.uniform(0, total_weight)
+        cumulative = 0.0
+        for index, (weight, _value) in enumerate(weighted_options):
+            cumulative += max(weight, 0.0)
+            if threshold <= cumulative:
+                return index
+        return len(weighted_options) - 1
+
+    def _strip_weight_prefix(self, value: str) -> str:
+        return _WEIGHTED_OPTION_PATTERN.sub("", value, count=1)
+
+    def _replace_wildcards(
+        self,
+        text: str,
+        rng: random.Random,
+        wildcard_dict: dict[str, list[str]],
+    ) -> tuple[str, bool]:
+        replaced_any = False
+
+        def replace_match(match: re.Match[str]) -> str:
+            nonlocal replaced_any
+            replacement = self._resolve_wildcard_match(match.group(1), rng, wildcard_dict)
+            if replacement is None:
+                return match.group(0)
+            replaced_any = True
+            return replacement
+
+        return _WILDCARD_PATTERN.sub(replace_match, text), replaced_any
+
+    def _resolve_wildcard_match(
+        self,
+        raw_key: str,
+        rng: random.Random,
+        wildcard_dict: dict[str, list[str]],
+    ) -> str | None:
+        keyword = _normalize_wildcard_key(raw_key)
+        if keyword in wildcard_dict:
+            return rng.choice(wildcard_dict[keyword])
+
+        if "*" in keyword:
+            regex_pattern = keyword.replace("*", ".*").replace("+", r"\+")
+            compiled = re.compile(f"^{regex_pattern}$")
+            aggregated: list[str] = []
+            for key, values in wildcard_dict.items():
+                if compiled.match(key):
+                    aggregated.extend(values)
+            if aggregated:
+                return rng.choice(aggregated)
+
+        if "/" not in keyword:
+            fallback_keyword = _normalize_wildcard_key(f"*/{keyword}")
+            if fallback_keyword != keyword:
+                return self._resolve_wildcard_match(fallback_keyword, rng, wildcard_dict)
+
+        return None
+
+
+def is_trigger_words_input(name: str) -> bool:
+    return bool(_TRIGGER_WORD_PATTERN.match(name))
+
+
+def get_wildcard_service() -> WildcardService:
+    return WildcardService.get_instance()
+
+
+__all__ = [
+    "WildcardService",
+    "WildcardMetadata",
+    "contains_dynamic_syntax",
+    "get_wildcard_service",
+    "get_wildcards_dir",
+    "is_trigger_words_input",
+]

py/utils/cache_paths.py

@@ -11,6 +11,8 @@ Target structure:
         │   └── symlink_map.json
         ├── model/
         │   └── {library_name}.sqlite
+        ├── model_update/
+        │   └── {library_name}.sqlite
         ├── recipe/
         │   └── {library_name}.sqlite
         └── fts/
@@ -36,6 +38,7 @@ class CacheType(Enum):
     """Types of cache files managed by the cache path resolver."""
 
     MODEL = "model"
+    MODEL_UPDATE = "model_update"
     RECIPE = "recipe"
     RECIPE_FTS = "recipe_fts"
     TAG_FTS = "tag_fts"
@@ -45,6 +48,7 @@ class CacheType(Enum):
 # Subdirectory structure for each cache type
 _CACHE_SUBDIRS = {
     CacheType.MODEL: "model",
+    CacheType.MODEL_UPDATE: "model_update",
     CacheType.RECIPE: "recipe",
     CacheType.RECIPE_FTS: "fts",
     CacheType.TAG_FTS: "fts",
@@ -54,6 +58,7 @@ _CACHE_SUBDIRS = {
 # Filename patterns for each cache type
 _CACHE_FILENAMES = {
     CacheType.MODEL: "{library_name}.sqlite",
+    CacheType.MODEL_UPDATE: "{library_name}.sqlite",
     CacheType.RECIPE: "{library_name}.sqlite",
     CacheType.RECIPE_FTS: "recipe_fts.sqlite",
     CacheType.TAG_FTS: "tag_fts.sqlite",

py/utils/civitai_utils.py

@@ -2,10 +2,13 @@
 
 from __future__ import annotations
 
+import re
 from typing import Any, Dict, Iterable, Mapping, Sequence
-from urllib.parse import urlparse, urlunparse
+from urllib.parse import parse_qs, urlparse, urlunparse
 
 
+_SUPPORTED_CIVITAI_PAGE_HOSTS = frozenset({"civitai.com", "civitai.red"})
+DEFAULT_CIVITAI_PAGE_HOST = "civitai.com"
 _DEFAULT_ALLOW_COMMERCIAL_USE: Sequence[str] = ("Sell",)
 _LICENSE_DEFAULTS: Dict[str, Any] = {
     "allowNoCredit": True,
@@ -17,12 +20,141 @@ _COMMERCIAL_ALLOWED_VALUES = {"sell", "rent", "rentcivit", "image"}
 _COMMERCIAL_SHIFT = 1
 
 
+def is_supported_civitai_page_host(hostname: str | None) -> bool:
+    """Return whether the hostname is a supported Civitai page domain."""
+
+    if not hostname:
+        return False
+    return hostname.lower() in _SUPPORTED_CIVITAI_PAGE_HOSTS
+
+
+def normalize_civitai_page_host(hostname: str | None) -> str:
+    """Return a supported Civitai page host or the default host."""
+
+    if not isinstance(hostname, str):
+        return DEFAULT_CIVITAI_PAGE_HOST
+
+    normalized = hostname.strip().lower()
+    if is_supported_civitai_page_host(normalized):
+        return normalized
+
+    return DEFAULT_CIVITAI_PAGE_HOST
+
+
+def build_civitai_model_page_url(
+    model_id: str | int | None,
+    version_id: str | int | None = None,
+    *,
+    host: str | None = None,
+) -> str | None:
+    """Build a Civitai model or model-version page URL."""
+
+    normalized_host = normalize_civitai_page_host(host)
+    normalized_model_id = str(model_id).strip() if model_id is not None else ""
+    normalized_version_id = str(version_id).strip() if version_id is not None else ""
+
+    if normalized_model_id:
+        path = f"/models/{normalized_model_id}"
+        query = f"modelVersionId={normalized_version_id}" if normalized_version_id else ""
+        return urlunparse(("https", normalized_host, path, "", query, ""))
+
+    if normalized_version_id:
+        return urlunparse(
+            ("https", normalized_host, f"/model-versions/{normalized_version_id}", "", "", "")
+        )
+
+    return None
+
+
+def _parse_supported_civitai_page_url(url: str | None):
+    if not url:
+        return None
+
+    try:
+        parsed = urlparse(url)
+    except ValueError:
+        return None
+
+    if parsed.scheme not in {"http", "https"}:
+        return None
+
+    if not is_supported_civitai_page_host(parsed.hostname):
+        return None
+
+    return parsed
+
+
+def extract_civitai_model_url_parts(
+    url: str | None,
+) -> tuple[str | None, str | None]:
+    """Extract model and version identifiers from a supported Civitai model URL."""
+
+    parsed = _parse_supported_civitai_page_url(url)
+    if parsed is None:
+        return None, None
+
+    path_match = re.search(r"/models/(\d+)", parsed.path)
+    if not path_match:
+        return None, None
+
+    model_id = path_match.group(1)
+
+    query_params = parse_qs(parsed.query)
+    version_values = query_params.get("modelVersionId") or []
+    version_id = version_values[0] if version_values else None
+    return model_id, version_id
+
+
+def extract_civitai_image_id(url: str | None) -> str | None:
+    """Extract the image identifier from a supported Civitai image page URL."""
+
+    parsed = _parse_supported_civitai_page_url(url)
+    if parsed is None:
+        return None
+
+    path_match = re.search(r"/images/(\d+)", parsed.path)
+    if not path_match:
+        return None
+
+    return path_match.group(1)
+
+
+def normalize_civitai_download_url(url: str | None) -> str | None:
+    """Rewrite Civitai download URLs to the canonical authenticated host."""
+
+    if not url:
+        return url
+
+    try:
+        parsed = urlparse(url)
+    except ValueError:
+        return url
+
+    hostname = parsed.hostname.lower() if parsed.hostname else None
+    if hostname != "civitai.red" or not parsed.path.startswith("/api/download/"):
+        return url
+
+    return urlunparse(parsed._replace(netloc="civitai.com"))
+
+
+def extract_civitai_page_host(url: str | None) -> str | None:
+    """Extract the supported Civitai page host from a URL."""
+
+    parsed = _parse_supported_civitai_page_url(url)
+    if parsed is None:
+        return None
+
+    return parsed.hostname.lower() if parsed.hostname else None
+
+
 def _normalize_commercial_values(value: Any) -> Sequence[str]:
     """Return a normalized list of commercial permissions preserving source values."""
 
     def _split_aggregate(value_str: str) -> list[str]:
         stripped = value_str.strip()
-        looks_aggregate = "," in stripped or (stripped.startswith("{") and stripped.endswith("}"))
+        looks_aggregate = "," in stripped or (
+            stripped.startswith("{") and stripped.endswith("}")
+        )
         if not looks_aggregate:
             return [value_str]
 
@@ -141,14 +273,18 @@ def build_license_flags(payload: Mapping[str, Any] | None) -> int:
     return flags
 
 
-def resolve_license_info(model_data: Mapping[str, Any] | None) -> tuple[Dict[str, Any], int]:
+def resolve_license_info(
+    model_data: Mapping[str, Any] | None,
+) -> tuple[Dict[str, Any], int]:
     """Return normalized license payload and its encoded bitset."""
 
     payload = resolve_license_payload(model_data)
     return payload, build_license_flags(payload)
 
 
-def rewrite_preview_url(source_url: str | None, media_type: str | None = None) -> tuple[str | None, bool]:
+def rewrite_preview_url(
+    source_url: str | None, media_type: str | None = None
+) -> tuple[str | None, bool]:
     """Rewrite Civitai preview URLs to use optimized renditions.
 
     Args:
@@ -168,7 +304,12 @@ def rewrite_preview_url(source_url: str | None, media_type: str | None = None) -
     except ValueError:
         return source_url, False
 
-    if parsed.netloc.lower() != "image.civitai.com":
+    hostname = parsed.hostname
+    if hostname is None:
+        return source_url, False
+
+    hostname = hostname.lower()
+    if hostname == "civitai.com" or not hostname.endswith(".civitai.com"):
         return source_url, False
 
     replacement = "/width=450,optimized=true"
@@ -188,6 +329,10 @@ def rewrite_preview_url(source_url: str | None, media_type: str | None = None) -
 
 __all__ = [
     "build_license_flags",
+    "extract_civitai_image_id",
+    "extract_civitai_page_host",
+    "extract_civitai_model_url_parts",
+    "is_supported_civitai_page_host",
     "resolve_license_payload",
     "resolve_license_info",
     "rewrite_preview_url",

py/utils/constants.py

@@ -101,6 +101,7 @@ DEFAULT_PRIORITY_TAG_CONFIG = {
 DIFFUSION_MODEL_BASE_MODELS = frozenset(
     [
         "ZImageTurbo",
+        "ZImageBase",
         "Wan Video 1.3B t2v",
         "Wan Video 14B t2v",
         "Wan Video 14B i2v 480p",
@@ -110,6 +111,71 @@ DIFFUSION_MODEL_BASE_MODELS = frozenset(
         "Wan Video 2.2 T2V-A14B",
         "Wan Video 2.5 T2V",
         "Wan Video 2.5 I2V",
+        "CogVideoX",
+        "Mochi",
         "Qwen",
     ]
 )
+
+# Supported baseModel values for download exclusion settings.
+# Keep this aligned with static/js/utils/constants.js, excluding the generic "Other" value.
+SUPPORTED_DOWNLOAD_SKIP_BASE_MODELS = frozenset(
+    [
+        "SD 1.4",
+        "SD 1.5",
+        "SD 1.5 LCM",
+        "SD 1.5 Hyper",
+        "SD 2.0",
+        "SD 2.1",
+        "SD 3",
+        "SD 3.5",
+        "SD 3.5 Medium",
+        "SD 3.5 Large",
+        "SD 3.5 Large Turbo",
+        "SDXL 1.0",
+        "SDXL Lightning",
+        "SDXL Hyper",
+        "Flux.1 D",
+        "Flux.1 S",
+        "Flux.1 Krea",
+        "Flux.1 Kontext",
+        "Flux.2 D",
+        "Flux.2 Klein 9B",
+        "Flux.2 Klein 9B-base",
+        "Flux.2 Klein 4B",
+        "Flux.2 Klein 4B-base",
+        "AuraFlow",
+        "Chroma",
+        "PixArt a",
+        "PixArt E",
+        "Hunyuan 1",
+        "Lumina",
+        "Kolors",
+        "NoobAI",
+        "Illustrious",
+        "Pony",
+        "Pony V7",
+        "HiDream",
+        "Qwen",
+        "ZImageTurbo",
+        "ZImageBase",
+        "SVD",
+        "LTXV",
+        "LTXV2",
+        "LTXV 2.3",
+        "CogVideoX",
+        "Mochi",
+        "Wan Video",
+        "Wan Video 1.3B t2v",
+        "Wan Video 14B t2v",
+        "Wan Video 14B i2v 480p",
+        "Wan Video 14B i2v 720p",
+        "Wan Video 2.2 TI2V-5B",
+        "Wan Video 2.2 T2V-A14B",
+        "Wan Video 2.2 I2V-A14B",
+        "Wan Video 2.5 T2V",
+        "Wan Video 2.5 I2V",
+        "Hunyuan Video",
+        "Anima",
+    ]
+)

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: